1093 lines
1.2 MiB
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1093 lines
1.2 MiB
Plaintext
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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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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"source": [
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"import os\n",
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"import expipe\n",
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"import pathlib\n",
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"import numpy as np\n",
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"import spatial_maps.stats as stats\n",
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"import septum_mec\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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"import head_direction.head as head\n",
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"import spatial_maps as sp\n",
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"import speed_cells.speed as spd\n",
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"import re\n",
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"import joblib\n",
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"import multiprocessing\n",
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"import shutil\n",
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"import psutil\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib\n",
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"import statsmodels\n",
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"import seaborn as sns\n",
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"from distutils.dir_util import copy_tree\n",
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"from neo import SpikeTrain\n",
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"import scipy\n",
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"from functools import reduce\n",
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"from tqdm.notebook import tqdm_notebook as tqdm\n",
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"tqdm.pandas()\n",
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"\n",
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"\n",
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"from septum_mec.analysis.statistics import load_data_frames, make_paired_tables, make_statistics_table\n",
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"\n",
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"from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features\n",
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"\n",
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"from septum_mec.analysis.plotting import violinplot, despine"
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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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"%matplotlib inline\n",
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"plt.rc('axes', titlesize=12)\n",
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"plt.rcParams.update({\n",
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" 'font.size': 12, \n",
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" 'figure.figsize': (8, 4), \n",
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" 'figure.dpi': 150\n",
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"})\n",
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"\n",
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"output_path = pathlib.Path(\"output\") / (\"theta-rhythmicity\")\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)\n",
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"output_path.mkdir(exist_ok=True)"
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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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"source": [
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"data_loader = dp.Data()\n",
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"actions = data_loader.actions\n",
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"project = data_loader.project"
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Number of sessions above threshold 194\n",
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"Number of animals 4\n",
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"Number of individual gridcells 139\n",
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"Number of gridcell recordings 230\n"
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]
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}
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],
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"source": [
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"data, labels, colors, queries = load_data_frames()"
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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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"theta_action = actions['theta-rhythmicity']\n",
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"theta_results = pd.read_csv(theta_action.data_path('results'))"
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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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"stim_action = actions['stimulus-response']\n",
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"stim_results = pd.read_csv(stim_action.data_path('results'))"
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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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = data.drop(columns='theta_score').merge(theta_results, how='left')"
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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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = data.merge(stim_results, how='left')"
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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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"data['stim_strength'] = data.stim_p_max / data.theta_peak"
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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": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"keys = [\n",
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" 'theta_score',\n",
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" 'theta_bandpower',\n",
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" 'theta_relpower',\n",
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" 'theta_relpeak',\n",
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" 'theta_peak',\n",
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" 'theta_freq',\n",
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" 'theta_half_width',\n",
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" 'stim_bandpower',\n",
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" 'stim_relpower',\n",
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" 'stim_relpeak',\n",
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" 'stim_half_width',\n",
