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{
"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": [
2019-10-17 17:49:59 +00:00
"19:42:33 [I] klustakwik KlustaKwik2 version 0.2.6\n",
2019-10-16 05:30:40 +00:00
"/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",
"from matplotlib import colors\n",
"import seaborn as sns\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\n",
"\n",
"from tqdm import tqdm_notebook as tqdm\n",
"from tqdm._tqdm_notebook import tqdm_notebook\n",
"tqdm_notebook.pandas()"
]
},
{
"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\") / \"longitudinal-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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" }\n",
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" .dataframe thead th {\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>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",
" <th>stim_location</th>\n",
" <th>stimulated</th>\n",
" <th>tag</th>\n",
" <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",
" <tbody>\n",
" <tr>\n",
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" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
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" <td>baseline ii</td>\n",
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" <td>0.397921</td>\n",
" <td>0.676486</td>\n",
" <td>-0.459487</td>\n",
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" <td>0.277121</td>\n",
" <td>-0.615405</td>\n",
" <td>0.311180</td>\n",
" <td>0.191375</td>\n",
" <td>0.032266</td>\n",
" <td>1.821598</td>\n",
" <td>0.014624</td>\n",
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" <td>True</td>\n",
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" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
" <td>...</td>\n",
" <td>0.373466</td>\n",
" <td>0.658748</td>\n",
" <td>-0.527711</td>\n",
" <td>0.131660</td>\n",
" <td>3.833587</td>\n",
" <td>0.336590</td>\n",
" <td>4.407614</td>\n",
" <td>0.121115</td>\n",
" <td>0.542877</td>\n",
" <td>27.758541</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",
" <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>0.097464</td>\n",
" <td>0.196189</td>\n",
" <td>-0.641543</td>\n",
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" <td>0.153740</td>\n",
" <td>0.068626</td>\n",
" <td>6.128601</td>\n",
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" <td>0.484916</td>\n",
" <td>11.309932</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>0.248036</td>\n",
" <td>0.461250</td>\n",
" <td>-0.085292</td>\n",
" <td>0.198676</td>\n",
" <td>0.526720</td>\n",
" <td>0.033667</td>\n",
" <td>1.602362</td>\n",
" <td>0.051825</td>\n",
" <td>0.646571</td>\n",
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"</table>\n",
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"<p>5 rows ×
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"</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 ... burst_event_ratio \\\n",
"0 NaN False baseline ii ... 0.397921 \n",
"1 NaN False baseline ii ... 0.146481 \n",
"2 NaN False baseline ii ... 0.373466 \n",
"3 NaN False baseline ii ... 0.097464 \n",
"4 NaN False baseline ii ... 0.248036 \n",
"\n",
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" bursty_spike_ratio gridness border_score information_rate \\\n",
"0 0.676486 -0.459487 0.078474 0.965845 \n",
"1 0.277121 -0.615405 0.311180 0.191375 \n",
"2 0.658748 -0.527711 0.131660 3.833587 \n",
"3 0.196189 -0.641543 0.274989 0.153740 \n",
"4 0.461250 -0.085292 0.198676 0.526720 \n",
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"\n",
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" information_specificity head_mean_ang head_mean_vec_len spacing \\\n",
"0 0.309723 5.788704 0.043321 0.624971 \n",
"1 0.032266 1.821598 0.014624 0.753333 \n",
"2 0.336590 4.407614 0.121115 0.542877 \n",
"3 0.068626 6.128601 0.099223 0.484916 \n",
"4 0.033667 1.602362 0.051825 0.646571 \n",
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"\n",
" orientation \n",
"0 22.067900 \n",
"1 0.000000 \n",
"2 27.758541 \n",
"3 11.309932 \n",
"4 0.000000 \n",
"\n",
