2594 lines
22 MiB
Plaintext
2594 lines
22 MiB
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"cells": [
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"cell_type": "code",
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"execution_count": 1,
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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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{
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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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"09:05:14 [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\n",
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"\n",
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"from spike_statistics.core import permutation_resampling\n",
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"\n",
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"from tqdm import tqdm_notebook as tqdm\n",
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"from tqdm._tqdm_notebook import tqdm_notebook\n",
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"tqdm_notebook.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-gridcells\"\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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" <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",
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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>0.397921</td>\n",
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|||
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" <td>0.676486</td>\n",
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|||
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" <td>-0.459487</td>\n",
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" <td>0.078474</td>\n",
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" <td>0.965845</td>\n",
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" <td>0.309723</td>\n",
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" <td>5.788704</td>\n",
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" <td>0.043321</td>\n",
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" <td>0.624971</td>\n",
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" <td>22.067900</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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" <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",
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" <td>NaN</td>\n",
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" <td>False</td>\n",
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" <td>baseline ii</td>\n",
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" <td>...</td>\n",
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" <td>0.146481</td>\n",
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" <td>0.277121</td>\n",
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" <td>-0.615405</td>\n",
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" <td>0.311180</td>\n",
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" <td>0.191375</td>\n",
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" <td>0.032266</td>\n",
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" <td>1.821598</td>\n",
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" <td>0.014624</td>\n",
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" <td>0.753333</td>\n",
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" <td>0.000000</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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" <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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" <td>False</td>\n",
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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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" <td>False</td>\n",
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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>0.373466</td>\n",
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|||
|
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" <td>0.658748</td>\n",
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|||
|
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" <td>-0.527711</td>\n",
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|||
|
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" <td>0.131660</td>\n",
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|||
|
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" <td>3.833587</td>\n",
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|||
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" <td>0.336590</td>\n",
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|||
|
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" <td>4.407614</td>\n",
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|||
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" <td>0.121115</td>\n",
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|||
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" <td>0.542877</td>\n",
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|||
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" <td>27.758541</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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|||
|
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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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|||
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" <td>0.097464</td>\n",
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|||
|
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" <td>0.196189</td>\n",
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|||
|
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" <td>-0.641543</td>\n",
|
|||
|
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" <td>0.274989</td>\n",
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|||
|
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" <td>0.153740</td>\n",
|
|||
|
|
" <td>0.068626</td>\n",
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|||
|
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" <td>6.128601</td>\n",
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|||
|
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" <td>0.099223</td>\n",
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|||
|
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" <td>0.484916</td>\n",
|
|||
|
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" <td>11.309932</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</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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|||
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" <td>False</td>\n",
|
|||
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" <td>True</td>\n",
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|||
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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>0.248036</td>\n",
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|||
|
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" <td>0.461250</td>\n",
|
|||
|
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" <td>-0.085292</td>\n",
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|||
|
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" <td>0.198676</td>\n",
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|||
|
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" <td>0.526720</td>\n",
|
|||
|
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" <td>0.033667</td>\n",