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" 'stim_p_max',\n",
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" 'stim_strength',\n",
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"]"
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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": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"results, labels = make_paired_tables(data, keys)"
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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": 13,
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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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|
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" <th></th>\n",
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" <th>entity</th>\n",
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" <th>unit_idnum</th>\n",
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|||
|
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" <th>channel_group</th>\n",
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|||
|
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" <th>date</th>\n",
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|||
|
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" <th>Baseline I</th>\n",
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|||
|
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" <th>11 Hz</th>\n",
|
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|
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" <th>Baseline II</th>\n",
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|
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" <th>30 Hz</th>\n",
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|
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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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|
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" <th>51</th>\n",
|
|||
|
|
" <td>1833</td>\n",
|
|||
|
|
" <td>8</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>20719</td>\n",
|
|||
|
|
" <td>1.248894</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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|||
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" <th>85</th>\n",
|
|||
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" <td>1833</td>\n",
|
|||
|
|
" <td>13</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>20719</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>0.037071</td>\n",
|
|||
|
|
" <td>1.287301</td>\n",
|
|||
|
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" <td>0.176807</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>86</th>\n",
|
|||
|
|
" <td>1833</td>\n",
|
|||
|
|
" <td>14</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>20719</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>0.107253</td>\n",
|
|||
|
|
" <td>1.573884</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
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|||
|
|
" <th>58</th>\n",
|
|||
|
|
" <td>1833</td>\n",
|
|||
|
|
" <td>23</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>200619</td>\n",
|
|||
|
|
" <td>1.032539</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>127</th>\n",
|
|||
|
|
" <td>1833</td>\n",
|
|||
|
|
" <td>26</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>200619</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>0.851083</td>\n",
|
|||
|
|
" <td>0.265955</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
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" <tr>\n",
|
|||
|
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" <th>...</th>\n",
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|||
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" <td>...</td>\n",
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|||
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" <td>...</td>\n",
|
|||
|
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" <td>...</td>\n",
|
|||
|
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" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
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" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>139</th>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>835</td>\n",
|
|||
|
|
" <td>4</td>\n",
|
|||
|
|
" <td>150319</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>2.182959</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>43</th>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>851</td>\n",
|
|||
|
|
" <td>5</td>\n",
|
|||
|
|
" <td>60319</td>\n",
|
|||
|
|
" <td>2.999367</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>65</th>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>932</td>\n",
|
|||
|
|
" <td>7</td>\n",
|
|||
|
|
" <td>280219</td>\n",
|
|||
|
|
" <td>1.869360</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>74</th>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>937</td>\n",
|
|||
|
|
" <td>7</td>\n",
|
|||
|
|
" <td>280219</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>0.264037</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>105</th>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>939</td>\n",
|
|||
|
|
" <td>7</td>\n",
|
|||
|
|
" <td>280219</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>2.499672</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
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" </tbody>\n",
|
|||
|
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"</table>\n",
|
|||
|
|
"<p>137 rows × 8 columns</p>\n",
|
|||
|
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"</div>"
|
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],
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"text/plain": [
|
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|
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" entity unit_idnum channel_group date Baseline I 11 Hz \\\n",
|
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|
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"51 1833 8 0 20719 1.248894 NaN \n",
|
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|
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"85 1833 13 0 20719 NaN 0.037071 \n",
|
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|
|
"86 1833 14 0 20719 NaN 0.107253 \n",
|
|||
|
|
"58 1833 23 0 200619 1.032539 NaN \n",
|
|||
|
|
"127 1833 26 0 200619 NaN NaN \n",
|
|||
|
|
".. ... ... ... ... ... ... \n",
|
|||
|
|
"139 1849 835 4 150319 NaN NaN \n",
|
|||
|
|
"43 1849 851 5 60319 2.999367 NaN \n",
|
|||
|
|
"65 1849 932 7 280219 1.869360 NaN \n",
|
|||
|
|
"74 1849 937 7 280219 NaN 0.264037 \n",
|
|||
|
|
"105 1849 939 7 280219 NaN NaN \n",
|
|||
|
|
"\n",
|
|||
|
|
" Baseline II 30 Hz \n",
|
|||
|
|
"51 NaN NaN \n",
|
|||
|
|
"85 1.287301 0.176807 \n",
|
|||
|
|
"86 1.573884 NaN \n",
|
|||
|
|
"58 NaN NaN \n",
|
|||
|
|
"127 0.851083 0.265955 \n",
|
|||
|
|
".. ... ... \n",
|
|||
|
|
"139 NaN 2.182959 \n",
|
|||
|
|
"43 NaN NaN \n",
|
|||
|
|
"65 NaN NaN \n",
|
|||
|
|
"74 NaN NaN \n",
|
|||
|
|
"105 2.499672 NaN \n",
|
|||
|
|
"\n",
|
|||
|
|
