3005 lines
7.9 MiB
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3005 lines
7.9 MiB
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{
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"cells": [
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"cell_type": "code",
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2"
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]
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},
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"14:38:14 [I] klustakwik KlustaKwik2 version 0.2.6\n",
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"/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",
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" return f(*args, **kwds)\n",
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"/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",
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" return f(*args, **kwds)\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import pathlib\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from matplotlib import colors\n",
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"import seaborn as sns\n",
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"import re\n",
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"import shutil\n",
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"import pandas as pd\n",
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"import scipy.stats\n",
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"\n",
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"import exdir\n",
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"import expipe\n",
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"from distutils.dir_util import copy_tree\n",
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"import septum_mec\n",
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"import spatial_maps as sp\n",
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"import head_direction.head as head\n",
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"import septum_mec.analysis.data_processing as dp\n",
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"import septum_mec.analysis.registration\n",
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"from septum_mec.analysis.plotting import violinplot, despine\n",
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"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",
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"\n",
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"import speed_cells.speed as spd\n",
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"from septum_mec.analysis.plotting import plot_bootstrap_timeseries\n",
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"\n",
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"from tqdm.notebook import tqdm_notebook as tqdm\n",
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"tqdm.pandas()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"project_path = dp.project_path()\n",
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"project = expipe.get_project(project_path)\n",
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"actions = project.actions\n",
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"\n",
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"output_path = pathlib.Path(\"output\") / \"longitudinal-comparisons-speed\"\n",
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"(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n",
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"(output_path / \"figures\").mkdir(exist_ok=True, parents=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Load cell statistics and shuffling quantiles"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>action</th>\n",
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" <th>baseline</th>\n",
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" <th>entity</th>\n",
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" <th>frequency</th>\n",
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" <th>i</th>\n",
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" <th>ii</th>\n",
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" <th>session</th>\n",
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" <th>stim_location</th>\n",
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" <th>stimulated</th>\n",
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" <th>tag</th>\n",
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" <th>...</th>\n",
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" <th>burst_event_ratio</th>\n",
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" <th>bursty_spike_ratio</th>\n",
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" <th>gridness</th>\n",
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" <th>border_score</th>\n",
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" <th>information_rate</th>\n",
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" <th>information_specificity</th>\n",
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" <th>head_mean_ang</th>\n",
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" <th>head_mean_vec_len</th>\n",
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" <th>spacing</th>\n",
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" <th>orientation</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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|||
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" <td>1849-060319-3</td>\n",
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|||
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" <td>True</td>\n",
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|||
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" <td>1849</td>\n",
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" <td>NaN</td>\n",
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|||
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" <td>False</td>\n",
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|||
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" <td>True</td>\n",
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" <td>3</td>\n",
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|||
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" <td>NaN</td>\n",
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|||
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" <td>False</td>\n",
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|||
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" <td>baseline ii</td>\n",
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" <td>...</td>\n",
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|||
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" <td>0.398230</td>\n",
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|||
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" <td>0.678064</td>\n",
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|||
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" <td>-0.466923</td>\n",
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" <td>0.029328</td>\n",
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" <td>1.009215</td>\n",
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" <td>0.317256</td>\n",
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" <td>5.438033</td>\n",
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|||
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" <td>0.040874</td>\n",
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|||
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" <td>0.628784</td>\n",
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|||
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" <td>20.224859</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1849-060319-3</td>\n",
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|||
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" <td>True</td>\n",
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|||
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" <td>1849</td>\n",
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|||
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" <td>NaN</td>\n",
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|||
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" <td>False</td>\n",
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|||
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" <td>True</td>\n",
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" <td>3</td>\n",
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" <td>NaN</td>\n",
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" <td>False</td>\n",
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|||
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" <td>baseline ii</td>\n",
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" <td>...</td>\n",
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|||
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" <td>0.138014</td>\n",
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|||
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" <td>0.263173</td>\n",
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|||
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" <td>-0.666792</td>\n",
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|||
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" <td>0.308146</td>\n",
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|||
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" <td>0.192524</td>\n",
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|||
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" <td>0.033447</td>\n",
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|||
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" <td>1.951740</td>\n",
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|||
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" <td>0.017289</td>\n",
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|||
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" <td>0.789388</td>\n",
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|||
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" <td>27.897271</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>1849-060319-3</td>\n",
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|||
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" <td>True</td>\n",
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|||
|
|
" <td>1849</td>\n",
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|||
|
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" <td>NaN</td>\n",
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|||
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" <td>False</td>\n",
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|||
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" <td>True</td>\n",
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" <td>3</td>\n",