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|||
|
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" <td>1.602362</td>\n",
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|||
|
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" <td>0.051825</td>\n",
|
|||
|
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" <td>0.646571</td>\n",
|
|||
|
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" <td>0.000000</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>5 rows × 34 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 ... 0.397921 \n",
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"1 NaN False baseline ii ... 0.146481 \n",
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"2 NaN False baseline ii ... 0.373466 \n",
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"3 NaN False baseline ii ... 0.097464 \n",
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"4 NaN False baseline ii ... 0.248036 \n",
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"\n",
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" bursty_spike_ratio gridness border_score information_rate \\\n",
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"0 0.676486 -0.459487 0.078474 0.965845 \n",
|
|||
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"1 0.277121 -0.615405 0.311180 0.191375 \n",
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|||
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"2 0.658748 -0.527711 0.131660 3.833587 \n",
|
|||
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"3 0.196189 -0.641543 0.274989 0.153740 \n",
|
|||
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"4 0.461250 -0.085292 0.198676 0.526720 \n",
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"\n",
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" information_specificity head_mean_ang head_mean_vec_len spacing \\\n",
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|||
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"0 0.309723 5.788704 0.043321 0.624971 \n",
|
|||
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"1 0.032266 1.821598 0.014624 0.753333 \n",
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"2 0.336590 4.407614 0.121115 0.542877 \n",
|
|||
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"3 0.068626 6.128601 0.099223 0.484916 \n",
|
|||
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"4 0.033667 1.602362 0.051825 0.646571 \n",
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"\n",
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" orientation \n",
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"0 22.067900 \n",
|
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"1 0.000000 \n",
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"2 27.758541 \n",
|
|||
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"3 11.309932 \n",
|
|||
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"4 0.000000 \n",
|
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"\n",
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"[5 rows x 34 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": [
|
|||
|
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"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",
|
|||
|
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"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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},
|
|||
|
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{
|
|||
|
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"cell_type": "code",
|
|||
|
|
"execution_count": 5,
|
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|
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"metadata": {},
|
|||
|
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"outputs": [],
|
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"source": [
|
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|
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"stim_response_action = actions['stimulus-response']\n",
|
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|
|
"stim_response_results = pd.read_csv(stim_response_action.data_path('results'))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 6,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"statistics = pd.merge(statistics, stim_response_results, how='left')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 7,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"N cells: 1298\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"print('N cells:',statistics.shape[0])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"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": 8,
|
|||
|
|
"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": 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>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>0.445206</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>0.097485</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>0.393687</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>0.262612</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>1849-060319-3</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>baseline ii</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>0.342799</td>\n",
|
|||
|
|
" <td>0.218967</td>\n",
|
|||
|
|
" <td>5.768170</td>\n",
|
|||
|
|
" <td>0.054762</td>\n",
|
|||
|
|
" <td>0.524990</td>\n",
|
|||
|
|
" <td>0.144702</td>\n",
|
|||
|
|
" <td>0.133677</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>5 rows × 45 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 0.445206 \n",
|
|||
|
|
"1 0.052594 0.097485 \n",
|
|||
|
|
"2 0.027120 0.393687 \n",
|
|||
|
|
"3 0.054074 0.262612 \n",
|
|||
|
|
"4 0.144702 0.133677 \n",
|
|||
|
|
"\n",
|
|||
|
|
"[5 rows x 45 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"action_columns = ['action', 'channel_group', 'unit_name']\n",
|
|||
|
|
"data = pd.merge(statistics, quantiles_95, on=action_columns, suffixes=(\"\", \"_threshold\"))\n",
|
|||
|
|
"\n",
|
|||
|
|
"data['specificity'] = np.log10(data['in_field_mean_rate'] / data['out_field_mean_rate'])\n",
|
|||
|
|
"\n",
|
|||
|
|
"data.head()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Statistics about all cell-sessions"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"stimulated\n",
|
|||
|
|
"False 624\n",
|
|||
|
|
"True 674\n",
|
|||
|
|
"Name: action, dtype: int64"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"data.groupby('stimulated').count()['action']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"data['date'] = data.apply(lambda row: row.action.split('-')[1], axis=1)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Find all cells with gridness above threshold"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Number of gridcells 226\n",
|
|||
|
|
"Number of animals 4\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"query = 'gridness > gridness_threshold and information_rate > information_rate_threshold'\n",
|
|||
|
|
"sessions_above_threshold = data.query(query)\n",
|
|||
|
|
"print(\"Number of gridcells\", len(sessions_above_threshold))\n",
|
|||
|
|
"print(\"Number of animals\", len(sessions_above_threshold.groupby(['entity'])))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"columns = [\n",
|
|||
|
|
" 'average_rate', 'gridness', 'sparsity', 'selectivity', 'information_specificity',\n",
|
|||
|
|
" 'max_rate', 'information_rate', 'interspike_interval_cv', \n",
|
|||
|
|
" 'in_field_mean_rate', 'out_field_mean_rate', \n",
|
|||
|
|
" 'burst_event_ratio', 'specificity', 'speed_score'\n",
|
|||
|
|
