4002 lines
24 MiB
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4002 lines
24 MiB
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"cells": [
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2"
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]
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},
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"18:00:49 [I] klustakwik KlustaKwik2 version 0.2.6\n",
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"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
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" return f(*args, **kwds)\n",
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"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
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" return f(*args, **kwds)\n",
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"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
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" return f(*args, **kwds)\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import pathlib\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from matplotlib import colors\n",
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"import seaborn as sns\n",
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"import re\n",
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"import shutil\n",
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"import pandas as pd\n",
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"import scipy.stats\n",
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"\n",
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"import exdir\n",
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"import expipe\n",
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"from distutils.dir_util import copy_tree\n",
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"import septum_mec\n",
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"import spatial_maps as sp\n",
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"import head_direction.head as head\n",
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"import septum_mec.analysis.data_processing as dp\n",
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"import septum_mec.analysis.registration\n",
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"from septum_mec.analysis.plotting import violinplot, despine\n",
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"from spatial_maps.fields import (\n",
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" find_peaks, calculate_field_centers, separate_fields_by_laplace, \n",
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" map_pass_to_unit_circle, calculate_field_centers, distance_to_edge_function, \n",
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" which_field, compute_crossings)\n",
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"from phase_precession import cl_corr\n",
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"from spike_statistics.core import permutation_resampling\n",
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"import matplotlib.mlab as mlab\n",
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"import scipy.signal as ss\n",
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"from scipy.interpolate import interp1d\n",
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"from septum_mec.analysis.plotting import regplot\n",
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"from skimage import measure\n",
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"from tqdm.notebook import tqdm_notebook as tqdm\n",
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"tqdm.pandas()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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 notebook\n",
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"%matplotlib inline"
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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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"project_path = dp.project_path()\n",
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"project = expipe.get_project(project_path)\n",
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"actions = project.actions\n",
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"\n",
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"output_path = pathlib.Path(\"output\") / \"phase-precession\"\n",
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"(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n",
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"(output_path / \"figures\").mkdir(exist_ok=True, parents=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Load cell statistics and shuffling quantiles"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>action</th>\n",
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" <th>baseline</th>\n",
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" <th>entity</th>\n",
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" <th>frequency</th>\n",
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" <th>i</th>\n",
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" <th>ii</th>\n",
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" <th>session</th>\n",
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" <th>stim_location</th>\n",
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" <th>stimulated</th>\n",
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" <th>tag</th>\n",
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" <th>...</th>\n",
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" <th>burst_event_ratio</th>\n",
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" <th>bursty_spike_ratio</th>\n",
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" <th>gridness</th>\n",
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" <th>border_score</th>\n",
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" <th>information_rate</th>\n",
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" <th>information_specificity</th>\n",
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" <th>head_mean_ang</th>\n",
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" <th>head_mean_vec_len</th>\n",
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" <th>spacing</th>\n",
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" <th>orientation</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1849-060319-3</td>\n",
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|||
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" <td>True</td>\n",
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" <td>1849</td>\n",
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" <td>NaN</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>3</td>\n",
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" <td>NaN</td>\n",
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" <td>False</td>\n",
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" <td>baseline ii</td>\n",
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" <td>...</td>\n",
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|||
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" <td>0.398230</td>\n",
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" <td>0.678064</td>\n",
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|||
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" <td>-0.466923</td>\n",
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" <td>0.029328</td>\n",
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" <td>1.009215</td>\n",
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" <td>0.317256</td>\n",
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" <td>5.438033</td>\n",
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|||
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" <td>0.040874</td>\n",
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|||
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" <td>0.628784</td>\n",
|
|||
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" <td>20.224859</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1849-060319-3</td>\n",
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|||
|
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" <td>True</td>\n",
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|||
|
|
" <td>1849</td>\n",
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|||
|
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" <td>NaN</td>\n",
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|||
|
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" <td>False</td>\n",
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|||
|
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" <td>True</td>\n",
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" <td>3</td>\n",
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|||
|
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" <td>NaN</td>\n",
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|||
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" <td>False</td>\n",
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|||
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" <td>baseline ii</td>\n",
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|||
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" <td>...</td>\n",
|
|||
|
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" <td>0.138014</td>\n",
|
|||
|
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" <td>0.263173</td>\n",
|
|||
|
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" <td>-0.666792</td>\n",
|
|||
|
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" <td>0.308146</td>\n",
|
|||
|
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" <td>0.192524</td>\n",
|
|||
|
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" <td>0.033447</td>\n",
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|||
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" <td>1.951740</td>\n",
|
|||
|
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" <td>0.017289</td>\n",
|
|||
|
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" <td>0.789388</td>\n",
|
|||
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" <td>27.897271</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>1849-060319-3</td>\n",
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|||
|
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" <td>True</td>\n",
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|||
|
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" <td>1849</td>\n",
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|||
|
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" <td>NaN</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>3</td>\n",
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|||
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" <td>NaN</td>\n",
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|||
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" <td>False</td>\n",
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|||
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" <td>baseline ii</td>\n",
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|||
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" <td>...</td>\n",
|
|||
|
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" <td>0.373986</td>\n",
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|||
|
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" <td>0.659259</td>\n",
|
|||
|
|
" <td>-0.572566</td>\n",
|
|||
|
|
" <td>0.143252</td>\n",
|
|||
|
|
" <td>4.745836</td>\n",
|
|||
|
|
" <td>0.393704</td>\n",
|
|||
|
|
" <td>4.439721</td>\n",
|
|||
|
|
" <td>0.124731</td>\n",
|
|||
|
|
" <td>0.555402</td>\n",
|
|||
|
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" <td>28.810794</td>\n",
|
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" </tr>\n",
|
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|
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" <tr>\n",
|
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|
" <th>3</th>\n",
|
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" <td>1849-060319-3</td>\n",
|
|||
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" <td>True</td>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
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" <td>False</td>\n",
|
|||
|
|
" <td>baseline ii</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>0.087413</td>\n",
|
|||
|
|
" <td>0.179245</td>\n",
|
|||
|
|
" <td>-0.437492</td>\n",
|
|||
|
|
" <td>0.268948</td>\n",
|
|||
|
|
" <td>0.157394</td>\n",
|
|||
|
|
" <td>0.073553</td>\n",
|
|||
|
|
" <td>6.215195</td>\n",
|
|||
|
|
" <td>0.101911</td>\n",
|
|||
|
|
" <td>0.492250</td>\n",
|
|||
|
|
" <td>9.462322</td>\n",
|
|||
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|
" </tr>\n",
|
|||
|
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" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
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" <td>1849-060319-3</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>baseline ii</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>0.248771</td>\n",
|
|||
|
|
" <td>0.463596</td>\n",
|
|||
|
|
" <td>-0.085938</td>\n",
|
|||
|
|
" <td>0.218744</td>\n",
|
|||
|
|
" <td>0.519153</td>\n",
|
|||
|
|
" <td>0.032683</td>\n",
|
|||
|
|
" <td>1.531481</td>\n",
|
|||
|
|
" <td>0.053810</td>\n",
|
|||
|
|
" <td>0.559905</td>\n",
|
|||
|
|
" <td>0.000000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>5 rows × 39 columns</p>\n",
|
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"</div>"
|
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],
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"text/plain": [
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" action baseline entity frequency i ii session \\\n",
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"0 1849-060319-3 True 1849 NaN False True 3 \n",
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"1 1849-060319-3 True 1849 NaN False True 3 \n",
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"2 1849-060319-3 True 1849 NaN False True 3 \n",
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"3 1849-060319-3 True 1849 NaN False True 3 \n",
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"4 1849-060319-3 True 1849 NaN False True 3 \n",
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"\n",
|
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" stim_location stimulated tag ... burst_event_ratio \\\n",
|
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"0 NaN False baseline ii ... 0.398230 \n",
|
|||
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"1 NaN False baseline ii ... 0.138014 \n",
|
|||
|
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"2 NaN False baseline ii ... 0.373986 \n",
|
|||
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"3 NaN False baseline ii ... 0.087413 \n",
|
|||
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"4 NaN False baseline ii ... 0.248771 \n",
|
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"\n",
|
|||
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" bursty_spike_ratio gridness border_score information_rate \\\n",
|
|||
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"0 0.678064 -0.466923 0.029328 1.009215 \n",
|
|||
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"1 0.263173 -0.666792 0.308146 0.192524 \n",
|
|||
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"2 0.659259 -0.572566 0.143252 4.745836 \n",
|
|||
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"3 0.179245 -0.437492 0.268948 0.157394 \n",
|
|||
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"4 0.463596 -0.085938 0.218744 0.519153 \n",
|
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"\n",
|
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" information_specificity head_mean_ang head_mean_vec_len spacing \\\n",
|
|||
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"0 0.317256 5.438033 0.040874 0.628784 \n",
|
|||
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"1 0.033447 1.951740 0.017289 0.789388 \n",
|
|||
|
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"2 0.393704 4.439721 0.124731 0.555402 \n",
|
|||
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"3 0.073553 6.215195 0.101911 0.492250 \n",
|
|||
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"4 0.032683 1.531481 0.053810 0.559905 \n",
|
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"\n",
|
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" orientation \n",
|
|||
|
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"0 20.224859 \n",
|
|||
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"1 27.897271 \n",
|
|||
|
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"2 28.810794 \n",
|
|||
|
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"3 9.462322 \n",
|
|||
|
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"4 0.000000 \n",
|
|||
|
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"\n",
|
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|
|
