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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": [
2019-12-16 15:16:33 +00:00
"13:55:01 [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",
2019-12-13 10:43:57 +00:00
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:25: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
"Please use `tqdm.notebook.*` instead of `tqdm._tqdm_notebook.*`\n"
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]
}
],
"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",
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"from septum_mec.analysis.plotting import violinplot, despine\n",
"from spatial_maps.fields import find_peaks, calculate_field_centers, separate_fields_by_laplace\n",
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"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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" <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",
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" <td>0.398230</td>\n",
" <td>0.678064</td>\n",
" <td>-0.466923</td>\n",
" <td>0.029328</td>\n",
" <td>1.009215</td>\n",
" <td>0.317256</td>\n",
" <td>5.438033</td>\n",
" <td>0.040874</td>\n",
" <td>0.628784</td>\n",
" <td>20.224859</td>\n",
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" </tr>\n",
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" <td>0.138014</td>\n",
" <td>0.263173</td>\n",
" <td>-0.666792</td>\n",
" <td>0.308146</td>\n",
" <td>0.192524</td>\n",
" <td>0.033447</td>\n",
" <td>1.951740</td>\n",
" <td>0.017289</td>\n",
" <td>0.789388</td>\n",
" <td>27.897271</td>\n",
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" </tr>\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",
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" <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",
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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",
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" <td>0.087413</td>\n",
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" <td>-0.437492</td>\n",
" <td>0.268948</td>\n",
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" <td>0.073553</td>\n",
" <td>6.215195</td>\n",
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" <td>9.462322</td>\n",
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" </tr>\n",
" <tr>\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",
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" <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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" <td>0.000000</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",
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"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",
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"\n",
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" bursty_spike_ratio gridness border_score information_rate \\\n",
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"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",
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"\n",
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" information_specificity head_mean_ang head_mean_vec_len spacing \\\n",
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"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",
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"\n",
" orientation \n",
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"0 20.224859 \n",
"1 27.897271 \n",
"2 28.810794 \n",
"3 9.462322 \n",
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"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"
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]
}
],
"source": [
"print('N cells:',statistics.shape[0])"
]
},
{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
"outputs": [
{
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" <td>0.367498</td>\n",
" <td>0.266865</td>\n",
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" <td>0.182843</td>\n",
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" <th>3</th>\n",
" <td>0.331942</td>\n",
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" <td>0.0</td>\n",
" <td>223.0</td>\n",
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" <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",
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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",
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"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>6.120055</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.264610</td>\n",
" <td>-0.121081</td>\n",
" <td>5.759406</td>\n",
" <td>0.150827</td>\n",
" <td>4.964984</td>\n",
" <td>0.027120</td>\n",
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" <td>0.400770</td>\n",
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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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</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",
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" <td>0.269461</td>\n",
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" </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",
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" <td>0.133410</td>\n",
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" </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",
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"0 0.066736 0.451741 \n",
"1 0.052594 0.098517 \n",
"2 0.027120 0.400770 \n",
"3 0.054074 0.269461 \n",
"4 0.144702 0.133410 \n",
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"\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 sessions above threshold 194\n",
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"Number of animals 4\n"
]
}
],
"source": [
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"query = (\n",
" 'gridness > gridness_threshold and '\n",
" 'information_rate > information_rate_threshold and '\n",
" 'gridness > .2 and '\n",
" 'average_rate < 25'\n",
")\n",
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"sessions_above_threshold = data.query(query)\n",
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"print(\"Number of sessions above threshold\", len(sessions_above_threshold))\n",
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"print(\"Number of animals\", len(sessions_above_threshold.groupby(['entity'])))"
]
},
{
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"cell_type": "markdown",
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"metadata": {},
"source": [
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"## select neurons that have been characterized as a grid cell on the same day"
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]
},
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{
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"cell_type": "code",
"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"once_a_gridcell = statistics[statistics.unit_day.isin(sessions_above_threshold.unit_day.values)]"
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]
},
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{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of gridcells 139\n",
"Number of gridcell recordings 231\n",
"Number of animals 4\n"
]
}
],
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"source": [
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"print(\"Number of gridcells\", once_a_gridcell.unit_idnum.nunique())\n",
"print(\"Number of gridcell recordings\", len(once_a_gridcell))\n",
"print(\"Number of animals\", len(once_a_gridcell.groupby(['entity'])))"
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]
},
{
"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 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"
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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": [
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"# Plotting"
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]
},
{
"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
"outputs": [],
"source": [
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"max_speed = .5 # m/s only used for speed score\n",
"min_speed = 0.02 # m/s only used for speed score\n",
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"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",
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"smoothing_high = 0.06\n",
"\n",
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"speed_binsize = 0.02\n",
"\n",
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"stim_mask = True\n",
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"# baseline_duration = 600\n",
"baseline_duration = None"
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]