"]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"once_a_gridcell = data[data.unit_id.isin(sessions_above_threshold.unit_id)]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Number of gridcells in baseline i sessions 83\n",
|
|||
|
|
"Number of gridcells in stimulated 11Hz ms sessions 72\n",
|
|||
|
|
"Number of gridcells in baseline ii sessions 73\n",
|
|||
|
|
"Number of gridcells in stimulated 30Hz ms sessions 58\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"baseline_i = once_a_gridcell.query('baseline and i')\n",
|
|||
|
|
"stimulated_11 = once_a_gridcell.query('stimulated and frequency==11 and stim_location==\"ms\" and i')\n",
|
|||
|
|
"\n",
|
|||
|
|
"baseline_ii = once_a_gridcell.query('baseline and ii')\n",
|
|||
|
|
"stimulated_30 = once_a_gridcell.query('stimulated and frequency==30 and stim_location==\"ms\" and ii')\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Number of gridcells in baseline i sessions\", len(baseline_i))\n",
|
|||
|
|
"print(\"Number of gridcells in stimulated 11Hz ms sessions\", len(stimulated_11))\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Number of gridcells in baseline ii sessions\", len(baseline_ii))\n",
|
|||
|
|
"print(\"Number of gridcells in stimulated 30Hz ms sessions\", len(stimulated_30))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Plotting\n",
|
|||
|
|
"## TODO select units that are grid in baseline i"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 17,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"max_speed = 1, # m/s only used for speed score\n",
|
|||
|
|
"min_speed = 0.02, # m/s only used for speed score\n",
|
|||
|
|
"position_sampling_rate = 100 # for interpolation\n",
|
|||
|
|
"position_low_pass_frequency = 6 # for low pass filtering of position\n",
|
|||
|
|
"\n",
|
|||
|
|
"box_size = [1.0, 1.0]\n",
|
|||
|
|
"bin_size = 0.02\n",
|
|||
|
|
"smoothing_low = 0.03\n",
|
|||
|
|
"smoothing_high = 0.06"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"data_loader = dp.Data(\n",
|
|||
|
|
" position_sampling_rate=position_sampling_rate, \n",
|
|||
|
|
" position_low_pass_frequency=position_low_pass_frequency,\n",
|
|||
|
|
" box_size=box_size, bin_size=bin_size\n",
|
|||
|
|
")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"neuron_ids = once_a_gridcell.unit_id.unique()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"results_corr = [[], [], []]\n",
|
|||
|
|
"results_gridness = [[], [], []]\n",
|
|||
|
|
"results_unit_name = [[], [], []]\n",
|
|||
|
|
"results_unit_id = [[], [], []]\n",
|
|||
|
|
"results_id_map = {}\n",
|
|||
|
|
"nuid = 0\n",
|
|||
|
|
"for nid in neuron_ids:\n",
|
|||
|
|
" unit_sessions = once_a_gridcell.query(f'unit_id==\"{nid}\"')\n",
|
|||
|
|
" base_i = unit_sessions.query(\"baseline and i\")\n",
|
|||
|
|
" base_ii = unit_sessions.query(\"baseline and ii\")\n",
|
|||
|
|
" base = unit_sessions.query(\"baseline\")\n",
|
|||
|
|
" stim_i = unit_sessions.query(\"stimulated and i\")\n",
|
|||
|
|
" stim_ii = unit_sessions.query(\"stimulated and ii\")\n",
|
|||
|
|
" dfs = [(base_i, base_ii), (base_i, stim_i), (base_ii, stim_ii)]\n",
|
|||
|
|
" for i, pair in enumerate(dfs):\n",
|
|||
|
|
" for (_, row_1), (_, row_2) in zip(pair[0].iterrows(), pair[1].iterrows()):\n",
|
|||
|
|
" rate_map_1 = data_loader.rate_map(\n",
|
|||
|
|
" row_1['action'], row_1['channel_group'], row_1['unit_name'], smoothing_low)\n",
|
|||
|
|
" rate_map_2 = data_loader.rate_map(\n",
|
|||
|
|
" row_2['action'], row_2['channel_group'], row_2['unit_name'], smoothing_low)\n",
|
|||
|
|
" results_corr[i].append(np.corrcoef(rate_map_1.ravel(), rate_map_2.ravel())[0,1])\n",
|
|||
|
|
" results_gridness[i].append((row_1.gridness, row_2.gridness))\n",
|
|||
|
|
" results_unit_name[i].append((\n",
|
|||
|
|
" f'{row_1.action}_{row_1.channel_group}_{row_1.unit_name}', \n",
|
|||
|
|
" f'{row_2.action}_{row_2.channel_group}_{row_2.unit_name}'))\n",
|
|||
|
|
" assert row_1.unit_id == row_2.unit_id\n",
|
|||
|
|
" uid = row_2.unit_id\n",
|
|||
|
|
" if uid not in results_id_map:\n",
|
|||
|
|
" nuid += 1\n",
|
|||
|
|
" results_id_map[uid] = nuid\n",
|
|||
|
|
" results_unit_id[i].append(results_id_map[uid])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
|
|||
|
|
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
|||
|
|
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
|||
|
|
"\n",
|
|||
|
|
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
|
|||
|
|
" \n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"def session_id(row):\n",
|
|||
|
|
" if row.baseline and row.i:\n",
|
|||
|
|
" n = 0\n",
|
|||
|
|
" elif row.stimulated and row.i:\n",
|
|||
|
|
" n = 1\n",
|
|||
|
|
" elif row.baseline and row.ii:\n",
|
|||
|
|
" n = 2\n",
|
|||
|
|
" elif row.stimulated and row.ii:\n",
|
|||
|
|
" n = 3\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" raise ValueError('what')\n",
|
|||
|
|
" return n\n",
|
|||
|
|
" \n",
|
|||
|
|
"once_a_gridcell['session_id'] = once_a_gridcell.apply(session_id, axis=1)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 24,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"plt.rc('axes', titlesize=12)\n",
|
|||
|
|
"plt.rcParams.update({\n",
|
|||
|
|
" 'font.size': 12, \n",
|
|||
|
|
" 'figure.figsize': (6, 4), \n",
|
|||
|
|
" 'figure.dpi': 150\n",
|
|||
|
|
"})"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 58,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# exclude = [i for i, n in results_id_map.items() if n in [25,41]]\n",
|
|||
|
|
"exclude = [\n",
|
|||
|
|
" '751d2de8-faf1-4048-82db-34cbd64a7c1d',\n",
|
|||
|
|
" '9f6eb181-321a-4ef7-8e2d-870bac6ceb37'\n",
|
|||
|
|
"]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 52,
|
|||
|
|
"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 1200x1800 with 12 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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|
},
|
|||
|
|
"metadata": {
|
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"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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"text/plain": [
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"<Figure size 1200x600 with 4 Axes>"
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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": [
|
|||
|
|
"<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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"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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"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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"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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"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 1200x1200 with 8 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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"data": {
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
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"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": [
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"<Figure size 1200x600 with 4 Axes>"
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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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},
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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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"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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"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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|||