"[5 rows x 39 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 5,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"statistics_action = actions['calculate-statistics']\n",
|
|||
|
|
"identification_action = actions['identify-neurons']\n",
|
|||
|
|
"sessions = pd.read_csv(identification_action.data_path('sessions'))\n",
|
|||
|
|
"units = pd.read_csv(identification_action.data_path('units'))\n",
|
|||
|
|
"session_units = pd.merge(sessions, units, on='action')\n",
|
|||
|
|
"statistics_results = pd.read_csv(statistics_action.data_path('results'))\n",
|
|||
|
|
"statistics = pd.merge(session_units, statistics_results, how='left')\n",
|
|||
|
|
"statistics.head()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 6,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"statistics['unit_day'] = statistics.apply(lambda x: str(x.unit_idnum) + '_' + x.action.split('-')[1], axis=1)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 7,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"stim_response_action = actions['stimulus-response']\n",
|
|||
|
|
"stim_response_results = pd.read_csv(stim_response_action.data_path('results'))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"statistics = pd.merge(statistics, stim_response_results, how='left')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"N cells: 1284\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"print('N cells:',statistics.shape[0])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>border_score</th>\n",
|
|||
|
|
" <th>gridness</th>\n",
|
|||
|
|
" <th>head_mean_ang</th>\n",
|
|||
|
|
" <th>head_mean_vec_len</th>\n",
|
|||
|
|
" <th>information_rate</th>\n",
|
|||
|
|
" <th>speed_score</th>\n",
|
|||
|
|
" <th>action</th>\n",
|
|||
|
|
" <th>channel_group</th>\n",
|
|||
|
|
" <th>unit_name</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <td>0.348023</td>\n",
|
|||
|
|
" <td>0.275109</td>\n",
|
|||
|
|
" <td>3.012689</td>\n",
|
|||
|
|
" <td>0.086792</td>\n",
|
|||
|
|
" <td>0.707197</td>\n",
|
|||
|
|
" <td>0.149071</td>\n",
|
|||
|
|
" <td>1833-010719-1</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>127.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>0.362380</td>\n",
|
|||
|
|
" <td>0.166475</td>\n",
|
|||
|
|
" <td>3.133138</td>\n",
|
|||
|
|
" <td>0.037271</td>\n",
|
|||
|
|
" <td>0.482486</td>\n",
|
|||
|
|
" <td>0.132212</td>\n",
|
|||
|
|
" <td>1833-010719-1</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>161.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>0.367498</td>\n",
|
|||
|
|
" <td>0.266865</td>\n",
|
|||
|
|
" <td>5.586395</td>\n",
|
|||
|
|
" <td>0.182843</td>\n",
|
|||
|
|
" <td>0.271188</td>\n",
|
|||
|
|
" <td>0.062821</td>\n",
|
|||
|
|
" <td>1833-010719-1</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>191.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>0.331942</td>\n",
|
|||
|
|
" <td>0.312155</td>\n",
|
|||
|
|
" <td>5.955767</td>\n",
|
|||
|
|
" <td>0.090786</td>\n",
|
|||
|
|
" <td>0.354018</td>\n",
|
|||
|
|
" <td>0.052009</td>\n",
|
|||
|
|
" <td>1833-010719-1</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>223.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>0.325842</td>\n",
|
|||
|
|
" <td>0.180495</td>\n",
|
|||
|
|
" <td>5.262721</td>\n",
|
|||
|
|
" <td>0.103584</td>\n",
|
|||
|
|
" <td>0.210427</td>\n",
|
|||
|
|
" <td>0.094041</td>\n",
|
|||
|
|
" <td>1833-010719-1</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>225.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" border_score gridness head_mean_ang head_mean_vec_len information_rate \\\n",
|
|||
|
|
"0 0.348023 0.275109 3.012689 0.086792 0.707197 \n",
|
|||
|
|
"1 0.362380 0.166475 3.133138 0.037271 0.482486 \n",
|
|||
|
|
"2 0.367498 0.266865 5.586395 0.182843 0.271188 \n",
|
|||
|
|
"3 0.331942 0.312155 5.955767 0.090786 0.354018 \n",
|
|||
|
|
"4 0.325842 0.180495 5.262721 0.103584 0.210427 \n",
|
|||
|
|
"\n",
|
|||
|
|
" speed_score action channel_group unit_name \n",
|
|||
|
|
"0 0.149071 1833-010719-1 0.0 127.0 \n",
|
|||
|
|
"1 0.132212 1833-010719-1 0.0 161.0 \n",
|
|||
|
|
"2 0.062821 1833-010719-1 0.0 191.0 \n",
|
|||
|
|
"3 0.052009 1833-010719-1 0.0 223.0 \n",
|
|||
|
|
"4 0.094041 1833-010719-1 0.0 225.0 "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"shuffling = actions['shuffling']\n",
|
|||
|
|
"quantiles_95 = pd.read_csv(shuffling.data_path('quantiles_95'))\n",
|
|||
|
|
"quantiles_95.head()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>action</th>\n",
|
|||
|
|
" <th>baseline</th>\n",
|
|||
|
|
" <th>entity</th>\n",
|
|||
|
|
" <th>frequency</th>\n",
|
|||
|
|
" <th>i</th>\n",
|
|||
|
|
" <th>ii</th>\n",
|
|||
|
|
" <th>session</th>\n",
|
|||
|
|
" <th>stim_location</th>\n",
|
|||
|
|
" <th>stimulated</th>\n",
|
|||
|
|
" <th>tag</th>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <th>p_e_peak</th>\n",
|
|||
|
|
" <th>t_i_peak</th>\n",
|
|||
|
|
" <th>p_i_peak</th>\n",
|
|||
|
|
" <th>border_score_threshold</th>\n",
|
|||
|
|
" <th>gridness_threshold</th>\n",
|
|||
|
|
" <th>head_mean_ang_threshold</th>\n",
|
|||
|
|
" <th>head_mean_vec_len_threshold</th>\n",
|
|||
|
|
" <th>information_rate_threshold</th>\n",
|
|||
|
|
" <th>speed_score_threshold</th>\n",
|
|||
|
|
" <th>specificity</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <td>1849-060319-3</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>baseline ii</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>0.332548</td>\n",
|
|||
|
|
" <td>0.229073</td>\n",
|
|||
|
|
" <td>6.029431</td>\n",
|
|||
|
|
" <td>0.205362</td>\n",
|
|||
|
|
" <td>1.115825</td>\n",
|
|||
|
|
" <td>0.066736</td>\n",
|
|||
|
|
" <td>0.451741</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>1849-060319-3</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>baseline ii</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>0.354830</td>\n",
|
|||
|
|
" <td>0.089333</td>\n",
|
|||
|
|
" <td>6.120055</td>\n",
|
|||
|
|
" <td>0.073566</td>\n",
|
|||
|
|
" <td>0.223237</td>\n",
|
|||
|
|
" <td>0.052594</td>\n",
|
|||
|
|
" <td>0.098517</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>1849-060319-3</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>baseline ii</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>0.264610</td>\n",
|
|||
|
|
" <td>-0.121081</td>\n",
|
|||
|
|
" <td>5.759406</td>\n",
|
|||
|
|
" <td>0.150827</td>\n",
|
|||
|
|
" <td>4.964984</td>\n",
|
|||
|
|
" <td>0.027120</td>\n",
|
|||
|
|
" <td>0.400770</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>1849-060319-3</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>baseline ii</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>0.344280</td>\n",
|
|||
|
|
" <td>0.215829</td>\n",
|
|||
|
|
" <td>6.033364</td>\n",
|
|||
|
|
" <td>0.110495</td>\n",
|
|||
|
|
" <td>0.239996</td>\n",
|
|||
|
|
" <td>0.054074</td>\n",
|
|||
|
|
" <td>0.269461</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>1849-060319-3</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>baseline ii</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>0.342799</td>\n",
|
|||
|
|
" <td>0.218967</td>\n",
|
|||
|
|
" <td>5.768170</td>\n",
|
|||
|
|
" <td>0.054762</td>\n",
|
|||
|
|
" <td>0.524990</td>\n",
|
|||
|
|
" <td>0.144702</td>\n",
|
|||
|
|
" <td>0.133410</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>5 rows × 51 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" action baseline entity frequency i ii session \\\n",
|
|||
|
|
"0 1849-060319-3 True 1849 NaN False True 3 \n",
|
|||
|
|
"1 1849-060319-3 True 1849 NaN False True 3 \n",
|
|||
|
|
"2 1849-060319-3 True 1849 NaN False True 3 \n",
|
|||
|
|
"3 1849-060319-3 True 1849 NaN False True 3 \n",
|
|||
|
|
"4 1849-060319-3 True 1849 NaN False True 3 \n",
|
|||
|
|
"\n",
|
|||
|
|
" stim_location stimulated tag ... p_e_peak t_i_peak p_i_peak \\\n",
|
|||
|
|
"0 NaN False baseline ii ... NaN NaN NaN \n",
|
|||
|
|
"1 NaN False baseline ii ... NaN NaN NaN \n",
|
|||
|
|
"2 NaN False baseline ii ... NaN NaN NaN \n",
|
|||
|
|
"3 NaN False baseline ii ... NaN NaN NaN \n",
|
|||
|
|
"4 NaN False baseline ii ... NaN NaN NaN \n",
|
|||
|
|
"\n",
|
|||
|
|
" border_score_threshold gridness_threshold head_mean_ang_threshold \\\n",
|
|||
|
|
"0 0.332548 0.229073 6.029431 \n",
|
|||
|
|
"1 0.354830 0.089333 6.120055 \n",
|
|||
|
|
"2 0.264610 -0.121081 5.759406 \n",
|
|||
|
|
"3 0.344280 0.215829 6.033364 \n",
|
|||
|
|
"4 0.342799 0.218967 5.768170 \n",
|
|||
|
|
"\n",
|
|||
|
|
" head_mean_vec_len_threshold information_rate_threshold \\\n",
|
|||
|
|
"0 0.205362 1.115825 \n",
|
|||
|
|
"1 0.073566 0.223237 \n",
|
|||
|
|
"2 0.150827 4.964984 \n",
|
|||
|
|
"3 0.110495 0.239996 \n",
|
|||
|
|
"4 0.054762 0.524990 \n",
|
|||
|
|
"\n",
|
|||
|
|
" speed_score_threshold specificity \n",
|
|||
|
|
"0 0.066736 0.451741 \n",
|
|||
|
|
"1 0.052594 0.098517 \n",
|
|||
|
|
"2 0.027120 0.400770 \n",
|
|||
|
|
"3 0.054074 0.269461 \n",
|
|||
|
|
"4 0.144702 0.133410 \n",
|
|||
|
|
"\n",
|
|||
|
|
"[5 rows x 51 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"action_columns = ['action', 'channel_group', 'unit_name']\n",
|
|||
|
|
"data = pd.merge(statistics, quantiles_95, on=action_columns, suffixes=(\"\", \"_threshold\"))\n",
|
|||
|
|
"\n",
|
|||
|
|
"data['specificity'] = np.log10(data['in_field_mean_rate'] / data['out_field_mean_rate'])\n",
|
|||
|
|
"\n",
|
|||
|
|
"data.head()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Statistics about all cell-sessions"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"stimulated\n",
|
|||
|
|
"False 624\n",
|
|||
|
|
"True 660\n",
|
|||
|
|
"Name: action, dtype: int64"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"data.groupby('stimulated').count()['action']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Find all cells with gridness above threshold"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Number of sessions above threshold 194\n",
|
|||
|
|
"Number of animals 4\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"query = (\n",
|
|||
|
|
" 'gridness > gridness_threshold and '\n",
|
|||
|
|
" 'information_rate > information_rate_threshold and '\n",
|
|||
|
|
" 'gridness > .2 and '\n",
|
|||
|
|
" 'average_rate < 25'\n",
|
|||
|
|
")\n",
|
|||
|
|
"sessions_above_threshold = data.query(query)\n",
|
|||
|
|
"print(\"Number of sessions above threshold\", len(sessions_above_threshold))\n",
|
|||
|
|
"print(\"Number of animals\", len(sessions_above_threshold.groupby(['entity'])))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"## select neurons that have been characterized as a grid cell on the same day"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"once_a_gridcell = statistics[statistics.unit_day.isin(sessions_above_threshold.unit_day.values)]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Number of gridcells 139\n",
|
|||
|
|
"Number of gridcell recordings 231\n",
|
|||
|
|
"Number of animals 4\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"print(\"Number of gridcells\", once_a_gridcell.unit_idnum.nunique())\n",
|
|||
|
|
"print(\"Number of gridcell recordings\", len(once_a_gridcell))\n",
|
|||
|
|
"print(\"Number of animals\", len(once_a_gridcell.groupby(['entity'])))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# divide into stim not stim"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Number of gridcells in baseline i sessions 66\n",
|
|||
|
|
"Number of gridcells in stimulated 11Hz ms sessions 61\n",
|
|||
|
|
"Number of gridcells in baseline ii sessions 56\n",
|
|||
|
|
"Number of gridcells in stimulated 30Hz ms sessions 40\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"baseline_i = once_a_gridcell.query('baseline and Hz11')\n",
|
|||
|
|
"stimulated_11 = once_a_gridcell.query('stimulated and frequency==11 and stim_location==\"ms\"')\n",
|
|||
|
|
"\n",
|
|||
|
|
"baseline_ii = once_a_gridcell.query('baseline and Hz30')\n",
|
|||
|
|
"stimulated_30 = once_a_gridcell.query('stimulated and frequency==30 and stim_location==\"ms\"')\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Number of gridcells in baseline i sessions\", len(baseline_i))\n",
|
|||
|
|
"print(\"Number of gridcells in stimulated 11Hz ms sessions\", len(stimulated_11))\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Number of gridcells in baseline ii sessions\", len(baseline_ii))\n",
|
|||
|
|
"print(\"Number of gridcells in stimulated 30Hz ms sessions\", len(stimulated_30))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 17,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"baseline_ids = baseline_i.unit_idnum.unique()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"array([ 30, 31, 32, 78, 79, 150, 205, 243, 263, 265, 45, 46, 47,\n",
|
|||
|
|
" 49, 96, 118, 121, 185, 186, 106, 168, 231, 232, 233, 379, 609,\n",
|
|||
|
|
" 658, 615, 616, 666, 667, 179, 214, 278, 279, 317, 613, 661, 361,\n",
|
|||
|
|
" 362, 851, 357, 358, 359, 332, 338, 655, 715, 8, 56, 57, 58,\n",
|
|||
|
|
" 129, 130, 132, 23, 174, 250, 251, 252, 253, 304, 932])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"baseline_ids"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"stimulated_11_sub = stimulated_11[stimulated_11.unit_idnum.isin(baseline_ids)]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"baseline_ids_11 = stimulated_11_sub.unit_idnum.unique()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"baseline_i_sub = baseline_i[baseline_i.unit_idnum.isin(baseline_ids_11)]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Plotting"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 22,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"max_speed = .5 # m/s only used for speed score\n",
|
|||
|
|
"min_speed = 0.02 # m/s only used for speed score\n",
|
|||
|
|
"position_sampling_rate = 100 # for interpolation\n",
|
|||
|
|
"position_low_pass_frequency = 6 # for low pass filtering of position\n",
|
|||
|
|
"\n",
|
|||
|
|
"box_size = [1.0, 1.0]\n",
|
|||
|
|
"bin_size = 0.02\n",
|
|||
|
|
"smoothing_low = 0.03\n",
|
|||
|
|
"smoothing_high = 0.06\n",
|
|||
|
|
"\n",
|
|||
|
|
"speed_binsize = 0.02\n",
|
|||
|
|
"\n",
|
|||
|
|
"stim_mask = True\n",
|
|||
|
|
"baseline_duration = 600"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 23,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"data_loader = dp.Data(\n",
|
|||
|
|
" position_sampling_rate=position_sampling_rate, \n",
|
|||
|
|
" position_low_pass_frequency=position_low_pass_frequency,\n",
|
|||
|
|
" box_size=box_size, bin_size=bin_size, \n",
|
|||
|
|
" stim_mask=stim_mask, baseline_duration=baseline_duration\n",
|
|||
|
|
")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 24,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from scipy.signal import butter, filtfilt\n",
|
|||
|
|
"\n",
|
|||
|
|
"def butter_bandpass(lowcut, highcut, fs, order=5):\n",
|
|||
|
|
" nyq = 0.5 * fs\n",
|
|||
|
|
" low = lowcut / nyq\n",
|
|||
|
|
" high = highcut / nyq\n",
|
|||
|
|
" b, a = butter(order, [low, high], btype='band')\n",
|
|||
|
|
" return b, a\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):\n",
|
|||
|
|
" b, a = butter_bandpass(lowcut, highcut, fs, order=order)\n",
|
|||
|
|
" y = filtfilt(b, a, data)\n",
|
|||
|
|
" return y"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 25,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def signaltonoise(a, axis=0, ddof=0):\n",
|
|||
|
|
" a = np.asanyarray(a)\n",
|
|||
|
|
" m = a.mean(axis)\n",
|
|||
|
|
" sd = a.std(axis=axis, ddof=ddof)\n",
|
|||
|
|
" return np.where(sd == 0, 0, m / sd)\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def remove_artifacts(anas, spikes=None, width=500, threshold=2, sampling_rate=None, fillval=0):\n",
|
|||
|
|
" sampling_rate = sampling_rate or anas.sampling_rate.magnitude\n",
|
|||
|
|
" times = np.arange(anas.shape[0]) / sampling_rate\n",
|
|||
|
|
" anas = np.array(anas)\n",
|
|||
|
|
" if anas.ndim == 1:\n",
|
|||
|
|
" anas = np.reshape(anas, (anas.size, 1))\n",
|
|||
|
|
" assert len(times) == anas.shape[0]\n",
|
|||
|
|
" nchan = anas.shape[1]\n",
|
|||
|
|
" if spikes is not None:\n",
|
|||
|
|
" spikes = np.array(spikes)\n",
|
|||
|
|
" for ch in range(nchan):\n",
|
|||
|
|
" idxs, = np.where(abs(anas[:, ch]) > threshold)\n",
|
|||
|
|
" for idx in idxs:\n",
|
|||
|
|
" if spikes is not None:\n",
|
|||
|
|
" t0 = times[idx-width]\n",
|
|||
|
|
" stop = idx+width\n",
|
|||
|
|
" if stop > len(times) - 1:\n",
|
|||
|
|
" stop = len(times) - 1 \n",
|
|||
|
|
" t1 = times[stop]\n",
|
|||
|
|
" mask = (spikes > t0) & (spikes < t1)\n",
|
|||
|
|
" spikes = spikes[~mask]\n",
|
|||
|
|
" anas[idx-width:idx+width, ch] = fillval\n",
|
|||
|
|
" if spikes is not None:\n",
|
|||
|
|
" spikes = spikes[spikes <= times[-1]]\n",
|
|||
|
|
" return anas, times, spikes\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" return anas, times"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 26,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def find_theta_peak(p, f, f1, f2):\n",
|
|||
|
|
" if np.all(np.isnan(p)):\n",
|
|||
|
|
" return np.nan, np.nan\n",
|
|||
|
|
" mask = (f > f1) & (f < f2)\n",
|
|||
|
|
" p_m = p[mask]\n",
|
|||
|
|
" f_m = f[mask]\n",
|
|||
|
|