"[137 rows x 8 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"results['gridcell']['theta_relpeak']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 525x330 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 525x330 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
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"text/plain": [
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"<Figure size 525x330 with 1 Axes>"
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"metadata": {
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 525x330 with 1 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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"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 525x330 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 525x330 with 1 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 525x330 with 1 Axes>"
|
|||
|
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]
|
|||
|
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},
|
|||
|
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"metadata": {
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|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
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|
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},
|
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|
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{
|
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|
|
"data": {
|
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"text/plain": [
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"metadata": {
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{
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"text/plain": [
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"<Figure size 525x330 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"metadata": {
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{
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"data": {
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"text/plain": [
|
|||
|
|
"<Figure size 525x330 with 1 Axes>"
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]
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},
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|
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"metadata": {
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},
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|||
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 525x330 with 1 Axes>"
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"metadata": {
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{
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"data": {
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"text/plain": [
|
|||
|
|
"<Figure size 525x330 with 1 Axes>"
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]
|
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},
|
|||
|
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"metadata": {
|
|||
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},
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|||
|
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"output_type": "display_data"
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},
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{
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|
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"data": {
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"text/plain": [
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"metadata": {
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{
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"data": {
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"text/plain": [
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|||
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|
"<Figure size 525x330 with 1 Axes>"
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]
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},
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"metadata": {
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},
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|
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 525x330 with 1 Axes>"
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 525x330 with 1 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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"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": [
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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 525x330 with 1 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 525x330 with 1 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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|
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|
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"text/plain": [
|
|||
|
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"<Figure size 525x330 with 1 Axes>"
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]
|
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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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|||
|
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{
|
|||
|
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"data": {
|
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"text/plain": [
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"<Figure size 525x330 with 1 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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|
"<Figure size 525x330 with 1 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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{
|
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"data": {
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"text/plain": [
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"metadata": {
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 525x330 with 1 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 525x330 with 1 Axes>"
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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 525x330 with 1 Axes>"
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]
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},
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|
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"metadata": {
|
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},
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|||
|
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"output_type": "display_data"
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},
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{
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"data": {
|
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"text/plain": [
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"<Figure size 525x330 with 1 Axes>"
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"metadata": {
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 525x330 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 525x330 with 1 Axes>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
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{
