2284 lines
274 KiB
Plaintext
2284 lines
274 KiB
Plaintext
{
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"cells": [
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{
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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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"19:18:12 [I] klustakwik KlustaKwik2 version 0.2.6\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 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 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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"\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()\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 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"
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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': (6, 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\") / \"stimulus-response\"\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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"source": [
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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')"
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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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"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": 9,
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||
"metadata": {},
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"outputs": [],
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"source": [
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"# lfp_results has old unit id's but correct on (action, unit_name, channel_group)\n",
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"stim_results = stim_results.drop('unit_id', axis=1)"
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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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"statistics_action = actions['calculate-statistics']\n",
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"shuffling = actions['shuffling']\n",
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||
"\n",
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||
"statistics_results = pd.read_csv(statistics_action.data_path('results'))\n",
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||
"statistics_results = session_units.merge(statistics_results, how='left')\n",
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||
"quantiles_95 = pd.read_csv(shuffling.data_path('quantiles_95'))\n",
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||
"action_columns = ['action', 'channel_group', 'unit_name']\n",
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||
"data = pd.merge(statistics_results, quantiles_95, on=action_columns, suffixes=(\"\", \"_threshold\"))"
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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['unit_day'] = data.apply(lambda x: str(x.unit_idnum) + '_' + x.action.split('-')[1], axis=1)"
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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 = 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": 11,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"waveform_action = actions['waveform-analysis']\n",
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||
"waveform_results = pd.read_csv(waveform_action.data_path('results')).drop('template', axis=1)"
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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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"data = data.merge(waveform_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": 13,
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||
"metadata": {},
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||
"outputs": [],
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"source": [
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"colors = ['#d95f02','#e7298a']\n",
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||
"labels = ['11 Hz', '30 HZ']\n",
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"queries = ['frequency==11', 'frequency==30']"
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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": 14,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"data.bs = data.bs.astype(bool)"
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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": 15,
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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": [
|
||
"Number of gridcells 225\n"
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||
]
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||
}
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||
],
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||
"source": [
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||
"grid_query = 'gridness > gridness_threshold and information_rate > information_rate_threshold'\n",
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||
"gridcell_sessions = data.query(grid_query)\n",
|
||
"print(\"Number of gridcells\", len(gridcell_sessions))\n",
|
||
"# print(\"Number of animals\", len(gridcell_sessions.groupby(['entity'])))"
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||
]
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||
},
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||
{
|
||
"cell_type": "code",
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||
"execution_count": 22,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"data['gridcell'] = data.isin(data.query(grid_query))"
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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": 23,
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"metadata": {},
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"outputs": [
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|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>48</th>\n",
|
||
" <td>1833-260619-1</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>112</td>\n",
|
||
" <td>5.891356</td>\n",
|
||
" <td>0.226892</td>\n",
|
||
" <td>4.028409</td>\n",
|
||
" <td>10.441355</td>\n",
|
||
" <td>13.169649</td>\n",
|
||
" <td>24.406383</td>\n",
|
||
" <td>0.643995</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.282336</td>\n",
|
||
" <td>0.711705</td>\n",
|
||
" <td>5.890705</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>49</th>\n",
|
||
" <td>1833-260619-1</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>124</td>\n",
|
||
" <td>7.915120</td>\n",
|
||
" <td>0.182376</td>\n",
|
||
" <td>4.543545</td>\n",
|
||
" <td>14.013583</td>\n",
|
||
" <td>17.035745</td>\n",
|
||
" <td>30.787249</td>\n",
|
||
" <td>0.646322</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.285816</td>\n",
|
||
" <td>0.603160</td>\n",
|
||
" <td>7.914246</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>57</th>\n",
|
||
" <td>1834-150319-3</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>61</td>\n",
|
||
" <td>17.163920</td>\n",
|
||
" <td>0.021890</td>\n",
|
||
" <td>12.070353</td>\n",
|
||
" <td>23.188083</td>\n",
|
||
" <td>24.427655</td>\n",
|
||
" <td>44.829894</td>\n",
|
||
" <td>0.837844</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.277867</td>\n",
|
||
" <td>0.588852</td>\n",
|
||
" <td>17.162446</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>124</th>\n",
|
||
" <td>1833-010719-1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>219</td>\n",
|
||
" <td>2.868256</td>\n",
|
||
" <td>0.170572</td>\n",
|
||
" <td>1.391229</td>\n",
|
||
" <td>6.759410</td>\n",
|
||
" <td>8.941986</td>\n",
|
||
" <td>21.915347</td>\n",
|
||
" <td>0.446442</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.271262</td>\n",
|
||
" <td>0.615002</td>\n",
|
||
" <td>2.868000</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>125</th>\n",
|
||
" <td>1833-010719-1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>221</td>\n",
|
||
" <td>6.912671</td>\n",
|
||
" <td>0.090486</td>\n",
|
||
" <td>4.070879</td>\n",
|
||
" <td>11.915337</td>\n",
|
||
" <td>24.220877</td>\n",
|
||
" <td>32.274461</td>\n",
|
||
" <td>0.661683</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.307694</td>\n",
|
||
" <td>0.659653</td>\n",
|
||