" peaks = find_peaks(p_m)\n",
|
|||
|
|
" idx = np.argmax(p_m[peaks])\n",
|
|||
|
|
" return f_m[peaks[idx]], p_m[peaks[idx]]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 27,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def compute_spike_phase(lfp, times, return_degrees=False):\n",
|
|||
|
|
" x_a = ss.hilbert(lfp)\n",
|
|||
|
|
" x_phase = np.angle(x_a)\n",
|
|||
|
|
" if return_degrees:\n",
|
|||
|
|
" x_phase = x_phase * 180 / np.pi\n",
|
|||
|
|
" return interp1d(times, x_phase)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 28,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def find_grid_fields(rate_map, sigma=3, seed=2.5):\n",
|
|||
|
|
" # find fields with laplace\n",
|
|||
|
|
" fields_laplace = sp.fields.separate_fields_by_dilation(rate_map, sigma=sigma, seed=seed)\n",
|
|||
|
|
" fields = fields_laplace.copy() # to be cleaned by Ismakov\n",
|
|||
|
|
" fields_areas = scipy.ndimage.measurements.sum(\n",
|
|||
|
|
" np.ones_like(fields), fields, index=np.arange(fields.max() + 1))\n",
|
|||
|
|
" fields_area = fields_areas[fields]\n",
|
|||
|
|
" fields[fields_area < 9.0] = 0\n",
|
|||
|
|
"\n",
|
|||
|
|
" # find fields with Ismakov-method\n",
|
|||
|
|
" fields_ismakov, radius = sp.separate_fields_by_distance(rate_map)\n",
|
|||
|
|
" fields_ismakov_real = fields_ismakov * bin_size\n",
|
|||
|
|
" approved_fields = []\n",
|
|||
|
|
"\n",
|
|||
|
|
" # remove fields not found by both methods\n",
|
|||
|
|
" for point in fields_ismakov:\n",
|
|||
|
|
" field_id = fields[tuple(point)]\n",
|
|||
|
|
" approved_fields.append(field_id)\n",
|
|||
|
|
"\n",
|
|||
|
|
" for field_id in np.arange(1, fields.max() + 1):\n",
|
|||
|
|
" if not field_id in approved_fields:\n",
|
|||
|
|
" fields[fields == field_id] = 0\n",
|
|||
|
|
" \n",
|
|||
|
|
" return fields"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 29,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def normalize(a):\n",
|
|||
|
|
" _a = a - a.min()\n",
|
|||
|
|
" return _a / _a.max()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 30,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def distance(x, y):\n",
|
|||
|
|
" _x = x - x.min()\n",
|
|||
|
|
" _y = y - y.min()\n",
|
|||
|
|
" dx, dy = np.diff(x), np.diff(y)\n",
|
|||
|
|
" s = np.sqrt(dx**2 + dy**2)\n",
|
|||
|
|
" distance = np.cumsum(s) \n",
|
|||
|
|
" # first index is distance from first point, \n",
|
|||
|
|
" # to match len(x) we put a zero as first index to initialize distance 0\n",
|
|||
|
|
" return np.concatenate(([0], distance))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 31,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def model(x, slope, phi0):\n",
|
|||
|
|
" return 2 * np.pi * slope * x + phi0"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 32,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def compute_data(row, flim=[6,10]):\n",
|
|||
|
|
" lfp = data_loader.lfp(row.action, row.channel_group)\n",
|
|||
|
|
" spikes = data_loader.spike_train(row.action, row.channel_group, row.unit_name)\n",
|
|||
|
|
" rate_map = data_loader.rate_map(row.action, row.channel_group, row.unit_name, smoothing=0.04)\n",
|
|||
|
|
" pos_x, pos_y, pos_t, pos_speed = map(data_loader.tracking(row.action).get, ['x', 'y', 't', 'v'])\n",
|
|||
|
|
" \n",
|
|||
|
|
" spikes = np.array(spikes)\n",
|
|||
|
|
" spikes = spikes[(spikes > pos_t.min()) & (spikes < pos_t.max())]\n",
|
|||
|
|
"\n",
|
|||
|
|
" cleaned_lfp_, times_ = remove_artifacts(lfp)\n",
|
|||
|
|
" peak_amp = {}\n",
|
|||
|
|
" for i, ch in enumerate(cleaned_lfp_.T):\n",
|
|||
|
|
" pxx, freqs = mlab.psd(ch, Fs=lfp.sampling_rate.magnitude, NFFT=4000)\n",
|
|||
|
|
" f, p = find_theta_peak(pxx, freqs, *flim)\n",
|
|||
|
|
" peak_amp[i] = p\n",
|
|||
|
|
"\n",
|
|||
|
|
" theta_channel = max(peak_amp, key=lambda x: peak_amp[x])\n",
|
|||
|
|
" filtered_lfp = butter_bandpass_filter(\n",
|
|||
|
|
" lfp.magnitude[:,theta_channel], *flim, fs=lfp.sampling_rate.magnitude, order=3)\n",
|
|||
|
|
" \n",
|
|||
|
|
" cleaned_lfp, times, cleaned_spikes = remove_artifacts(\n",
|
|||
|
|
" filtered_lfp, spikes, threshold=2, sampling_rate=lfp.sampling_rate.magnitude, fillval=0)\n",
|
|||
|
|
" \n",
|
|||
|
|
" cleaned_lfp = cleaned_lfp.ravel()\n",
|
|||
|
|
"\n",
|
|||
|
|
" spike_phase_func = compute_spike_phase(cleaned_lfp, times)\n",
|
|||
|
|
" \n",
|
|||
|
|
" fields = find_grid_fields(rate_map, sigma=3, seed=2.5)\n",
|
|||
|
|
" \n",
|
|||
|
|
" return spike_phase_func, cleaned_spikes, pos_x, pos_y, pos_t, rate_map, fields"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 386,
|
|||
|
|
"metadata": {
|
|||
|
|
"scrolled": false
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def compute_phase_precession(row, flim=[6, 10], return_runs=False, field_num=None, \n",
|
|||
|
|
" plot=False, plot_grid=False, plot_lines=True, save=False):\n",
|
|||
|
|
" spike_phase_func, cleaned_spikes, pos_x, pos_y, pos_t, rate_map, fields = compute_data(row, flim)\n",
|
|||
|
|
" \n",
|
|||
|
|
" if field_num is not None:\n",
|
|||
|
|
" fields = np.where(fields == field_num, fields, 0)\n",
|
|||
|
|
" \n",
|
|||
|
|
" in_field_indices = which_field(pos_x, pos_y, fields, box_size)\n",
|
|||
|
|
" in_field_enter, in_field_exit = compute_crossings(in_field_indices)\n",
|
|||
|
|
"\n",
|
|||
|
|
" if plot:\n",
|
|||
|
|
" if plot_grid:\n",
|
|||
|
|
" fig, axs = plt.subplots(2, 2)\n",
|
|||
|
|
" plt.suptitle(f'{row.action} {row.channel_group} {row.unit_idnum}')\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" fig, ax = plt.subplots(1, 1)\n",
|
|||
|
|
" axs = [[ax]]\n",
|
|||
|
|
" ax.set_title(f'{row.action} {row.channel_group} {row.unit_idnum}')\n",
|
|||
|
|
" dot_size = 1\n",
|
|||
|
|
"\n",
|
|||
|
|
" in_field_spikes, in_field_dur, in_field_dist, spike_phase = [], [], [], []\n",
|
|||
|
|
"\n",
|
|||
|
|
" sx, sy = interp1d(pos_t, pos_x), interp1d(pos_t, pos_y)\n",
|
|||
|
|
" results = []\n",
|
|||
|
|
" for en, ex in zip(in_field_enter, in_field_exit):\n",
|
|||
|
|
" x, y, t = pos_x[en:ex+1], pos_y[en:ex+1], pos_t[en:ex+1]\n",
|
|||
|
|
"\n",
|
|||
|
|
" s = cleaned_spikes[(cleaned_spikes > t[0]) & (cleaned_spikes < t[-1])]\n",
|
|||
|
|
" if len(s) < 5:\n",
|
|||
|
|
" continue\n",
|
|||
|
|
"\n",
|
|||
|
|
" in_field_spikes.append(s)\n",
|
|||
|
|
" \n",
|
|||
|
|
" dist = distance(x, y)\n",
|
|||
|
|
" t_to_dist_norm = interp1d(t, normalize(dist))\n",
|
|||
|
|
" t_to_dist = interp1d(t, dist)\n",
|
|||
|
|
" in_field_dist.append(t_to_dist_norm(s))\n",
|
|||
|
|
" \n",
|
|||
|
|
" t_to_dur = interp1d(t, t)\n",
|
|||
|
|
" t_to_dur_norm = interp1d(t, normalize(t))\n",
|
|||
|
|
" in_field_dur.append(t_to_dur_norm(s))\n",
|
|||
|
|
" \n",
|
|||
|
|
" spike_phase.append(spike_phase_func(s))\n",
|
|||
|
|
" if return_runs:\n",
|
|||
|
|
" circ_lin_corr_dist, pval_dist, slope_dist, phi0_dist, RR_dist = cl_corr(\n",
|
|||
|
|
" t_to_dist(s), spike_phase_func(s), -100, 100, return_pval=True)\n",
|
|||
|
|
" circ_lin_corr_dur, pval_dur, slope_dur, phi0_dur, RR_dur = cl_corr(\n",
|
|||
|
|
" s - t[0], spike_phase_func(s), -100, 100, return_pval=True)\n",
|
|||
|
|
" result_run = {\n",
|
|||
|
|
" 'action': row.action, \n",
|
|||
|
|
" 'channel_group': row.channel_group, \n",
|
|||
|
|
" 'unit_name': row.unit_name,\n",
|
|||
|
|
" 'circ_lin_corr_dist': circ_lin_corr_dist, \n",
|
|||
|
|
" 'pval_dist': pval_dist, \n",
|
|||
|
|
" 'slope_dist': slope_dist, \n",
|
|||
|
|
" 'phi0_dist': phi0_dist, \n",
|
|||
|
|
" 'RR_dist': RR_dist,\n",
|
|||
|
|
" 'circ_lin_corr_dur': circ_lin_corr_dur, \n",
|
|||
|
|
" 'pval_dur': pval_dur, \n",
|
|||
|
|
" 'slope_dur': slope_dur, \n",
|
|||
|
|
" 'phi0_dur': phi0_dur, \n",
|
|||
|
|
" 'RR_dur': RR_dur\n",
|
|||
|
|
" }\n",
|
|||
|
|
" results.append(result_run)\n",
|
|||
|
|
" if plot:\n",
|
|||
|
|
" p = axs[0][0].scatter(t_to_dist(s), spike_phase_func(s), s=dot_size)\n",
|
|||
|
|
" axs[0][0].scatter(\n",
|
|||
|
|
" t_to_dist(s), spike_phase_func(s) + 2 * np.pi, \n",
|
|||
|
|
" s=dot_size, color=p.get_facecolor()[0])\n",
|
|||
|
|
" axs[0][0].set_yticks([-np.pi, np.pi, 3*np.pi])\n",
|
|||
|
|
" axs[0][0].set_yticklabels([r'$-\\pi$', r'$\\pi$', r'$3\\pi$'])\n",
|
|||
|
|
" if plot_lines:\n",
|
|||
|
|
" line_fit = model(np.array([0, .4]), slope_dist, phi0_dist)\n",
|
|||
|
|
" axs[0][0].plot([0, .4], line_fit, lw=2, label=\n",
|
|||
|
|
" f'corr = {circ_lin_corr_dist:.3f}, '\n",
|
|||
|
|
" f'pvalue = {pval_dist:.3f}, '\n",
|
|||
|
|
" f'R = {RR_dist:.3f}')\n",
|
|||
|
|
" \n",
|
|||
|
|
" if plot and plot_grid:\n",
|
|||
|
|
" axs[0][1].plot(x, y)\n",
|
|||
|
|
" axs[0][1].scatter(sx(s), sy(s), s=dot_size, color='r', zorder=100000)\n",
|
|||
|
|
" \n",
|
|||
|
|
" dist = np.array([d for di in in_field_dist for d in di])\n",
|
|||
|
|
" dur = np.array([d for di in in_field_dur for d in di])\n",
|
|||
|
|
" phase = np.array([d for di in spike_phase for d in di])\n",
|
|||
|
|
" if not return_runs:\n",
|
|||
|
|
" circ_lin_corr_dist, pval_dist, slope_dist, phi0_dist, RR_dist = cl_corr(\n",
|
|||
|
|
" dist, phase, -2, 2, return_pval=True)\n",
|
|||
|
|
" circ_lin_corr_dur, pval_dur, slope_dur, phi0_dur, RR_dur = cl_corr(\n",
|
|||
|
|
" dur, phase, -2, 2, return_pval=True)\n",
|
|||
|
|
"\n",
|
|||
|
|
" results = {\n",
|
|||
|
|
" 'action': row.action, \n",
|
|||
|
|
" 'channel_group': row.channel_group, \n",
|
|||
|
|
" 'unit_name': row.unit_name,\n",
|
|||
|
|
" 'circ_lin_corr_dist': circ_lin_corr_dist, \n",
|
|||
|
|
" 'pval_dist': pval_dist, \n",
|
|||
|
|
" 'slope_dist': slope_dist, \n",
|
|||
|
|
" 'phi0_dist': phi0_dist, \n",
|
|||
|
|
" 'RR_dist': RR_dist,\n",
|
|||
|
|
" 'circ_lin_corr_dur': circ_lin_corr_dur, \n",
|
|||
|
|
" 'pval_dur': pval_dur, \n",
|
|||
|
|
" 'slope_dur': slope_dur, \n",
|
|||
|
|
" 'phi0_dur': phi0_dur, \n",
|
|||
|
|
" 'RR_dur': RR_dur\n",
|
|||
|
|
" }\n",
|
|||
|
|
" if plot:\n",
|
|||
|
|
" axs[0][0].scatter(dist, phase, s=dot_size, color='k')\n",
|
|||
|
|
" axs[0][0].scatter(dist, phase + 2 * np.pi, s=dot_size, color='k')\n",
|
|||
|
|
" axs[0][0].set_yticks([-np.pi, np.pi, 3*np.pi])\n",
|
|||
|
|
" axs[0][0].set_yticklabels([r'$-\\pi$', r'$\\pi$', r'$3\\pi$'])\n",
|
|||
|
|
" if plot_lines:\n",
|
|||
|
|
" line_fit = model(np.array([0, 1]), slope_dist, phi0_dist)\n",
|
|||
|
|
" axs[0][0].plot([0, 1], line_fit, lw=2, color='k')\n",
|
|||
|
|
" axs[0][0].set_title(\n",
|
|||
|
|
" f'corr = {circ_lin_corr_dist:.3f}, '\n",
|
|||
|
|
" f'pvalue = {pval_dist:.3f}, '\n",
|
|||
|
|
" f'R = {RR_dist:.3f}')\n",
|
|||
|
|
" if plot and plot_grid:\n",
|
|||
|
|
" contours = measure.find_contours(fields, 0.8)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Display the image and plot all contours found\n",
|
|||
|
|
" axs[1][0].imshow(rate_map.T, extent=[0, box_size[0], 0, box_size[1]], origin='lower')\n",
|
|||
|
|
" axs[1][1].plot(pos_x, pos_y, color='k', alpha=.2, zorder=1000)\n",
|
|||
|
|
" axs[1][1].scatter(\n",
|
|||
|
|
" interp1d(pos_t, pos_x)(cleaned_spikes), interp1d(pos_t, pos_y)(cleaned_spikes), \n",
|
|||
|
|
" s=1, zorder=10001)\n",
|
|||
|
|
"\n",
|
|||
|
|
" for ax in axs.ravel()[1:]:\n",
|
|||
|
|
" for n, contour in enumerate(contours):\n",
|
|||
|
|
" ax.plot(contour[:, 0] * bin_size, contour[:, 1] * bin_size, linewidth=2)\n",
|
|||
|
|
" \n",
|
|||
|
|
" for ax in axs.ravel()[1:]:\n",
|
|||
|
|
" ax.axis('image')\n",
|
|||
|
|
" ax.set_xticks([])\n",
|
|||
|
|
" ax.set_yticks([])\n",
|
|||
|
|
" \n",
|
|||
|
|
" axs[0][0].set_aspect(1 / (4*np.pi))\n",
|
|||
|
|
" if plot:\n",
|
|||
|
|
" despine()\n",
|
|||
|
|
" if plot_lines:\n",
|
|||
|
|
" plt.legend()\n",
|
|||
|
|
" if plot and save:\n",
|
|||
|
|
" figname = f'{row.action}_{row.channel_group}_{row.unit_idnum}_f{flim[0]}-{flim[1]}'\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)\n",
|
|||
|
|
" \n",
|
|||
|
|
" return results"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 370,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"plt.rc('axes', titlesize=12)\n",
|
|||
|
|
"plt.rcParams.update({\n",
|
|||
|
|
" 'font.size': 12, \n",
|
|||
|
|
" 'figure.figsize': (2, 2), \n",
|
|||
|
|
" 'figure.dpi': 150\n",
|
|||
|
|
"})"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 371,
|
|||
|
|
"metadata": {
|
|||
|
|
"scrolled": true
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"{'action': '1833-260619-2',\n",
|
|||
|
|
" 'channel_group': 0,\n",
|
|||
|
|
" 'unit_name': 174,\n",
|
|||
|
|
" 'circ_lin_corr_dist': 0.015035045939466871,\n",
|
|||
|
|
" 'pval_dist': 0.58032151303154,\n",
|
|||
|
|
" 'slope_dist': 0.31943787776044974,\n",
|
|||
|
|
" 'phi0_dist': 4.843627836303633,\n",
|
|||
|
|
" 'RR_dist': 0.03720602786152166,\n",
|
|||
|
|
" 'circ_lin_corr_dur': 0.008726766011604742,\n",
|
|||
|
|
" 'pval_dur': 0.7501129594935525,\n",
|
|||
|
|
" 'slope_dur': 0.12178247278219806,\n",
|
|||
|
|
" 'phi0_dur': 5.408259850880937,\n",
|
|||
|
|
" 'RR_dur': 0.034326500937119704}"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 371,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 300x300 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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"text/plain": [
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"<Figure size 300x300 with 1 Axes>"
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},
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"metadata": {
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},
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"output_type": "display_data"
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{
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"data": {
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|
|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 300x300 with 1 Axes>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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"text/plain": [
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"<Figure size 300x300 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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|||
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|
"<Figure size 300x300 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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|
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"data": {
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|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 300x300 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"compute_phase_precession(\n",
|
|||
|
|
" data.query('action==\"1833-010719-1\" and unit_idnum==121').iloc[0], \n",
|
|||
|
|
" plot=True, save=True)\n",
|
|||
|
|
"compute_phase_precession(\n",
|
|||
|
|
" data.query('action==\"1833-120619-1\" and unit_idnum==168').iloc[0], \n",
|
|||
|
|
" plot=True, save=True)\n",
|
|||
|
|
"compute_phase_precession(\n",
|
|||
|
|
" data.query('action==\"1833-260619-1\" and unit_idnum==32').iloc[0], \n",
|
|||
|
|
" plot=True, save=True)\n",
|
|||
|
|
"compute_phase_precession(\n",
|
|||
|
|
" data.query('action==\"1833-010719-2\" and unit_idnum==121').iloc[0], \n",
|
|||
|
|
" plot=True, save=True)\n",
|
|||
|
|
"compute_phase_precession(\n",
|
|||
|
|
" data.query('action==\"1833-120619-2\" and unit_idnum==168').iloc[0], \n",
|
|||
|
|
" plot=True, save=True)\n",
|
|||
|
|
"compute_phase_precession(\n",
|
|||
|
|
" data.query('action==\"1833-260619-2\" and unit_idnum==32').iloc[0], \n",
|
|||
|
|
" plot=True, save=True)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 372,
|
|||
|
|
"metadata": {
|
|||
|
|
"scrolled": true