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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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"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 1200x1800 with 12 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
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"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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|
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"data": {
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x3000 with 20 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 1200x2400 with 16 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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"text/plain": [
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|||
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"<Figure size 1200x1800 with 12 Axes>"
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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 1200x1800 with 12 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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{
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"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 1200x1800 with 12 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
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"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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"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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{
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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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"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"
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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|
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"text/plain": [
|
|||
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"<Figure size 1200x1200 with 8 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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"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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},
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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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|
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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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"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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"data": {
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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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"metadata": {
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{
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"data": {
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"text/plain": [
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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": "iVBORw0KGgoAAAANSUhEUgAABJgAAASbCAYAAADawAUeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3XmcZFV9/vGnqrp6nZ6ZnoWZYRfU475AohKNuEEkRJQICuGngjGiwQ2ICyQsoqJxCRrAEFRURolLUBL3uKAhEkJIMCrqAQUEh23Wnt67uqp+f9xqpqfP0901c2uG6enP+/WaV3efvsu591b3PfPtW88p1Ot1AQAAAAAAADur+Eh3AAAAAAAAAPMbBSYAAAAAAADkQoEJAAAAAAAAuVBgAgAAAAAAQC4UmAAAAAAAAJALBSYAAAAAAADkQoEJAAAAAAAAuVBgAgAAAAAAQC4UmAAAAAAAAJALBSYAAAAAAADkQoEJAAAAAAAAuVBgAgAAAAAAQC4UmAAAAAAAAJALBSYAAAAAAADk0vZIdwAAAOx5Qgg/lHRk48u/iTG+r8n1LpN0RuPLR8UY725979CsEMLTJd2sbMx3WozxM3Ms/yhJb5N0tKQDJRUk3Svpe5I+GmO8Y5d2GAAAzFs8wQQAAOZyXgjh8Y90J7BjQghlSZ9Wk39QDCGcKOnnkt4i6XGSuiV1SXqspL+U9LMQwqt3TW8BAMB8R4EJAADMpUPSp0IIjBvml3MlPbWZBUMIz5D0eWVFpaqkSyX9saSjJL1f0riy18GnQwhH7ZLeAgCAeY23yAEAgGYcoezJlo8+0h3B3EIIT5H01zuwynsllRufnxxj/PKU730vhPAdST9Q9sfJj0h6Sks6CgAA9hr8JRIAAMymJmmi8fn7QgiHPJKdwdxCCG3K3hpXlrShieU7JD2/8eXN04pLkqQY448kfbXx5ZNDCAe1qLsAAGAvQYEJAADMpiLpQ43PuyV94hHsC5rzDkmHSdok6cImll+mbU+13z7Lcj+f8vmaneoZAADYa/EWOQAAMJd3SzpeWfDzC0IIfxFjzFVoaoSGv0nSCyXtr22zlV0v6dIY4y9mWO+Hyma3G4sxds6y/Z9LeqKk38YYD572vXrj0zMlfUPSZZKeo6yY9mtJ74oxfm/K8oslvU7SSyU9SVKvpI2SbpX0ZUlrY4wTmiaEcLCkuxpfHi/pXyWdKunVjb71Slon6TuSPhJj/M1Mx9OsEMITJJ3f+PIsSUNNrLZe2VNqbcqu8UwePeXz+3aqgwAAYK/FE0wAsOvU+ce/+frvGc94xpGS1N7e3hFjHL3mmmseVyxmw4be3t4rH3zwQbveKaeccoYavv/979/llrn88svrpVLpF8pmJguSepQ9HRUkvaFYLN526aWX1uv1+qz9mq3/j3nMY54oSfvtt99B5vuSpNNOO+2SZcuW3S7p6Mb+lxQKhcO///3vf3dy2Ztuuqm+cuXKfmW5Q89V9rRPWdJqScdIukrSrSGEQzW7bknfk/QpZQWyFcpCsw+R9EZJt4UQ/niObcwqhFBS9ta4DknfiTF+tpn1GsWxbza+/L0QwvFm20+XdGLjy5tjjPfk6SuAeecRvy/xj3/826X/WoInmAAAwJwOP/xwnXLKKVq7dq0GBgZ0wQUX6Iorrtjh7Vx66aW67LLLJEkhBP3Zn/2ZQgiq1Wq67bbbtHbtWt1zzz0PL/PmN7+5pccx1Wc/+1nV63W97nWv0/Of/3xt2LBBv/zlL7X//vtLkm699VadfvrpGh0dVaFQ0Ete8hIdc8wxWrFihU488cSTJL1WWXHqSZJuCCEcHmO8f4bdfURZUeomSZcreyvaGklnKJuprUPSZ0IIh8QYB3fykM6S9AxJg5Jev4Prvr2x7mpJXwohXCrp3ySNKXu6652S2pU9ubWj2wYAAAsABSYAANCUs846Sz/4wQ+0bt06XX/99fra176ml7zkJU2vf9ttt+njH/+4JOmlL32pLr74YrW1bRuKHH744TrhhBN0+umn6+abb9bll1+uY445Ro9+9KNn2mQutVpNb3jDG3TmmWc+3PbiF79YklStVnXuuedqdHRUxWJRl1xyycPfk6QY4xclfTGEcL6ytxCukfSPko6bYXerJa2VdGqMsTbZGEL4V0lfl/THklZKOlbSF3f0WEIIQdJFjS/ftaNPGMUYbw8hPEvZbHKnKHv74JnTFrtW0ttjjHftaP8AAMDej7fIAQCApnR3d+s973nPw1+/733v06ZNm5pe/6qrrlKtVtPSpUv17ne/e7vi0tR9XHzxxSoUCqrX61q7dm1L+j6Tk08+2bZff/31uvPOOx9eZmpxaaoY40WSftj48iWNDCRnVNLbphaXGuvXtX1w+lOb7fukEEJR2Vv1OiX9h6SP7+g2Gv5AWTZUYYbv/6Gklzf2BwAAsB0GCAAAoGnPfvaz9ad/+qeSpM2bN29XcJpNvV7XDTfcIEk67LDD1NXVNeOyBxxwgA49NIs0uummm3L2eGarVq3S6tWr7fcm+ypJr3zlK+fa1NSCjq9ESf8TY5ypGjc13Lt3rp0Zb1FWHBqV9LpG0WqHhBAulnSNpKdLulHZ2/YWNf69UNKPJO2jbEbBT1FkAgAA0/EWOQAAsEPOOecc3XDDDVq/fr2++c1v6thjj9WLXvSiWdf53e9+p/7+fknSD37wA2Xv6Jrb7373u9z9ncmaNWtm/N4dd9whKXui6rGPfexcm5paBXvyDMvcPcv6UzOXdmhs1ggXf1/jy3fHGOOOrN/YxnGSzml8+W1Jx8UYK1MW+UEI4UeSPifpJGUz4f2XpB0P4QIAAHst/voEAAB2yOLFi3XBBRc8/PWFF16orVu3zrrO5s2bd2pfExMTGhzc2czr2S1atGjG723ZskWS1NfXp0JhpneMPezBKZ8vm2GZ2Q5i6hNHc+5sUgihoGxWum5Jt0r6cLPrTjM5819N0unTikuSpBhjVdlsd5MX+k07uS8AALCX4gkmAACww4466ii9+MUv1re//W2tX79eH/jAB3TxxRfPuHy1Wn3485e//OV61ate1fS+Zns73UxqtdrcC82iXt+hd5mVpu461453zOmSjmx8/veSnmSeDDt4yucHhhCe1vj811Nmq/v9xsefzhYOHmPcEkK4XtJLJT0xhLAox4x3AABgL0OBCQAA7JTzzz9fN910k7Zs2aJrr71Wxx577IzLLlmy5OHPS6WSHv/4x+fa91wFoIGBgVzbn+zv5s2bVa/X53qKadWUz5tPPc/vWVM+/3QTy7+78U+Snq9t4eSTj3JtaWIbD035fLFmfzILAAAsILxFDgAA7JTly5frnHPOefjr8847TyMjI3bZAw444OEnkX7yk5/Mue0rr7xSX/jCF3TjjTdu1z4581ylUtnuqaipRkdHd/oteZMmnwQaHh5+OI9pFlMLPb/KteNHxvrGx0OaWHa/xseapA27pjsAAGA+osAEAAB22ste9jI997nPlSStW7dOX/va1+xy5XJZz3zmMyVJt99+u2655ZYZt/mf//mf+shHPqILLrhAV1yxfY50b++2SdbWrVtn17/xxhtVqSQxQjvkOc95zsOff/GLX5xr8TdM+fy7uXa8A2KMp8YYC7P9k3TilFVOm/K9H05pn5wy78AQwpGaQQhhtbInnyTp5hjjeEsPCAAAzGsUmAAAQC4XXXSRenp6JGnWws6pp5768OfnnHOO7r///mSZjRs36vzzz3/461e/+tXbfX9qxtDatWuT9Tds2KAPfvCDTfd9Ji94wQt00EEHSZKuueYaffe7vm4UQjhP23KQvh9jnPvxrD3P5VM+/0QIYb/pC4QQuiV9XtJkINalu6NjAABg/qDABAAAclmzZo3+6q/+as7ljjjiCJ188smSpHvuuUcvfelLddlll+nmm2/WzTffrKuuukove9nLdM89Wc70UUcdpRe96EXbbeNP/uRPHn6b3NVXX61zzz1XP/7xj3XLLbfok5/8pI4//njdddddOvDAA3MdU6lU0gc/+EGVy2XVajW95S1v0Tvf+U5df/3
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x1200 with 8 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"for unit_id, id_num in results_id_map.items():\n",
|
|||
|
|
" sessions = once_a_gridcell.query(f'unit_id==\"{unit_id}\"')\n",
|
|||
|
|
" n_action = sessions.date.nunique()\n",
|
|||
|
|
" fig, axs = plt.subplots(n_action, 4, sharey=True, sharex=True, figsize=(8, n_action*4))\n",
|
|||
|
|
" sns.despine(left=True, bottom=True)\n",
|
|||
|
|
" fig.suptitle(f'Neuron {id_num}')\n",
|
|||
|
|
" if n_action == 1:\n",
|
|||
|
|
" axs = [axs]\n",
|
|||
|
|
" waxs = None\n",
|
|||
|
|