" <td>6.912052</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>126</th>\n",
|
||
" <td>1833-010719-1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>229</td>\n",
|
||
" <td>4.230245</td>\n",
|
||
" <td>0.018811</td>\n",
|
||
" <td>1.546702</td>\n",
|
||
" <td>8.504585</td>\n",
|
||
" <td>15.581766</td>\n",
|
||
" <td>33.782863</td>\n",
|
||
" <td>0.456739</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.267708</td>\n",
|
||
" <td>0.630543</td>\n",
|
||
" <td>4.229867</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>128</th>\n",
|
||
" <td>1833-010719-1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>16.737459</td>\n",
|
||
" <td>0.254297</td>\n",
|
||
" <td>12.420895</td>\n",
|
||
" <td>25.377508</td>\n",
|
||
" <td>23.273238</td>\n",
|
||
" <td>52.301684</td>\n",
|
||
" <td>0.802417</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.289100</td>\n",
|
||
" <td>0.673221</td>\n",
|
||
" <td>16.735961</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>129</th>\n",
|
||
" <td>1833-010719-1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>202</td>\n",
|
||
" <td>25.977054</td>\n",
|
||
" <td>0.226032</td>\n",
|
||
" <td>21.598716</td>\n",
|
||
" <td>37.463629</td>\n",
|
||
" <td>43.547728</td>\n",
|
||
" <td>66.169116</td>\n",
|
||
" <td>0.862176</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.290402</td>\n",
|
||
" <td>0.650772</td>\n",
|
||
" <td>25.974728</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>131</th>\n",
|
||
" <td>1833-010719-1</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>171</td>\n",
|
||
" <td>14.687550</td>\n",
|
||
" <td>0.163959</td>\n",
|
||
" <td>11.038136</td>\n",
|
||
" <td>20.488701</td>\n",
|
||
" <td>21.342234</td>\n",
|
||
" <td>45.144706</td>\n",
|
||
" <td>0.858017</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.272160</td>\n",
|
||
" <td>0.620429</td>\n",
|
||
" <td>14.686236</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>132</th>\n",
|
||
" <td>1833-010719-1</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>198</td>\n",
|
||
" <td>18.659249</td>\n",
|
||
" <td>0.282318</td>\n",
|
||
" <td>15.427596</td>\n",
|
||
" <td>26.715844</td>\n",
|
||
" <td>33.932272</td>\n",
|
||
" <td>51.441681</td>\n",
|
||
" <td>0.860475</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.241405</td>\n",
|
||
" <td>0.595513</td>\n",
|
||
" <td>18.657578</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>134</th>\n",
|
||
" <td>1833-010719-1</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>240</td>\n",
|
||
" <td>3.107182</td>\n",
|
||
" <td>0.076765</td>\n",
|
||
" <td>1.059941</td>\n",
|
||
" <td>7.228602</td>\n",
|
||
" <td>12.831970</td>\n",
|
||
" <td>33.059125</td>\n",
|
||
" <td>0.383354</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.269911</td>\n",
|
||
" <td>0.609574</td>\n",
|
||
" <td>3.106903</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>135</th>\n",
|
||
" <td>1833-010719-1</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>134</td>\n",
|
||
" <td>6.214363</td>\n",
|
||
" <td>0.168450</td>\n",
|
||
" <td>4.835608</td>\n",
|
||
" <td>9.832902</td>\n",
|
||
" <td>18.534635</td>\n",
|
||
" <td>33.761835</td>\n",
|
||
" <td>0.750893</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.273069</td>\n",
|
||
" <td>0.651265</td>\n",
|
||
" <td>6.213807</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>136</th>\n",
|
||
" <td>1833-010719-1</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>144</td>\n",
|
||
" <td>2.226506</td>\n",
|
||
" <td>0.119543</td>\n",
|
||
" <td>1.188425</td>\n",
|
||
" <td>5.927293</td>\n",
|
||
" <td>13.273928</td>\n",
|
||
" <td>26.877971</td>\n",
|
||
" <td>0.358918</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.263251</td>\n",
|
||
" <td>0.629310</td>\n",
|
||
" <td>2.226306</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>209</th>\n",
|
||
" <td>1833-050619-1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>99</td>\n",
|
||
" <td>3.350056</td>\n",
|
||
" <td>0.095012</td>\n",
|
||
" <td>1.224499</td>\n",
|
||
" <td>7.669547</td>\n",
|
||
" <td>14.470606</td>\n",
|
||
" <td>29.613931</td>\n",
|
||
" <td>0.384212</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.251027</td>\n",
|
||
" <td>0.593786</td>\n",
|
||
" <td>3.347881</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>214</th>\n",
|
||
" <td>1833-050619-1</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>60</td>\n",
|
||
" <td>7.177620</td>\n",
|
||
" <td>0.259306</td>\n",
|
||
" <td>5.263129</td>\n",
|
||
" <td>11.558126</td>\n",
|
||
" <td>13.097257</td>\n",
|
||
" <td>24.533320</td>\n",
|
||
" <td>0.764622</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.296577</td>\n",
|
||
" <td>0.631283</td>\n",
|
||
" <td>7.172961</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>215</th>\n",
|
||
" <td>1833-050619-1</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>64</td>\n",
|
||
" <td>16.944449</td>\n",
|
||
" <td>0.243525</td>\n",
|
||
" <td>13.371230</td>\n",
|
||
" <td>26.025889</td>\n",
|
||
" <td>33.591762</td>\n",
|
||
" <td>60.449939</td>\n",
|
||
" <td>0.824103</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.295235</td>\n",
|
||
" <td>0.633010</td>\n",
|
||
" <td>16.933450</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>216</th>\n",
|
||
" <td>1833-050619-1</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>91</td>\n",
|
||
" <td>3.325889</td>\n",
|
||
" <td>0.155904</td>\n",
|
||
" <td>2.039584</td>\n",
|
||
" <td>7.702821</td>\n",
|
||
" <td>9.078369</td>\n",
|
||
" <td>21.975777</td>\n",
|
||
" <td>0.462461</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.268553</td>\n",
|
||
" <td>0.618949</td>\n",
|
||
" <td>3.323730</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>221</th>\n",
|
||
" <td>1833-060619-1</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>172</td>\n",
|
||
" <td>2.654829</td>\n",
|
||
" <td>0.119661</td>\n",
|
||
" <td>1.666324</td>\n",
|
||
" <td>6.169001</td>\n",
|
||
" <td>7.323174</td>\n",
|
||
" <td>22.931784</td>\n",
|
||
" <td>0.457083</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.263816</td>\n",
|
||
" <td>0.607601</td>\n",
|
||
" <td>2.654511</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>223</th>\n",
|
||
" <td>1833-060619-1</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>164</td>\n",
|
||
" <td>3.083686</td>\n",
|
||
" <td>0.021853</td>\n",
|
||
" <td>1.755081</td>\n",
|
||
" <td>5.101697</td>\n",
|
||
" <td>8.325821</td>\n",
|
||
" <td>21.146134</td>\n",
|
||
" <td>0.566049</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.313833</td>\n",
|
||
" <td>0.646825</td>\n",
|
||
" <td>3.083316</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>227</th>\n",
|
||
" <td>1833-060619-1</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>170</td>\n",
|
||
" <td>3.080462</td>\n",
|
||
" <td>0.155454</td>\n",
|
||
" <td>1.816201</td>\n",
|
||
" <td>6.197439</td>\n",
|
||
" <td>8.744690</td>\n",
|
||
" <td>18.172981</td>\n",
|
||
" <td>0.529688</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.261029</td>\n",
|
||
" <td>0.596500</td>\n",
|
||
" <td>3.080092</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>262</th>\n",
|
||
" <td>1834-150319-1</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>95</td>\n",
|
||
" <td>19.609185</td>\n",
|
||
" <td>0.063354</td>\n",
|
||
" <td>14.334866</td>\n",
|
||
" <td>25.933220</td>\n",
|
||
" <td>29.106613</td>\n",
|
||
" <td>53.460587</td>\n",
|
||
" <td>0.857509</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.282343</td>\n",
|
||
" <td>0.604147</td>\n",
|
||
" <td>19.498454</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>274</th>\n",
|
||
" <td>1839-120619-1</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>158</td>\n",
|
||
" <td>12.579822</td>\n",
|
||
" <td>0.285708</td>\n",
|
||
" <td>9.656518</td>\n",
|
||
" <td>23.105339</td>\n",
|
||
" <td>25.311402</td>\n",
|
||
" <td>59.566964</td>\n",
|
||
" <td>0.660943</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.265655</td>\n",
|
||
" <td>0.574791</td>\n",
|
||
" <td>12.578109</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1130</th>\n",
|
||
" <td>1834-010319-4</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>7</td>\n",
|
||
" <td>18.428099</td>\n",
|
||
" <td>0.073675</td>\n",
|
||
" <td>13.995565</td>\n",
|
||
" <td>25.034061</td>\n",
|
||
" <td>27.551569</td>\n",
|
||
" <td>45.574876</td>\n",
|
