{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12:51:51 [I] klustakwik KlustaKwik2 version 0.2.6\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pathlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import shutil\n",
    "import pandas as pd\n",
    "import scipy.stats\n",
    "\n",
    "import exdir\n",
    "import expipe\n",
    "from distutils.dir_util import copy_tree\n",
    "import septum_mec\n",
    "import spatial_maps as sp\n",
    "import head_direction.head as head\n",
    "import septum_mec.analysis.data_processing as dp\n",
    "import septum_mec.analysis.registration\n",
    "from septum_mec.analysis.plotting import violinplot\n",
    "\n",
    "from spike_statistics.core import permutation_resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "project_path = dp.project_path()\n",
    "project = expipe.get_project(project_path)\n",
    "actions = project.actions\n",
    "\n",
    "output_path = pathlib.Path(\"output\") / \"comparisons-gridcells\"\n",
    "(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n",
    "(output_path / \"figures\").mkdir(exist_ok=True, parents=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load cell statistics and shuffling quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>action</th>\n",
       "      <th>baseline</th>\n",
       "      <th>entity</th>\n",
       "      <th>frequency</th>\n",
       "      <th>i</th>\n",
       "      <th>ii</th>\n",
       "      <th>session</th>\n",
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       "      <th>...</th>\n",
       "      <th>burst_event_ratio</th>\n",
       "      <th>bursty_spike_ratio</th>\n",
       "      <th>gridness</th>\n",
       "      <th>border_score</th>\n",
       "      <th>information_rate</th>\n",
       "      <th>information_specificity</th>\n",
       "      <th>head_mean_ang</th>\n",
       "      <th>head_mean_vec_len</th>\n",
       "      <th>spacing</th>\n",
       "      <th>orientation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>-0.466923</td>\n",
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       "      <td>0.317256</td>\n",
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       "      <td>0.628784</td>\n",
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       "      <th>1</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>baseline ii</td>\n",
       "      <td>...</td>\n",
       "      <td>0.138014</td>\n",
       "      <td>0.263173</td>\n",
       "      <td>-0.666792</td>\n",
       "      <td>0.308146</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>baseline ii</td>\n",
       "      <td>...</td>\n",
       "      <td>0.373986</td>\n",
       "      <td>0.659259</td>\n",
       "      <td>-0.572566</td>\n",
       "      <td>0.143252</td>\n",
       "      <td>4.745836</td>\n",
       "      <td>0.393704</td>\n",
       "      <td>4.439721</td>\n",
       "      <td>0.124731</td>\n",
       "      <td>0.555402</td>\n",
       "      <td>28.810794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1849-060319-3</td>\n",
       "      <td>True</td>\n",
       "      <td>1849</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>baseline ii</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087413</td>\n",
       "      <td>0.179245</td>\n",
       "      <td>-0.437492</td>\n",
       "      <td>0.268948</td>\n",
       "      <td>0.157394</td>\n",
       "      <td>0.073553</td>\n",
       "      <td>6.215195</td>\n",
       "      <td>0.101911</td>\n",
       "      <td>0.492250</td>\n",
       "      <td>9.462322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1849-060319-3</td>\n",
       "      <td>True</td>\n",
       "      <td>1849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>baseline ii</td>\n",
       "      <td>...</td>\n",
       "      <td>0.248771</td>\n",
       "      <td>0.463596</td>\n",
       "      <td>-0.085938</td>\n",
       "      <td>0.218744</td>\n",
       "      <td>0.519153</td>\n",
       "      <td>0.032683</td>\n",
       "      <td>1.531481</td>\n",
       "      <td>0.053810</td>\n",
       "      <td>0.559905</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
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      ],
      "text/plain": [
       "          action  baseline  entity  frequency      i    ii  session  \\\n",
       "0  1849-060319-3      True    1849        NaN  False  True        3   \n",
       "1  1849-060319-3      True    1849        NaN  False  True        3   \n",
       "2  1849-060319-3      True    1849        NaN  False  True        3   \n",
       "3  1849-060319-3      True    1849        NaN  False  True        3   \n",
       "4  1849-060319-3      True    1849        NaN  False  True        3   \n",
       "\n",
       "  stim_location  stimulated          tag  ...  burst_event_ratio  \\\n",
       "0           NaN       False  baseline ii  ...           0.398230   \n",
       "1           NaN       False  baseline ii  ...           0.138014   \n",
       "2           NaN       False  baseline ii  ...           0.373986   \n",
       "3           NaN       False  baseline ii  ...           0.087413   \n",
       "4           NaN       False  baseline ii  ...           0.248771   \n",
       "\n",
       "  bursty_spike_ratio  gridness  border_score  information_rate  \\\n",
       "0           0.678064 -0.466923      0.029328          1.009215   \n",
       "1           0.263173 -0.666792      0.308146          0.192524   \n",
       "2           0.659259 -0.572566      0.143252          4.745836   \n",
       "3           0.179245 -0.437492      0.268948          0.157394   \n",
       "4           0.463596 -0.085938      0.218744          0.519153   \n",
       "\n",
       "   information_specificity  head_mean_ang head_mean_vec_len   spacing  \\\n",
       "0                 0.317256       5.438033          0.040874  0.628784   \n",
       "1                 0.033447       1.951740          0.017289  0.789388   \n",
       "2                 0.393704       4.439721          0.124731  0.555402   \n",
       "3                 0.073553       6.215195          0.101911  0.492250   \n",
       "4                 0.032683       1.531481          0.053810  0.559905   \n",
       "\n",
       "   orientation  \n",
       "0    20.224859  \n",
       "1    27.897271  \n",
       "2    28.810794  \n",
       "3     9.462322  \n",
       "4     0.000000  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics_action = actions['calculate-statistics']\n",
    "identification_action = actions['identify-neurons']\n",
    "sessions = pd.read_csv(identification_action.data_path('sessions'))\n",
    "units = pd.read_csv(identification_action.data_path('units'))\n",
    "session_units = pd.merge(sessions, units, on='action')\n",
    "statistics_results = pd.read_csv(statistics_action.data_path('results'))\n",
    "statistics = pd.merge(session_units, statistics_results, how='left')\n",
    "statistics.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>border_score</th>\n",
       "      <th>gridness</th>\n",
       "      <th>head_mean_ang</th>\n",
       "      <th>head_mean_vec_len</th>\n",
       "      <th>information_rate</th>\n",
       "      <th>speed_score</th>\n",
       "      <th>action</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.348023</td>\n",
       "      <td>0.275109</td>\n",
       "      <td>3.012689</td>\n",
       "      <td>0.086792</td>\n",
       "      <td>0.707197</td>\n",
       "      <td>0.149071</td>\n",
       "      <td>1833-010719-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>127.0</td>\n",
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       "      <td>0.362380</td>\n",
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       "      <td>3.133138</td>\n",
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       "      <td>0.132212</td>\n",
       "      <td>1833-010719-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>161.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.367498</td>\n",
       "      <td>0.266865</td>\n",
       "      <td>5.586395</td>\n",
       "      <td>0.182843</td>\n",
       "      <td>0.271188</td>\n",
       "      <td>0.062821</td>\n",
       "      <td>1833-010719-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>191.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.331942</td>\n",
       "      <td>0.312155</td>\n",
       "      <td>5.955767</td>\n",
       "      <td>0.090786</td>\n",
       "      <td>0.354018</td>\n",
       "      <td>0.052009</td>\n",
       "      <td>1833-010719-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.325842</td>\n",
       "      <td>0.180495</td>\n",
       "      <td>5.262721</td>\n",
       "      <td>0.103584</td>\n",
       "      <td>0.210427</td>\n",
       "      <td>0.094041</td>\n",
       "      <td>1833-010719-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   border_score  gridness  head_mean_ang  head_mean_vec_len  information_rate  \\\n",
       "0      0.348023  0.275109       3.012689           0.086792          0.707197   \n",