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"[5 rows x 39 columns]"
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]
},
"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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"source": [
"statistics['unit_day'] = statistics.apply(lambda x: str(x.unit_idnum) + '_' + x.action.split('-')[1], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
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"source": [
"stim_response_action = actions['stimulus-response']\n",
"stim_response_results = pd.read_csv(stim_response_action.data_path('results'))"
]
},
{
"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
"outputs": [],
"source": [
"statistics = pd.merge(statistics, stim_response_results, how='left')"
]
},
{
"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"N cells: 1284\n"
2019-10-16 05:30:40 +00:00
]
}
],
"source": [
"print('N cells:',statistics.shape[0])"
]
},
{
"cell_type": "code",
2019-10-17 17:49:59 +00:00
"execution_count": 9,
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"metadata": {},
"outputs": [
{
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" <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",
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],
"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 "
]
},
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"execution_count": 9,
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"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",
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"execution_count": 10,
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"metadata": {},
"outputs": [
{
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>3</td>\n",
" <td>NaN</td>\n",
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" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>0.239996</td>\n",
" <td>0.054074</td>\n",
" <td>0.262612</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</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>0.133677</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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"<p>5 rows ×
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"</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",
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" stim_location stimulated tag ... p_e_peak t_i_peak p_i_peak \\\n",
"0 NaN False baseline ii ... NaN NaN NaN \n",
"1 NaN False baseline ii ... NaN NaN NaN \n",
"2 NaN False baseline ii ... NaN NaN NaN \n",
"3 NaN False baseline ii ... NaN NaN NaN \n",
"4 NaN False baseline ii ... NaN NaN NaN \n",
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"\n",
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" border_score_threshold gridness_threshold head_mean_ang_threshold \\\n",
"0 0.332548 0.229073 6.029431 \n",
"1 0.354830 0.089333 6.120055 \n",
"2 0.264610 -0.121081 5.759406 \n",
"3 0.344280 0.215829 6.033364 \n",
"4 0.342799 0.218967 5.768170 \n",
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"\n",
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" head_mean_vec_len_threshold information_rate_threshold \\\n",
"0 0.205362 1.115825 \n",
"1 0.073566 0.223237 \n",
"2 0.150827 4.964984 \n",
"3 0.110495 0.239996 \n",
"4 0.054762 0.524990 \n",
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"\n",
" speed_score_threshold specificity \n",
"0 0.066736 0.445206 \n",
"1 0.052594 0.097485 \n",
"2 0.027120 0.393687 \n",
"3 0.054074 0.262612 \n",
"4 0.144702 0.133677 \n",
"\n",
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"[5 rows x 51 columns]"
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]
},
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"execution_count": 10,
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"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",
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"execution_count": 11,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"stimulated\n",
"False 624\n",
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"True 660\n",
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"Name: action, dtype: int64"
]
},