||
" <td>0.864388</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.284016</td>\n",
|
||
" <td>0.615742</td>\n",
|
||
" <td>18.426477</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1151</th>\n",
|
||
" <td>1833-200619-1</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>165</td>\n",
|
||
" <td>4.093726</td>\n",
|
||
" <td>0.112030</td>\n",
|
||
" <td>1.560769</td>\n",
|
||
" <td>9.952907</td>\n",
|
||
" <td>16.871964</td>\n",
|
||
" <td>34.400735</td>\n",
|
||
" <td>0.371794</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.272936</td>\n",
|
||
" <td>0.784972</td>\n",
|
||
" <td>4.093056</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1152</th>\n",
|
||
" <td>1833-200619-1</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>163</td>\n",
|
||
" <td>17.705502</td>\n",
|
||
" <td>0.202908</td>\n",
|
||
" <td>14.631392</td>\n",
|
||
" <td>24.895637</td>\n",
|
||
" <td>34.144570</td>\n",
|
||
" <td>51.462522</td>\n",
|
||
" <td>0.877996</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.303012</td>\n",
|
||
" <td>0.661133</td>\n",
|
||
" <td>17.702603</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1153</th>\n",
|
||
" <td>1833-200619-1</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>171</td>\n",
|
||
" <td>4.061107</td>\n",
|
||
" <td>0.058014</td>\n",
|
||
" <td>1.879235</td>\n",
|
||
" <td>7.260758</td>\n",
|
||
" <td>11.252257</td>\n",
|
||
" <td>20.574695</td>\n",
|
||
" <td>0.561983</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.310060</td>\n",
|
||
" <td>0.632763</td>\n",
|
||
" <td>4.060442</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1154</th>\n",
|
||
" <td>1833-200619-1</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>206</td>\n",
|
||
" <td>3.982277</td>\n",
|
||
" <td>0.150630</td>\n",
|
||
" <td>2.316705</td>\n",
|
||
" <td>7.168058</td>\n",
|
||
" <td>9.286450</td>\n",
|
||
" <td>19.626376</td>\n",
|
||
" <td>0.618229</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.290294</td>\n",
|
||
" <td>0.618949</td>\n",
|
||
" <td>3.981625</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1155</th>\n",
|
||
" <td>1833-200619-1</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>240</td>\n",
|
||
" <td>4.089649</td>\n",
|
||
" <td>0.098818</td>\n",
|
||
" <td>1.539874</td>\n",
|
||
" <td>10.560745</td>\n",
|
||
" <td>15.374288</td>\n",
|
||
" <td>32.783007</td>\n",
|
||
" <td>0.358157</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.263375</td>\n",
|
||
" <td>0.622896</td>\n",
|
||
" <td>4.088979</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1156</th>\n",
|
||
" <td>1833-200619-1</td>\n",
|
||
" <td>7</td>\n",
|
||
" <td>143</td>\n",
|
||
" <td>9.300587</td>\n",
|
||
" <td>0.218310</td>\n",
|
||
" <td>6.750717</td>\n",
|
||
" <td>13.150023</td>\n",
|
||
" <td>13.197378</td>\n",
|
||
" <td>25.067697</td>\n",
|
||
" <td>0.825910</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.289628</td>\n",
|
||
" <td>0.650032</td>\n",
|
||
" <td>9.299064</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1162</th>\n",
|
||
" <td>1839-120619-3</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>131</td>\n",
|
||
" <td>17.773050</td>\n",
|
||
" <td>0.076020</td>\n",
|
||
" <td>9.779864</td>\n",
|
||
" <td>29.707618</td>\n",
|
||
" <td>42.215165</td>\n",
|
||
" <td>77.486029</td>\n",
|
||
" <td>0.651012</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.245031</td>\n",
|
||
" <td>0.528413</td>\n",
|
||
" <td>17.770859</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1165</th>\n",
|
||
" <td>1839-120619-3</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>133</td>\n",
|
||
" <td>2.612293</td>\n",
|
||
" <td>0.053873</td>\n",
|
||
" <td>1.055067</td>\n",
|
||
" <td>6.992168</td>\n",
|
||
" <td>9.603099</td>\n",
|
||
" <td>17.060484</td>\n",
|
||
" <td>0.372375</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.239301</td>\n",
|
||
" <td>0.531126</td>\n",
|
||
" <td>2.611971</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1167</th>\n",
|
||
" <td>1839-120619-3</td>\n",
|
||
" <td>7</td>\n",
|
||
" <td>119</td>\n",
|
||
" <td>4.950355</td>\n",
|
||
" <td>0.132893</td>\n",
|
||
" <td>3.636504</td>\n",
|
||
" <td>7.175598</td>\n",
|
||
" <td>7.291281</td>\n",
|
||
" <td>14.571674</td>\n",
|
||
" <td>0.836244</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.284221</td>\n",
|
||
" <td>0.610068</td>\n",
|
||
" <td>4.949745</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1168</th>\n",
|
||
" <td>1839-120619-3</td>\n",
|
||
" <td>7</td>\n",
|
||
" <td>127</td>\n",
|
||
" <td>5.407801</td>\n",
|
||
" <td>0.091931</td>\n",
|
||
" <td>3.251329</td>\n",
|
||
" <td>15.356306</td>\n",
|
||
" <td>18.617758</td>\n",
|
||
" <td>37.590469</td>\n",
|
||
" <td>0.414271</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.273572</td>\n",
|
||
" <td>0.611548</td>\n",
|
||
" <td>5.407135</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1199</th>\n",
|
||
" <td>1833-260619-3</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>140</td>\n",
|
||
" <td>3.564682</td>\n",
|
||
" <td>0.063184</td>\n",
|
||
" <td>2.498756</td>\n",
|
||
" <td>5.782665</td>\n",
|
||
" <td>8.770230</td>\n",
|
||
" <td>17.134986</td>\n",
|
||
" <td>0.720704</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.189559</td>\n",
|
||
" <td>0.248665</td>\n",
|
||
" <td>3.564358</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1200</th>\n",
|
||
" <td>1833-260619-3</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>141</td>\n",
|
||
" <td>2.694224</td>\n",
|
||
" <td>0.094154</td>\n",
|
||
" <td>1.691471</td>\n",
|
||
" <td>5.502054</td>\n",
|
||
" <td>10.395725</td>\n",
|
||
" <td>20.328752</td>\n",
|
||
" <td>0.519950</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.225575</td>\n",
|
||
" <td>0.277528</td>\n",
|
||
" <td>2.693978</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1202</th>\n",
|
||
" <td>1833-260619-3</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>182</td>\n",
|
||
" <td>5.289030</td>\n",
|
||
" <td>0.148720</td>\n",
|
||
" <td>3.342163</td>\n",
|
||
" <td>10.892485</td>\n",
|
||
" <td>16.803801</td>\n",
|
||
" <td>30.523793</td>\n",
|
||
" <td>0.544679</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.275930</td>\n",
|
||
" <td>0.594526</td>\n",
|
||
" <td>5.288548</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1203</th>\n",
|
||
" <td>1833-260619-3</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>194</td>\n",
|
||
" <td>6.485358</td>\n",
|
||
" <td>0.096207</td>\n",
|
||
" <td>3.706339</td>\n",
|
||
" <td>12.069498</td>\n",
|
||
" <td>18.212336</td>\n",
|
||
" <td>29.243464</td>\n",
|
||
" <td>0.590584</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.222604</td>\n",
|
||
" <td>0.576271</td>\n",
|
||
" <td>6.484767</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1205</th>\n",
|
||
" <td>1833-260619-3</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>209</td>\n",
|
||
" <td>3.425497</td>\n",
|
||
" <td>0.085117</td>\n",
|
||
" <td>1.306754</td>\n",
|
||
" <td>8.551145</td>\n",
|
||
" <td>11.161798</td>\n",
|
||
" <td>29.652423</td>\n",
|
||
" <td>0.378044</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.244049</td>\n",
|
||
" <td>0.571337</td>\n",
|
||
" <td>3.425185</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1207</th>\n",
|
||
" <td>1833-260619-3</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>170</td>\n",
|
||
" <td>26.841716</td>\n",
|
||
" <td>0.218178</td>\n",
|
||
" <td>22.328079</td>\n",
|
||
" <td>38.090240</td>\n",
|
||
" <td>50.981983</td>\n",
|
||
" <td>74.601637</td>\n",
|
||
" <td>0.857579</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.257469</td>\n",
|
||
" <td>0.636957</td>\n",
|
||
" <td>26.839270</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1208</th>\n",
|
||
" <td>1833-260619-3</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>207</td>\n",
|
||
" <td>4.589791</td>\n",
|
||
" <td>0.088439</td>\n",
|
||
" <td>2.309667</td>\n",
|
||
" <td>8.938164</td>\n",
|
||
" <td>10.731362</td>\n",
|
||
" <td>25.229471</td>\n",
|
||
" <td>0.538208</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.252255</td>\n",
|
||
" <td>0.587372</td>\n",
|
||
" <td>4.589373</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1211</th>\n",
|
||
" <td>1833-260619-3</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>176</td>\n",
|
||
" <td>7.407735</td>\n",
|
||
" <td>0.156101</td>\n",