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 300x300 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAUMAAAFECAYAAAC9Nly/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nO19eZxlRXn2w4CCy6CgOKwCzcypuEK73A5GPoltEpc7rUnQNi4RMbh8idoT4/JFG82MYlAjo8ZIxBU0Zowa5V41xrSIqOAYHVBHLUFFDEZlMTBENpn7/VHndJ+uruWt5Zx7b/f7/H71u91nqe1UPfUutewzGAzAYDAYax3rhp0BBoPBGAUwGTIYDAaYDBkMBgMAkyGDwWAAYDJkMBgMAEyGDAaDAYDJkMFgMAAwGTIYDAYAJkMGg8EAwGTIYDAYAJgMGQwGAwCTIYPBYABgMmQwGAwATIYMBoMBANhv2BkYZQghXgDgHACnSynf43juPgBeCeDJAI4GcDuA7wD4AID3SCn3Wt57OIBXAPg/AO4D4JcAvgjgbVLKrzvSmwYwB+BEAOsB/AzAvwN4q5RSBhXSASHEGwG8CsDvSSn/w/HcbwF4GYDHAjgCwB0ArgDwCaiy7LG8dx8Ar4GqtyMB/ArAVwC8SUp5qSO9dQCeC+A5AB4M4B4AfgLgUwDOlFL+ili+jwB4OoBNUsorHc89uizf70DV91UAPg3gbCnlNZS0LPFG1VtgGusBfBuqXf6ulPKLGeIktYvy2T8GcDqAhwO4F1Rb/TcAr5dS/pflnfsD+H8AHg9VL/8L4JsA3iWl/Fhq/m1gydACIcQjAbyZ8NzRUB/q5QAmAPwQqlOfCOAfAXxKCHEXw3vPBfA1AE8DcHcAuwHsD+CZAC4RQrzYkt4ZAP4DQLe8tBvAQQCeD+CysvElQwjRBfBXhOeeCuAyAH8GRWhXArgewAkAtgH4TyHEkYb3NkCVfw7ABgDfAjAA8IcAviyEOM2S3j0AfB7AewCcBOBaKHI6rszvN03pGeJ5IRQR+p57JYAvAXgKgLtBDXIHQZHYd4QQJ/visMQbVW8R2A5FhFkQ0C72E0J8CMDHAPwBFKH9AIrcXgDgciHEQw3vPQyqXl4I4DAA3wdwK9SA8S9CiHMyFWUFmAwNKBv456CkAB/eB+D+UKT0QCnlg6SURwOYgfqIXSjprx7/A6Akzn2hGusGKeUkFCm8vLpeEnL9vWkAf1P++/LyvYeV720HcACAD6V2IiHE06AasVNzEEJMADgfisTPA3A/KeWDpZTHAHgYgO8BKAB81PD6DigC+zyAI6WUjwBwOJTEsS+Ac8p60vEuqI7xMwBTUkohpRRQJHIFgGOgBiFXvucA/IPrmfK5zQD+FsA+AM4FcKiU8pFlPl8O4N4APiOEONYXlxZvSr2FpPMkAMZBJTI+UrsocQbUwH4TgCdJKY+RUj4Y6pvvBHAwgA8LIfapxb8vgI9ADTZfAnCMlPKhUsrDoAb7AYAXCCGematMdTAZ1iCEOEAI8TooyesgwvNHQXVMAHi+lPKK6p6UsgfgTeW/z9NefSmAuwL4KoC/lFLeWr6zV0r5Fig1Yh1UA6jj5eXvR6SUb5FS3lm+dxuAv4TqRAdAqY/BEELcWwjxD1BEtT/hlZeUz10G4DQp5Y3VDSnlZQD+CMCdAE4sVc0qnZMBPAbAzQCeUam1ZfnPAvAhAHcB8Gotfx0Azy7jfLyUcmctvW9DSRwA8AQhxBGG8h0mhPgYgLOhCM6HauD5dynl86WU/1vL51sA/DOUtPhWQlx1RNVbCIQQB0MR+K9j3tfiCmoXZd2/svz36VLKz1T3pJRXA3gGFLE9GMBv1149EWoQAFS7+HntvXOhBhBgZX/KAibDEkKIjVBi/GvLS6+BskO5UJfALjfcr+x+R2nXvwXg4wDOkVKazl34VvmrqzdfAXABlDS6DGU837a854UQ4kQoVe1FAG4BTaL43fL3oxUxa3n6PpSaAwB1KffU8vdTUsrrDPFWqtBThBB3q12vSP6DJfnp+CLUd3sJFJksQgjxh1CS4x9DmTH+3PB+/flDAUyW/77J8tj28ndzST5UxNZbCN4JpWb+deT7AKLbxTOgBvsvSCk/q9+UUv4QyjwyB6D+/at+cp3FFlv1p/vTch8GdqAs4Uioj3EpgL+QUn5DCHG6552ra39PAviydr+yiSwjVSnlP8Ctpj2i/L2iflFKuc32QqliVJ33CttzDggoJ85nAbxESnmlEGIF6Wp4KYBjoWx/NlQS2L61ayeWv3p9VdgJ4DdQjpFHALi4vP575e8nTC+VA8IbLHEeD2Wb/TCUzesAR56B5QPKNyzPVM6qfct8/rsnzgqx9UaCEOIUKHvolwC8HUukHYOYduH8TgAgpXy74XLVn+4rhDjS4GCp+tNVnvSjwGS4hP+Csm18xvtkCSnlNUKIT0F5Q98lhHhKOepBCPFYKI8YQFSjhBCHQUk2j4VSIU0NxvTecVC2rU0Afg6D5EjAdwA8Wkr5FeoLpWfyi458PQjAA8t/d5fX1kE5mgDlbDLFe4cQ4hooQioAXCyEuDuUvQkAdpde0mdB1dVBUAPOR6WUn7Nk50sAjq8kSiHEMZ7i1SX2OyzP1B1jvvgWEVNvVJSOqXdBOSyeK6UcCCFCotAR3C6wRFq7hRB3hSLmx0PZtv8byuv/MYNW9FUo08EJAM4XQjxdSvkLABBCPB1KKh0gjdytYDIsUU6tsE6vcOBZAN4L4KkAvi+E+AGUHelYAP8DYK6UBK0QQvwFgL+A6uz7AfgugOf5pskIIc4EMAvVEdcBuATKBnV9aCGklP8Z+o4nb/tiSfq9BsAXyr8PwlK7u9YRxfVQZHjf8v+jsGTWORLARVipLp0mhNgB4DmlHXURUsovIAw/rv19ApSJQseDan97bcwUOOqNin+EqrMXSyl/lJqf0HYhhNgfivQAJYl/A8o2WMczAVwohPgjKeX/1NIaCCGeAGUbfByAnwghJJSz5UgoIv1LKWU/qjAesM0wHQMoe+ENUJ38gVBECCgypBiwT4JSRyqS2ADgyaYpORqmoaSs6jsehaUpN0ND6SE8B2r+JAC8rEZOd689eqsjmlu05+ue/U9A1ftToFTp+0INJrdADQ7JkoOU8lqowQUAXlP3etZQd/DcNTVNT71R3n8OlJZyIZTNcBiof6cPQHnenwPleb8XlPBwPZTd9COG938D4D+h+s3+UFJmZZu/DnYpPRlMhgkQQhwINXK/AUpNeyyUVHhfqImm9wbw7tIT58IryveOBrAFqhG8Cv6pFU+Hsn1tgpqbtgHAm4UQZ8eUJwdKyeZcqPlzAPAOKeWO2iMrHAYeVKpU3ZGyP4DHSCk/JaX8tZTyeinlO7HkFHm+SNQNS/w1gL1QKt6/CiEeLIS4ixBikxDiPKjvXU3wTuqkhHrzvX8kgLdBmVdOszjm2kD9Ox0M4A+klOdJKW+UUt4kpfwwlBYFAI8XQlT2xaoMl0C1/a8C6EC178Oh+kgB4GNCiGVT1XKByTANL4f6YD8DMC2lvFBKeWvZOd8D4PehOv+LXJNzpZQ/Kd+7Wkq5HUvS3VM87/1YSnmblPJKKeUZWOpILy7tiK2inBD9CSxNfXg/lMewjptrf7ucGFWn+rX2CwDvl1KaPP0fgBqU1gHYTMiyE6Vt7zSoFUVPhvLW3w416+CpUAPeD8rHb4pNh1hvPrwXSvJ6hZTyqti8ZED9O/VNaraU8kIoGy6g6rXCGwFshNK0niil/HrZvv9bSvlmKC81AJxZzv7ICrYZpqEa4d5Wt31UkFJ+XQjRh/rgz4DDaK69d5EQ4qsAHgXg5ID3zhNqqdThUKr3D4UQfw3giZZXTqnP5UqBEOJwAH0sebTPhlLzdAnlZgC3QUl393FEWdkKf1n+1uv3MtMLpc1pN5SEPWF6JhRSyg8KIS6GIr5JqMFtF4D3Sil/LISYLx+9BgBC6zug3qwQQrwIauBdwNK0pGHhJihpeh0s36nEd6DMARPAoonglPLemVLKFZK2lPITQojLoWYGPA3
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 300x300 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 300x300 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"compute_phase_precession(\n",
|
|||
|
|
" data.query('action==\"1833-010719-2\" and unit_idnum==121').iloc[0], \n",
|
|||
|
|
" plot=True, save=True, flim=[10,12])\n",
|
|||
|
|
"compute_phase_precession(\n",
|
|||
|
|
" data.query('action==\"1833-120619-2\" and unit_idnum==168').iloc[0], \n",
|
|||
|
|
" plot=True, save=True, flim=[10,12])\n",
|
|||
|
|
"compute_phase_precession(\n",
|
|||
|
|
" data.query('action==\"1833-260619-2\" and unit_idnum==32').iloc[0], \n",
|
|||
|
|
" plot=True, save=True, flim=[10,12]);"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 373,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 300x300 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
" compute_phase_precession(baseline_i.sort_values('gridness', ascending=False).iloc[18], plot=True, plot_grid=True);"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 388,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"[{'action': '1833-260619-1',\n",
|
|||
|
|
" 'channel_group': 0,\n",
|
|||
|
|
" 'unit_name': 132,\n",
|
|||
|
|
" 'circ_lin_corr_dist': 0.4129467750189508,\n",
|
|||
|
|
" 'pval_dist': 0.28464111660579006,\n",
|
|||
|
|
" 'slope_dist': 28.093945031625527,\n",
|
|||
|
|
" 'phi0_dist': 0.16742503048943053,\n",
|
|||
|
|
" 'RR_dist': 0.8263134195104782,\n",
|
|||
|
|
" 'circ_lin_corr_dur': 0.45019436077125674,\n",
|
|||
|
|
" 'pval_dur': 0.23466570092972083,\n",
|
|||
|
|
" 'slope_dur': 7.60530119306991,\n",
|
|||
|
|
" 'phi0_dur': 2.559041546574352,\n",
|
|||
|
|
" 'RR_dur': 0.9151777474060984},\n",
|
|||
|
|
" {'action': '1833-260619-1',\n",
|
|||
|
|
" 'channel_group': 0,\n",
|
|||
|
|
" 'unit_name': 132,\n",
|
|||
|
|
" 'circ_lin_corr_dist': 0.9172178573980904,\n",
|
|||
|
|
" 'pval_dist': 0.04558526041179123,\n",
|
|||
|
|
" 'slope_dist': 20.643552625318872,\n",
|
|||
|
|
" 'phi0_dist': 3.7770417048542133,\n",
|
|||
|
|
" 'RR_dist': 0.9494493337637299,\n",
|
|||
|
|
" 'circ_lin_corr_dur': -0.7812146888876957,\n",
|
|||
|
|
" 'pval_dur': 0.060057201741145594,\n",
|
|||
|
|
" 'slope_dur': -68.05547576848763,\n",
|
|||
|
|
" 'phi0_dur': 1.8139821995133372,\n",
|
|||
|
|
" 'RR_dur': 0.6676959218271774},\n",
|
|||
|
|
" {'action': '1833-260619-1',\n",
|
|||
|
|
" 'channel_group': 0,\n",
|
|||
|
|
" 'unit_name': 132,\n",
|
|||
|
|
" 'circ_lin_corr_dist': -0.265840449081054,\n",
|
|||
|
|
" 'pval_dist': 0.4625226050621034,\n",
|
|||
|
|
" 'slope_dist': -23.301088851788954,\n",
|
|||
|
|
" 'phi0_dist': 2.566564620765877,\n",
|
|||
|
|
" 'RR_dist': 0.5693840101370895,\n",
|
|||
|
|
" 'circ_lin_corr_dur': -0.4630967615992036,\n",
|
|||
|
|
" 'pval_dur': 0.2042202329379157,\n",
|
|||
|
|
" 'slope_dur': -23.34510043249202,\n",
|
|||
|
|
" 'phi0_dur': 4.516455459791437,\n",
|
|||
|
|
" 'RR_dur': 0.5965273552272874},\n",
|
|||
|
|
" {'action': '1833-260619-1',\n",
|
|||
|
|
" 'channel_group': 0,\n",
|
|||
|
|
" 'unit_name': 132,\n",
|
|||
|
|
" 'circ_lin_corr_dist': 0.30367944771269895,\n",
|
|||
|
|
" 'pval_dist': 0.026135127305834782,\n",
|
|||
|
|
" 'slope_dist': 14.149754716354465,\n",
|
|||
|
|
" 'phi0_dist': 1.1693565535871226,\n",
|
|||
|
|
" 'RR_dist': 0.3314870454477721,\n",
|
|||
|
|
" 'circ_lin_corr_dur': 0.4618892719151249,\n",
|
|||
|
|
" 'pval_dur': 0.0006395229268481017,\n",
|
|||
|
|
" 'slope_dur': 9.021986257398721,\n",
|
|||
|
|
" 'phi0_dur': 2.783446591667775,\n",
|
|||
|
|
" 'RR_dur': 0.39308123478746143},\n",
|
|||
|
|
" {'action': '1833-260619-1',\n",
|
|||
|
|
" 'channel_group': 0,\n",
|
|||
|
|
" 'unit_name': 132,\n",
|
|||
|
|
" 'circ_lin_corr_dist': 0.9645237355164202,\n",
|
|||
|
|
" 'pval_dist': 0.001823411910458983,\n",
|
|||
|
|
" 'slope_dist': 26.70323060703037,\n",
|
|||
|
|
" 'phi0_dist': 1.3158184207558894,\n",
|
|||
|
|
" 'RR_dist': 0.9629331089459956,\n",
|
|||
|
|
" 'circ_lin_corr_dur': 0.6072925403004289,\n",
|
|||
|
|
" 'pval_dur': 0.009703337725364802,\n",
|
|||
|
|
" 'slope_dur': -23.62288151655366,\n",
|
|||
|
|
" 'phi0_dur': 2.6856436154554024,\n",
|
|||
|
|
" 'RR_dur': 0.40446535590794314},\n",
|
|||
|
|
" {'action': '1833-260619-1',\n",
|
|||
|
|
" 'channel_group': 0,\n",
|
|||
|
|
" 'unit_name': 132,\n",
|
|||
|
|
" 'circ_lin_corr_dist': 0.9911167344823372,\n",
|
|||
|
|
" 'pval_dist': 0.050122642544521145,\n",
|
|||
|
|
" 'slope_dist': 48.001098030230565,\n",
|
|||
|
|
" 'phi0_dist': 0.8814362136350085,\n",
|
|||
|
|
" 'RR_dist': 0.912890955119381,\n",
|
|||
|
|
" 'circ_lin_corr_dur': -0.1333551438932498,\n",
|
|||
|
|
" 'pval_dur': 0.7443122589388493,\n",
|
|||
|
|
" 'slope_dur': -24.93268402473139,\n",
|
|||
|
|
" 'phi0_dur': 6.274996051779313,\n",
|
|||
|
|
" 'RR_dur': 0.6249452967636183},\n",
|
|||
|
|
" {'action': '1833-260619-1',\n",
|
|||
|
|
" 'channel_group': 0,\n",
|
|||
|
|
" 'unit_name': 132,\n",
|
|||
|
|
" 'circ_lin_corr_dist': 0.16547965733786388,\n",
|
|||
|
|
" 'pval_dist': 0.14124946518803294,\n",
|
|||
|
|
" 'slope_dist': -12.455079310491033,\n",
|
|||
|
|
" 'phi0_dist': 6.207897911524018,\n",
|
|||
|
|
" 'RR_dist': 0.27009312976403244,\n",
|
|||
|
|
" 'circ_lin_corr_dur': -0.11643443056432957,\n",
|
|||
|
|
" 'pval_dur': 0.31967716933659585,\n",
|
|||
|
|
" 'slope_dur': -23.47550060316967,\n",
|
|||
|
|
" 'phi0_dur': 2.5651006117534303,\n",
|
|||
|
|
" 'RR_dur': 0.13813365449174708},\n",
|
|||
|
|
" {'action': '1833-260619-1',\n",
|
|||
|
|
" 'channel_group': 0,\n",
|
|||
|
|
" 'unit_name': 132,\n",
|
|||
|
|
" 'circ_lin_corr_dist': 0.746534745210693,\n",
|
|||
|
|
" 'pval_dist': 0.00023103425988657555,\n",
|
|||
|
|
" 'slope_dist': 28.759505218226185,\n",
|
|||
|
|
" 'phi0_dist': 1.5684112725295802,\n",
|
|||
|
|
" 'RR_dist': 0.9347072516993947,\n",
|
|||
|
|
" 'circ_lin_corr_dur': -0.23985224003814648,\n",
|
|||
|
|
" 'pval_dur': 0.3070938529180911,\n",
|
|||
|
|
" 'slope_dur': -22.425631682208603,\n",
|
|||
|
|
" 'phi0_dur': 3.973167364069665,\n",
|
|||
|
|
" 'RR_dur': 0.27989097932985585},\n",
|
|||
|
|
" {'action': '1833-260619-1',\n",
|
|||
|
|
" 'channel_group': 0,\n",
|
|||
|
|
" 'unit_name': 132,\n",
|
|||
|
|
" 'circ_lin_corr_dist': 0.9193528711271779,\n",
|
|||
|
|
" 'pval_dist': 0.010992921886874285,\n",
|
|||
|
|
" 'slope_dist': 25.82566687705631,\n",
|
|||
|
|
" 'phi0_dist': 0.03756099795784066,\n",
|
|||
|
|
" 'RR_dist': 0.8000390143854937,\n",
|
|||
|
|
" 'circ_lin_corr_dur': 0.9957206567761857,\n",
|
|||
|
|
" 'pval_dur': 0.00823766690802552,\n",
|
|||
|
|
" 'slope_dur': 7.232062114847552,\n",
|
|||
|
|
" 'phi0_dur': 1.3430528701524695,\n",
|
|||
|
|
" 'RR_dur': 0.9937332903207565},\n",
|
|||
|
|
" {'action': '1833-260619-1',\n",
|
|||
|
|
" 'channel_group': 0,\n",
|
|||
|
|
" 'unit_name': 132,\n",
|
|||
|
|
" 'circ_lin_corr_dist': 0.712693653840821,\n",
|
|||
|
|
" 'pval_dist': 0.04188142051134802,\n",
|
|||
|
|
" 'slope_dist': 25.702044480941627,\n",
|
|||
|
|
" 'phi0_dist': 2.460403243905358,\n",
|
|||
|
|
" 'RR_dist': 0.6969993151612308,\n",
|
|||
|
|
" 'circ_lin_corr_dur': -0.1574673579760977,\n",
|
|||
|
|
" 'pval_dur': 0.6654107468230355,\n",
|
|||
|
|
" 'slope_dur': -14.70173580978851,\n",
|
|||
|
|
" 'phi0_dur': 4.151621446378472,\n",
|
|||
|
|
" 'RR_dur': 0.505881569847836},\n",
|
|||
|
|
" {'action': '1833-260619-1',\n",
|
|||
|
|
" 'channel_group': 0,\n",
|
|||
|
|
" 'unit_name': 132,\n",
|
|||
|
|
" 'circ_lin_corr_dist': -0.3272773950469095,\n",
|
|||
|
|
" 'pval_dist': 0.19157360520736888,\n",
|
|||
|
|
" 'slope_dist': -4.56009392702431,\n",
|
|||
|
|
" 'phi0_dist': 4.533929895678337,\n",
|
|||
|
|
" 'RR_dist': 0.5532784366113003,\n",
|
|||
|
|
" 'circ_lin_corr_dur': 0.27336783776006485,\n",
|
|||
|
|
" 'pval_dur': 0.23109078232440483,\n",
|
|||
|
|
" 'slope_dur': 55.84599943412176,\n",
|
|||
|
|
" 'phi0_dur': 3.077875813963241,\n",
|
|||
|
|
" 'RR_dur': 0.44463970728712504}]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 388,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 300x300 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
" compute_phase_precession(\n",
|
|||
|
|
" baseline_i.sort_values('gridness', ascending=False).iloc[18], \n",
|
|||
|
|
" plot=True, field_num=1, return_runs=True, plot_lines=False)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 195,
|
|||
|
|
"metadata": {
|
|||
|
|
"scrolled": true
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:11: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
|
|||
|
|
" # This is added back by InteractiveShellApp.init_path()\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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"text/plain": [
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"<Figure size 1152x648 with 4 Axes>"
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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},
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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"metadata": {
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"data": {
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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|
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"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
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]
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},
|
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|
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"metadata": {
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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|||
|
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"<Figure size 1152x648 with 4 Axes>"
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},
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"metadata": {
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{
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"data": {
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"image/png": "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|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
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|||
|
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]
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|
|
},
|
|||
|
|
"metadata": {
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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"metadata": {
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{
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"data": {
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|||
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"text/plain": [
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|||
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|
"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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},
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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"metadata": {
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"data": {
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|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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"metadata": {
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{
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"data": {
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|
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"text/plain": [
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|||
|
|
"<Figure size 1152x648 with 4 Axes>"
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]
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},
|
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"metadata": {
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"text/plain": [
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"<Figure size 1152x648 with 4 Axes>"
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"text/plain": [
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"<Figure size 1152x648 with 4 Axes>"
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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},
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{
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"data": {
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"text/plain": [
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"data": {
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"text/plain": [
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"<Figure size 1152x648 with 4 Axes>"
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},
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"metadata": {
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"for row in baseline_i.sort_values('gridness', ascending=False).itertuples():\n",
|
|||
|
|
" compute_phase_precession(row, plot=True)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 196,
|
|||
|
|
"metadata": {
|
|||
|
|
"scrolled": true
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:11: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
|
|||
|
|
" # This is added back by InteractiveShellApp.init_path()\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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|||
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1152x648 with 4 Axes>"
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1152x648 with 4 Axes>"
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"data": {
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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|
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"text/plain": [
|
|||
|
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"<Figure size 1152x648 with 4 Axes>"
|
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]
|
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},
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"metadata": {
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
|
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|
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]
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|||
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},
|
|||
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"metadata": {
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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|
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"text/plain": [
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|||
|
|
"<Figure size 1152x648 with 4 Axes>"
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]
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},
|
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"metadata": {