},
{
"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",
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" box_size=box_size, bin_size=bin_size, \n",
" stim_mask=stim_mask, baseline_duration=baseline_duration\n",
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")"
]
},
{
"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
"outputs": [],
"source": [
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"def fftcorrelate2d(arr1, arr2, normalize=False, **kwargs):\n",
" from copy import copy\n",
" arr1 = copy(arr1)\n",
" arr2 = copy(arr2)\n",
" from astropy.convolution import convolve_fft\n",
" if normalize:\n",
" # https://stackoverflow.com/questions/53436231/normalized-cross-correlation-in-python\n",
" a_ = arr1.ravel()\n",
" v_ = arr2.ravel()\n",
" arr1 = (arr1 - np.mean(a_)) / (np.std(a_) * len(a_))\n",
" arr2 = (arr2 - np.mean(v_)) / np.std(v_)\n",
" corr = convolve_fft(arr1, np.fliplr(np.flipud(arr2)), normalize_kernel=False, **kwargs)\n",
" return corr\n",
"\n",
"\n",
"def cross_correlation_distance(r1, r2):\n",
" r12 = fftcorrelate2d(r1, r2)\n",
" labels = separate_fields_by_laplace(r12, threshold=0)\n",
" peaks = calculate_field_centers(r12, labels)\n",
" centered_peaks = peaks - np.array(r1.shape) / 2\n",
" offset = np.linalg.norm(centered_peaks, axis=1)\n",
" distance_idx = np.argmin(offset)\n",
" distance = offset[distance_idx]\n",
" angle = np.arctan2(*centered_peaks[distance_idx])\n",
" \n",
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" return distance, angle\n",
"\n",
"\n",
"def cross_correlation_centre_of_mass(r1, r2):\n",
" from scipy import ndimage\n",
" r12 = fftcorrelate2d(r1, r2)\n",
" cntr = ndimage.center_of_mass(r12)\n",
" \n",
" centered_cntr = cntr - np.array(r1.shape) / 2\n",
" distance = np.linalg.norm(centered_cntr)\n",
" angle = np.arctan2(*centered_cntr)\n",
" \n",
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" return distance, angle"
]
},
{
"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mikkel/apps/expipe-project/septum-mec/septum_mec/analysis/data_processing.py:590: RuntimeWarning: divide by zero encountered in true_divide\n",
" rate_map_1 = smooth_spike_map_1 / smooth_occupancy_map_1\n",
"/home/mikkel/apps/expipe-project/septum-mec/septum_mec/analysis/data_processing.py:590: RuntimeWarning: invalid value encountered in true_divide\n",
" rate_map_1 = smooth_spike_map_1 / smooth_occupancy_map_1\n",
"/home/mikkel/apps/expipe-project/septum-mec/septum_mec/analysis/data_processing.py:591: RuntimeWarning: divide by zero encountered in true_divide\n",
" rate_map_2 = smooth_spike_map_2 / smooth_occupancy_map_2\n",
"/home/mikkel/apps/expipe-project/septum-mec/septum_mec/analysis/data_processing.py:591: RuntimeWarning: invalid value encountered in true_divide\n",
" rate_map_2 = smooth_spike_map_2 / smooth_occupancy_map_2\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/fromnumeric.py:90: RuntimeWarning: invalid value encountered in reduce\n",
" return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n"
]
}
],
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"source": [
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"results_xcorr_displacement = [[], [], [], [], []]\n",
"results_xcorr_cntr_mass = [[], [], [], [], []]\n",
"results_gridness = [[], [], [], [], []]\n",
"results_maxrate = [[], [], [], [], []]\n",
"results_avgrate = [[], [], [], [], []]\n",
"results_unit_name = [[], [], [], [], []]\n",
"results_unit_id = [[], [], [], [], []]\n",
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"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_i), (base_i, base_ii), (base_i, stim_i), (base_ii, stim_ii), (base_i, stim_ii)]\n",
2019-10-16 05:30:40 +00:00
" for i, pair in enumerate(dfs):\n",
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" same_frame = pair[0].equals(pair[1])\n",
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" for (_, row_1), (_, row_2) in zip(pair[0].iterrows(), pair[1].iterrows()):\n",
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" if same_frame:\n",
" assert row_1.equals(row_2)\n",
" rate_map_1, rate_map_2 = data_loader.rate_map_split(\n",
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" row_1['action'], row_1['channel_group'], row_1['unit_name'], smoothing_low)\n",
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" results_gridness[i].append((sp.gridness(rate_map_1), sp.gridness(rate_map_2)))\n",
"\n",
" results_maxrate[i].append((rate_map_1.max(), rate_map_2.max()))\n",
"\n",
" results_avgrate[i].append((np.nanmean(rate_map_1), np.nanmean(rate_map_2)))\n",
"\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",
" else:\n",
" assert not row_1.equals(row_2)\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",
"\n",
" results_gridness[i].append((row_1.gridness, row_2.gridness))\n",
"\n",
" results_maxrate[i].append((row_1.max_rate, row_2.max_rate))\n",
"\n",
" results_avgrate[i].append((row_1.average_rate, row_2.average_rate))\n",
"\n",
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" 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",
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" results_unit_id[i].append(idnum)\n",
" \n",
" results_xcorr_displacement[i].append(cross_correlation_distance(rate_map_1, rate_map_2))\n",
" results_xcorr_cntr_mass[i].append(cross_correlation_centre_of_mass(rate_map_1, rate_map_2))"
]
},
{
"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"action True\n",
"channel_group True\n",
"unit_name True\n",
"Name: 1030, dtype: bool"
]
},
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"execution_count": 20,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"row_id = ['action', 'channel_group', 'unit_name']\n",
"row_1.loc[row_id].eq(row_2.loc[row_id])"
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]
},
{
"cell_type": "code",
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"execution_count": 21,
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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",
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"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
2019-10-16 05:30:40 +00:00
" \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": 22,
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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": 22,
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"metadata": {
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"scrolled": true
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},
"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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"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": {
2019-10-22 10:22:00 +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": {
2019-10-22 10:22:00 +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": {
2019-10-22 10:22:00 +00:00
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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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"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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"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>"
2019-10-16 05:30:40 +00:00
]
},
"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": [
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"<Figure size 1200x1200 with 8 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-22 10:22:00 +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": {
2019-10-22 10:22:00 +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": {
2019-10-22 10:22:00 +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": {
2019-10-22 10:22:00 +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": {
2019-10-22 10:22:00 +00:00
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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": {
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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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"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": {
2019-10-22 10:22:00 +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": {
2019-10-22 10:22:00 +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": {
2019-10-22 10:22:00 +00:00
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"text/plain": [
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"<Figure size 1200x600 with 4 Axes>"
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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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{
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"text/plain": [
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"<Figure size 1200x600 with 4 Axes>"
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]
},