" for ax, (date, rows) in zip(axs, sessions.groupby('date')):\n",
|
|||
|
|
" entity = rows.iloc[0].entity\n",
|
|||
|
|
" ax[0].set_ylabel(f'{entity}-{date}')\n",
|
|||
|
|
" for _, row in rows.iterrows():\n",
|
|||
|
|
" action_id = row['action']\n",
|
|||
|
|
" channel_id = row['channel_group']\n",
|
|||
|
|
" unit_name = row['unit_name']\n",
|
|||
|
|
" rate_map = data_loader.rate_map(action_id, channel_id, unit_name, smoothing_low)\n",
|
|||
|
|
" idx = row.session_id\n",
|
|||
|
|
" ax[idx].imshow(rate_map)\n",
|
|||
|
|
" ax[idx].set_title(f'{row.gridness:.2f} {row.max_rate:.2f}')\n",
|
|||
|
|
" ax[idx].set_yticklabels([])\n",
|
|||
|
|
" ax[idx].set_xticklabels([])\n",
|
|||
|
|
" plt.tight_layout()\n",
|
|||
|
|
" fig.savefig(output_path / 'figures' / f'neuron_{id_num}_rate_map.png', bbox_inches='tight')\n",
|
|||
|
|
" fig.savefig(output_path / 'figures' / f'neuron_{id_num}_rate_map.svg', bbox_inches='tight')\n",
|
|||
|
|
" \n",
|
|||
|
|
" # waveforms\n",
|
|||
|
|
"# template = data_loader.template(action_id, channel_id, unit_name)\n",
|
|||
|
|
"# if waxs is None:\n",
|
|||
|
|
"# wfig, waxs = plt.subplots(1, template.data.shape[0], sharey=True, sharex=True)\n",
|
|||
|
|
"# for i, wax in enumerate(waxs):\n",
|
|||
|
|
"# wax.plot(template.data[i,:]) \n",
|
|||
|
|
"# if i > 0:\n",
|
|||
|
|
"# ax.set_yticklabels([])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 51,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x1800 with 12 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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"text/plain": [
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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": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
|
]
|
|||
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|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"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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|||
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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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{
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"data": {
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"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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|
},
|
|||
|
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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": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"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 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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"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": {},
|
|||
|
|
"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": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABJgAAAPZCAYAAABd95D3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xl83FW9//HXzCSTrU23dElLm7Sl/dKWpYAsIlcBUdlUvCJevFfvVVERBAHxp3ARARGuoICCIAKCgGwtgmwqILK2bIUWWsq3G0m6p+nebJNk5vfHTNMkzdoplMrr+Xj00ZnvfM/5nu9Mmkfy7jmfE0mlUkiSJEmSJEk7KrqrByBJkiRJkqTdmwGTJEmSJEmSsmLAJEmSJEmSpKwYMEmSJEmSJCkrBkySJEmSJEnKigGTJEmSJEmSsmLAJEmSJEmSpKwYMEmSJEmSJCkrBkySJEmSJEnKigGTJEmSJEmSsmLAJEmSJEmSpKwYMEmSJEmSJCkrBkySJEmSJEnKigGTJEmSJEmSspKzqwcgSZI+eIIgeAb4RObphWEY/ryX7a4Hzsg8HRuGYcXOH516IwiCOPA6MAX4aBiGL/WizQHAacDHgT2AGLAamAH8PgzDZ96zAUuSpN2aM5gkSVJPfhIEwaRdPQj12RWkw6UeBUEQCYLgSuA14FtAABQB+UAZcArwzyAIbssEV5IkSe0YMEmSpJ7kAbcGQeDPDbuJIAjOB87tQ5OLgB8CEWBF5vG/AYcBZwJLMuf9D3DDThuoJEn6l+ESOUmS1BsfBc4Crt3VA1HXMrOLfk16mVtv25QBF2SeLgAOD8NwTZtTZgZBcAfwD+AjwDeDIPhDGIYzdtKwJUnSvwD/J1KSJHUnCTRnHv88CIJxu3Iw6loQBAcDL7ItXGrpZdP/BLYuezu7Q7gEQBiGm4DvtDn0tR0dpyRJ+tdkwCRJkrrTBFyVeVwI3LwLx6IuBEHwf8BLpGcYAfyF3s82+7fM3/XAk12dFIbh68C6zNP9dmCYkiTpX5hL5CRJUk8uAb4A7AUcFQTBt8IwzCpoyhQN/x7wSdK7lUWApcA/gevCMHy7i3bPkN7drjEMw/xu+p9LusB1ZRiG5R1eS2UengM8BlwPHE46TFsE/DgMw6fanF8MnAp8Htgb6A+sBd4ApgF3hmHYTAdBEJQD72aefgF4mHQNo69lxtYfWA78HfhVGIaLu7qfXjiU9Hu4Dvh/YRjeGgTBxb1sex8wD8jt7D46iGT+7vK9lyRJH07OYJIkSd0Kw7CRdMCSzBy6KgiCUTvaXxAEPwHeAk5n225lhZnHpwFvBUFwcRAEka572SlGk15S9unM9QcAB5AOmbaO9UjgHeBXwMeBwUAuMAI4FvgD8EYQBON7uFYh8BRwK+mArIR08fRxwHeBeUEQHJfFvawHfgHsGYbhrX1pGIbh7WEYnheG4fe7Oy8Ign2BQZmnlTs2TEmS9K/KgEmSJPUoDMMXgd9mng4Afrcj/WRm1VwKxIA3SQdKh5GeQfR9YDHpn09+mvnzXjqbdNBzJellYl8CLg/DsCIz1o+SnuFUCqSAu4DPAYcA/wE8kelnb+D5IAhKu7nWr4AjSS9j+2qmjxPZtiQtD7g9CIJ+O3gvXwzD8MdhGK7fwfa9cX6bx39/D68jSZJ2Qy6RkyRJvXU+8FmgHDghCIKvhGF4d28bB0FwAPCTzNM7gW90WJL1YhAEtwKPAkcAFwVBcH9Xy+V2gijpQOl/2xybnhlrjPTspALSM7e+HIbh9DbnvQLcFwTBRaSXEJYCN5EOoDozgvQ9/08YhltnghEEwcOk7/c4YChwPOkla33Sts/3QhAEXyIdqgFUk74XSZKkVs5gkiRJvRKGYS3w7TaHfh0EwdA+dPED0j97rAVO66zeT+Ya3yA9YygCnLnjI+6VG7s4/lnSNacAbuwQLrUKw/BS4JmtbYIgmNxFfw2kd2hrFwSFYZiifeH0D1zx7CAIDgNub3Po3DAMt+yi4UiSpA8oAyZJktRrYRg+CdyWeVoCXNebdpl6Ssdmnr4YhmFdN9d4F5ifefrJHRxqbywPw3BZF699ps3jm3ro54Y2j4/p4pxZYRiu6+K1tsW9+/dwrfdVEAQfAx4nXUMK4HdhGP5pFw5JkiR9QLlETpIk9dW5pIOUUuDLQRDcE4bhX3poU862AtGfa7OTW0/G7tgQe2VpN6/tnfl7CzC3h35eavN4ny7OqeimfdvZQB+Yn80yRcensS1ceoD0zn+SJEnbcQaTJEnqkzAMNwBntDl0YxAEA3toVrKDl8sJguC9mtWzqZvXhmT+rsksY+vO6jaPB3dxTndLytr2/17vnNcrQRCcBjzMtnDpPuA/wjBs2XWjkiRJH2QfmP8lkyRJu48wDB8MgmAa6Z3XSknvkvbNbpq0/ZnjD/RyaV1Gl8vputGb/0TrLjjqS9ATa/P4PS22/V7LLGW8EjivzeHfA999rwuJS5Kk3ZsBkyRJ2lHfA44iPdvnG0EQ3NvNuW3rD7WEYTh7B6+5NRTqKQAasIP9b7V1vCVBEER6mMU0vJN2u53Mznl/BP6zzeGLwzC8ZBcNSZIk7UZcIidJknZIGIbVwDltDv0eKOri9CVsm4l0aE99B0HwoyAIvhMEwdEdXtq681w8E4h01rYA6Mvudp15M/N3P2BKD+e2vZ93srzuLpGZuXQn28KlZuAbhkuSJKm3DJgkSdIOC8PwTuCvmafltJ/90va8JuCfmaf7BEFweFd9BkFwFPB/wO+ACzq8vKHN4/IuujgayO1u3L3wRJvH3+nh3NPaPH4yy+vuKpcAp2QeNwBfCMPwtm7OlyRJaseASZIkZes7wObM4+6CnavbPL49CILRHU8IgmAY6ZlQW/2mwylvtnl8ZifthwNXdTva3nkYWJR5fHoQBF/o7KQgCH4CfCLz9B9ZLP3bZYIgOJRtQV6KdDHvR3fhkCRJ0m7IGkySJCkrYRguDYLgR8ANPZz3dBAENwLfBcYDc4IguBZ4NnPKR4BzgZGZ5w+GYfhQh27uAS4i/TPM9zM7zN1LetbNocDZmfaLM9fY0XtqCYLgq5mxxYHpQRDcBUwDqoEy0kXNP5NpUgP8945ebxe7jG2Fyv8CVAZBMLWHNokwDN9+b4clSZJ2JwZMkiRpZ/gd8B/Ax3s470zSYdDZwCDSS7M682fgvzoeDMNwURAEZ5Oe2RQFvpH5s1WS9GycIcAP+jD+7YRh+FIQBMcA95Gu6fS1zJ+OXge+HIbh8myutysEQTAO+GSbQydm/vSkkq6XKEqSpA8hl8hJkqSsZXZZOxWo7+G8ljAMzwX2Jx1KvQNsAZqA5cADwPFhGH4xDMNO+wrD8LfAQcBdwDIgAawE7gcOD8Pwip1yU+lr/RPYE/gx8CLpXeISQAXwKHAycGgYhou66uMD7oBdPQBJkvSvIZJKdbfrriRJkiRJktQ9ZzBJkiRJkiQpKwZMkiRJkiRJyooBkyRJkiRJkrJiwCRJkiRJkqSsGDBJkiRJkiQpKwZMkiRJkiRJyooBkyRJkiRJkrJiwCRJkiRJkqSsGDBJkiRJkiQpKwZMkiRJkiRJyooBkyRJkiRJkrJiwCRJkiRJkqSsGDBJkiRJkiQpKwZMkiRJkiRJyooBkyRJkiRJkrJiwCRJkiRJkqSsGDBJkiRJkiQpKwZMkiRJkiRJyooBkyRJkiRJkrJiwCRJkiRJkqSsGDBJkiRJkiQpKwZMkiRJkiRJyooBkyRJkiRJkrJiwCRJkiRJkqSsGDBJkiRJkiQpKwZMkiRJkiRJyooBkyRJkiRJkrJiwCRJkiRJkqSsGDBJkiRJkiQpKwZMkiRJkiRJyooBkyRJkiRJkrJiwCRJkiRJkqSsGDBJkiRJkiQpKwZMkiRJkiRJyooBkyRJkiRJkrJiwCRJkiRJkqSsGDBJkiRJkiQpKwZMkiRJkiRJyooBkyRJkiRJkrJiwCRJkiRJkqSsGDBJkiRJkiQpKwZMkiRJkiRJyooBkyRJkiRJkrJiwCRJkiRJkqSsGDBJkiRJkiQpKwZMkiRJkiRJyoo
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x1200 with 8 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"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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"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": {},
|
|||
|
|
"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 1200x1200 with 8 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"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": {},
|
|||
|
|
"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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]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"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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"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
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]
|
|||