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1152x648 with 4 Axes>"
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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]
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},
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"image/png": "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|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
|
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|
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]
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},
|
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"text/plain": [
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"<Figure size 1152x648 with 4 Axes>"
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{
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"text/plain": [
|
|||
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"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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|
|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA1EAAAI4CAYAAACLCWOMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydeZhcRdX/v7NkJhlDgkgECTBItAtkWAdEoyIKBN+g4QeMyOoCKmqCoijByasoEhQMiyxCXsBEAYOyzhAWYwIxrGELkAAp2ZOwhBAgC5PM0n1+f9StdHV1Vd17e7pnIefzPPXMdN9761bVXfp861SdqiIiMAzDMAzDMAzDMMmo7u8CMAzDMAzDMAzDDCZYRDEMwzAMwzAMw6SARRTDMAzDMAzDMEwKWEQxDMMwDMMwDMOkgEUUwzAMwzAMwzBMClhEMQzDMAzDMAzDpKC2vwvAMAzDbJ4IIaoAzACwREo5LfquBsBlAL4Y7XYngF9IKUkI8SUAfwQwBMAGAD+WUj4S5fM7AEdGxzwK4IdSyg7HOUcBmA7gE1C/gXcAmCylzAkhtgJwKYBPARgGYKqU8trouMMA/B5APYCnAZwspVwbbfsRgO9Gxzwebes0zjkOwPlSyr2M704FMCmqx3MAJkop33GUdxiAywHsB9XxuTDad4Nj36MAtEZlfBXAN6WUqx37nQ7gJAA9AFYBOEVK+aK1zy0AXpdSToo+7wHgCgAfAkAAWqWUd9l5MwzDbC6wJ4phGIbpc4QQuwKYB+Boa9OJAASA3QHsCSWmWoQQdQD+AeB7Uso9AZwD4NromCMAjAOwF4DdADQA+Inn1BcBeFZKuQeAfQDsD+Db0baZAFZIKfcGcDCAS4QQ20fCawaAo6SUAsBLAP4Q1eNIAKdG++8GJaR+Gm0bJoQ4B8A/YXRaRmJwMoCDImF1J4D/85R3SnTsngD2iPL/pb2TEGJfKPF5lJSyCcB/AUx17HcwgJMBfDZqx1uiupn7nAHgC9ah1wH4Y1TeEwH8M7omDMMwmyUsohiGYZj+YCKU8f5P6/saKG9HfZTqAGyUUnYBGC2lXBR5nnYGsBoApJS3APhctM8WAD6qtzm4FUpsQEq5EcASAI2RF+oQAL+Ntq2AEljvQAm0R6WUz0d5XAHg+Kgc3wRwgZTyHSllDsAPkBd3h0Z1OckqQzOAudE5ACVkvuYRJQsAnCOlzEkpswAWAWh07HcCgGuklK9En38D4HzHfm9CeenWRp8fM/OLBN5XAFxpHbcPgLbo/zEA3gOQdeTPMAyzWcDD+RiGYZg+xxgmdpC1aSaArwN4Deo3ao6U8vbomG4hxDYAngCwNYBvGPl1CyEmQXmoXoMSS67z3qz/F0LsDeA4AAdCDe97A8DPhBD/AyXgpkkp/yuE2AHAciObFQBGQAm2DICPCiHuBrAdgPsAnBGd6zYAtwkhDrSK8QiAHwshGqWUrwL4DpRY/EhUBrO8c4zyNgI4DcD3HVXLAHhaCNEGYCcAixF5xKz8lhj51UN51G6MPm8H4E9Q4u8U67geIUSVEOLFKP+fRKKOYRhms4Q9UQzDMMxA4iyoeTrbANgewFbRHB4AgJRypZRyNIDPApghhMgY2y4D8GEoAXVT6CRCiEMBzAFwqpTySah5Vh8HsFZK+TkAxwC4SAjRDP9vZTY67hCoYYn7AtgKjmF0JlLKBVAer1uFEI8ByEF5vLoC5W2GEmiXSSlnO3YZAuBrUOJnbyiP01WB/EZB1X89gFYhxBAANwA4TUr5husYKSVJKcdACc4zhRBfDtWTYRjmgwyLKIZhGGYgcSSAv0gpu6SUawD8FcCXhBAjhRBH6J2klE8AeArA7kKIPSOvEqSUBOBqAPsIIbYTQjxppO0AQAjxM6ghd8fqwBEAXo/+zozyeQHA/QA+DWAZgI8ZZRwN4F0p5fvRcbdKKddGwwmvgxJ4XoQQWwD4j5RyHynlvgC0d+wdq7z7RvsfA+DfAM6UUp7ryfZ1AP+SUr4ZDSuc4StHFCTiUSiP3hFRufeFEpEXCiGehBqW+A0hxNVCiDohxDFCiOqobV4GMBdKrDEMw2yW8HA+hmEYZiDxBJRX597IOzIBwMNQXp+/CCHeklI+IITYDcAuUNHqvgTgdCHE2Cgi3zcB3COlfB0q2MQmIgE1EcBnpJQv6e+llC8LIZ4A8C0Al0bDBsdCzSt6FcAFQohPRvOifoD8/KCbABwthLgKwEYA/w9KoITYDsA8IcSnorlJvwIwKxKAdnlbAFwCYJyU8rFAnjcBmCaEmBpF5DvSVQ4hxCcA3AsV8fAvRv0fArCDsd9vAGxtDLs8B6rj9e+RGP0SorllDMMwmyMsohiGYZiBxE+hRMxSKOE0D8B50Zyn/wfg4khcdQI4LgrOcG0kDh4TQvQAeAYqAl0BUeCG30EFRbhFCKE33SilnAoV5e9yIcQPoATD2VLKR6NjvwPgpiiPF6GEGgD8GWoI3+NQQTGeALBp+KELKaUUQvwBwMLIu3M/VLhzF78HUAXgaqO8D0gpJ1p53i6E2B7Af6I8X3W1AVRUwAaoOVk/jr7rlFLuHyoz8m1zBtTww1/EiDqGYZgPNFVE1N9lYBiGYRiGYRiGGTTwnCiGYRiGYRiGYZgUsIhiGIZhGIZhGIZJAYsohmEYhmEYhmGYFLCIYhiGYRiGYRiGSQGLKIZhGIZhGIZhmBSwiGIYhmEYhmEYhkkBiyiGYRiGYRiGYZgUsIhiGIZhGIZhGIZJAYsohmEYhmEYhmGYFLCIYhiGYRiGYRiGSQGLKIZhGIZhGIZhmBSwiGIYhmEYhmEYhkkBiyiGYRiGYRiGYZgUsIhiGIZhGIZhGIZJAYsohmEYhmEYhmGYFLCIYhiGYRiGYRiGSQGLKIZhGIZhGIZhmBSwiGIYhmEYhmEYhkkBiyiGYRiGYRiGYZgUsIhiGIZhGIZhGIZJAYsohmEYhmEYhmGYFLCIYhiGYRiGYRiGSQGLKIZhGIZhGIZhmBSwiGIYhmEYhmEYhkkBiyiGYRiGYRiGYZgUsIhiGIZhGIZhGIZJAYsohmEYhmEYhmGYFLCIYhiGYRiGYRiGSQGLKIZhGIZhGIZhmBSwiGIYhmEYhmEYhkkBiyiGYRiGYRiGYZgUsIhiGIZhGIZhGIZJAYsohmEYhmEYhmGYFLCIYhiGYRiGYRiGSQGLKIZhGIZhGIZhmBSwiGIYhmEYhmEYhkkBiyiGYRiGYRiGYZgUsIhiGIZhGIZhGIZJAYsohmEYhmEYhmGYFLCIYhiGYRiGYRiGSQGLKIZhGIZhGIZhmBSwiGIYhmEYhmEYhklBbX8XgAGEEPsCOFNK2VKm/AjAKABjARwspfxxOfIdSAgh9gdwOYAPAXgdwAlSyjeS7ieEqAbwBwCHAcgBeB7AKVLKVcaxdQAWALhJSjktQZmOBnAWgB4AKwD8SEr5arTtcQDDAHRFu18vpfyjI4+hAKYA+CqAKgA1AK4DcL6UkoQQ3wbQIqX8alx5AuWcCeAQALdLKX/g2H4SgF9AvR/mAvixlLI77X5CiD0B3C2l/Fj0+WAAZjsOA5ABsK+U8vEU5a8BcCGAQ6NzT5NSXlnKflEdjpBSfi36XAXgdwCOjHZ5FMAPAewOYDqAPQGMklK+HSjfgQDuAiCNr7cA8CyAb0spVyetqyPvTwL4C4CPAFgP4JtSyqVp9hNCnA7gJKj7dBXUff+iEGIrAFcA2AvA+wBmSCkvLbWsDMMwDPNBhj1RAwAp5WPlElBWvu0fUAFVB+AmAD+RUu4a/X9Nyv1OAtAMYB8p5e4AXgBwgZXFxQDGJCzTJ6GM7GOklHsCOC86H4QQH4ry2VNKuVeUXAKqCsBtAEYC+KyUci8ABwI4AsDZScqRgos8AqoJwG8BHABAANgSwE/T7CeEqBVC/BTAHCjxAACQUs416r8XgMUA/pBGQEWcAuCTAJoA7AfgNCHEp9PsJ4TYSghxJYBLocSq5ggA46CExG4AGqDun4VRmZPyolXXDIAsgJ+nyMPF9QCukFJ+Ckqw3xzdN4n2i4TsyVD3154AbgE
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"for row in stimulated_11_sub.sort_values('gridness', ascending=False).itertuples():\n",
|
|||
|
|
" compute_phase_precession(row, plot=True, flim=[6,10])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 197,
|
|||
|
|
"metadata": {
|
|||
|
|
"scrolled": true
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:11: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
|
|||
|
|
" # This is added back by InteractiveShellApp.init_path()\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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]
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"metadata": {
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"data": {
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|||
|
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"text/plain": [
|
|||
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|
"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
|
|||
|
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]
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},
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|
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"metadata": {
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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},
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"metadata": {
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{
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"data": {
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|
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"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
|
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]
|
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},
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"metadata": {
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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|||
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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]
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},
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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"metadata": {
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"data": {
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"text/plain": [
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"<Figure size 1152x648 with 4 Axes>"
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{
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"data": {
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"text/plain": [
|
|||
|
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"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
|
|||
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]
|
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|
|
},
|
|||
|
|
"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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|||
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"text/plain": [
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|||
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|
"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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]
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"metadata": {
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{
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"data": {
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|
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"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
|
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]
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},
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"metadata": {
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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"metadata": {
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"data": {
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|||
|
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"text/plain": [
|
|||
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|
"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"text/plain": [
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"<Figure size 1152x648 with 4 Axes>"
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{
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"data": {
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|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"for row in stimulated_11_sub.sort_values('gridness', ascending=False).itertuples():\n",
|
|||
|
|
" compute_phase_precession(row, plot=True, flim=[9,12])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 198,
|
|||
|
|
"metadata": {
|
|||
|
|
"scrolled": true
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"ename": "ValueError",
|
|||
|
|
"evalue": "The length of x is too small: len(x) < 2.",
|
|||
|
|
"output_type": "error",
|
|||
|
|
"traceback": [
|
|||
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|||
|
|
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
|
|||
|
|
"\u001b[0;32m<ipython-input-198-e9ca3d18003a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbaseline_ii\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'gridness'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitertuples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcompute_phase_precession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
|||
|
|
"\u001b[0;32m<ipython-input-194-7839614a8a2f>\u001b[0m in \u001b[0;36mcompute_phase_precession\u001b[0;34m(row, flim, field_num, plot)\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0mphase\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mspike_phase\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0mcirc_lin_corr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphi0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcl_corr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0mline_fit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphi0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[0;32m~/apps/expipe-project/phase-precession/phase_precession/core.py\u001b[0m in \u001b[0;36mcl_corr\u001b[0;34m(x, phase, min_slope, max_slope, ci, bootstrap_iter)\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[0mgoodness\u001b[0m \u001b[0mof\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 138\u001b[0m '''\n\u001b[0;32m--> 139\u001b[0;31m \u001b[0mphi0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcl_regression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_slope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_slope\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# fit line to data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 140\u001b[0m \u001b[0mcirc_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslope\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# convert linear variable to circular one\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[0;32m~/apps/expipe-project/phase-precession/phase_precession/core.py\u001b[0m in \u001b[0;36mcl_regression\u001b[0;34m(x, phase, min_slope, max_slope)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'The length of x is too small: len(x) < 2.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_slope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[0;31mValueError\u001b[0m: The length of x is too small: len(x) < 2."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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"metadata": {
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"data": {
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|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
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]
|
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},
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|
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"metadata": {
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"output_type": "display_data"
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},
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{
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"data": {
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|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
|
|||
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]
|
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},
|
|||
|
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"metadata": {
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|||
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},
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|
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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 1152x648 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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"text/plain": [
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"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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|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"for row in baseline_ii.sort_values('gridness', ascending=False).itertuples():\n",
|
|||
|
|
" compute_phase_precession(row, plot=True)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 198,
|
|||
|
|
"metadata": {
|
|||
|
|
"scrolled": true
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"ename": "ValueError",
|
|||
|
|
"evalue": "The length of x is too small: len(x) < 2.",
|
|||
|
|
"output_type": "error",
|
|||
|
|
"traceback": [
|
|||
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|||
|
|
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
|
|||
|
|
"\u001b[0;32m<ipython-input-198-e9ca3d18003a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbaseline_ii\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'gridness'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitertuples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcompute_phase_precession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
|||
|
|
"\u001b[0;32m<ipython-input-194-7839614a8a2f>\u001b[0m in \u001b[0;36mcompute_phase_precession\u001b[0;34m(row, flim, field_num, plot)\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0mphase\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mspike_phase\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0mcirc_lin_corr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphi0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcl_corr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0mline_fit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphi0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/apps/expipe-project/phase-precession/phase_precession/core.py\u001b[0m in \u001b[0;36mcl_corr\u001b[0;34m(x, phase, min_slope, max_slope, ci, bootstrap_iter)\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[0mgoodness\u001b[0m \u001b[0mof\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 138\u001b[0m '''\n\u001b[0;32m--> 139\u001b[0;31m \u001b[0mphi0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcl_regression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_slope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_slope\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# fit line to data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 140\u001b[0m \u001b[0mcirc_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslope\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# convert linear variable to circular one\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/apps/expipe-project/phase-precession/phase_precession/core.py\u001b[0m in \u001b[0;36mcl_regression\u001b[0;34m(x, phase, min_slope, max_slope)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'The length of x is too small: len(x) < 2.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_slope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mValueError\u001b[0m: The length of x is too small: len(x) < 2."