       "1      0.362380  0.166475       3.133138           0.037271          0.482486   \n",
       "2      0.367498  0.266865       5.586395           0.182843          0.271188   \n",
       "3      0.331942  0.312155       5.955767           0.090786          0.354018   \n",
       "4      0.325842  0.180495       5.262721           0.103584          0.210427   \n",
       "\n",
       "   speed_score         action  channel_group  unit_name  \n",
       "0     0.149071  1833-010719-1            0.0      127.0  \n",
       "1     0.132212  1833-010719-1            0.0      161.0  \n",
       "2     0.062821  1833-010719-1            0.0      191.0  \n",
       "3     0.052009  1833-010719-1            0.0      223.0  \n",
       "4     0.094041  1833-010719-1            0.0      225.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shuffling = actions['shuffling']\n",
    "quantiles_95 = pd.read_csv(shuffling.data_path('quantiles_95'))\n",
    "quantiles_95.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1849-060319-3</td>\n",
       "      <td>True</td>\n",
       "      <td>1849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>baseline ii</td>\n",
       "      <td>...</td>\n",
       "      <td>0.101911</td>\n",
       "      <td>0.492250</td>\n",
       "      <td>9.462322</td>\n",
       "      <td>0.344280</td>\n",
       "      <td>0.215829</td>\n",
       "      <td>6.033364</td>\n",
       "      <td>0.110495</td>\n",
       "      <td>0.239996</td>\n",
       "      <td>0.054074</td>\n",
       "      <td>0.269461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1849-060319-3</td>\n",
       "      <td>True</td>\n",
       "      <td>1849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>baseline ii</td>\n",
       "      <td>...</td>\n",
       "      <td>0.053810</td>\n",
       "      <td>0.559905</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.342799</td>\n",
       "      <td>0.218967</td>\n",
       "      <td>5.768170</td>\n",
       "      <td>0.054762</td>\n",
       "      <td>0.524990</td>\n",
       "      <td>0.144702</td>\n",
       "      <td>0.133410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          action  baseline  entity  frequency      i    ii  session  \\\n",
       "0  1849-060319-3      True    1849        NaN  False  True        3   \n",
       "1  1849-060319-3      True    1849        NaN  False  True        3   \n",
       "2  1849-060319-3      True    1849        NaN  False  True        3   \n",
       "3  1849-060319-3      True    1849        NaN  False  True        3   \n",
       "4  1849-060319-3      True    1849        NaN  False  True        3   \n",
       "\n",
       "  stim_location  stimulated          tag  ...  head_mean_vec_len   spacing  \\\n",
       "0           NaN       False  baseline ii  ...           0.040874  0.628784   \n",
       "1           NaN       False  baseline ii  ...           0.017289  0.789388   \n",
       "2           NaN       False  baseline ii  ...           0.124731  0.555402   \n",
       "3           NaN       False  baseline ii  ...           0.101911  0.492250   \n",
       "4           NaN       False  baseline ii  ...           0.053810  0.559905   \n",
       "\n",
       "   orientation  border_score_threshold  gridness_threshold  \\\n",
       "0    20.224859                0.332548            0.229073   \n",
       "1    27.897271                0.354830            0.089333   \n",
       "2    28.810794                0.264610           -0.121081   \n",
       "3     9.462322                0.344280            0.215829   \n",
       "4     0.000000                0.342799            0.218967   \n",
       "\n",
       "   head_mean_ang_threshold  head_mean_vec_len_threshold  \\\n",
       "0                 6.029431                     0.205362   \n",
       "1                 6.120055                     0.073566   \n",
       "2                 5.759406                     0.150827   \n",
       "3                 6.033364                     0.110495   \n",
       "4                 5.768170                     0.054762   \n",
       "\n",
       "  information_rate_threshold  speed_score_threshold  specificity  \n",
       "0                   1.115825               0.066736     0.451741  \n",
       "1                   0.223237               0.052594     0.098517  \n",
       "2                   4.964984               0.027120     0.400770  \n",
       "3                   0.239996               0.054074     0.269461  \n",
       "4                   0.524990               0.144702     0.133410  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "action_columns = ['action', 'channel_group', 'unit_name']\n",
    "data = pd.merge(statistics, quantiles_95, on=action_columns, suffixes=(\"\", \"_threshold\"))\n",
    "\n",
    "data['specificity'] = np.log10(data['in_field_mean_rate'] / data['out_field_mean_rate'])\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistics about all cell-sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stimulated\n",
       "False    624\n",
       "True     660\n",
       "Name: action, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('stimulated').count()['action']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['unit_day'] = data.apply(lambda x: str(x.unit_idnum) + '_' + x.action.split('-')[1], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find all cells with gridness above threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of sessions above threshold 194\n",
      "Number of animals 4\n"
     ]
    }
   ],
   "source": [
    "query = (\n",
    "    'gridness > gridness_threshold and '\n",
    "    'information_rate > information_rate_threshold and '\n",
    "    'gridness > .2 and '\n",
    "    'average_rate < 25'\n",
    ")\n",
    "sessions_above_threshold = data.query(query)\n",
    "print(\"Number of sessions above threshold\", len(sessions_above_threshold))\n",
    "print(\"Number of animals\", len(sessions_above_threshold.groupby(['entity'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "gridcell_sessions = data[data.unit_day.isin(sessions_above_threshold.unit_day.values)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of gridcells 139\n",
      "Number of gridcell recordings 231\n",
      "Number of animals 4\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of gridcells\", gridcell_sessions.unit_idnum.nunique())\n",
    "print(\"Number of gridcell recordings\", len(gridcell_sessions))\n",
    "print(\"Number of animals\", len(gridcell_sessions.groupby(['entity'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of gridcells in baseline i sessions 66\n",
      "Number of gridcells in stimulated 11Hz ms sessions 61\n",
      "Number of gridcells in baseline ii sessions 56\n",
      "Number of gridcells in stimulated 30Hz ms sessions 40\n"
     ]
    }
   ],
   "source": [
    "baseline_i = gridcell_sessions.query('baseline and Hz11')\n",
    "stimulated_11 = gridcell_sessions.query('frequency==11 and stim_location==\"ms\"')\n",
    "\n",
    "baseline_ii = gridcell_sessions.query('baseline and Hz30')\n",
    "stimulated_30 = gridcell_sessions.query('frequency==30 and stim_location==\"ms\"')\n",
    "\n",
    "print(\"Number of gridcells in baseline i sessions\", len(baseline_i))\n",
    "print(\"Number of gridcells in stimulated 11Hz ms sessions\", len(stimulated_11))\n",
    "\n",
    "print(\"Number of gridcells in baseline ii sessions\", len(baseline_ii))\n",
    "print(\"Number of gridcells in stimulated 30Hz ms sessions\", len(stimulated_30))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# slice unique units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline_i = baseline_i.drop_duplicates('unit_id')\n",
    "stimulated_11 = stimulated_11.drop_duplicates('unit_id')\n",
    "baseline_ii = baseline_ii.drop_duplicates('unit_id')\n",
    "stimulated_30 = stimulated_30.drop_duplicates('unit_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of gridcells in baseline i sessions 63\n",
      "Number of gridcells in stimulated 11Hz ms sessions 58\n",
      "Number of gridcells in baseline ii sessions 52\n",
      "Number of gridcells in stimulated 30Hz ms sessions 38\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of gridcells in baseline i sessions\", len(baseline_i))\n",
    "print(\"Number of gridcells in stimulated 11Hz ms sessions\", len(stimulated_11))\n",
    "\n",
    "print(\"Number of gridcells in baseline ii sessions\", len(baseline_ii))\n",
    "print(\"Number of gridcells in stimulated 30Hz ms sessions\", len(stimulated_30))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    'average_rate', 'gridness', 'sparsity', 'selectivity', 'information_specificity',\n",
    "    'max_rate', 'information_rate', 'interspike_interval_cv', \n",
    "    'in_field_mean_rate', 'out_field_mean_rate', \n",
    "    'burst_event_ratio', 'specificity', 'speed_score'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>average_rate</th>\n",
       "      <th>gridness</th>\n",
       "      <th>sparsity</th>\n",
       "      <th>selectivity</th>\n",
       "      <th>information_specificity</th>\n",
       "      <th>max_rate</th>\n",
