3973 lines
16 MiB
Plaintext
3973 lines
16 MiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"INFO: Pandarallel will run on 8 workers.\n",
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"INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
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]
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}
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],
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"source": [
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"import exdir\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.mlab as mlab\n",
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"from scipy.interpolate import interp1d\n",
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"import os\n",
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"import expipe\n",
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"import pathlib\n",
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"import numpy as np\n",
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"import pnnmec\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 re\n",
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"import joblib\n",
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"import multiprocessing\n",
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"from distutils.dir_util import copy_tree\n",
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"import copy\n",
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"import pandas as pd\n",
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"from scipy.io import loadmat\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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"import pnnmec.spikes\n",
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"from spike_statistics.core import permutation_resampling\n",
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"import scipy\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 skimage import measure\n",
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"from tqdm.notebook import tqdm_notebook as tqdm\n",
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"tqdm.pandas()\n",
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"import seaborn as sns\n",
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"from pandarallel import pandarallel\n",
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"pandarallel.initialize(progress_bar=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"sample_rate = 250\n",
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"\n",
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"max_speed = .5 # m/s only used for speed score\n",
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"min_speed = 0.02 # m/s only used for speed score\n",
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"\n",
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"box_size = [1.0, 1.0]\n",
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"bin_size = 0.02\n",
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"\n",
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"speed_binsize = 0.02\n",
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"\n",
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"smoothing = 0.04"
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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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"# %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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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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": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"data_path = pathlib.Path('sargolini2006/all_data/')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = {}\n",
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"for fname in data_path.iterdir():\n",
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" if not fname.is_file():\n",
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" continue\n",
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" try:\n",
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" action, ftype = fname.stem.split('_')\n",
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" except Exception as e:\n",
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" print(fname)\n",
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" raise e\n",
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" if ftype == 'EGF':\n",
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" continue\n",
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" if action not in data:\n",
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" data[action] = {}\n",
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" data[action][ftype] = loadmat(fname)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"def rm_nans(*args):\n",
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" \"\"\"\n",
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" Removes nan from all corresponding arrays\n",
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" Parameters\n",
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" ----------\n",
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" args : arrays, lists or quantities which should have removed nans in\n",
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" all the same indices\n",
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" Returns\n",
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" -------\n",
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" out : args with removed nans\n",
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" \"\"\"\n",
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" nan_indices = []\n",
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" for arg in args:\n",
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" nan_indices.extend(np.where(np.isnan(arg))[0].tolist())\n",
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" nan_indices = np.unique(nan_indices)\n",
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" out = []\n",
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" for arg in args:\n",
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" out.append(np.delete(arg, nan_indices))\n",
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" return out"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:18: DeprecationWarning: using a non-integer array as obj in delete will result in an error in the future\n"
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]
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}
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],
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"source": [
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"analog, cells = [], []\n",
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"for k, v in data.items():\n",
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" if 'POS' not in v:\n",
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" continue\n",
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" if set(['EEG', 'EG2']).intersection(set(v.keys())) == set():\n",
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" continue\n",
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" x, y, t = rm_nans(v['POS']['posx'], v['POS']['posy'], v['POS']['post'])\n",
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" analog.append({\n",
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" 'action': k, \n",
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" 'eeg': None if 'EEG' not in v else v['EEG']['EEG'], \n",
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" 'eeg2': None if 'EG2' not in v else v['EG2']['EEG'],\n",
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" 'x': x,\n",
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" 'y': y,\n",
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" 't': t\n",
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" })\n",
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" for kk, vv in v.items():\n",
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" if kk.startswith('T'):\n",
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" cells.append({\n",
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" 'action': k,\n",
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" 'channel': kk[1],\n",
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" 'unit': kk[3:],\n",
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" 'spikes': vv['cellTS']\n",
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" })\n",
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"analog = pd.DataFrame(analog)\n",
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"cells = pd.DataFrame(cells)"
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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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"# Plotting"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"from scipy.signal import butter, filtfilt\n",
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"\n",
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"def butter_bandpass(lowcut, highcut, fs, order=5):\n",
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" nyq = 0.5 * fs\n",
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" low = lowcut / nyq\n",
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" high = highcut / nyq\n",
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" b, a = butter(order, [low, high], btype='band')\n",
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" return b, a\n",
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"\n",
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"\n",
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"def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):\n",
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" b, a = butter_bandpass(lowcut, highcut, fs, order=order)\n",
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" y = filtfilt(b, a, data)\n",
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" return y"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"def compute_spike_phase(lfp, times):\n",
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" x_a = ss.hilbert(lfp)\n",
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" x_phase = np.angle(x_a)\n",
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" return interp1d(times, x_phase)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"def find_grid_fields(rate_map, sigma=3, seed=2.5):\n",
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" # find fields with laplace\n",
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" fields_laplace = sp.fields.separate_fields_by_dilation(rate_map, sigma=sigma, seed=seed)\n",
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" fields = fields_laplace.copy() # to be cleaned by Ismakov\n",
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" fields_areas = scipy.ndimage.measurements.sum(\n",
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" np.ones_like(fields), fields, index=np.arange(fields.max() + 1))\n",
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" fields_area = fields_areas[fields]\n",
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" fields[fields_area < 9.0] = 0\n",
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"\n",
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" # find fields with Ismakov-method\n",
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" fields_ismakov, radius = sp.separate_fields_by_distance(rate_map)\n",
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" fields_ismakov_real = fields_ismakov * bin_size\n",
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" approved_fields = []\n",
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"\n",
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" # remove fields not found by both methods\n",
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" for point in fields_ismakov:\n",
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" field_id = fields[tuple(point)]\n",
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" approved_fields.append(field_id)\n",
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"\n",
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" for field_id in np.arange(1, fields.max() + 1):\n",
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" if not field_id in approved_fields:\n",
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" fields[fields == field_id] = 0\n",
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" \n",
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" return fields"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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|
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"def normalize(a):\n",
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" _a = a - a.min()\n",
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" return _a / _a.max()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"def distance(x, y):\n",
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" _x = x - x.min()\n",
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" _y = y - y.min()\n",
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" dx, dy = np.diff(_x), np.diff(_y)\n",
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" s = np.sqrt(dx**2 + dy**2)\n",