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]
|
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1152x648 with 4 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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"data": {
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"text/plain": [
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|||
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"<Figure size 1152x648 with 4 Axes>"
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},
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"metadata": {
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{
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"data": {
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|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
|
|||
|
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"<Figure size 1152x648 with 4 Axes>"
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]
|
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},
|
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|
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAA08AAAI4CAYAAACybtLXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdeXxcdb3/8ddkT9omTQoUylpZvi0UKJtwATdaqlcxKhSQRSiKFoWryMWrUFd+FuUiixdQKktLARFaQKIgVAplFRWw0LJ8EdBCCy1pmyZNkyaZZH5/nDPJZDLLmcmZ/f18PPpoZs7MOd+ZM5Oc9/l8v98TCIVCiIiIiIiISGJluW6AiIiIiIhIIVB4EhERERER8UDhSURERERExAOFJxEREREREQ8UnkRERERERDxQeBIREREREfGgItcNEBGR0mSMCQALgdXW2l+495UD1wMfcx/2EPAda23IGPMJ4EqgEugGvmmt/Zu7nv8HnApsA54FLrLWbk+w7YOBh621u0Tc90lgPs7fxgHgEmvtI+6yLwPfcZc96m67z23vD4BmYIzb3oustaGI9X4Z+IK19rMRr9tTe40xuwO3ABOBcuBKa+1tMR6XtB0Rj/0GcC5QC7wAfMVa22OM2Qf4NbAjUAXcYq29yn3OKcCPgCCwFviGtXZNvPdXRKRYqfIkIiJZZ4yZCiwHTola9CXAAAcCB+OEqNnGmCrgbuCr1tqDgZ8Ct7vPmQOcABxhrZ0OvO8uj7XdCmPMt4FlwLiI+xuA3wJnu+uYA9xtjBlnjJkG/AT4qNu28cC33ad+C/g4cAxwEPAfOKEIY0yTMeZG4DogENEMz+0FbgAecl/zDOA6Y8xuMR4Xtx1Rr/9E4L+AmcABOAEq/FoWAXe7bfoPYK4x5jhjzL7AAuCLbjuuAJbGaa+ISFFTeBIRkVw4H6fqdE/U/eU4lZNq918VsN1a2wvsaq39h1u5+RCwyX3OYcDvrbVb3Nv3AbPjbPdQnHARvbwSp5ryinv7VZzAswPwOaDFWttqrR3ACRJnuo87C/iptbbbWtsDnIQTCsEJhu8DF0dtK5X2fh4nfAHsgVP56Y7xuETtiH7cVdbaze5rOY+hEHoLToDEWtsOvAnsiRNiX7LWrnKXPQnsZYzZK06bRUSKlrrtiYhI1llrLwAwxsyIWrQIOBlYh/M3apm19g/uc/qMMROBF3FCTbiy8lfg28aY64HNOAFhF2Kw1v4N+Fv0gb+1diNOZSvsMuANa+2/3K5z/45YthYIV3/2A/Y3xlyC092tBad7G9baG93XOCeqGam0d8BdxwrgWOBqa+2mGA+N244Yj9vJGPMwMAl4Cvgfd1sLww8yxnwKOBr4ClAHTDPGTLfWrjTGfBaY4Lb534iIlBBVnkREJJ/8CGjFGeOzG9BkjPnv8EJr7QZr7a443coWGmP2s9beDiwBHgOeAV4HetPZuNut7/9wAtxJ7t2x/lb2u/9XAkcBn8bpMncsTre4uNJpr7X24zhhZZYx5pwYD/HajkrgeJyq2OFAE844r0HGmLOBO4DZ1tr3rbVvAV8GbjTG/AOncvZSsjaLiBQjVZ5ERCSfnAj8l9tNr9cYcxvOmKebgeOstfcDWGtfNMa8BBxojNkI/NZa+zMAY8yRwJvGmEk4EyeEfdpa+168DRtjGnHG8gSAoyIqPO8wvDK0K071CeA94HduV7keY8wSnLFR1ybYTpPX9uJUfx6x1m611rYaY36P0/VwYdRqvbbjPeB+a22Hu+07gB+6PweAX+B0IZxprV3p3l8NvGmtPcq9XQFcCPwr3msUESlWCk8iIpJPXsSpijxujKnEmT3uOZxKz63GmA+stc8YYw4ApuB0gTsc+LkbQkLAJcCdblCa7mWjbkBYhlNROc9aG4xY3AI8YIyZj1MV+xrwe3fZUuBMY8wfccZrnUDssUaRPLfXGPN1nK52l7uTWnwOp0thNK/tWAqcYoy5CdiOM6bq7+6yX+JU9A631rZGPKcaeMYYc5C19l2cCSaettZuTvI6RUSKjsKTiIjkk2/jzCj3Ok5gWg5c4Y53+jxwrRuqeoDTrbVrgbXGmI8BL+N0sfs9cE2K252NE2pqgOeNMeH7v2StfdkYcxlON7tKnMB2hbv8++7Pq3H+pv6ZBFUnAGvtshTaOwdYYIx52b19U7j6FsVrO36F01XvBZyQ9SLw3+64rguANcCfI17/L621C40xXwX+5E6J/prbLhGRkhMIhUZcAkJERERERESiaMIIERERERERDxSeREREREREPFB4EhERERER8UDhSURERERExAOFJxEREREREQ8UnkRERERERDxQeBIREREREfFA4UlERERERMQDhScREREREREPFJ5EREREREQ8UHgSERERERHxQOFJRERERETEA4UnERERERERDxSeREREREREPFB4EhERERER8UDhSURERERExAOFJxEREREREQ8UnkRERERERDxQeBIREREREfFA4UlERERERMQDhScREREREREPFJ5EREREREQ8UHgSERERERHxQOFJRERERETEA4UnERERERERDxSeREREREREPFB4EhERERER8UDhSURERERExAOFJxEREREREQ8UnkRERERERDxQeBIREREREfFA4UlERERERMQDhScREREREREPFJ5EREREREQ8UHgSERERERHxQOFJRERERETEA4UnERERERERDxSeREREREREPFB4EhERERER8UDhSURERERExAOFJxEREREREQ8UnkRERERERDyoyHUDJD5jzOHA96y1s31aXwjYETgamGmt/aYf680lY8yRwA3AGOA94Exr7fsxHncK8H335kZgrrX2n+6yk4BLgWpgDXCWtXaTMWZ34BZgIlAOXGmtvc1Dm/4LuADoBl4DzrfWbnaXtQLrIh5+pbX2zhjrGA9cBnwcGABCwPXW2lvc5T8GdrDWXpCsPQnauQLYE1horb3MGHMpcBbO74U7gJ9Ya0NRz6kCrgM+4t71J+B/rLX9EY9pBF5w719qjJkJ/CJiNbXAfsDh1toXErSvHLga+KTbpl9Ya2+M8bgGnP00BeeE0G3W2ivcZZ8FbgPeiXjKR4AvABdF3NcA7AbsAzwA7A8cba19PkH79gLeAlZF3D0WWAt82Vr7drznJmOM2RFYjLN/BoCvWWufTfD4Ye+5e1/c74aXfS0iIiIjqfKUx6y1z/sVnKLW21IkwakKWAp8y1o71f35lhiPmwjcCHzGWnsQcB9wvbvscPfnk6y104A3gPnuU28AHrLWHgzMAK4zxuyWpE2fAL4LzLDWTgceAn7jLjNAm7V2esS/WMGpBngC5yD8UHc9nwcuMcZ8xfs75Ml33OD0aeBk4DBgGvAJ93a0C3AC+DTgIJwgfkpE2wM4B/0N4fustY9GvmacsPHzRMHJNRfY193WEcCFxpgPx3jc/wPWuvvvCODrxpj/cJcdjRO6It/zrdbaxRHtOQJYD1xgrV3j3vdekraFdUe9tn3d1zc/yfOSuQF4ylq7P3AmsMQYUxfrgbHe80TfjRT2tYiIiERR5SlPGGO+DPw30I9TGTkb2Bun2jDNGLMIaHLv+yPOAeN1wDFAEPg9MM/L2WNjzBxgtrX2BLf68Bd3PXsATwFnW2sHEjy/Avhf4AR3288C38CpjlyNEzT6gb8C37bWbjXG/Nu9fRBOleeayNvW2vsj1v894IsxNj3DWrsp4vYRQIe19hn39i3AtcaYCZGPs9ZuMMZMtNb2uW3fEwgvPxO4xVr7b/f2j4EJ7s+fBwLuz3u4r7U73vviOgx41Fq71r19H3CzezB7NNBvjHnc3cZSYH5k1cZ1KtBprf3fiNewxq2eVSXaeIwqT9h3rbWPJHjqF4DfWmu3uetZiPPe3BP5IGvt1caY66y1A251ZDywOeIh3wdeBsbFad+ZwF7E3r+x2vQba20QaDPG/M5t09+iHvctnMogwC44FcR29/bRQJ9bXezC+Y48GfX87wIfWGsXeGhTMjVuGzZEL/C6b9zP6AnA+QDW2pXGmH8Cn8L5PEWL9Z7
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|
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"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
|
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|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
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|
|
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},
|
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|
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"output_type": "display_data"
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},
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|||
|
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{
|
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|
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"data": {
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"text/plain": [
|
|||
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"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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|||
|
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"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
|
|||
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"<Figure size 1152x648 with 4 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
|
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|
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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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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"for row in stimulated_30.sort_values('gridness', ascending=False).itertuples():\n",
|
|||
|
|
" compute_phase_precession(row, plot=True)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Analysis"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 390,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"model_id": "45ec19625a034ec79e6f979e7f5d6241",
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0
|
|||
|
|
},
|
|||
|
|
"text/plain": [
|
|||
|
|
"HBox(children=(IntProgress(value=0, max=40), HTML(value='')))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"baseline_i_pp = []\n",
|
|||
|
|
"for row in tqdm(baseline_i.itertuples(), total=len(baseline_i)):\n",
|
|||
|
|
" result_cell = compute_phase_precession(row)\n",
|
|||
|
|
" baseline_i_pp.append(result_cell)\n",
|
|||
|
|
"\n",
|
|||
|
|
"stimulated_11_pp = []\n",
|
|||
|
|
"for row in tqdm(stimulated_11.itertuples(), total=len(stimulated_11)):\n",
|
|||
|
|
" result_cell = compute_phase_precession(row)\n",
|
|||
|
|
" stimulated_11_pp.append(result_cell)\n",
|
|||
|
|
"\n",
|
|||
|
|
"stimulated_11_stim_pp = []\n",
|
|||
|
|
"for row in tqdm(stimulated_11.itertuples(), total=len(stimulated_11)):\n",
|
|||
|
|
" result_cell = compute_phase_precession(row, flim=[9,12])\n",
|
|||
|
|
" stimulated_11_stim_pp.append(result_cell)\n",
|
|||
|
|
"\n",
|
|||
|
|
"baseline_ii_pp = []\n",
|
|||
|
|
"for row in tqdm(baseline_ii.itertuples(), total=len(baseline_ii)):\n",
|
|||
|
|
" result_cell = compute_phase_precession(row)\n",
|
|||
|
|
" baseline_ii_pp.append(result_cell)\n",
|
|||
|
|
" \n",
|
|||
|
|
"stimulated_30_pp = []\n",
|
|||
|
|
"for row in tqdm(stimulated_30.itertuples(), total=len(stimulated_30)):\n",
|
|||
|
|
" result_cell = compute_phase_precession(row, flim=[6,10])\n",
|
|||
|
|
" stimulated_30_pp.append(result_cell)\n",
|
|||
|
|
" \n",
|
|||
|
|
"stimulated_stim_30_pp = []\n",
|
|||
|
|
"for row in tqdm(stimulated_30.itertuples(), total=len(stimulated_30)):\n",
|
|||
|
|
" result_cell = compute_phase_precession(row, flim=[29,31])\n",
|
|||
|
|
" stimulated_stim_30_pp.append(result_cell)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 391,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"baseline_i_pp = pd.DataFrame(baseline_i_pp)\n",
|
|||
|
|
"\n",
|
|||
|
|
"stimulated_11_pp = pd.DataFrame(stimulated_11_pp)\n",
|
|||
|
|
"\n",
|
|||
|
|
"stimulated_11_stim_pp = pd.DataFrame(stimulated_11_stim_pp)\n",
|
|||
|
|
"\n",
|
|||
|
|
"baseline_ii_pp = pd.DataFrame(baseline_ii_pp)\n",
|
|||
|
|
"\n",
|
|||
|
|
"stimulated_30_pp = pd.DataFrame(stimulated_30_pp)\n",
|
|||
|
|
"\n",
|
|||
|
|
"stimulated_stim_30_pp = pd.DataFrame(stimulated_stim_30_pp)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 392,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"stuff = ['action', 'channel_group', 'unit_name', 'unit_idnum', 'unit_id']\n",
|
|||
|
|
"baseline_i_pp = baseline_i_pp.merge(data.loc[:,stuff], on=stuff[:3])\n",
|
|||
|
|
"\n",
|
|||
|
|
"stimulated_11_pp = stimulated_11_pp.merge(data.loc[:,stuff], on=stuff[:3])\n",
|
|||
|
|
"\n",
|
|||
|
|
"stimulated_11_stim_pp = stimulated_11_stim_pp.merge(data.loc[:,stuff], on=stuff[:3])\n",
|
|||
|
|
"\n",
|
|||
|
|
"baseline_ii_pp = baseline_ii_pp.merge(data.loc[:,stuff], on=stuff[:3])\n",
|
|||
|
|
"\n",
|
|||
|
|
"stimulated_30_pp = stimulated_30_pp.merge(data.loc[:,stuff], on=stuff[:3])\n",
|
|||
|
|
"\n",
|
|||
|
|
"stimulated_stim_30_pp = stimulated_stim_30_pp.merge(data.loc[:,stuff], on=stuff[:3])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 393,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"baseline_i_pp['baseline_i'] = True\n",
|
|||
|
|
"\n",
|
|||
|
|
"stimulated_11_pp['stimulated_11'] = True\n",
|
|||
|
|
"\n",
|
|||
|
|
"stimulated_11_stim_pp['stimulated_11_stim'] = True\n",
|
|||
|
|
"\n",
|
|||
|
|
"baseline_ii_pp['baseline_ii'] = True\n",
|
|||
|
|
"\n",
|
|||
|
|
"stimulated_30_pp['stimulated_30'] = True\n",
|
|||
|
|
"\n",
|
|||
|
|
"stimulated_stim_30_pp['stimulated_stim_30'] = True"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 394,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:7: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
|
|||
|
|
"of pandas will change to not sort by default.\n",
|
|||
|
|
"\n",
|
|||
|
|
"To accept the future behavior, pass 'sort=False'.\n",
|
|||
|
|
"\n",
|
|||
|
|
"To retain the current behavior and silence the warning, pass 'sort=True'.\n",
|
|||
|
|
"\n",
|
|||
|
|
" import sys\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"results = pd.concat([\n",
|
|||
|
|
" baseline_i_pp,\n",
|
|||
|
|
" stimulated_11_pp,\n",
|
|||
|
|
" stimulated_11_stim_pp,\n",
|
|||
|
|
" baseline_ii_pp,\n",
|
|||
|
|
" stimulated_30_pp,\n",
|
|||
|
|
" stimulated_stim_30_pp,\n",
|
|||
|
|
"])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 396,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"results.reset_index(drop=True).to_feather(output_path / 'results.feather')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 397,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def compute_date_idnum(row):\n",
|
|||
|
|
" return '-'.join(row.action.split('-')[:2]) + '_' + str(row.unit_idnum)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 398,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"baseline_i_pp['date_idnum'] = baseline_i_pp.apply(compute_date_idnum, axis=1)\n",
|
|||
|
|
"\n",
|
|||
|
|
"stimulated_11_pp['date_idnum'] = stimulated_11_pp.apply(compute_date_idnum, axis=1)\n",
|
|||
|
|
"\n",
|
|||
|
|
"stimulated_11_stim_pp['date_idnum'] = stimulated_11_stim_pp.apply(compute_date_idnum, axis=1)\n",
|
|||
|
|
"\n",
|
|||
|
|
"baseline_ii_pp['date_idnum'] = baseline_ii_pp.apply(compute_date_idnum, axis=1)\n",
|
|||
|
|
"\n",
|
|||
|
|
"stimulated_30_pp['date_idnum'] = stimulated_30_pp.apply(compute_date_idnum, axis=1)\n",
|
|||
|
|
"\n",
|
|||
|
|
"stimulated_stim_30_pp['date_idnum'] = stimulated_stim_30_pp.apply(compute_date_idnum, axis=1)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# barplot"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 474,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"plt.rc('axes', titlesize=12)\n",
|
|||
|
|
"plt.rcParams.update({\n",
|
|||
|
|
" 'font.size': 12, \n",
|
|||
|
|
" 'figure.figsize': (2, 2), \n",
|
|||
|
|
" 'figure.dpi': 150\n",
|
|||
|
|
"})"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 475,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"query = 'pval_dist < 0.01 and RR_dist > .1'"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 476,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"precess_i = sum(baseline_i_pp.query(query).circ_lin_corr_dist < 0) / len(baseline_i_pp) * 100\n",
|
|||
|
|
"recess_i = sum(baseline_i_pp.query(query).circ_lin_corr_dist > 0) / len(baseline_i_pp) * 100\n",
|
|||
|
|
"stim_11 = len(stimulated_11_pp.query(query).circ_lin_corr_dist)\n",
|
|||
|
|
"precess_ii = sum(baseline_ii_pp.query(query).circ_lin_corr_dist < 0) / len(baseline_ii_pp) * 100\n",
|
|||
|
|
"recess_ii = sum(baseline_ii_pp.query(query).circ_lin_corr_dist > 0) / len(baseline_ii_pp) * 100\n",
|
|||
|
|
"stim_30 = len(stimulated_30_pp.query(query).circ_lin_corr_dist)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 479,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 300x300 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"fig = plt.figure()\n",
|
|||
|
|
"sns.barplot(data=[\n",
|
|||
|
|
" [precess_i], \n",
|
|||
|
|
" [recess_i], \n",
|
|||
|
|
" [stim_11],\n",
|
|||
|
|
" [stim_11],\n",
|
|||
|
|
" [precess_ii], \n",
|
|||
|
|
" [recess_ii], \n",
|
|||
|
|
" [stim_30],\n",
|
|||
|
|
" [stim_30]\n",
|
|||
|
|
"], color='k')\n",
|
|||
|
|
"plt.xticks(\n",
|
|||
|
|
" range(8),\n",
|
|||
|
|
" [\n",
|
|||
|
|
" 'Baseline I precession', \n",
|
|||
|
|
" 'Baseline I recession', \n",
|
|||
|
|
" '11 Hz precession',\n",
|
|||
|
|
" '11 Hz recession',\n",
|
|||
|
|