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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": {
"needs_background": "light"
},
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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": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-22 10:22:00 +00:00
"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": {
2019-10-22 10:22:00 +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": {
2019-10-22 10:22:00 +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 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": [
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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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"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": [
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"<Figure size 1200x600 with 4 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-22 10:22:00 +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": {
2019-10-22 10:22:00 +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": {
2019-10-22 10:22:00 +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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
2019-10-16 05:30:40 +00:00
"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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
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": {
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"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"
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}
],
"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",
" rows = rows.sort_values('session')\n",
" entity = rows.iloc[0].entity\n",
" ax[0].set_ylabel(f'{entity}-{date}')\n",
" vmax = None\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",
" if vmax is None:\n",
" vmax = rate_map.max()\n",
" ax[idx].imshow(rate_map, origin='lower', vmax=vmax)\n",
" ax[idx].set_title(f'{row.gridness:.2f} {row.max_rate:.2f} {row.average_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",
"execution_count": 23,
"metadata": {
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"scrolled": true
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},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
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},
{
"data": {
2019-10-22 10:22:00 +00:00
"image/png": "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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
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"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
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},
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"metadata": {},
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"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
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"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1200x1800 with 12 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1200x1800 with 12 Axes>"
]
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},
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"metadata": {},
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"output_type": "display_data"
},
{
"data": {
2019-10-22 10:22:00 +00:00
"image/png": "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
2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
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"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x1200 with 8 Axes>"
]
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},
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"metadata": {},
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"output_type": "display_data"
},
{
"data": {
2019-10-22 10:22:00 +00:00
"image/png": "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
2019-10-16 05:30:40 +00:00
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
2019-10-22 10:22:00 +00:00
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x1200 with 8 Axes>"
]
2019-10-16 05:30:40 +00:00
},
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"metadata": {},
2019-10-16 05:30:40 +00:00
"output_type": "display_data"
},
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAABJgAAAG/CAYAAAAUxW2ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xl8nGW9///XTPakbdI06d4kXejdlrUFARUERQ96RDjHjaPI+SkelKMg4nHhy+GguCDIIoIoHtz1CAiKO4Lsm2UpaxfupkvSNG3TpGmSZl9mfn/MZDpJk3SZQou8no9HH5nlvu+5ZiadufK+r+tzReLxOJIkSZIkSdK+ih7oBkiSJEmSJOm1zYBJkiRJkiRJGTFgkiRJkiRJUkYMmCRJkiRJkpQRAyZJkiRJkiRlxIBJkiRJkiRJGTFgkiRJkiRJUkYMmCRJkiRJkpQRAyZJkiRJkiRlxIBJkiRJkiRJGTFgkiRJkiRJUkYMmCRJkiRJkpQRAyZJkiRJkiRlxIBJkiRJkiRJGck+0A2QJEmvviAIHgJOSl69NAzDb+zhft8FPp28OjsMw5r93zqNJAiC6cCngFOBeUAR0Aw8B9wG/F8Yhv1j7P9D4ON7+HCjvrdBELwJ+CzwZqAc2Aa8APwoDMM79vD4kiTpH4wjmCRJ0v8EQbDwQDdCowuC4INACPw3cAxQAuQAU4B3Aj8FngiCYMYYh1m8H9rxZeAx4APA9GQbppIIvX4dBMFdQRDkZfo4kiTptceASZIk5QE/CoLAfsFBKAiCU4BfAeOAbuA64J+A44APAY8kN30D8JcgCApHOEY2cGjy6g9JhE1j/ds0wjH+A/gKEAHWAOcAxwNnAk8mN/sX4HsZPF1JkvQa5RQ5SZIE8EbgM8D1B7oh2ikIggjwXSCLRLj01jAMl6Zt8lQQBLeTCHXOA44gMX3timGHWkgiSAS4LwzD5/eyHaXA1cmr1cBxYRhuT15/MgiC3wK/AU4HzgmC4AdhGD61N48hSZJe2zxTKUnS61sMGKzb840gCOYcyMZoF28EFiQv3zAsXAIgDMM4cBGwNXnTv49wnKPSLu9VuJT0MRLT8gAuTguXBtvQD3wC6Eze9IV9eAxJkvQaZsAkSdLrWx87R6YUArccwLZoVyemXf7DaBuFYdhNojYSQDBCHaTB+ksdJEYg7a33Jn+2Ar8fpQ0NwJ+TV/95pKl6kiTpH5dT5CRJ0uXAv5IYKfO2IAjODcMwo6ApWTT8fOAUYCaJuj11wIPAjWEYrhxlv4dIrG7XE4Zh/hjHX06iplBtGIZVw+6LJy9eRCLw+C5wAokwbQ2JETj3pW0/AfgP4AzgMGA8iZXRngPuAH4x0upsQRBUAeuTV/+VRAD0URIjiA5NHqceuAe4NgzDtaM9nzE8BXyTREHtNbvZNpJ2OR/oSbs+OILphTAMY3vTgCAIckjUdwJ4LAzDgTE2f4REAfBCEvWZHtibx5IkSa9dBkySJL3OhWHYkyzg/AiJ0c1XB0HwlzAM6/fleEEQ/A/wZRJ1g4bclfz3iSAIvgZcnpze9UqZBTwOlKfdtoS0oCYIgrcC/wdMG7bvVOBdyX+fC4LgX3YTEBUC9wFvHXb7HOA/SdQlem8Yhn/ZmycQhuGDJEK5MSVDoDcnr7aGYdg6bJMjkz+fD4LgdHYW6C4Fmki8Tt9LPt5w80isFge7H/2U/hotxIBJkqTXDafISZIkwjB8HLgpebUYuHlfjhMEwVeAr5IIl14kUXj6TSRGEF1IIoCIkgigvpxRo3fvs0AZ8C0SU80+AFwRhmFNsq1vJDHCaRoQB35Jokj1ccC/Afcmj3MY8GgQBMNDqHTXkgiXlgJnJ4/xL8DfkvfnAT8NgmDcfnpuw50DTE5evif9jiAIKkgESQAfITHF7QxgCongaBrwfuCBIAh+kFxxLt2MtMsbdtOOulH2kyRJ/+AcwSRJkgb9P+A9QBVwWhAEHw7D8Fd7unMQBEuA/0le/QVwzrCpZY8HQfAj4E/AycBlQRD8erTpcvtBlESg9N9pt92ZbGsW8GOggESh8zPDMLwzbbungNuDILiMxBTCacAPSARQI5lK4jl/NH0KWhAEfyDxfP+ZxEiqdwO3Z/7UdgqCYB5wZdpN1w7bZHHa5QnACyRWnVtOIvg6GbgAmEiiUHecRDA4qDTt8o7dNKcj7XLJqFtJkqR/OI5gkiRJAIRh2EEiYBj0nSAIykfbfgT/RaJvsQ04b6S6RcnHOIdEiBEhEWy8kr4/yu3vYefqbN8fFi6lhGH4VeChwX2CIFg0yvG6gc8Or2+UnAKYXs/qSPajIAgmkwiwBsOcH4Zh+NSwzdJXkPsRcEwYhv8bhuETYRg+GIbhl0mEULXJbT4ZBMHJafukFwzv3k2TukbZT5Ik/YMzYJIkSSlhGP4N+Enyahlw457sFwRBhES9IoDHwzDsHG3bMAzXA6uSV0/Zx6buifowDDeOct+paZd/sJvjfC/t8jtH2WZZGIbNo9yXXpdo/G4ea48FQTAVuJ9EXStIFCX/zAibXk0i2HoPowd/tSQKnQ/6bNrl9KLee1Mz65WsryVJkg4yTpGTJEnDfY5EkDINODMIglvDMBxxafo0VSSmWAGcnraS2+7M3rcm7pG6Me47LPmzncRUsbEsTbt8+Cjb1Iyxf3va5f3S9wqCYC6JWktzkzeFwLvCMOwavm0y7Hsx+W9UYRjeFwTBehLvyduCIIgkR2Clt3/Ulf2SCtIu7260kyRJ+gfiCCZJkjREGIYtwKfTbvp+EAS7q6dTto8Plx0EwX4b1TNM2xj3TUr+bNqDlewa0i6XjrJN+yi3w9CRPJHdPNZuJYuT/52d4dIK4K1hGDaMvtceeyH5czw7A8P0uktFu9k//f7RRnRJkqR/QI5gkiRJuwjD8K4gCO4gsfLaNBKFoz8+xi7pfYofs4dT65JGnU43hj05STZWcLQ3QU9W2uXYqFu9CoIg+ADwc3aOJHoSeHcYhtv200Okvxe5yZ+1abfN2s3+6fdv2i8tkiRJrwkGTJIkaTTnA28jMdrnnCAIbhtj2/TRKgNhGD6/j485GArtLgAq3sfjDxpsb1naVLDRTBlhv1ddEASfAr7Lztfmz8AHx6p3FQRBlMR7WA50h2F4124eZnLy5wA7n+t6EsFTITtHTY0m/f5XanVASZJ0EHKKnCRJGlEYhluBi9Ju+l9GnyK1jp2jX47f3bGDIPhSEASfDILg7cPuGixAnRsEQdbw/ZL7FpAITDIxWI9oHHDobrZNfz4vZ/i4+yQIgv8EbmJnuHQLcMZY4RJAclW7O4FfAd9LFmMf7THygDckr74YhmFv8hhxYHBluhPGOgbwluTPHuDpsdomSZL+sRgwSZKkUYVh+Avg7uTVKuCsUbbrAx5MXj08CIITRjtmEARvA64EbgYuGXZ3S9rlqlEO8XYgZ6x274F70y5/cjfbnpd2+W8ZPu5eS4Zw30276RthGH4iDMOB0fYZ5pHkz6nAP42x3TnsHBk2fLTancmf5cC7R2nnlLT7/jpSwXFJkvSPy4BJkiTtzifZWeh5rGDnurTLPw2CYJd6PUEQTCYxEmrQDcM2SV/p7IIR9p8CXD1ma/fMH4A1ycufCoLgX0faKAiC/wFOSl69P4Opf/skCIJi4Gfs7LN9OwzDS/fyMN9Lu3xDEAS7FGQPguA44FvJq1sY+h4B3MrOKXM3JN+H9P2zk/sUDrZzL9soSZJe46zBJEmSxhSGYV0QBF9iaFAx0nYPBEHwfeA/SdTieSEIguuBh5ObHAN8DpievH5XGIa/G3aYW4HLSPRRLkyuMHcbiSXvjwc+m9x/LbuvBzRWWweCIDg72bZc4M4gCH4J3AFsBSpJFDU/NblLE/D/7evjZeACdr5eNcCvgiA4ag/2W5k2xe2vQRD8CvgwMB94LgiCb5GYwlZEYtTRp0m8Dn3AR5MrCaaEYdgcBMEXgR8Cs4FngiD4BvA8icLen2PnVMJfhGH4MJIk6XXFgEmSJO2Jm4F/Y2eNndFcQCIM+iyJZe4vH2W73wIfGX5jGIZ
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
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"metadata": {},
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"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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]
},
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"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
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},
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"metadata": {},
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"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