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|||
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" <td>NaN</td>\n",
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|||
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" <td>False</td>\n",
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|||
|
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" <td>baseline ii</td>\n",
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|||
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|
" <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",
|
|||
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" <td>28.810794</td>\n",
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" </tr>\n",
|
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1849-060319-3</td>\n",
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|||
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" <td>True</td>\n",
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|||
|
|
" <td>1849</td>\n",
|
|||
|
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" <td>NaN</td>\n",
|
|||
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" <td>False</td>\n",
|
|||
|
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" <td>True</td>\n",
|
|||
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" <td>3</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
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" <td>False</td>\n",
|
|||
|
|
" <td>baseline ii</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>0.087413</td>\n",
|
|||
|
|
" <td>0.179245</td>\n",
|
|||
|
|
" <td>-0.437492</td>\n",
|
|||
|
|
" <td>0.268948</td>\n",
|
|||
|
|
" <td>0.157394</td>\n",
|
|||
|
|
" <td>0.073553</td>\n",
|
|||
|
|
" <td>6.215195</td>\n",
|
|||
|
|
" <td>0.101911</td>\n",
|
|||
|
|
" <td>0.492250</td>\n",
|
|||
|
|
" <td>9.462322</td>\n",
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" </tr>\n",
|
|||
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" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
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|||
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" <td>1849-060319-3</td>\n",
|
|||
|
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" <td>True</td>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
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" <td>3</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
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" <td>False</td>\n",
|
|||
|
|
" <td>baseline ii</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>0.248771</td>\n",
|
|||
|
|
" <td>0.463596</td>\n",
|
|||
|
|
" <td>-0.085938</td>\n",
|
|||
|
|
" <td>0.218744</td>\n",
|
|||
|
|
" <td>0.519153</td>\n",
|
|||
|
|
" <td>0.032683</td>\n",
|
|||
|
|
" <td>1.531481</td>\n",
|
|||
|
|
" <td>0.053810</td>\n",
|
|||
|
|
" <td>0.559905</td>\n",
|
|||
|
|
" <td>0.000000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
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"<p>5 rows × 39 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" action baseline entity frequency i ii session \\\n",
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"0 1849-060319-3 True 1849 NaN False True 3 \n",
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"1 1849-060319-3 True 1849 NaN False True 3 \n",
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"2 1849-060319-3 True 1849 NaN False True 3 \n",
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"3 1849-060319-3 True 1849 NaN False True 3 \n",
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"4 1849-060319-3 True 1849 NaN False True 3 \n",
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"\n",
|
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" stim_location stimulated tag ... burst_event_ratio \\\n",
|
|||
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"0 NaN False baseline ii ... 0.398230 \n",
|
|||
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"1 NaN False baseline ii ... 0.138014 \n",
|
|||
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"2 NaN False baseline ii ... 0.373986 \n",
|
|||
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"3 NaN False baseline ii ... 0.087413 \n",
|
|||
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"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",
|
|||
|
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"2 0.659259 -0.572566 0.143252 4.745836 \n",
|
|||
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"3 0.179245 -0.437492 0.268948 0.157394 \n",
|
|||
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"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",
|
|||
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"1 0.033447 1.951740 0.017289 0.789388 \n",
|
|||
|
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"2 0.393704 4.439721 0.124731 0.555402 \n",
|
|||
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"3 0.073553 6.215195 0.101911 0.492250 \n",
|
|||
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"4 0.032683 1.531481 0.053810 0.559905 \n",
|
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"\n",
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" orientation \n",
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"0 20.224859 \n",
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|||
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"1 27.897271 \n",
|
|||
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"2 28.810794 \n",
|
|||
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"3 9.462322 \n",
|
|||
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"4 0.000000 \n",
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|||
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"\n",
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"[5 rows x 39 columns]"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
|
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],
|
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"source": [
|
|||
|
|
"statistics_action = actions['calculate-statistics']\n",
|
|||
|
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"identification_action = actions['identify-neurons']\n",
|
|||
|
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"sessions = pd.read_csv(identification_action.data_path('sessions'))\n",
|
|||
|
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"units = pd.read_csv(identification_action.data_path('units'))\n",
|
|||
|
|
"session_units = pd.merge(sessions, units, on='action')\n",
|
|||
|
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"statistics_results = pd.read_csv(statistics_action.data_path('results'))\n",
|
|||
|
|
"statistics = pd.merge(session_units, statistics_results, how='left')\n",
|
|||
|
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"statistics.head()"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
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{
|
|||
|
|
"cell_type": "code",
|
|||
|
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"execution_count": 5,
|
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"metadata": {},
|
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"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": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"## stim response"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 6,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"stim_response_action = actions['stimulus-response']\n",
|
|||
|
|
"stim_response_results = pd.read_csv(stim_response_action.data_path('results'))\n",
|
|||
|
|
"stim_response_results = stim_response_results.drop('unit_id', axis=1)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 7,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"statistics = pd.merge(statistics, stim_response_results, how='left')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"N cells: 1284\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"print('N cells:',statistics.shape[0])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"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>border_score</th>\n",
|
|||
|
|
" <th>gridness</th>\n",
|
|||
|
|
" <th>head_mean_ang</th>\n",
|
|||
|
|
" <th>head_mean_vec_len</th>\n",
|
|||
|
|
" <th>information_rate</th>\n",
|
|||
|
|
" <th>speed_score</th>\n",
|
|||
|
|
" <th>action</th>\n",
|
|||
|
|
" <th>channel_group</th>\n",
|
|||
|
|
" <th>unit_name</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <td>0.348023</td>\n",
|
|||
|
|
" <td>0.275109</td>\n",
|
|||
|
|
" <td>3.012689</td>\n",
|
|||
|
|
" <td>0.086792</td>\n",
|
|||
|
|
" <td>0.707197</td>\n",
|
|||
|
|
" <td>0.149071</td>\n",
|
|||
|
|
" <td>1833-010719-1</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>127.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>0.362380</td>\n",
|
|||
|
|
" <td>0.166475</td>\n",
|
|||
|
|
" <td>3.133138</td>\n",
|
|||
|
|
" <td>0.037271</td>\n",
|
|||
|
|
" <td>0.482486</td>\n",
|
|||
|
|
" <td>0.132212</td>\n",
|
|||
|
|
" <td>1833-010719-1</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>161.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>0.367498</td>\n",
|
|||
|
|
" <td>0.266865</td>\n",
|
|||
|
|
" <td>5.586395</td>\n",
|
|||
|
|
" <td>0.182843</td>\n",
|
|||
|
|
" <td>0.271188</td>\n",
|
|||
|
|
" <td>0.062821</td>\n",
|
|||
|
|
" <td>1833-010719-1</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>191.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>0.331942</td>\n",
|
|||
|
|
" <td>0.312155</td>\n",
|
|||
|
|
" <td>5.955767</td>\n",
|
|||
|
|
" <td>0.090786</td>\n",
|
|||
|
|
" <td>0.354018</td>\n",
|
|||
|
|
" <td>0.052009</td>\n",
|
|||
|
|
" <td>1833-010719-1</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>223.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>0.325842</td>\n",
|
|||
|
|
" <td>0.180495</td>\n",
|
|||
|
|
" <td>5.262721</td>\n",
|
|||
|
|
" <td>0.103584</td>\n",
|
|||
|
|
" <td>0.210427</td>\n",
|
|||
|
|
" <td>0.094041</td>\n",
|
|||
|
|
" <td>1833-010719-1</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>225.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" border_score gridness head_mean_ang head_mean_vec_len information_rate \\\n",
|
|||
|
|
"0 0.348023 0.275109 3.012689 0.086792 0.707197 \n",
|
|||
|
|
"1 0.362380 0.166475 3.133138 0.037271 0.482486 \n",
|
|||
|
|
"2 0.367498 0.266865 5.586395 0.182843 0.271188 \n",
|
|||
|
|
"3 0.331942 0.312155 5.955767 0.090786 0.354018 \n",
|
|||
|
|
"4 0.325842 0.180495 5.262721 0.103584 0.210427 \n",
|
|||
|
|
"\n",
|
|||
|
|
" speed_score action channel_group unit_name \n",
|
|||
|
|
"0 0.149071 1833-010719-1 0.0 127.0 \n",
|
|||
|
|
"1 0.132212 1833-010719-1 0.0 161.0 \n",
|
|||
|
|
"2 0.062821 1833-010719-1 0.0 191.0 \n",
|
|||
|
|
"3 0.052009 1833-010719-1 0.0 223.0 \n",
|
|||
|
|
"4 0.094041 1833-010719-1 0.0 225.0 "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"shuffling = actions['shuffling']\n",
|
|||
|
|
"quantiles_95 = pd.read_csv(shuffling.data_path('quantiles_95'))\n",
|
|||
|
|