       "      <th>information_rate</th>\n",
       "      <th>interspike_interval_cv</th>\n",
       "      <th>in_field_mean_rate</th>\n",
       "      <th>out_field_mean_rate</th>\n",
       "      <th>burst_event_ratio</th>\n",
       "      <th>specificity</th>\n",
       "      <th>speed_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stimulated</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>8.904501</td>\n",
       "      <td>0.521371</td>\n",
       "      <td>0.618384</td>\n",
       "      <td>5.934539</td>\n",
       "      <td>0.234632</td>\n",
       "      <td>37.437808</td>\n",
       "      <td>1.246546</td>\n",
       "      <td>2.404647</td>\n",
       "      <td>14.717635</td>\n",
       "      <td>6.346875</td>\n",
       "      <td>0.211840</td>\n",
       "      <td>0.478775</td>\n",
       "      <td>0.135495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>8.392252</td>\n",
       "      <td>0.440296</td>\n",
       "      <td>0.655698</td>\n",
       "      <td>5.977408</td>\n",
       "      <td>0.215736</td>\n",
       "      <td>33.716478</td>\n",
       "      <td>0.964787</td>\n",
       "      <td>2.223636</td>\n",
       "      <td>12.936021</td>\n",
       "      <td>6.122228</td>\n",
       "      <td>0.197264</td>\n",
       "      <td>0.455878</td>\n",
       "      <td>0.104697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            average_rate  gridness  sparsity  selectivity  \\\n",
       "stimulated                                                  \n",
       "False           8.904501  0.521371  0.618384     5.934539   \n",
       "True            8.392252  0.440296  0.655698     5.977408   \n",
       "\n",
       "            information_specificity   max_rate  information_rate  \\\n",
       "stimulated                                                         \n",
       "False                      0.234632  37.437808          1.246546   \n",
       "True                       0.215736  33.716478          0.964787   \n",
       "\n",
       "            interspike_interval_cv  in_field_mean_rate  out_field_mean_rate  \\\n",
       "stimulated                                                                    \n",
       "False                     2.404647           14.717635             6.346875   \n",
       "True                      2.223636           12.936021             6.122228   \n",
       "\n",
       "            burst_event_ratio  specificity  speed_score  \n",
       "stimulated                                               \n",
       "False                0.211840     0.478775     0.135495  \n",
       "True                 0.197264     0.455878     0.104697  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridcell_sessions.groupby('stimulated')[columns].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "        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>average_rate</th>\n",
       "      <th>gridness</th>\n",
       "      <th>sparsity</th>\n",
       "      <th>selectivity</th>\n",
       "      <th>information_specificity</th>\n",
       "      <th>max_rate</th>\n",
       "      <th>information_rate</th>\n",
       "      <th>interspike_interval_cv</th>\n",
       "      <th>in_field_mean_rate</th>\n",
       "      <th>out_field_mean_rate</th>\n",
       "      <th>burst_event_ratio</th>\n",
       "      <th>specificity</th>\n",
       "      <th>speed_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>129.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>129.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.904501</td>\n",
       "      <td>0.521371</td>\n",
       "      <td>0.618384</td>\n",
       "      <td>5.934539</td>\n",
       "      <td>0.234632</td>\n",
       "      <td>37.437808</td>\n",
       "      <td>1.246546</td>\n",
       "      <td>2.404647</td>\n",
       "      <td>14.717635</td>\n",
       "      <td>6.346875</td>\n",
       "      <td>0.211840</td>\n",
       "      <td>0.478775</td>\n",
       "      <td>0.135495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.605598</td>\n",
       "      <td>0.337607</td>\n",
       "      <td>0.187934</td>\n",
       "      <td>3.217366</td>\n",
       "      <td>0.200726</td>\n",
       "      <td>16.300117</td>\n",
       "      <td>0.605971</td>\n",
       "      <td>0.756407</td>\n",
       "      <td>9.267522</td>\n",
       "      <td>6.805499</td>\n",
       "      <td>0.080143</td>\n",
       "      <td>0.209531</td>\n",
       "      <td>0.072831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.478349</td>\n",
       "      <td>-0.684924</td>\n",
       "      <td>0.200066</td>\n",
       "      <td>1.533216</td>\n",
       "      <td>0.007807</td>\n",
       "      <td>3.346027</td>\n",
       "      <td>0.117638</td>\n",
       "      <td>1.304387</td>\n",
       "      <td>0.924066</td>\n",
       "      <td>0.159076</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.071681</td>\n",
       "      <td>-0.025629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.518392</td>\n",
       "      <td>0.316326</td>\n",
       "      <td>0.437499</td>\n",
       "      <td>3.729863</td>\n",
       "      <td>0.093252</td>\n",
       "      <td>26.948843</td>\n",
       "      <td>0.786753</td>\n",
       "      <td>1.872991</td>\n",
       "      <td>7.701156</td>\n",
       "      <td>1.669844</td>\n",
       "      <td>0.160795</td>\n",
       "      <td>0.310822</td>\n",
       "      <td>0.084280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.456882</td>\n",
       "      <td>0.529243</td>\n",
       "      <td>0.642167</td>\n",
       "      <td>4.794970</td>\n",
       "      <td>0.180286</td>\n",
       "      <td>35.064991</td>\n",
       "      <td>1.156087</td>\n",
       "      <td>2.221185</td>\n",
       "      <td>12.212289</td>\n",
       "      <td>4.314913</td>\n",
       "      <td>0.210240</td>\n",
       "      <td>0.436340</td>\n",
       "      <td>0.128603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.721755</td>\n",
       "      <td>0.783682</td>\n",
       "      <td>0.758097</td>\n",
       "      <td>7.439464</td>\n",
       "      <td>0.312487</td>\n",
       "      <td>44.324873</td>\n",
       "      <td>1.592948</td>\n",
       "      <td>2.770624</td>\n",
       "      <td>20.974026</td>\n",
       "      <td>9.121505</td>\n",
       "      <td>0.267568</td>\n",
       "      <td>0.624834</td>\n",
       "      <td>0.188948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>59.365312</td>\n",
       "      <td>1.148979</td>\n",
       "      <td>0.976157</td>\n",
       "      <td>18.975875</td>\n",
       "      <td>1.243307</td>\n",
       "      <td>90.160158</td>\n",
       "      <td>3.456796</td>\n",
       "      <td>5.671362</td>\n",
       "      <td>66.350754</td>\n",
       "      <td>56.255544</td>\n",
       "      <td>0.393306</td>\n",
       "      <td>1.066391</td>\n",
       "      <td>0.297548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       average_rate    gridness    sparsity  selectivity  \\\n",
       "count    129.000000  129.000000  129.000000   129.000000   \n",
       "mean       8.904501    0.521371    0.618384     5.934539   \n",
       "std        7.605598    0.337607    0.187934     3.217366   \n",
       "min        0.478349   -0.684924    0.200066     1.533216   \n",
       "25%        3.518392    0.316326    0.437499     3.729863   \n",
       "50%        6.456882    0.529243    0.642167     4.794970   \n",
       "75%       12.721755    0.783682    0.758097     7.439464   \n",
       "max       59.365312    1.148979    0.976157    18.975875   \n",
       "\n",
       "       information_specificity    max_rate  information_rate  \\\n",
       "count               129.000000  129.000000        129.000000   \n",
       "mean                  0.234632   37.437808          1.246546   \n",
       "std                   0.200726   16.300117          0.605971   \n",
       "min                   0.007807    3.346027          0.117638   \n",
       "25%                   0.093252   26.948843          0.786753   \n",
       "50%                   0.180286   35.064991          1.156087   \n",
       "75%                   0.312487   44.324873          1.592948   \n",
       "max                   1.243307   90.160158          3.456796   \n",
       "\n",
       "       interspike_interval_cv  in_field_mean_rate  out_field_mean_rate  \\\n",
       "count              129.000000          129.000000           129.000000   \n",
       "mean                 2.404647           14.717635             6.346875   \n",
       "std                  0.756407            9.267522             6.805499   \n",
       "min                  1.304387            0.924066             0.159076   \n",
       "25%                  1.872991            7.701156             1.669844   \n",
       "50%                  2.221185           12.212289             4.314913   \n",
       "75%                  2.770624           20.974026             9.121505   \n",
       "max                  5.671362           66.350754            56.255544   \n",
       "\n",