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"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
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},
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"metadata": {},
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"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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]
},
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"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
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},
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"metadata": {},
2019-10-16 05:30:40 +00:00
"output_type": "display_data"
2019-10-22 10:22:00 +00:00
},
{
"data": {
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x1800 with 12 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x1200 with 8 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x1800 with 12 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1200x1200 with 8 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x1200 with 8 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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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": 50,
"metadata": {
"scrolled": true
},
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"outputs": [
{
"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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{
"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"
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},
{
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"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": [
"<Figure size 1200x1800 with 12 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABJMAAAbwCAYAAADXjBBZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3XecZGlZ9/9vxe6uzt2Tc9q5Z5PLIrAs8pMoBhRFDPAAygMIiAqrPIKJqKCIIqgIKkmXBzAiSHhEguS4S9h4787OzsxO6JnpUFVdOf7+OKdC91RVn6ru6qqe/rxfr35VnTr3OeeumZ2zVVdf13X7yuWyAAAAAAAAAC/8vZ4AAAAAAAAANg6CSQAAAAAAAPCMYBIAAAAAAAA8I5gEAAAAAAAAzwgmAQAAAAAAwDOCSQAAAAAAAPCMYBIAAAAAAAA8I5gEAAAAAAAAzwgmAQAAAAAAwDOCSQAAAAAAAPCMYBIAAAAAAAA8I5gEAAAAAAAAzwgmAQAAAAAAwDOCSQAAAAAAAPAs2OsJAJ0wxkQkvVLSMyUdlLQo6TZJb7PWfmqNrvEh9/xXWWuPt3HcXkl3ShqTdNBae7LJuCdJukXSzZJGJZ2T9GlJb7XW2tXNHkAn+vze8i+Sfk4t7ivuuB+R9HJJj5Y0Ium8pP+S9OfW2vtXMXUAHerGvcUYs0/SayT9mKRtki5J+qykP7bW3tPiuMdI+j+SHitpQtKcpC9K+lNr7W1NjnmepPetMKW3W2tvafNtAAA2KDKTsOEYY4YlfU7SayUdknSXpKSkp0j6pDHmtWtwjZfI+cDX7nE+Se+VE0hqNe41kj4j6Sfdl+6SNCnpRZK+a4x5RrvXBrA6fX5v+XU5gaSVxr1BTlD6qe5Ld0uakvRiSd8zxvxMu9cGsDrduLcYY4yk2yW9QE7Q+HuSBiU9V9LtxpgfbXLcCyR9SdLTJYXduUQk/YKkrxtjntvkkje4jw9K+kqTnxPtvg8AwMZFMAkb0Tsk3STpu5IOW2sfbq3dL+mXJBUkvc4Y8+ROT26MuUXS33R4+Esltby2m5H0enfztyVtt9Y+XNJ2SW+T82HwA8aYPR3OAUBn+vLeYox5haS/9DDuRyW92t38LUnb6u4tfyVpSNL/NcbsbHcOAFZlTe8txpigpI9LmpZ0q6Sd1tpHStop6a/lfI74sDFmetlxhyW9U87n/7+S8/njRkk7JL1bTsXCu40xhxpcthJM+l1r7WOb/Kx4nwIAXDkIJmFDcT8IPUdSSdKzrbUPVfZZa2+V9Cfu5us6OPdOY8y/SvoLSb4O5/ZmSakVhv62+/gha+2fWWuLkmStzcr5AniPnA+Cv9zuHAB0ph/vLcaY3caYf5f0Zx6Pq9xbbrXW/oW1tiRJ1tqMnJLa++RkIPxSO/MH0Lku3VueI+mIpNOSXmitTbvny0l6mZzMowlJv7nsuGdJCkm6V9It7ucOucf/qpzMorB7/uUqwaQ72pgnAOAKRjAJG81zJQUkfc1ae3eD/e9yH3/I7SXgiTHm6ZLul/QMSQuSfq2dSRlj/JL+QdKwpN9bYfhXJH1MTjncEtbasmof1Pa3MwcAq9JX9xZjzM/JCf48XdK8pF/3cNiXJP2nGvQ1cQNLd7qb3FuA9dONe8vz3Mdb3QBSlfs54m/dzWctO26v+3hnJdhcd1xB0nfczSXzcDOlpyTl5NyXAAAgmIQN52b38cuNdlprz0o65W4+ro3z3iDnN/b/V9I1kj7Z5rxeIemHJH1A0kdbDbTW/qG19qettZ9Zvs8YE5B0o7tJo1xg/fTbveVhcsrSbnWP+6+VDrDWvt5a+zRr7eeX73PLYh7mbnJvAdbPmt5b3F9eParVOeX80kqSDrmLglScdh9/wD3P8vNe626eXHa+SlbSvW7QCQAAVnPDhnPEfXygxZiTcn7zfrSN835R0g3W2jskyRhzwOuBxphrJP2hnBWTXiZpvI3r1p/nsJx096skzahB5hKArum3e8vnJX3YWnune9xoG9dcwhhzRE4J7iE5q0a+v9NzAWjbWt9bdssJNLc650OSinIyoo6625KTQf277mtvNca80lqbM8aE5dwjjkmK6fLPH5Vg0p3GmMfLadZ9VFJGTjbTe621D3qYOwDgCkIwCRvNNvfxUosxc+7jFq8ntdZ+rpPJuL/t/0dJA5JeZK1dMMa0FUwyxrxJ0i9KOiAnW/Brkp5vrZ1rdRyANdVX9xZr7Wc7Oa6eMebNclaAOyin59JX5NxbFlZ7bgCerfW9ZVvd84bntNYWjTExOaVpW+peP2OMeYqcUtiXS3qeMeaEnHvEhJzV4V5orT237JSVYNJPSfpfy/Y9VdIrjTEvt9a+SwCATYMyN2w0Efcx02JMetnYbvp9ST8o6R+stR/v8BxPkpMxUPn3uFfST67B3AB412/3lrXwZDn3lkrz7r1yvvgBWD9rfW+pH9PJOeOSvu0+H5dTWj/hbp9vcq5KMMkv6f9I2iPnl2jXyynvD0t6pzHmF1aaPADgykFmEjaaorwHQcvdnIgx5uFygkln5ayU1Klnyik92StnlaXfkfQWY8xua+3ylVgAdEff3FvW0M/LuT/tk7M65KvklLbskvRI1fqz/IG19o1eTmiM+WvVmogftNaeXNMZwzO3NOl2OX1ubrbWft3jcT8nZ7WuR0jaKmlRTqnS++SsMtryv29jzGPl/DfwQ5K2ywlo3CenX+A7yHy7zFrfW4ptXr96TmPMk+T8PQ1L+itJb5N0Rk5m0islPV/SE4wxT7XW/k/dOT4op1zv7621X6p7/U5JzzXGZCS9UNJfGGP+nb5KALA5EEzCRpOQNClpsMWYSi+BVLcmYYwZkNN7ICTpV6y10U7PVddn4Lik1xhjjrvn/g1jzF9ba1v1WQCwNvri3rKWrLUn3Kf3S/oDt5zlPXKC37fVDX21+wXwnvWeI1blj1VrmLwitwT73+Rkw9ablpPF9mRJzzHG/Ky1tmHGizHmLXIyU+qF5QSmHiHpJcaYp1lrb/c6r01gre8tibrng2qenbTknMaYkKS/lxNI+htr7cvqxlpJLzDGJCX9hqR3G2OOVYJC1to3rDCn18kJJu2S0xz8qx7eBwBgg6PMDRvNrPs43WJMpT/AxS7O4w2SrpPTdPJTa3lia+0/yslUCkj6/9by3ACa6pd7Sze9T9IFOb9Iqu/tNiDpPctXd0L/Msb8rqTfamN8SM6KgJVA0tfl9L55tJyl6+9yX/9xSe9sco5bVAskReVkuj1e0tMlfdh9fbekTxljdnid2yaw1veW2brnDc/p9nOs/BuvnPMH5WQgSdLrm5z79XIynw6rtmLcitwV6SrXOdhqLADgykFmEjaae+SsdnagxZjKvvu6OI9fdB+fb4x5fotxDxpjJOn11trXGWN8knbK+bD1NWttqclxp+T8ho8P5MD66Jd7S0fce8suOXP8aqNSJWtt2RhzWk5pUnjZ7pvlrEb5ti5PFavglra9XdJL2jz0FZJucp9/SNJzrbWVcqlvGGM+Imfxh+vlNGV+i7X27rrrDqkWgIhLesSyrNn/MMbcLecXLdsk/Z6c/56wxvcWa+05t7n2uHtco+zlvXJ+IVV/zv3u44K1tmHQylo7Z4y5qNrnlGqGkTFmyFqbbnScqxKMzq/0HgAAVwZ+C4mN5hvu482Ndhpj9sjpDyJ1N836W3JWRmr08+26cd92Xzvtbu+R08Pky3JKApqpfOg7u3ZTBtBCv9xbOnVATu+TL8tpqHsZN+BUeQ9Z97EkqdLf5I3GmENdnCNWwRjzKDn/P6kEkjz1zjHGDMpZDl5yAg+/XBdIkiRZa5NyegBWPGPZaR4jacx9/q4m5ddvUi075ae8zG2T6Ma95Zutzinn70uSTtWtzBZ3H8eMMcNN5hJSrRl33H3tqcaYhKSEMabhanPGmL2qZVfd3WgMAODKQzAJG82/uI+PN27KzzKVD9lf6GZjWGvtz1trH9voR07T24rKuPe6xz0k6V533682Ore7GsouSTlJn+7WewCwRF/cWzrl9l477m42vLdIepZqDZM
"text/plain": [
"<Figure size 1200x1800 with 12 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": {
"image/png": "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
"text/plain": [
"<Figure size 1200x1200 with 8 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x1200 with 8 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"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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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"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": [
"<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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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
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},
"output_type": "display_data"
},
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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x1800 with 12 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"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": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABJMAAAJPCAYAAADIX5XqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3XmYZGdd9/93T88+2SYJJMFAQhK4ZYeICgiKIviIsgnqIwiyyaI/FRCVR2VVQUERRRZRWQUFZVNc2REwoLKHeCdkI5nJTKZnprfaz/L745zqPt1T1V3dXd1d3Xm/rmuu2s45dfdM+qTOp7739x7L8xxJkiRJkiRpEDs2ewCSJEmSJEnaOgyTJEmSJEmSNDDDJEmSJEmSJA3MMEmSJEmSJEkDM0ySJEmSJEnSwAyTJEmSJEmSNDDDJEmSJEmSJA3MMEmSJEmSJEkDM0ySJEmSJEnSwAyTJEmSJEmSNDDDJEmSJEmSJA3MMEmSJEmSJEkDM0ySJEmSJEnSwAyTJEmSJEmSNLCdmz0ASZK0sUIInwJ+oHz42zHG3xtwvz8DfrF8eOcY4w3DH536CSF8N/Bs4AeBC4AEiMD7gT+LMc4us//lwHOA7wcuBMaBo8DngbfEGD81wBgeAzwd+F7gbOAE8AXgL2OM/7iqH0ySJG05Y3meb/YYJEnSBloUJrWA+8UYrxpgP8OkTRBCGANeA7wAGOuz2beA/xNjvLbP/n8AvHCJ/QHeDjw7xtjucYzTgPcAj1pi/w8BT4wxNpbYRpIkbQNOc5Mk6bZtD/BXIQQ/E4yuPwJ+lSIIugn4JeDBFMHOR8ptLgM+EkLY02P/lwC/Vu5/uLz/EOBB5bGuK7d7KvDGxTuX/218gPkg6Rjw68D3AQ8H3gSkwGOBj4UQdq36J5UkSVuC09wkSdIDgV8GXrfZA9FCIYQHAs8rH34D+KEY47HKJh8JIbwVeBrwnRRT0N5U2f8i4DfLh1cDD160/3+GEN4JfBy4P/CMEMJbY4yfr2zzdIrQqHuMH4wxHq68/rEQwr9QVCY9CPgV4A9X+zNLkqTR57eQkiTddmUUfXcAfi+EcMlmDkY9vYyioigBHr8oCOp6IdAp7z9h0WtPAnaX95/Xa/8Y4zRFL6aupyza5JfL2xz4v4uCpO4x/hF4S/nwJeW0OEmStE0ZJkmSdNvVoejFA7Af+ItNHIsWCSGcBzysfPi2GOPVvbaLMZ4AXkUxRe2fF738kPK2AXy033vFGL9E0Uwb4D6VMdwOuFf58DMxxi8vMeS3lbenA49cYjtJkrTFOc1NkqTbtpcDj6OYIvVDIYSfjzGuKVQKIdwN+P8ogpALme/180ng9THGb/bZ71MUjcFbMca9Sxz/G8A9gBtjjBcveq27ssjzgX8C/oyiv1CHokn1i2KMH6tsfwbwTOAxwD0pgpDjwJeBvwPeFWNMWCSEcDFwffnwccA/UPQceko5ttOBQ8C/AX/UqzH2AB5OseIawHuX2jDG+NI+L70XuBLY1evnWKTbnLv6d3+nyv0vLLN/9d/1gcD7ltlekiRtUYZJkiTdhsUYWyGEZwKfoahYfk0I4Z9jjIdWc7wQwouBlzIfgsy9VP55Vgjhd4CXxxjXc0nZOwKfA25Xee5yikCpO9YfBN4NXLBo3/OBHy3/vCCE8NhlwqD9wMeAH1z0/CXAc4GnhxB+Isa4uGpoOfeq3P/vyrh3UoR0O4GbYoytfgeIMb59kDcKIdwbOFg+vLHy0u7K/ZllDtOp3L/rIO8rSZK2Jqe5SZJ0Gxdj/BzwhvLhmcCbV3OcEMLLgFdQBElfA55D0ZD5wRRNma+l+Ozx0vLPenoecC7waoqpXj8JvDLGeEM51gdSVC5dQNEL6K+BRwPfC/xf4N/L49wT+I8QwuLAqeqPKIKkK4Anl8d4LPPTyvYAb19FH6G7l7eTMcapEMLFZbPsSYqqqGuAkyGEvw0hXLrCYy/2/yr3/61yf6Jy/8JljnHHyv3z1zgeSZI0wqxMkiRJUIQJjwIuBn48hPDEGON7Bt05hHA58OLy4buApy+aVvW5EMJfUSxl/1CKJs3v6zflbQh2UIRHv1V57u/LsY4DbwX2UTQh/+kY499Xtvsi8N4QwksopgFeAPw5RdjUy/kUP/NTY4xZ98kQwj9Q/LyPpKiQ+jGWma62