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"execution_count": 11,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby('stimulated').count()['action']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Find all cells with gridness above threshold"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Number of gridcells 225\n",
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"Number of animals 4\n"
]
}
],
"source": [
"query = 'gridness > gridness_threshold and information_rate > information_rate_threshold'\n",
"sessions_above_threshold = data.query(query)\n",
"print(\"Number of gridcells\", len(sessions_above_threshold))\n",
"print(\"Number of animals\", len(sessions_above_threshold.groupby(['entity'])))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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",
"]"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"## select neurons that have been characterized as a grid cell on the same day"
]
},
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{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
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"once_a_gridcell = statistics[statistics.unit_day.isin(sessions_above_threshold.unit_day.values)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# divide into stim not stim"
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]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Number of gridcells in baseline i sessions 76\n",
"Number of gridcells in stimulated 11Hz ms sessions 68\n",
"Number of gridcells in baseline ii sessions 64\n",
"Number of gridcells in stimulated 30Hz ms sessions 52\n"
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]
}
],
"source": [
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"baseline_i = once_a_gridcell.query('baseline and Hz11')\n",
"stimulated_11 = once_a_gridcell.query('stimulated and frequency==11 and stim_location==\"ms\"')\n",
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"\n",
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"baseline_ii = once_a_gridcell.query('baseline and Hz30')\n",
"stimulated_30 = once_a_gridcell.query('stimulated and frequency==30 and stim_location==\"ms\"')\n",
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"\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": [
"# Plotting\n",
"## TODO select units that are grid in baseline i"
]
},
{
"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
"outputs": [],
"source": [
"max_speed = 1, # m/s only used for speed score\n",
"min_speed = 0.02, # m/s only used for speed score\n",
"position_sampling_rate = 100 # for interpolation\n",
"position_low_pass_frequency = 6 # for low pass filtering of position\n",
"\n",
"box_size = [1.0, 1.0]\n",
"bin_size = 0.02\n",
"smoothing_low = 0.03\n",
"smoothing_high = 0.06"
]
},
{
"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
"outputs": [],
"source": [
"data_loader = dp.Data(\n",
" position_sampling_rate=position_sampling_rate, \n",
" position_low_pass_frequency=position_low_pass_frequency,\n",
" box_size=box_size, bin_size=bin_size\n",
")"
]
},
{
"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
"outputs": [],
"source": [
"results_corr = [[], [], []]\n",
"results_gridness = [[], [], []]\n",
"results_unit_name = [[], [], []]\n",
"results_unit_id = [[], [], []]\n",
"results_id_map = {}\n",
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"for nid, unit_sessions in once_a_gridcell.groupby('unit_id'):\n",
" base_i = unit_sessions.query(\"baseline and Hz11\")\n",
" base_ii = unit_sessions.query(\"baseline and Hz30\")\n",
" stim_i = unit_sessions.query(\"frequency==11\")\n",
" stim_ii = unit_sessions.query(\"frequency==30\")\n",
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" dfs = [(base_i, base_ii), (base_i, stim_i), (base_ii, stim_ii)]\n",
" for i, pair in enumerate(dfs):\n",
" for (_, row_1), (_, row_2) in zip(pair[0].iterrows(), pair[1].iterrows()):\n",
" rate_map_1 = data_loader.rate_map(\n",
" row_1['action'], row_1['channel_group'], row_1['unit_name'], smoothing_low)\n",
" rate_map_2 = data_loader.rate_map(\n",