"quantiles_95.head()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"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>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>p_e_peak</th>\n",
|
|||
|
|
" <th>t_i_peak</th>\n",
|
|||
|
|
" <th>p_i_peak</th>\n",
|
|||
|
|
" <th>border_score_threshold</th>\n",
|
|||
|
|
" <th>gridness_threshold</th>\n",
|
|||
|
|
" <th>head_mean_ang_threshold</th>\n",
|
|||
|
|
" <th>head_mean_vec_len_threshold</th>\n",
|
|||
|
|
" <th>information_rate_threshold</th>\n",
|
|||
|
|
" <th>speed_score_threshold</th>\n",
|
|||
|
|
" <th>specificity</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</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>0.332548</td>\n",
|
|||
|
|
" <td>0.229073</td>\n",
|
|||
|
|
" <td>6.029431</td>\n",
|
|||
|
|
" <td>0.205362</td>\n",
|
|||
|
|
" <td>1.115825</td>\n",
|
|||
|
|
" <td>0.066736</td>\n",
|
|||
|
|
" <td>0.451741</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</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>0.354830</td>\n",
|
|||
|
|
" <td>0.089333</td>\n",
|
|||
|
|
" <td>6.120055</td>\n",
|
|||
|
|
" <td>0.073566</td>\n",
|
|||
|
|
" <td>0.223237</td>\n",
|
|||
|
|
" <td>0.052594</td>\n",
|
|||
|
|
" <td>0.098517</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</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>0.264610</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",
|
|||
|
|
" <td>0.400770</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>1849-060319-3</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>baseline ii</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>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",
|
|||
|
|
" <td>0.269461</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>1849-060319-3</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>baseline ii</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>0.342799</td>\n",
|
|||
|
|
" <td>0.218967</td>\n",
|
|||
|
|
" <td>5.768170</td>\n",
|
|||
|
|
" <td>0.054762</td>\n",
|
|||
|
|
" <td>0.524990</td>\n",
|
|||
|
|
" <td>0.144702</td>\n",
|
|||
|
|
" <td>0.133410</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>5 rows × 51 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" action baseline entity frequency i ii session \\\n",
|
|||
|
|
"0 1849-060319-3 True 1849 NaN False True 3 \n",
|
|||
|
|
"1 1849-060319-3 True 1849 NaN False True 3 \n",
|
|||
|
|
"2 1849-060319-3 True 1849 NaN False True 3 \n",
|
|||
|
|
"3 1849-060319-3 True 1849 NaN False True 3 \n",
|
|||
|
|
"4 1849-060319-3 True 1849 NaN False True 3 \n",
|
|||
|
|
"\n",
|
|||
|
|
" stim_location stimulated tag ... 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",
|
|||
|
|
"\n",
|
|||
|
|
" 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",
|
|||
|
|
"\n",
|
|||
|
|
" 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",
|
|||
|
|
"\n",
|
|||
|
|
" speed_score_threshold specificity \n",
|
|||
|
|
"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",
|
|||
|
|
"\n",
|
|||
|
|
"[5 rows x 51 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"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": [
|
|||
|
|
"## waveform"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"waveform_action = actions['waveform-analysis']\n",
|
|||
|
|
"waveform_results = pd.read_csv(waveform_action.data_path('results')).drop('template', axis=1)\n",
|
|||
|
|
"waveform_results = waveform_results.drop('unit_id', axis=1)\n",
|
|||
|
|
"\n",
|
|||
|
|
"data = data.merge(waveform_results, how='left')\n",
|
|||
|
|
"data.bs = data.bs.astype(bool)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Statistics about all cell-sessions"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"stimulated\n",
|
|||
|
|
"False 624\n",
|
|||
|
|
"True 660\n",
|
|||
|
|
"Name: action, dtype: int64"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"data.groupby('stimulated').count()['action']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Find interneurons"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"data.loc[data.eval('t_i_peak == t_i_peak and not bs'), 'ns_inhibited'] = True\n",
|
|||
|
|
"data.ns_inhibited.fillna(False, inplace=True)\n",
|
|||
|
|
"\n",
|
|||
|
|
"data.loc[data.eval('t_i_peak != t_i_peak and not bs'), 'ns_not_inhibited'] = True\n",
|
|||
|
|
"data.ns_not_inhibited.fillna(False, inplace=True)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# make baseline for inhibited vs not inhibited\n",
|
|||
|
|
"data.loc[data.unit_id.isin(data.query('ns_inhibited').unit_id.values), 'ns_inhibited'] = True\n",
|
|||
|
|
"data.loc[data.unit_id.isin(data.query('ns_not_inhibited').unit_id.values), 'ns_not_inhibited'] = True"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Find all cells with gridness above threshold"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Number of sessions above threshold 194\n",
|
|||
|
|
"Number of animals 4\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"query = (\n",
|
|||
|
|
" 'gridness > gridness_threshold and '\n",
|
|||
|
|
" 'information_rate > information_rate_threshold and '\n",
|
|||
|
|
" 'gridness > .2 and '\n",
|
|||
|
|
" 'average_rate < 25'\n",
|
|||
|
|
")\n",
|
|||
|
|
"sessions_above_threshold = data.query(query)\n",
|
|||
|
|
"print(\"Number of sessions above threshold\", len(sessions_above_threshold))\n",
|
|||
|
|
"print(\"Number of animals\", len(sessions_above_threshold.groupby(['entity'])))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"## select neurons that have been characterized as a grid cell on the same day"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"once_a_gridcell = statistics[statistics.unit_day.isin(sessions_above_threshold.unit_day.values)]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 17,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Number of gridcells 139\n",
|
|||
|
|
"Number of gridcell recordings 231\n",
|
|||
|
|
"Number of animals 4\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"print(\"Number of gridcells\", 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'])))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"data.loc[:,'gridcell'] = np.nan\n",
|
|||
|
|
"data['gridcell'] = data.isin(once_a_gridcell)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# divide into stim not stim"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"109"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"data.ns_inhibited.sum()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Number of gridcells in baseline i sessions 66\n",
|
|||
|
|
"Number of gridcells in stimulated 11Hz ms sessions 61\n",
|
|||
|
|
"Number of gridcells in baseline ii sessions 56\n",
|
|||
|
|
"Number of gridcells in stimulated 30Hz ms sessions 40\n",
|
|||
|
|
"\n",
|
|||
|
|
"Number of NSi in baseline i sessions 17\n",
|
|||
|
|
"Number of NSi in stimulated 11Hz ms sessions 31\n",
|
|||
|
|
"Number of NSi in baseline ii sessions 21\n",
|
|||
|
|
"Number of NSi in stimulated 30Hz ms sessions 36\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"query_baseline_i = 'baseline and Hz11'\n",
|
|||
|
|
"query_stimulated_11 = 'stimulated and frequency==11 and stim_location==\"ms\"'\n",
|
|||
|
|
"\n",
|
|||
|
|
"query_baseline_ii = 'baseline and Hz30'\n",
|
|||
|
|
"query_stimulated_30 = 'stimulated and frequency==30 and stim_location==\"ms\"'\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Number of gridcells in baseline i sessions\", \n",
|
|||
|
|
" len(data.query('gridcell and ' + f'{query_baseline_i}')))\n",
|
|||
|
|
"print(\"Number of gridcells in stimulated 11Hz ms sessions\", \n",
|
|||
|
|
" len(data.query('gridcell and ' + f'{query_stimulated_11}')))\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Number of gridcells in baseline ii sessions\", \n",
|
|||
|
|
" len(data.query('gridcell and ' + f'{query_baseline_ii}')))\n",
|
|||
|
|
"print(\"Number of gridcells in stimulated 30Hz ms sessions\", \n",
|
|||
|
|
" len(data.query('gridcell and ' + f'{query_stimulated_30}')))\n",
|
|||
|
|
"print()\n",
|
|||
|
|
"print(\"Number of NSi in baseline i sessions\", \n",
|
|||
|
|
" len(data.query('ns_inhibited and ' + f'{query_baseline_i}')))\n",
|
|||
|
|
"print(\"Number of NSi in stimulated 11Hz ms sessions\", \n",
|
|||
|
|
" len(data.query('ns_inhibited and ' + f'{query_stimulated_11}')))\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Number of NSi in baseline ii sessions\", \n",
|
|||
|
|
" len(data.query('ns_inhibited and ' + f'{query_baseline_ii}')))\n",
|
|||
|
|
"print(\"Number of NSi in stimulated 30Hz ms sessions\", \n",
|
|||
|
|
" len(data.query('ns_inhibited and ' + f'{query_stimulated_30}')))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Plotting"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"max_speed = .5 # m/s only used for speed score\n",
|
|||
|
|
"min_speed = 0.02 # m/s only used for speed score\n",
|
|||
|
|
"position_sampling_rate = 100 # for interpolation\n",
|
|||
|
|
"position_low_pass_frequency = 6 # for low pass filtering of position\n",
|
|||
|
|
"\n",
|
|||
|
|
"box_size = [1.0, 1.0]\n",
|
|||
|
|
"bin_size = 0.02\n",
|
|||
|
|
"smoothing_low = 0.03\n",
|
|||
|
|
"smoothing_high = 0.06\n",
|
|||
|
|
"\n",
|
|||
|
|
"speed_binsize = 0.02\n",
|
|||
|
|
"\n",
|
|||
|
|
"stim_mask = True\n",
|
|||
|
|
"# baseline_duration = 600\n",
|
|||
|
|
"baseline_duration = None"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 22,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"data_loader = dp.Data(\n",
|
|||
|
|
" position_sampling_rate=position_sampling_rate, \n",
|
|||
|
|
" position_low_pass_frequency=position_low_pass_frequency,\n",
|
|||
|
|
" box_size=box_size, bin_size=bin_size, \n",
|
|||
|
|
" stim_mask=stim_mask, baseline_duration=baseline_duration\n",
|
|||
|
|
")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 38,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def compute_mask(search, t1, t2, z1, z2, z3):\n",
|
|||
|
|
" idxs = []\n",
|
|||
|
|
" idx = np.searchsorted(search, [t1 + z1, t1 + z2], side='right')\n",
|
|||
|
|
" idxs.extend(np.arange(idx[0], idx[1]).tolist())\n",
|
|||
|
|
"\n",
|
|||
|
|
" idx = np.searchsorted(search, [t1 + z3, t2], side='right')\n",
|
|||
|
|
" idxs.extend(np.arange(idx[0], idx[1]).tolist())\n",
|
|||
|
|
" return idxs\n",
|
|||
|
|
"\n",
|
|||
|
|
" \n",
|
|||
|
|
"def load_speed(action_id, channel_id, unit_name, z1, z2, z3, split=False, stim_action=None):\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",
|
|||
|
|
" stim_action = stim_action if stim_action is not None else action_id\n",
|
|||
|
|
" stim_times = data_loader.stim_times(stim_action)\n",
|
|||
|
|
" \n",
|
|||
|
|
" if stim_times is not None:\n",
|
|||
|
|
" idxs = []\n",
|
|||
|
|
" stim_times = np.array(stim_times)\n",
|
|||
|
|
" for t1, t2 in zip(stim_times, stim_times[1:]):\n",
|
|||
|
|
" idx = compute_mask(np.array(t), t1, t2, z1, z2, z3)\n",
|
|||
|
|
" idxs.extend(idx)\n",
|
|||
|
|
" \n",
|
|||
|
|
" idxs = np.sort(np.unique(idxs))\n",
|
|||
|
|
" mask = ~np.in1d(np.arange(len(t)), idxs)\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" mask = np.zeros_like(t).astype(bool)\n",
|