       "       burst_event_ratio  specificity  speed_score  \n",
       "count         129.000000   129.000000   129.000000  \n",
       "mean            0.211840     0.478775     0.135495  \n",
       "std             0.080143     0.209531     0.072831  \n",
       "min             0.025000     0.071681    -0.025629  \n",
       "25%             0.160795     0.310822     0.084280  \n",
       "50%             0.210240     0.436340     0.128603  \n",
       "75%             0.267568     0.624834     0.188948  \n",
       "max             0.393306     1.066391     0.297548  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridcell_sessions.query('baseline')[columns].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_rate</th>\n",
       "      <th>gridness</th>\n",
       "      <th>sparsity</th>\n",
       "      <th>selectivity</th>\n",
       "      <th>information_specificity</th>\n",
       "      <th>max_rate</th>\n",
       "      <th>information_rate</th>\n",
       "      <th>interspike_interval_cv</th>\n",
       "      <th>in_field_mean_rate</th>\n",
       "      <th>out_field_mean_rate</th>\n",
       "      <th>burst_event_ratio</th>\n",
       "      <th>specificity</th>\n",
       "      <th>speed_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.392252</td>\n",
       "      <td>0.440296</td>\n",
       "      <td>0.655698</td>\n",
       "      <td>5.977408</td>\n",
       "      <td>0.215736</td>\n",
       "      <td>33.716478</td>\n",
       "      <td>0.964787</td>\n",
       "      <td>2.223636</td>\n",
       "      <td>12.936021</td>\n",
       "      <td>6.122228</td>\n",
       "      <td>0.197264</td>\n",
       "      <td>0.455878</td>\n",
       "      <td>0.104697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.057001</td>\n",
       "      <td>0.357038</td>\n",
       "      <td>0.211704</td>\n",
       "      <td>3.702400</td>\n",
       "      <td>0.235916</td>\n",
       "      <td>13.249312</td>\n",
       "      <td>0.572972</td>\n",
       "      <td>0.819734</td>\n",
       "      <td>7.211895</td>\n",
       "      <td>5.366332</td>\n",
       "      <td>0.082164</td>\n",
       "      <td>0.236777</td>\n",
       "      <td>0.081989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.198337</td>\n",
       "      <td>-0.516914</td>\n",
       "      <td>0.172684</td>\n",
       "      <td>1.930026</td>\n",
       "      <td>0.013088</td>\n",
       "      <td>2.846281</td>\n",
       "      <td>0.063173</td>\n",
       "      <td>1.110672</td>\n",
       "      <td>0.524639</td>\n",
       "      <td>0.099060</td>\n",
       "      <td>0.008475</td>\n",
       "      <td>0.097718</td>\n",
       "      <td>-0.138128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.579184</td>\n",
       "      <td>0.265949</td>\n",
       "      <td>0.458493</td>\n",
       "      <td>3.044303</td>\n",
       "      <td>0.066656</td>\n",
       "      <td>25.555110</td>\n",
       "      <td>0.564279</td>\n",
       "      <td>1.620472</td>\n",
       "      <td>7.555760</td>\n",
       "      <td>1.733624</td>\n",
       "      <td>0.146755</td>\n",
       "      <td>0.248057</td>\n",
       "      <td>0.056903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.838561</td>\n",
       "      <td>0.399053</td>\n",
       "      <td>0.699561</td>\n",
       "      <td>4.891855</td>\n",
       "      <td>0.128562</td>\n",
       "      <td>31.402558</td>\n",
       "      <td>0.862413</td>\n",
       "      <td>2.084020</td>\n",
       "      <td>11.451560</td>\n",
       "      <td>4.234871</td>\n",
       "      <td>0.192948</td>\n",
       "      <td>0.376143</td>\n",
       "      <td>0.106314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.934599</td>\n",
       "      <td>0.749561</td>\n",
       "      <td>0.842332</td>\n",
       "      <td>8.001587</td>\n",
       "      <td>0.300713</td>\n",
       "      <td>42.334786</td>\n",
       "      <td>1.190324</td>\n",
       "      <td>2.673991</td>\n",
       "      <td>17.335356</td>\n",
       "      <td>8.583415</td>\n",
       "      <td>0.247405</td>\n",
       "      <td>0.684623</td>\n",
       "      <td>0.149313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>24.858738</td>\n",
       "      <td>1.155123</td>\n",
       "      <td>0.967003</td>\n",
       "      <td>19.911477</td>\n",
       "      <td>1.359164</td>\n",
       "      <td>65.990793</td>\n",
       "      <td>3.182285</td>\n",
       "      <td>6.526960</td>\n",
       "      <td>34.489913</td>\n",
       "      <td>21.696265</td>\n",
       "      <td>0.393037</td>\n",
       "      <td>1.091064</td>\n",
       "      <td>0.390079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       average_rate    gridness    sparsity  selectivity  \\\n",
       "count    102.000000  102.000000  102.000000   102.000000   \n",
       "mean       8.392252    0.440296    0.655698     5.977408   \n",
       "std        6.057001    0.357038    0.211704     3.702400   \n",
       "min        0.198337   -0.516914    0.172684     1.930026   \n",
       "25%        3.579184    0.265949    0.458493     3.044303   \n",
       "50%        6.838561    0.399053    0.699561     4.891855   \n",
       "75%       11.934599    0.749561    0.842332     8.001587   \n",
       "max       24.858738    1.155123    0.967003    19.911477   \n",
       "\n",
       "       information_specificity    max_rate  information_rate  \\\n",
       "count               102.000000  102.000000        102.000000   \n",
       "mean                  0.215736   33.716478          0.964787   \n",
       "std                   0.235916   13.249312          0.572972   \n",
       "min                   0.013088    2.846281          0.063173   \n",
       "25%                   0.066656   25.555110          0.564279   \n",
       "50%                   0.128562   31.402558          0.862413   \n",
       "75%                   0.300713   42.334786          1.190324   \n",
       "max                   1.359164   65.990793          3.182285   \n",
       "\n",
       "       interspike_interval_cv  in_field_mean_rate  out_field_mean_rate  \\\n",
       "count              102.000000          102.000000           102.000000   \n",
       "mean                 2.223636           12.936021             6.122228   \n",
       "std                  0.819734            7.211895             5.366332   \n",
       "min                  1.110672            0.524639             0.099060   \n",
       "25%                  1.620472            7.555760             1.733624   \n",
       "50%                  2.084020           11.451560             4.234871   \n",
       "75%                  2.673991           17.335356             8.583415   \n",
       "max                  6.526960           34.489913            21.696265   \n",
       "\n",
       "       burst_event_ratio  specificity  speed_score  \n",
       "count         102.000000   102.000000   102.000000  \n",
       "mean            0.197264     0.455878     0.104697  \n",
       "std             0.082164     0.236777     0.081989  \n",
       "min             0.008475     0.097718    -0.138128  \n",
       "25%             0.146755     0.248057     0.056903  \n",
       "50%             0.192948     0.376143     0.106314  \n",
       "75%             0.247405     0.684623     0.149313  \n",
       "max             0.393037     1.091064     0.390079  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridcell_sessions.query(\"stimulated\")[columns].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create nice table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def summarize(data):\n",
    "    return \"{:.2f} ± {:.2f} ({})\".format(data.mean(), data.sem(), sum(~np.isnan(data)))\n",
    "\n",
    "\n",
    "def MWU(column, stim, base):\n",
    "    '''\n",
    "    Mann Whitney U\n",
    "    '''\n",
    "    Uvalue, pvalue = scipy.stats.mannwhitneyu(\n",
    "        stim[column].dropna(), \n",
    "        base[column].dropna(),\n",
    "        alternative='two-sided')\n",
    "\n",
    "    return \"{:.2f}, {:.3f}\".format(Uvalue, pvalue)\n",
    "\n",
    "\n",
    "def PRS(column, stim, base):\n",
    "    '''\n",
    "    Permutation ReSampling\n",
    "    '''\n",
    "    pvalue, observed_diff, diffs = permutation_resampling(\n",
    "        stim[column].dropna(), \n",
    "        base[column].dropna(), statistic=np.median)\n",
    "\n",
    "    return \"{:.2f}, {:.3f}\".format(observed_diff, pvalue)\n",
    "\n",
    "\n",
    "def rename(name):\n",
    "    return name.replace(\"_field\", \"-field\").replace(\"_\", \" \").capitalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Baseline I</th>\n",
       "      <th>Stimulated</th>\n",
       "      <th>MWU</th>\n",
       "      <th>PRS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Average rate</th>\n",
       "      <td>8.61 ± 0.75 (71)</td>\n",
       "      <td>8.39 ± 0.60 (102)</td>\n",
       "      <td>3599.00, 0.947</td>\n",
       "      <td>0.55, 0.757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gridness</th>\n",
       "      <td>0.51 ± 0.04 (71)</td>\n",