" row_2['action'], row_2['channel_group'], row_2['unit_name'], smoothing_low)\n",
" results_corr[i].append(np.corrcoef(rate_map_1.ravel(), rate_map_2.ravel())[0,1])\n",
" results_gridness[i].append((row_1.gridness, row_2.gridness))\n",
" results_unit_name[i].append((\n",
" f'{row_1.action}_{row_1.channel_group}_{row_1.unit_name}', \n",
" f'{row_2.action}_{row_2.channel_group}_{row_2.unit_name}'))\n",
" assert row_1.unit_id == row_2.unit_id\n",
" uid = row_2.unit_id\n",
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" idnum = row_1.unit_idnum\n",
" results_id_map[uid] = idnum\n",
" results_unit_id[i].append(idnum)"
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]
},
{
"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n"
]
}
],
"source": [
"def session_id(row):\n",
" if row.baseline and row.i:\n",
" n = 0\n",
" elif row.stimulated and row.i:\n",
" n = 1\n",
" elif row.baseline and row.ii:\n",
" n = 2\n",
" elif row.stimulated and row.ii:\n",
" n = 3\n",
" else:\n",
" raise ValueError('what')\n",
" return n\n",
" \n",
"once_a_gridcell['session_id'] = once_a_gridcell.apply(session_id, axis=1)"
]
},
{
"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
"outputs": [],
"source": [
"plt.rc('axes', titlesize=12)\n",
"plt.rcParams.update({\n",
" 'font.size': 12, \n",
" 'figure.figsize': (6, 4), \n",
" 'figure.dpi': 150\n",
"})"
]
},
{
"cell_type": "code",
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"execution_count": 21,
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"metadata": {
"scrolled": true
},
"outputs": [
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{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/pyplot.py:514: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
" max_open_warning, RuntimeWarning)\n"
]
},
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{
"data": {
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 1200x600 with 4 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:49:59 +00:00
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2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 1200x1200 with 8 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 1200x1800 with 12 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1200x600 with 4 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 1200x600 with 4 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:49:59 +00:00
"image/png": "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
2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x1200 with 8 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:49:59 +00:00
"image/png": "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
2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:49:59 +00:00
"image/png": "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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1200x1800 with 12 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1200x1200 with 8 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:49:59 +00:00
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2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
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},
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},
{
"data": {
2019-10-17 17:49:59 +00:00
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2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:49:59 +00:00
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2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:49:59 +00:00
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2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:49:59 +00:00
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 1200x600 with 4 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:49:59 +00:00
"image/png": "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"text/plain": [
"<Figure size 1200x1800 with 12 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1200x600 with 4 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 1200x600 with 4 Axes>"
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]
},
"metadata": {
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},