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" distance = np.cumsum(s) \n",
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" # first index is distance from first point, \n",
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" # to match len(x) we put a zero as first index to initialize distance 0\n",
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" return np.concatenate(([0], distance))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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|||
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"source": [
|
|||
|
|
"def model(x, slope, phi0):\n",
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" return 2 * np.pi * slope * x + phi0"
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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": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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|
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"# channel_to_eeg = {str(i): '' if i < 4 else '2' for i in range(1,9)}\n",
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"\n",
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"channel_to_eeg = {str(i): '' if i < 4 else '2' for i in reversed(range(1,9))}"
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]
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},
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{
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|||
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"cell_type": "code",
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"execution_count": 17,
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|||
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"metadata": {},
|
|||
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"outputs": [
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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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"{'8': '2', '7': '2', '6': '2', '5': '2', '4': '2', '3': '', '2': '', '1': ''}"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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|||
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],
|
|||
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"source": [
|
|||
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"channel_to_eeg"
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|
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]
|
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|
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},
|
|||
|
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{
|
|||
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"cell_type": "code",
|
|||
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"execution_count": 18,
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|||
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"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def compute_statistics(row):\n",
|
|||
|
|
" a = analog.query(f'action==\"{row.action}\"')\n",
|
|||
|
|
" lfp = a.get(f'eeg{channel_to_eeg[row.channel]}')\n",
|
|||
|
|
" get_values = lambda x: a.get(x).values[0]\n",
|
|||
|
|
" pos_x, pos_y, pos_t = map(get_values, ['x', 'y', 't'])\n",
|
|||
|
|
" box_size_, bin_size_ = sp.maps._adjust_bin_size(box_size=box_size, bin_size=bin_size)\n",
|
|||
|
|
" xbins, ybins = sp.maps._make_bins(box_size_, bin_size_)\n",
|
|||
|
|
" \n",
|
|||
|
|
" pos_x, pos_y, pos_t = pos_x.ravel(), pos_y.ravel(), pos_t.ravel()\n",
|
|||
|
|
" \n",
|
|||
|
|
" if sum(np.isnan(np.concatenate([pos_x, pos_y]))) > 0:\n",
|
|||
|
|
" print(\n",
|
|||
|
|
" 'Nan in position',\n",
|
|||
|
|
" f'nnanx = {sum(np.isnan(pos_x))}',\n",
|
|||
|
|
" f'nnany = {sum(np.isnan(pos_y))}',\n",
|
|||
|
|
" f'shape = {pos_x.shape}', \n",
|
|||
|
|
" row.action, row.channel, row.unit)\n",
|
|||
|
|
" return\n",
|
|||
|
|
" \n",
|
|||
|
|
" pos_x, pos_y = normalize(pos_x.ravel()), normalize(pos_y.ravel())\n",
|
|||
|
|
" \n",
|
|||
|
|
" occupancy_map = sp.maps._occupancy_map(pos_x, pos_y, pos_t, xbins, ybins)\n",
|
|||
|
|
" \n",
|
|||
|
|
" occupancy_map = sp.maps.smooth_map(occupancy_map, bin_size=bin_size_, smoothing=smoothing)\n",
|
|||
|
|
"\n",
|
|||
|
|
" spikes = row.spikes.ravel()\n",
|
|||
|
|
"\n",
|
|||
|
|
" # common\n",
|
|||
|
|
" spike_map = sp.maps._spike_map(pos_x, pos_y, pos_t, spikes, xbins, ybins)\n",
|
|||
|
|
"\n",
|
|||
|
|
" spike_map = sp.maps.smooth_map(spike_map, bin_size=bin_size_, smoothing=smoothing)\n",
|
|||
|
|
"\n",
|
|||
|
|
" rate_map = spike_map / occupancy_map\n",
|
|||
|
|
" \n",
|
|||
|
|
" fields = find_grid_fields(rate_map, sigma=3, seed=2.5)\n",
|
|||
|
|
" \n",
|
|||
|
|
" prob_dist = sp.stats.prob_dist(pos_x, pos_y, bins=(xbins, ybins))\n",
|
|||
|
|
" \n",
|
|||
|
|
" average_rate = len(spikes) / (pos_t.max() - pos_t.min())\n",
|
|||
|
|
" \n",
|
|||
|
|
" max_rate = rate_map.max()\n",
|
|||
|
|
"\n",
|
|||
|
|
" out_field_mean_rate = rate_map[np.where(fields == 0)].mean()\n",
|
|||
|
|
" in_field_mean_rate = rate_map[np.where(fields != 0)].mean()\n",
|
|||
|
|
" max_field_mean_rate = rate_map[np.where(fields == 1)].mean()\n",
|
|||
|
|
"\n",
|
|||
|
|
" interspike_interval = np.diff(spikes)\n",
|
|||
|
|
" interspike_interval_cv = interspike_interval.std() / interspike_interval.mean()\n",
|
|||
|
|
"\n",
|
|||
|
|
" autocorrelogram = sp.autocorrelation(rate_map)\n",
|
|||
|
|
" peaks = sp.fields.find_peaks(autocorrelogram)\n",
|
|||
|
|
" real_peaks = peaks * bin_size\n",
|
|||
|
|
" autocorrelogram_box_size = np.array(box_size) * autocorrelogram.shape[0] / rate_map.shape[0]\n",
|
|||
|
|
" spacing, orientation = sp.spacing_and_orientation(real_peaks, autocorrelogram_box_size)\n",
|
|||
|
|
" orientation *= 180 / np.pi\n",
|
|||
|
|
"\n",
|
|||
|
|
" selectivity = sp.stats.selectivity(rate_map, prob_dist)\n",
|
|||
|
|
"\n",
|
|||
|
|
" sparsity = sp.stats.sparsity(rate_map, prob_dist)\n",
|
|||
|
|
"\n",
|
|||
|
|
" gridness = sp.gridness(rate_map)\n",
|
|||
|
|
"\n",
|
|||
|
|
" information_rate = sp.stats.information_rate(rate_map, prob_dist)\n",
|
|||
|
|
"\n",
|
|||
|
|
" single_spikes, bursts, bursty_spikes = pnnmec.spikes.find_bursts(spikes, threshold=0.01)\n",
|
|||
|
|
" burst_event_ratio = np.sum(bursts) / (np.sum(single_spikes) + np.sum(bursts))\n",
|
|||
|
|
" bursty_spike_ratio = np.sum(bursty_spikes) / (np.sum(bursty_spikes) + np.sum(single_spikes))\n",
|
|||
|
|
" mean_spikes_per_burst = np.sum(bursty_spikes) / np.sum(bursts)\n",
|
|||
|
|
" results = {\n",
|
|||
|
|
" 'action': row.action,\n",
|
|||
|
|
" 'channel': row.channel,\n",
|
|||
|
|
" 'unit': row.unit,\n",
|
|||
|
|
" 'average_rate': average_rate,\n",
|
|||
|
|
" 'out_field_mean_rate': out_field_mean_rate,\n",
|
|||
|
|
" 'in_field_mean_rate': in_field_mean_rate,\n",
|
|||
|
|
" 'max_field_mean_rate': max_field_mean_rate,\n",
|
|||
|
|
" 'max_rate': max_rate,\n",
|
|||
|
|
" 'sparsity': sparsity,\n",
|
|||
|
|
" 'selectivity': selectivity,\n",
|
|||
|
|
" 'interspike_interval_cv': interspike_interval_cv,\n",
|
|||
|
|
" 'burst_event_ratio': burst_event_ratio,\n",
|
|||
|
|
" 'bursty_spike_ratio': bursty_spike_ratio,\n",
|
|||
|
|
" 'gridness': gridness,\n",
|
|||
|
|
" 'information_rate': information_rate,\n",
|
|||
|
|
" 'spacing': spacing,\n",
|
|||
|
|
" 'orientation': orientation\n",
|
|||
|
|
" }\n",
|
|||
|
|
" return results"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:45: RuntimeWarning: Mean of empty slice.\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
|
|||
|
|
" ret = ret.dtype.type(ret / rcount)\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:45: RuntimeWarning: Mean of empty slice.\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:45: RuntimeWarning: Mean of empty slice.\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
|
|||
|
|
" ret = ret.dtype.type(ret / rcount)\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:45: RuntimeWarning: Mean of empty slice.\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
|
|||
|
|
" ret = ret.dtype.type(ret / rcount)\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
|
|||
|
|
" ret = ret.dtype.type(ret / rcount)\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:45: RuntimeWarning: Mean of empty slice.\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
|
|||
|
|
" ret = ret.dtype.type(ret / rcount)\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:45: RuntimeWarning: Mean of empty slice.\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
|
|||
|
|
" ret = ret.dtype.type(ret / rcount)\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:45: RuntimeWarning: Mean of empty slice.\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
|
|||
|
|
" ret = ret.dtype.type(ret / rcount)\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:45: RuntimeWarning: Mean of empty slice.\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
|
|||
|
|
" ret = ret.dtype.type(ret / rcount)\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:68: RuntimeWarning: invalid value encountered in long_scalars\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:68: RuntimeWarning: invalid value encountered in long_scalars\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply\n",
|
|||
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"cells_stats = cells.parallel_apply(compute_statistics, axis=1, result_type='expand')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"cells = cells.merge(cells_stats, on=['action', 'channel', 'unit'])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def plot_spikes_and_rate_map(row, rate_map, fields, pos_x, pos_y, sy, sx, spikes, box_size, bin_size, dot_size, flim, output_path, save, axs):\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(sx(spikes), sy(spikes), s=dot_size, 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",
|
|||
|
|
" if save:\n",
|
|||
|
|
" figname = f'{row.action}_{row.channel}_{row.unit_id}_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",
|
|||
|
|
"def plot_spike_phase(spike_dist, spike_phase, dot_size, slope, phi0, circ_lin_corr, pval, RR, plot_regression_line, ax):\n",
|
|||
|
|
" p = ax.scatter(spike_dist, spike_phase, s=dot_size)\n",
|
|||
|
|
" ax.scatter(\n",
|
|||
|
|
" spike_dist, spike_phase + 2 * np.pi, \n",
|
|||
|
|
" s=dot_size, color=p.get_facecolor()[0])\n",
|
|||
|
|
" ax.set_yticks([-np.pi, np.pi, 3*np.pi])\n",
|
|||
|
|
" ax.set_yticklabels([r'$-\\pi$', r'$\\pi$', r'$3\\pi$'])\n",
|
|||
|
|
" if plot_regression_line:\n",
|
|||
|
|
" line_fit = model(spike_dist, slope, phi0)\n",
|
|||
|
|
" ax.plot(spike_dist, line_fit, lw=2, label=\n",
|
|||
|
|
" f'corr = {circ_lin_corr:.3f}, '\n",
|
|||
|
|
" f'pvalue = {pval:.3f}, '\n",
|
|||
|
|
" f'R = {RR:.3f}')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 22,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def compute_rate_crossings(spikes, times, threshold=0):\n",
|
|||
|
|
" from elephant.statistics import instantaneous_rate\n",
|
|||
|
|
" from elephant.kernels import GaussianKernel\n",
|
|||
|
|
" import quantities as pq\n",
|
|||
|
|
" import neo\n",
|
|||
|
|
" spikes = neo.SpikeTrain(spikes, t_stop=times[-1], units='s')\n",
|
|||
|
|
" \n",
|
|||
|
|
" kernel = GaussianKernel(100 * pq.ms)\n",
|
|||
|
|
" \n",
|
|||
|
|
" rate = instantaneous_rate(spikes, np.diff(times).min() * pq.s, kernel=kernel)\n",
|
|||
|
|
" \n",
|
|||
|
|
" rate = rate.magnitude\n",
|
|||
|
|
" mean = np.mean(rate)\n",
|
|||
|
|
" std = np.std(rate)\n",
|
|||
|
|
" indices = np.where(rate > mean + std * threshold)[0]\n",
|
|||
|
|
" \n",
|
|||
|
|
" field_indices = np.concatenate(([0], indices.astype(int), [0]))\n",
|
|||
|
|
" enter, = np.where(np.diff(field_indices) > 1)\n",
|
|||
|
|
" exit = enter[1:] - 1\n",
|
|||
|
|
" return rate, indices, indices[enter], indices[exit]\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def plot_map_spikes(rate_map, fields, spikes, x, y, t, box_size, dot_size=1, axs=None):\n",
|
|||
|
|
" if axs is None:\n",
|
|||
|
|
" fig, axs = plt.subplots(1, 2)\n",
|
|||
|
|
" contours = measure.find_contours(fields, 0.8)\n",
|
|||
|
|
" sx, sy = interp1d(t, x), interp1d(t, y)\n",
|
|||
|
|
" # Display the image and plot all contours found\n",
|
|||
|
|
" axs[0].imshow(rate_map.T, extent=[0, box_size[0], 0, box_size[1]], origin='lower')\n",
|
|||
|
|
" axs[1].plot(x, y, color='k', alpha=.2, zorder=1000)\n",
|
|||
|
|
" axs[1].scatter(sx(spikes), sy(spikes), s=dot_size, zorder=10001)\n",
|
|||
|
|
"\n",
|
|||
|
|
" for ax in axs:\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:\n",
|
|||
|
|
" ax.axis('image')\n",
|
|||
|
|
" ax.set_xticks([])\n",
|
|||
|
|
" ax.set_yticks([])\n",
|
|||
|
|
" return axs"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 23,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def compute_data(row, flim=[6,10]):\n",
|
|||
|
|
" a = analog.query(f'action==\"{row.action}\"')\n",
|
|||
|
|
" lfp = a.get(f'eeg{channel_to_eeg[row.channel]}').values[0]\n",
|
|||
|
|
" if lfp is None:\n",
|
|||
|
|
" print('Wrong hemisphere?', row.action, row.channel, row.unit)\n",
|
|||
|
|
" l = ['eeg', 'eeg2']\n",
|
|||
|
|
" l.pop(l.index(f'eeg{channel_to_eeg[row.channel]}'))\n",
|
|||
|
|
" lfp = a.get(l[0]).values[0]\n",
|
|||
|
|
" if lfp is not None:\n",
|
|||
|
|
" print('Warning: using lfp from other hemisphere')\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" return [None] * 7\n",