|||
|
|
" \n",
|
|||
|
|
" if split:\n",
|
|||
|
|
" t_split = t[-1] / 2\n",
|
|||
|
|
" mask_speed = t < t_split\n",
|
|||
|
|
" speed1 = speed[mask_speed]\n",
|
|||
|
|
" speed2 = speed[~mask_speed]\n",
|
|||
|
|
" t1 = t[mask_speed]\n",
|
|||
|
|
" t2 = t[~mask_speed]\n",
|
|||
|
|
" mask1 = mask[mask_speed]\n",
|
|||
|
|
" mask2 = mask[~mask_speed]\n",
|
|||
|
|
" \n",
|
|||
|
|
" spike_mask = spike_times < t_split\n",
|
|||
|
|
" spike_times1 = spike_times[spike_mask]\n",
|
|||
|
|
" spike_times2 = spike_times[~spike_mask]\n",
|
|||
|
|
" \n",
|
|||
|
|
" return speed1, speed2, t1, t2, spike_times1, spike_times2, mask1, mask2\n",
|
|||
|
|
" \n",
|
|||
|
|
" return speed, t, spike_times, mask"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 45,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"model_id": "c4e5c309c03c46e3b5203c1a3f28c7d9",
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0
|
|||
|
|
},
|
|||
|
|
"text/plain": [
|
|||
|
|
"HBox(children=(IntProgress(value=0, max=190), HTML(value='')))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"z1, zg2, zi2, z3 = 0, 5e-3, 3e-3, 15e-3\n",
|
|||
|
|
"gridcell_id_map = {}\n",
|
|||
|
|
"gridcell_speed = [[], [], [], [], []]\n",
|
|||
|
|
"gridcell_data = data.query('gridcell')\n",
|
|||
|
|
"\n",
|
|||
|
|
"nsi_id_map = {}\n",
|
|||
|
|
"nsi_speed = [[], [], [], [], []]\n",
|
|||
|
|
"nsi_data = data.query('ns_inhibited')\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"n_iter = gridcell_data.unit_id.unique().shape[0] + nsi_data.unit_id.unique().shape[0]\n",
|
|||
|
|
"pbar = tqdm(total=n_iter)\n",
|
|||
|
|
"\n",
|
|||
|
|
"for nid, unit_sessions in gridcell_data.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",
|
|||
|
|
" dfs = [(base_i, base_i), (base_i, base_ii), (base_i, stim_i), (base_ii, stim_ii), (base_i, stim_ii)]\n",
|
|||
|
|
" sample = [False, False, True, True, True]\n",
|
|||
|
|
" for i, pair in enumerate(dfs):\n",
|
|||
|
|
" same_frame = pair[0].equals(pair[1])\n",
|
|||
|
|
" for (_, row_1), (_, row_2) in zip(pair[0].iterrows(), pair[1].iterrows()):\n",
|
|||
|
|
" if same_frame:\n",
|
|||
|
|
" assert row_1.equals(row_2)\n",
|
|||
|
|
" speed1, speed2, t1, t2, spike_times1, spike_times2, mask1, mask2 = load_speed(\n",
|
|||
|
|
" row_1['action'], row_1['channel_group'], row_1['unit_name'], \n",
|
|||
|
|
" z1, zg2, z3, split=True)\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" assert not row_1.equals(row_2)\n",
|
|||
|
|
" stim_action = row_2['action'] if sample[i] else None\n",
|
|||
|
|
" speed1, t1, spike_times1, mask1 = load_speed(\n",
|
|||
|
|
" row_1['action'], row_1['channel_group'], row_1['unit_name'], \n",
|
|||
|
|
" z1, zg2, z3, split=False, stim_action=stim_action)\n",
|
|||
|
|
" speed2, t2, spike_times2, mask2 = load_speed(\n",
|
|||
|
|
" row_2['action'], row_2['channel_group'], row_2['unit_name'], \n",
|
|||
|
|
" z1, zg2, z3, split=False)\n",
|
|||
|
|
"\n",
|
|||
|
|
" speed_score1 = spd.speed_correlation(\n",
|
|||
|
|
" speed1, t1, spike_times1, return_data=False, mask=mask1)\n",
|
|||
|
|
" speed_score2 = spd.speed_correlation(\n",
|
|||
|
|
" speed2, t2, spike_times2, return_data=False, mask=mask2)\n",
|
|||
|
|
" \n",
|
|||
|
|
" gridcell_speed[i].append((speed_score1, speed_score2))\n",
|
|||
|
|
"\n",
|
|||
|
|
" assert row_1.unit_id == row_2.unit_id\n",
|
|||
|
|
" uid = row_2.unit_id\n",
|
|||
|
|
" idnum = row_1.unit_idnum\n",
|
|||
|
|
" gridcell_id_map[uid] = idnum\n",
|
|||
|
|
" pbar.update()\n",
|
|||
|
|
"\n",
|
|||
|
|
"for nid, unit_sessions in nsi_data.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",
|
|||
|
|
" dfs = [(base_i, base_i), (base_i, base_ii), (base_i, stim_i), (base_ii, stim_ii), (base_i, stim_ii)]\n",
|
|||
|
|
" sample = [False, False, True, True, True]\n",
|
|||
|
|
" for i, pair in enumerate(dfs):\n",
|
|||
|
|
" same_frame = pair[0].equals(pair[1])\n",
|
|||
|
|
" for (_, row_1), (_, row_2) in zip(pair[0].iterrows(), pair[1].iterrows()):\n",
|
|||
|
|
" if same_frame:\n",
|
|||
|
|
" assert row_1.equals(row_2)\n",
|
|||
|
|
" speed1, speed2, t1, t2, spike_times1, spike_times2, mask1, mask2 = load_speed(\n",
|
|||
|
|
" row_1['action'], row_1['channel_group'], row_1['unit_name'], \n",
|
|||
|
|
" z1, zi2, z3, split=True)\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" assert not row_1.equals(row_2)\n",
|
|||
|
|
" stim_action = row_2['action'] if sample[i] else None\n",
|
|||
|
|
" speed1, t1, spike_times1, mask1 = load_speed(\n",
|
|||
|
|
" row_1['action'], row_1['channel_group'], row_1['unit_name'], \n",
|
|||
|
|
" z1, zi2, z3, split=False, stim_action=stim_action)\n",
|
|||
|
|
" speed2, t2, spike_times2, mask2 = load_speed(\n",
|
|||
|
|
" row_2['action'], row_2['channel_group'], row_2['unit_name'], \n",
|
|||
|
|
" z1, zi2, z3, split=False)\n",
|
|||
|
|
" \n",
|
|||
|
|
" speed_score1 = spd.speed_correlation(\n",
|
|||
|
|
" speed1, t1, spike_times1, return_data=False, mask=mask1)\n",
|
|||
|
|
" speed_score2 = spd.speed_correlation(\n",
|
|||
|
|
" speed2, t2, spike_times2, return_data=False, mask=mask2)\n",
|
|||
|
|
" \n",
|
|||
|
|
" nsi_speed[i].append((speed_score1, speed_score2))\n",
|
|||
|
|
"\n",
|
|||
|
|
" assert row_1.unit_id == row_2.unit_id\n",
|
|||
|
|
" uid = row_2.unit_id\n",
|
|||
|
|
" idnum = row_1.unit_idnum\n",
|
|||
|
|
" nsi_id_map[uid] = idnum\n",
|
|||
|
|
" pbar.update()\n",
|
|||
|
|
"\n",
|
|||
|
|
"pbar.close()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 52,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"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",
|
|||
|
|
"data['session_id'] = data.apply(session_id, axis=1)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 53,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"plt.rc('axes', titlesize=12)\n",
|
|||
|
|
"plt.rcParams.update({\n",
|
|||
|
|
" 'font.size': 12, \n",
|
|||
|
|
" 'figure.figsize': (4, 3), \n",
|
|||
|
|
" 'figure.dpi': 150\n",
|
|||
|
|
"})"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 66,
|
|||
|
|
"metadata": {
|
|||
|
|
"scrolled": false
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|||
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/septum-mec/septum_mec/analysis/plotting.py:86: UserWarning: Warning: converting a masked element to nan.\n",
|
|||
|
|
" values = np.array([statistic(signal) for signal in signals])\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.\n",
|
|||
|
|
" out=out, **kwargs)\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:154: RuntimeWarning: invalid value encountered in true_divide\n",
|
|||
|
|
" ret, rcount, out=ret, casting='unsafe', subok=False)\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:6: 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",
|
|||
|
|
" \n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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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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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
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]
|
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},
|
|||
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"metadata": {
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},
|
|||
|
|
"output_type": "display_data"
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|
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},
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|||
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{
|
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|
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"data": {
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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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},
|
|||
|
|
"metadata": {
|
|||
|
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},
|
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"output_type": "display_data"
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
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{
|
|||
|
|
"data": {
|
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"text/plain": [
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|||
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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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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
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]
|
|||
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},
|
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|
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"metadata": {
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},
|
|||
|
|
"output_type": "display_data"
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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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},
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|
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"metadata": {
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{
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"data": {
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|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
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]
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},
|
|||
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"metadata": {
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},
|
|||
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"output_type": "display_data"
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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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]
|
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},
|
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|
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"image/png": "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|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
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]
|
|||
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|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
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|
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},
|
|||
|
|
"output_type": "display_data"
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|||
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},
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|
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{
|
|||
|
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"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": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x1800 with 12 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x1800 with 12 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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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 1200x1200 with 8 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