       "      <td>0.44 ± 0.04 (102)</td>\n",
       "      <td>3208.00, 0.203</td>\n",
       "      <td>0.13, 0.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparsity</th>\n",
       "      <td>0.61 ± 0.02 (71)</td>\n",
       "      <td>0.66 ± 0.02 (102)</td>\n",
       "      <td>4170.00, 0.091</td>\n",
       "      <td>0.06, 0.179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Selectivity</th>\n",
       "      <td>5.91 ± 0.37 (71)</td>\n",
       "      <td>5.98 ± 0.37 (102)</td>\n",
       "      <td>3460.00, 0.620</td>\n",
       "      <td>0.10, 0.874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information specificity</th>\n",
       "      <td>0.25 ± 0.03 (71)</td>\n",
       "      <td>0.22 ± 0.02 (102)</td>\n",
       "      <td>2944.00, 0.037</td>\n",
       "      <td>0.05, 0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max rate</th>\n",
       "      <td>36.55 ± 1.78 (71)</td>\n",
       "      <td>33.72 ± 1.31 (102)</td>\n",
       "      <td>3291.00, 0.309</td>\n",
       "      <td>3.19, 0.194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information rate</th>\n",
       "      <td>1.30 ± 0.07 (71)</td>\n",
       "      <td>0.96 ± 0.06 (102)</td>\n",
       "      <td>2385.00, 0.000</td>\n",
       "      <td>0.32, 0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Interspike interval cv</th>\n",
       "      <td>2.42 ± 0.10 (71)</td>\n",
       "      <td>2.22 ± 0.08 (102)</td>\n",
       "      <td>3034.00, 0.070</td>\n",
       "      <td>0.12, 0.398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>In-field mean rate</th>\n",
       "      <td>14.43 ± 1.00 (71)</td>\n",
       "      <td>12.94 ± 0.71 (102)</td>\n",
       "      <td>3368.00, 0.436</td>\n",
       "      <td>0.39, 0.817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Out-field mean rate</th>\n",
       "      <td>6.05 ± 0.62 (71)</td>\n",
       "      <td>6.12 ± 0.53 (102)</td>\n",
       "      <td>3600.00, 0.950</td>\n",
       "      <td>0.08, 0.944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burst event ratio</th>\n",
       "      <td>0.22 ± 0.01 (71)</td>\n",
       "      <td>0.20 ± 0.01 (102)</td>\n",
       "      <td>3090.00, 0.102</td>\n",
       "      <td>0.02, 0.129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Specificity</th>\n",
       "      <td>0.48 ± 0.03 (71)</td>\n",
       "      <td>0.46 ± 0.02 (102)</td>\n",
       "      <td>3268.00, 0.277</td>\n",
       "      <td>0.06, 0.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Speed score</th>\n",
       "      <td>0.14 ± 0.01 (71)</td>\n",
       "      <td>0.10 ± 0.01 (102)</td>\n",
       "      <td>2546.00, 0.001</td>\n",
       "      <td>0.05, 0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Baseline I          Stimulated  \\\n",
       "Average rate              8.61 ± 0.75 (71)   8.39 ± 0.60 (102)   \n",
       "Gridness                  0.51 ± 0.04 (71)   0.44 ± 0.04 (102)   \n",
       "Sparsity                  0.61 ± 0.02 (71)   0.66 ± 0.02 (102)   \n",
       "Selectivity               5.91 ± 0.37 (71)   5.98 ± 0.37 (102)   \n",
       "Information specificity   0.25 ± 0.03 (71)   0.22 ± 0.02 (102)   \n",
       "Max rate                 36.55 ± 1.78 (71)  33.72 ± 1.31 (102)   \n",
       "Information rate          1.30 ± 0.07 (71)   0.96 ± 0.06 (102)   \n",
       "Interspike interval cv    2.42 ± 0.10 (71)   2.22 ± 0.08 (102)   \n",
       "In-field mean rate       14.43 ± 1.00 (71)  12.94 ± 0.71 (102)   \n",
       "Out-field mean rate       6.05 ± 0.62 (71)   6.12 ± 0.53 (102)   \n",
       "Burst event ratio         0.22 ± 0.01 (71)   0.20 ± 0.01 (102)   \n",
       "Specificity               0.48 ± 0.03 (71)   0.46 ± 0.02 (102)   \n",
       "Speed score               0.14 ± 0.01 (71)   0.10 ± 0.01 (102)   \n",
       "\n",
       "                                    MWU          PRS  \n",
       "Average rate             3599.00, 0.947  0.55, 0.757  \n",
       "Gridness                 3208.00, 0.203  0.13, 0.145  \n",
       "Sparsity                 4170.00, 0.091  0.06, 0.179  \n",
       "Selectivity              3460.00, 0.620  0.10, 0.874  \n",
       "Information specificity  2944.00, 0.037  0.05, 0.034  \n",
       "Max rate                 3291.00, 0.309  3.19, 0.194  \n",
       "Information rate         2385.00, 0.000  0.32, 0.001  \n",
       "Interspike interval cv   3034.00, 0.070  0.12, 0.398  \n",
       "In-field mean rate       3368.00, 0.436  0.39, 0.817  \n",
       "Out-field mean rate      3600.00, 0.950  0.08, 0.944  \n",
       "Burst event ratio        3090.00, 0.102  0.02, 0.129  \n",
       "Specificity              3268.00, 0.277  0.06, 0.360  \n",
       "Speed score              2546.00, 0.001  0.05, 0.000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_stim_data = gridcell_sessions.query('stimulated')\n",
    "_base_data = gridcell_sessions.query('baseline and i')\n",
    "\n",
    "result = pd.DataFrame()\n",
    "\n",
    "result['Baseline I'] = _base_data[columns].agg(summarize)\n",
    "result['Stimulated'] = _stim_data[columns].agg(summarize)\n",
    "\n",
    "\n",
    "result.index = map(rename, result.index)\n",
    "\n",
    "result['MWU'] = list(map(lambda x: MWU(x, _stim_data, _base_data), columns))\n",
    "result['PRS'] = list(map(lambda x: PRS(x, _stim_data, _base_data), columns))\n",
    "\n",
    "result.to_latex(output_path / \"statistics\" / \"statistics.tex\")\n",
    "result.to_csv(output_path / \"statistics\" / \"statistics.csv\")\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>Baseline I</th>\n",
       "      <th>11 Hz</th>\n",
       "      <th>MWU</th>\n",
       "      <th>PRS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Average rate</th>\n",
       "      <td>8.96 ± 0.80 (63)</td>\n",
       "      <td>8.80 ± 0.85 (58)</td>\n",
       "      <td>1781.00, 0.813</td>\n",
       "      <td>0.04, 0.968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gridness</th>\n",
       "      <td>0.53 ± 0.05 (63)</td>\n",
       "      <td>0.41 ± 0.05 (58)</td>\n",
       "      <td>1459.00, 0.057</td>\n",
       "      <td>0.21, 0.041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparsity</th>\n",
       "      <td>0.63 ± 0.02 (63)</td>\n",
       "      <td>0.67 ± 0.03 (58)</td>\n",
       "      <td>2138.00, 0.107</td>\n",
       "      <td>0.07, 0.128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Selectivity</th>\n",
       "      <td>5.76 ± 0.40 (63)</td>\n",
       "      <td>5.69 ± 0.50 (58)</td>\n",
       "      <td>1687.00, 0.469</td>\n",
       "      <td>0.00, 0.983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information specificity</th>\n",
       "      <td>0.24 ± 0.03 (63)</td>\n",
       "      <td>0.21 ± 0.03 (58)</td>\n",
       "      <td>1452.00, 0.052</td>\n",
       "      <td>0.06, 0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max rate</th>\n",
       "      <td>37.39 ± 1.91 (63)</td>\n",
       "      <td>33.11 ± 1.85 (58)</td>\n",
       "      <td>1538.00, 0.134</td>\n",
       "      <td>4.06, 0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information rate</th>\n",
       "      <td>1.31 ± 0.08 (63)</td>\n",
       "      <td>0.94 ± 0.08 (58)</td>\n",
       "      <td>1143.00, 0.000</td>\n",
       "      <td>0.32, 0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Interspike interval cv</th>\n",
       "      <td>2.39 ± 0.10 (63)</td>\n",
       "      <td>2.19 ± 0.12 (58)</td>\n",
       "      <td>1462.00, 0.059</td>\n",
       "      <td>0.18, 0.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>In-field mean rate</th>\n",
       "      <td>14.88 ± 1.05 (63)</td>\n",
       "      <td>13.27 ± 1.04 (58)</td>\n",
       "      <td>1633.00, 0.315</td>\n",
       "      <td>0.77, 0.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Out-field mean rate</th>\n",
       "      <td>6.37 ± 0.67 (63)</td>\n",
       "      <td>6.57 ± 0.77 (58)</td>\n",
       "      <td>1795.00, 0.870</td>\n",
       "      <td>0.47, 0.719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burst event ratio</th>\n",
       "      <td>0.22 ± 0.01 (63)</td>\n",
       "      <td>0.22 ± 0.01 (58)</td>\n",
       "      <td>1897.00, 0.718</td>\n",
       "      <td>0.00, 0.824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Specificity</th>\n",
       "      <td>0.47 ± 0.03 (63)</td>\n",
       "      <td>0.44 ± 0.03 (58)</td>\n",
       "      <td>1605.00, 0.250</td>\n",
       "      <td>0.06, 0.414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Speed score</th>\n",
       "      <td>0.14 ± 0.01 (63)</td>\n",
       "      <td>0.11 ± 0.01 (58)</td>\n",
       "      <td>1378.00, 0.020</td>\n",
       "      <td>0.04, 0.022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Baseline I              11 Hz             MWU  \\\n",
       "Average rate              8.96 ± 0.80 (63)   8.80 ± 0.85 (58)  1781.00, 0.813   \n",
       "Gridness                  0.53 ± 0.05 (63)   0.41 ± 0.05 (58)  1459.00, 0.057   \n",
       "Sparsity                  0.63 ± 0.02 (63)   0.67 ± 0.03 (58)  2138.00, 0.107   \n",