yLnl7WQI4eHAB4DFgdQ+4KeBR5bVTx9jhUIIP0kRoAHcWv4sXd+imPZ3DvBjIYRdMcYOvT2qcv/ASschSZK2DiuTJEkSMcYa8KzKU39SNl8e1K9SfK44DjynV3+e8j2eTlEJNAb80upHPJA39Xn+URQ9ogDetChImhNjfAXwqe4+IYS799oOaFKslJZVnyyn8VX7T92HlekGR2cB76foZfS7wKUU1U53paiKyil6NL0/hHDZSt4ghPAg4O2Vp14QY5ztPih/hneXDy8EXtnnOBcBv1l5atdKxiFJkrYWK5MkSRIAMcaPhhDeBjyNoirm9cxXrPQVQhij6C8E8LkYY32J97g+hHAVxRSuh/XbbggOxRhv7vPaj1Tu//kyx3kjRSUVwP9hYZPprv8pV1Trpdpr6fRl3mux/eXtWRSB0eNjjB+svH4N8MIQwnUU0xTPoAh7fmqQg4cQvo9iql/3fd4cY3x3j027xzy/fL87UUwf/AZF4PVjFKvJHQSOlNu1B/wZJUnSFmSYJEmSql5AEZpcAPx0COFvYowfXmafi5lv3vzoyopqy7nz6oY4kJuWeO2e5e0sRSCylCsq9+/VZ5sblth/tnJ/pZ+7GpX7H1wUJM2JMb4xhPAsisqnx4YQDpRVYH2FEB5JsVpdN0h6P8UKfL2OfzSE8GiK4Ol2FMHS4sCqQ9Fs/IkUYdKS7y9JkrY2p7lJkqQ5McZJ4BcrT70phHDWMrudu8zr/ewMIay0WmdQ00u8dk55OzHAinJHK/fP7rPNbJ/noago6hpb5r0Wq66e1jNIqvjH8nYXxap1fYUQngP8A/NB0nuB/xtjTPvtE2P8L+C+FNP2qn+3CfBh4AExxr9gPlQ8iiRJ2rasTJIkSQvEGD8YQvg7ihXQLqDoy/OMJXapfp54K8X0uEH1nRK3hEG+DFsqJFpJqDNeuZ/13Wp93FK5f2iZbauVWD3DvXI64quBF1aefgvw3MX9nnqJMR4GnhVC+EWK/knjwE0xxlZ5/B3AXcrNr1/ueJIkaesyTJIkaQVCCE+hmM5zL4rAIgJvo2jkvOKwIYSwH/h1it5Ed6aoRvkf4HUxxn9ZwXFeBbwIeHivFb36TD3b0+f5d1BMefohiiqep4cQ/naJt6/2C0pjjF8ZdNyLdMeyXNhz5iqP39Ud77khhLFlqpPO67HfRvk68Ljy/sGlNqRoyN11cvGL5Qp27wCeVHn6ZTHGl690UOVqbr3ConszX+202v8Gtr1h/c4vOuadgJdQTFG9PXAM+DjwqhjjVUvs9yCKcPHBFL25jgOfAV4dY/yfPvvsoAiXn0ZxHtwBXEex2uBrY4xTq/kZJK3OiJ1T9lB8fvhpIAC7KXoH/i3wRzHGRp/9HlXu993lPtdRTMX+sxjjKf9P02hwmpskSQMKIbye4oL8ARSVIDcC3wX8GfAvIYQVrWAVQjgAfAJ4KXAJcCVFr5lHAP8cQnjpgMf5cRZWm/Tyucqf7jSlvPJcrGx7TYzxVuD5lefeQv/l3q9jvsLoAQOM9zdCCM8OIfzwope6K8DtLsOPXvvuo+jbsxZfK29PA+6xzLbVn+d/1/i+K1Xt17Tc32v157ih+kJZkfQu5oOkBHj6oEFSCOGZIYQ/DiEst/3jKvdPCTQ1vN/5RccMwJcoAp7TgK9SrPz3ZOBLIYQf6bPfM4D/oPh3212OZT9FP6wrQghP7rHPbuAjFOeDB1IErNdSVKS9BLgyhHDXlf4MklZnxM4p5wFfBP6QYrr1TRSLMtwD+B3gsyGEU74MCiG8gWLq9SMoPpdcRVH9+grgayGEey7eR6PBMEmSpAGEEJ5K8a3ZFPCQGOPdYoz3BO5PMR3pERQXUyvxBuB7Kao4Lo0xXh5jvAh4CsU
"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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"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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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"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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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"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": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x1800 with 12 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x1200 with 8 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"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": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
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},
"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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"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABJMAAAJPCAYAAADIX5XqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3XmcZFdZ8PFfzz6TTCZ7MmFJSIATNgPIFpY3EVQERORFX1zAFxAFFxRUQBEQXBBQUBEFX0F2WRRQVJBNFgk7REhCchJCEiCTmcns3dVdy7233j/Orenqnq7qqu7qpap+38+nP1W37r2nTs1kbvo+9TzPmWg2m0iSJEmSJEm92LDWE5AkSZIkSdLwMJgkSZIkSZKknhlMkiRJkiRJUs8MJkmSJEmSJKlnBpMkSZIkSZLUM4NJkiRJkiRJ6pnBJEmSJEmSJPXMYJIkSZIkSZJ6ZjBJkiRJkiRJPTOYJEmSJEmSpJ4ZTJIkSZIkSVLPDCZJkiRJkiSpZwaTJEmSJEmS1DODSZIkSZIkSerZprWegCRJWn0hhE8Dl5WbL44x/kmP570e+LVy8y4xxpsHPzstJIRwV+C5wI8AdwaqwE3AB4D/F2Pcv8j5nwAe1ct7xRgnOoyxEfgZ4BeA+wGnAseAK4F3Ae+MMWa9vIckSRpeZiZJkqSXhBDusdaTUGchhKcBV5ECeXcHtpECOfcD/gi4JoTw2EWGue8y57AT+BjwTuBHgbOAzcAZwA8DbwE+FUI4fTnvI0mS1j+DSZIkaSvw5hCCvxesQyGExwD/QAogzQB/TArmPBr4U6AGnAn8cwjh/h3GuBMp6APwh6QgVLefhbwDeGT5/DrgGcDDgacCXytffzjwgRDCgplNkiRpNFjmJkmSAC4FfgP4y7WeiGaVAb7XAxNAHXhEjPFrbYd8LITwn8CngO3AK0mBpvnas5I+HGP8nz7n8VDgCeXm14GHxhhr5fYVIYR3Ax8u3/sy4Imk8jtJkjSC/AZSkqTxVgCtHjd/EkK4cC0noxM8Emj9nbx+XiAJgBjjZ4H/KDd/JIRw2gLjtIJJBalcrl+PaXv+4rZAUmsOOfDbbS89fgnvIUmShoTBJEmSxlsD+LPy+Q7g79dwLlrYvwPfBf61yzHXtj2/0wL7W6VrMcY4vYQ5nN32/PoucyjK57uX8B6SJGlIWOYmSZJeTipLuhh4ZAjhl2KMywoqlQ29f520etgdSWVa3yOVY/11jPFbHc77NKlMqhZj3NZl/KuBewG3xBgvmLevWT59Hilj5/WkXj4N4NvA78YYP9F2/CnAM0llXPcGdgIHSSuU/RPwjoVWKAshXEBaTQ3Sn9+HgKeRVjq7VznOrcBHgdfEGG/s9Hk6Kef5iUUPhPPbnt+2wP5WZtKV/c6htKft+cXAQp/lLsx+Ublngf2SJGlEmJkkSdKYK0uWnslsVsmfhRDusNTxQggvIZVS/SoQgJNIWU8BeDZwVQjhZavQpPlOwBWkPj47gF3A/UkBpdZcf4jUTPo1wP8CTietUHYuqbTrH4ArQwgXLfJeO0hBnzeTgmFnkhqbXwj8Cr2ttrYkIYQHAj9Zbn4qxnj7vP2nABeUm98MITwlhPDREMKBEEIthPDdEMLbQgidGm8D/Fvb85eFEDbPe48J4BVtL/3Tkj6MJEkaCgaTJEkSMcYrgL8pN3cBb1zKOCGEl5FWC9sIfJMUPHooKTPoN0kZLRuAPyh/VtJzSUGdVwOPAH4aeEWM8eZyrpeSMpd2A03Skvc/ATwY+BngY+U49wb+O4TQrXTrNcAPAV8krW72YFKA5+Pl/q3AW0MIJy/3Q4UQJkIIO0MI9w8h/AXw6XL8w6RssPnuS8oMA3gRaVW2HyWt7raFFHT7BeBrIYQF/05ijF8H/qLcfADw9RDC00IIDw0hPJmUcfbT5f43xxg/ssyPKUmS1jHL3CRJUsvvkRonXwD8eAjh52KM/9jryeWy9C8pN98BPGNeedgVIYQ3k3oAXQ68NITwvk4lbwOwgRQ8+v221/65nOtGUtbRdlJG1pNjjP/cdtyXgfeGEF5KKgPcDfwdKdi0kHNJn/lpMcZWhhchhA+RPu9jgbOAxwHvXebn+vnyvdpdATwzxnjdAse3ZxydAnwWeBNwQ7n9aFL21HZS1lEtxvjK+YPEGH8rhHAV8DJSgO0t8w45ADwfeFu/H0iSJA0XM5MkSRIAMcYK8MttL/1VCOGsPob4bdLvFgeBZy/UZ6h8j2eQMoEmgOcsfcY9eUOH1x9P6v0D8IZ5gaTjYox/SMr8AXh8COGeHcarAs9tDySV5zeZ29T8kl4mvYjzF3jtPsBzFlnJDeBlMcbLYozviDF+Mcb4sRjjb5Oyx46Ux/xxCOGu8wcJIZxHyrg6s8O8ziRlJ3X6M5IkSSPCYJIkSTouxvhxZjNOzgT+upfzyp45reXjr+i2YliM8SZmVx971BKn2otbY4zf77Dv0W3P/26Rcf627fmPdTjmazHGQx32tTer3rnIe/XiM8CPkAI7TyWV1p1C6lH12RDC2fOO/03gB4HHxBhfvtCAMcb/IWUVQSpRnFMuF0K4G/AF4Fmk3x9/n9Rwe2v5+LtAhZSB9d8hhB9c3keUJEnrmWVukiRpvt8iBU12A08OIbw7xthtWXpIpXGtrJifaFtRbTF3WdoUe/K9LvvuXT5OAVcvMs4X257fp8MxN3c5f6rt+bJ/94oxfq5t88shhH8kZT89g/S5/pzUA6l1/DHg6z0M/Q5S8HAb8MPz9r0XuDOQAY+NMX6qbd/NwKtCCJ8kBbpOA94fQghlc3dJkjRizEySJElzxBiPAL/W9tIbQginLnJap9KnxWwKIQwiW2chx7rsO6N8PFCWonWzr+356R2OmerwOqSSvpaBr2BXltb9KnBr+dKTQwg7ljBOjbSyHaTAEXC8UXmr79Jb5wWS2s//KimQBakU7/H9zkGSJA0Hg0mSJOkEMcYPMru8+27SamXdtGfc/AMp+NDrT8eSuC56+R2mW5Con6DOxrbnRcej1lAZCPqPcnMLs/2g+tX6u9jS9toD257/+yLnf7Dt+YOXOAdJkrTOWeamkVJ+E/sC0pLOdwEmga8Bf7nUZYpDCHcGXkoq+TgbuB34JPCnMcZru53bNsYG0uo5DwM2z29KG0K4nLSscq+eHmN8ax/HS1qi9XRdCSH8FGnVrR8k9ar5Hunm/s9jjHu6nPcwUk+bhwInledt7WGqvw48kpTF84wQwnu6HNveLygve/AsRSsAtFiwZ9cSx29pzffMEMLEItlJ5yxw3qooG2pfBJwbY1wskHOw7fmW8vztwCNI/53tjTF+YpExWv2W9re9dnLb8yN0137ecv+ORs5KXE8WeI93l+PfLcb47S7HPYjUT+sRpNUIZ4BvAe8G3hhjrHc47+GkZvsPI/UAu5kUyPyLGOOtC50jSRo9ZiZpZIQQTgL+C/gD4ELgGlIz0B8FPhxC+IMljBlIfSZ+kfTL9DdIvSSeCnw9hPDoLqe3ewXpl65OjpKWde72s7c8tmBuM1dJK2Q9XVdCCG8iZQo9knTTdx1wHvA84JoyYLTQef+HFMz+8fK8a4A7lT9dxRj3l+O3/D9SMGoh32E2q+Uhi40dQnhhCOFZIYT5vXlawfYtIYSN888rz90O9LPK3EK+WT6eDNxrkWPbP891HY9aGe8AvgJ8qIeV9S5qe95qPL4N+Gg5zp90OzmEcG7bGF9t23V7h/dYyB3anu/veNQYWonryQLv8WxSIGmx436T1FD950glqteSSjUfAvwVqZH7KQuc90LS9eQnge2kfmOnkYJLV5dfjkmSxoDBJI2SvyGl1P8PcFGM8f4xxvNJTUgz4GUL3LR0FELYRPrG/wzSL+G7Y4wPJJV7vJ70C/p7QghndBljYwjhz4AXdnuvGOOVMcaHd/oh/WLYKjn4vRjjf/f6OSQty7q4roQQfpEUfMqAp8QYd8cY70cKJr0XOBX4wPw+OWXg6h2k/98/B7hTjPEHSf1sjpaHtZcznSDG+A6glTFxAfDzHY5rMJtheZ8ye6HTn8M
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x1200 with 8 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import speed_cells.speed as spd\n",
"from septum_mec.analysis.plotting import plot_bootstrap_timeseries\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",
" despine()\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",
" idx = row.session_id\n",
" x, y, t, speed = map(data_loader.tracking(action_id).get, ['x', 'y', 't', 'v'])\n",
"\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",
" \n",
" speed_score, inst_speed, rate, times = spd.speed_correlation(\n",
" speed, t, spike_times, return_data=True)\n",
"\n",
" speed_bins = np.arange(min_speed, max_speed + speed_binsize, speed_binsize)\n",
" ia = np.digitize(inst_speed, bins=speed_bins, right=True)\n",
" rates = []\n",
"\n",
" for i in range(len(speed_bins)):\n",
" rates.append(rate[ia==i])\n",
"\n",
" ax[idx].set_title(f'{speed_score:.3f}')\n",
" plot_bootstrap_timeseries(speed_bins, rates, ax=ax[idx])\n",
" rr = [rr for r in rates for rr in r]\n",
" aspect = (max_speed - min_speed) / (np.nanmax(rr) - np.nanmin(rr))\n",
" for a in ax:\n",
" a.set_aspect('auto')\n",
"# break\n",
"# break\n",
" plt.tight_layout()\n",
" fig.savefig(\n",
" output_path / 'figures' / f'neuron_{id_num}_speed_map.png', \n",
" bbox_inches='tight', transparent=True)\n",
" fig.savefig(\n",
" output_path / 'figures' / f'neuron_{id_num}_speed_map.svg', \n",
" bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"import speed_cells.speed as spd\n",
"from septum_mec.analysis.plotting import plot_bootstrap_timeseries\n",
"speed_dist = [[], [], [], []]\n",
"speed_bins = np.arange(min_speed, 1 + speed_binsize, speed_binsize)\n",
"for unit_id, id_num in results_id_map.items():\n",
" sessions = once_a_gridcell.query(f'unit_id==\"{unit_id}\"')\n",
"\n",
" for date, rows in sessions.groupby('date'):\n",
" entity = rows.iloc[0].entity\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",
" hist, _ = np.histogram(speed, bins=speed_bins, density=True, )\n",