|
|||
|
|
" lfp = lfp.ravel()\n",
|
|||
|
|
" get_values = lambda x: a.get(x).values[0]\n",
|
|||
|
|
" pos_x, pos_y, pos_t = map(get_values, ['x', 'y', 't'])\n",
|
|||
|
|
" \n",
|
|||
|
|
" pos_x, pos_y, pos_t = pos_x.ravel(), pos_y.ravel(), pos_t.ravel()\n",
|
|||
|
|
" pos_x, pos_y = normalize(pos_x.ravel()), normalize(pos_y.ravel())\n",
|
|||
|
|
" \n",
|
|||
|
|
" box_size_, bin_size_ = sp.maps._adjust_bin_size(box_size=box_size, bin_size=bin_size)\n",
|
|||
|
|
" xbins, ybins = sp.maps._make_bins(box_size_, bin_size_)\n",
|
|||
|
|
" occupancy_map = sp.maps._occupancy_map(pos_x, pos_y, pos_t, xbins, ybins)\n",
|
|||
|
|
" \n",
|
|||
|
|
" occupancy_map = sp.maps.smooth_map(occupancy_map, bin_size=bin_size_, smoothing=smoothing)\n",
|
|||
|
|
"\n",
|
|||
|
|
" spikes = row.spikes\n",
|
|||
|
|
" \n",
|
|||
|
|
" spikes = spikes[(spikes >= pos_t[0]) & (spikes <= pos_t[-1])]\n",
|
|||
|
|
"\n",
|
|||
|
|
" # common\n",
|
|||
|
|
" spike_map = sp.maps._spike_map(pos_x, pos_y, pos_t, spikes, xbins, ybins)\n",
|
|||
|
|
"\n",
|
|||
|
|
" spike_map = sp.maps.smooth_map(spike_map, bin_size=bin_size_, smoothing=smoothing)\n",
|
|||
|
|
"\n",
|
|||
|
|
" rate_map = spike_map / occupancy_map\n",
|
|||
|
|
"\n",
|
|||
|
|
" filtered_lfp = butter_bandpass_filter(\n",
|
|||
|
|
" lfp, *flim, fs=sample_rate, order=3)\n",
|
|||
|
|
" \n",
|
|||
|
|
" times = np.arange(len(lfp)) / sample_rate\n",
|
|||
|
|
" spike_phase_func = compute_spike_phase(filtered_lfp, times)\n",
|
|||
|
|
" \n",
|
|||
|
|
" fields = find_grid_fields(rate_map, sigma=3, seed=2.5)\n",
|
|||
|
|
" \n",
|
|||
|
|
" return spike_phase_func, spikes, pos_x, pos_y, pos_t, rate_map, fields"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 24,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def compute_phase_precession_runs(row, flim=[6, 10], field_num=None, slope_limit_dist=[-1,1], slope_limit_dur=[-1,1], norm=False, store_runs=False,\n",
|
|||
|
|
" plot=False, plot_regression_line=True, save=False, dot_size=1, crossing_type='field'):\n",
|
|||
|
|
" '''\n",
|
|||
|
|
" flim : [flow, fhigh]\n",
|
|||
|
|
" field_num : int\n",
|
|||
|
|
" which field to plot\n",
|
|||
|
|
" reg_limit : [low, high]\n",
|
|||
|
|
" Regression intervall\n",
|
|||
|
|
" plot : str\n",
|
|||
|
|
" \"all\" or \"scatter\"\n",
|
|||
|
|
" plot_regression_line : bool\n",
|
|||
|
|
" plot the regression line in scatter\n",
|
|||
|
|
" save : bool\n",
|
|||
|
|
" save plot\n",
|
|||
|
|
" dot_size : size of scatter dots\n",
|
|||
|
|
" crossing_type : str\n",
|
|||
|
|
" type of signal to compute crossings must be \"rate\" or \"field\"\n",
|
|||
|
|
" '''\n",
|
|||
|
|
" spike_phase_func, spikes, pos_x, pos_y, pos_t, rate_map, fields = compute_data(\n",
|
|||
|
|
" row, flim)\n",
|
|||
|
|
" if spike_phase_func is None:\n",
|
|||
|
|
" return\n",
|
|||
|
|
" \n",
|
|||
|
|
" if field_num is not None:\n",
|
|||
|
|
" fields = np.where(fields == field_num, fields, 0)\n",
|
|||
|
|
" \n",
|
|||
|
|
" if crossing_type == 'field':\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",
|
|||
|
|
" elif crossing_type == 'rate':\n",
|
|||
|
|
" _, _, in_field_enter, in_field_exit = compute_rate_crossings(spikes, pos_t)\n",
|
|||
|
|
" \n",
|
|||
|
|
" spikes = np.array(spikes)\n",
|
|||
|
|
" spikes = spikes[(spikes > pos_t.min()) & (spikes < pos_t.max())]\n",
|
|||
|
|
"\n",
|
|||
|
|
" if plot == 'all':\n",
|
|||
|
|
" fig, axs = plt.subplots(2, 2)\n",
|
|||
|
|
" plt.suptitle(f'{row.action} {row.channel} {row.unit}')\n",
|
|||
|
|
" elif plot == 'scatter':\n",
|
|||
|
|
" fig, ax = plt.subplots(1, 1)\n",
|
|||
|
|
" axs = [[ax]]\n",
|
|||
|
|
" ax.set_title(f'{row.action} {row.channel} {row.unit}')\n",
|
|||
|
|
"\n",
|
|||
|
|
" sx, sy = interp1d(pos_t, pos_x), interp1d(pos_t, pos_y)\n",
|
|||
|
|
" results = []\n",
|
|||
|
|
" max_dist = 0\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",
|
|||
|
|
" if len(t) <= 1:\n",
|
|||
|
|
" continue\n",
|
|||
|
|
" s = spikes[(spikes > t[0]) & (spikes < t[-1])]\n",
|
|||
|
|
" if len(s) < 5:\n",
|
|||
|
|
" continue\n",
|
|||
|
|
"\n",
|
|||
|
|
" spike_phase = spike_phase_func(s)\n",
|
|||
|
|
" \n",
|
|||
|
|
" dist = distance(x, y)\n",
|
|||
|
|
" if norm:\n",
|
|||
|
|
" t_to_dist = interp1d(t, normalize(dist))\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" t_to_dist = interp1d(t, dist)\n",
|
|||
|
|
" \n",
|
|||
|
|
" spike_dist = t_to_dist(s)\n",
|
|||
|
|
" spike_dur = s - t[0]\n",
|
|||
|
|
" \n",
|
|||
|
|
" circ_lin_corr_dist, pval_dist, slope_dist, phi0_dist, RR_dist = cl_corr(\n",
|
|||
|
|
" spike_dist, spike_phase, *slope_limit_dist, return_pval=True)\n",
|
|||
|
|
" circ_lin_corr_dur, pval_dur, slope_dur, phi0_dur, RR_dur = cl_corr(\n",
|
|||
|
|
" spike_dur, spike_phase, *slope_limit_dur, return_pval=True)\n",
|
|||
|
|
" result_run = {\n",
|
|||
|
|
" 'action': row.action, \n",
|
|||
|
|
" 'channel': row.channel, \n",
|
|||
|
|
" 'unit': row.unit,\n",
|
|||
|
|
" 'slope_limit_dist': slope_limit_dist,\n",
|
|||
|
|
" 'slope_limit_dur': slope_limit_dur,\n",
|
|||
|
|
" 'flim': flim,\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 store_runs:\n",
|
|||
|
|
" result_run.update({\n",
|
|||
|
|
" 'spike_dist': spike_dist,\n",
|
|||
|
|
" 'spike_dur': spike_dur,\n",
|
|||
|
|
" 'spike_phase': spike_phase\n",
|
|||
|
|
" })\n",
|
|||
|
|
" results.append(result_run)\n",
|
|||
|
|
" if plot:\n",
|
|||
|
|
" plot_spike_phase(spike_dist, spike_phase, dot_size, slope_dist, phi0_dist, circ_lin_corr_dist, pval_dist, RR_dist, plot_regression_line, axs[0][0])\n",
|
|||
|
|
" \n",
|
|||
|
|
" if plot == 'all':\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",
|
|||
|
|
" if plot == 'all':\n",
|
|||
|
|
" plot_spikes_and_rate_map(row, rate_map, fields, pos_x, pos_y, sy, sx, spikes, box_size, bin_size, dot_size, flim, output_path, save, axs)\n",
|
|||
|
|
" \n",
|
|||
|
|
" return results"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 25,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"plt.rc('axes', titlesize=12)\n",
|
|||
|
|
"plt.rcParams.update({\n",
|
|||
|
|
" 'font.size': 12, \n",
|
|||
|
|
" 'figure.figsize': (8, 6), \n",
|
|||
|
|
" 'figure.dpi': 150\n",
|
|||
|
|
"})"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 26,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"row = cells.sort_values('gridness', ascending=False).iloc[0]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 27,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|||
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/phase-precession/phase_precession/corr_cc.py:35: RuntimeWarning: invalid value encountered in double_scalars\n",
|
|||
|
|
" rho = num / den\t# correlation coefficient\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/phase-precession/phase_precession/corr_cc.py:41: RuntimeWarning: invalid value encountered in double_scalars\n",
|
|||
|
|
" ts = np.sqrt((n * l20 * l02) / l22) * rho\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/phase-precession/phase_precession/corr_cc.py:84: RuntimeWarning: invalid value encountered in double_scalars\n",
|
|||
|
|
" rho = n* (R_aminusb - R_aplusb) / den\n",
|
|||
|
|
"/home/mikkel/apps/expipe-project/phase-precession/phase_precession/corr_cc.py:92: RuntimeWarning: invalid value encountered in double_scalars\n",
|
|||
|
|
" ts = np.sqrt((n * l20 * l02) / l22) * rho\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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>channel</th>\n",
|
|||
|
|
" <th>unit</th>\n",
|
|||
|
|
" <th>slope_limit_dist</th>\n",
|
|||
|
|
" <th>slope_limit_dur</th>\n",
|
|||
|
|
" <th>flim</th>\n",
|
|||
|
|
" <th>circ_lin_corr_dist</th>\n",
|
|||
|
|
" <th>pval_dist</th>\n",
|
|||
|
|
" <th>slope_dist</th>\n",
|
|||
|
|
" <th>phi0_dist</th>\n",
|
|||
|
|
" <th>RR_dist</th>\n",
|
|||
|
|
" <th>circ_lin_corr_dur</th>\n",
|
|||
|
|
" <th>pval_dur</th>\n",
|
|||
|
|
" <th>slope_dur</th>\n",
|
|||
|
|
" <th>phi0_dur</th>\n",
|
|||
|
|
" <th>RR_dur</th>\n",
|
|||
|
|
" <th>spike_dist</th>\n",
|
|||
|
|
" <th>spike_dur</th>\n",
|
|||
|
|
" <th>spike_phase</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <td>11138-20040502</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>[-20, 20]</td>\n",
|
|||
|
|
" <td>[-1, 1]</td>\n",
|
|||
|
|
" <td>[20, 25]</td>\n",
|
|||
|
|
" <td>-0.057014</td>\n",
|
|||
|
|
" <td>0.878528</td>\n",
|
|||
|
|
" <td>-19.999995</td>\n",
|
|||
|
|
" <td>2.907198</td>\n",
|
|||
|
|
" <td>0.165186</td>\n",
|
|||
|
|
" <td>0.064951</td>\n",
|
|||
|
|
" <td>0.836527</td>\n",
|
|||
|
|
" <td>-0.958127</td>\n",
|
|||
|
|
" <td>4.483757</td>\n",
|
|||
|
|
" <td>0.388724</td>\n",
|
|||
|
|
" <td>[0.0010296753702724094, 0.001817031819833984, ...</td>\n",
|
|||
|
|
" <td>[0.02847916666666306, 0.0478541666666632, 0.32...</td>\n",
|
|||
|
|
" <td>[1.2000947270293003, -2.3461875911029755, 2.72...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>11138-20040502</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>[-20, 20]</td>\n",
|
|||
|
|
" <td>[-1, 1]</td>\n",
|
|||
|
|
" <td>[20, 25]</td>\n",
|
|||
|
|
" <td>0.131524</td>\n",
|
|||
|
|
" <td>0.563850</td>\n",
|
|||
|
|
" <td>3.885511</td>\n",
|
|||
|
|
" <td>0.086344</td>\n",
|
|||
|
|
" <td>0.334172</td>\n",
|
|||
|
|
" <td>-0.072538</td>\n",
|
|||
|
|
" <td>0.752826</td>\n",
|
|||
|
|
" <td>-0.406430</td>\n",
|
|||
|
|
" <td>2.614404</td>\n",
|
|||
|
|
" <td>0.244867</td>\n",
|
|||
|
|
" <td>[0.049559492047359446, 0.09828326225275039, 0....</td>\n",
|
|||
|
|
" <td>[0.0644270833333227, 0.15601041666665605, 0.17...</td>\n",
|
|||
|
|
" <td>[1.7077833533155713, 2.1292424841744704, -0.91...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>11138-20040502</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>[-20, 20]</td>\n",
|
|||
|
|
" <td>[-1, 1]</td>\n",
|
|||
|
|
" <td>[20, 25]</td>\n",
|
|||
|
|
" <td>0.188106</td>\n",
|
|||
|
|
" <td>0.283691</td>\n",
|
|||
|
|
" <td>15.242794</td>\n",
|
|||
|
|
" <td>3.825074</td>\n",
|
|||
|
|
" <td>0.226141</td>\n",
|
|||
|
|
" <td>0.182584</td>\n",
|
|||
|
|
" <td>0.323239</td>\n",
|
|||
|
|
" <td>0.296876</td>\n",
|
|||
|
|
" <td>4.297967</td>\n",
|
|||
|
|
" <td>0.233404</td>\n",
|
|||
|
|
" <td>[0.0031409918384566787, 0.0037455194326765674,...</td>\n",
|
|||
|
|
" <td>[0.08618749999998876, 0.09827083333332176, 0.1...</td>\n",
|
|||
|
|
" <td>[-0.856849038171823, 0.8129509104365495, 1.751...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>11138-20040502</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>[-20, 20]</td>\n",
|
|||
|
|
" <td>[-1, 1]</td>\n",
|
|||
|
|
" <td>[20, 25]</td>\n",
|
|||
|
|
" <td>-0.163215</td>\n",
|
|||
|
|
" <td>0.658520</td>\n",
|
|||
|
|
" <td>-3.456314</td>\n",
|
|||
|
|
" <td>1.836874</td>\n",
|
|||
|
|
" <td>0.309684</td>\n",
|
|||
|
|
" <td>-0.129448</td>\n",
|
|||
|
|
" <td>0.728305</td>\n",
|
|||
|
|
" <td>-0.982799</td>\n",
|
|||
|
|
" <td>1.897397</td>\n",
|
|||
|
|
" <td>0.289831</td>\n",
|
|||
|
|
" <td>[0.008278960511390213, 0.011733955097490707, 0...</td>\n",
|
|||
|
|
" <td>[0.02919791666667315, 0.05157291666667341, 0.1...</td>\n",
|
|||
|
|
" <td>[1.706753592099695, -1.4574268368886827, 1.170...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>11138-20040502</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>[-20, 20]</td>\n",
|
|||
|
|
" <td>[-1, 1]</td>\n",
|
|||
|
|
" <td>[20, 25]</td>\n",
|
|||
|
|
" <td>-0.115085</td>\n",
|
|||
|
|
" <td>0.641511</td>\n",
|
|||
|
|
" <td>-5.432632</td>\n",
|
|||
|
|
" <td>6.027699</td>\n",
|
|||
|
|
" <td>0.196856</td>\n",
|
|||
|
|
" <td>-0.092728</td>\n",
|
|||
|
|
" <td>0.709784</td>\n",
|
|||
|
|
" <td>-0.699412</td>\n",
|
|||
|
|
" <td>5.567881</td>\n",
|
|||
|
|
" <td>0.212273</td>\n",
|
|||
|
|
" <td>[0.017771141003186743, 0.04181666630671976, 0....</td>\n",
|
|||
|
|
" <td>[0.09056249999999721, 0.20097916666666293, 0.2...</td>\n",
|
|||
|
|
" <td>[0.10783296702320369, 2.496075744564212, -2.38...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>104</th>\n",
|
|||
|
|
" <td>11138-20040502</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>[-20, 20]</td>\n",
|
|||
|
|
" <td>[-1, 1]</td>\n",
|
|||
|
|
" <td>[20, 25]</td>\n",
|
|||
|
|
" <td>0.445130</td>\n",
|
|||
|
|
" <td>0.295925</td>\n",
|
|||
|
|
" <td>13.499483</td>\n",
|
|||
|
|
" <td>4.692939</td>\n",
|
|||
|
|
" <td>0.528767</td>\n",
|
|||
|
|
" <td>0.409080</td>\n",
|
|||
|
|
" <td>0.333955</td>\n",
|
|||
|
|
" <td>0.688516</td>\n",
|
|||
|
|
" <td>4.652379</td>\n",
|
|||
|
|
" <td>0.504698</td>\n",
|
|||
|
|
" <td>[0.0032512325138726733, 0.0035440202351017212,...</td>\n",
|
|||
|
|
" <td>[0.08663541666658148, 0.09234374999994088, 0.1...</td>\n",
|
|||
|
|
" <td>[-2.2651495674824766, -1.140410096840983, 0.33...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>105</th>\n",
|
|||
|
|
" <td>11138-20040502</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>[-20, 20]</td>\n",
|
|||
|
|
" <td>[-1, 1]</td>\n",
|
|||
|
|
" <td>[20, 25]</td>\n",
|
|||
|
|
" <td>0.079285</td>\n",
|
|||
|
|
" <td>0.771025</td>\n",
|
|||
|
|
" <td>-11.773172</td>\n",
|
|||
|
|
" <td>2.538374</td>\n",
|
|||
|
|
" <td>0.215287</td>\n",
|
|||
|
|
" <td>-0.069550</td>\n",
|
|||
|
|
" <td>0.787010</td>\n",
|
|||
|
|
" <td>-0.164432</td>\n",
|
|||
|
|
" <td>5.055850</td>\n",
|
|||
|
|
" <td>0.217357</td>\n",
|
|||
|
|
" <td>[0.015594637525964256, 0.019669560707982706, 0...</td>\n",
|
|||
|
|
" <td>[0.05721874999994725, 0.07401041666662422, 0.0...</td>\n",
|
|||
|
|