       "Selectivity               5.76 ± 0.40 (63)   5.69 ± 0.50 (58)  1687.00, 0.469   \n",
       "Information specificity   0.24 ± 0.03 (63)   0.21 ± 0.03 (58)  1452.00, 0.052   \n",
       "Max rate                 37.39 ± 1.91 (63)  33.11 ± 1.85 (58)  1538.00, 0.134   \n",
       "Information rate          1.31 ± 0.08 (63)   0.94 ± 0.08 (58)  1143.00, 0.000   \n",
       "Interspike interval cv    2.39 ± 0.10 (63)   2.19 ± 0.12 (58)  1462.00, 0.059   \n",
       "In-field mean rate       14.88 ± 1.05 (63)  13.27 ± 1.04 (58)  1633.00, 0.315   \n",
       "Out-field mean rate       6.37 ± 0.67 (63)   6.57 ± 0.77 (58)  1795.00, 0.870   \n",
       "Burst event ratio         0.22 ± 0.01 (63)   0.22 ± 0.01 (58)  1897.00, 0.718   \n",
       "Specificity               0.47 ± 0.03 (63)   0.44 ± 0.03 (58)  1605.00, 0.250   \n",
       "Speed score               0.14 ± 0.01 (63)   0.11 ± 0.01 (58)  1378.00, 0.020   \n",
       "\n",
       "                                 PRS  \n",
       "Average rate             0.04, 0.968  \n",
       "Gridness                 0.21, 0.041  \n",
       "Sparsity                 0.07, 0.128  \n",
       "Selectivity              0.00, 0.983  \n",
       "Information specificity  0.06, 0.030  \n",
       "Max rate                 4.06, 0.125  \n",
       "Information rate         0.32, 0.004  \n",
       "Interspike interval cv   0.18, 0.139  \n",
       "In-field mean rate       0.77, 0.690  \n",
       "Out-field mean rate      0.47, 0.719  \n",
       "Burst event ratio        0.00, 0.824  \n",
       "Specificity              0.06, 0.414  \n",
       "Speed score              0.04, 0.022  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_stim_data = stimulated_11\n",
    "_base_data = baseline_i\n",
    "\n",
    "result = pd.DataFrame()\n",
    "\n",
    "result['Baseline I'] = _base_data[columns].agg(summarize)\n",
    "result['11 Hz'] = _stim_data[columns].agg(summarize)\n",
    "\n",
    "\n",
    "result.index = map(rename, result.index)\n",
    "\n",
    "result['MWU'] = list(map(lambda x: MWU(x, _stim_data, _base_data), columns))\n",
    "result['PRS'] = list(map(lambda x: PRS(x, _stim_data, _base_data), columns))\n",
    "\n",
    "\n",
    "result.to_latex(output_path / \"statistics\" / \"statistics_11.tex\")\n",
    "result.to_csv(output_path / \"statistics\" / \"statistics_11.csv\")\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>Baseline II</th>\n",
       "      <th>30 Hz</th>\n",
       "      <th>MWU</th>\n",
       "      <th>PRS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Average rate</th>\n",
       "      <td>8.29 ± 0.87 (52)</td>\n",
       "      <td>7.61 ± 0.87 (38)</td>\n",
       "      <td>958.00, 0.810</td>\n",
       "      <td>0.27, 0.808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gridness</th>\n",
       "      <td>0.54 ± 0.04 (52)</td>\n",
       "      <td>0.48 ± 0.06 (38)</td>\n",
       "      <td>914.00, 0.548</td>\n",
       "      <td>0.04, 0.598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparsity</th>\n",
       "      <td>0.63 ± 0.03 (52)</td>\n",
       "      <td>0.64 ± 0.03 (38)</td>\n",
       "      <td>1040.00, 0.674</td>\n",
       "      <td>0.06, 0.398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Selectivity</th>\n",
       "      <td>5.96 ± 0.46 (52)</td>\n",
       "      <td>6.42 ± 0.60 (38)</td>\n",
       "      <td>1019.00, 0.803</td>\n",
       "      <td>0.20, 0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information specificity</th>\n",
       "      <td>0.21 ± 0.02 (52)</td>\n",
       "      <td>0.22 ± 0.03 (38)</td>\n",
       "      <td>950.00, 0.759</td>\n",
       "      <td>0.04, 0.506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max rate</th>\n",
       "      <td>36.27 ± 2.34 (52)</td>\n",
       "      <td>33.49 ± 1.89 (38)</td>\n",
       "      <td>943.00, 0.716</td>\n",
       "      <td>2.90, 0.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information rate</th>\n",
       "      <td>1.13 ± 0.08 (52)</td>\n",
       "      <td>0.98 ± 0.09 (38)</td>\n",
       "      <td>827.00, 0.190</td>\n",
       "      <td>0.07, 0.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Interspike interval cv</th>\n",
       "      <td>2.37 ± 0.09 (52)</td>\n",
       "      <td>2.23 ± 0.11 (38)</td>\n",
       "      <td>869.00, 0.333</td>\n",
       "      <td>0.17, 0.482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>In-field mean rate</th>\n",
       "      <td>13.79 ± 1.12 (52)</td>\n",
       "      <td>12.21 ± 0.98 (38)</td>\n",
       "      <td>912.00, 0.537</td>\n",
       "      <td>1.06, 0.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Out-field mean rate</th>\n",
       "      <td>5.80 ± 0.72 (52)</td>\n",
       "      <td>5.36 ± 0.73 (38)</td>\n",
       "      <td>959.00, 0.816</td>\n",
       "      <td>0.13, 0.912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burst event ratio</th>\n",
       "      <td>0.20 ± 0.01 (52)</td>\n",
       "      <td>0.16 ± 0.01 (38)</td>\n",
       "      <td>676.00, 0.011</td>\n",
       "      <td>0.05, 0.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Specificity</th>\n",
       "      <td>0.47 ± 0.03 (52)</td>\n",
       "      <td>0.48 ± 0.04 (38)</td>\n",
       "      <td>976.00, 0.925</td>\n",
       "      <td>0.00, 0.988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Speed score</th>\n",
       "      <td>0.12 ± 0.01 (52)</td>\n",
       "      <td>0.11 ± 0.01 (38)</td>\n",
       "      <td>784.00, 0.096</td>\n",
       "      <td>0.01, 0.242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Baseline II              30 Hz             MWU  \\\n",
       "Average rate              8.29 ± 0.87 (52)   7.61 ± 0.87 (38)   958.00, 0.810   \n",
       "Gridness                  0.54 ± 0.04 (52)   0.48 ± 0.06 (38)   914.00, 0.548   \n",
       "Sparsity                  0.63 ± 0.03 (52)   0.64 ± 0.03 (38)  1040.00, 0.674   \n",
       "Selectivity               5.96 ± 0.46 (52)   6.42 ± 0.60 (38)  1019.00, 0.803   \n",
       "Information specificity   0.21 ± 0.02 (52)   0.22 ± 0.03 (38)   950.00, 0.759   \n",
       "Max rate                 36.27 ± 2.34 (52)  33.49 ± 1.89 (38)   943.00, 0.716   \n",
       "Information rate          1.13 ± 0.08 (52)   0.98 ± 0.09 (38)   827.00, 0.190   \n",
       "Interspike interval cv    2.37 ± 0.09 (52)   2.23 ± 0.11 (38)   869.00, 0.333   \n",
       "In-field mean rate       13.79 ± 1.12 (52)  12.21 ± 0.98 (38)   912.00, 0.537   \n",
       "Out-field mean rate       5.80 ± 0.72 (52)   5.36 ± 0.73 (38)   959.00, 0.816   \n",
       "Burst event ratio         0.20 ± 0.01 (52)   0.16 ± 0.01 (38)   676.00, 0.011   \n",
       "Specificity               0.47 ± 0.03 (52)   0.48 ± 0.04 (38)   976.00, 0.925   \n",
       "Speed score               0.12 ± 0.01 (52)   0.11 ± 0.01 (38)   784.00, 0.096   \n",
       "\n",
       "                                 PRS  \n",
       "Average rate             0.27, 0.808  \n",
       "Gridness                 0.04, 0.598  \n",
       "Sparsity                 0.06, 0.398  \n",
       "Selectivity              0.20, 0.845  \n",
       "Information specificity  0.04, 0.506  \n",
       "Max rate                 2.90, 0.565  \n",
       "Information rate         0.07, 0.335  \n",
       "Interspike interval cv   0.17, 0.482  \n",
       "In-field mean rate       1.06, 0.444  \n",
       "Out-field mean rate      0.13, 0.912  \n",
       "Burst event ratio        0.05, 0.009  \n",
       "Specificity              0.00, 0.988  \n",
       "Speed score              0.01, 0.242  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_stim_data = stimulated_30\n",
    "_base_data = baseline_ii\n",
    "\n",
    "result = pd.DataFrame()\n",
    "\n",
    "result['Baseline II'] = _base_data[columns].agg(summarize)\n",
    "result['30 Hz'] = _stim_data[columns].agg(summarize)\n",
    "\n",
    "result.index = map(rename, result.index)\n",
    "\n",
    "result['MWU'] = list(map(lambda x: MWU(x, _stim_data, _base_data), columns))\n",
    "result['PRS'] = list(map(lambda x: PRS(x, _stim_data, _base_data), columns))\n",
    "\n",
    "\n",
    "result.to_latex(output_path / \"statistics\" / \"statistics_30.tex\")\n",
    "result.to_csv(output_path / \"statistics\" / \"statistics_30.csv\")\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Baseline I</th>\n",
       "      <th>30 Hz</th>\n",
       "      <th>MWU</th>\n",
       "      <th>PRS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Average rate</th>\n",
       "      <td>8.96 ± 0.80 (63)</td>\n",
       "      <td>7.61 ± 0.87 (38)</td>\n",
       "      <td>1081.00, 0.418</td>\n",
       "      <td>0.27, 0.804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gridness</th>\n",
       "      <td>0.53 ± 0.05 (63)</td>\n",
       "      <td>0.48 ± 0.06 (38)</td>\n",
       "      <td>1094.00, 0.472</td>\n",
       "      <td>0.08, 0.354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparsity</th>\n",
       "      <td>0.63 ± 0.02 (63)</td>\n",
       "      <td>0.64 ± 0.03 (38)</td>\n",
       "      <td>1261.00, 0.656</td>\n",
       "      <td>0.03, 0.648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Selectivity</th>\n",