|
||
" <td>5.622472</td>\n",
|
||
" <td>11.694017</td>\n",
|
||
" <td>16.474141</td>\n",
|
||
" <td>32.870310</td>\n",
|
||
" <td>0.757528</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.261129</td>\n",
|
||
" <td>0.592306</td>\n",
|
||
" <td>7.407060</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1213</th>\n",
|
||
" <td>1833-260619-3</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>111</td>\n",
|
||
" <td>9.222663</td>\n",
|
||
" <td>0.179913</td>\n",
|
||
" <td>6.341652</td>\n",
|
||
" <td>14.990045</td>\n",
|
||
" <td>17.803066</td>\n",
|
||
" <td>32.423819</td>\n",
|
||
" <td>0.732917</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.277189</td>\n",
|
||
" <td>0.615988</td>\n",
|
||
" <td>9.221822</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1216</th>\n",
|
||
" <td>1833-260619-3</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>142</td>\n",
|
||
" <td>9.359639</td>\n",
|
||
" <td>0.129023</td>\n",
|
||
" <td>6.738758</td>\n",
|
||
" <td>14.564994</td>\n",
|
||
" <td>20.758052</td>\n",
|
||
" <td>44.189302</td>\n",
|
||
" <td>0.773930</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.300175</td>\n",
|
||
" <td>0.610068</td>\n",
|
||
" <td>9.358786</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1218</th>\n",
|
||
" <td>1833-260619-3</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>192</td>\n",
|
||
" <td>7.836336</td>\n",
|
||
" <td>0.170862</td>\n",
|
||
" <td>4.889011</td>\n",
|
||
" <td>13.019928</td>\n",
|
||
" <td>17.648343</td>\n",
|
||
" <td>34.791219</td>\n",
|
||
" <td>0.715811</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.287132</td>\n",
|
||
" <td>0.616235</td>\n",
|
||
" <td>7.835622</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1222</th>\n",
|
||
" <td>1833-200619-3</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>91</td>\n",
|
||
" <td>7.072750</td>\n",
|
||
" <td>0.074100</td>\n",
|
||
" <td>4.679924</td>\n",
|
||
" <td>11.282597</td>\n",
|
||
" <td>18.578196</td>\n",
|
||
" <td>35.109099</td>\n",
|
||
" <td>0.713088</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.293775</td>\n",
|
||
" <td>0.657679</td>\n",
|
||
" <td>7.071948</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1228</th>\n",
|
||
" <td>1833-200619-3</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>82</td>\n",
|
||
" <td>15.697615</td>\n",
|
||
" <td>0.127761</td>\n",
|
||
" <td>12.267443</td>\n",
|
||
" <td>21.346293</td>\n",
|
||
" <td>27.567344</td>\n",
|
||
" <td>38.706425</td>\n",
|
||
" <td>0.874674</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.252895</td>\n",
|
||
" <td>0.600200</td>\n",
|
||
" <td>15.695836</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1229</th>\n",
|
||
" <td>1833-200619-3</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>113</td>\n",
|
||
" <td>11.770313</td>\n",
|
||
" <td>0.136640</td>\n",
|
||
" <td>6.835310</td>\n",
|
||
" <td>20.280536</td>\n",
|
||
" <td>22.248766</td>\n",
|
||
" <td>44.143227</td>\n",
|
||
" <td>0.676058</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.271023</td>\n",
|
||
" <td>0.699617</td>\n",
|
||
" <td>11.768979</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1231</th>\n",
|
||
" <td>1833-200619-3</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>59</td>\n",
|
||
" <td>4.442527</td>\n",
|
||
" <td>0.110165</td>\n",
|
||
" <td>2.926793</td>\n",
|
||
" <td>7.344323</td>\n",
|
||
" <td>8.786494</td>\n",
|
||
" <td>20.320606</td>\n",
|
||
" <td>0.722984</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.343906</td>\n",
|
||
" <td>0.698383</td>\n",
|
||
" <td>4.442023</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1232</th>\n",
|
||
" <td>1833-200619-3</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>120</td>\n",
|
||
" <td>22.461229</td>\n",
|
||
" <td>0.268466</td>\n",
|
||
" <td>18.182326</td>\n",
|
||
" <td>32.115585</td>\n",
|
||
" <td>33.640870</td>\n",
|
||
" <td>62.235139</td>\n",
|
||
" <td>0.833921</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.294291</td>\n",
|
||
" <td>0.639177</td>\n",
|
||
" <td>22.458685</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1233</th>\n",
|
||
" <td>1833-200619-3</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>126</td>\n",
|
||
" <td>3.102942</td>\n",
|
||
" <td>0.090727</td>\n",
|
||
" <td>1.447857</td>\n",
|
||
" <td>6.981766</td>\n",
|
||
" <td>9.945472</td>\n",
|
||
" <td>21.048478</td>\n",
|
||
" <td>0.436204</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.304748</td>\n",
|
||
" <td>0.641151</td>\n",
|
||
" <td>3.102590</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1234</th>\n",
|
||
" <td>1833-200619-3</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>132</td>\n",
|
||
" <td>6.901437</td>\n",
|
||
" <td>0.072648</td>\n",
|
||
" <td>4.231220</td>\n",
|
||
" <td>14.073295</td>\n",
|
||
" <td>20.697950</td>\n",
|
||
" <td>36.231604</td>\n",
|
||
" <td>0.612146</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.277708</td>\n",
|
||
" <td>0.585645</td>\n",
|
||
" <td>6.900656</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1235</th>\n",
|
||
" <td>1833-200619-3</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>150</td>\n",
|
||
" <td>3.767582</td>\n",
|
||
" <td>0.114920</td>\n",
|
||
" <td>1.422876</td>\n",
|
||
" <td>10.607271</td>\n",
|
||
" <td>13.651769</td>\n",
|
||
" <td>34.348592</td>\n",
|
||
" <td>0.332963</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.258204</td>\n",
|
||
" <td>0.608094</td>\n",
|
||
" <td>3.767155</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>130 rows × 51 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" action channel_group unit_name average_rate speed_score \\\n",
|
||
"32 1833-260619-1 0 118 5.946164 0.169495 \n",
|
||
"34 1833-260619-1 0 130 2.860363 0.081075 \n",
|
||
"35 1833-260619-1 0 132 3.366046 0.072301 \n",
|
||
"39 1833-260619-1 1 116 17.473449 0.193373 \n",
|
||
"40 1833-260619-1 1 126 5.892390 0.183633 \n",
|
||
"42 1833-260619-1 3 114 13.438331 0.224642 \n",
|
||
"43 1833-260619-1 5 100 17.448630 0.144593 \n",
|
||
"45 1833-260619-1 6 102 10.841667 0.235736 \n",
|
||
"48 1833-260619-1 6 112 5.891356 0.226892 \n",
|
||
"49 1833-260619-1 6 124 7.915120 0.182376 \n",
|
||
"57 1834-150319-3 3 61 17.163920 0.021890 \n",
|
||
"124 1833-010719-1 1 219 2.868256 0.170572 \n",
|
||
"125 1833-010719-1 1 221 6.912671 0.090486 \n",
|
||
"126 1833-010719-1 1 229 4.230245 0.018811 \n",
|
||
"128 1833-010719-1 1 8 16.737459 0.254297 \n",
|
||
"129 1833-010719-1 2 202 25.977054 0.226032 \n",
|
||
"131 1833-010719-1 3 171 14.687550 0.163959 \n",
|
||
"132 1833-010719-1 3 198 18.659249 0.282318 \n",
|
||
"134 1833-010719-1 3 240 3.107182 0.076765 \n",
|
||
"135 1833-010719-1 5 134 6.214363 0.168450 \n",
|
||
"136 1833-010719-1 5 144 2.226506 0.119543 \n",
|
||
"209 1833-050619-1 2 99 3.350056 0.095012 \n",
|
||
"214 1833-050619-1 6 60 7.177620 0.259306 \n",
|
||
"215 1833-050619-1 6 64 16.944449 0.243525 \n",
|
||
"216 1833-050619-1 6 91 3.325889 0.155904 \n",
|
||
"221 1833-060619-1 4 172 2.654829 0.119661 \n",
|
||
"223 1833-060619-1 5 164 3.083686 0.021853 \n",
|
||
"227 1833-060619-1 6 170 3.080462 0.155454 \n",
|
||
"262 1834-150319-1 3 95 19.609185 0.063354 \n",
|
||
"274 1839-120619-1 5 158 12.579822 0.285708 \n",
|
||
"... ... ... ... ... ... \n",
|
||
"1130 1834-010319-4 0 7 18.428099 0.073675 \n",
|
||
"1151 1833-200619-1 4 165 4.093726 0.112030 \n",
|
||
"1152 1833-200619-1 6 163 17.705502 0.202908 \n",
|
||
"1153 1833-200619-1 6 171 4.061107 0.058014 \n",
|
||
"1154 1833-200619-1 6 206 3.982277 0.150630 \n",
|
||
"1155 1833-200619-1 6 240 4.089649 0.098818 \n",
|
||
"1156 1833-200619-1 7 143 9.300587 0.218310 \n",
|
||
"1162 1839-120619-3 5 131 17.773050 0.076020 \n",
|
||
"1165 1839-120619-3 6 133 2.612293 0.053873 \n",
|
||
"1167 1839-120619-3 7 119 4.950355 0.132893 \n",
|
||
"1168 1839-120619-3 7 127 5.407801 0.091931 \n",
|
||
"1199 1833-260619-3 0 140 3.564682 0.063184 \n",
|
||
"1200 1833-260619-3 0 141 2.694224 0.094154 \n",
|
||
"1202 1833-260619-3 0 182 5.289030 0.148720 \n",
|
||
"1203 1833-260619-3 0 194 6.485358 0.096207 \n",
|
||
"1205 1833-260619-3 0 209 3.425497 0.085117 \n",
|
||
"1207 1833-260619-3 1 170 26.841716 0.218178 \n",
|
||
"1208 1833-260619-3 1 207 4.589791 0.088439 \n",
|
||
"1211 1833-260619-3 3 176 7.407735 0.156101 \n",
|
||
"1213 1833-260619-3 5 111 9.222663 0.179913 \n",
|
||