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},
{
"data": {
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"text/plain": [
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"<Figure size 1200x600 with 4 Axes>"
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]
},
"metadata": {
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},
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{
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"text/plain": [
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"<Figure size 1200x600 with 4 Axes>"
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]
},
"metadata": {
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},
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},
{
"data": {
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1200x1200 with 8 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:49:59 +00:00
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2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:49:59 +00:00
"image/png": "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
2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:49:59 +00:00
"image/png": "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
2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x1800 with 12 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:49:59 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAABJgAAAJPCAYAAAAwv24dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3XmYJFWV/vG3srbuhm5W2ZVNPTqIG7gwbihuDKIjLugoiOgI7qD+xHFB0FFQRsUFBRXFjREVUEcHR0VBRR0dZRRGOcqOLfve3VVdVZn1++NG2tl5T1VFVURXZVd/P8/TT1bejOVGZOTNqtMRb/RNTk4KAAAAAAAAmKvGQncAAAAAAAAAGzcKTAAAAAAAAKiEAhMAAAAAAAAqocAEAAAAAACASigwAQAAAAAAoBIKTAAAAAAAAKiEAhMAAAAAAAAqocAEAAAAAACASigwAQAAAAAAoBIKTAAAAAAAAKiEAhMAAAAAAAAqocAEAAAAAACASigwAQAAAAAAoBIKTAAAAAAAAKhkYKE7AAAA5peZXSTpScXTd7r7+0rO9wlJry2e7u7u19bfO0TM7P6SjpH0NEn3kzQq6RpJ50n6tLvfUmIZ/ZKOkHSopL0lbSPpDkm/kvQpd79gDv16h6R/lXSGux892/kBAMDiwRlMAABs2t5lZg9e6E5gamZ2hKTLlIp7D5S0RNKWkh4h6b2S/s/M/mGGZewi6X8kfVapSLWDpEFJ20s6WNJ/mtlnzaz074Zm9ihJ75rt9gAAgMWJAhMAAJu2YUlnzqawgPljZgdK+pxSUWlE6Wyhp0t6hqSTJK2VtK2kb5jZI6dYxpaSLpb08KLpAkmHSNpP0tGSbijaX6GSBSMze0ixnOFZbxQAAFiUuEQOAADsJ+kNkk5d6I5gnaLo9wlJfZLGJD3B3X/TMcn3zex7kn4saamkk5WKT91OlrRH8fMH3f24jtd+aWbflPS/Smc1vc3MPubud07Tr4MlfUnSFnPbMgAAsBjxv5UAAGy6WpImip/fZ2Z7TDcx5t1TtK4w9Imu4pIkyd1/Ium7xdOnmdlWna8Xl8b9c/H0J13FpfYybpb0/uLpEknPijpjZluZ2UclfUupuNSc3eYAAIDFjAITAACbrnFJpxQ/L5P0mQXsC2LfkXS9UlFnKn/s+Pm+Xa8dqnW/7/3LNMs4V+mspI9I+kv3i2b295KuVDrTrU/SjZL+abqOAwCATQuXyAEAsGk7UdJzJT1I0lPM7J/dvVKhqQgNf52kAyTtolSQuEHpUq6Pu/sfppjvIqW726119yXTLP9ySXtJus7dd+t6bbL48VilM3s+IenxSsW0KyW9zd1/2DH9CkmvlPQcSQ+RtFzS7ZIulfR1SV9y9wl1MbPdlO7iJqX9922lO7QdXvRtuaSVkv5L0ofc/aqptmcqRT9/OOOE0q4dP9/Y9dqBxeMN7v7zadb1V6W+T+WBkraWNCnpi0r7l0vkAADA33AGEwAAmzB3X6tUYGkVTaeY2c5zXZ6ZvUvpjmevkWSSNlM6O8qUAqUvM7MTzKyvUsdndl9JlyhlEi1TKoY8UqnI1O7rkyVdIelDkp6oVEAZVMoiaodrX2pme86wrmVKhaAzlQpk2yqFX+8h6dUqcZe3uSru5PaPxdMfu/utXZPsXTz+T9d8yy3ZqeSqWpL+U9Jj3P2I6TKaAADApokzmAAA2MS5+yVmdpqk1ysVYk5XunX9rJjZCZLeXTz9vaRPFo8NSfsoXV61Z8c0J1Tp9wyOUTpz6oOS/kOpaPRwd7+26Ot+Smc4LVU6K+crkr4m6WZJu0s6Uqk49RBJPzWzfdy9++ygtg8Vy/+lpNMk/UnSjpJeK+lpSsWms8xsD3dfVWWjisLc5pIeIOkwSa8qln+n0lljndNuI2m74ul1Rdshkt4s6e87prtB6b36SFFwjHzZ3b9Ype8AAGBxo8AEAACklM9zsKTdJD3LzP7J3c8uO7OZPVLrbnH/JUlHdl1adomZnamUKbS/pOPN7GtTXS5Xg4ak97v7OzravlH0tV/p7KSlSmfmHOru3+iY7leSzjGz45UuIdxR0hmSnj3FunZQ2uYj3L19JpjM7NtK2/sPku4j6SBJ51TcrpcU6+p0iaRXuvsVXe3bdvx8l5mdLumoYJn3lXSSpIPN7FnR2Umd2wUAABDhEjkAACB3X610NkzbR83sPrNYxJuVfq+4XdLRUW5RsY4jlc4Y6lM6Y2pD+tQU7QcrZU5J0qe6ikt/4+7vkXRRex4z+7spljcq6ZjuIoy7T2r94PSHlen0DHYN2vaW9PruO8gpnenUdqRScelqpeDvrZQuXzxA6cwrKZ3V9OUa+ggAADZBFJgAAIAkyd1/IOnzxdNtJX28zHzFZVvtMOlL3H3NNOu4RuvuenbAHLtaxkp3z+6GVnhGx89nzLCcT3b8/MwppvmNu98xxWud4d7LZ1hXGRcrXXb3GKVL5H4paYVS5tVPzGy7jmmXdfx8v6Ivj3b3r7n7Xe6+xt1/pHRG2c+K6f5hQ+VFAQCAxY1L5AAAQKc3KRVSdpR0qJn9u7t/a4Z5dlM6I0aSnt1xJ7eZ7D63LpZywzSvPaR4XCXp8hmW88uOn/eeYpprp5m/M3Op8u9d7v6zjqe/MrOzlc6SOlJpu/5N6+4GN9I1+7HufnuwzLVm9nqlO+dJ0kuVAr0BAABK4wwmAADwN+5+l1I4ddunzGzLGWbbdobXpzJgZnWc1RO5Z5rXtikebysuY5vOzR0/bz3FNNMFd3cuv/Y75xWX5b1G0sqi6VAza5+5dG/HpCOSvjfNcv5XUvuMr8fU3U8AALD4UWACAADrcffzJX29eLqj0l3SptN5Zs7nJD