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{
|
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|
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"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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]
|
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
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]
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},
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"metadata": {
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},
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|||
|
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"output_type": "display_data"
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},
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{
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"data": {
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
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},
|
|||
|
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"output_type": "display_data"
|
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},
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x1200 with 8 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
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]
|
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},
|
|||
|
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"metadata": {
|
|||
|
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},
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"output_type": "display_data"
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},
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{
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"data": {
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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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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]
|
|||
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},
|
|||
|
|
"metadata": {
|
|||
|
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},
|
|||
|
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"output_type": "display_data"
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},
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{
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"data": {
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
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{
|
|||
|
|
"data": {
|
|||
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"text/plain": [
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|||
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"<Figure size 1200x600 with 4 Axes>"
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|
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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|||
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|
"<Figure size 1200x600 with 4 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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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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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x1800 with 12 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
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},
|
|||
|
|
"output_type": "display_data"
|
|||
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},
|
|||
|
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{
|
|||
|
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"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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|||
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"<Figure size 1200x600 with 4 Axes>"
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]
|
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},
|
|||
|
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"metadata": {
|
|||
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},
|
|||
|
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"output_type": "display_data"
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{
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"data": {
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"image/png": "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
|
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|
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"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
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|||
|
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]
|
|||
|
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},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
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},
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|
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{
|
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|
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"data": {
|
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
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},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
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|
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{
|
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|
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"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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|||
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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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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
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]
|
|||
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|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
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|||
|
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{
|
|||
|
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"data": {
|
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"text/plain": [
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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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"data": {
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"text/plain": [
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|||
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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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"metadata": {
|
|||
|
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},
|
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|
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"output_type": "display_data"
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{
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"data": {
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|
|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
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]
|
|||
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|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
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},
|
|||
|
|
"output_type": "display_data"
|
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|
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},
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|||
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{
|
|||
|
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"data": {
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"text/plain": [
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"metadata": {
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 1200x600 with 4 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"metadata": {
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 1200x600 with 4 Axes>"
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]
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},
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"metadata": {
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},
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|||
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"output_type": "display_data"
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},
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{
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"data": {
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|
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"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
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]
|
|||
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|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
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},
|
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|
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x1200 with 8 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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"text/plain": [
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"<Figure size 1200x600 with 4 Axes>"
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"metadata": {
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 1200x600 with 4 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"<Figure size 1200x600 with 4 Axes>"
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{
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"text/plain": [
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|||
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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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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
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|
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]
|
|||
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},
|
|||
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"metadata": {
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},
|
|||
|
|
"output_type": "display_data"
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},
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{
|
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|
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"data": {
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x1800 with 12 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
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"output_type": "display_data"
|
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},
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{
|
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|
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x1200 with 8 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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"text/plain": [