       "      <td>5.76 ± 0.40 (63)</td>\n",
       "      <td>6.42 ± 0.60 (38)</td>\n",
       "      <td>1276.00, 0.582</td>\n",
       "      <td>0.86, 0.292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information specificity</th>\n",
       "      <td>0.24 ± 0.03 (63)</td>\n",
       "      <td>0.22 ± 0.03 (38)</td>\n",
       "      <td>1076.00, 0.398</td>\n",
       "      <td>0.05, 0.159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max rate</th>\n",
       "      <td>37.39 ± 1.91 (63)</td>\n",
       "      <td>33.49 ± 1.89 (38)</td>\n",
       "      <td>1027.00, 0.235</td>\n",
       "      <td>3.99, 0.191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information rate</th>\n",
       "      <td>1.31 ± 0.08 (63)</td>\n",
       "      <td>0.98 ± 0.09 (38)</td>\n",
       "      <td>797.00, 0.005</td>\n",
       "      <td>0.32, 0.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Interspike interval cv</th>\n",
       "      <td>2.39 ± 0.10 (63)</td>\n",
       "      <td>2.23 ± 0.11 (38)</td>\n",
       "      <td>1100.00, 0.499</td>\n",
       "      <td>0.01, 0.991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>In-field mean rate</th>\n",
       "      <td>14.88 ± 1.05 (63)</td>\n",
       "      <td>12.21 ± 0.98 (38)</td>\n",
       "      <td>1018.00, 0.211</td>\n",
       "      <td>1.74, 0.273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Out-field mean rate</th>\n",
       "      <td>6.37 ± 0.67 (63)</td>\n",
       "      <td>5.36 ± 0.73 (38)</td>\n",
       "      <td>1079.00, 0.410</td>\n",
       "      <td>0.51, 0.641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burst event ratio</th>\n",
       "      <td>0.22 ± 0.01 (63)</td>\n",
       "      <td>0.16 ± 0.01 (38)</td>\n",
       "      <td>675.00, 0.000</td>\n",
       "      <td>0.05, 0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Specificity</th>\n",
       "      <td>0.47 ± 0.03 (63)</td>\n",
       "      <td>0.48 ± 0.04 (38)</td>\n",
       "      <td>1206.00, 0.952</td>\n",
       "      <td>0.01, 0.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Speed score</th>\n",
       "      <td>0.14 ± 0.01 (63)</td>\n",
       "      <td>0.11 ± 0.01 (38)</td>\n",
       "      <td>835.00, 0.011</td>\n",
       "      <td>0.06, 0.004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Baseline I              30 Hz             MWU  \\\n",
       "Average rate              8.96 ± 0.80 (63)   7.61 ± 0.87 (38)  1081.00, 0.418   \n",
       "Gridness                  0.53 ± 0.05 (63)   0.48 ± 0.06 (38)  1094.00, 0.472   \n",
       "Sparsity                  0.63 ± 0.02 (63)   0.64 ± 0.03 (38)  1261.00, 0.656   \n",
       "Selectivity               5.76 ± 0.40 (63)   6.42 ± 0.60 (38)  1276.00, 0.582   \n",
       "Information specificity   0.24 ± 0.03 (63)   0.22 ± 0.03 (38)  1076.00, 0.398   \n",
       "Max rate                 37.39 ± 1.91 (63)  33.49 ± 1.89 (38)  1027.00, 0.235   \n",
       "Information rate          1.31 ± 0.08 (63)   0.98 ± 0.09 (38)   797.00, 0.005   \n",
       "Interspike interval cv    2.39 ± 0.10 (63)   2.23 ± 0.11 (38)  1100.00, 0.499   \n",
       "In-field mean rate       14.88 ± 1.05 (63)  12.21 ± 0.98 (38)  1018.00, 0.211   \n",
       "Out-field mean rate       6.37 ± 0.67 (63)   5.36 ± 0.73 (38)  1079.00, 0.410   \n",
       "Burst event ratio         0.22 ± 0.01 (63)   0.16 ± 0.01 (38)   675.00, 0.000   \n",
       "Specificity               0.47 ± 0.03 (63)   0.48 ± 0.04 (38)  1206.00, 0.952   \n",
       "Speed score               0.14 ± 0.01 (63)   0.11 ± 0.01 (38)   835.00, 0.011   \n",
       "\n",
       "                                 PRS  \n",
       "Average rate             0.27, 0.804  \n",
       "Gridness                 0.08, 0.354  \n",
       "Sparsity                 0.03, 0.648  \n",
       "Selectivity              0.86, 0.292  \n",
       "Information specificity  0.05, 0.159  \n",
       "Max rate                 3.99, 0.191  \n",
       "Information rate         0.32, 0.049  \n",
       "Interspike interval cv   0.01, 0.991  \n",
       "In-field mean rate       1.74, 0.273  \n",
       "Out-field mean rate      0.51, 0.641  \n",
       "Burst event ratio        0.05, 0.004  \n",
       "Specificity              0.01, 0.875  \n",
       "Speed score              0.06, 0.004  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_stim_data = stimulated_30\n",
    "_base_data = baseline_i\n",
    "\n",
    "result = pd.DataFrame()\n",
    "\n",
    "result['Baseline I'] = _base_data[columns].agg(summarize)\n",
    "result['30 Hz'] = _stim_data[columns].agg(summarize)\n",
    "\n",
    "result.index = map(rename, result.index)\n",
    "\n",
    "result['MWU'] = list(map(lambda x: MWU(x, _stim_data, _base_data), columns))\n",
    "result['PRS'] = list(map(lambda x: PRS(x, _stim_data, _base_data), columns))\n",
    "\n",
    "\n",
    "result.to_latex(output_path / \"statistics\" / \"statistics_b_i_30.tex\")\n",
    "result.to_csv(output_path / \"statistics\" / \"statistics_b_i_30.csv\")\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_stim_data = stimulated_30\n",
    "_base_data = stimulated_11\n",
    "\n",
    "result = pd.DataFrame()\n",
    "\n",
    "result['11 Hz'] = _base_data[columns].agg(summarize)\n",
    "result['30 Hz'] = _stim_data[columns].agg(summarize)\n",
    "\n",
    "\n",
    "result.index = map(rename, result.index)\n",
    "\n",
    "result['MWU'] = list(map(lambda x: MWU(x, _stim_data, _base_data), columns))\n",
    "result['PRS'] = list(map(lambda x: PRS(x, _stim_data, _base_data), columns))\n",
    "\n",
    "\n",
    "result.to_latex(output_path / \"statistics\" / \"statistics_11_vs_30.tex\")\n",
    "result.to_csv(output_path / \"statistics\" / \"statistics_11_vs_30.csv\")\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_stim_data = baseline_i\n",
    "_base_data = baseline_ii\n",
    "\n",
    "result = pd.DataFrame()\n",
    "\n",
    "result['Baseline I'] = _stim_data[columns].agg(summarize)\n",
    "result['Baseline II'] = _base_data[columns].agg(summarize)\n",
    "\n",
    "result.index = map(rename, result.index)\n",
    "\n",
    "result['MWU'] = list(map(lambda x: MWU(x, _stim_data, _base_data), columns))\n",
    "result['PRS'] = list(map(lambda x: PRS(x, _stim_data, _base_data), columns))\n",
    "\n",
    "\n",
    "result.to_latex(output_path / \"statistics\" / \"statistics_base_i_vs_base_ii.tex\")\n",
    "result.to_csv(output_path / \"statistics\" / \"statistics_base_i_vs_base_ii.csv\")\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Violinplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.rc('axes', titlesize=12)\n",
    "plt.rcParams.update({\n",
    "    'font.size': 12, \n",
    "    'figure.figsize': (1.7, 3), \n",
    "    'figure.dpi': 150\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# colors = ['#1b9e77','#d95f02','#7570b3','#e7298a']\n",
    "# labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz']\n",
    "\n",
    "stuff = {\n",
    "    '': {\n",
    "        'base': gridcell_sessions.query('baseline and i'),\n",
    "        'stim': gridcell_sessions.query('stimulated')\n",
    "    },\n",
    "    '_11': {\n",
    "        'base': baseline_i,\n",
    "        'stim': stimulated_11\n",
    "    },\n",
    "    '_30': {\n",
    "        'base': baseline_ii,\n",
    "        'stim': stimulated_30\n",
    "    }\n",
    "}\n",
    "\n",
    "label = {\n",
    "    '': ['Baseline I   ', '   Stimulated'],\n",
    "    '_11': ['Baseline I ', '  11 Hz'],\n",
    "    '_30': ['Baseline II ', '  30 Hz']\n",
    "}\n",
    "\n",
    "colors = {\n",
    "    '': ['#1b9e77', '#b2182b'],\n",
    "    '_11': ['#1b9e77', '#d95f02'],\n",
    "    '_30': ['#7570b3', '#e7298a']\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Information rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, dd in stuff.items():\n",
    "    baseline = dd['base']['information_specificity'].to_numpy()\n",
    "    stimulated = dd['stim']['information_specificity'].to_numpy()\n",
    "    print(key)\n",
    "    plt.figure()\n",
    "    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
    "    plt.title(\"Spatial information specificity\")\n",
    "    plt.ylabel(\"bits/spike\")\n",
    "    plt.ylim(-0.2, 1.6)\n",
    "\n",
    "    plt.savefig(output_path / \"figures\" / f\"information_specificity{key}.svg\", bbox_inches=\"tight\")\n",
    "    plt.savefig(output_path / \"figures\" / f\"information_specificity{key}.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for key, dd in stuff.items():\n",
    "    baseline = dd['base']['information_rate'].to_numpy()\n",
    "    stimulated = dd['stim']['information_rate'].to_numpy()\n",
    "    print(key)\n",
    "    plt.figure()\n",
    "    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
    "    plt.title(\"Spatial information\")\n",
    "    plt.ylabel(\"bits/s\")\n",
    "    plt.ylim(-0.2, 4)\n",
    "\n",
    "    plt.savefig(output_path / \"figures\" / f\"spatial_information{key}.svg\", bbox_inches=\"tight\")\n",