1iFv+mvJxuGmV+f5mucDSbQk9/x889GXRd3Pntu8XTIa3Ll7qpY7Jb3X18hkW1z/qaa8EQAABswrhEDgAARF4n6SlKZ/scaWZfnWbazvyhZnE2zFy0i0IzFYC2mOPy29r93dbM+mY4i2n7YL55UYR27ylpB3f/zgyTd176NiRJ7n63mV2vlL/UHQAeGS4es7vIAQAAzIQzmAAAQMbdb5F0bEfTp5XuOha5WuvORHrsTMs2s+PM7Cgze2rXS+07zw2ZWX/3fMW8SyXN5u52kd8Xj5tL2muGaTu354qK652tL0n6taRvl7ij354dP3eGm7czpJZPcxc8mdmAJCueXjvLfgIAAFBgAgAAMXf/kqQLiqe7SXrJFNONS/px8XRvM3v8VMs0s6dIOlnS6ZLe3vXyXR0/7zbFIp4qaXC6fpfw/Y6fj5ph2qM7fv5BxfXO1k+Lxz6lEO+Qme0g6aDi6RVdd8/rPPPsNdOs6xCtKyB+c5b9BAAAoMAEAACmdZTWhUVPV9j5cMfPZ5nZfbsnMLPtlM6EavtY1yS/7/j59cH820s6ZdrelvNtSVcWP7/GzJ4bTWRm75L0pOLphRUu/ZurL0haXfz8djPL7mJXhKR/TeuKQyd3TfIdSX8ofn61mR0SLGM3SacWT++RdHa1bgMAgE0RGUwAAGBK7n6DmR0n6ZMzTPcjM/uUpFcrXa71OzM7VdLFxST7SnqTpJ2K5+e7e/eZMv8u6Xil30/eWBRPvippVOlStWOK+a/S+peEzXabmmZ2WNG3IUnfMLMvKwWb3yJpV0mvkPSMYpbbJL1sruur0M+bzOwtkj4laYWkXxX79CKlQtCjdt5554+uXJluIHfQQQfpQx/60FmSzupYhn7/+9/rsMMO0+joaKPRaJz7tre9TQceeKBWrFihSy+9VFtttZXuvDPFLp100kkrDjnkkM47503pwgsv1AEHHCBJOvTQQ4/SzGeDAZi92u8+OUcz3XETwMatlrGGAhMAAJjJ6ZJeJOmJM0z3eqVi0DFKodInTjHdeZJe2t3o7lea2TFKZzY1lC4L67w0rKV0Wd02kt48i/5n3P2XZvZMSecoZTodXvzr9ltJh7r7yirrmyt3P93MhpXO3Foi6W3FP0lSu7j04he/WO94xzvU15f/fvjQhz5UZ555po455hjdeuutOv/883X++eevN83AwIC
2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:49:59 +00:00
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2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:49:59 +00:00
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2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1200x1800 with 12 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 1200x1200 with 8 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:49:59 +00:00
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2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:49:59 +00:00
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2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
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},
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},
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"data": {
2019-10-17 17:49:59 +00:00
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2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:49:59 +00:00
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2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:49:59 +00:00
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2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 1200x1200 with 8 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1200x600 with 4 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for unit_id, id_num in results_id_map.items():\n",
" sessions = once_a_gridcell.query(f'unit_id==\"{unit_id}\"')\n",
" n_action = sessions.date.nunique()\n",
" fig, axs = plt.subplots(n_action, 4, sharey=True, sharex=True, figsize=(8, n_action*4))\n",
" sns.despine(left=True, bottom=True)\n",
" fig.suptitle(f'Neuron {id_num}')\n",
" if n_action == 1:\n",
" axs = [axs]\n",
" waxs = None\n",
" for ax, (date, rows) in zip(axs, sessions.groupby('date')):\n",
" entity = rows.iloc[0].entity\n",
" ax[0].set_ylabel(f'{entity}-{date}')\n",
" for _, row in rows.iterrows():\n",
" action_id = row['action']\n",
" channel_id = row['channel_group']\n",
" unit_name = row['unit_name']\n",
" rate_map = data_loader.rate_map(action_id, channel_id, unit_name, smoothing_low)\n",
" idx = row.session_id\n",
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" ax[idx].imshow(rate_map, origin='lower')\n",
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" ax[idx].set_title(f'{row.gridness:.2f} {row.max_rate:.2f}')\n",
" ax[idx].set_yticklabels([])\n",
" ax[idx].set_xticklabels([])\n",
" plt.tight_layout()\n",
" fig.savefig(output_path / 'figures' / f'neuron_{id_num}_rate_map.png', bbox_inches='tight')\n",
" fig.savefig(output_path / 'figures' / f'neuron_{id_num}_rate_map.svg', bbox_inches='tight')\n",
" \n",
" # waveforms\n",
"# template = data_loader.template(action_id, channel_id, unit_name)\n",
"# if waxs is None:\n",
"# wfig, waxs = plt.subplots(1, template.data.shape[0], sharey=True, sharex=True)\n",
"# for i, wax in enumerate(waxs):\n",
"# wax.plot(template.data[i,:]) \n",