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|||
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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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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
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|
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},
|
|||
|
|
"output_type": "display_data"
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|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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|||
|
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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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|
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},
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|||
|
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAABI8AAAJPCAYAAAD1zfSMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdebxkeV3f/1fd/d7ee/ZhZhgYwpdVECEExTBCQoy4JnEHghMUVFSU/IAEcECjuCvKahQNEBRcIiSBCAi4QEAwgMryhQFmH3qm17vVds6p3x/n1L2nqqvq1q1bt7tv9ev5ePSjtnO+deoOXXS/+/P5fCutVgtJkiRJkiSpl6nzfQGSJEmSJEm6cBkeSZIkSZIkqS/DI0mSJEmSJPVleCRJkiRJkqS+DI8kSZIkSZLUl+GRJEmSJEmS+jI8kiRJkiRJUl+GR5IkSZIkSerL8EiSJEmSJEl9GR5JkiRJkiSpL8MjSZIkSZIk9WV4JEmSJEmSpL4MjyRJkiRJktSX4ZEkSZIkSZL6mjnfFyBJks6tEMIHgScVD18aY/y5Ic97NfCjxcMHxBhvHf/VqZcQwtXAjwD/CngQsA84CXwC+EPgv8cYkwHn/w7wH4Z8u6H/24YQXgL8F+ANMcbnDrm+JEnaY6w8kiTp4vayEMJDz/dFqL8QwncBEXgJ8FjgMDALXAF8I/D7wIdDCPcbsMxX78J1PQ542bjXlSRJFx7DI0mSLm7zwO+GEPwzwQUohPAU4K3AfqAG/BrwVODxwPcCf1Uc+jjgXSGEpR5rzAAPLx7+DnmQNOjX3UNc1yOAd5P/70eSJE0429YkSdITgB8HfuN8X4g2hRAqwKuBafLg6BtijB8pHfK3IYS3Aa8Fngt8FfB84Oe7lnoomyHP+2KMn9zhdX0L8Gbg0E7WkSRJe4f/yihJ0sUrA9pzcn4uhPDA83kxOssTgIcU93+zKzgCIMbYAn4SuLd46pk91nl06f7IwVEI4UgI4VXAO8iDo3TUtSRJ0t5ieCRJ0sWrCfxycX8J+K/n8Vp0tq8v3X9nv4NijDXgb4qHIYTQ3UrWnne0BnxhlAsJIXwtcAt5hVoFuAf4vlHWkiRJe49ta5IkXdxeAXwHeYXLk0MIPxhj3FGIVAzgfh7wFOAa8rDhDuADwG/FGD/T57wPku8CV48xLgxY/x/JZ/jcFmO8vuu1VnH3J4H/Td729UTyoOwW4MUxxveVjj8IPBv4NuARwAHgBPkuZn8EvLnXLmYhhOuBLxcPv4M83HkWeeXPw4t17gL+HPjVGOMX+32eAf4WeCVwdXHtg1RK9xeAeulxu/LoUzHGbITrAHgwcBRoAW8i//natiZJ0kXC8EiSpItYjLEeQng2+eDlKeCXQwjvijHeNcp6IYSXATeTz+npeKn49UMhhJ8FXlG0XO2Wa4EPAZeVnnsMpRAmhPANwH8Hruo690rgXxe/fiqE8O1bhD9LwPuAb+h6/oHADwM3hRD+TYzxXdv5ADHGD5AHbgOFEGaBrysenokxnuk65FHF7SdDCN8K3AT8M/Iw6Dj5z+m1xfv1kwHvAl4eY/xY8b6GR5IkXSRsW5Mk6SIXY/wQ8Jri4SHg9aOsE0J4OfAz5MHR35MPcf5a8sqfnwC+SP5nj5uLX7vp+cClwC+Rt399J/DzMcZbi2t9Anll0lXk1TRvAb6VfBez7wHeU6zzCOCvQwjdAVPZr5IHRx8BnlGs8e3Ae4vX54HfDyHsH9Nn63YTcHlx/8/LL4QQriMPiQCeTj6v6NuAK4BZ8s//74D3hxDeUOzM1stbYoxPawdHkiTp4mLlkSRJAvhPwLcA1wPfHEL4vhjjW4c9OYTwGOBlxcM3Azd1tXt9KITwu8D/Am4EfjqE8PZ+LWxjMEUeFr2k9NwfF9c6DbwRWCSvqPnuGOMfl477W+BtIYSfJm/ruwp4A3m41MuV5J/5WeW2sBDCO8k/7zeRV0A9DXjbzj/aphDCg4BfKD31q12HfHXp/kHgU+S7s/0jeah1I/BjwBHgh8iDtOd2v88O2t0kSdIEsPJIkiQRY1wjDw/aXhVCuKzf8T28gPzPFSeA5/aaE1S8x03kAUWFPLTYTa/r8/y3sLmL2eu6gqMNMcafAT7YPieE8LA+69WA53cHLEVbXnl+1KMYoxDC5eTh1OHiqd+JMf5t12HlndZ+F3hsjPG3Y4wfjjF+IMZ4M3nAdFtxzHNCCDeO8zolSdLeZ3gkSZIAiDG+F/i94uGlwG8Nc14IoUI+HwjgQzHG9QHv8WXgs8XDp4x4qcO4K8Z4Z5/X/lXp/hu2WOe1pfvf2OeYv4sxnuzzWnlW0oEt3mtoIYQrgb8gnyMF+YDvH+9x6C+Th1bfQv9Q7zbyoeFtzx/XdUqSpMlg25okSSr7KfKQ5Crgu0MIfxBjfMcW51xP3vYE8K2lHc+28oDRLnEodwx47RHF7Sp5+9YgHyndf2SfY24dcP5q6f5Y/twVQriBfLbRDcVTEfjXMcZq97FFkPf3xa++YozvCyF8mfy/yZNDCJVdHmguSZL2ECuPJEnShhjjaeBHS0+9LoRwuN/xhUtHfLuZEMLYqnG6LA947ZLi9vgQAcmx0v2jfY5Z7fM85C16bZUt3mtLxaDv/8tmcPRp4BtijMf6nzW0TxW3B9gMAyVJkqw8kiRJnWKM/yOE8EfkO5RdRT6E+T8MOKX854k3MmS7W6Fvi9sAw/zj16BQaDshznTp/nkdGh1C+E7gTcBC8dRHgafFGE+M6S3K/y3mxrSmJEmaAIZHmjghhCXgheRbLT8AWAH+DviNGOO7R1zzOuCnyVs5LgfuI5818coY42f7nHMj8IEtln5HjPHbe5z7TeTbWj8O2A98BXhf8X5fGOUzSNI2PQ94MnmVzk0hhD8ccGx53k8aY/zkiO/ZDny2CncOjbh+W/t6Lx2iPeuKHuedcyGEHwFezebP5n8D3zVovlQIYYr8v+FlQC3G+D+2eJvLi9uU8/hZJUnShce2NU2UEMI+4P3AzcADycv514CnAu8KIdw8wpoB+H/k/+q+n7ysfwF4BvD/Qgj/qs+p7V117gE+1OfXWVtUhxBeQf6XgqeS/wH+M+R/efsB4BMhhH+x3c8gSdsVY7wX+MnSU78N7Otz+JfYrFr5Z1utHUJ4UQjhOT2+z9rDnOdCCNPd5xXnLpKHITvRnv+zH3j4FseWP8/ndvi+Iwkh/DDwGjaDo/8KfNug4Aig2P3tj4G3Aq8tBpv3e4958n+wAPj7GGNjxxcuSZImhuGRJs1rgMcDnwRuiDE+JsZ4f+CZ5H8pefl2wpcQwgz5NsiXAG8GrooxPo68jePV5CHSH4YQLulxejs8elWM8Yl9fv3nrvf7evIKJ4D/BFwRY3w0cE1xHfuAPwgh7PRf3SVpSzHGNwPtis3rge/vc1yTzUrLR4YQnthvzRDCk4FfAF4P/Oeul0+X7l/fZ4l/AcwOuu4hvKd0/zlbHPvc0v337vB9t634/6xXl576uRjjD8UY0yGX+Kvi9kryf5To5yY2K7oGVZlJkqSLkOGRJkax+8zTyWdSfH+McWOnneIvQL9QPHz5NpZ9OvAg4Hbg2e2dbIp/kf1x4K+Bw3T+63xbOzz6h228378vbt8bY/yF4l+NiTGeIv9L2wr5YNpv28aakrQTzyH/7oHBoc2vle7/fgjh2u4DQgiXk1cwtf1m1yHlHcF+rMf5V5BvPb9T7wRuKe7/SAjhO3odFEJ4GfCk4uFf7KAdbyTFPxT8Nzb/vPbrMcaXbnOZ15bu/2YI4azh5iGExwO/VDz8Cp3/jSRJkpx5pInyDPLBph+KMZ7VDkb+r9wvBb4uhHBdjPH2IdZ8VnH75u4S/hhjK4TwBuDrge8t1gY2KpbarRBbbQNd1v7L1qe6X4gxLocQPg98DXDdNtaUpJHFGO8IIbyIzhCi13HvDyG8Dvhh8p3APhVC+A3gL4tDHgv8FHB18fh/xBj/rGuZPyCvvpwBfqLYie0PgRp5+9jzi/O/yOZuY6N8pjSE8Izi2uaAPw4hvAX4I+Be4P7krcrttuTjbIb
|
|||
|
|
"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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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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|||
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|
"<Figure size 1200x600 with 4 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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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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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
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]
|
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},
|
|||
|
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"metadata": {
|
|||
|
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},
|
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|
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"output_type": "display_data"
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{
|
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"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": [
|
|||
|
|
"<Figure size 1200x1200 with 8 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
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{
|
|||
|
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"data": {
|
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|
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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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|
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
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]
|
|||
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},
|
|||
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"metadata": {
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},
|
|||
|
|
"output_type": "display_data"
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},
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{
|
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|
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"data": {
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|
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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": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from septum_mec.analysis.plotting import plot_uncertainty\n",
|
|||
|
|
"\n",
|
|||
|
|
"for unit_id, id_num in gridcell_id_map.items():\n",
|
|||
|
|
" sessions = data.query(f'gridcell and 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",
|
|||
|
|
" idx = row.session_id\n",
|
|||
|
|
" \n",
|
|||
|
|
" speed, t, spike_times, mask = load_speed(\n",
|
|||
|
|
" row['action'], row['channel_group'], row['unit_name'], \n",
|
|||
|
|
" z1, zg2, z3, split=False)\n",
|
|||
|
|
"\n",
|
|||
|
|
" speed_score, inst_speed, rate, times = spd.speed_correlation(\n",
|
|||
|
|
" speed, t, spike_times, return_data=True, mask=mask)\n",
|
|||
|
|
" \n",
|
|||
|
|
" inst_speed = inst_speed[~inst_speed.mask]\n",
|
|||
|
|
" rate = rate[~rate.mask]\n",
|
|||
|
|
" times = times[~times.mask]\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",
|
|||
|
|
" ax[idx].set_title(f'{speed_score:.3f}')\n",
|
|||
|
|
"# plot_uncertainty(speed_bins, rates, ax=ax[idx], normalize_values=True)\n",
|
|||
|
|
" plot_bootstrap_timeseries(speed_bins, rates, ax=ax[idx], normalize_values=True)\n",
|
|||
|
|
"# rr = [rr for r in rates for rr in r]\n",
|
|||
|
|
"# aspect = (np.nanmax(rr) - np.nanmin(rr)) / (max_speed - min_speed)\n",
|
|||
|
|
" for a in ax:\n",
|
|||
|
|
" a.set_aspect('auto')\n",
|
|||
|
|
"\n",
|
|||
|
|
" plt.tight_layout()\n",
|
|||
|
|
" fig.savefig(\n",
|
|||
|
|
" output_path / 'figures' / f'gridcell_neuron_{id_num}_speed_map.png', \n",
|
|||
|
|
" bbox_inches='tight', transparent=True)\n",
|
|||
|
|
" fig.savefig(\n",
|
|||
|
|
" output_path / 'figures' / f'gridcell_neuron_{id_num}_speed_map.svg', \n",
|
|||
|
|
" bbox_inches='tight', transparent=True)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 67,
|
|||
|
|
"metadata": {
|
|||
|
|
"scrolled": true
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"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 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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|
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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|||
|
|
"<Figure size 1200x600 with 4 Axes>"
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]
|