" <td>[2.4722928434603078, -1.3131957520032655, 0.44...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>106</th>\n",
|
|||
|
|
" <td>11138-20040502</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>[-20, 20]</td>\n",
|
|||
|
|
" <td>[-1, 1]</td>\n",
|
|||
|
|
" <td>[20, 25]</td>\n",
|
|||
|
|
" <td>0.452029</td>\n",
|
|||
|
|
" <td>0.131712</td>\n",
|
|||
|
|
" <td>8.737603</td>\n",
|
|||
|
|
" <td>5.069350</td>\n",
|
|||
|
|
" <td>0.653502</td>\n",
|
|||
|
|
" <td>0.459736</td>\n",
|
|||
|
|
" <td>0.141185</td>\n",
|
|||
|
|
" <td>0.605074</td>\n",
|
|||
|
|
" <td>5.505432</td>\n",
|
|||
|
|
" <td>0.617714</td>\n",
|
|||
|
|
" <td>[0.0017002443178809272, 0.0018301956937179047,...</td>\n",
|
|||
|
|
" <td>[0.05375000000003638, 0.06277083333327482, 0.0...</td>\n",
|
|||
|
|
" <td>[0.2297067793093115, -2.041161329643188, -0.41...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>107</th>\n",
|
|||
|
|
" <td>11138-20040502</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>[-20, 20]</td>\n",
|
|||
|
|
" <td>[-1, 1]</td>\n",
|
|||
|
|
" <td>[20, 25]</td>\n",
|
|||
|
|
" <td>0.699380</td>\n",
|
|||
|
|
" <td>0.083434</td>\n",
|
|||
|
|
" <td>7.217852</td>\n",
|
|||
|
|
" <td>4.917169</td>\n",
|
|||
|
|
" <td>0.760941</td>\n",
|
|||
|
|
" <td>0.682985</td>\n",
|
|||
|
|
" <td>0.088925</td>\n",
|
|||
|
|
" <td>0.999996</td>\n",
|
|||
|
|
" <td>6.024202</td>\n",
|
|||
|
|
" <td>0.753521</td>\n",
|
|||
|
|
" <td>[0.03589687648547473, 0.048606408433837324, 0....</td>\n",
|
|||
|
|
" <td>[0.11198958333329756, 0.16214583333328392, 0.1...</td>\n",
|
|||
|
|
" <td>[0.22944773386426603, 0.7291734013827129, 2.44...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>108</th>\n",
|
|||
|
|
" <td>11138-20040502</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>[-20, 20]</td>\n",
|
|||
|
|
" <td>[-1, 1]</td>\n",
|
|||
|
|
" <td>[20, 25]</td>\n",
|
|||
|
|
" <td>0.237864</td>\n",
|
|||
|
|
" <td>0.164136</td>\n",
|
|||
|
|
" <td>6.027968</td>\n",
|
|||
|
|
" <td>5.642149</td>\n",
|
|||
|
|
" <td>0.286743</td>\n",
|
|||
|
|
" <td>0.345264</td>\n",
|
|||
|
|
" <td>0.048720</td>\n",
|
|||
|
|
" <td>0.183132</td>\n",
|
|||
|
|
" <td>5.899758</td>\n",
|
|||
|
|
" <td>0.284605</td>\n",
|
|||
|
|
" <td>[0.010510995966230776, 0.027472739743439972, 0...</td>\n",
|
|||
|
|
" <td>[0.04120833333331575, 0.10558333333335668, 0.1...</td>\n",
|
|||
|
|
" <td>[0.13522186477845663, 0.7241862965321739, -0.4...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>109 rows × 19 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" action channel unit slope_limit_dist slope_limit_dur flim \\\n",
|
|||
|
|
"0 11138-20040502 3 2 [-20, 20] [-1, 1] [20, 25] \n",
|
|||
|
|
"1 11138-20040502 3 2 [-20, 20] [-1, 1] [20, 25] \n",
|
|||
|
|
"2 11138-20040502 3 2 [-20, 20] [-1, 1] [20, 25] \n",
|
|||
|
|
"3 11138-20040502 3 2 [-20, 20] [-1, 1] [20, 25] \n",
|
|||
|
|
"4 11138-20040502 3 2 [-20, 20] [-1, 1] [20, 25] \n",
|
|||
|
|
".. ... ... ... ... ... ... \n",
|
|||
|
|
"104 11138-20040502 3 2 [-20, 20] [-1, 1] [20, 25] \n",
|
|||
|
|
"105 11138-20040502 3 2 [-20, 20] [-1, 1] [20, 25] \n",
|
|||
|
|
"106 11138-20040502 3 2 [-20, 20] [-1, 1] [20, 25] \n",
|
|||
|
|
"107 11138-20040502 3 2 [-20, 20] [-1, 1] [20, 25] \n",
|
|||
|
|
"108 11138-20040502 3 2 [-20, 20] [-1, 1] [20, 25] \n",
|
|||
|
|
"\n",
|
|||
|
|
" circ_lin_corr_dist pval_dist slope_dist phi0_dist RR_dist \\\n",
|
|||
|
|
"0 -0.057014 0.878528 -19.999995 2.907198 0.165186 \n",
|
|||
|
|
"1 0.131524 0.563850 3.885511 0.086344 0.334172 \n",
|
|||
|
|
"2 0.188106 0.283691 15.242794 3.825074 0.226141 \n",
|
|||
|
|
"3 -0.163215 0.658520 -3.456314 1.836874 0.309684 \n",
|
|||
|
|
"4 -0.115085 0.641511 -5.432632 6.027699 0.196856 \n",
|
|||
|
|
".. ... ... ... ... ... \n",
|
|||
|
|
"104 0.445130 0.295925 13.499483 4.692939 0.528767 \n",
|
|||
|
|
"105 0.079285 0.771025 -11.773172 2.538374 0.215287 \n",
|
|||
|
|
"106 0.452029 0.131712 8.737603 5.069350 0.653502 \n",
|
|||
|
|
"107 0.699380 0.083434 7.217852 4.917169 0.760941 \n",
|
|||
|
|
"108 0.237864 0.164136 6.027968 5.642149 0.286743 \n",
|
|||
|
|
"\n",
|
|||
|
|
" circ_lin_corr_dur pval_dur slope_dur phi0_dur RR_dur \\\n",
|
|||
|
|
"0 0.064951 0.836527 -0.958127 4.483757 0.388724 \n",
|
|||
|
|
"1 -0.072538 0.752826 -0.406430 2.614404 0.244867 \n",
|
|||
|
|
"2 0.182584 0.323239 0.296876 4.297967 0.233404 \n",
|
|||
|
|
"3 -0.129448 0.728305 -0.982799 1.897397 0.289831 \n",
|
|||
|
|
"4 -0.092728 0.709784 -0.699412 5.567881 0.212273 \n",
|
|||
|
|
".. ... ... ... ... ... \n",
|
|||
|
|
"104 0.409080 0.333955 0.688516 4.652379 0.504698 \n",
|
|||
|
|
"105 -0.069550 0.787010 -0.164432 5.055850 0.217357 \n",
|
|||
|
|
"106 0.459736 0.141185 0.605074 5.505432 0.617714 \n",
|
|||
|
|
"107 0.682985 0.088925 0.999996 6.024202 0.753521 \n",
|
|||
|
|
"108 0.345264 0.048720 0.183132 5.899758 0.284605 \n",
|
|||
|
|
"\n",
|
|||
|
|
" spike_dist \\\n",
|
|||
|
|
"0 [0.0010296753702724094, 0.001817031819833984, ... \n",
|
|||
|
|
"1 [0.049559492047359446, 0.09828326225275039, 0.... \n",
|
|||
|
|
"2 [0.0031409918384566787, 0.0037455194326765674,... \n",
|
|||
|
|
"3 [0.008278960511390213, 0.011733955097490707, 0... \n",
|
|||
|
|
"4 [0.017771141003186743, 0.04181666630671976, 0.... \n",
|
|||
|
|
".. ... \n",
|
|||
|
|
"104 [0.0032512325138726733, 0.0035440202351017212,... \n",
|
|||
|
|
"105 [0.015594637525964256, 0.019669560707982706, 0... \n",
|
|||
|
|
"106 [0.0017002443178809272, 0.0018301956937179047,... \n",
|
|||
|
|
"107 [0.03589687648547473, 0.048606408433837324, 0.... \n",
|
|||
|
|
"108 [0.010510995966230776, 0.027472739743439972, 0... \n",
|
|||
|
|
"\n",
|
|||
|
|
" spike_dur \\\n",
|
|||
|
|
"0 [0.02847916666666306, 0.0478541666666632, 0.32... \n",
|
|||
|
|
"1 [0.0644270833333227, 0.15601041666665605, 0.17... \n",
|
|||
|
|
"2 [0.08618749999998876, 0.09827083333332176, 0.1... \n",
|
|||
|
|
"3 [0.02919791666667315, 0.05157291666667341, 0.1... \n",
|
|||
|
|
"4 [0.09056249999999721, 0.20097916666666293, 0.2... \n",
|
|||
|
|
".. ... \n",
|
|||
|
|
"104 [0.08663541666658148, 0.09234374999994088, 0.1... \n",
|
|||
|
|
"105 [0.05721874999994725, 0.07401041666662422, 0.0... \n",
|
|||
|
|
"106 [0.05375000000003638, 0.06277083333327482, 0.0... \n",
|
|||
|
|
"107 [0.11198958333329756, 0.16214583333328392, 0.1... \n",
|
|||
|
|
"108 [0.04120833333331575, 0.10558333333335668, 0.1... \n",
|
|||
|
|
"\n",
|
|||
|
|
" spike_phase \n",
|
|||
|
|
"0 [1.2000947270293003, -2.3461875911029755, 2.72... \n",
|
|||
|
|
"1 [1.7077833533155713, 2.1292424841744704, -0.91... \n",
|
|||
|
|
"2 [-0.856849038171823, 0.8129509104365495, 1.751... \n",
|
|||
|
|
"3 [1.706753592099695, -1.4574268368886827, 1.170... \n",
|
|||
|
|
"4 [0.10783296702320369, 2.496075744564212, -2.38... \n",
|
|||
|
|
".. ... \n",
|
|||
|
|
"104 [-2.2651495674824766, -1.140410096840983, 0.33... \n",
|
|||
|
|
"105 [2.4722928434603078, -1.3131957520032655, 0.44... \n",
|
|||
|
|
"106 [0.2297067793093115, -2.041161329643188, -0.41... \n",
|
|||
|
|
"107 [0.22944773386426603, 0.7291734013827129, 2.44... \n",
|
|||
|
|
"108 [0.13522186477845663, 0.7241862965321739, -0.4... \n",
|
|||
|
|
"\n",
|
|||
|
|
"[109 rows x 19 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 27,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"res = compute_phase_precession_runs(\n",
|
|||
|
|
" row,\n",
|
|||
|
|
" plot='all', \n",
|
|||
|
|
" save=False, \n",
|
|||
|
|
" plot_regression_line=False, \n",
|
|||
|
|
" slope_limit_dist=[-20,20], \n",
|
|||
|
|
" flim=[20,25],\n",
|
|||
|
|
" norm=False, \n",
|
|||
|
|
" store_runs=True, \n",
|
|||
|
|
" crossing_type='rate', \n",
|
|||
|
|
" field_num=None)\n",
|
|||
|
|
"res = pd.DataFrame(res)\n",
|
|||
|
|
"res"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 28,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.lines.Line2D at 0x7f4c8e1ee198>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 28,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x900 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"res.slope_dist.plot.density()\n",
|
|||
|
|
"# res.slope_dist.hist()\n",
|
|||
|
|
"plt.axvline(0)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 29,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.lines.Line2D at 0x7f4c8e19ce80>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 29,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x900 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"res.pval_dist.hist(bins=30)\n",
|
|||
|
|
"plt.title(f'min={res.pval_dist.min():.4f}')\n",
|
|||
|
|
"plt.axvline(0.05)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 30,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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"text/plain": [
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"<Figure size 600x600 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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|
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"text/plain": [
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|||
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"<Figure size 600x600 with 1 Axes>"
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]
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},
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|
"metadata": {
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"output_type": "display_data"
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{
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"data": {
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|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAiIAAAIJCAYAAACY31bfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdeXxddZ3/8fc3e9OkSVq60JU2XWjSlrZJCiMoriDb/IZliojI4oILjiPr+NOxhcEZFVD5OQwyM7KKDAyCCCqICgoqkKQrSfeGNt1Lk2Zt9u/vj7twb2+Snpucm3Puzev5eNxHc+8533O/+SZN3vme7+ccY60VAACAF9K87gAAABi9CCIAAMAzBBEAAOAZgggAAPAMQQQAAHiGIAIAADxDEAEAAJ4hiAAAAM8QRAAAgGcIIgAAwDMEEQAA4BmCCAAA8EyG1x3wC2PMAUm5kuq97gsAAElmhqR2a+2UeBsa7r4bYIxpzs7Ozi8uLva6KwAAJJUdO3aos7OzxVo7Lt62zIi8p764uLikpqbG634AAJBUSktLVVtbO6QzCqwRAQAAniGIAAAAzxBEAACAZwgiAADAMwQRAADgGYIIAADwDEEEAAB4hiACAAA8QxABAACe4cqqGJWsteL2BgAgGWNkjPHs/QkiGDWOHTumpqYmtbS0qKenx+vuAIBvZGRkKD8/XwUFBRozZszIvveIvhvgkebmZu3du9frbgCAL/X09KixsVGNjY2aNm2axo2L+951Q0YQGQUOt3TqycrderOuQa2dPcrLztAZcyZoZfkMTczP9rp7CXfs2LFwCMnLy1NRUZFycnKUlsYSKQDo6+tTR0eHGhsb1draqr179yozM3PEZkYIIimso7tXtz9fo6er96i7N3o9xGvb3tUPf7dVl5XN0KqLSpSTme5RLxOvqalJUiCETJ8+3dNzoQDgN2lpacrLy9PYsWO1Z88etba2qqmpacSCCH8SpqiO7l5d/eBbeuKt+pgQEtLda/XEW7t19YNvqaO7d4R7OHJaWlokSUVFRYQQABiAMUZFRUWS3vu5ORIIIinq9udr9GZdg6N936xr0O3P1ya4R96w1oYXpubk5HjcGwDwt9DPyZ6enhGrLCSIpKBDLR16unpPXG2erq7X4ZbOBPXIO5H/kVgTAgCDi/w5SRDBkD1VOfDpmIF091o9VVWfoB4BANA/gkgKcnpK5nhv7Dzick8AABgcQSQFtXYO7WJdQ20HAMBQEURSUF720Kqyh9oOAIChIoikoNNnjx9SuzPmTHC5JwAADI4gkoJWVsxQZnp818vITDdaWT4jQT0CAKB/BJEUNCk/R5eVTY+rzWVlo+Ny74BTzzzzjM4991xNmjRJOTk5mj17tq6//npt377dleP39fXp0Ucf1QUXXKBp06YpOztbkyZNUnl5uf7xH/9RtbWx1/bZt2+fvv/97+vKK6/UkiVLNGXKFGVlZWncuHFasmSJ/uEf/kGbN292pX+jXaLHetu2bfrOd76jyy67TKWlpZo0aZIyMzNVWFio8vJyff3rX9eePSe+DMNrr72myy+/XNOnTw9/D33sYx/TE088MeS+jbjQ7dBH+0NSTUlJiU0Vx7p67Mof/8XOuu2FEz5W/vgv9lhXj9ddToje3l5bW1tra2trbW9vr9fdQRLo6+uz1157rZVkJdm0tDQ7bty48PPc3Fz7q1/9aljvUV9fb8vKyqLeo6ioyKalpYVf+8EPfhDT7n//93/D2yXZjIwMW1RUZI0x4dcyMzPtfffdN6z+IfFjfdddd0UdPysryxYWFka9NnbsWPvss88OeIzbbrstav/CwkKbmZkZfn7xxRfb7u7uuPo11J+ZJSUlVlKNHcLvX2ZEUlROZroeuW6Frlgxc8DTNJnpRlesmKlHrluR0veaAeJx11136aGHHpIkrVq1Sk1NTWpqatLmzZv1vve9T+3t7Vq5cqXq6uqGdPwjR47oAx/4gKqrq7V06VK98MILam9vV0NDgzo7O7V161b94Ac/0Pz582PazpgxQ9/85jf14osv6uDBg+rq6lJDQ4M6Ojr08ssvq6ysTN3d3brhhhv017/+dVjjMNoleqwXLlyoO++8U6+88oqOHDmizs5ONTY26tixY3rmmWdUXFystrY2ffKTn+z3e+2BBx7Qd7/7XUnSJz7xCdXX16uxsVEtLS16+OGHNXbsWD377LO69dZbhz0WCTeU9JKKD6XYjEikQ80d9t//sM1+6r/fsH933+v2U//9hv33P2yzh5o7vO5awjEjgng0NDTY/Px8K8lef/31/W6fMmWKlWQ/9alPDek9Lr/8civJnn766batrW24XY7pX25urpVkP/vZz7p6bERL9Fhv2bIlPLNx5513Rm3r7u62kydPtpLs8uXL+/3Zdv/994dncnbs2OH4fZkRQUJMzM/Wlz80V4995nQ9+6Uz9dhnTteXPzSXNSGI2wc/+EEZY7R69Wp1d3frnnvuUXl5uQoLC2WM0auvvup1F4fl2WefDd/s6+tf/3rM9qKiIn3hC1+QJP385z9XW1tbXMffvHmznnzySUmBv2hzc3OH2ePY/i1YsECSHK0vGIqXXnpJl1xyiaZPnx5eMzFnzhydc845uvvuu9XQMLQLKiabRI/1/PnzwzegO/741dXVOnjwoCTppptu6vf2FZ/73OdUWFionp4e/fSnP3W9f24iiACIW0dHhz74wQ/q5ptv1vr165WWlpYSdzZ++eWXJUklJSWaNWtWv/ucd955kqRjx47p9ddfj+v4jz32mCRp8eLFOu2004bR0/4dPnxYW7ZskSQVFxe7fvw77rhDH//4x/Xss89q7969yszMlLVWdXV1evnll3XLLbdow4YNrr+vHyV6rGtqatTY2Njv8Xft2hX+uKSkpN/26enp4dN7v/3tb13vn5u4ghWgwCnKpqYmr7vhuoKCgoQEhPvuu0+S9NBDD+nyyy/XmDFjdOTIkaQPI2+//bYkadGiRQPuE7mtpqZG5557ruPjh4JLWVmZ2tvbddddd+nJJ59UXV2dcnJyVFpaqiuuuEKf+9znlJWV5eiYvb29OnTokP76179q9erVam9vV2Zmpr7yla847pcTu3bt0u233y5JuvHGG3XTTTdp6tSpkqSmpiZt3LhRTzzxhPLz8119Xz9J9Fh3d3fr4MGD+v3vf69vfetbkqTx48frmmuuGbRPJ9oW+r72raGcz0nFh1J4jcho5vR8Z2NjY9Tq81R5NDY2ujqeZ599dvjYv/zlL4d1rOF8XldffbU7n9Bxxo8fbyXZr33ta4PuF6puuOmmm+I6fmh9yXXXXWcXLlw4YMXMihUr7JEjRwY91rnnntvv2MyYMcP+4Q9/iKtfTjz55JNWkp0/f76rx62rqxvW98KqVatc7U9/Ej3WCxYs6Pf4JSUldv369TH7v/nmm+F9Hn744X6P2dnZGVXt1dra6qgvXqwRYUYEQNxKS0t10UUXDesYkydPHnLbgoKCYb33QELrQ060diM3N1dHjx4N7+9UaKr94YcfliTdeeed+spXvqJx48apublZ9957r1avXq233npL11xzjX75y18OeKzx48dr8uTJ6urqCh931qxZ+uEPf6izzz47rn45UVhYKCkwRm1tbRo7dqwrx01PTx/W90JeXp4r/RhMosd64sSJOnr0qI4dO6bm5mZJ0pIlS/SDH/xAS5Ysidl/+fLlmjx5sg4ePKjvfve7uvLKK5WREf3r/Ec/+lH4WJLU3Nzs2tfMdUNJL6n4EDMiKYkZkcTMiHz+85939bh+EboGwze+8Y1B95s6deqQxiHyGg9f/epX+93nhhtuCO+zZs0aR8dtbm62P//5z8N/WZ9//vm2paUlrr6dSGNjoz3ppJOsJFtaWmp/9KMf2U2bNtm+vj5X38fvEj3WR44csT/5yU/stGnTwrNn/V0L5L777gt/n5x77rm2urradnZ22v3799vvfe97NjMzM+r77cCBA47enxkRwCMFBQXhv3RSSaJmDiZNmpSQ4ybK3XffrbvvvrvfbZWVlZoxI3B7g/z