" speed_dist[idx].append(hist)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 375x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.rc('axes', titlesize=12)\n",
"plt.rcParams.update({\n",
" 'font.size': 12, \n",
" 'figure.figsize': (2.5, 2), \n",
" 'figure.dpi': 150\n",
"})\n",
"colors = ['#1b9e77','#d95f02','#7570b3','#e7298a']\n",
"labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz']\n",
"fig = plt.figure()\n",
"for i in range(len(speed_dist)):\n",
" plt.plot(\n",
" speed_bins[:-1], np.cumsum(np.array(speed_dist[i]).mean(0))*speed_binsize, \n",
" c=colors[i], label=labels[i])\n",
"plt.legend(bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n",
"despine()\n",
"plt.xlabel('Running speed (m/s)')\n",
"fig.savefig(output_path / 'figures' / 'running_speed.png', bbox_inches='tight', transparent=True)\n",
"fig.savefig(output_path / 'figures' / 'running_speed.svg', bbox_inches='tight')"
]
},
{
"cell_type": "code",
2019-12-16 15:16:33 +00:00
"execution_count": 28,
2019-12-13 10:43:57 +00:00
"metadata": {},
"outputs": [],
"source": [
"cmap = ['#252525', '#1b9e77','#d95f02','#7570b3', '#e7298a']\n",
"labels = [\n",
" 'Baseline I vs baseline I',\n",
" 'Baseline I vs baseline II', \n",
" 'Baseline I vs stim I', \n",
" 'Baseline II vs stim II', \n",
" 'Baseline I vs stim II'\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x600 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"msize = 9\n",
"fig = plt.figure()\n",
"ticks = []\n",
"nuids = {}\n",
"n = 0\n",
"\n",
"for i, pairs in enumerate(results_gridness):\n",
" for j, pair in enumerate(pairs):\n",
" nuid = results_unit_id[i][j]\n",
" if nuid not in nuids:\n",
" nuids[nuid] = n\n",
" n += 1\n",
" \n",
" plt.plot(\n",
" nuids[nuid], np.diff(pair), \n",
" color=cmap[i], marker='.', ls='none', markersize=msize)\n",
"for l in range(n):\n",
" plt.axvline(l, color='k', lw=.1, alpha=.5)\n",
"\n",
"from matplotlib.lines import Line2D\n",
"\n",
"\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.ylabel('Difference in gridness')\n",
"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",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x600 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\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",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x600 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"for i, pairs in enumerate(results_maxrate):\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('Max rate')\n",
"plt.xlabel('Baseline max rate')\n",
"lim = [-.7, 100]\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_max_rate_vs_other.png', bbox_inches='tight')\n",
"fig.savefig(output_path / 'figures' / 'baseline_max_rate_vs_other.svg', bbox_inches='tight')"
]
2019-10-22 10:22:00 +00:00
},
{
"cell_type": "code",
2019-12-13 10:43:57 +00:00
"execution_count": 85,
2019-10-22 10:22:00 +00:00
"metadata": {},
2019-12-13 10:43:57 +00:00
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x600 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
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"source": [
"fig = plt.figure()\n",
"for i, pairs in enumerate(results_avgrate):\n",
" for j, pair in enumerate(pairs):\n",
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" 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('Average rate')\n",
"plt.xlabel('Baseline average rate')\n",
"lim = [-.7, 40]\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_average_rate_vs_other.png', bbox_inches='tight')\n",
"fig.savefig(output_path / 'figures' / 'baseline_average_rate_vs_other.svg', bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x600 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"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",
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"\n",
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"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 I',\n",
" 'Baseline I', 'Baseline II', \n",
" 'Baseline I', 'Stimulation I', \n",
" 'Baseline II', 'Stimulation II', \n",
" 'Baseline I', '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": 23,
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"metadata": {},
"outputs": [],
"source": [
"pairwise_gridness = [[], [], [], [], []]\n",
"for i, pairs in enumerate(results_gridness):\n",
" for j, pair in enumerate(pairs):\n",
" pairwise_gridness[i].append(np.diff(pair))\n",
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"\n",
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" \n",
"pairwise_maxrate = [[], [], [], [], []]\n",
"for i, pairs in enumerate(results_maxrate):\n",
" for j, pair in enumerate(pairs):\n",
" val = np.diff(pair) / pair[0]\n",
" if np.isnan(val):\n",
" continue\n",
" pairwise_maxrate[i].append(val)\n",
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" \n",
" \n",
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"pairwise_avgrate = [[], [], [], [], []]\n",
"for i, pairs in enumerate(results_avgrate):\n",
" for j, pair in enumerate(pairs):\n",
" val = np.diff(pair) / pair[0]\n",
" if np.isnan(val):\n",
" continue\n",
" pairwise_avgrate[i].append(val)\n",
" \n",
"pairwise_xcorr_cntr_mass = [[], [], [], [], []]\n",
"for i, pairs in enumerate(results_xcorr_cntr_mass):\n",
" for j, pair in enumerate(pairs):\n",
" pairwise_xcorr_cntr_mass[i].append(pair[0] * bin_size)"
]
},
{
"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
"outputs": [],
"source": [
"def violin(data, ax=None):\n",
" if ax is None:\n",
" fig, ax = plt.subplots()\n",
" ticks = [0,1,2,3,4]\n",
" violins = ax.violinplot(\n",
" data, ticks, showmedians=True, showextrema=False, points=1000, bw_method=.3)\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",
" for pc, c in zip(violins['bodies'], cmap):\n",
" pc.set_facecolor(c)\n",
" pc.set_edgecolor(c)\n",
" pc.set_alpha(0.8)\n",
" ax.spines['top'].set_visible(False)\n",
" ax.spines['right'].set_visible(False)"
]
},
{
"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
"outputs": [],
"source": [
"def swarm_violin(data, ax=None, clip=None):\n",
" if ax is None:\n",
" fig, ax = plt.subplots()\n",
" sns.set_palette(palette=cmap)\n",
" \n",
" ticks = [0,1,2,3,4]\n",
"\n",
" violins = ax.violinplot(\n",
" data, ticks, showmedians=True, showextrema=False, points=1000, bw_method=.3)\n",
"\n",
" for category in ['cbars', 'cmins', 'cmaxes', 'cmedians']:\n",
" if category in violins:\n",
" violins[category].set_color(['w', 'w'])\n",
" violins[category].set_linewidth(2.0)\n",
" violins[category].set_zorder(10000)\n",
"\n",
" for pc in violins['bodies']:\n",
" pc.set_facecolor('gray')\n",
"# pc.set_edgecolor(c)\n",
" pc.set_alpha(0.4)\n",
"\n",
" sns.stripplot(data=data, size=4, ax=ax, color='k')\n",
" ax.spines['top'].set_visible(False)\n",
" ax.spines['right'].set_visible(False)\n",
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" \n",
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" y = -np.inf\n",
" if clip is None:\n",
" for val in data[1:]:\n",
" data_max = np.max([max(data[0]), max(val)])\n",
" data_min = np.min([min(data[0]), min(val)])\n",
" y_ = data_max * 1.05 + 0.025 * (data_max - data_min)\n",
" if y_ > y:\n",
" y = y_\n",
" else:\n",
" y = clip\n",
" ax.set_ylim(0, clip)\n",
" \n",
" x = 1\n",
" for val in data[1:]:\n",
" Uvalue, pvalue = scipy.stats.mannwhitneyu(data[0], val, alternative='two-sided')\n",
" # significance\n",
" if pvalue < 0.0001:\n",
" significance = \"****\"\n",
" elif pvalue < 0.001:\n",
" significance = \"***\"\n",
" elif pvalue < 0.01:\n",
" significance = \"**\"\n",
" elif pvalue < 0.05:\n",
" significance = \"*\"\n",
" else:\n",
" significance = \"ns\"\n",
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"\n",
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" ax.text(x, y, significance, ha='center', va='bottom')\n",
" x += 1"
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]
},
{
"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
"outputs": [],
"source": [
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"plt.rc('axes', titlesize=12)\n",
"plt.rcParams.update({\n",
" 'font.size': 12, \n",
" 'figure.figsize': (4, 6), \n",
" 'figure.dpi': 150\n",
"})"
]
},
{
"cell_type": "code",
2019-12-16 15:16:33 +00:00
"execution_count": 29,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 600x900 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axs = plt.subplots(4, 1, sharex=True)\n",
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"\n",
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"swarm_violin(pairwise_xcorr_cntr_mass, ax=axs[0], clip=.1)\n",
"axs[0].set_ylabel('Spatial shift')\n",
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"\n",
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"swarm_violin(pairwise_gridness, ax=axs[1])\n",
"axs[1].set_ylabel('Difference in gridness')\n",