    "    plt.savefig(output_path / \"figures\" / f\"spatial_information{key}.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, dd in stuff.items():\n",
    "    baseline = dd['base']['specificity'].to_numpy()\n",
    "    stimulated = dd['stim']['specificity'].to_numpy()\n",
    "    plt.figure()\n",
    "    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
    "    plt.title(\"Spatial specificity\")\n",
    "    plt.ylabel(\"\")\n",
    "    plt.ylim(-0.02, 1.25)\n",
    "    plt.savefig(output_path / \"figures\" / f\"specificity{key}.svg\", bbox_inches=\"tight\")\n",
    "    plt.savefig(output_path / \"figures\" / f\"specificity{key}.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "\n",
    "for key, dd in stuff.items():\n",
    "    baseline = dd['base']['average_rate'].to_numpy()\n",
    "    stimulated = dd['stim']['average_rate'].to_numpy()\n",
    "    plt.figure()\n",
    "    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
    "    plt.title(\"Average rate\")\n",
    "    plt.ylabel(\"spikes/s\")\n",
    "    plt.ylim(-0.2, 40)\n",
    "\n",
    "    plt.savefig(output_path / \"figures\" / f\"average_rate{key}.svg\", bbox_inches=\"tight\")\n",
    "    plt.savefig(output_path / \"figures\" / f\"average_rate{key}.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "for key, dd in stuff.items():\n",
    "    baseline = dd['base']['max_rate'].to_numpy()\n",
    "    stimulated = dd['stim']['max_rate'].to_numpy()\n",
    "    plt.figure()\n",
    "    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
    "    plt.title(\"Max rate\")\n",
    "    plt.ylabel(\"spikes/s\")\n",
    "    # plt.ylim(-0.2, 45)\n",
    "\n",
    "    plt.savefig(output_path / \"figures\" / f\"max_rate{key}.svg\", bbox_inches=\"tight\")\n",
    "    plt.savefig(output_path / \"figures\" / f\"max_rate{key}.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for key, dd in stuff.items():\n",
    "    baseline = dd['base']['interspike_interval_cv'].to_numpy()\n",
    "    stimulated = dd['stim']['interspike_interval_cv'].to_numpy()\n",
    "    plt.figure()\n",
    "    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
    "    plt.title(\"ISI CV\")\n",
    "    plt.ylabel(\"Coefficient of variation\")\n",
    "    # plt.ylim(0.9, 5)\n",
    "\n",
    "    plt.savefig(output_path / \"figures\" / f\"isi_cv{key}.svg\", bbox_inches=\"tight\")\n",
    "    plt.savefig(output_path / \"figures\" / f\"isi_cv{key}.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for key, dd in stuff.items():\n",
    "    baseline = dd['base']['in_field_mean_rate'].to_numpy()\n",
    "    stimulated = dd['stim']['in_field_mean_rate'].to_numpy()\n",
    "    plt.figure()\n",
    "    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
    "    plt.title(\"In-field rate\")\n",
    "    plt.ylabel(\"spikes/s\")\n",
    "    # plt.ylim(-0.1, 18)\n",
    "\n",
    "    plt.savefig(output_path / \"figures\" / f\"in_field_mean_rate{key}.svg\", bbox_inches=\"tight\")\n",
    "    plt.savefig(output_path / \"figures\" / f\"in_field_mean_rate{key}.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for key, dd in stuff.items():\n",
    "    baseline = dd['base']['out_field_mean_rate'].to_numpy()\n",
    "    stimulated = dd['stim']['out_field_mean_rate'].to_numpy()\n",
    "    plt.figure()\n",
    "    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
    "    plt.title(\"Out-of-field rate\")\n",
    "    plt.ylabel(\"spikes/s\")\n",
    "    # plt.ylim(-0.2, 8)\n",
    "\n",
    "    plt.savefig(output_path / \"figures\" / f\"out_field_mean_rate{key}.svg\", bbox_inches=\"tight\")\n",
    "    plt.savefig(output_path / \"figures\" / f\"out_field_mean_rate{key}.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, dd in stuff.items():\n",
    "    baseline = dd['base']['burst_event_ratio'].to_numpy()\n",
    "    stimulated = dd['stim']['burst_event_ratio'].to_numpy()\n",
    "    plt.figure()\n",
    "    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
    "    plt.title(\"Bursting ratio\")\n",
    "    plt.ylabel(\"\")\n",
    "    # plt.ylim(-0.02, 0.60)\n",
    "\n",
    "    plt.savefig(output_path / \"figures\" / f\"burst_event_ratio{key}.svg\", bbox_inches=\"tight\")\n",
    "    plt.savefig(output_path / \"figures\" / f\"burst_event_ratio{key}.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, dd in stuff.items():\n",
    "    baseline = dd['base']['max_field_mean_rate'].to_numpy()\n",
    "    stimulated = dd['stim']['max_field_mean_rate'].to_numpy()\n",
    "    plt.figure()\n",
    "    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
    "    plt.title(\"Mean rate of max field\")\n",
    "    plt.ylabel(\"(spikes/s)\")\n",
    "    # plt.ylim(-0.5,25)\n",
    "\n",
    "    plt.savefig(output_path / \"figures\" / f\"max_field_mean_rate{key}.svg\", bbox_inches=\"tight\")\n",
    "    plt.savefig(output_path / \"figures\" / f\"max_field_mean_rate{key}.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, dd in stuff.items():\n",
    "    baseline = dd['base']['bursty_spike_ratio'].to_numpy()\n",
    "    stimulated = dd['stim']['bursty_spike_ratio'].to_numpy()\n",
    "    plt.figure()\n",
    "    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
    "    plt.title(\"ratio of spikes per burst\")\n",
    "    plt.ylabel(\"\")\n",
    "    # plt.ylim(-0.03,0.9)\n",
    "\n",
    "    plt.savefig(output_path / \"figures\" / f\"bursty_spike_ratio{key}.svg\", bbox_inches=\"tight\")\n",
    "    plt.savefig(output_path / \"figures\" / f\"bursty_spike_ratio{key}.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for key, dd in stuff.items():\n",
    "    baseline = dd['base']['gridness'].to_numpy()\n",
    "    stimulated = dd['stim']['gridness'].to_numpy()\n",
    "    plt.figure()\n",
    "    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
    "    plt.title(\"Gridness\")\n",
    "    plt.ylabel(\"Gridness\")\n",
    "    plt.ylim(-0.6, 1.5)\n",
    "\n",
    "    plt.savefig(output_path / \"figures\" / f\"gridness{key}.svg\", bbox_inches=\"tight\")\n",
    "    plt.savefig(output_path / \"figures\" / f\"gridness{key}.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, dd in stuff.items(): #TODO narrow broad spiking\n",
    "    baseline = dd['base']['speed_score'].to_numpy()\n",
    "    stimulated = dd['stim']['speed_score'].to_numpy()\n",
    "    plt.figure()\n",
    "    violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
    "    plt.title(\"Speed score\")\n",
    "    plt.ylabel(\"Speed score\")\n",
    "    # plt.ylim(-0.1, 0.5)\n",
    "\n",
    "    plt.savefig(output_path / \"figures\" / f\"speed_score{key}.svg\", bbox_inches=\"tight\")\n",
    "    plt.savefig(output_path / \"figures\" / f\"speed_score{key}.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# inihibitory cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stim_action = actions['stimulus-response']\n",
    "stim_results = pd.read_csv(stim_action.data_path('results'))\n",
    "# stim_results has old unit id's but correct on (action, unit_name, channel_group)\n",
    "stim_results = stim_results.drop('unit_id', axis=1)\n",
    "\n",
    "data = data.merge(stim_results, how='left')\n",
    "\n",
    "waveform_action = actions['waveform-analysis']\n",
    "waveform_results = pd.read_csv(waveform_action.data_path('results')).drop('template', axis=1)\n",
    "\n",
    "data = data.merge(waveform_results, how='left')\n",
    "\n",
    "data.bs = data.bs.astype(bool)\n",
    "\n",
    "data.loc[data.eval('t_i_peak == t_i_peak and not bs'), 'ns_inhibited'] = True\n",
    "data.ns_inhibited.fillna(False, inplace=True)\n",
    "\n",
    "data.loc[data.eval('t_i_peak != t_i_peak and not bs'), 'ns_not_inhibited'] = True\n",
    "data.ns_not_inhibited.fillna(False, inplace=True)\n",
    "\n",
    "# make baseline for inhibited vs not inhibited\n",
    "data.loc[data.unit_id.isin(data.query('ns_inhibited').unit_id.values), 'ns_inhibited'] = True\n",
    "data.loc[data.unit_id.isin(data.query('ns_not_inhibited').unit_id.values), 'ns_not_inhibited'] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "baseline = data.query('ns_inhibited and baseline and i')['speed_score'].to_numpy()\n",
    "stimulated = data.query('ns_inhibited and stimulated')['speed_score'].to_numpy()\n",
    "plt.figure()\n",
    "violinplot(baseline, stimulated, xticks=label[''], colors=colors[''])\n",
    "plt.title(\"Speed score\")\n",
    "plt.ylabel(\"Speed score\")\n",
    "# plt.ylim(-0.1, 0.5)\n",
    "\n",
    "plt.savefig(output_path / \"figures\" / f\"speed_score_ns_inhibited.svg\", bbox_inches=\"tight\")\n",
    "plt.savefig(output_path / \"figures\" / f\"speed_score_ns_inhibited.png\", dpi=600, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Register in Expipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "action = project.require_action(\"comparisons-gridcells\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy_tree(output_path, str(action.data_path()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "septum_mec.analysis.registration.store_notebook(action, \"20_comparisons_gridcells.ipynb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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