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 600x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 600x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"ds = res.sort_values('circ_lin_corr_dist')\n",
|
|||
|
|
"for i, (_, d) in enumerate(ds.iloc[:4].iterrows()):\n",
|
|||
|
|
" plt.figure(figsize=(4,4))\n",
|
|||
|
|
" plt.scatter(d.spike_dist, d.spike_phase)\n",
|
|||
|
|
" plt.plot(\n",
|
|||
|
|
" d.spike_dist, \n",
|
|||
|
|
" model(d.spike_dist, d.slope_dist, d.phi0_dist), \n",
|
|||
|
|
" label=f'r = {d.circ_lin_corr_dist:.2f}, s = {d.slope_dist:.2f}',\n",
|
|||
|
|
" color='k'\n",
|
|||
|
|
" )\n",
|
|||
|
|
" plt.plot(\n",
|
|||
|
|
" d.spike_dist, \n",
|
|||
|
|
" model(d.spike_dist, d.slope_dist, d.phi0_dist - 2*np.pi), \n",
|
|||
|
|
" color='k'\n",
|
|||
|
|
" )\n",
|
|||
|
|
" plt.legend()\n",
|
|||
|
|
" if i > 3:\n",
|
|||
|
|
" break"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 31,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def compute_phase_precession(row, flim=[6, 10], field_num=None, slope_limit_dist=[-1,1], slope_limit_dur=[-1,1], norm=True, store_runs=False,\n",
|
|||
|
|
" plot=False, plot_regression_line=True, save=False, dot_size=1, crossing_type='field'):\n",
|
|||
|
|
" '''\n",
|
|||
|
|
" flim : [flow, fhigh]\n",
|
|||
|
|
" field_num : int\n",
|
|||
|
|
" which field to plot\n",
|
|||
|
|
" reg_limit : [low, high]\n",
|
|||
|
|
" Regression intervall\n",
|
|||
|
|
" plot : str\n",
|
|||
|
|
" \"all\" or \"scatter\"\n",
|
|||
|
|
" plot_regression_line : bool\n",
|
|||
|
|
" plot the regression line in scatter\n",
|
|||
|
|
" save : bool\n",
|
|||
|
|
" save plot\n",
|
|||
|
|
" dot_size : size of scatter dots\n",
|
|||
|
|
" '''\n",
|
|||
|
|
" spike_phase_func, 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",
|
|||
|
|
" \n",
|
|||
|
|
" if crossing_type == 'field':\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",
|
|||
|
|
" elif crossing_type == 'rate':\n",
|
|||
|
|
" _, _, in_field_enter, in_field_exit = compute_rate_crossings(spikes, pos_t)\n",
|
|||
|
|
"\n",
|
|||
|
|
" if plot == 'all':\n",
|
|||
|
|
" fig, axs = plt.subplots(2, 2)\n",
|
|||
|
|
" plt.suptitle(f'{row.action} {row.channel} {row.unit}')\n",
|
|||
|
|
" elif plot == 'scatter':\n",
|
|||
|
|
" fig, ax = plt.subplots(1, 1)\n",
|
|||
|
|
" axs = [[ax]]\n",
|
|||
|
|
" ax.set_title(f'{row.action} {row.channel} {row.unit}')\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",
|
|||
|
|
" if len(t) <= 1:\n",
|
|||
|
|
" continue\n",
|
|||
|
|
" s = spikes[(spikes > t[0]) & (spikes < t[-1])]\n",
|
|||
|
|
" if len(s) < 5:\n",
|
|||
|
|
" continue\n",
|
|||
|
|
"\n",
|
|||
|
|
" in_field_spikes.append(s)\n",
|
|||
|
|
" \n",
|
|||
|
|
" dist = distance(x, y)\n",
|
|||
|
|
" if norm:\n",
|
|||
|
|
" t_to_dist = interp1d(t, normalize(dist))\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" t_to_dist = interp1d(t, dist)\n",
|
|||
|
|
" in_field_dist.append(t_to_dist(s))\n",
|
|||
|
|
" \n",
|
|||
|
|
" if norm:\n",
|
|||
|
|
" t_to_dur = interp1d(t, normalize(t))\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" t_to_dur = interp1d(t, t)\n",
|
|||
|
|
" in_field_dur.append(t_to_dur(s))\n",
|
|||
|
|
" \n",
|
|||
|
|
" spike_phase.append(spike_phase_func(s))\n",
|
|||
|
|
" \n",
|
|||
|
|
" if plot == 'all':\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",
|
|||
|
|
"\n",
|
|||
|
|
" circ_lin_corr_dist, pval_dist, slope_dist, phi0_dist, RR_dist = cl_corr(\n",
|
|||
|
|
" dist, phase, *slope_limit_dist, return_pval=True)\n",
|
|||
|
|
" circ_lin_corr_dur, pval_dur, slope_dur, phi0_dur, RR_dur = cl_corr(\n",
|
|||
|
|
" dur, phase, *slope_limit_dur, return_pval=True)\n",
|
|||
|
|
" \n",
|
|||
|
|
" if len(dist) < 2:\n",
|
|||
|
|
" return\n",
|
|||
|
|
" results = {\n",
|
|||
|
|
" 'action': row.action, \n",
|
|||
|
|
" 'channel': row.channel, \n",
|
|||
|
|
" 'unit': row.unit,\n",
|
|||
|
|
" 'flim': flim,\n",
|
|||
|
|
" 'slope_limit_dist': slope_limit_dist,\n",
|
|||
|
|
" 'slope_limit_dur': slope_limit_dur,\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 store_runs:\n",
|
|||
|
|
" results.update({\n",
|
|||
|
|
" 'dist': dist,\n",
|
|||
|
|
" 'dur': dur,\n",
|
|||
|
|
" 'phase': phase\n",
|
|||
|
|
" })\n",
|
|||
|
|
" if plot:\n",
|
|||
|
|
" plot_spike_phase(dist, phase, dot_size, slope_dist, phi0_dist, circ_lin_corr_dist, pval_dist, RR_dist, plot_regression_line, axs[0][0])\n",
|
|||
|
|
" if plot == 'all':\n",
|
|||
|
|
" plot_spikes_and_rate_map(row, rate_map, fields, pos_x, pos_y, sy, sx, spikes, box_size, bin_size, dot_size, flim, output_path, save, axs)\n",
|
|||
|
|
" \n",
|
|||
|
|
" return results"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 32,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide\n",
|
|||
|
|
" This is separate from the ipykernel package so we can avoid doing imports until\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\n",
|
|||
|
|
"NaN result encountered.\n",
|
|||
|
|
"Minimization did not converge\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"{'action': '11138-20040502',\n",
|
|||
|
|
" 'channel': '3',\n",
|
|||
|
|
" 'unit': '2',\n",
|
|||
|
|
" 'flim': [6, 10],\n",
|
|||
|
|
" 'slope_limit_dist': [-1, 1],\n",
|
|||
|
|
" 'slope_limit_dur': [-1, 1],\n",
|
|||
|
|
" 'circ_lin_corr_dist': nan,\n",
|
|||
|
|
" 'pval_dist': nan,\n",
|
|||
|
|
" 'slope_dist': -0.2360679774997898,\n",
|
|||
|
|
" 'phi0_dist': nan,\n",
|
|||
|
|
" 'RR_dist': nan,\n",
|
|||
|
|
" 'circ_lin_corr_dur': -0.059989191365075634,\n",
|
|||
|
|
" 'pval_dur': 0.011890634000119693,\n",
|
|||
|
|
" 'slope_dur': -0.2497326328984342,\n",
|
|||
|
|
" 'phi0_dur': 0.19120562436143232,\n",
|
|||
|
|
" 'RR_dur': 0.24012797353166987}"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 32,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA7sAAAMmCAYAAAA0YpZrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdeXwV9b3/8dc3C4EQIKwhQAABwYAFVMS9KiB1AbXXWre21Lr03tvWtvZ2V9vaxf56W7t4u6pVr1XU6lWLOwb3DRVBhbCKELYQCFsSsn9/f8w5yZzJzDlzkpOFw/v5eJwHOXNmvvOd78w5zGe+m7HWIiIiIiIiIpJOMro7AyIiIiIiIiKppmBXRERERERE0o6CXREREREREUk7CnZFREREREQk7SjYFRERERERkbSjYFdERERERETSjoJdERERERERSTsKdkVERERERCTtKNgVERERERGRtKNgV0RERERERNKOgl0RERERERFJOwp2RUREREREJO0o2BUREREREZG0o2BXRERERERE0o6CXREREREREUk7CnZFRA4BxphMY8zRxpgvGmNuM8a8YYypMcbYyOvHSaRljDETjTGXG2N+Y4x50Riz35XW3Unmbawx5iJjzC3GmOeMMbtdab2YZDqfM8b83hjzsjFmnTFmjzGmIZLmUmPMrcaYqUmkmWuMucYY86QxZosxpjby2hbJ69eNMfnJHG+C/RljzCnGmB8ZY54xxpRF9ldjjNlkjHnUGPN5Y0xOkumOMcb80hjzgTFmnzGmyhizOnItTEkyraHGmBuNMe8aYyojedtgjPm7MebE5I64TdoDjTE7XOffGmPGhtw2O3KuSowx240xdZFztsgYc4kxxoRI40XPvuO9Pk7iuM42xjwYOYe1xpidxpjXjDHfNMb0DbF9ljFmTuQcLnEdX1Wk7B8wxlxojMkMm6cE+8s2xswyxnzXGPOwMWaFMWZrJO/VxpiPjTGPGWOuNMb0ScU+I/s9JrLPRcaYjZFrqzay76eMMV8xxvRL1f5ERBKy1uqll1566dXDX8AjgI3z+nESaf0mQVp3J5HW1xKk9WISaT2QIK3oqxn4E5CVIL0ZwEch0isH5qbgHBUDW0MewxrguJDpXgEciJNWHfDNkGnNiRxvvLL97w6UwV0+aY4Nsd1YYFmCMlsM5CdI58WQ5W+Bj0PkKwdYmCCd9cDUOGmcCewOmaelwPgUXItHJVEOG4GTO7i/QZFyCLO/ban4vumll156hXllISIihwJvjU8lzg30kSlI6wBQBkxOQVoHgXVA6NpXjxrgHeDdSDqVODfII3ECtXMAA/wHkAd8wS8RY8wY4HlgQGTRDuDuSJqNwLjItkcAw4BFxpgTrLXL25lvgMHAiMjf1ZH9vw5siSybCnwRKAAmAiXGmFOstSuDEjTGnAfcg1POFngYeBZoAE4HPg/0Am41xhyw1t4RJ63jgMeB3MiixTgPUaqAmcBVQF/gv4wxddbaG5I5eGPMWZHjawbqgd4ht8sHnsYJ0ABKgb/jlNsE4FqgCOf8/58xZq61tjFE0p9O8HlNiDTuAS6J/L0b+BvwATAE+BxOuY0HnolcP2U+aYzECQbBuZ6fB94EtgPZkTS+APQHjgdeMMbMtNbuCJG/RD4C3gJWAptxjrkf8InIcY3EedCwOJL/D9u5n1yccgDn3L8AvBrZZz0wCViA830rxPm+nW2tfaGd+xMRCae7o2299NJLL70Sv4AfALcAnwGOiCz7Iu2r2b0WuBW4HOcm1ABnuNK6O4m0LgT+B7gSJ5jLxLl5bk/N7pFAToJ1ZgG1rvRPCFjvTtc6zwB9fNbJwglmous91sFzdCpOLdmXgX4B6+QTW/v4Upz0comtKV7gs85ZOIGvxXloURCQlsF5gBBN60c+60wD9kU+bwI+kcSx59Jai/4H4GPXvsYm2Pa3rnWfBnp7Ph9EbK3vV+Kk1VK2HTmXkbQucO1zEzDa83kGTlAeXeefAel8DngfuCzo+sYJOj90pXVPB/Pel8jvRJx1+uA8/Ggp+w7sbxROi4FvA0MC1ulNbOuNDSRonaGXXnrp1dFXt2dAL7300kuv9r1oZ7AbkNYZrrTu7mBaY11pvdgJx/17V/o3B6yzxbXO5Dhp5buCxd0dzFcekB1ivWE4Nb/R/PkGJcDXXes8FCe9X7nW822C7Anc3gRMwHr/mSh4C9ju1sg2W3BqDj92pTM2QVnURdarAoYFrHc0To2xxakRzQxY78XoflNwnb3nOoZzA9bpgxMIR9c7OuAa8y1vn2OMplMD5Hb0GELscyhOSwcb+R70amc6vYC+IdbrjdOKJHqcZ3b2Meqll16H90sDVImIyKFmlevv4QHrDHP9vS4oIWvtXqAi8jbhQEPxWGurrLUNIdbbCbzsWvSJgFUvcf39+zhJ3oYTOAB8NkRat1lrbcB6dwP7I3+fF3LwpZk4gTnA16y1BxJt43IhTqAEsDBSNm1Yp3ntksjb4ThNuDuNMeZIYHrk7Tpr7VMB+ToI3O5a1Kb8rbV745S3e70PgdWRt31wmnB3KmttBa3XfxZO8+z2pFNvra0OsV4t8IRrUdC1LyKSEgp2RUTkUDPe9XdQv8Zy19+B/Zoj/UWHRt6WdjBfyXAHhG1GwzXG9AeiIyPvA94ISsg6/USjDwBGG2P8+l7Pdf39bJy0aoBXXPmKG1QaY7KBO3DuJx631j4ab/0E+Xomwbruz89Ocj/J+pTr78DyikhlvuJeF6lmjBmA09ccnJrdys7eJ118jCJyeFOwKyIihwxjzAycwanAqc0MCq4ed/19q9/0KsaYLOB30DJY4+9Slc8Q3NMFbfL5fDJOP1uA5dba5gTpveP6+2j3B8aY4bQGNJustbvam5aP7+HUzh0AvppgXT/u9N9NYb4wznRT240x9caZump5ZKqm6Ym2TTJfy3H6OANMDjNFUkB+exH7YMbvukiZyPX/PziDZAE8Fal57Wzusu3UYxQR0WjMIiLS40RqJydG3mbhNF39JM4Iu9H/u35irX0vIIkf49TOTYj8+5Ex5i5aR2MeT+tozM04Azbdk/ojacsYcyqtI19XAG/7rDbR9ffHIZJ1Bw0TPZ+lMq0Wxphi4IeRtzdYa7cErRuwfQattfRNtI5a3aF8uZzr+ntQ5DUN+GrkWvhKpBmyn9BlZq1tNMZsBUbjNIUfSeJj8fNZnP69AMtsakZjjpbz+a5FeTjTZF1Ca/l/TGtT9E4TmXP5rMjbBpwRwUVEOo2CXRER6Ym+AHw34LMVwC+stQ8FbWyt3WWMOQH4K06APBz4vs+qDwG32I5NORRapPbuj65Fv7LWNvmsmu/6O1FNLDjT4vhtm+q0gJYA6g6ceWjfwakhTFYerfche23i6YQS5su13rM4NbLbcGrIxwLzgJMj61yJ0+T77ID9tqfMRru2TTbwH4gz0FjULclsn0AvgltAVOF8B74bosa/QyI13n+m9Zz/zVq7O84mIiIdpmBXREQOJQeA53AC3ristZXGmO8Ce4GrA1a7EMAY8w1r7faU5TLYn2idg/g9nGl6/OS5/g7TtNRdQ9mvE9OK+k+cwLEJuDZEM2s/nZGv7wPvBAwUdosx5tPAP3CmSpqN80Dl512UN1/GmEycKXkKI4uetNY+nEwaHbAcZ+CvfV2wrxto7dNcBtzUBfsUkcOc+uyKiEiPY639nrXWWGsNznQlE4Gv4dyUfxtYYYxZEC8NY8z3cZotX41TCzkDJ8jJjfx9B06t12eBN40xYZrGtpsx5tvAVZG3+4BLrLX1nbnPzmCMGU1rzePv4jQl73LW2jfijYgdGUDrGteibxtjcjo/Z3H9ntaBujbjTCmWMtbaWtd3KQOn//ZsYCHO3ND/AJ43xgyNk0yHGGMuAX4SeVsPXG6t7YrBsETkMKeaXRER6dGstXU4Qes6Y8x9wEs4gyLdbYyp8JsWxhjzc+AHkbfXWWtv86zyLnCNMeYDnGBjNHAfcLwnnQvjZK3GWvtcmGMwxlxLazPVapx5WwOnRMJpXhrVO8Qu3ANweaf+SWVa4DR
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"compute_phase_precession(\n",
|
|||
|
|
" cells.sort_values('gridness', ascending=False).iloc[0], \n",
|
|||
|
|
" plot='all', \n",
|
|||
|
|
" save=False,\n",
|
|||
|
|
" flim=[6,10],\n",
|
|||
|
|
" plot_regression_line=True, \n",
|
|||
|
|
" field_num=None, \n",
|
|||
|
|
" crossing_type='rate')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 33,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4c8e5e77f0>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 33,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x900 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"cells.gridness.plot.density()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Analysis"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 34,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Wrong hemisphere? 11265-07020602 5 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-31010502 6 3\n",
|
|||
|
|
"Wrong hemisphere? 10884-03080402 4 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10938-08100401 7 3\n",
|
|||
|
|
"Wrong hemisphere? 11025-20050501 6 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-26040501 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10884-03080402 4 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-20050501 6 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-31010502 6 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-20050501 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-29010503 6 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-31010502 8 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10962-28110406 4 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-20040502 6 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 6 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10962-28110402 4 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 6 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-03020601 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-13020601 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 4 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 5 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-01060511 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-01020602 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10884-03080409 4 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 5 5\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11340-22110501 7 4\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-01060511 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11340-22110501 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10938-12100410 7 1\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11340-01120501 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11278-31080502 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-12110501 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-11040509 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11207-14060501 6 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 4 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11340-25110501 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11207-14060501 6 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-07020602 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-13040502 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-31010601 8 2\n",
|
|||
|
|