"1216 1833-260619-3 6 142 9.359639 0.129023 \n",
|
||
"1218 1833-260619-3 6 192 7.836336 0.170862 \n",
|
||
"1222 1833-200619-3 0 91 7.072750 0.074100 \n",
|
||
"1228 1833-200619-3 3 82 15.697615 0.127761 \n",
|
||
"1229 1833-200619-3 4 113 11.770313 0.136640 \n",
|
||
"1231 1833-200619-3 5 59 4.442527 0.110165 \n",
|
||
"1232 1833-200619-3 6 120 22.461229 0.268466 \n",
|
||
"1233 1833-200619-3 6 126 3.102942 0.090727 \n",
|
||
"1234 1833-200619-3 6 132 6.901437 0.072648 \n",
|
||
"1235 1833-200619-3 6 150 3.767582 0.114920 \n",
|
||
"\n",
|
||
" out_field_mean_rate in_field_mean_rate max_field_mean_rate max_rate \\\n",
|
||
"32 4.138169 10.175750 16.836097 29.863371 \n",
|
||
"34 1.362852 6.837975 10.333063 21.846576 \n",
|
||
"35 1.204876 8.320200 11.903539 24.820419 \n",
|
||
"39 12.435315 25.886509 35.066123 58.438209 \n",
|
||
"40 4.008668 10.376607 11.424828 22.616252 \n",
|
||
"42 10.451118 18.904366 20.482248 37.829102 \n",
|
||
"43 12.651420 25.885399 31.780144 50.983827 \n",
|
||
"45 7.896926 16.159949 15.994156 37.844022 \n",
|
||
"48 4.028409 10.441355 13.169649 24.406383 \n",
|
||
"49 4.543545 14.013583 17.035745 30.787249 \n",
|
||
"57 12.070353 23.188083 24.427655 44.829894 \n",
|
||
"124 1.391229 6.759410 8.941986 21.915347 \n",
|
||
"125 4.070879 11.915337 24.220877 32.274461 \n",
|
||
"126 1.546702 8.504585 15.581766 33.782863 \n",
|
||
"128 12.420895 25.377508 23.273238 52.301684 \n",
|
||
"129 21.598716 37.463629 43.547728 66.169116 \n",
|
||
"131 11.038136 20.488701 21.342234 45.144706 \n",
|
||
"132 15.427596 26.715844 33.932272 51.441681 \n",
|
||
"134 1.059941 7.228602 12.831970 33.059125 \n",
|
||
"135 4.835608 9.832902 18.534635 33.761835 \n",
|
||
"136 1.188425 5.927293 13.273928 26.877971 \n",
|
||
"209 1.224499 7.669547 14.470606 29.613931 \n",
|
||
"214 5.263129 11.558126 13.097257 24.533320 \n",
|
||
"215 13.371230 26.025889 33.591762 60.449939 \n",
|
||
"216 2.039584 7.702821 9.078369 21.975777 \n",
|
||
"221 1.666324 6.169001 7.323174 22.931784 \n",
|
||
"223 1.755081 5.101697 8.325821 21.146134 \n",
|
||
"227 1.816201 6.197439 8.744690 18.172981 \n",
|
||
"262 14.334866 25.933220 29.106613 53.460587 \n",
|
||
"274 9.656518 23.105339 25.311402 59.566964 \n",
|
||
"... ... ... ... ... \n",
|
||
"1130 13.995565 25.034061 27.551569 45.574876 \n",
|
||
"1151 1.560769 9.952907 16.871964 34.400735 \n",
|
||
"1152 14.631392 24.895637 34.144570 51.462522 \n",
|
||
"1153 1.879235 7.260758 11.252257 20.574695 \n",
|
||
"1154 2.316705 7.168058 9.286450 19.626376 \n",
|
||
"1155 1.539874 10.560745 15.374288 32.783007 \n",
|
||
"1156 6.750717 13.150023 13.197378 25.067697 \n",
|
||
"1162 9.779864 29.707618 42.215165 77.486029 \n",
|
||
"1165 1.055067 6.992168 9.603099 17.060484 \n",
|
||
"1167 3.636504 7.175598 7.291281 14.571674 \n",
|
||
"1168 3.251329 15.356306 18.617758 37.590469 \n",
|
||
"1199 2.498756 5.782665 8.770230 17.134986 \n",
|
||
"1200 1.691471 5.502054 10.395725 20.328752 \n",
|
||
"1202 3.342163 10.892485 16.803801 30.523793 \n",
|
||
"1203 3.706339 12.069498 18.212336 29.243464 \n",
|
||
"1205 1.306754 8.551145 11.161798 29.652423 \n",
|
||
"1207 22.328079 38.090240 50.981983 74.601637 \n",
|
||
"1208 2.309667 8.938164 10.731362 25.229471 \n",
|
||
"1211 5.622472 11.694017 16.474141 32.870310 \n",
|
||
"1213 6.341652 14.990045 17.803066 32.423819 \n",
|
||
"1216 6.738758 14.564994 20.758052 44.189302 \n",
|
||
"1218 4.889011 13.019928 17.648343 34.791219 \n",
|
||
"1222 4.679924 11.282597 18.578196 35.109099 \n",
|
||
"1228 12.267443 21.346293 27.567344 38.706425 \n",
|
||
"1229 6.835310 20.280536 22.248766 44.143227 \n",
|
||
"1231 2.926793 7.344323 8.786494 20.320606 \n",
|
||
"1232 18.182326 32.115585 33.640870 62.235139 \n",
|
||
"1233 1.447857 6.981766 9.945472 21.048478 \n",
|
||
"1234 4.231220 14.073295 20.697950 36.231604 \n",
|
||
"1235 1.422876 10.607271 13.651769 34.348592 \n",
|
||
"\n",
|
||
" sparsity ... p_e_peak t_i_peak p_i_peak half_width peak_to_trough \\\n",
|
||
"32 0.633240 ... NaN NaN NaN 0.272875 0.602667 \n",
|
||
"34 0.424446 ... NaN NaN NaN 0.226452 0.274814 \n",
|
||
"35 0.393028 ... NaN NaN NaN 0.247266 0.570104 \n",
|
||
"39 0.760804 ... NaN NaN NaN 0.284542 0.644111 \n",
|
||
"40 0.698596 ... NaN NaN NaN 0.259920 0.581698 \n",
|
||
"42 0.841781 ... NaN NaN NaN 0.263630 0.596746 \n",
|
||
"43 0.823859 ... NaN NaN NaN 0.281399 0.607354 \n",
|
||
"45 0.799767 ... NaN NaN NaN 0.279177 0.585152 \n",
|
||
"48 0.643995 ... NaN NaN NaN 0.282336 0.711705 \n",
|
||
"49 0.646322 ... NaN NaN NaN 0.285816 0.603160 \n",
|
||
"57 0.837844 ... NaN NaN NaN 0.277867 0.588852 \n",
|
||
"124 0.446442 ... NaN NaN NaN 0.271262 0.615002 \n",
|
||
"125 0.661683 ... NaN NaN NaN 0.307694 0.659653 \n",
|
||
"126 0.456739 ... NaN NaN NaN 0.267708 0.630543 \n",
|
||
"128 0.802417 ... NaN NaN NaN 0.289100 0.673221 \n",
|
||
"129 0.862176 ... NaN NaN NaN 0.290402 0.650772 \n",
|
||
"131 0.858017 ... NaN NaN NaN 0.272160 0.620429 \n",
|
||
"132 0.860475 ... NaN NaN NaN 0.241405 0.595513 \n",
|
||
"134 0.383354 ... NaN NaN NaN 0.269911 0.609574 \n",
|
||
"135 0.750893 ... NaN NaN NaN 0.273069 0.651265 \n",
|
||
"136 0.358918 ... NaN NaN NaN 0.263251 0.629310 \n",
|
||
"209 0.384212 ... NaN NaN NaN 0.251027 0.593786 \n",
|
||
"214 0.764622 ... NaN NaN NaN 0.296577 0.631283 \n",
|
||
"215 0.824103 ... NaN NaN NaN 0.295235 0.633010 \n",
|
||
"216 0.462461 ... NaN NaN NaN 0.268553 0.618949 \n",
|
||
"221 0.457083 ... NaN NaN NaN 0.263816 0.607601 \n",
|
||
"223 0.566049 ... NaN NaN NaN 0.313833 0.646825 \n",
|
||
"227 0.529688 ... NaN NaN NaN 0.261029 0.596500 \n",
|
||
"262 0.857509 ... NaN NaN NaN 0.282343 0.604147 \n",
|
||
"274 0.660943 ... NaN NaN NaN 0.265655 0.574791 \n",
|
||
"... ... ... ... ... ... ... ... \n",
|
||
"1130 0.864388 ... NaN NaN NaN 0.284016 0.615742 \n",
|
||
"1151 0.371794 ... NaN NaN NaN 0.272936 0.784972 \n",
|
||
"1152 0.877996 ... NaN NaN NaN 0.303012 0.661133 \n",
|
||
"1153 0.561983 ... NaN NaN NaN 0.310060 0.632763 \n",
|
||
"1154 0.618229 ... NaN NaN NaN 0.290294 0.618949 \n",
|
||
"1155 0.358157 ... NaN NaN NaN 0.263375 0.622896 \n",
|
||
"1156 0.825910 ... NaN NaN NaN 0.289628 0.650032 \n",
|
||
"1162 0.651012 ... NaN NaN NaN 0.245031 0.528413 \n",
|
||
"1165 0.372375 ... NaN NaN NaN 0.239301 0.531126 \n",
|
||
"1167 0.836244 ... NaN NaN NaN 0.284221 0.610068 \n",
|
||
"1168 0.414271 ... NaN NaN NaN 0.273572 0.611548 \n",
|
||
"1199 0.720704 ... NaN NaN NaN 0.189559 0.248665 \n",
|
||
"1200 0.519950 ... NaN NaN NaN 0.225575 0.277528 \n",
|
||
"1202 0.544679 ... NaN NaN NaN 0.275930 0.594526 \n",
|
||
"1203 0.590584 ... NaN NaN NaN 0.222604 0.576271 \n",
|
||
"1205 0.378044 ... NaN NaN NaN 0.244049 0.571337 \n",
|
||
"1207 0.857579 ... NaN NaN NaN 0.257469 0.636957 \n",
|
||
"1208 0.538208 ... NaN NaN NaN 0.252255 0.587372 \n",
|
||
"1211 0.757528 ... NaN NaN NaN 0.261129 0.592306 \n",
|
||
"1213 0.732917 ... NaN NaN NaN 0.277189 0.615988 \n",
|
||
"1216 0.773930 ... NaN NaN NaN 0.300175 0.610068 \n",
|
||
"1218 0.715811 ... NaN NaN NaN 0.287132 0.616235 \n",
|
||
"1222 0.713088 ... NaN NaN NaN 0.293775 0.657679 \n",
|
||
"1228 0.874674 ... NaN NaN NaN 0.252895 0.600200 \n",
|
||
"1229 0.676058 ... NaN NaN NaN 0.271023 0.699617 \n",
|
||
"1231 0.722984 ... NaN NaN NaN 0.343906 0.698383 \n",
|
||
"1232 0.833921 ... NaN NaN NaN 0.294291 0.639177 \n",
|
||
"1233 0.436204 ... NaN NaN NaN 0.304748 0.641151 \n",
|
||
"1234 0.612146 ... NaN NaN NaN 0.277708 0.585645 \n",
|
||
"1235 0.332963 ... NaN NaN NaN 0.258204 0.608094 \n",
|
||
"\n",
|
||
" average_firing_rate bs bs_stim bs_ctrl gridcell \n",
|
||
"32 5.945508 True NaN 1.0 True \n",
|
||
"34 2.860048 False NaN 0.0 True \n",
|
||
"35 3.365674 True NaN 1.0 True \n",
|
||
"39 17.471520 True NaN 1.0 True \n",
|
||
"40 5.891739 True NaN 1.0 True \n",
|
||
"42 13.436847 True NaN 1.0 True \n",
|
||
"43 17.446704 True NaN 1.0 True \n",
|
||
"45 10.840470 True NaN 1.0 True \n",
|
||
"48 5.890705 True NaN 1.0 True \n",
|
||
"49 7.914246 True NaN 1.0 True \n",
|
||
"57 17.162446 True NaN 1.0 True \n",
|
||
"124 2.868000 True NaN 1.0 True \n",
|
||
"125 6.912052 True NaN 1.0 True \n",
|
||
"126 4.229867 True NaN 1.0 True \n",
|
||
"128 16.735961 True NaN 1.0 True \n",
|
||
"129 25.974728 True NaN 1.0 True \n",
|
||
"131 14.686236 True NaN 1.0 True \n",
|
||
"132 18.657578 True NaN 1.0 True \n",
|
||
"134 3.106903 True NaN 1.0 True \n",
|
||
"135 6.213807 True NaN 1.0 True \n",
|
||