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAABKwAAAugCAYAAAAjLaDbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdeZicVZ3w/W9n6ewkENYQFgE5LDqGRRgGFFD0BVHZdBg2RdRR51VGfRx9AAFBBcFnxBHIg74OIlEBBUdHh1lEUUTEoLJIgAMEAlkg3UnvS3VXddX7x31Xp7pT1Xunqzvfz3V53V11n3Pfp7iuHO/61e/8Tk2hUECSJEmSJEmqFtMmegCSJEmSJElSKQNWkiRJkiRJqioGrCRJkiRJklRVDFhJkiRJkiSpqhiwkiRJkiRJUlUxYCVJkiRJkqSqYsBKkiRJkiRJVcWAlSRJkiRJkqqKAStJkiRJkiRVFQNWkiRJkiRJqioGrCRJkiRJklRVDFhJkiRJkiSpqhiwkiRJkiRJUlUxYCVJkiRJkqSqMmOiByBNdiGEucBngb8DXgO0An8Cvh5j/M8RXvOtwCeBY4AFwAbgf4CvxRjjAP3eBXwceCNQC7wA/Ai4KcbYOEC/dwD/mPabD7wK3AdcG2N8biSfQdLIVNOcUuY6RwAPAzNijDUDtHs78A/A0cBioA14HPgOsCLGWBjJ55A0fOMxp5S5x17Ak8AOwGtijGsqtDuK5HnjTcDuQCfwFHAHcEuMsbtCvzGZwyRJk0tNoeAzozRSIYR5wC9JvpRlSR7WFgN7p02+EGO8apjXvAIo9tkMvAzsBywEMsD5McZ7yvS7meQLIkADSbBqf2BHYB1wSozxyTL9rgKuSF9uAtYDrwXmAu3A6THG+4bzGSSNTDXNKWWuMwv4M3AIQKWAVQjhn4FPpy9bgdXAnsAu6Xv/DrwnxpgdzueQNHzjMaeUuUcNSfDopPStsgGrEMI/Al8jWeHRCTwH7AwsSZv8AXh7jLGlX78xmcMkSZOPSwKl0bmZ5CHwMWD/GOPhMcZ9gPcBOeALIYSTBrpAqfQXxOJD2T8Bu8UYDwd2A74OzAa+F0JY2q/fJ9gSrLoa2D3G+EZgj7TfUuC+EMKifv3exJZg1SXp/Zal7X8OzAPuCCEsHOpnkDQqVTGnVPAl0mDVAPc7jyRY1ZMeF8UYD4sx7gq8lySA9W6SeUrS+BvTOaWCf2BLsKqsEMKxwA0k3z2uB3aMMb4hxrgn8BaSjKmjgW/26zeWc5gkaZIxYCWNUAhhf+B8IA+cF2NcWzwXY1wBfCV9+YVhXPaf0uMdMcb/E2PsSa/XRfLl72mSh7P3l4xjBnB5+vJbMcYri5kLMcauGOOngN+TPNxd2e9+xev8Isb4lRhjPu3XCJxH8uVyZ+C0YXwGSSNQLXNKhbEdm7bvGOL9lscYbyjOKek972ZL5tUn0owtSeNknOaUcve4jqHNDTXAz2KMn0vnoOJY7mfLHPR36fLC0n4wyjlMkjQ5GbCSRu4CYDrw+xjjU2XO35Iejw0h7F3mfDm/I1kuc2v/E2nNl7+kL/cpOXUkW5baXF/huv9SHHOaul9UfCh8vMz9WoBn05dDHb+kkauWOaWPdEnRbSRfeq8YoN1OwBvSl3dUaPaT9DiPQbK1JI3aeMwpvUII04Dvkvx7vnSQ5iemx0pzwy9JfiSD5LmmaNRzmCRp8rLoujRyx6THB8udjDGuDyG8RPIQdTywYrALxhi/WOlcCGE6cFj6srQQevEhrTnGuLrSpdPjYpL6VMVA1Mvp8bD+HdIirfunL9cMOHBJY6Fa5pT+rgcOIFkS+KcB2mWAd5EsKd6qXl6qNGA+fYBrSRq9MZ9T+vlfwLHA94CfkizR20oa2Dqb5Eey31a4Vtm5YYzmMEnSJGXAShq5A9JjpSARJIGefYADR3OjNOX+KyTBplfp+0tjceeEgQoYzyz5e1+2BKy+CXwAeGsI4TMku+3kQwjzgW8Di4CXAIuZSuOvWuaU0nZvBT4GPEFSd+rYSteMMXaQ1L4byHvTYxa/YErjbdzmlBDCIcAXgVeAi0kKoJeVLg3+r0EueTLJ7n8Aq4Zw/yHNYZKkyc0lgdLI7Zoe6wdoszk97jySG4QQrgkhrCYJML2HpBbViTHGzSXNXijeY4Cio4eW/L1j8Y8Y4x+Bs0iKnX4V2BhCeJTkAfRs4H7ghBhj50jGL2lYqmVOKbbdgeRLYA64cLS7+oUQ9mBL8eSfxhibR3M9SYMalzklrZ15OzAL+Pu07uWIpT+SfS19+ccY49MDtB3yHCZJmvwMWEkjNzc9ZgZoUwz0zB2gzUDeSrJ1c/Hf6l7AO/u1eRRYl/59eb9zxQfLz5a8VduvySa21LDaGVgGzCepV7MBMzGlbaVa5pSifyGpX3dNjPHREd4PgHSn0Z+TzDFtJLuSShpf4zWnXAYcAXw3xjhYVuWAQgi1wA+BQLK76CcH6TKcOUySNMkZsJJGrmcYbQuDNynr70h2v3ktSer9bsBXQwg3FBukO+YUi53+fQjhWyGE/UMIM0MIbyD5kvgaoD1t05slEUI4H/gN8DaSHQSXkvxienja7zzgoRBCaYaWpPFRFXMKQAjhXcCFwGPAl0d4r+K1dgbuI5lXCsAHYozPj+aakoZkzOeUEMLhJAGr9QweXBrsWnOAfwNOSd/6bIzxd4N0G9IcJkmaGgxYSSPXlh5nD9BmTnocbLvnsmKML8YYu2KMz8cYrwA+lJ76RFq/odhuBckDZB74MPA80E3yZfMokmV/xfG2QO+OXjeTFDe9NMZ4dYxxfYyxO82mOJ1kZ55d0naSxldVzCkhhMXAt0iC26NaChhCOIBkl68jSeenGOPdI72epGEZ0zklhDCLZFfAmST/lptGOrAQwq7Ar4B3pG9dHWP82gBdgKE/F0mSpgYDVtLIbUqPiwdoU6wJUTcWN4wx3k6yTG868KZ+564h2S3nG8AvSIJNlwIHp693SpuuT49vB3YgeaDd6iEx3S76yvTl8SGEJWPxGSRVVC1zynJgd+CLMcbHK/UdTAjhOOBhkmLO3cA5McZ/Hd2IJQ3DWM8pVwOvA26NMf7nSAcVQjgY+APw1ySZXZ+OMV45cK/yBnoukiRNftamkUbuaZKU9H0HaFM89+wAbQAIIdQAe5As3/t9uqtOOS8BS0i+UPYRY3wC+Mcy1z6c5BfRHPBM+vY+6fG5dFlhOU+V/P0akodCSeOjWuaUv02PV4cQrh7g+sUlRB+IMd7W79zZJJkYs4AG4IwY4wODjVnSmBrTOYVkMxaAi0IIFw3Q7sUQAsBVMcYvlJ4IIZxAsgxwEUltrQsqZV2OxXORJGlyM2AljdwfgHcDx5Q7me7Yt3f68qEhXG8p8HL699HAygrtioGm9el9aoCLSOo4fLPCTjmnpccHY4xd6d8t6XH3EEJNmlHV364lf7eUOS9p7FTFnEKyhK+ShSQZFqXtNvYb598B3yfJ4n4ROCXGGIcwXklja6znlEfYsslLf7NIlv4C/BHoYsv8U7zf8cC9JMsQNwPvijH+foD7jXQOkyRNEQaspJH7EUkx4hNCCKHMF7KPpsffxBjXDHaxGOPaEMIzwEHAxyjzYBZC+FuSXxG7gf9J+xVCCJeS7JqziaT2TGmfxcA/pC//peTU/elxD5Iddn5WZljFX1A3AqsG+wySRqVa5pTjKl0zzY64v1K7EMLRJNvdTwP+Arw9xvjqYGOVNC7Gek55b6VzIYR9SQLUAO/tf70QwmuAn5IEq9YBJw0WyB7pHCZJmjqsYSWNUIzxOeAHJHUTfpwWFwZ6d9/7XPryS/37prv4HRRC2KPfqeLymwtDCJeFEGaW9PlboFj/5boYY2lWw3fS45dDCMtK+uxH8mvmziTZVT8pGf8zwB3F/umuYMV+00IIHwMuT9+6coBUfEljoMrmlGELIUwnCVbNJKmHc7LBKmnijNOcMlLfJsnQ7AROHUbW5TabwyRJ1aemUBjpztiS0uyl+4HXk2wf/RdgR7akp1+WFkPv329N2ua7McYL+527mi2
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x3000 with 20 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>"
|
|||
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]
|
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},
|
|||
|
|
"metadata": {
|
|||
|
|
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},
|
|||
|
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"output_type": "display_data"
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{
|
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"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": {
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{
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"data": {
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"text/plain": [
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|||
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|
"<Figure size 1200x1200 with 8 Axes>"
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|||
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]
|
|||
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|
},
|
|||
|
|
"metadata": {
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|||
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},
|
|||
|
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"output_type": "display_data"
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},
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{
|
|||
|
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"data": {
|
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"text/plain": [
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|||
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"<Figure size 1200x600 with 4 Axes>"
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|
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},
|
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"metadata": {
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{
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"data": {
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"text/plain": [
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|||
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|
"<Figure size 1200x600 with 4 Axes>"
|
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]
|
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},
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"metadata": {
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},
|
|||
|
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"output_type": "display_data"
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},
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{
|
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"data": {
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x1200 with 8 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
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{
|
|||
|
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"data": {
|
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x1200 with 8 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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"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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"metadata": {
|
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"output_type": "display_data"
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{
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"data": {
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|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
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|||
|
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]
|
|||
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},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
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|
},
|
|||
|
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"output_type": "display_data"
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},
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|||
|
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{
|
|||
|
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"data": {
|
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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": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"\n",
|
|||
|
|
"for unit_id, id_num in nsi_id_map.items():\n",
|
|||
|
|
" sessions = data.query(f'ns_inhibited and 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",
|
|||
|
|
" idx = row.session_id\n",
|
|||
|
|
" \n",
|
|||
|
|
" speed, t, spike_times, mask = load_speed(\n",
|
|||
|
|
" row['action'], row['channel_group'], row['unit_name'], \n",
|
|||
|
|
" z1, zi2, z3, split=False)\n",
|
|||
|
|
"\n",
|
|||
|
|
" speed_score, inst_speed, rate, times = spd.speed_correlation(\n",
|
|||
|
|
" speed, t, spike_times, return_data=True, mask=mask)\n",
|
|||
|
|
" \n",
|
|||
|
|
" inst_speed = inst_speed[~inst_speed.mask]\n",
|
|||
|
|
" rate = rate[~rate.mask]\n",
|
|||
|
|
" times = times[~times.mask]\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], normalize_values=True)\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",
|
|||
|
|
" plt.tight_layout()\n",
|
|||
|
|
" fig.savefig(\n",
|
|||
|
|
" output_path / 'figures' / f'nsi_neuron_{id_num}_speed_map.png', \n",
|
|||
|
|
" bbox_inches='tight', transparent=True)\n",
|
|||
|
|
" fig.savefig(\n",
|
|||
|
|
" output_path / 'figures' / f'nsi_neuron_{id_num}_speed_map.svg', \n",
|
|||
|
|
" bbox_inches='tight', transparent=True)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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",
|
|||
|
|
"execution_count": 46,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"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",
|
|||
|
|
"]\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def swarm_violin(data, ax=None, clip=None):\n",
|
|||
|
|
" if ax is None:\n",
|
|||
|
|
" fig, ax = plt.subplots()\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",
|
|||
|
|
" \n",
|
|||
|
|
" 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",
|
|||
|
|
"\n",
|
|||
|
|
" ax.text(x, y, significance, ha='center', va='bottom')\n",
|
|||
|
|
" x += 1"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 47,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"pairwise_gridcell = [[], [], [], [], []]\n",
|
|||
|
|
"for i, pairs in enumerate(gridcell_speed):\n",
|
|||
|
|
" for j, pair in enumerate(pairs):\n",
|
|||
|
|
" pairwise_gridcell[i].append(np.diff(pair))\n",
|
|||
|
|
" \n",
|
|||
|
|
"pairwise_nsi = [[], [], [], [], []]\n",
|
|||
|
|
"for i, pairs in enumerate(nsi_speed):\n",
|
|||
|
|
" for j, pair in enumerate(pairs):\n",
|
|||
|
|
" pairwise_nsi[i].append(np.diff(pair))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 72,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"plt.rc('axes', titlesize=12)\n",
|
|||
|
|
"plt.rcParams.update({\n",
|
|||
|
|
" 'font.size': 12, \n",
|
|||
|
|
" 'figure.figsize': (4, 2), \n",
|
|||
|
|
" 'figure.dpi': 150\n",
|
|||
|
|
"})"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 73,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 600x300 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"fig, axs = plt.subplots(2, 1, sharex=True)\n",
|
|||
|
|
"\n",
|
|||
|
|
"swarm_violin(pairwise_gridcell, ax=axs[0])\n",
|
|||
|
|
"axs[0].set_ylabel('Grid cells')\n",
|
|||
|
|
"\n",
|
|||
|
|
"swarm_violin(pairwise_nsi, ax=axs[1])\n",
|
|||
|
|
"axs[1].set_ylabel('NSi cells')\n",
|
|||
|
|