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"\n",
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"swarm_violin(pairwise_maxrate, ax=axs[2])\n",
"axs[2].set_ylabel('Relative change in max rate')\n",
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"\n",
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"swarm_violin(pairwise_avgrate, ax=axs[3])\n",
"axs[3].set_ylabel('Relative change in mean rate')\n",
"\n",
"plt.xticks([0,1,2,3,4], labels, rotation=-45, ha='center')\n",
"# plt.tight_layout()\n",
"fig.savefig(output_path / 'figures' / 'violins_swarm.png', bbox_inches='tight')\n",
"fig.savefig(output_path / 'figures' / 'violins_swarm.svg', bbox_inches='tight')"
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]
},
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{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"def summarize(data):\n",
" return \"{:.2f} ± {:.2f} ({})\".format(data.mean(), data.sem(), len(data))\n",
"\n",
"\n",
"def MWU(a, b):\n",
" '''\n",
" Mann Whitney U\n",
" '''\n",
" Uvalue, pvalue = scipy.stats.mannwhitneyu(\n",
" a, b, alternative='two-sided')\n",
"\n",
" return \"{:.2f}, {:.3f}\".format(Uvalue, pvalue)\n",
"\n",
"\n",
"def PRS(df, keys):\n",
" '''\n",
" Permutation ReSampling\n",
" '''\n",
" pvalue, observed_diff, diffs = permutation_resampling(\n",
" a, b, statistic=np.median)\n",
"\n",
" return \"{:.2f}, {:.3f}\".format(observed_diff, pvalue)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"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>Baseline I vs baseline I</th>\n",
" <th>Baseline I vs baseline II</th>\n",
" <th>Baseline I vs stim I</th>\n",
" <th>Baseline II vs stim II</th>\n",
" <th>Baseline I vs stim II</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Spatial shift</th>\n",
" <td>0.03 ± 0.01 (66)</td>\n",
" <td>0.03 ± 0.00 (21)</td>\n",
" <td>0.02 ± 0.00 (22)</td>\n",
" <td>0.02 ± 0.00 (22)</td>\n",
" <td>0.03 ± 0.00 (12)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Difference in gridness</th>\n",
" <td>-0.02 ± 0.03 (66)</td>\n",
" <td>-0.05 ± 0.08 (21)</td>\n",
" <td>-0.09 ± 0.11 (22)</td>\n",
" <td>-0.03 ± 0.09 (22)</td>\n",
" <td>-0.20 ± 0.14 (12)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Relative change in max rate</th>\n",
" <td>0.05 ± 0.03 (65)</td>\n",
" <td>0.00 ± 0.07 (21)</td>\n",
" <td>-0.04 ± 0.07 (22)</td>\n",
" <td>0.03 ± 0.07 (22)</td>\n",
" <td>-0.12 ± 0.09 (12)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Relative change in mean rate</th>\n",
" <td>0.02 ± 0.03 (65)</td>\n",
" <td>0.09 ± 0.11 (21)</td>\n",
" <td>-0.03 ± 0.08 (22)</td>\n",
" <td>0.10 ± 0.08 (22)</td>\n",
" <td>0.20 ± 0.16 (12)</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Baseline I vs baseline I \\\n",
"Spatial shift 0.03 ± 0.01 (66) \n",
"Difference in gridness -0.02 ± 0.03 (66) \n",
"Relative change in max rate 0.05 ± 0.03 (65) \n",
"Relative change in mean rate 0.02 ± 0.03 (65) \n",
"\n",
" Baseline I vs baseline II Baseline I vs stim I \\\n",
"Spatial shift 0.03 ± 0.00 (21) 0.02 ± 0.00 (22) \n",
"Difference in gridness -0.05 ± 0.08 (21) -0.09 ± 0.11 (22) \n",
"Relative change in max rate 0.00 ± 0.07 (21) -0.04 ± 0.07 (22) \n",
"Relative change in mean rate 0.09 ± 0.11 (21) -0.03 ± 0.08 (22) \n",
"\n",
" Baseline II vs stim II Baseline I vs stim II \n",
"Spatial shift 0.02 ± 0.00 (22) 0.03 ± 0.00 (12) \n",
"Difference in gridness -0.03 ± 0.09 (22) -0.20 ± 0.14 (12) \n",
"Relative change in max rate 0.03 ± 0.07 (22) -0.12 ± 0.09 (12) \n",
"Relative change in mean rate 0.10 ± 0.08 (22) 0.20 ± 0.16 (12) "
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary = pd.DataFrame()\n",
"\n",
"for label, data in zip(labels, pairwise_xcorr_cntr_mass):\n",
" summary.loc['Spatial shift', label] = summarize(pd.Series(data))\n",
" \n",
"for label, data in zip(labels, pairwise_gridness):\n",
" data = [d for a in data for d in a]\n",
" summary.loc['Difference in gridness', label] = summarize(pd.Series(data))\n",
"\n",
"for label, data in zip(labels, pairwise_maxrate):\n",
" data = [d for a in data for d in a]\n",
" summary.loc['Relative change in max rate', label] = summarize(pd.Series(data))\n",
"\n",
"for label, data in zip(labels, pairwise_avgrate):\n",
" data = [d for a in data for d in a]\n",
" summary.loc['Relative change in mean rate', label] = summarize(pd.Series(data))\n",
"\n",
"# for val in data[1:]:\n",
"# Uvalue, pvalue = scipy.stats.mannwhitneyu(data[0], val, alternative='two-sided')\n",
"\n",
"# labels[1:]\n",
"summary"
]
},
{
"cell_type": "code",
"execution_count": 43,
"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>Baseline I vs baseline II</th>\n",
" <th>Baseline I vs stim I</th>\n",
" <th>Baseline II vs stim II</th>\n",
" <th>Baseline I vs stim II</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Spatial shift</th>\n",
" <td>526.00, 0.099</td>\n",
" <td>688.00, 0.718</td>\n",
" <td>689.00, 0.725</td>\n",
" <td>276.00, 0.098</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Difference in gridness</th>\n",
" <td>819.00, 0.213</td>\n",
" <td>841.00, 0.270</td>\n",
" <td>757.00, 0.769</td>\n",
" <td>495.00, 0.173</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Relative change in max rate</th>\n",
" <td>777.00, 0.345</td>\n",
" <td>833.00, 0.251</td>\n",
" <td>786.00, 0.491</td>\n",
" <td>543.00, 0.032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Relative change in mean rate</th>\n",
" <td>691.00, 0.936</td>\n",
" <td>887.00, 0.094</td>\n",
" <td>649.00, 0.522</td>\n",
" <td>347.00, 0.551</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Baseline I vs baseline II Baseline I vs stim I \\\n",
"Spatial shift 526.00, 0.099 688.00, 0.718 \n",
"Difference in gridness 819.00, 0.213 841.00, 0.270 \n",
"Relative change in max rate 777.00, 0.345 833.00, 0.251 \n",
"Relative change in mean rate 691.00, 0.936 887.00, 0.094 \n",
"\n",
" Baseline II vs stim II Baseline I vs stim II \n",
"Spatial shift 689.00, 0.725 276.00, 0.098 \n",
"Difference in gridness 757.00, 0.769 495.00, 0.173 \n",
"Relative change in max rate 786.00, 0.491 543.00, 0.032 \n",
"Relative change in mean rate 649.00, 0.522 347.00, 0.551 "
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats = pd.DataFrame()\n",
"\n",
"for label, val in zip(labels[1:], pairwise_xcorr_cntr_mass[1:]):\n",
" stats.loc['Spatial shift', label] = MWU(pairwise_xcorr_cntr_mass[0], val)\n",
" \n",
"for label, val in zip(labels[1:], pairwise_gridness[1:]):\n",
" data = [d for a in val for d in a]\n",
" stats.loc['Difference in gridness', label] = MWU(pairwise_gridness[0], val)\n",
"\n",
"for label, val in zip(labels[1:], pairwise_maxrate[1:]):\n",
" data = [d for a in val for d in a]\n",
" stats.loc['Relative change in max rate', label] = MWU(pairwise_maxrate[0], val)\n",
"\n",
"for label, val in zip(labels[1:], pairwise_avgrate[1:]):\n",
" data = [d for a in val for d in a]\n",
" stats.loc['Relative change in mean rate', label] = MWU(pairwise_avgrate[0], val)\n",
"\n",
"stats"
]
},
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{
"cell_type": "code",
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"execution_count": 212,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 600x900 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axs = plt.subplots(4, 1, sharex=True)\n",
"\n",
"violin(pairwise_xcorr_cntr_mass, ax=axs[0])\n",
"axs[0].set_ylabel('Spatial shift')\n",
"axs[0].set_ylim(0, .1)\n",
"\n",
"violin(pairwise_gridness, ax=axs[1])\n",
"axs[1].set_ylabel('Difference in gridness')\n",
"\n",
"violin(pairwise_maxrate, ax=axs[2])\n",
"axs[2].set_ylabel('Relative change in max rate')\n",
"\n",
"violin(pairwise_avgrate, ax=axs[3])\n",
"axs[3].set_ylabel('Relative change in mean rate')\n",
"\n",
"plt.xticks([0,1,2,3,4], labels, rotation=-45, ha='center')\n",
"\n",
"fig.savefig(output_path / 'figures' / 'violins.png', bbox_inches='tight')\n",
"fig.savefig(output_path / 'figures' / 'violins.svg', bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 35,
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"metadata": {
"scrolled": false
},
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"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 900x600 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
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"source": [
"plt.imshow([np.arange(100), np.arange(100)])\n",
"despine(bottom=True, left=True, xticks=False, yticks=False)\n",
"plt.gcf().savefig('rocket_colorbar.svg')"
]
},
{
"cell_type": "code",
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"execution_count": 36,
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"metadata": {},
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"outputs": [
{
"data": {
"text/plain": [
"'Baseline i'"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
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"source": [