"136 2.226306 True NaN 1.0 True \n",
|
||
"209 3.347881 True NaN 1.0 True \n",
|
||
"214 7.172961 True NaN 1.0 True \n",
|
||
"215 16.933450 True NaN 1.0 True \n",
|
||
"216 3.323730 True NaN 1.0 True \n",
|
||
"221 2.654511 True NaN 1.0 True \n",
|
||
"223 3.083316 True NaN 1.0 True \n",
|
||
"227 3.080092 True NaN 1.0 True \n",
|
||
"262 19.498454 True NaN 1.0 True \n",
|
||
"274 12.578109 True NaN 1.0 True \n",
|
||
"... ... ... ... ... ... \n",
|
||
"1130 18.426477 True NaN 1.0 True \n",
|
||
"1151 4.093056 True NaN 1.0 True \n",
|
||
"1152 17.702603 True NaN 1.0 True \n",
|
||
"1153 4.060442 True NaN 1.0 True \n",
|
||
"1154 3.981625 True NaN 1.0 True \n",
|
||
"1155 4.088979 True NaN 1.0 True \n",
|
||
"1156 9.299064 True NaN 1.0 True \n",
|
||
"1162 17.770859 True NaN 1.0 True \n",
|
||
"1165 2.611971 True NaN 1.0 True \n",
|
||
"1167 4.949745 True NaN 1.0 True \n",
|
||
"1168 5.407135 True NaN 1.0 True \n",
|
||
"1199 3.564358 False NaN 0.0 True \n",
|
||
"1200 2.693978 False NaN 0.0 True \n",
|
||
"1202 5.288548 True NaN 1.0 True \n",
|
||
"1203 6.484767 True NaN 1.0 True \n",
|
||
"1205 3.425185 True NaN 1.0 True \n",
|
||
"1207 26.839270 True NaN 1.0 True \n",
|
||
"1208 4.589373 True NaN 1.0 True \n",
|
||
"1211 7.407060 True NaN 1.0 True \n",
|
||
"1213 9.221822 True NaN 1.0 True \n",
|
||
"1216 9.358786 True NaN 1.0 True \n",
|
||
"1218 7.835622 True NaN 1.0 True \n",
|
||
"1222 7.071948 True NaN 1.0 True \n",
|
||
"1228 15.695836 True NaN 1.0 True \n",
|
||
"1229 11.768979 True NaN 1.0 True \n",
|
||
"1231 4.442023 True NaN 1.0 True \n",
|
||
"1232 22.458685 True NaN 1.0 True \n",
|
||
"1233 3.102590 True NaN 1.0 True \n",
|
||
"1234 6.900656 True NaN 1.0 True \n",
|
||
"1235 3.767155 True NaN 1.0 True \n",
|
||
"\n",
|
||
"[130 rows x 51 columns]"
|
||
]
|
||
},
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data.query('baseline and gridcell')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 61,
|
||
"metadata": {
|
||
"scrolled": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 525x330 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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77ruP/fbbjy222IIlS5Zw2223ceaZZ3LHHeXsvnnz5rHjjjtOcqulqWHcgjsidmVwpOdotn8GJaS3A56gzPS0FWXGov0j4j2Z+ZU2VWgC3H///bz+9d2eAvx0J510Em984+gvU3zsscfyi18MdwGozuy2224sWDCa2T9XTZ/97Gc55JBDWLhwIRdffDEXX3xxy3Jvf/vbOfrooye4ddLUNS7BXc2ffB5lQMponU0J7YuAt2bmw9WsRkdSZpo6LSKuyczfjrW9krq39dZbc8EFF3Deeedx4YUXkpk8+uijrLXWWmy66aa86EUv4o1vfCMveMF4zXgqrZp6Oqq8mhbww5Tu7RlNq7rqKq+C/1LK6SPPGjoPdUQsoJw/+o2mi9qPpr03zZ07d8fvf//7o61CUn9zcJqmnJ6dxx0RcynnLH60euhYyixRozG/Wn53mItHnFYtXx8Ra43yOSRJqp1edpVvBWxNmW3o/Zn5y4h49yjr2qNaXjnM+l9QJuRYhzIH8RXDlJM0RgMrVrB88aIuyg8w8Gj3k6TNWGcW06Z3ti8xbcO1mDbdnWmtmnoZ3L8H9svMH4ylkuo4duMqSS2HK1fzLN9Lmd5wewxuadwsX7yIOz64Wcflpz25Dutd9/TTw3pp0yvfx7SNejphn1QbPQvuzLyNwTmJx2IWg+16oE25RZTgHpdLBkqS1I/6ca7y5p/RS9uUW9KivCRJU1o/zpy2fOQiK3G60nHW7TFOTS3LF7caHyp1ZrQTcUXE+yjXOz84M0/v8jlXo1wkBuCgzDxjhPKHUK6Utzwz+zEXV9KPDWy+gtDMYUuVS+VBmZxF46jbY5ya+uaceCMz1m19lGrFw0t56DVnrfTYht98PdM3HPxzXv74In7/8b8cUudNrLbexi3qW8KDr3WupToa7URcEbE75ZK6aqFfg/tPwJrA0/+KBzW+Ne4f9xZJWsmMdWez2vqbtFy34qmn/5ZeY+stmd40mOypP85kYPXHVyozfdZMpq/vka+pYrQTcUXEy4H/ppw1pBb6Lrgzc0VEJLAzsE2rMhGxOtC4HNEtE9S0vjdeXdp2lUrqVJuJuEbabi3gn6p//Tj+qm/0XXBXfk4J7j2AL7dYvxul7UuB6yawXX3NLm1Jk6maiOsnlDk9llPC+92UM4DabRfAxZT5QJ4CjgHeD2w5nu2tq34N7nMoH/abI+KozHxoyPrDquXZmbkETbh2xzg19c1Yt91RLK3CRjsR19bVtldX210XEe8fx3aOaMgAt5F8OTMPGc/2NJvU4I6IZ1JO53oiM+9uWnUJ5cpgewLfiYgDMvN/q8lZjgAOpLyhDl6YJO2OcTYbWDHAwCP+tppqBh5ZOuzpHCsenpqf9y3zZ8wANprsdvTIQ9ufvrzbM3g6MdqJuO4GXpOZPxqHNo3WACWHhjMbiOr2aKf3HpXJ3uP+OrA3cBmwT+PBzByIiHdWj78UuCsibqR0m2xGeUMP9spg/W/gkSXcv9epk90MaUxumT/jAMqpSZtOdlt65P5b5s94//anLz+3l5WOdiKuzLyFPhuvlJnLgb1arYuI9RickvtC4MSJahdMfnAPKzPviIhdKMc69geeRzn160fAJzPz0slsX12MV5e2XaVaxXwR2GCyG9FDm1JeU0+Du08tqK4o2RNVF/q5lHFYvwXeUoX8hBnX4M7MbUZYv88I6x8EPlT90yh02qUtSVPULbSfPhvgGcDcDus7FXgVZdrtv8nMR8fQtlHp2z3uqW48Tt3ytC1p3LybKdZVThm1vSr4v13MnNZWRBwNvAdYBrwxM+/oTRO7Y3BPklX51K3ZF7yL6bO8jPqqZNqG9f68tz99+bm3zJ9xHg5OW2VFxAHASdXd92bm5ZPVFoNbE276rLVWmkVLqoMq6EbqctUUFBF7UAZTTwNO7nbu9F5zdhpJkoYREdsB51OunfEd4COT2yKDW5KkliJiI+AHlHO2rwfmZeakX5HSrvI+Mh6nbnnaliR1LyLWpOxhbw8sBP46Mx9vu9EEMbj7SD+cutXrmc6m6ixakqa8v6dMAAbwIPDFiFgHWL1F2adGOr25lwxurcSZziQJgPWbbr94hLJTZwIWSdKqbaSJuNpst9UYnvMpygjwTst/CfjSkMeOpVzdrO84OE2SpBoxuCVJqhG7yjWiXs90VvdZtCRpMhncGpEznUlS/7CrXJKkGjG4JUmqEbvKa6zXk6WAE6ZIUr8zuGvMyVIkadVjV7kkSTVicEuSVCMGtyRJNeIx7imm15OlgBOmSFI/MbinGCdLkaSpza5ySZJqxOCWJKlGDG5JkmrEY9ySpJ6JiGcDHwZeCWwGPAT8DDg1My9qs93GwLHA64CtgIeBq4BPZubPumzDPsCl1d1nZebCEcqfDvwtcFlm7tP0+E+Bvbt57k6eb6wMbklST0TEq4BvA2sBTwA3A5tQwvh1EfGvmXlki+2eQQnp7artfkMJ7zcA+0fEezLzKxPzKlZyA53l5C5AY1Twk+PXnMLgliSNWUTMBr5JCe2zgEMz89Fq3TuArwNHRMTPMvO/h2x+NiW0LwLempkPR8R04EjgE8BpEXFNZv52gl4OAJn5gZHKRMQ8YEF19zOZee/4tspj3JKk3jgEmAUsBOY3QhsgM88EvljdPbR5o6pbe29gMfCOzHy42mZFZp4MnAGsDhwzzu3vWkTsCnypuns1cNREPK/BLUnqhTspe9yfz8w/tVj/m2o5Z8jj86vldzPzwRbbnVYtXx8RfTMbVERsBHwLWBN4AHhLZo57NznYVS5J6oHMPJvS5T2cF1fLW4c8vke1vHKY7X4BPAWsU9VxxWjb2CsRMY3S9f9MYAXw9onoIm8wuCWpA/fteMoMYKPJbkePPLTZzUcun4gniogNgb8HDqYE8MlN66YD21Z3b2+1fWY+GRH3UvbUt6cPghs4Gtivuv3RzLxkIp/c4JakEdy34ykHAP8BbDrZbemR++/b8ZT3b3bzkeeO1xNExJuAE4C5lO7ke4DDMvPypmKzGMyhB9pUt4gS3LPHoaldiYiXAh+r7v4Q+PhEt8HglqSRfRHYYLIb0UObUl7TuAU3sBuwU9P9WcDfRMTlmflY9VjzhRWWtqlrSYvynbozIkax2dNFxCaUEfMzgLuAeZk50JPKu2BwS5LGw+eAfwHWB/YFTqGMKH9xROyRmU8B3XbXjyYk/wdoNViu2bMZoTel6tb/BrAFsAw4IDMfGkV7xszglqSRvZsp1lUOvH88nyAzf1/dfBz4WkT8DLieMsBsHnA65RSwhpltqmuMJn9iFE05oIuZ09o5hjIbHMCHMvPaUbSlJwxuSRrBZjcfee59O55yHg5OG7XMzIg4D3gHsA+Dwf0nyjHwjdts3ji2ff84NnFYEfEy4Pjq7pmZ+fnJaEeDwS1JHaiCrt0AqlVadV7zs4C7hjkfG8pxYShzmJOZKyIigZ2BbYapd3VK9zTALT1rcIciYjPK+enTKVO4vmei2zCUE7BIknrhWsrx5He1KdOYfKX5nOefV8s9aG03yk7mUuC6sTSwWxExAzgTeAald+BNmfn4RLahFYNbktQLF1bLQ6q95JVExDaUi4YAXNC06pxq+eZqr32ow6rl2Zm5pMX68XQ88LLq9rsz83cT/PwtGdySpF44hXLa1rOBM6uLjgAQEbsAP6YMMrsc+G7TdpdQrgy2AfCd6kphRMT0iDgKOJByxa2TmUARsS/wT9XdUzLzrIl8/nY8xi1JGrPMvCMi3kKZ9vTNwGur49czKTOeQbku95uaz33OzIGIeCdwGfBS4K6IuBHYknIsfAA4eKKvDEa5pnhj53aviBhuStahPp6ZPxynNgEGtySpRzLzexHxfMrlOPcFnkM5hetKylW+vtLqQhxV6O9COeVqf+B51XY/Aj6ZmZdO0Eto1twjPdzx91ae0euGDGVwS5J6JjNvA947iu0eBD5U/RtrG34KTOui/HwGr1LWeGyfsbZjvBjcE2RgxQADjwyOq1jx2FKmPbnOSmVWPLyUFU91Pr/AiocnepyGJGmyGdwTZOCRJdy/16krPbYex610/6HX9M3YB0lSn3JUuSRJNWJwS5JUIwa3JEk14jHuSbT4eZ9iYLXBwWhzTryJ1dZrN8/+yKZtuNbIhSRJtWVwT6KB1Z5gYPXBaW+nz5rJ9PVHc514SdKqwq5ySZJqxOCWJKlGDG5JkmrE4JYkqUYMbkmSasTgliSpRgxuSZJqxOCWJKlGDG5JkmrE4JYkqUYMbkmSasTgliSpRgxuSZJqxKuDdWBgxQqWL140pjpWPLa0R62RJK3KDO4OLF+8iDs+uNmY6pj25Dqsx3E9apEkaVVlV7kkSTVicEuSVCMGtyRJNeIx7lGac+KNzFh3dsflVzy8lIdec9aQOm5i+qyZf74/Y92Ne9Y+SdLUZHCP0ox1Z7Pa+pt0XH7FU0887bHV1tuY6euv3ctmSZKmOLvKJUmqEYNbkqQaMbglSaoRg1uSpBoxuCVJqhGDW5KkGjG4JUmqkZ6dxx0RawNHAW8DngU8BvwS+Exm/nAU9W0D3DlCsV9n5gu6rVuSpLrqSXBHxDrAJcDuwJPAjcDGwL7AvhFxfGae0GW1z6+WDwG/HabMraNoriRJtdWrPe5TKaF9PbB/Zt4DEBEHAV8Bjo+IqzLz4i7qbAT3OZl5WI/aKUlSrY35GHdEbAfMA1YABzZCGyAzFwCfqO4e32XVjeC+YaxtlCRpqujF4LSDgBnANZl5c4v1p1XLPSPimV3U2wjuG8fSOEmSppJeBPce1fLKVisz817gruru3p1UGBHrAttWd93jliSp0otj3HOr5e1tyiwE5gDbd1jnzsA04A/AJhFxBLALpb23AN/MzKtG1VpJkmqsF8G9abV8oE2ZRdWy0wtYN7rJZwE3U7riG14JvC8ivgIcmplPdtpQSZLqrhdd5Y0LSi9tU2bJkLIjaQT3TOCLwE7AmpS99mMpp5y9C/hsVy2VJKnmerHHvZzOfwAMdFjuiqrO6zLzP5sevxv4eEQsBM4ADo2IUzPzpg7rlSSp1noR3IspXdoz25RZq1o+0UmFmfkN4Bvt1kfER4FnA68DDG5J0iqhF13lD1bLjduUaRzbvr8Hz9dwXbV8Vg/rlCSpr/UiuBvTkW7Tpkxj3S2dVhoRq0fEjDZFGm13cJokaZXRi+D+ebXco9XKiNgKaEy8cvVIlUXErIh4CFhG6QYfzi7VstWkL5IkTUm9CO5zq+U+EREt1h9aLS/LzIUjVZaZDwP3VXfntyoTEW8GtqOE+3ndNFaSpDobc3Bn5q3AmZRzrc+LiMaELETEPODo6u7Hhm4bEdtFxA4RsfmQVSdVy9dGxEkRsWbTNm8Gvlrd/WRm/mGsr0GSpLro1dXBPgg8r/r3u4i4gTLSfE61/phhrgx2SVXmazTtXWfmgojYGTgC+DBlwpVbgWcAW1bFvgR8tEftlySpFnrRVU5mLqIc4z6BMgDtOZRR5pcBb8rME0dR55GUWdK+S5nAZWdgdeB7wN9k5rszc0Uv2i9JUl30ao+bzHyccunO47vYZpsR1l8MdHMNb0mSprSe7HFLkqSJYXBLklQjBrckSTVicEuSVCMGtyRJNWJwS5JUIwa3JEk1YnBLklQjBrckSTVicEuSVCMGtyRJNWJwS5JUIwa3JEk1YnBLklQjBrckSTVicEuSVCMGtyRJNWJwS5JUIwa3JEk1YnBLklQjBrckSTVicEuSVCMGtyRJNWJwS5JUIwa3JEk1YnBLklQjBrckSTVicEuSVCMGtyRJNWJwS5JUIwa3JEk1YnBLklQjBrckSTVicEuSVCMGtyRJNWJwS5JUIwa3JEk1YnBLklQjBrckSTVicEuSVCMGtyRJNWJwS5JUIwa3JEk1YnBLklQjBrckSTVicEuSVCMGtyRJNWJwS5JUIwa3JEk1YnBLklQjBrckSTVicEuSVCMGtyRJNWJwS5JUIwa3JEk1YnBLklQjBrckSTVicEuSVCMGtyRJNWJwS5JUIwa3JEk1YnBLklQjBrckSTVicEuSVCMGtyRJNWJwS5JUIwa3JEk1YnBLklQjBrckSTVicEuSVCMGtyRJNWJwS5JUIwa3JEk1YnBLklQX5EelAAAKpUlEQVQjBrckSTVicEuSVCMGtyRJNbJaryqKiLWBo4C3Ac8CHgN+CXwmM384yjqfCfwz8GpgU+AB4BLgpMz8bS/aLUlSnfRkjzsi1gF+AnwU2Ba4CXgc2Bf4QUR8dBR1BvAr4O+AdYFfAzOBg4BfRcSretF2SZLqpFdd5