"Wrong hemisphere? 11138-11040501 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-07020602 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 4 5\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-07020602 5 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-11040501 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11207-21060503 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10938-12100406 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11207-11060501 6 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-20050501 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-31010502 5 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10884-03080402 4 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# results_runs = data_cells.query('group==\"control\"').iloc[:100].parallel_apply(\n",
|
|||
|
|
"results_theta = cells.query('gridness > 0.3').parallel_apply(\n",
|
|||
|
|
" compute_phase_precession_runs, \n",
|
|||
|
|
" axis=1, \n",
|
|||
|
|
" slope_limit_dist=[-20, 20], \n",
|
|||
|
|
" slope_limit_dur=[-5, 5], \n",
|
|||
|
|
" flim=[6, 10],\n",
|
|||
|
|
" norm=False, \n",
|
|||
|
|
" store_runs=True, \n",
|
|||
|
|
" crossing_type='rate')\n",
|
|||
|
|
"results_theta = pd.DataFrame([b for a in results_theta for b in a])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 35,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Wrong hemisphere? 11265-07020602 5 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10884-03080402 4 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10938-08100401 7 3\n",
|
|||
|
|
"Wrong hemisphere? 11016-31010502 6 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-20050501 6 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-26040501 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10884-03080402 4 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-20050501 6 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-31010502 6 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-20050501 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-29010503 6 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-31010502 8 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10962-28110406 4 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-13020601 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-20040502 6 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-03020601 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 6 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10962-28110402 4 2\n",
|
|||
|
|
"Wrong hemisphere? 11265-01020602 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 6 1\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 5 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11340-22110501 7 4\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10938-12100410 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 4 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11340-22110501 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10884-03080409 4 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-01060511 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 5 5\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-01060511 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-12110501 5 1\n",
|
|||
|
|
"Wrong hemisphere? 11340-01120501 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11207-14060501 6 2\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 4 5\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11340-25110501 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11278-31080502 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11207-14060501 6 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-11040509 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 4 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-13040502 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-31010601 8 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-11040501 5 1\n",
|
|||
|
|
"Wrong hemisphere? 11265-07020602 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-07020602 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-11040501 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-07020602 5 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11207-11060501 6 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10938-12100406 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10884-03080402 4 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-20050501 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11207-21060503 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-31010502 5 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"results_25 = cells.query('gridness > 0.3').parallel_apply(\n",
|
|||
|
|
" compute_phase_precession_runs, \n",
|
|||
|
|
" axis=1, \n",
|
|||
|
|
" slope_limit_dist=[-20, 20], \n",
|
|||
|
|
" slope_limit_dur=[-5, 5], \n",
|
|||
|
|
" flim=[20, 25],\n",
|
|||
|
|
" norm=False, \n",
|
|||
|
|
" store_runs=True, \n",
|
|||
|
|
" crossing_type='rate')\n",
|
|||
|
|
"results_25 = pd.DataFrame([b for a in results_25 for b in a])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 36,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"results_runs = pd.concat([results_theta, results_25])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 37,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"results_runs['flim_str'] = results_runs.apply(lambda x: '_'.join(str(a) for a in x.flim), axis=1)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 38,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x900 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x900 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"for i, d in results_runs.groupby('flim_str'):\n",
|
|||
|
|
" d.loc[:, ['RR_dist', 'RR_dur']].hist()\n",
|
|||
|
|
" plt.suptitle(i)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 39,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x900 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x900 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"for i, d in results_runs.groupby('flim_str'):\n",
|
|||
|
|
" plt.figure()\n",
|
|||
|
|
" plt.scatter(d.circ_lin_corr_dist, d.circ_lin_corr_dur, s=1)\n",
|
|||
|
|
" plt.title(i)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 40,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 600x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.figure(figsize=(4,4))\n",
|
|||
|
|
"for i, d in results_runs.groupby('flim_str'):\n",
|
|||
|
|
" q = 'pval_dist < 0.01 and RR_dist > .1'\n",
|
|||
|
|
"# q_sig = 'RR > .1'\n",
|
|||
|
|
" s = d.query(q).slope_dist\n",
|
|||
|
|
" s.plot.density(label='-'.join(i.split('_')) + ' Hz')\n",
|
|||
|
|
" # s.hist(label=i, bins=50, histtype='step')\n",
|
|||
|
|
" plt.axvline(0, c='grey', lw=1)\n",
|
|||
|
|
" # plt.xlim(-2, 2)\n",
|
|||
|
|
" plt.legend()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 41,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 600x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.figure(figsize=(4,4))\n",
|
|||
|
|
"for i, d in results_runs.groupby('flim_str'):\n",
|
|||
|
|
" q = 'pval_dist < 0.01 and RR_dist > .1'\n",
|
|||
|
|
" s = d.query(q).circ_lin_corr_dist\n",
|
|||
|
|
" s.plot.density(label='-'.join(i.split('_')) + ' Hz')\n",
|
|||
|
|
" # s.hist(label=i, bins=50, histtype='step')\n",
|
|||
|
|
" plt.axvline(0, c='grey', lw=1)\n",
|
|||
|
|
" # plt.xlim(-2, 2)\n",
|
|||
|
|
" plt.legend()\n",
|
|||
|
|
" plt.xlabel('Correlation')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Aggregated runs"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 42,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Wrong hemisphere? 11265-07020602 5 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10884-03080402 4 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10938-08100401 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-20050501 6 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-31010502 6 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|||
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|||
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Wrong hemisphere? 11138-26040501 5 1\n",
|
|||
|
|
"Wrong hemisphere? 10884-03080402 4 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|||
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|||
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|||
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Wrong hemisphere? 11025-20050501 6 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|||
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|||
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Wrong hemisphere? 11016-31010502 6 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-29010503 6 1\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|||
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-20050501 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-31010502 8 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10962-28110406 4 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-20040502 6 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 6 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-13020601 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-01020602 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10962-28110402 4 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-03020601 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 5 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 6 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide\n",
|
|||
|
|
" This is separate from the ipykernel package so we can avoid doing imports until\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\n",
|
|||
|
|
"NaN result encountered.\n",
|
|||
|
|
"Minimization did not converge\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 4 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10938-12100410 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11340-22110501 7 4\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 7 2\n",
|
|||
|
|
"Wrong hemisphere? 11340-22110501 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10884-03080409 4 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 5 5\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-01060511 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-12110501 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 4 5\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-01060511 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11340-01120501 7 1\n",
|
|||
|
|
"Wrong hemisphere? 11340-25110501 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11207-14060501 6 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-11040501 5 1\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-31010601 8 2\n",
|
|||
|
|
"Wrong hemisphere? 11207-14060501 6 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11278-31080502 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-13040502 5 1\n",
|
|||
|
|
"Wrong hemisphere? 11138-11040501 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 4 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide\n",
|
|||
|
|
" This is separate from the ipykernel package so we can avoid doing imports until\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\n",
|
|||
|
|
"NaN result encountered.\n",
|
|||
|
|
"Minimization did not converge\n",
|
|||
|
|
"Wrong hemisphere? 11138-11040509 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-07020602 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10938-12100406 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10884-03080402 4 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-07020602 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-07020602 5 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-31010502 5 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11207-11060501 6 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-20050501 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11207-21060503 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"results_theta = cells.query('gridness > 0.3').parallel_apply(\n",
|
|||
|
|
" compute_phase_precession, \n",
|
|||
|
|
" store_runs=False, \n",
|
|||
|
|
" axis=1,\n",
|
|||
|
|
" slope_limit_dist=[-2, 2], \n",
|
|||
|
|
" slope_limit_dur=[-2, 2], \n",
|
|||
|
|
" flim=[6, 10],\n",
|
|||
|
|
" result_type='expand', \n",
|
|||
|
|
" crossing_type='rate')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 43,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Wrong hemisphere? 11265-07020602 5 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10884-03080402 4 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-20050501 6 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-31010502 6 3\n",
|
|||
|
|
"Wrong hemisphere? 10938-08100401 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|||
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Wrong hemisphere? 10884-03080402 4 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|||
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|||
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|||
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Wrong hemisphere? 11138-26040501 5 1\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|||
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Warning: using lfp from other hemisphere\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|||
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Wrong hemisphere? 11025-20050501 6 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|||
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Wrong hemisphere? 11016-31010502 6 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-20050501 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|||