"# if i > 0:\n",
"# ax.set_yticklabels([])"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"from scipy.interpolate import interp1d\n",
"\n",
"\n",
"for unit_id, id_num in results_id_map.items():\n",
" sessions = once_a_gridcell.query(f'unit_id==\"{unit_id}\"')\n",
" n_action = sessions.date.nunique()\n",
" fig, axs = plt.subplots(n_action, 4, sharey=True, sharex=True, figsize=(8, n_action*4))\n",
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" sns.despine(left=True, bottom=True)\n",
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" fig.suptitle(f'Neuron {id_num}')\n",
" if n_action == 1:\n",
" axs = [axs]\n",
" waxs = None\n",
" for ax, (date, rows) in zip(axs, sessions.groupby('date')):\n",
" entity = rows.iloc[0].entity\n",
" ax[0].set_ylabel(f'{entity}-{date}')\n",
" for _, row in rows.iterrows():\n",
" action_id = row['action']\n",
" channel_id = row['channel_group']\n",
" unit_name = row['unit_name']\n",
" idx = row.session_id\n",
" x, y, t, speed = map(data_loader.tracking(action_id).get, ['x', 'y', 't', 'v'])\n",
" ax[idx].plot(x, y, 'k', alpha=0.3)\n",
" spike_times = data_loader.spike_train(action_id, channel_id, unit_name)\n",
" spike_times = spike_times[(spike_times > min(t)) & (spike_times < max(t))]\n",
" x_spike = interp1d(t, x)(spike_times)\n",
" y_spike = interp1d(t, y)(spike_times)\n",
" ax[idx].set_xticks([])\n",
" ax[idx].set_yticks([])\n",
" ax[idx].scatter(x_spike, y_spike, marker='.', color=(0.7, 0.2, 0.2), s=1.5)\n",
" ax[idx].set_title(f'{row.session}')\n",
" ax[idx].set_yticklabels([])\n",
" ax[idx].set_xticklabels([])\n",
" for a in ax:\n",
" a.set_aspect(1)\n",
" plt.tight_layout()\n",
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" fig.savefig(\n",
" output_path / 'figures' / f'neuron_{id_num}_spike_map.png', \n",
" bbox_inches='tight', transparent=True)\n",
" fig.savefig(\n",
" output_path / 'figures' / f'neuron_{id_num}_spike_map.svg', \n",
" bbox_inches='tight')"
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]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
"outputs": [],
"source": [
"plt.rc('axes', titlesize=12)\n",
"plt.rcParams.update({\n",
" 'font.size': 12, \n",
" 'figure.figsize': (6, 4), \n",
" 'figure.dpi': 150\n",
"})\n",
"cmap = ['#1b9e77','#d95f02','#7570b3']"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
"source": [
"len(results_gridness)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
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"source": [
"msize = 9\n",
"fig = plt.figure()\n",
"ticks = []\n",
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"nuids = {}\n",
"n = 0\n",
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"for i, pairs in enumerate(results_gridness):\n",
" for j, pair in enumerate(pairs):\n",
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" nuid = results_unit_id[i][j]\n",
" if nuid not in nuids:\n",
" nuids[nuid] = n\n",
" n += 1\n",
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" plt.plot(\n",
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" nuids[nuid], np.diff(pair), \n",
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" color=cmap[i], marker='.', ls='none', markersize=msize)\n",
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"for l in range(n):\n",
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" plt.axvline(l, color='k', lw=.1, alpha=.5)\n",
"\n",
"from matplotlib.lines import Line2D\n",
"\n",
"labels = ['Baseline I vs baseline II', 'Baseline I vs stim I', 'Baseline II vs stim II']\n",
"custom_lines = [\n",
" Line2D([],[], color=cmap[i], marker='.', ls='none', label=label, markersize=msize) \n",
" for i, label in enumerate(labels)\n",
"]\n",
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"plt.ylabel('Difference in gridness')\n",
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"plt.xlabel('Neuron')\n",
"plt.legend(handles=custom_lines, bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n",
"fig.savefig(output_path / 'figures' / 'neuron_gridness.png', bbox_inches='tight')\n",
"fig.savefig(output_path / 'figures' / 'neuron_gridness.svg', bbox_inches='tight')"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"fig = plt.figure()\n",