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},
|
|||
|
|
"metadata": {},
|
|||
|
|
"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>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"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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|
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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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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"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>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"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 1200x1800 with 12 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x3000 with 20 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x2400 with 16 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x1800 with 12 Axes>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"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 1200x1800 with 12 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"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": {},
|
|||
|
|
"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": {},
|
|||
|
|
"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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{
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"data": {
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"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": {},
|
|||
|
|
"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 1200x1800 with 12 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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|
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|
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"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"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": {},
|
|||
|
|
"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": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x1200 with 8 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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|
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|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x1200 with 8 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"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": {},
|
|||
|
|
"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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{
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"data": {
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"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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"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": {},
|
|||
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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": {},
|
|||
|
|
"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": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x1200 with 8 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from scipy.interpolate import interp1d\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"for unit_id, id_num in results_id_map.items():\n",
|
|||
|
|
" sessions = once_a_gridcell.query(f'unit_id==\"{unit_id}\"')\n",
|
|||
|
|
" n_action = sessions.date.nunique()\n",
|
|||
|
|
" fig, axs = plt.subplots(n_action, 4, sharey=True, sharex=True, figsize=(8, n_action*4))\n",
|
|||
|
|
"# sns.despine(left=True, bottom=True)\n",
|
|||
|
|
" fig.suptitle(f'Neuron {id_num}')\n",
|
|||
|
|
" if n_action == 1:\n",
|
|||
|
|
" axs = [axs]\n",
|
|||
|
|
" waxs = None\n",
|
|||
|
|
" for ax, (date, rows) in zip(axs, sessions.groupby('date')):\n",
|
|||
|
|
" entity = rows.iloc[0].entity\n",
|
|||
|
|
" ax[0].set_ylabel(f'{entity}-{date}')\n",
|
|||
|
|
" for _, row in rows.iterrows():\n",
|
|||
|
|
" action_id = row['action']\n",
|
|||
|
|
" channel_id = row['channel_group']\n",
|
|||
|
|
" unit_name = row['unit_name']\n",
|
|||
|
|
" idx = row.session_id\n",
|
|||
|
|
" x, y, t, speed = map(data_loader.tracking(action_id).get, ['x', 'y', 't', 'v'])\n",
|
|||
|
|
" ax[idx].plot(x, y, 'k', alpha=0.3)\n",
|
|||
|
|
" spike_times = data_loader.spike_train(action_id, channel_id, unit_name)\n",
|
|||
|
|
" spike_times = spike_times[(spike_times > min(t)) & (spike_times < max(t))]\n",
|
|||
|
|
" x_spike = interp1d(t, x)(spike_times)\n",
|
|||
|
|
" y_spike = interp1d(t, y)(spike_times)\n",
|
|||
|
|
" ax[idx].set_xticks([])\n",
|
|||
|
|
" ax[idx].set_yticks([])\n",
|
|||
|
|
" ax[idx].scatter(x_spike, y_spike, marker='.', color=(0.7, 0.2, 0.2), s=1.5)\n",
|
|||
|
|
" ax[idx].set_title(f'{row.session}')\n",
|
|||
|
|
" ax[idx].set_yticklabels([])\n",
|
|||
|
|
" ax[idx].set_xticklabels([])\n",
|
|||
|
|
" for a in ax:\n",
|
|||
|
|
" a.set_aspect(1)\n",
|
|||
|
|
" plt.tight_layout()\n",
|
|||
|
|
" fig.savefig(output_path / 'figures' / f'neuron_{id_num}_spike_map.png', bbox_inches='tight')\n",
|
|||
|
|
" fig.savefig(output_path / 'figures' / f'neuron_{id_num}_spike_map.svg', bbox_inches='tight')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 42,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"plt.rc('axes', titlesize=12)\n",
|
|||
|
|
"plt.rcParams.update({\n",
|
|||
|
|
" 'font.size': 12, \n",
|
|||
|
|
" 'figure.figsize': (6, 4), \n",
|
|||
|
|
" 'figure.dpi': 150\n",
|
|||
|
|
"})\n",
|
|||
|
|
"cmap = ['#1b9e77','#d95f02','#7570b3']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 60,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"msize = 9\n",
|
|||
|
|
"fig = plt.figure()\n",
|
|||
|
|
"ticks = []\n",
|
|||
|
|
"for i, pairs in enumerate(results_gridness):\n",
|
|||
|
|
" for j, pair in enumerate(pairs):\n",
|
|||
|
|
" if results_unit_id[i][j] in [results_id_map[i] for i in exclude]:\n",
|
|||
|
|
" continue\n",
|
|||
|
|
" plt.plot(\n",
|
|||
|
|
" results_unit_id[i][j], abs(np.diff(pair)), \n",
|
|||
|
|
" color=cmap[i], marker='.', ls='none', markersize=msize)\n",
|
|||
|
|
"for l in range(nuid):\n",
|
|||
|
|
" plt.axvline(l, color='k', lw=.1, alpha=.5)\n",
|
|||
|
|
"\n",
|
|||
|
|
"from matplotlib.lines import Line2D\n",
|
|||
|
|
"\n",
|
|||
|
|
"labels = ['Baseline I vs baseline II', 'Baseline I vs stim I', 'Baseline II vs stim II']\n",
|
|||
|
|
"custom_lines = [\n",
|
|||
|
|
" Line2D([],[], color=cmap[i], marker='.', ls='none', label=label, markersize=msize) \n",
|
|||
|
|
" for i, label in enumerate(labels)\n",
|
|||
|
|
"]\n",
|
|||
|
|
"plt.ylabel('Absolute difference in gridness')\n",
|
|||
|
|
"plt.xlabel('Neuron')\n",
|
|||
|
|
"plt.legend(handles=custom_lines, bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n",
|
|||
|
|
"fig.savefig(output_path / 'figures' / 'neuron_gridness.png', bbox_inches='tight')\n",
|
|||
|
|
"fig.savefig(output_path / 'figures' / 'neuron_gridness.svg', bbox_inches='tight')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 63,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"fig = plt.figure()\n",
|
|||
|
|
"labels = ['Baseline I vs baseline II', 'Baseline I vs stim I', 'Baseline II vs stim II']\n",
|
|||
|
|
"for i, pairs in enumerate(results_gridness):\n",
|
|||
|
|
" for j, pair in enumerate(pairs):\n",
|
|||
|
|
" if results_unit_id[i][j] in [results_id_map[i] for i in exclude]:\n",
|
|||
|
|
" continue\n",
|
|||
|
|
" plt.plot(*pair, color=cmap[i], marker='.', ls='none', markersize=msize)\n",
|
|||
|
|
"# plt.scatter(*np.array(pairs).T, label=labels[i], color=cmap[i])\n",
|
|||
|
|
"# plt.legend(bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n",
|
|||
|
|
"custom_lines = [\n",
|
|||
|
|
" Line2D([],[], color=cmap[i], marker='.', ls='none', label=label, markersize=msize) \n",