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Wrong hemisphere? 11025-19050503 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-29010503 6 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-31010502 8 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10962-28110406 4 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-13020601 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-20040502 6 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-01020602 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 6 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10962-28110402 4 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 5 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-03020601 7 1\n",
|
|||
|
|
"Wrong hemisphere? 11340-22110501 7 4\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 6 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10938-12100410 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11340-22110501 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide\n",
|
|||
|
|
" This is separate from the ipykernel package so we can avoid doing imports until\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\n",
|
|||
|
|
"NaN result encountered.\n",
|
|||
|
|
"Minimization did not converge\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 4 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 7 2\n",
|
|||
|
|
"Wrong hemisphere? 10884-03080409 4 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-01060511 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11340-25110501 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 5 5\n",
|
|||
|
|
"Wrong hemisphere? 11138-12110501 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 4 5\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-01060511 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11207-14060501 6 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-31010601 8 2\n",
|
|||
|
|
"Wrong hemisphere? 11340-01120501 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11278-31080502 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide\n",
|
|||
|
|
" This is separate from the ipykernel package so we can avoid doing imports until\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\n",
|
|||
|
|
"NaN result encountered.Wrong hemisphere? 11207-14060501 6 3\n",
|
|||
|
|
"\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 7 1\n",
|
|||
|
|
"Minimization did not converge\n",
|
|||
|
|
"Wrong hemisphere? 11138-11040509 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-07020602 7 3\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-13040502 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-11040501 5 1\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-07020602 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 5 1\n",
|
|||
|
|
"Wrong hemisphere? 11265-07020602 5 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10938-12100406 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 4 2\n",
|
|||
|
|
"Wrong hemisphere? 10884-03080402 4 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-11040501 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11207-11060501 6 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-31010502 5 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-20050501 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11207-21060503 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"results_25 = cells.query('gridness > 0.3').parallel_apply(\n",
|
|||
|
|
" compute_phase_precession, \n",
|
|||
|
|
" store_runs=False, \n",
|
|||
|
|
" axis=1,\n",
|
|||
|
|
" slope_limit_dist=[-2, 2], \n",
|
|||
|
|
" slope_limit_dur=[-2, 2], \n",
|
|||
|
|
" flim=[20, 25],\n",
|
|||
|
|
" result_type='expand', \n",
|
|||
|
|
" crossing_type='rate')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 44,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"results = pd.concat([results_theta, results_25])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 45,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"results['flim_str'] = results.apply(lambda x: '_'.join(str(a) for a in x.flim), axis=1)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 46,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 600x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 600x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"for i, d in results.groupby('flim_str'):\n",
|
|||
|
|
" plt.figure(figsize=(4,4))\n",
|
|||
|
|
" plt.scatter(d.circ_lin_corr_dist, d.circ_lin_corr_dur, s=1)\n",
|
|||
|
|
" plt.title(i)\n",
|
|||
|
|
" plt.plot([-1,1], [-1,1], c='k', lw=1)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 47,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 600x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 600x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"for i, d in results.groupby('flim_str'):\n",
|
|||
|
|
" plt.figure(figsize=(4,4))\n",
|
|||
|
|
" q_sig = 'pval_dist < 0.01 and RR_dist > .1'\n",
|
|||
|
|
" s = d.query(q).slope_dist\n",
|
|||
|
|
" if s.empty:\n",
|
|||
|
|
" print(i, 'empty')\n",
|
|||
|
|
" continue\n",
|
|||
|
|
" s.plot.density(label='-'.join(i.split('_')) + ' Hz')\n",
|
|||
|
|
" # s.hist(label=i, bins=50, histtype='step')\n",
|
|||
|
|
"\n",
|
|||
|
|
" plt.axvline(0, c='grey', lw=1)\n",
|
|||
|
|
" # plt.xlim(-2, 2)\n",
|
|||
|
|
"\n",
|
|||
|
|
" plt.legend()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 48,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 600x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 600x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"for i, d in results.groupby('flim_str'):\n",
|
|||
|
|
" plt.figure(figsize=(4,4))\n",
|
|||
|
|
" q_sig = 'pval_dist < 0.01 and RR_dist > .1'\n",
|
|||
|
|
" s = d.query(q).circ_lin_corr_dist\n",
|
|||
|
|
" if s.empty:\n",
|
|||
|
|
" print(i, 'empty')\n",
|
|||
|
|
" continue\n",
|
|||
|
|
" s.plot.density(label='-'.join(i.split('_')) + ' Hz')\n",
|
|||
|
|
" # s.hist(label=i, bins=50, histtype='step')\n",
|
|||
|
|
"\n",
|
|||
|
|
" plt.axvline(0, c='grey', lw=1)\n",
|
|||
|
|
" # plt.xlim(-2, 2)\n",
|
|||
|
|
"\n",
|
|||
|
|
" plt.legend()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 49,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# results.reset_index(drop=True).to_feather(output_path / 'results.feather')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 50,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x900 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"q_sig = 'pval_dist < 0.01 and RR_dist > .1'\n",
|
|||
|
|
"q = 'flim_str==\"6_10\"'\n",
|
|||
|
|
"\n",
|
|||
|
|
"d = results.query(q)\n",
|
|||
|
|
"percentages = {\n",
|
|||
|
|
" 'precess': sum(d.query(q_sig).circ_lin_corr_dist < 0) / len(d) * 100,\n",
|
|||
|
|
" 'recess': sum(d.query(q_sig).circ_lin_corr_dist > 0) / len(d) * 100,\n",
|
|||
|
|
" 'n': len(d)\n",
|
|||
|
|
"}\n",
|
|||
|
|
"\n",
|
|||
|
|
"fig = plt.figure()\n",
|
|||
|
|
"sns.barplot(data=[\n",
|
|||
|
|
" [percentages['precess']], \n",
|
|||
|
|
" [percentages['recess']],\n",
|
|||
|
|
"], color='k')\n",
|
|||
|
|
"plt.xticks(\n",
|
|||
|
|
" range(8),\n",
|
|||
|
|
" [\n",
|
|||
|
|
" 'Baseline precession', \n",
|
|||
|
|
" 'Baseline recession',\n",
|
|||
|
|
" ], rotation=90)\n",
|
|||
|
|
"plt.ylabel('Percentage')\n",
|
|||
|
|
"plt.text(0, 49, f'n = {percentages[\"n\"]}')\n",
|
|||
|
|
"\n",
|
|||
|
|
"sns.despine()\n",
|
|||
|
|
"\n",
|
|||
|
|
"\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": "code",
|
|||
|
|
"execution_count": 51,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"plt.rc('axes', titlesize=12)\n",
|
|||
|
|
"plt.rcParams.update({\n",
|
|||
|
|
" 'font.size': 12, \n",
|
|||
|
|
" 'figure.figsize': (6, 6), \n",
|
|||
|
|
" 'figure.dpi': 150\n",
|
|||
|
|
"})"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 52,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"results_with_data = cells.merge(results, on=['action', 'unit', 'channel'])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 53,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|||
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Wrong hemisphere? 11025-19050503 6 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-07020602 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-07020602 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-07020602 5 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-03020601 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11340-01120501 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11207-21060503 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-01020602 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:30: 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"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Wrong hemisphere? 11265-06020601 4 5\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-12110501 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10962-28110406 4 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10962-28110402 4 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11207-14060501 6 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-13040502 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-20050501 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11340-22110501 7 4\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11340-22110501 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11340-25110501 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-31010601 8 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10938-12100406 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-31010502 5 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-31010502 6 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-20040502 6 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-11040501 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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|
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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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|
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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": [
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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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"data": {
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|||
|
|
"text/plain": [
|
|||
|
|
"<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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},
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|
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{
|
|||
|
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"data": {
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|
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"text/plain": [
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|||
|
|
"<Figure size 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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"image/png": "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|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {
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},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
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|||
|
|
{
|
|||
|
|
"data": {
|
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"text/plain": [
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|||
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"<Figure size 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": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
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]
|
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|
},
|
|||
|
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"metadata": {
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},
|
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"output_type": "display_data"
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|||
|
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},
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|||
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{
|
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|
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"data": {
|
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|
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"text/plain": [
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|||
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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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"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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|
|
"needs_background": "light"
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},
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|
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"output_type": "display_data"
|
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|
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},
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|||
|
|
{
|
|||
|
|
"data": {
|
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|
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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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"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 900x900 with 4 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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|||