"labels = ['Baseline I vs baseline II', 'Baseline I vs stim I', 'Baseline II vs stim II']\n",
"for i, pairs in enumerate(results_gridness):\n",
" for j, pair in enumerate(pairs):\n",
" plt.plot(*pair, color=cmap[i], marker='.', ls='none', markersize=msize)\n",
"# plt.scatter(*np.array(pairs).T, label=labels[i], color=cmap[i])\n",
"# plt.legend(bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n",
"custom_lines = [\n",
" Line2D([],[], color=cmap[i], marker='.', ls='none', label=label, markersize=msize) \n",
" for i, label in enumerate(labels)\n",
"]\n",
"plt.legend(handles=custom_lines, bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n",
"plt.ylabel('Gridness')\n",
"plt.xlabel('Baseline gridness')\n",
"lim = [-.7, 1.35]\n",
"plt.ylim(lim)\n",
"plt.xlim(lim)\n",
"plt.plot(lim, lim, '--k', alpha=.5, lw=1)\n",
"fig.savefig(output_path / 'figures' / 'baseline_gridness_vs_other.png', bbox_inches='tight')\n",
"fig.savefig(output_path / 'figures' / 'baseline_gridness_vs_other.svg', bbox_inches='tight')"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"fig = plt.figure()\n",
"import matplotlib\n",
"cNorm = matplotlib.colors.Normalize(vmin=-np.pi/2, vmax=np.pi/2)\n",
"scalarMap = plt.cm.ScalarMappable(norm=cNorm, cmap=plt.cm.Blues)\n",
"\n",
"ticks = []\n",
"for i, pairs in enumerate(results_gridness):\n",
" for j, pair in enumerate(pairs):\n",
" angle = float(np.arctan(np.diff(pair) / 0.9))\n",
" color = scalarMap.to_rgba(angle)\n",
"# color = plt.cm.Paired((np.sign(angle)+1)/14)\n",
" tick = (i, i+.8)\n",
" plt.plot(tick, pair, marker='.', color=color)\n",
" ticks.append(tick)\n",
"plt.xticks(\n",
" [t for tick in ticks for t in tick], \n",
" ['Baseline I', 'Baseline II', 'Baseline I', 'Stimulation I', 'Baseline II', 'Stimulation II'],\n",
" rotation=-45, ha='left'\n",
")\n",
"plt.ylabel('Gridness')\n",
"fig.savefig(output_path / 'figures' / 'stickplot_gridness.png', bbox_inches='tight')\n",
"fig.savefig(output_path / 'figures' / 'stickplot_gridness.svg', bbox_inches='tight')"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"fig = plt.figure()\n",
"ticks = [0,0.6,1.2]\n",
"\n",
"diff_res = [[], [], []]\n",
"for i, pairs in enumerate(results_gridness):\n",
" for j, pair in enumerate(pairs):\n",
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"# if results_unit_id[i][j] in [results_id_map[i] for i in exclude]:\n",
"# continue\n",
" diff_res[i].append(np.diff(pair))\n",
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"violins = plt.violinplot(\n",
" diff_res, ticks, showmedians=True, showextrema=False, points=1000, bw_method=.2)\n",
"\n",
"\n",
"for category in ['cbars', 'cmins', 'cmaxes', 'cmedians']:\n",
" if category in violins:\n",
" violins[category].set_color(['k', 'k'])\n",
" violins[category].set_linewidth(2.0)\n",
" \n",
"colors = plt.cm.Paired(np.linspace(0,1,12))\n",
" \n",
"for pc, c in zip(violins['bodies'], cmap):\n",
" pc.set_facecolor(c)\n",
" pc.set_edgecolor(c)\n",
" \n",
"plt.xticks(ticks, ['baseline', 'stim i', 'stim ii'])\n",
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"plt.ylabel('Difference in gridness')\n",
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"\n",
"plt.gca().spines['top'].set_visible(False)\n",
"plt.gca().spines['right'].set_visible(False)\n",
"fig.savefig(output_path / 'figures' / 'violins_gridness_difference.png', bbox_inches='tight')\n",
"fig.savefig(output_path / 'figures' / 'violins_gridness_difference.svg', bbox_inches='tight')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Save to expipe"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
"outputs": [],
"source": [
"action = project.require_action(\"longitudinal-comparisons-gridcells\")"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"copy_tree(output_path, str(action.data_path()))"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
"outputs": [],
"source": [
"septum_mec.analysis.registration.store_notebook(action, \"20_longitudinal_comparisons_gridcells.ipynb\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
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