acCuwPXA9tl5gszcw7wTuAp4PiIeEWnlUXEasD3gI2BBcDmmbkrsDnwH5QAPysiNu5R+yVJqoUxB3dEbAfMA1YAB2bmPY11mbkA+ER19/guqp0HzAXuBg7JzCVVfcuADwJXABsCHxpr+yVJqpNe7HEfBMwArsnMm1usP61a7lkds+7E/Gq5oArrP8vMAeAL1d23d9lWSZJqrRfBvUe1vLLVysy8F7irurv3SJVFxHRgt3Z1AldVy20jYusO2ylJUu31IrjnVsvb25RZWC2376C+LYG1RqjzHmB5F3VKkjQl9CK4N62WD7Qps6hazu6ivmHrzMzlwKNd1ClJ0pTQi/O4166WS9uUWTKkbCf19bLO4Wx99913s99++7UtNLD8KZbdt3KP/Bpvmce0GZ2/fQPLV7B8+UMrPTbjHT9h2gznwNGqp5u/qbH87dx2223nZ+b+Y2ut1F96EdzL6XzPfaDD+rrRSZ3DeWLZsmXcdttt94xcdM2V7965cAxP26jjkbHXIdXWGP6m/NvRKqwXwb0YmEU5t3o4jWPWT3RYX8NMht/r7qbOljJzs9FuK0nSZOhFP+2D1bLdZCiN49D3d1HfsHVWE7Rs0EWdkiRNCb0I7sac4du0KdNYd8tIlWXmHxgceDZcnVtTzh3vqE5JkqaKXgT3z6vlHq1WRsRWQGPilas7rPMX7eoEXlIt76qCXpKkVUIvgvvcarlPdWGQoQ6tlpdl5sIO6zynWh4cEWu0qfP0DuuTJGlKGHNwZ+atwJmUruvzIqIxIQsRMQ84urr7saHbRsR2EbFDRGw+ZNUZlMlXtgXOjIj1qvJrRMS/A3tRutM/N9b2S5JUJ9MGBsZyNlVRXaXrUuB5lNO5bqCMNJ9TFTkmM09ssd3CqszXMnP+kHW7AhdRBqEtBn5HCfKNgGXAqzPz0jE3XpKkGunJ7B+ZuYhyPPoEymCx51BGhF8GvKlVaHdQ57XA84EvA49Ut1cA/w3sbmhLklZFPdnjliRJE8P5NiVJqhGDW5KkGjG4JUmqEYNbkqQaMbglSaqRXlwdTD0QEWsDRwFvA54FPAb8EvhMZv5wlHU+E/hn4NXApsADwCXASZn523bbNtUxHbgc2BNYPTOfGk1bprJ++uwiYl/gcGB3yimZi4FfA18FFmSmp5FINefpYH0gItahfCnvDjwJ3Ej50m3M8X58Zp7QZZ0BXFXV8yhwK4MT2CwFXp+ZP+6gnk8wOPudwT1EP312EfEp4P9Udx+jzD64JbBJ9dj5wJsz88lu2iOpv9hV3h9OpXzxXw9sl5kvzMw5wDuBp4DjI+IVnVZWXfb0e5Qv/gXA5pm5K7A58B+U65yfVc14N1wdMyLiFAZDW631xWcXEQdSQnt5tdwwM3fJzE2BAyhBvj/wL2N5sZImn8E9ySJiO2AeZVa4AzPznsa6zFwAfKK6e3wX1c4D5gJ3A4dk5pKqvmXAB4ErgA2BDw3TpmdT9iKP6Oa1rGr67LM7slp+PjM/nZkrmtryLQb3xD8QEWt20R5JfcbgnnwHUS7Qck1m3txi/WnVcs/quGcn5lfLBdUX/p9Vxzi/UN19+9ANI+J9wE3A3sA9lGO3aq0vPruI2IgyJTDAN4ep9zvVch1gxw7bIqkPGdyTr3HN8StbrczMe4G7qrt7j1RZNZhst3Z1Uo6fAmwbEVsPWbdrtfwc8Fzg2pGecxXWL5/dUuC1wGGUY+ytTGu6PWOktkjqX44qn3yNy6De3qbMQspV1LbvoL4tgbVGqPMeyrHQGVWd9zSt+2/ghMy8E6D1JdZV6YvPLjOfoBwXb+eAavkkZbCbpJpyj3vybVotH2hTZlG1nN1FfcPWmZnLKaOVn1ZnZl7QCG2NqK8+u+FU17tvjGz/bmY+2q68pP5mcE++tavl0jZllgwp20l9vaxTrfX9ZxcRG1D2xmdTzun+SAftkNTHDO7Jt7yLsp2cdN9NfZ3Wqdb6+rOLiNnAxcALq7IHZ+ZtXT6HpD5jcE++xdVyZpsyjeOeT3RRXy/rVGt9+9lFxFzKQLYXU05Xe3d1WpikmjO4J9+D1XLYyVAYPJZ5fxf1DVtnNcnHBl3Uqdb68rOLiL2An1EGry0D3p6ZX+7g+SXVgME9+RrzTm/Tpkxj3S0jVZaZf2Bw8NJwdW7N4ClBI9apYfXdZxcRb6V0j28MPAS8MjPPGem5JdWHwT35fl4t92i1MiK2YnDe66s7rPMX7eoEXlIt76rCQqPTV59dRLwNOBNYE7gTeElmXt7h80qqCYN78p1bLfeJ1idNH1otL8vMhR3W2djDOjgi1mhT5+kd1qfW+uazi4jdga9T/qZvoIR2dvickmrE4J5kmXkrZS9pBnBeNagIgIiYx+BFPj42dNuI2C4idqjO0212BmUCj22BMyNivar8GhHx78BelC7Zz/X69axK+uWzi4gZlNBenXLc+9WZeV9vXqWkfuNlPftAdaWnS4HnUU4JugGYRZlxC+CYzDyxxXYLqzJfy8z5Q9btClxEGci0GPgdg5eGXEb5cr+0g7btU7UNvKzn0/TDZxcRb2Zw7/9eymxt7XwgM6/r8CVK6jPucfeBzFxEOaZ5AmXA0XMog4suA97U6ou/gzqvpVx44svAI9XtFZQpTXfvJLQ1sj757JrnQd8S2HOEfxsgqbbc45YkqUbc45YkqUYMbkmSasTgliSpRgxuSZJqxOCWJKlGDG5JkmrE4JYkqUYMbkmSasTgliSpRgxuSZJqxOCWJKlGDG5JkmrE4JYkqUYMbkmSasTgliSpRgxuSZJqxOCWJKlGVpvsBkjjLSK2BI4GXg3MAZYB9wAXAZ/OzIWT1zpJ6s60gYGByW6DNG4iYjvgamBT4HHgjmrV9sCawB+BfTLzuslpoSR1x65yTXUfp4T2t4DNMnPnzNyZsud9NbA+cNIktk+SumJwa6p7frX8RmYubjyYmf8L/D3wI+DmyWiYJI2GXeWa0iLifOC1QFKOc1+YmUsmt1WSNHoGt6a0iHgBcAWwbvXQnyhd5BcBP8jMX09W2yRpNAxuTXkRsS3wEeANwMZDVt8AHJ6ZV054wyRpFAxurTIiYjrwImAf4OXAXwGrA08AO2TmPZPXOknqjMGtKSsiplFGj8/NzItbrN8euJYysvwfMvOzE9xESeqao8o1lW0E3ApcFBEvHroyM28B7q7uzpjIhknSaBncmrIycxHww+ruVyNih8a6iJgeEYcDzwVWUE4Lk6S+Z1e5prSI2Bz4GfBMSkDfCTxC6UKfXRX7cGaePDktlKTuGNya8iJiU+AI4DXAtpQBaf8LXAl8LjOvnsTmSVJXDG5JkmrEY9ySJNWIwS1JUo0Y3JIk1YjBLUlSjRjckiTViMEtSVKNGNySJNWIwS1JUo0Y3JIk1YjBLUlSjRjckiTViMEtSVKNGNySJNWIwS1JUo0Y3JIk1YjBLUlSjRjckiTViMEtSVKNGNySJNXI/wdNUl3B+oG4qAAAAABJRU5ErkJggg==\n",
|
||
"text/plain": [
|
||
"<Figure size 525x330 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 525x330 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 525x330 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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PgCPd/fIK37Mf4WE+AIe7+69L7H804Sl57e7eiLm4hkasYPbTfwYU2S8zmrx7U2X1EeX0aYvU2shbj6J1WPdu/OhYuJx5n9JcSymqdiIuM9uN8DhdyaNRg/sdYF1gRJH9Mn3bc3q8RiJSldZhA2nVILI+Kc7HcRMVTsRlZh8BbiTcNSR5NFxwu3uHmTmwAzAu3z5m1p8wixqEe8dFRKQBFJmIq9RxAwnPVT+dxhx/1TAa9ZfzaFzuXmD7roQvHSuAJ3qlRgnq7Ohg9dtzWf32XPVpi0iPq3YiLjOzeNyZQAfwLeD1Hqpm8hruiju6DjgGOMTMTnX3BTnbj4/La929d2Z8SFCpfu2xU5+hbXDocWhfPJdZZ2zfW1UTkeZU7URcm8ZjH4rHPWFmJ/RgPUvKGeBWyi/d/eierE+2uga3mW1GuJ1rmbu/lrXpHsKTwfYAfmdmh7r7W3FylpOBwwi/UA1e6Ia2wSM1wYpImV6Y3NYGDK93PWpkwZaXt1d6B085qp2I6zVgP3f/fQ/UqVqdhBwqZCRg8edqp/euSr2vuK8E9gbuAyZmXnT3TjP7Unx9L2CWmT0DbAyMJvxCj3T353u9xiLS57wwue1Qwq1Jo+pdlxqZ88LkthO2vLz9+loWWu1EXO7+Ag02Xsnd24E9820zsyF0Tcn9R2Bqb9UL6h/cBbn7K2a2E3AGcACwPeHWr98D57v7vfWsn4j0Kb8ANqh3JWpoFOEz1TS4G9T0+ETJmohN6NcTBlA/D3w2hnyv6dHgdvdxJbZPLLF9HnBS/Cc1kN2v3Ta42N12IiJN4QWKT58NsBEwoczypgH7Eqbd/qS7/7sbdatKw15xS89Qv7ZIVY6hyZrKgboO/upF361g5rSizOw04FhgJfAZd3+lNlWsjIJbRKSELS9vv/6FyW03ocFpfZaZHQqcG1e/7O7316suCu4movnIRXpODLpSTa7ShMxsd8Jg6hbgvErnTq81BXcT0XzkIiK1ZWbjgVsIz874HWFymLpScIs0oM6OTjoXpTW3UMfCtOorUoqZDQfuINyz/SQwyd3r/kRKBbdIA+pctJw5e06rdzVE+iwzW5dwhb0lMBP4hLsvrWulIgW3iIjI2r5OmAAMYB7wCzNbD+ifZ9/VpW5vriUFd5PLvm8bdO+2iEiZ1s/6eecS+zbPBCxSf7pvW0TqqdREXEWO26Qb77maMAK83P0vBS7Nee1MwtPKGo6CWyQRI289itZhA+tdjYq0DE2rviIpUHCLJKJ12EBahw+qdzVEpM4U3NL0dGuViDQTBbc0Pd1aJSLNpLXeFRAREZHyKbhFREQSoqZyqYj6i0VE6kvBLRVplv5i3VolIqlScEufpFurRCRV6uMWERFJiK64m1VnCy2rB9GxcAUdq5eV3L1j8QpaVq235mt5jlV/sYhIfSm4m1TL6kEMeeLbLNjvmrKPGcK311gv91j1F4uI9B4Ft3Sb+otFRHqP+rhFREQSouAWERFJiJrK+5BifdGrF89n1unbrvHa2KnP0m/IiJLlqr9YRKT3KLj7kGJ90a39ltLZf2nO/gNoXV991yJSPjN7L/BN4GPAaGAB8Agwzd3vKnLcCOBM4NPAJsBC4EHgfHd/pMI6TATujaubu/vMEvtfDhwB3OfuE7Ne/zOwdyXvXc77dZeCW0REasLM9gV+CwwElgHPARsSwvjTZnahu5+S57iNCCE9Ph73d0J4HwQcYGbHuvtlvfMp1vA05eXkTkDmKmdVz1UnUHAnrLOjg/Yl899db18yr461EZG+zMxGAr8hhPY1wHHu/u+47YvAlcDJZvaIu9+Yc/i1hNC+C/icuy80s1bgFOD7wCVm9rC7P99LHwcAd/9aqX3MbBIwPa5e5O6v92ytFNxJa18yn1dOHF3vaoiIABwNDANmApPd/Z3MBne/2sz2Ao6L/94N7tisvTewBPiiuy+Mx3QA55nZdsAk4Iy4bBhmtgtwaVx9CDi1N95Xo8pFRKQWXiVccf80O7Sz/D0ux+a8Pjkub3b3fM2Gl8TlgWbWMCNhzWw4cAOwLjAX+Ky793gzOeiKW0REasDdryU0eReyc1y+mPP67nE5o8BxjwGrgfViGQ9UW8daMbMWQtP/ZkAH8IXeaCLPUHCLiJThzW0uaAOG17seNbJg9HOntPfGG5nZUODrwJGEAD4va1srsEVcfTnf8e6+ysxeJ1ypb0kDBDdwGrB//Pksd7+nN99cwd1kxk59hrbBI+lYuKKiecpFpLA3t7ngUOAnwKh616VG5ry5zQUnjH7ulOt76g3M7GDgbGACoTl5NnC8u9+ftdswunJobpHi5hOCe2QPVLUisa/+e3H1TuCc3q6DgrvJtA0eSb/1NyzriWAiUrZfABvUuxI1NIrwmXosuIFdgexZnYYBnzSz+919cXwte6KIFUXKyjyWsJqJJV41syoOW5uZbUgYMd8GzAImuXtnTQqvgIJbRER6wo+B7wDrA/sAFxBGlO9sZru7+2qg0ub6akLyL0C+wXLZ3kuJ1pTYrH8V8B5gJXCouy+ooj7dpuAWESntGJqsqRw4oSffwN3/GX9cClxhZo8ATxIGmE0CLifcApYxoEhxmdHk1TQlHlrBzGnFnEGYDQ7gJHd/vIq61ISCW0SkhNHPnXL9m9tccBManFY1d3czuwn4IjCRruB+h9AHXuzBCJm+7Tk9WMWCzOxDwJS4erW7/7Qe9chQcIuIlCEGXbEBVH1avK95c2BWgfuxIfQLQ5jDHHfvMDMHdgDGFSi3P6F5GuCFmlW4TGY2mnB/eithCtdje7sOuTQBi4iI1MLjhP7ko4rsk5l8Jfue50fjcnfy25VwkbkCeKI7FayUmbUBVwMbEVoHDnb3pcWP6nkK7sR0dnSw+u25rH57ruYmF5FG8se4PDpeJa/BzMYRHhoCcGvWpuvi8pB41Z7r+Li81t2X59nek6YAH4o/H+Pu/+jl989LwZ2YzPzkr5w4mlmnb1fv6oiIZFxAuG3rvcDV8aEjAJjZTsAfCIPM7gduzjruHsKTwTYAfhefFIaZtZrZqcBhhCdunUcvMrN9gNPj6gXu3jATY6iPW0REus3dXzGzzxKmPT0E+FTsvx5AmPEMwnO5D86+99ndO83sS8B9wF7ALDN7BtiY0BfeCRzZ208GIzxTPHNxu6eZFZqSNdc57n5nD9UJUHCLiEiNuPttZrYj4XGc+wBbE27hmgH8Grgs34M4YujvRLjl6gBg+3jc74Hz3f3eXvoI2bJbpAv1v+ezUa0rkkvBLSIiNePuLwFfruK4ecBJ8V936/BnoKWC/SfT9ZSyzGsTu1uPnqLgbgKZ+ckB2gYXuxVSRERSp+BuApn5yUVEpPlpVLmIiEhCFNwiIiIJUXCLiIgkRMEtIiKSEAW3iIhIQhTcIiIiCVFwi4iIJETBLSIikhBNwFJHnR2ddC6q7Cl1HYtX0LJqvTVfW7iCjtXLcl7r7affiYhIb1Bw11HnouXM2XNaxccN4dtrrC/Yr2GeNiciIj1MTeUiIiIJUXCLiIgkRMEtIiKSEPVxN5iRtx5F67CBBbevXjyfWadvu8ZrY6c+S78hpR/n2TK0cLkiIpIGBXeDaR02kNbhgwpv77eUzv5Lc44ZQOv6hY8REZHmoaZyERGRhCi4RUREEqLgFhERSYiCW0REJCEKbhERkYQouEVERBKi4BYREUmIgltERCQhCm4REZGEKLhFREQSouAWERFJiIJbREQkIQpuERGRhCi4RUREEqLgFhERSYiCW0REJCEKbhERkYT0q1VBZjYIOBX4PLA5sBj4K3CRu99ZRXnjgFdL7PaUu7+v0rJFRERSVZPgNrP1gHuA3YBVwDPACGAfYB8zm+LuZ1dY7I5xuQB4vsA+L1ZRXRERkWTV6op7GiG0nwQOcPfZAGZ2OHAZMMXMHnT3uysoMxPc17n78TWqp4iISNK63cdtZuOBSUAHcFgmtAHcfTrw/bg6pcKiM8H9dHfrKCIi0ixqMTjtcKANeNjdn8uz/ZK43MPMNqug3ExwP9OdyomIiDSTWgT37nE5I99Gd38dmBVX9y6nQDMbDGwRV3XFLSIiEtWij3tCXL5cZJ+ZwFhgyzLL3AFoAd4ANjSzk4GdCPV9AfiNuz9YVW1FREQSVovgHhWXc4vsMz8uR5ZZZqaZfBjwHKEpPuNjwFfN7DLgOHdfVW5FRUREUleLpvJBcbmiyD7Lc/YtJRPcA4BfANsC6xKu2s8k3HJ2FPCjimoqIiKSuFpccbdT/heAzjL3eyCW+YS7/yzr9deAc8xsJvBr4Dgzm+buz5ZZroiISNJqEdxLCE3aA4rsMzAul5VToLtfBVxVbLuZnQW8F/g0oOAWEZE+oRZN5fPickSRfTJ923Nq8H4ZT8Tl5jUsU0REpKHVIrgz05GOK7JPZtsL5RZqZv3NrK3ILpm6a3CaiIj0GbUI7kfjcvd8G81sEyAz8cpDpQozs2FmtgBYSWgGL2SnuMw36YuIiEhTqkVwXx+XE83M8mw/Li7vc/eZpQpz94XAm3F1cr59zOwQYDwh3G+qpLIiIiIp63Zwu/uLwNWEe61vMrPMhCyY2STgtLj6vdxjzWy8mW1lZmNyNp0bl58ys3PNbN2sYw4BfhVXz3f3N7r7GURERFJRq6eDnQhsH//