|
|||
|
|
" for i, label in enumerate(labels)\n",
|
|||
|
|
"]\n",
|
|||
|
|
"plt.legend(handles=custom_lines, bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n",
|
|||
|
|
"plt.ylabel('Gridness')\n",
|
|||
|
|
"plt.xlabel('Baseline gridness')\n",
|
|||
|
|
"lim = [-.7, 1.35]\n",
|
|||
|
|
"plt.ylim(lim)\n",
|
|||
|
|
"plt.xlim(lim)\n",
|
|||
|
|
"plt.plot(lim, lim, '--k', alpha=.5, lw=1)\n",
|
|||
|
|
"fig.savefig(output_path / 'figures' / 'baseline_gridness_vs_other.png', bbox_inches='tight')\n",
|
|||
|
|
"fig.savefig(output_path / 'figures' / 'baseline_gridness_vs_other.svg', bbox_inches='tight')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 64,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"fig = plt.figure()\n",
|
|||
|
|
"import matplotlib\n",
|
|||
|
|
"cNorm = matplotlib.colors.Normalize(vmin=-np.pi/2, vmax=np.pi/2)\n",
|
|||
|
|
"scalarMap = plt.cm.ScalarMappable(norm=cNorm, cmap=plt.cm.Blues)\n",
|
|||
|
|
"\n",
|
|||
|
|
"ticks = []\n",
|
|||
|
|
"for i, pairs in enumerate(results_gridness):\n",
|
|||
|
|
" for j, pair in enumerate(pairs):\n",
|
|||
|
|
" if results_unit_id[i][j] in [results_id_map[i] for i in exclude]:\n",
|
|||
|
|
" continue\n",
|
|||
|
|
" angle = float(np.arctan(np.diff(pair) / 0.9))\n",
|
|||
|
|
" color = scalarMap.to_rgba(angle)\n",
|
|||
|
|
"# color = plt.cm.Paired((np.sign(angle)+1)/14)\n",
|
|||
|
|
" tick = (i, i+.8)\n",
|
|||
|
|
" plt.plot(tick, pair, marker='.', color=color)\n",
|
|||
|
|
" ticks.append(tick)\n",
|
|||
|
|
"plt.xticks(\n",
|
|||
|
|
" [t for tick in ticks for t in tick], \n",
|
|||
|
|
" ['Baseline I', 'Baseline II', 'Baseline I', 'Stimulation I', 'Baseline II', 'Stimulation II'],\n",
|
|||
|
|
" rotation=-45, ha='left'\n",
|
|||
|
|
")\n",
|
|||
|
|
"plt.ylabel('Gridness')\n",
|
|||
|
|
"fig.savefig(output_path / 'figures' / 'stickplot_gridness.png', bbox_inches='tight')\n",
|
|||
|
|
"fig.savefig(output_path / 'figures' / 'stickplot_gridness.svg', bbox_inches='tight')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 66,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"fig = plt.figure()\n",
|
|||
|
|
"ticks = [0,0.6,1.2]\n",
|
|||
|
|
"\n",
|
|||
|
|
"diff_res = [[], [], []]\n",
|
|||
|
|
"for i, pairs in enumerate(results_gridness):\n",
|
|||
|
|
" for j, pair in enumerate(pairs):\n",
|
|||
|
|
" if results_unit_id[i][j] in [results_id_map[i] for i in exclude]:\n",
|
|||
|
|
" continue\n",
|
|||
|
|
" diff_res[i].append(abs(np.diff(pair)))\n",
|
|||
|
|
"violins = plt.violinplot(\n",
|
|||
|
|
" diff_res, ticks, showmedians=True, showextrema=False, points=1000, bw_method=.2)\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"for category in ['cbars', 'cmins', 'cmaxes', 'cmedians']:\n",
|
|||
|
|
" if category in violins:\n",
|
|||
|
|
" violins[category].set_color(['k', 'k'])\n",
|
|||
|
|
" violins[category].set_linewidth(2.0)\n",
|
|||
|
|
" \n",
|
|||
|
|
"colors = plt.cm.Paired(np.linspace(0,1,12))\n",
|
|||
|
|
" \n",
|
|||
|
|
"for pc, c in zip(violins['bodies'], cmap):\n",
|
|||
|
|
" pc.set_facecolor(c)\n",
|
|||
|
|
" pc.set_edgecolor(c)\n",
|
|||
|
|
" \n",
|
|||
|
|
"plt.xticks(ticks, ['baseline', 'stim i', 'stim ii'])\n",
|
|||
|
|
"plt.ylabel('Absolute difference in gridness')\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.gca().spines['top'].set_visible(False)\n",
|
|||
|
|
"plt.gca().spines['right'].set_visible(False)\n",
|
|||
|
|
"fig.savefig(output_path / 'figures' / 'violins_gridness_difference.png', bbox_inches='tight')\n",
|
|||
|
|
"fig.savefig(output_path / 'figures' / 'violins_gridness_difference.svg', bbox_inches='tight')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Save to expipe"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 67,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"action = project.require_action(\"longitudinal-comparisons-gridcells\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 68,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"['/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_45_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_23_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_24_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_36_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_3_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_10_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_18_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_41_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_15_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_7_spike_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_6_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_19_spike_map.svg',\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_17_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_2_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_22_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_17_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_15_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_44_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_15_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_18_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_5_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_18_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_8_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_11_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_5_spike_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_47_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_29_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_29_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_39_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/violins_gridness_difference.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_41_spike_map.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_39_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_33_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_46_rate_map.png',\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_16_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_43_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_4_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_9_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_22_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_11_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_16_spike_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_44_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_10_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_48_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_36_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_40_rate_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_33_spike_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_33_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_21_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_26_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_23_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_4_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_34_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_2_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_36_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_34_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_3_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_48_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_40_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_26_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_22_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_17_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_39_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_gridness.