" 'Baseline II precession', \n",
|
|||
|
|
" 'Baseline II recession', \n",
|
|||
|
|
" '30 Hz precession',\n",
|
|||
|
|
" '30 Hz recession'\n",
|
|||
|
|
" ], rotation=90)\n",
|
|||
|
|
"plt.ylabel('Percentage')\n",
|
|||
|
|
"despine()\n",
|
|||
|
|
"figname = f'phase-precession-quantification'\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": [
|
|||
|
|
"# hist"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 448,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd10cb721d0>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 448,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"bins = np.arange(-2, 2, .1)\n",
|
|||
|
|
"\n",
|
|||
|
|
"baseline_i_pp.query('pval_dist < 0.01').slope_dist.hist(density=False, bins=bins)\n",
|
|||
|
|
"stimulated_11_pp.query('pval_dist < 0.01').slope_dist.hist(\n",
|
|||
|
|
" density=False, bins=bins, alpha=.5)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 447,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd10cd4c0f0>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 447,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"bins = np.arange(-.25, .25, .01)\n",
|
|||
|
|
"density=False\n",
|
|||
|
|
"baseline_i_pp.query('pval_dist < 0.01 and RR_dist > .1').circ_lin_corr_dist.hist(\n",
|
|||
|
|
" density=density, bins=bins)\n",
|
|||
|
|
"stimulated_11_pp.query('pval_dist < 0.01 and RR_dist > .1').circ_lin_corr_dist.hist(\n",
|
|||
|
|
" density=density, bins=bins, alpha=.5)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 408,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"baseline_i_pp['slope_R_dist'] = baseline_i_pp.slope_dist * baseline_i_pp.RR_dist\n",
|
|||
|
|
"stimulated_11_pp['slope_R_dist'] = stimulated_11_pp.slope_dist * stimulated_11_pp.RR_dist"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 409,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"baseline_i_pp['r_R_dist'] = baseline_i_pp.circ_lin_corr_dist * baseline_i_pp.RR_dist\n",
|
|||
|
|
"stimulated_11_pp['r_R_dist'] = stimulated_11_pp.circ_lin_corr_dist * stimulated_11_pp.RR_dist"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 410,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd12d465a58>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 410,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 300x300 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"bins = np.arange(-2, 2, .1)\n",
|
|||
|
|
"baseline_i_pp_sig = baseline_i_pp.query('pval_dist < 0.05')\n",
|
|||
|
|
"baseline_i_pp_sig_date_idnum = baseline_i_pp_sig.date_idnum.unique()\n",
|
|||
|
|
"stimulated_11_pp_sig = stimulated_11_pp.query('date_idnum in @baseline_i_pp_sig_date_idnum')\n",
|
|||
|
|
"stimulated_11_pp_sig_date_idnum = stimulated_11_pp_sig.date_idnum.unique()\n",
|
|||
|
|
"baseline_i_pp_sig = baseline_i_pp_sig.query('date_idnum in @stimulated_11_pp_sig_date_idnum')\n",
|
|||
|
|
"\n",
|
|||
|
|
"baseline_i_pp_sig.slope_dist.hist(density=False, bins=bins)\n",
|
|||
|
|
"stimulated_11_pp_sig.slope_dist.hist(density=False, bins=bins, alpha=.5)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 411,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd12e3665c0>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 411,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 300x300 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"bins = np.arange(-.25, .25, .01)\n",
|
|||
|
|
"baseline_i_pp_sig.circ_lin_corr_dist.hist(density=False, bins=bins)\n",
|
|||
|
|
"stimulated_11_pp_sig.circ_lin_corr_dist.hist(density=False, bins=bins, alpha=.5)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 412,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd12d5567b8>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 412,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAASMAAAEmCAYAAAAp7faeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAATiklEQVR4nO3dfZBddX3H8fcm2SwkxIKESFDCk/jFpIoKiBZRcESxONM6UiqKNiojWp2qY33oCAUfsCpTH1BnbK0dfAKxglqRilRREYUiVrSJ/QrlKWA6EHyCbNjdxO0f51x2SXc3u3vP3f3t4f2a2Tn33PM7v9+559793HPP069vdHQUSZpvi+Z7ASQJDCNJhTCMJBXBMJJUBMNIUhEMI0lFMIwkFcEwklQEw0hSEQwjSUUwjCQVwTCSVATDSFIRlsz3AsxERPwvsAzYNN/LIun/2R8YzMx9ZzPzggojYNnSpUtXrFmzZu18ND40NATAwMDAfDTfWq7X3pjr9XrHHXcwPDw86/kXWhhtWrNmzdqvf/3r89L4hg0bAFi3bt28tN9WrtfemOv1etJJJ3HzzTfP+ldLY2EUEYcCbwdOAPYFfgVcC3w8M69sqh1J7dTIDuyIeB5wI/BKYG9gI7AD+BPgmxFxXhPtSGqvrsMoIlYCFwG7A18A9svMJ2Xmo4GXUoXSX0fEi7ptS1J7NbFldDqwF3AbsD4zf9uZkJkXAp+sR1/TQFuSWqqJfUa3Um0Z/WdmDk0w/af18IAG2pLUUl2HUWZeDFw8RZEj6+FN3bYlqb16dmg/IvYE3gC8AtgOvL9XbUla+BoPo3pH9TuBxwIDVGdLvzYzv9d0W5LaoxdbRk8Fxp9ltRfwgoj4Xmbe123lQ0NDD57MNdc6Z7TOV/ttVcJ67evrY+3ayU/s37hxIwutw9O5Xq+d9marFxfKfhTYA9gPWA9sozqS9u2IWGhnfEuaI42HQ2beWT/cCnw6Iq4FfkK1I/s04IJu6h8YGJi3ywa8bKE3SluvR7z7SgaHd7Bs6WJuOOsEgCm3mko11+u122vgen4LkcxM4NJ69Lhetyd1a3B4B9tGdjA4vGO+F+Vhpesto4h4JHAQcHtmbpmk2O31cFa3FpDUfk1sGV0P/IjqurTJdE54vKuB9iS1UBNh9M16eHpE9O88MSIOBF5Yj36tgfYktVATYXQe1RGzQ4EL6wtnAYiIJwNXUF1E+z3gqw20J6mFug6jzLwFOAUYBE4G7oyIGyMigR8Dj6O6r9GLMnNhnaghac40cjQtMy8DDgf+EdgMPB54FPB9qnOMnjnFzm1Jau48o8y8GTijqfokPbzYVZGkIhhGkopgGEkqgmEkqQiGkaQiGEaSimAYSSqCYSSpCIaRpCIYRpKKYBhJKoJhJKkIhpGkIhhGkopgGEkqgmEkqQiGkaQiGEaSimAYSSqCYSSpCIaRpCIYRpKKYBhJKoJhJKkIhpGkIhhGkopgGEkqgmEkqQiGkaQiGEaSimAYSSqCYSSpCIaRpCIYRpKKYBhJKoJhJKkIhpGkIhhGkopgGEkqgmEkqQiGkaQiGEaSimAYSSqCYSSpCIaRpCIYRpKKYBhJKoJhJKkIhpGkIhhGkopgGEkqgmEkqQiGkaQiLGmysoh4DPBm4ETggPrpW4HLgL/PzLubbE9SezS2ZRQRxwI/A94IHArcAdwFBPBW4MaIeGJT7Ulql0bCKCL2BC4B9gS+AeyfmYdl5qHA44BrgH2Br0TEbk20KaldmtoyWg/sA/wSOCUzN3cmZOYtwAuBXwMHASc31KakFmkqjI6vh5dl5n07T8zMe4Af1KNHNdSmpBZpagf2e4AvAb+YokxfPVzcUJuSWqSRMMrM64HrJ5seESuB4+rRDU20Kald5uo8o48Ay4BBqh3dkvQQjZ5nNJGIOBN4ST36rm7PNRoaGmLDhvnZuBoaGgKYt/bbalfrta+vj5UrVz7kueXLlwMwODjI6Ojog89v2bKF0dHRCefZuczObaxdu3bSZdy4cWMj88yluf68dtqbrZ6GUUScDZxTj34V+EAv21O7Lb/nx9WDvj76OQR+cwfLRkaA6h9+6z5PmXye2kRlVIaehFFELAE+Dry6fuoK4MWZ2fXXxMDAAOvWreu2mlnpfMPMV/ttNe31unU53HEd9C2C1YdBfz9LN9dhs+Zolq9axapVqyaeZ6oy0zDVFhDAEe++ksHhHSxbupgbzjphWvP02lx/XgcGBrqav/EwiohHUB1ZO6F+6mLg5Zk53HRbehga3UFnS2hsfDrz9Nbg8A62jfS+nTZrdAd2fW3aNYwF0XnAqQaRpF1pbMsoIvYDvgMcAuwAXp+Zn2iqfknt1kgYRcRS4F+pgmiYav/Ql5uoW9LDQ1M/094GHFE/fp1BJGmmut4yqreK3lSPbgfWR8T6KWa5PDPf2227ktqliZ9pTwD2GlffMbsof3MDbUpqma7DKDNvYOwiWEmaFe+BLakIhpGkIhhGkopgGEkqgmEkqQiGkaQiGEaSimAYSSqCYSSpCIaRpCIYRpKKYBhJKoJhJKkIhpGkIhhGkopgGEkqgmEkqQiGkaQiGEaSimAYSSqCYSSpCIaRpCIYRpKKYBhJKoJhJKkIhpGkIhhGkopgGEkqgmEkqQiGkaQiGEaSimAYSSqCYSSpCIaRpCIYRpKKYBhJKoJhJKkIhpGkIhhGkopgGEkqgmEkqQiGkaQiGEaSimAYSSqCYSSpCIaRpCIYRpKKYBhJKoJhJKkIhpGkIhhGkopgGEkqgmEkqQg9DaOIOCMiRiPi9F62I2nh61kYRcRRwHm9ql9Su/QkjCLiOOAKYEUv6pfUPkuarCwidgPeDpwJLG6ybknt1tiWUUQ8FvgFcHb91JnA7U3VL6ndmvyZ9hhgf+Ba4OjMPLfBuiW1XJM/0+4ETsrMyxusU9LDRGNhlJk3Azc3VZ80M33zvQDqUqM7sOfC0NAQGzZsmLe2gXlrv612tV77+vpYuXIlywe3snXLPdC3mD1HRmBkhN9u2cJo3yL2XLsvI1u3Mjg4yOjoKADLly+nvy4DsM8BE7d/zz33VNP32QeA/sV9bBt5aJmNGzc+WO/45Vq7du2kr2vjxo0Ak5aZqM5d1TvZPBOZ689rp73ZWnBhJE2m/67rWDYyAoxCXx/9HLLrmW69mmWDg9Xj+5fBQcf2ZuFuvfqh471qZwFbcGE0MDDAunXr5qXtzjfMfLXfVtNer1uXs3zlPtC3CPr7ob+ffVaufMj40s0/htEdsKgfVh82Vmaco8/9dwCue8dzAFi+5WfVhDVHT9r0VFtA053nUxddBMCrTj112nUe8e4rAbjhrBNmvBxz/XkdGBjoav4FF0bSlEZ3wOjvq+Ekto3sNG2Ksk0aGtk+43kGh+dm2UrghbKSimAYSSqCYSSpCIaRpCIYRpKKYBhJKkJPD+1n5oG9rF9Se7hlJKkIhpGkIhhGkopgGEkqgmEkqQiGkaQiGEaSimAYSSqCYSSpCIaRpCIYRpKKYBhJKoJhJKkIhpGkIhhGkopgGEkqgmEkqQiGkaQiGEaSimAYSSqCYSSpCIaRpCIYRpKKYBhJKoJhJKkIhpGkIhhGkopgGEkqgmEkqQiGkaQiGEaSimAYSSqCYSSpCIaRpCIYRpKKYBhJKoJhJKkIhpGkIhhGkopgGEkqgmEkqQiGkaQiGEaSimAYSSqCYSSpCIaRpCIYRpKKYBhJKoJhJKkIhpGkIhhGkopgGEkqwpKmKoqIZcBbgRcDBwH3ATcAH87Mf2uqHUnt1MiWUUQsB74NnA0cDGwAtgLPBS6PiLObaEdSezX1M+3jwNHAT4BDMvMpmXkA8HJgO3BORDynobYktVDXYRQRhwCnAb8HXpqZmzrTMvOzwPvq0XO6bUtSezWxZfQyYDHww8zcOMH0T9TDYyJiTQPtSWqhJsLo6fXw+xNNzMy7gNvr0Wc10J6kFmoijB5bD/9nijK31cPHNdCepBZq4tD+qnp4zxRl7q2HKxtob9709fXN9yK00ozWa99i6Fs09fj44c6PgRUDO33sJ5m+e/9
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 300x300 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"baseline_i_pp_sig.RR_dist.hist(density=False, bins=bins)\n",
|
|||
|
|
"stimulated_11_pp_sig.RR_dist.hist(density=False, bins=bins, alpha=.5)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 413,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd12db50e10>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 413,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 300x300 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"baseline_i_pp_sig.slope_R_dist.hist(density=False, bins=bins)\n",
|
|||
|
|
"stimulated_11_pp_sig.slope_R_dist.hist(density=False, bins=bins, alpha=.5)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 414,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd12dbea400>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 414,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 300x300 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"bins = np.arange(-.25, .25, .01)\n",
|
|||
|
|
"baseline_i_pp.r_R_dist.hist(density=False, bins=bins)\n",
|
|||
|
|
"stimulated_11_pp.r_R_dist.hist(density=False, bins=bins, alpha=.5)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 415,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"baseline_i_pp_stimulated_11_pp = baseline_i_pp.merge(stimulated_11_pp, on='date_idnum')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 452,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"[<matplotlib.lines.Line2D at 0x7fd10241c7f0>]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 452,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.scatter(\n",
|
|||
|
|
" baseline_i_pp_stimulated_11_pp.circ_lin_corr_dist_x, \n",
|
|||
|
|
" baseline_i_pp_stimulated_11_pp.circ_lin_corr_dist_y,\n",
|
|||
|
|
")\n",
|
|||
|
|
"plt.plot([-.25, 0.25], [-.25, 0.25], '--k')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 446,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>RR_dist_x</th>\n",
|
|||
|
|
" <th>circ_lin_corr_dist_x</th>\n",
|
|||
|
|
" <th>RR_dist_y</th>\n",
|
|||
|
|
" <th>circ_lin_corr_dist_y</th>\n",
|
|||
|
|
" <th>action_y</th>\n",
|
|||
|
|
" <th>unit_idnum_y</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>13</th>\n",
|
|||
|
|
" <td>0.083627</td>\n",
|
|||
|
|
" <td>0.074713</td>\n",
|
|||
|
|
" <td>0.050477</td>\n",
|
|||
|
|
" <td>0.093518</td>\n",
|
|||
|
|
" <td>1833-120619-2</td>\n",
|
|||
|
|
" <td>233</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
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" RR_dist_x circ_lin_corr_dist_x RR_dist_y circ_lin_corr_dist_y \\\n",
|
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"13 0.083627 0.074713 0.050477 0.093518 \n",
|
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"\n",
|
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|
|
" action_y unit_idnum_y \n",
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"13 1833-120619-2 233 "
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]
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|
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|
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|||
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|
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|
|||
|
|
" 'RR_dist_x', 'circ_lin_corr_dist_x', \n",
|
|||
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" 'RR_dist_y', 'circ_lin_corr_dist_y', \n",
|
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|
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|
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|
|||
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|
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|
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|
|
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|
|||
|
|
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|
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|
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|||
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"source": [
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|||
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|
|||
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|
|||
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|
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|||
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"execution_count": 444,
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"text/plain": [
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" 'channel_group': 6,\n",
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|||
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|
" 'unit_name': 106,\n",
|
|||
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" 'circ_lin_corr_dist': -0.11445218085660203,\n",
|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
" 'RR_dist': 0.15160195974199475,\n",
|
|||
|
|
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|
|||
|
|
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|||
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|
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|
|
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|
|
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]
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|||
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]
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|
},
|
|||
|
|
"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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|
],
|
|||
|
|
"source": [
|
|||
|
|
"compute_phase_precession(\n",
|
|||
|
|
"# stimulated_11.query('action==\"1834-220319-2\" and unit_idnum==358').iloc[0],\n",
|
|||
|
|
" stimulated_11.query('action==\"1839-120619-2\" and unit_idnum==629').iloc[0],\n",
|
|||
|
|
" plot=True, plot_grid=True, flim=[6,10]\n",
|
|||
|
|
")"
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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": 450,
|
|||
|
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"metadata": {
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|||
|
|
"scrolled": false
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},
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"outputs": [
|
|||
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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 900x900 with 4 Axes>"
|
|||
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]
|
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},
|
|||
|
|
"metadata": {
|
|||
|
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},
|
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|
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
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]
|
|||
|
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},