9w8yeJow0Hxu3n1HgyWD3xH2uIOvq2t2nm9kOwMnANwkTrrwIbARsHHe7FDirRvUXERFJQi2aynH3+YQ+7rMJA9C2Jowyvw842N2nVlHmKYRZ0m4mTOCyA9AfuA34pLsf4+4dtai/iIhIKmp1xY27LyU8unNKBceMK7H9bqCSZ3iLiIg0tZpccYuIiEjvUHCLiIgkpGZN5dIzOjs6aF8y/9319iXziuwtIiLNTsHd4NqXzOeVE0fXuxoiItIg1FQuIiKSEAW3iIhIQhTcIiIiCVEfd4LGTn2GtsEj311vG1zsiaoiItJMFNwJahs8kn7rb1jvaoiISB2oqVxERCQhCm4REZGEKLhFREQSouAWERFJiIJbREQkIQpuERGRhCi4RUREEqLgFhERSYiCW0REJCEKbhERkYQouEVERBKi4BYREUmIgltERCQhCm4REZGEKLhFREQSouAWERFJiIJbREQkIQpuERGRhCi4RUREEqLgFhERSYiCW0REJCEKbhERkYQouEVERBKi4BYREUmIgltERCQhCm4REZGEKLhFREQSouAWERFJiIJbREQkIQpuERGRhCi4RUREEqLgFhERSYiCW0REJCEKbhERkYQouEVERBKi4BYREUmIgltERCQhCm4REZGE9Kt3BWRNnR0drH577rvr7Uvm1bE2IiLSaBTcDaZ96UJmnjyu3tUQEZEGpaZyERGRhCi4RUREEqLgFhERSYj6uBMwduoztA0e+e562+ARdayNiIjUk4I7AW2DR9Jv/Q3rXQ0REWkAaioXERFJiIJbREQkIQpuERGRhCi4RUREEqLgFhERSYiCW0REJCEKbhERkYQouEVERBKi4BYREUmIgltERCQhCm4REZGEKLhFREQSouAWERFJiJ4OVgedHR20L5lPx+IVa21rXzq/DjUSEZFUKLjroH3JfF45cTQtq9ZjCN9eY9s/z/kP6F+niomISMNTcJehs6OTzkXLa1Zex+IVtKxaj5bVg2pWpoiI9A0K7jJ0LlrOnD2n1bTM3CttERGRcmhwmoiISEJ0xd1gxk59ltZhA9Z4rW3wiDrVRkREGo2Cu8H0GzKC1vXV9y0iIvkpuKs08tajaB02sKpjVy+ez6zTt13jtbFTn6XfkBG0DK2uTBER6RsU3FVqHTaQ1uHVXRm39ltKZ/+lOeUN0JW2iIiUpMFpIiIiCanZFbeZDQJOBT4PbA4sBv4KXOTud1ZZ5mbA/wAfB0YBc4F7gHPd/fla1FtERCQlNbniNrP1gD8BZwFbAM8CS4F9gDvM7KwqyjTgb8B/AoOBp4ABwOHA38xs31rUXUREJCW1aiqfBuwGPAmMd/f3u/tY4EvAamCKmX203MLMrB9wGzACmA6McfddgDHATwgBfo2Z6T4pERHpU7od3GY2HpgEdACHufvszDZ3nw58P65OqaDYScAE4DXgaHdfHstbCZwIPAAMBU7e37wgAAALLElEQVTqbv1FRERSUosr7sOBNuBhd38uz/ZL4nKP2GddjslxOT2G9bvcvRP4eVz9QoV1FRERSVotgnv3uJyRb6O7vw7Miqt7lyrMzFqBXYuVCTwYl1uY2aZl1lNERCR5tQjuCXH5cpF9ZsbllmWUtzGQmYWkUJmzgfYKyhQREWkKtQjuUXE5t8g+8+NyZAXlFSzT3duBf1dQpoiISFOoxX3cmem+VhTZJ/Mw63KmBsvep1ZlFrLpa6+9xv777190p872DtrbF6zxWtsX/0RLW3XfezrbV7PyzTVb+Nf57CRa2uo3kV0j1kkEuvf/30svvXSLux/QU3UTqYda/FVup/wr984yy6tEOWUWsmzlypW89NJLs0vvmuPVRd14W4B1c8qb2c3yaqER6ySSR7f//xNJVy2CewkwjHBvdSGZPutlZZaXMYDCV92VlJmXu4+u9lgREZF6qEUf97y4LDYZSqYfek4F5RUsM07QskEFZYqIiDSFWgR3Zs7wcUX2yWx7oVRh7v4GXQPPCpW5KeHe8bLKFBERaRa1CO5H43L3fBvNbBMgM/HKQ2WW+VixMoEPxuWsGPQiIiJ9Qi2C+/q4nBgfDJLruLi8z91nllnmdXF5pJmtU6TMy8ssT0REpCl0O7jd/UXgakLT9U1mlpmQBTObBJwWV7+Xe6yZjTezrcxsTM6mXxMmX9kCuNrMhsT91zGzi4E9Cc3pP+5u/UVERFLS0tnZnbupgviUrnuB7Qm3cz1NGGk+Nu5yhrtPzXPczLjPFe4+OWfbLsBdhEFoS4B/EIJ8OLAS+Li739vtyouIiCSkJo/1dPf5hP7oswmDxbYmjAi/Dzg4X2iXUebjwI7AL4FF8ecO4EZgN4W2iIj0RTW54hYREZHeUZMrbhEREekdCm4REZGEKLhFREQSouAWERFJiIJbREQkIU3xsGUzGwScCnwe2BxYDPwVuMjd76yyzM2A/wE+DowC5gL3AOe6+/NFjtsOOBP4EDAU+BdwB3COu79eTV2aRSOdp5wyWoH7gT2A/u6+upq6NItGOk9mtg/wFWA3wi2mS4CngF8B091dt8VIn5P87WBmth7hD8BuwCrgGcL/4Jn50ae4+9kVlmnAg7GcfwMv0jX5ywrgQHf/Q57j9gL+SHgc6TxgFmDAYGAh8GF3f7LCj9gUGuk85Snn+3TN8Neng7uRzpOZ/QD477i6mDCb4sbAhvG1W4BD3H1VJfURSV0zNJVPI/yReRIY7+7vd/exwJeA1cAUM/touYXFR4beRvgjMx0Y4+67AGOAnxBC+Zo4W1z2ccOBm+P28+JxOwPvIUwaMwy4scDc631BQ5ynnDLazOwCukJbGuQ8mdlhhNBuj8uh7r6Tu48CDiUE+QHAd7rzYUVSlHRwm9l4YBJhRrXD3H12Zpu7Twe+H1enVFDsJGAC8BpwtLsvj+WtBE4EHiA0gZ+Uc9yJhHB+xN2/mblqc/fFwBeBVwhXGV+qoC5NocHOU6ZO7yVcWZ5cyWdpZg12nk6Jy5+6+w/dvSOrLjfQdSX+NTNbt4L6iCQv6eAGDic83ORhd38uz/ZL4nKP2MdWjslxOT3+cXlX7E/7eVz9QoHjfplbYCznsgLH9QWNdJ4ws68CzwJ7A7MJ/bnSIOcptl7tGFd/U6Dc38XlesA2ZdZFpCmkHtyZ53XPyLcxDgabFVf3LlVYHKS0a7EyCX11AFuY2abxuDF0PVCl1HF7mFn/UnVpMg1xnrLsEpc/BrYDHi/1nn1Eo5ynFcCngOMJfez5tGT93FaqLiLNJPVR5ZlHiL5cZJ+ZhFDdsozyNgYGlihzNqHfrS2WOTurHp3Aq0XqAbAuYaBPsTo3m0Y5Txk3Ame7+6sA+R8j3yc1xHly92WEfvFiDo3LVYTBbiJ9RupX3KPicm6RfebH5cgKyitYpru3E0bGZpeZOe5td3+nRD3KrUszaZTzlNl2aya0ZQ0NdZ4KiS1cmZHtN7v7v4vtL9JsUg/uQXG5osg+y3P2Lae8SsuspB7l1qWZNMp5kuIa/jyZ2QaEq/GRhHu6v1VGPUSaSurB3V7BvuXcsF5JedllVntcX9Eo50mKa+jzZGYjgbuB98d9j3T3lyp8D5HkpR7cS+JyQJF9Mn1syyoor9IyK6lHuXVpJo1ynqS4hj1PZjaBMJBtZ8LtasfE28JE+pzUg3teXBacZIOufrM5FZRXsMw4ocQGOWVmjhtSZMR4dv9dOXVpJo1ynqS4hjxPZrYn8Ahh8NpK4AvuvtZtlyJ9RerBnZnjeFyRfTLbXihVmLu/QddAmUJlbkrX7SeZMjP1aKVrashC9VjBmiOc+4JGOU9SXMOdJzP7HKF5fASwAPiYu19X6r1Fmlnqwf1oXO6eb6OZbUJXkD5UZpmPFSsT+GBczop/mHD3hXTdklLquEfjSNq+pCHOk5TUUOfJzD4PXE24hfJV4IPufn+Z7yvStFIP7uvjcqLlvxn3uLi8z91nlllm5tv8kQXmFc+UeXmB447NPSCWc1SB4/qCRjpPUljDnCcz2w24kvA36mlCaHuZ7ynS1JIObnd/kfCNvA24KQ5gAcDMJtH18Ijv5R5rZuPNbKt4T2i2XxMmi9gCuNrMhsT91zGzi4E9Cc1/P8457mJgEbCXmV2c+SMVj78qlvdK/LlPabDzJAU0ynkyszZCaPcn9Ht/3N3frM2nFElfMzzWcwRwL7A94faTpwkP+8hMQXqGu0/Nc9zMuM8V7j45Z9suwF2EQTNLgH/Q9RjClYQ/JPfmKfOThFm51iH0x71CeKznEEKo7+nuz3bn86aqkc5TnveYGOsGeqxn3c+TmR1C19X/63TNOljI19z9iTI/okjykr7iBnD3+YT+s7MJg1u2JgxkuQ84ON8fmTLKfJzwkINfEgJ3R8ItKDcCuxUKA3e/jXC7yrWEqRjfR/hDdQXwgb4a2tBY50kKa5DzlD0P+sbAHiX+bYBIH5L8FbeIiEhfkvwVt4iISF+i4BYREUmIgltERCQhCm4REZGEKLhFREQSouAWERFJiIJbREQkIQpuERGRhCi4RUREEqLgFhERSYiCW0REJCEKbhERkYQouEVERBKi4BYREUmIgltERCQhCm4REZGEKLhFREQS0q/eFRDpaWa2MXAa8HFgLLASmA3cBfzQ3WfWr3YiIpVp6ezsrHcdRHqMmY0HHgJGAUuBV+KmLYF1gbeBie7+RH1qKCJSGTWVS7M7hxDaNwCj3X0Hd9+BcOX9ELA+cG4d6yciUhEFtzS7HePyKndfknnR3d8Cvg78HniuHhUTEamGmsqlqZnZLcCnACf0c//R3ZfXt1YiItVTcEtTM7P3AQ8Ag+NL7xCayO8C7nD3p+pVNxGRaii4pemZ2RbAt4CDgBE5m58GvuLuM3q9YiIiVVBwS59hZq3AB4CJwEeADwP9gWXAVu4+u361ExEpj4JbmpaZtRBGj09w97vzbN8SeJwwsvy/3P1HvVxFEZGKaVS5NLPhwIvAXWa2c+5Gd38BeC2utvVmxUREqqXglqbl7vOBO+Pqr8xsq8w2M2s1s68A2wEdhNvCREQanprKpamZ2RjgEWAzQkC/CiwiNKGPjLt9093Pq08NRUQqo+CWpmdmo4CTgf2ALQgD0t4CZgA/dveH6lg9EZGKKLhFREQSoj5uERGRhCi4RUREEqLgFhERSYiCW0REJCEKbhERkYQouEVERBKi4BYREUmIgltERCQhCm4REZGEKLhFREQSouAWERFJiIJbREQkIQpuERGRhCi4RUREEqLgFhERSYiCW0REJCEKbhERkYQouEVERBKi4BYREUnI/wdfeknEPCFW6wAAAABJRU5ErkJggg==\n",
|
||
"text/plain": [
|
||
"<Figure size 525x330 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 525x330 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 525x330 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 525x330 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"density = True\n",
|
||
"cumulative = True\n",
|
||
"histtype = 'step'\n",
|
||
"lw = 2\n",
|
||
"bins = {\n",
|
||
" 't_i_peak': None,\n",
|
||
" 't_e_peak': None,\n",
|
||
" 'p_i_peak': None,\n",
|
||
" 'p_e_peak': None,\n",
|
||
"}\n",
|
||
"xlabel = {\n",
|
||
" 't_i_peak': 's',\n",
|
||
" 't_e_peak': 's',\n",
|
||
" 'p_i_peak': 'prob',\n",
|
||
" 'p_e_peak': 'prob',\n",
|
||
"}\n",
|
||
"\n",
|
||
"for cell_type in ['gridcell', 'not bs']:\n",
|
||
" for key in bins:\n",
|
||
" fig = plt.figure(figsize=(3.5,2.2))\n",
|
||
" plt.suptitle(key + ' ' + cell_type)\n",
|
||
" legend_lines = []\n",
|
||
" for color, query, label in zip(colors, queries, labels):\n",
|
||
" data.query(query + ' and ' + cell_type)[key].hist(\n",
|
||
" bins=bins[key], density=density, cumulative=cumulative, lw=lw, \n",
|
||
" histtype=histtype, color=color)\n",
|
||
" legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, lw=lw, label=label))\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",
|
||
" sns.despine()\n",
|
||
" figname = f'histogram-{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": "code",
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from septum_mec.analysis.plotting import plot_bootstrap_timeseries"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"psth = pd.read_feather(output_path / 'data' / 'psth.feather')\n",
|
||
"times = pd.read_feather(output_path / 'data' / 'times.feather')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"times = times.T.iloc[0].values"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 62,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"cs = ['#d95f02', '#e7298a', '#993404', '#980043']\n",
|
||
"lb = ['GC 11 Hz', 'GC 30 Hz', 'NS 11 Hz', 'NS 30 Hz']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 64,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 750x300 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig, axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(5,2))\n",
|
||
"ii = 0\n",
|
||
"for cell_type, ls in zip(['gridcell', 'not bs'], ['-', '--']):\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 = psth.loc[:, selection].dropna(axis=1).to_numpy()\n",
|
||
"\n",
|
||
" plot_bootstrap_timeseries(times, values, ax=ax, lw=2, label=lb[ii], color=cs[ii], ls=ls)\n",
|
||
" # ax.set_title(titles[i])\n",
|
||
" ax.set_xlabel('Time (s)')\n",
|
||
" ax.legend(frameon=False)\n",
|
||
" ii += 1\n",
|
||
" axs[0].set_ylabel('Probability density')\n",
|
||
" sns.despine()\n",
|
||
" plt.xlim(0, 0.029)\n",
|
||
" \n",
|
||
"figname = f'response-probability'\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": 144,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"action = project.require_action(\"stimulus-response\")"
|
||
]
|
||
}
|
||
],
|
||
"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
|
||
}
|