"\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')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Save to expipe"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 50,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"action = project.require_action(\"longitudinal-comparisons-speed\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 51,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"['/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/statistics/PRS.csv',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/statistics/summary.tex',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/statistics/PRS.tex',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/statistics/MWU.csv',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/statistics/summary.csv',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/statistics/MWU.tex',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_629_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_150_speed_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_317_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_26_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_79_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_317_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_233_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_79_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_121_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_106_speed_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_234_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_32_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_250_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_96_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_31_speed_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_32_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_132_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_381_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_715_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_13_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_31_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_130_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_655_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_233_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_659_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_361_speed_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_361_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_357_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_357_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_130_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_240_speed_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_232_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_656_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_234_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_35_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_195_speed_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_47_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_656_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_243_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_121_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_106_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_234_speed_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_243_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_32_speed_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_250_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/running_speed.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_35_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_32_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_47_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_106_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_35_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_35_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_231_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_58_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_195_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_659_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_358_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_30_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_150_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_253_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/violins.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_361_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_30_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_195_speed_map.svg',\n",
|
|||
|
|
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|||
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|||
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|
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|
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|
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|||
|
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|
|||
|
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|
|||
|
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|||
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|
|||
|
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|
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|||
|
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|
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|
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|||
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|
|||
|
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|||
|
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|
|||
|
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|
|||
|
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|
|||
|
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|
|||
|
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|
|||
|
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|
|||
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|
|||
|
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|
|||
|
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|
|||
|
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|
|||
|
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|
|||
|
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|
|||
|
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|
|||
|
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|
|||
|
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|
|||
|
|
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|
|||
|
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|
|||
|
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|
|||
|
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|
|||
|
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|
|||
|
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|
|||
|
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|
|||
|
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|
|||
|
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|
|||
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
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|
|||
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_58_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/baseline_average_rate_vs_other.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_357_rate_map.png',\n",
|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/baseline_gridness_vs_other.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_656_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/stickplot_gridness.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_149_speed_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_233_speed_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_332_spike_map.png',\n",
|
|||
|
|
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|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_47_speed_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_57_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_106_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_168_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_358_speed_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_358_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_243_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_31_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_243_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_31_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_35_speed_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_234_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_263_speed_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_304_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_26_speed_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_232_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_240_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_231_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_31_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_232_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_96_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_233_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_656_speed_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_32_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_30_speed_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_gridness.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_381_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_240_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_168_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_655_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_317_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_655_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_96_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_250_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_715_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_381_speed_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/violins.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_150_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_656_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_149_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_195_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_58_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_253_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_263_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_121_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_232_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_168_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_263_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_130_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_132_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_96_speed_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_253_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_58_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_715_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_231_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_332_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_263_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_253_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_361_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_149_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_13_speed_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_13_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_304_rate_map.png']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 51,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"copy_tree(output_path, str(action.data_path()))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 52,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"septum_mec.analysis.registration.store_notebook(action, \"20_longitudinal_comparisons_speed.ipynb\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 3
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
|
|||
|
|
"version": "3.6.8"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|