|
|||
|
|
"metadata": {
|
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|
"needs_background": "light"
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|
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},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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"text/plain": [
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|||
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"<Figure size 900x900 with 4 Axes>"
|
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]
|
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},
|
|||
|
|
"metadata": {
|
|||
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},
|
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|
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAxAAAAMmCAYAAABl2mdNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydd5gV1dnAfwuIICxNELAgrrpHBURUFrEb7C6r0UQ0NizYUoTEJMa4G4PRJH7GGitq1NhjBRI1BhUVy6IiFvBFXClKEQSk153vj3Nm79zZmXvn7t4tyPt7nvvcuTPnnDlT7/ue85YCz/NQFEVRFEVRFEVJQoum7oCiKIqiKIqiKJsPqkAoiqIoiqIoipIYVSAURVEURVEURUmMKhCKoiiKoiiKoiRGFQhFURRFURRFURKjCoSiKIqiKIqiKIlRBUJRFEVRFEVRlMSoAqEoiqIoiqIoSmJUgVAURVEURVEUJTGqQCiKoiiKoiiKkhhVIBRFURRFURRFSYwqEIqiKIqiKIqiJEYVCEVRFEVRFEVREqMKhKIoiqIoiqIoiWnV1B1QFOX7gzGmNfAB0AcYLCLvJKizCzASOBroBRQAc4H/ATeLyOdZ6rcEzgTOAvYBCoEFQCVwv4i8kKAPPYHLgBOAImATMAcYD9wtIl9mayMpxpjdgZ8CPwB2BtoAi11/HwSeFxEvSxstgeHAMKAfsC2wxLVxZ8JjPhB73g8CugHfAlOB+0TkXwmPZSBwEXAE0BPYCAjwNPB3EVmZpJ1Ae7sBHwEtRKRNwjonAucBg4Au2PPwLnCviIzLZf+5Yoz5PfAn7D1ycZ7abIs9p6cABugILALeBG4VkbfysI/DgQuAwdjrBvA18Dr2uk1J0EZn7H1cBuwKtAfmAa8AN4rIpxnqHgy8kbC7fxSRqxOWVRSlkSjwvIz/U4qiKIkxxvwN+KX7mVWBMMb8GHgA2CamyDrgQhF5KKZ+F2AccGCG3TwNnC0iq2PaOBZ4DOgUU3818DMR+UeGfSTCGHMZ8H/AVhmKvQCcJiLLY9rYEXvM+2Ro4z7seauOaeMPwB+wyloUz7k+rIupX4A9jl9maGMmcKyIfJGhn8E22wATsNdyXTYFwhjTHngUGJqh2HPAT0RkTZI+5IJTnt4AtiZPCoQxZm9sn3eJKeIBo+sqUDsF/z6swp2J60Tk9xna+QH2mdkupsgG4FIRuTem/s+BW7P3GFAFQlGaJWrCpChKXjDG/I6U8pCkfAnwCFZ52ATcBhwPHAX8GViPFc7+YYw5KqJ+C2AsKeXhbaxgdDB2NuI9t/4U4P6YPvTHCmydAn0oxc4OXAl85/p3rzHm+KTHFrOvc4CbscrDMuCP2JH7A7Aj6FNd0eOAp5yQHm6jEzCRlPLwAnAydiT5YuzMDcD5QHlMPy4ArsYK/jPdvg/Azma864qdBNyR4XD+BvyK1GzRz7HnfSh21gZgN2C8MWbrDO34fdoK+BeZFcFg+RbAM6SUh0XAb7CzKUcBd2Kv50nA/1z7ecMY0xd77rMeWw5t7oIdvd8Fqyg8ir0XBwO/xs6sFAB/cPdSXbiblPIwAzuDcCBwCHAFsNBtu9IYc1VMPwdhr/F2WEXhduBY7PUfjVW4twLuNsYcEdMP//79BhiQ5XNXHY5TUZQGRmcgFEWpF25U8xasABsk4wyEMea/WGEP4NSw2Ywx5jCsQNUC+FhE9g5tHw74swL/AoYFTX+ckPkf4Bi36qCw+Ycx5jXgMPezLGzyYowpxppktcOa5uyZzbwo5ljbA7OwpkaLsedmZqhMa+ApUkLxGSLyaKjMXVjzFoDrReS3oe3dgQ+BHsBaYHsRWRrY3gX4AqswfQ4MCm1vhZ2xKXOrBolIZWgfg4FJWGH2E+AHIrIoVOZ+4Fz381IRuTPDuemJvX4HBVZnnIFwStAY93MGcISIzAuVGYpVDlsAvxaRG+LaywXX7j+xpkU+9Z6BMMa8gBXEAX4qIneEtu+BvRfbYk2FeonIphzaHwz49/+b2NmhVaEyPV2Z3lgF3ojIrMD2VsAUoC9WefiRiIwNtXE49rktACpFZFBEX94H9gVeFJHjkh6DoijNB52BUBSlzrhZhEmklIdEAo0blfZHJyujbO5FZCLwrPvZzxizc6jIBe57A3BJWLB35jtXBladGurDbqSUh2ei7OVFZAap2QsD9M90XBkowyoPABVh5cHtaz0wAns8AGeH+ruj2w7welh5cG0sBK5zP9tgR7CDnEvKVOuKoPLg6m8ELsSOIoMd+Q5zNVY43AicElYeHJcHjuNHEdsBMMachhWKfeUhqUD8C/ftYU2t5oULuOt5j/tZ4ZS4OmOM6WyMuQV4Hqs8JBbeE7Q9mJTy8FBYeQAQkc+wMysA25NwtibAuYHlC8PKg9vHfOzMEkBr4LRQkR9jlQeAP4eVB9fGa6Se2xJjzE7B7U4J6eN+fpjLASiK0nxQBUJRlDphjPkL8A6wv1v1PNZEJwldSAVxmJGh3CeB5Z6hbR8Dk4H/isi3MfWnB5Z7hba1x5rBzCQl8OTaRlIOCSzXErp8nALwsfsZVlaGkXpn/y7Dvp7GjpDfBHwV2nay+/4Oe73i+vBv9/N4Y0yNf4qb4Rjifv7DKVhRbSzBmqHdgZ0FqoUx5m2sHX0PoBrrk/FmhuPy63XDOo6DVaQyOfz6M1SFWPO4OuEczmdiFZcCYD7wk7q2F4Hf1iYg0nTI8RDWZ+gGYEWO+/DvwZkiMj1DuZcDy+F70O/nEqwPTBx3YhXv64GWoW17kTL9yuqsrShK80SjMCmKUlcOwApTS4DfiMh9xpirE9ZdhB3BbgXskaHcboHltFFmEbkkwX6CsxbzQ/U/xPpH1LmNHHgJWI5VgmqNlofwfR/CJjy+qcfcTJF43Gj82eH1zg9goPv5Zhbzl9exo83bYK/zK279UaQEwicy1EdE/pBpu2sXrII2QkQmOefcbASVuHdjS1mmBZYHA08maD+KYqzS62GF+FGkmzDVF//avi0ic+MKichU0mcScuFurGnSkizlgr43NfegmznwTQ7/kynCloj8DxtFLYqg878qEIqymaIKhKIodWUp8Ffgr2FTmGyIyEZjzH+wpj37G2N+KCJpswDGmAFYIRasmdOcXPbhQp1eE1iVUeCNaWMXUqZSM4H3c20DQESew9rjZ9vftqRG12eHNvvr3wuuNMYUYk1aVkSZ8gTYjVT0p4yhcbF+Ej57klIg+gXW1/TDCZc7Yv9T5sZFbwpRhR2hvs+ZTiWldWA52yj8hsBycQ77CFONnUm5WkQmAxhj8qJAGGPaYUMHg51RC27rDHQHlojIN/XZj4gknR08PLAcvAd3JzVzEO5nN6ArsNDNPmXCVyBWAAXGmJuxfkq9sVHXpmOf1TsT3keKojQBqkAoilJXTokLE5qQXwMlWBOWJ40xtwH/xQoRBwO/xQqL32Lt8rPilIbtsTb1lwP7uU13i8jrCeoXYEddi7A+Ez8HOrs+jajn8SbhclLv5ZcC/dqWVMjM2W7dyVh79QMD5eZizYZuihC+dggsZ1PGgqPgwXp7ue9lIvKdMaY3NvLOyVhHc4A1xpixwO+zhHDdvY7nc3FgeccsZYP29z3qsC+fh+NCCeeBPUiN+s929+D52HuvJnCAMWYGNvrVvQ11H7p9B31rXgos7xVYnu2UxlFYp/5dA21MwYaBfSpmN74CsRXWRDEYIasNdmbqAOASY8wJUf5CiqI0PeoDoShKnaivEOPs5w8AHsaaxYzChsZ8BSuUtsPa8w90phuJmsUKx49hlYe1WKH80oT1D8I6EH8CVGCVh2nA4c45tMFwdva+A+s64O+BzV0Dy8tcNKanqe1IuxPW9+AVN3odpEtgOdvIfdDBNpgfw+/HMhda92NsyNx2gTJtsf4aU4wxR8btoB73z0ysUglwQpYQrcEcEe1iS2WhgRXH4LVdhfWRGUNAeXAUY82QnnYRuxqCoEL6CekKRLCf1dhoTdcTUB4cA4B/GWNujwpFTEqBaIN91v6CnYEYjA0p7M9uFGPv4251OxRFURoSVSA
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|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
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"needs_background": "light"
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},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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"text/plain": [
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|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
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]
|
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},
|
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|
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"metadata": {
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},
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"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 900x900 with 4 Axes>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
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},
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|
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"output_type": "display_data"
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|
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},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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"text/plain": [
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|||
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"<Figure size 900x900 with 4 Axes>"
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]
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},
|
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|
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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|||
|
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"text/plain": [
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|||
|
|
"<Figure size 900x900 with 4 Axes>"
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|||
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]
|
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},
|
|||
|
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"metadata": {
|
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},
|
|||
|
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"output_type": "display_data"
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},
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|||
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{
|
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|
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"data": {
|
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|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
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|
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},
|
|||
|
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{
|
|||
|
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"data": {
|
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAxMAAAMmCAYAAACO7dxOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydd5wdVdn4v5uEFCB0CKETwj6otFUg9GKsuFlU1AjYqSJqsLeNmKCiPzTxFWwBKa8IiIAkC76WKNIkEQkdHjqhhQQIkF7v748zs3fu7Mzcmblzy26e7+czn3tn5rQ5M3fuec55SlupVMIwDMMwDMMwDCMrg5rdAMMwDMMwDMMw+icmTBiGYRiGYRiGkQsTJgzDMAzDMAzDyIUJE4ZhGIZhGIZh5MKECcMwDMMwDMMwcmHChGEYhmEYhmEYuTBhwjAMwzAMwzCMXJgwYRiGYRiGYRhGLkyYMAzDMAzDMAwjFyZMGIZhGIZhGIaRCxMmDMMwDMMwDMPIhQkThmEYhmEYhmHkwoQJwzAMwzAMwzByYcKEYRiGYRiGYRi5GNLsBhiGMbARkaHA3cBbgENU9c4UeXYHJgHvAnYB2oBngb8D01X1sSr5BwMfAz4O7A+MBBYAc4Hfquqfc17LTsADwObA91T1nDzlRJS7A3Am8G5gLLAJ8CowD7gKuEJV11Yp463AGcCRwE7AYOAl4A7gN6p6c4p27A18GTgGGA28ATwMXA5coqrrquQfDXwReB8wBlgHzAd6gF+r6lPV2hBR5ljgPmCQqg5Pmec44DPAOGArXF/OAS5S1VlZ25AFEfk2cC7ues8ooLw2oAv4BHAgsB2wBtevs4H/UdXHC6jn/cDJXh1bAQuBe3H3/g+qWkrIuxPu95mGy1T1UzHljAK+QPn5GQq8CPwLd513p6zDMIwG0lYqxb4fDMMwakZEfgJ8ydutKkyIyIeBS4GNY5KsAk5T1ctj8m8FzAIOTajmWuATqro8qS0RZd8IHOvtFiJMiMhHgIuBTROS/Qf4gKo+H5G/DfgR8BWc0BXHpcDpqro6ph0nA78ENorJfwfQqaqLY/K/B7gS2CIm/3LgLFW9JKGN4TKH4wbMhwKrqgkTIrIp8HtgQkKyPwEnquqKtO1Ii4gcCNwKDKMAYUJEtgCuxgnVcawBJqnqL3LWMRx3396fkOxfwEdUdWFMGROAmSmrjBQmROQo3O9y65h864HvqOoPU9ZjGEaDMDUnwzDqhoh8k7IgkSb9QcAVOEFiHfBz3OD9ncAPgdW4gdolIvLOiPyDcIMaX5D4N26F4nDcKsVd3vHjgd9mvJZPUhYkCkFExuMGv5sCK4Gf4gaO44ATgFu8pAcCN4lIlIA1GfgqTpB4wft+BK4PPg886aX7FBA54BSRdwEzcILES16+Q4DjgL94yQ4FrvGEl3D+/XCD9C0o37dO4O3At4DXcff0IhFJ1YcishFwDclCYTD9IOA6yoLEIuBrwGG45+eXXtveD/zdK78wvFWdP+OezyLKa8MNrn1B4i7g08DBuJWjc4EluHt2oYh8LGdVl1AWJB7GrU4cDkwE/uodPwq4TkTitBn29z7XAwcBHQnb5HBmEdkVuIGyIPE7r03H4J6fN3DjlR+IyGfyXKRhGPXDViYMwygcT7XpZzi1myCJKxMi8lfcwA/cTOg1ofNHAf/ADSzuV9V9Q+c/hRscgRuITgyqZ3gDzptw6kQAh6nqHSmuZzTwILBl4HBNKxPeYPEhYC+cIHFMuG+8NL+g3I/fVtUfBM7vCjyKUwd5FDhcVReFytgMN7t/gHeo4pq9QfXDwB7Ay8ABqvpMqIwLcWpY4Pr0D6HzN+MGnABdYVUiEWnHqbptAijwpipqM6Nx9++wwOHElQkROQUnEIHri2NU9YVQmgk4oWcQ8FVVPT+uvCx45f4vTv3Np6aVCRH5KG7FAFxfnBhWdRORNwO34Z7LV4DdVXVJhjqOBv7p7f4bODq8ciUivwFO9XZPUtXfR5RzLfBB4BFVfVPa+mPq6HNfRGQv4L84gfQVYIe4FTbDMBqPrUwYhlEo3urC7ZQHwIl69oF8w3AzkQBzw4IEgKr+C7je293HG0wHOcX7XAN8NjxgVdX1uJlOn4+kaRvwK9yA7eWU6dNwCE6QAKcP3kfI8tp/Nk5/HZzefJCTcIIEOFWXRaHzqOobwOmBQ+EyunCCBMAPw4KEx5dwqx7gVj568WwafEHiuiibBFV9lPJKkAD7RdThl/dRnODhCxKpnh+crj1ACfhoWJDw2jEL+I23O9lTi8qNiGwpIj/DzapvnqGtafBn4FfgnuU+NjOq+hBuhQLcrP77MtZxSuB7nArcNwLf434v/srEPRnr93mv97kA+En4pKo+gltZAnedh+SsxzCMOmDChGEYhSEi5wF3Up4FvwGYnjL7VpSdQjyakO6BwPfRoXP34+wL/qqqr8TkfzjwfZdqjRKRE3ED7tU4u4SiOCLwPVbfXFVX4mafveZIUI3GL2MF8LeEMu7GGSFD34H8B73PEm52PSr/Kpz6GcABIrJb4PSmOPWixykLelFU7XcR+TduNn57nMrMdylfeywisi2wj7d7i6rOS0jur1yNpAa1NRE5FHfNX8CpmL0InJi3vFDZbZSFqdsTnmWovO+xQloMT+IE/3tU9f6oBKr6KmVhts9981a+dvd2k/o9ie28z8cTVqySfveGYTQR8+ZkGEaRHIwbWL0KfE1VLxaRc1LmXQSsxb2X9kpINzbwvWL2WVU/m6Ke4GrGi0kJRWQ74H+83R/gVJ2KYi7ODmQH3KA0iaCdwnCcETo449wHgY2qeXsKlBFWFfIHrQ9FrWwEuIXyqsTb8VYaVPUenA1KNdL0+8He58PAqap6u4i8PUXZwUHunCppHwp8PwT4Q1zCKrTjBOASzuPR2VSqOdXCMGAqsCOVQlgU4WcjNao6mQgbhiCesOCr90Xdt/0DbcgrTLwA7Aa0i0hbjEAR+7s3DKO5mDBhGEaRLMZ5FvpRnNefOFR1rYjchFsFOEBEPqCqFTPdItIBfNjbnauq87PU4bmMnRo4dHWVLL/AqVXcjxMmss78xqKq/6Ssrx6LZ9PgD/hfV9XXA2VcmqYuEdmX8oDwmcDxjSkPxBPd7QJPBL5n0ov3XP36KjWP4/Tfo3gS+DFwcQrhKMjQwPdqNgNrAt/bM9QRZj3O/uYcVf0PgIgUIkx4q1HnpUx+dOB7lIparZxD2cNX1O9l/8D3l0TkXJwR/J44tS9/xWq6p3IXxUzcCs92OJfQ04InPdezvs3Oc7jVT8MwWgQTJgzDKJLjPbuEvHwV5w1me+APIvJznEeZVTgPM1/HDRxfAU5LU6AnQOyAG5B/BXibd+rXqnpLQr4P42bc1wEnq+oaEclzTbXyGcpqIH9JSpjANwPfg2XsQHlWuZpgFowjsGNSQk9NZzguVsBHcN6htsTdx1MTnpE9cz4/QVuWnaqk3Tnwffscdfn8Ls49caPw3LpOChzK+3wEyxwEjML9Ts7GrUKBE5z6GF9T6cnpDpyRffj8/sBnReQ4VZ0bUcb3cU4RBPiJJ/xej5ucOABn57QlzlHBZ8z42jBaC7OZMAyjMGoUJHxD3YNxriEH4wYzf8Z5cJqCG6hcCxyoqvemLRY3UL4SN0BaiRMqzozLICLbABd4u9P8medG4xk3B2eo+xinpijjw8BHvd2FVNpFbBX4Xm1Gf1nge1wsCZ/DcHElHsCp0WyJUy86Oil4Xg3Pz+M4ARPgfVXcvgZjUIQHvqmp9VkviPMp2yv8WVWrqUSl4a84NaJZOEFiPe4ZPD5mtajD+xyEm6D8Bc4W5WCcDYlv07E98DfPs1cFXvyKw3G/ubU4N8Y34FTrfgpsg1vFG6eqsbZBhmE0BxMmDMNoNQ7FRcuOC8B2BHC8N4OaiDdDvnPo8HDcbHlnQtaf41YDHqeKTnm98Ow1eigP3C+KmdVNKuNQXLA6ny+p6tLAftCYe2VSWZ4eu2+rUS2WQtjLFrhVijNFpKrRe1a8tvkG4jvhVNL64Hn/CnrzKjTWRCMRkUnA57z
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
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]
|
|||
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},
|
|||
|
|
"metadata": {
|
|||
|
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"needs_background": "light"
|
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},
|
|||
|
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
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]
|
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},
|
|||
|
|
"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"image/png": "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|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
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|
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},
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|
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"output_type": "display_data"
|
|||
|
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},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"for row in baseline_i_pp.query('pval_dist < 0.01 and RR_dist > .1').itertuples():\n",
|
|||
|
|
" compute_phase_precession(\n",
|
|||
|
|
" row, plot=True, plot_grid=True, flim=[6,10]\n",
|
|||
|
|
" )"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# The inverted grid"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 31,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x648 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"fig, axs = plt.subplots(1, 2, figsize=(16,9))\n",
|
|||
|
|
"row = baseline_i.sort_values('gridness', ascending=False).iloc[9]\n",
|
|||
|
|
"lfp = data_loader.lfp(row.action, row.channel_group)\n",
|
|||
|
|
"spikes = data_loader.spike_train(row.action, row.channel_group, row.unit_name)\n",
|
|||
|
|
"rate_map = data_loader.rate_map(row.action, row.channel_group, row.unit_name, smoothing=0.04)\n",
|
|||
|
|
"pos_x, pos_y, pos_t, pos_speed = map(data_loader.tracking(row.action).get, ['x', 'y', 't', 'v'])\n",
|
|||
|
|
"spikes = np.array(spikes)\n",
|
|||
|
|
"spikes = spikes[(spikes > pos_t.min()) & (spikes < pos_t.max())]\n",
|
|||
|
|
"\n",
|
|||
|
|
"axs[0].imshow(rate_map.T, extent=[0, box_size[0], 0, box_size[1]], origin='lower')\n",
|
|||
|
|
"axs[1].plot(pos_x, pos_y, color='k', alpha=.2, zorder=1000)\n",
|
|||
|
|
"axs[1].scatter(interp1d(pos_t, pos_x)(spikes), interp1d(pos_t, pos_y)(spikes), s=10, zorder=10001)\n",
|
|||
|
|
"\n",
|
|||
|
|
"for ax in axs:\n",
|
|||
|
|
" ax.axis('image')\n",
|
|||
|
|
" ax.set_xticks([])\n",
|
|||
|
|
" ax.set_yticks([])\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 3
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
|
|||
|
|
"version": "3.6.8"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|