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_4_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_2_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_10_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_25_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_20_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_21_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_4_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_26_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_26_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_28_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/baseline_gridness_vs_other.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_20_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_14_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_37_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_19_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_40_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_34_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_6_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_21_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_12_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_1_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_17_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_25_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_33_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_24_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_40_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_29_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_2_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_30_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_24_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_25_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_45_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_46_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_13_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_35_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/violins_gridness_difference.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_11_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_5_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_6_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_48_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_3_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_46_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_38_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_31_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_39_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_7_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/stickplot_gridness.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_11_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_16_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_12_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_20_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_7_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_13_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_14_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_10_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_44_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_8_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_43_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_7_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_1_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_3_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_30_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_27_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_47_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_18_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_47_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_38_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_34_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_25_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_19_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_32_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_42_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_16_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_45_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_28_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_38_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_37_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_24_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_1_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_5_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_6_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_28_rate_map.svg',\n",
|
|||
|
|
" '/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_9_rate_map.svg',\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_28_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_9_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_42_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_20_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_9_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_1_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_23_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_12_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_43_spike_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_15_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_27_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_14_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_42_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_36_rate_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_23_spike_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_gridness.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_37_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_41_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_12_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_44_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_43_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_48_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_46_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_8_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_21_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_29_spike_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_27_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_38_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_14_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_19_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_22_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_41_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_37_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_8_rate_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_42_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_27_spike_map.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_45_rate_map.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/longitudinal-comparisons-gridcells/data/figures/neuron_13_rate_map.png']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 68,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"copy_tree(output_path, str(action.data_path()))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 55,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"septum_mec.analysis.registration.store_notebook(action, \"20_longitudinal_comparisons_gridcells.ipynb\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 3
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
|
|||
|
|
"version": "3.6.8"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|