"'baseline I'.capitalize()"
]
},
{
"cell_type": "code",
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"execution_count": 57,
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"metadata": {},
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"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAwoAAAQNCAYAAAD9mr0XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzde5glV0Ev7F/IEGEaEQzTgIQESMgKd8WDHox+BI8gIIoodwLEG4JyVOTmEY4JXhBFPxHl8h09giAqCgoBuRxUULkIGOUWcOEAAxHQRsRj2A2EkPn+WLUzO11793T39K7dl/d9nnlqdu+6rN57VXX9aq1addLRo0cDAAAw6VqLLgAAALDzCAoAAECPoAAAAPQICgAAQI+gAAAA9AgKAABAj6AAAAD0CAoAAECPoAAAAPQICgAAQI+gAAAA9AgKAABAj6AAAAD0CAoAAECPoAAAAPQICgAAQM+BRReglHKtJN+Z5AFJ7pLktCSnJPm3JDXJG5L8dq3137a4/ouSXJjkrbXWb97Eci9K8qgkL621nr+VbW90naWUI0nOSPJDtdbf3o5tbbJcC93+fldKOZhkudZ6ZODt3jDJJ5Ncp/vR2bXWf9qmdc9tvy6lXJDkhUk+UWs9bRPL3SLJR7uXt661Ht7sttkdtrNur6k3SfIbtdYf28ByT0zyrO7lpurqXrCo49qizfO42q3/oUl+v3v5/9VaH7Nd62axduo+s9AWhVLKNyR5X5JXJjk/7WT1siTvTvKlJOcl+cUkHyml/OCCiglzU0p5WJIPJfm2BWz+4Tn2xyxJvn87Vmq/ZgeYS93ufG8p5aQNzPfgbdzmrrLg49qizbPurV3fw0spX7nN62cBdvI+s7CgUEr5tiR/leS2aR/Og5J8da31NrXWb6i1npHk7CS/l+Qrk/xWKeVRW9jUbya5TZKHbU/J5+K/pZXxjxddEAb3jCQ3W9C2f6CbvrWbPqqUcvKJrHCg/fpP0/aXu51IWdnTtr1ud65M8jVJzl1vplLKrZL8l23Y3m61yOPaos2r7qWUcnqSb03y70n+Nsn10oIJu9+O3WcW0vWolHLjJH+Ulrr/Osl9a62Xr52va657RCnlU0melOTXSimvqrX+x0a31XVt2FK3paHUWj+86DKwv5RS7pzka9NOfM5P8t4kN01ynySv3uI6B9mva63/N8n/3UoZ2fvmUbcn/GWSeyZ5YJK3rDPfuDXhH5J83Qluk11iznUvSb4v7QLv27p//zXJDyd5wTasG6ZaVIvCRUlumPbH/oHTTibW+Jm0Pn83TDtAAydmfNXr/3T9IV/evT6RZvKLYr9m8eZRt8f+qJser/vRg5NcNTE/+8Pc6l5X38atr6/Nsbr1taWU/3qi64dZBm9RKKVcL8kju5cvqLWuHG+ZWusXSilPT3L9JH++Zn1H0vpA3yltJ31kkmun3TB5jyQ/lhk3M5dSrpOWxh+V5NZJvtit/39u7bfb2jpn3Uzc3RT1xCTfleSsJEfTTqzenOTXa63vW7OeN6d1x7h/kn9N8vQk39C9/cEk/yvJ79Zar9rE7/O1SX4kybekNYtdJ8lnk1yS5HdqrS+fsdxNuuW+O8kt00JpTfIHSZ5Ta/3ilGXul+TRaTe/3iDJp9O6sfxKrfXvp8x/tPvvtdNumv2xJHdIu5rzriQX1lrfXkpZSvLTaX+8b57kP9Jupv2pWusnp6z3K5I8JslD0rrQnJLk40n+LMmzaq2fWjP/eUnelOQd3ef0Y2n18NZJrkjy92nf16smlrkorV6O/VYp5beSPL3WetHaMm2nro6Ou+K9pJu+OO1q1X1LKTeutf7rJte5bfv1xM3KL0vyG0mem9bV6DNpN4d+NuvczNxd1XtSkm9OcqMkh9OuuL1uRtknt/cDSX4qrcvUGUlGaVfufrnW+jczlr9xkickuW+SWyT5cpJ/TPKHSZ5ba/3ClGVuluQpSe7VbeeKtPs43pjk19bezLbZ+feredTtNf4myafSjoXflGPdSybLUNL+Hv1lkn9Zp6x3S/KD3Xpukvb3+N/SupQ8r9b6FxPznpN2HLlukv9da/3BNetaTrt6feMkv1VrffTxfpHj7We11l/bQjkvygaOa1vZZ3a6Aere3dP+ll6V5FW11k+WUt6Z9jf+sWnfx2R5Lky7ePPeWuudZpT53LSWsdUkN621/ufEe5v6juZRn9as/78keXJaK8qhJB9L+5x/Je3c4owkt5xy7Ny2urZf95lFtCh8a5KD3f9ftd6Mk2qt/6vW+iu11o/OmOV5aSdon0z7Qr5Ya/33WesrpdwgyV8keXZa0/DHu2UfmHYSfPuNlm0e6yylfHWSd6ad4N46yUfSdoabpJ3M/F0p5V4zFr9n2gn2t6aN1vHvaTvX7yT541LKKRssw2O7cv9Q2h+gw0k+nOSrkty7W9cvTFnu3CTvSQtHt+nKfllak+wvJ3n9ZBlKKQdKKb+XdvPrfdIC0XvSQslDk7yzlPK4dYr6a2kB5KyujF+RFhLfVEq5e9oJ/P9Iq+//lGQ5ySOS/HU3ysBk2W+atjM/O8k3pn12H0hyepLHJ3l/9/tNc0rayeivpPVj/mDaAeLuSV5ZSpkcneLjaScZ48B0uHv98XV+z+3yvWlB7PIc2wf/qtv2gRw74d+MeezX56QFulskuTSt5eHS9dZXSnl42vf3kCRLSd6fts/8ZpL/fZzi3KBb9mlpfX8/0K3jvml16TumbO/crkxPSqt/H0n7HL8+rR78bReaJ5c5M+2k77+n1ZOatp/eKu0Y9p5Sytdtdf59bh51e9JVSV7R/X9WK9i429EfzlpJKeUX0y74nJ92r84HuzIuJ/meJH9eSrn6ZL/W+o9pdSxJfqC7F2jSC9OO0R9I8hMb/F3GZu5nmy1nNnBc28o+s0vMu+6NWyXePHGBazz60YO6C4uTfjftb+kdSymzzj0e0U1fsSYknMh3tJ31aVyeC9KOzQ9MOza/P+0i0M+nBfLrrF1mG36P9eyrfWYRQeHO3fRo2lXf7XJukofUWm9Xa71F2lX49TwrLe39c5I7d8vdMe3EdvwFbNZ2rvPJaRXirUlO69b1dWlXsv4k7aT02TOWfWxapTq71vp1tdZbpX0eo7SK+oTjbbyUcuskv55WR56W5Ma11jvXWm+TdrIybvZ80uQBqvv/H6ftFK9LcvNa651qreekXfn4dNqoN5MJ+ufSbsj65yT3qrXeuNZ6l24dP5ZWV55TSrnHjOI+Lu0q8Nd0n9HZaQHtK9KuVB9M8o211lvVWm+fFqS+nOTMTPyx75p2X54WaN6S5Da11lvWWr8+7WTzd5J8ddpJ/7Sd8uvSwsXDa62HuuVulhYek+TnSykHkqTW+jtdC9f4iuMv1Vq/udb6OzN+x+00bh5/ea318115jiZ5affzrTSTz2O/vlPa6Eln1FrvnDbE6htnzdzdQPq/01qY/t8kN+nq0U3SguJ5x9net6f98fn2WuvNum3eMu1K7clJrhGKu6v8r0pyapLfShvW7va11tulhft3dL/D2u4nv5BWt1/elfGO3XHijLTWi+unjQq11fn3s3nU7bWO1/3owWmje71iynvjFsifSgsd35/2nf6XWuut0+rbm7tZf660YYaTJLXW5+ZYq9hvda14KaX897QLLF9I+xu4usnfZ+p+tpVyHu+4dgL7zG4wt7pXSvmqtL/dSRsIYuwP0v6WXSfJBZPLdFfW39S97A3x3l2se1D38kUTPz/R72jb6lNXntt25Tg57Tg3eVx/apK7poXktb/fPOvavtpnFhEUxidYn6m1XrmN6/2bWuvLxi/qOuOzdyd54532/FrrP0ws96Ek90s70G/YHNY5bip8+eTvUtuNnD+WdgL816WU605Z9vIk31Fr/cjEcq9O8pPdy5/aQKvCPdO68FxSa/2FWuvVZe9aap7Yvbx2kjKx3KPTbt46kuR7a63/MrHc3+XY1a5HlVKu1TWnPb772f1qrW+YmP/LtdbfSDvpOynt6sE0r6+1/tK4PtVa/zntakrS6vgP1FqvPnmttb4xrQtBcuw
"text/plain": [
"<Figure size 900x1200 with 20 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
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"source": [
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"ncol, nrow = 4, 5\n",
"fig, axs = plt.subplots(nrow, ncol, sharey=True, figsize=(1.5 * ncol, 8))\n",
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"form = lambda x: x.capitalize().replace(' i', ' I').replace(' Ii', ' II')\n",
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"density = True\n",
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"bins = [10, 10, 10, 10]\n",
"for i, ax in enumerate(axs):\n",
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"# ax[0].set_ylabel('\\n'.join([form(l) for l in labels[i].split(' vs ')]))\n",
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" \n",
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" h, b, _ = ax[0].hist(\n",
" np.array(pairwise_xcorr_cntr_mass[i]) * 100, bins=bins[0], color='k',\n",
" density=density\n",
" )\n",
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" bins[0] = b\n",
" \n",
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" if i == 4:\n",
" ax[0].set_xlabel('Centre of mass (cm)')\n",
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" elif i == 0:\n",
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" ax[0].set_title('Grid displacement')\n",
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" ax[0].set_xticklabels([])\n",
" else:\n",
" ax[0].set_xticklabels([])\n",
" \n",
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" h, b, _ = ax[1].hist(\n",
" np.array(pairwise_gridness[i]), bins=bins[1], color='k',\n",
" density=density\n",
" )\n",
2019-10-22 10:22:00 +00:00
" bins[1] = b\n",
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" if i == 4:\n",
2019-10-22 10:22:00 +00:00
" ax[1].set_xlabel('Change')\n",
" elif i == 0:\n",
" ax[1].set_title('$\\\\Delta$ Gridness')\n",
" ax[1].set_xticklabels([])\n",
" else:\n",
" ax[1].set_xticklabels([])\n",
" \n",
2019-12-13 10:43:57 +00:00
" h, b, _ = ax[2].hist(\n",
" np.array(pairwise_maxrate[i]), bins=bins[2], color='k',\n",
" density=density\n",
" )\n",
2019-10-22 10:22:00 +00:00
" bins[2] = b\n",
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" if i == 4:\n",
2019-10-22 10:22:00 +00:00
" ax[2].set_xlabel('Relative change')\n",
" elif i == 0:\n",
" ax[2].set_title('$\\\\Delta$ Max rate')\n",
" ax[2].set_xticklabels([])\n",
" else:\n",
" ax[2].set_xticklabels([])\n",
" \n",
2019-12-13 10:43:57 +00:00
" h, b, _ = ax[3].hist(\n",
" np.array(pairwise_avgrate[i]), bins=bins[3], color='k',\n",
" density=density\n",
" )\n",
2019-10-22 10:22:00 +00:00
" bins[3] = b\n",
2019-12-13 10:43:57 +00:00
" if i == 4:\n",
2019-10-22 10:22:00 +00:00
" ax[3].set_xlabel('Relative change')\n",
" elif i == 0:\n",
" ax[3].set_title('$\\\\Delta$ Average rate')\n",
" ax[3].set_xticklabels([])\n",
" else:\n",
" ax[3].set_xticklabels([])\n",
" \n",
"despine()\n",
"fig.savefig(output_path / 'figures' / 'histogram_grid_all.svg')\n",
"fig.savefig(output_path / 'figures' / 'histogram_grid_all.png')"
]
},
2019-10-16 05:30:40 +00:00
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Save to expipe"
]
},
{
"cell_type": "code",
2019-12-16 15:16:33 +00:00
"execution_count": 213,
2019-10-16 05:30:40 +00:00
"metadata": {},
"outputs": [],
"source": [
"action = project.require_action(\"longitudinal-comparisons-gridcells\")"
]
},
{
"cell_type": "code",
2019-12-16 15:16:33 +00:00
"execution_count": 214,
2019-10-16 05:30:40 +00:00
"metadata": {},
2019-12-13 10:43:57 +00:00
"outputs": [
{
"data": {
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2019-12-16 15:16:33 +00:00
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]
},
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"execution_count": 214,
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"metadata": {},
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}
],
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"source": [
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]
},
{
"cell_type": "code",
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"execution_count": 215,
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"metadata": {},
"outputs": [],
"source": [
"septum_mec.analysis.registration.store_notebook(action, \"20_longitudinal_comparisons_gridcells.ipynb\")"
]
},
{
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"execution_count": null,
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"source": []
}
],
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