|
|||
|
|
"data": {
|
|||
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"text/plain": [
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"metadata": {
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{
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"data": {
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"text/plain": [
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|||
|
|
"<Figure size 525x330 with 1 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 525x330 with 1 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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|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 525x330 with 1 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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|||
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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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"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 525x330 with 1 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 525x330 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"xlabel = {\n",
|
|||
|
|
" 'theta_score': 'Theta rhythmicity',\n",
|
|||
|
|
" 'theta_bandpower': 'Theta energy (dB)',\n",
|
|||
|
|
" 'theta_relpower': 'Theta relative energy',\n",
|
|||
|
|
" 'theta_relpeak': 'Theta relative power',\n",
|
|||
|
|
" 'theta_peak': 'Peak PSD (dB/Hz)',\n",
|
|||
|
|
" 'theta_freq': '(Hz)',\n",
|
|||
|
|
" 'theta_half_width': '(Hz)',\n",
|
|||
|
|
" 'stim_bandpower': 'Energy (dB)',\n",
|
|||
|
|
" 'stim_relpower': 'Relative energy',\n",
|
|||
|
|
" 'stim_relpeak': 'Relative power',\n",
|
|||
|
|
" 'stim_half_width': '(Hz)',\n",
|
|||
|
|
" 'stim_p_max': 'Peak PSD (dB/Hz)',\n",
|
|||
|
|
" 'stim_strength': 'Ratio',\n",
|
|||
|
|
"}\n",
|
|||
|
|
"for cell_type in ['gridcell', 'ns_inhibited', 'ns_not_inhibited']:\n",
|
|||
|
|
" for key in keys:\n",
|
|||
|
|
" fig = plt.figure(figsize=(3.5,2.2))\n",
|
|||
|
|
" plt.suptitle(key + ' ' + cell_type)\n",
|
|||
|
|
" legend_lines = []\n",
|
|||
|
|
" for color, label in zip(colors, labels):\n",
|
|||
|
|
" legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, label=label))\n",
|
|||
|
|
" sns.kdeplot(data=results[cell_type][key].loc[:,labels], cumulative=True, legend=False, palette=colors, common_norm=False)\n",
|
|||
|
|
" plt.xlabel(xlabel[key])\n",
|
|||
|
|
" plt.legend(\n",
|
|||
|
|
" handles=legend_lines,\n",
|
|||
|
|
" bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n",
|
|||
|
|
" plt.tight_layout()\n",
|
|||
|
|
" plt.grid(False)\n",
|
|||
|
|
"# plt.xlim(-0.05, bins[key].max() - bins[key].max()*0.02)\n",
|
|||
|
|
" despine()\n",
|
|||
|
|
" figname = f'theta-rhythmicity-{key}-{cell_type}'.replace(' ', '-')\n",
|
|||
|
|
" fig.savefig(\n",
|
|||
|
|
" output_path / 'figures' / f'{figname}.png', \n",
|
|||
|
|
" bbox_inches='tight', transparent=True)\n",
|
|||
|
|
" fig.savefig(\n",
|
|||
|
|
" output_path / 'figures' / f'{figname}.svg', \n",
|
|||
|
|
" bbox_inches='tight', transparent=True)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# stats"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"stats = {}\n",
|
|||
|
|
"for cell_type, result in results.items():\n",
|
|||
|
|
" stats[cell_type], _ = make_statistics_table(result, labels)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"stats['gridcell']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"for cell_type, stat in stats.items():\n",
|
|||
|
|
" stat.to_latex(output_path / \"statistics\" / f\"statistics_{cell_type}.tex\")\n",
|
|||
|
|
" stat.to_csv(output_path / \"statistics\" / f\"statistics_{cell_type}.csv\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"for cell_type, cell_results in results.items():\n",
|
|||
|
|
" for key, result in cell_results.items():\n",
|
|||
|
|
" result.to_latex(output_path / \"statistics\" / f\"values_{cell_type}_{key}.tex\")\n",
|
|||
|
|
" result.to_csv(output_path / \"statistics\" / f\"values_{cell_type}_{key}.csv\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"## psd"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from septum_mec.analysis.plotting import plot_bootstrap_timeseries"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"coher = pd.read_feather(output_path / 'data' / 'spike_psd.feather')\n",
|
|||
|
|
"freqs = pd.read_feather(output_path / 'data' / 'freqs.feather')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"freq = freqs.T.iloc[0].values\n",
|
|||
|
|
"\n",
|
|||
|
|
"mask = (freq < 100)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"results['gridcell']['theta_relpeak']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"for cell_type in ['gridcell', 'ns_inhibited', 'ns_not_inhibited']:\n",
|
|||
|
|
" fig, axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(5,1.5))\n",
|
|||
|
|
" axs = axs.repeat(2)\n",
|
|||
|
|
" for i, (ax, query) in enumerate(zip(axs.ravel(), queries)):\n",
|
|||
|
|
" selection = [\n",
|
|||
|
|
" f'{r.action}_{r.channel_group}_{r.unit_name}' \n",
|
|||
|
|
" for i, r in data.query(query + ' and ' + cell_type).iterrows()]\n",
|
|||
|
|
" values = coher.loc[mask, selection].dropna(axis=1).to_numpy()\n",
|
|||
|
|
" values = 10 * np.log10(values)\n",
|
|||
|
|
" plot_bootstrap_timeseries(freq[mask], values, ax=ax, lw=1, label=labels[i], color=colors[i])\n",
|
|||
|
|
"# ax.set_title(titles[i])\n",
|
|||
|
|
" ax.set_xlabel('Frequency Hz')\n",
|
|||
|
|
" ax.legend(frameon=False)\n",
|
|||
|
|
"# ax.set_ylim(-30, 0)\n",
|
|||
|
|
" axs[0].set_ylabel('PSD')\n",
|
|||
|
|
" despine()\n",
|
|||
|
|
" figname = f'spike-psd-{cell_type}'.replace(' ', '-')\n",
|
|||
|
|
" fig.savefig(\n",
|
|||
|
|
" output_path / 'figures' / f'{figname}.png', \n",
|
|||
|
|
" bbox_inches='tight', transparent=True)\n",
|
|||
|
|
" fig.savefig(\n",
|
|||
|
|
" output_path / 'figures' / f'{figname}.svg', \n",
|
|||
|
|
" bbox_inches='tight', transparent=True)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Store results in Expipe action"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"action = project.require_action(\"theta-rhythmicity\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"copy_tree(output_path, str(action.data_path()))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"septum_mec.analysis.registration.store_notebook(action, \"20-theta-rhythmicity.ipynb\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
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"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
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|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
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|
"version": 3
|
|||
|
|
},
|
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|
|
"file_extension": ".py",
|
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|
"mimetype": "text/x-python",
|
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|
|
"name": "python",
|
|||
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|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
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|||
|
|
"version": "3.6.8"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 4
|
|||
|
|
}
|