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"<Figure size 900x900 with 4 Axes>"
|
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},
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|
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"metadata": {
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{
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"data": {
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"text/plain": [
|
|||
|
|
"<Figure size 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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|
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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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"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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},
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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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|||
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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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"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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},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
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{
|
|||
|
|
"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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"needs_background": "light"
|
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|
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},
|
|||
|
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"output_type": "display_data"
|
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},
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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"text/plain": [
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|||
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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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"image/png": "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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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"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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{
|
|||
|
|
"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": {
|
|||
|
|
"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"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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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|
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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": "iVBORw0KGgoAAAANSUhEUgAAAwgAAAMmCAYAAABRo6ZRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydd3xV5f343082WYRACAESNhI2iiDioIDiCOJWrNZt/X5rrfq11tbxtWq/7a9WO9Rqa7VucENBRTSIKKA4WLLCDGElQMgOmc/vj3PPveeee84dmaif9+t1XrnnnGedcW8+n+f5DKW1RhAEQRAEQRAEASCqswcgCIIgCIIgCMKxgygIgiAIgiAIgiB4EQVBEARBEARBEAQvoiAIgiAIgiAIguBFFARBEARBEARBELyIgiAIgiAIgiAIghdREARBEARBEARB8CIKgiAIgiAIgiAIXkRBEARBEARBEATBiygIgiAIgiAIgiB4EQVBEARBEARBEAQvoiAIgiAIgiAIguBFFARBEARBEARBELyIgiAIgiAIgiAIghdREARBEARBEARB8CIKgiD8AFBKRSulRiqlrlFKPa6UWqmUqlFKac/2QAvbPUsp9ZpSqlApdVQpVaKUWq6Uul0plRSi7hRL/5Fsu8IYVz+l1B+UUuuVUuVKqSql1GbPtY9oybVa2h5ku3e6Ne0F6SdDKXWfUuprpVSpp8/tSqnnlFInhVF/aQvv7zUh2lVKqcuUUguVUnuUUnVKqf1KqXyl1A1KqZgQ9a9p4biWhnHNIz3PeLPnmZd73oE/KKX6haofou1TlFLNkbyHlrqtepaeNnKUUj9VSr2ilPpWKVWhlKpXSh1USn2mlHpQKZXT4gsM7E8ppSYrpf5XKbVIKVXk+Y7XeL7v7yilrlJKxbdxf7d5rvEbT5+1nj73KKXeV0rdopRKa4s+BUEIgtZaNtlk+55vwFuADrI9EGF78cCcEG1uA0YHaWNKiPpu25IQY/sxUBmkfh1weyvu5RJ7m+3wvKYDxUGuoRl4JEQbS1t4f08L0mY3ID9E/a+BnCBtXNPCcT0X4nrvBOqD1K8ALm/h84gHNtna29WBz3Kep1yoe3QUuLMN3r9cYG+Yz2ULcEIb9JkQwbtQAsxq6++dbLLJ5tuCzvQIgvC9Idq2XwocBoa0sL0XgMs8nw8D/wTWAz2AK4EJwCBgkVJqota6yKGNb4ELwuzvcaCv5/O/3Qoppc71jC0aQ5B4E/gAaABOB64C4oDHlFKVWut/hdm/2f4NwI+AaiDoCklLUUqdAMwHEj2HPsRQ8Kow7uv1nr7vVErVaa3vdWnqXoznEYrJGMI1wHbgU5dxxXnGdarnUBHGc9+G8WyuwxAsjwfeV0pN0lpXODS1hPCeexTwMtDFsx/sud8MPOLZbQBeAj4BYoEZwMVACvCSUqpMa70ojP6t3AcMI8Ln3obPciSgPJ+/BD4GCjCUnr7ARRjPMR54RCkVr7X+XdhXF0h3oLfnczXwEbAC2OM5NhpD0csEhgL5SqnJWusNrejTZC/wBbAOKMRQ9hMx7v8lGL9ZGcBbSqmztdYftkGfgiDY6WwNRTbZZGv/DfgN8HsMQWmA59g1tGAFAZhlqVeIbbYYQ7B7zlLmjVaOfZilrXIg0aVcIv6znlc7lDkDQ4DUGIJHZgTjyAKOeOreYelHt+FzUhgz8Gbb/+tQZoznPmigCRjVyj7nWvq7J0i5X1jKfQ10s51PABZZygSdFQ9jXGdZ2ioI8VyqPeUagOkOZazv+m4gIYJxjMa3MmF97rs66lliKNNPAMcF6e92S18NwNBW3PtTgJ3AT4EUlzJp+K9SfdLK5x0FDA9RJhr4u6XPTa3pUzbZZHPfOn0AsskmW+dstFxBWG2pd45LmS4YyoNZbmQrxvlHSzv/DFLOKsC+HmZ7YQuxwNueOt/gW6FoawXBqnx9DiiXcv9tKddiBQzDZOioRUDt61IuBsOsQ2OYuoxwKdcTY3bcNHfp3oqxvW65xt8EKfdnS7k/htnez8IcQzTGjL0G/gP0t7Sxq6OeJTZlLEifb1ra+m0r7n0yEBtGuZ74lDONZ/KhPTeMVaFDlj4Htnefssn2Q9zESVkQhLBRSg0Bxnp2t2qt33Mqp7WuBZ6xHLq0hf1FY5gFmTwXpPhlls9/DVLucQzBIuxxKaUuwjCLaQZu0lo3hVOvBViv4XGttXYp9zyGeQnAuSqEQ3gQrsAwSwH4UGu9x6XcVAyzDoB87WJKorUuwViRwNPurJYMSimVDpzn2W3CMBtzKqcwzE7AeKaPB2n2b5bPl7mW8uc2YDyG0vOzMOs49dGqZ6m1PhJmn29YPo8Ks04AWusqrXVDGOVKgGVt0We4eMa11XKoV3v3KQg/RERBEAQhEmZYPn8QoqzVzvusFvZ3Dj4BYKPW+nOnQkqpVMCMBlMOrHRrUBv+EBs9uzlKqeHBBuCJmPKEZ/dxrfVXYY69JZxp+ex6f7XWNfh8Bbpg+Fe0hOssn4MpX9ZxhbLfb4vn/mN8istirfVel3IjgD6ezxu0s6+LyQp8gvhkpVRKsAEopQYCD3p27wvRthMd/SzBMJsz6eJaqm3p0D6VUlEYKzkmB9q7T0H4ISIKgiAIkTDS8vnrEGXXYMz+Agz3zPZGyrWWz/8OUm44PifONVrr5hDtWoX8ka6lDB7FUFKKMBx/2wWlVC8M51CAQq31oRBVIrkGp/5GYzgUg+G0Pj9I8Uiee6vG5cH63IMpLmGPy/NOrPbsRmE4VAfjnxh+LV8TfGUigI5+li51C1vRTiRYwwa3a5+e35CH8U0arNFa72jPPgXhh4pEMRIEIRKGWj7vClZQa92olNoL5GBEaumDLwpKSJRSGUCeZ7cRIzJNq8flwSrIDHUrpJSahm+W/RatdVUYbbeUdrmGIFhXD17RWtcFKRvJ2PZgKIbRwBCllApiXhOAUmoMMM6zewjD9r8txgWB92yVyxiuA6ZhXEdLTMo6+lniyT9hVazebUk7EfZ5CoZyDnAQw1+jrdo+C8PxHQxFbTBwIYZjNxjR065vq/4EQfBHFARBECLBmqAo1KwoGP/EzeRNaUSgIGCES431fH5Xa13cxuNyqutFKZWIMYsM8I7WOpig2ha0+TW4oZSKxTDjMQk2S29vP+jYPIphBYYDdAyGchiJYmVXXOrbYlwewnnuvYA/eXb/prX+Jox2231cYXAnRsQvMEKEtquC4Al7+6Tl0B/b2DfneYwwqnbqMZTGu7TWO9uwP0EQLIiJkSAIkZBs+Xw0jPK1ls9Bbb4dCNfMBNpnXA8CAzFsrH8eRputpSPv7Xn4ciSs1lqvCVG+Q8bmETojUVzaY1xPYCg3uzHyH7SEjnyWKKV+BDzk2W0Ebg7DzK61/B0jBCwYplt/C1K2LdmMkZehpIP6E4QfJKIgCIJwzKGUGo8vIsoBwDFaUjv3f5tn9zdBnGS/q0SifHUk5+Gz3f9aa72uIztXSl2AkXQMjFCo1R3Zf0tQSg3DiF5kWgT8Rmvt6qTfRn3+Ep95TzlwWYiVnojRWvfSWisMOaUrRiK4pzB8Hp4GvlBKDWrLPgVB8CEmRoIgRILVVCTBtZQPa1STStdSgVjNTF7SWjeGKN9m4/KY3zyLYUO/CmOmtEV4bLSDZTNe7IliAx10b5VSWfiiC9UBr4RRrQpjVt0cWyiTobZ47uEoLm353NPwmcy8qbVeGEZ77T6uYCilBmDMpptK1V+01o8EqdJqlFI3YeQSASMHwjla661BqrQKj/9KBUYUqhVKqfkY5lMjgA+VUqO+C4qcIHzXEAVBEIRIKLN8Dib4mnS3fC5zLWVBKZUAzLYcCkdQbMtx3YVhOtGI4aDaGlONhwketnIAPifWdr+3Hn6CofwAzAszxn4ZPgWhB0EUBI+zbKpntwFDiAyJUqo3vtCgR4FXwxyXSWvv2SMYWZnLgVvDaKujxuWIUiobWIIvzOvTWuvbwxtey1BK/QRj9h4Ms6iZWusV7dmnHa31B0qp5zFWMAZgvM9PdeQYBOGHgCgIgiBEQgHwI8/n/sE
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|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
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},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
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{
|
|||
|
|
"data": {
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|
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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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},
|
|||
|
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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": [
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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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"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": [
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|||
|
|
"<Figure size 900x900 with 4 Axes>"
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]
|
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},
|
|||
|
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
|
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|
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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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"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 900x900 with 4 Axes>"
|
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]
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},
|
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"metadata": {
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"output_type": "display_data"
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},
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{
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|
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"data": {
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|||
|
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"text/plain": [
|
|||
|
|
"<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>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
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},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
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},
|
|||
|
|
"output_type": "display_data"
|
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},
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{
|
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|
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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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"metadata": {
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{
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"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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]
|
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},
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"metadata": {
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},
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|||
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"output_type": "display_data"
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},
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{
|
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"data": {
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|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
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|
},
|
|||
|
|
"metadata": {
|
|||
|
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|
|||
|
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
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|
|
"needs_background": "light"
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},
|
|||
|
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"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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"text/plain": [
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|||
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"<Figure size 900x900 with 4 Axes>"
|
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]
|
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},
|
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|
|
"metadata": {
|
|||
|
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},
|
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|
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"output_type": "display_data"
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{
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"data": {
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"image/png": "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|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 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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},
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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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"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": "iVBORw0KGgoAAAANSUhEUgAAAwgAAAMmCAYAAABRo6ZRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydd5wV1fn/32d7hWUp6wq7dMiCUizYokFAYwwoxkTFEkuM0SQSv+aXmOLXJMb0WKLma2KLHU2swRhFIEbFAkpRYellF1hgWcou29k9vz/mzuzM3Jm5c3fvFuB5v173xb0zZ849Uy77fM55itJaIwiCIAiCIAiCAJDU3QMQBEEQBEEQBKHnIAJBEARBEARBEAQLEQiCIAiCIAiCIFiIQBAEQRAEQRAEwUIEgiAIgiAIgiAIFiIQBEEQBEEQBEGwEIEgCIIgCIIgCIKFCARBEARBEARBECxEIAiCIAiCIAiCYCECQRAEQRAEQRAECxEIgiAIgiAIgiBYiEAQBEEQBEEQBMFCBIIgCIIgCIIgCBYiEARBEARBEARBsBCBIAiCIAiCIAiChQgEQTgCUEolK6WOUUpdpZS6Tyn1vlKqTimlI6+ft7Pfc5RSzymltiilGpRSu5RSi5RS/6OUyo6jn6OUUr9QSn2glKpSSjUqpbYqpd5QSl2jlEoJ0ccQpdTlSqk/KaXeVkqtU0rtVUo1R/pcrJS6Syk1Lo5xZSmlvqmU+ldkPA2R13al1Dyl1PeUUnlh+wv5nYVKqRlKqZ8rpV5VSlXY7tPmOPuaqJS6RSk1Vym1KXLPG5RS25RSrymlvqOUyg3ZVy+l1GSl1PeVUnOUUmuVUq22sQ1px7lmK6VujjwzuyJj2xJ5pr4YZ1+pkXu1IHLNzGdorlLqYqWUind8rv7fsJ2rVkpdFcexCfmd2PobHnk+PlRK7Yica4VSaqlS6gGl1NeUUsnx9mvr/zHXuYZ9/by93xn53oQ/Y4IgtBOttbzkJa/D/AW8AOiA18/j7C8dmBOjz/XAuBB9XQHUxuhrKTAsRj/PxujDfLUC/wekxOjvBGBjiP52Amcn6D7NiPFdm0P2kx+5/mGux/ZY4wd6R65bUD9D4jzXicCGGH0+BaSF6GtI5BkJ6utNIK+d9+VKj/6u6srfSaS/ZOCXQGOI+9quc418z2Mhnx336+sd+M6EP2Pykpe82v+KOSsnCMJhgXs2cQ9QBYxsZ3+PAxdH3lcBDwKfAv2Ay4FJwHDgdaXUSVrrcq9OlFJXAE/YNs0DXgEqgSJgFoahPhGYp5Q6RWtdGTCuOuAj4GNgHcZ5amAgMA34EqCAG4Ac4Os+4xoMzMcwWgB2YBhN64CDwLDIsUOBAcDcyHkuDxhbGNz3qRn4DOP84yEL4/oDNAH/Ad4FyiKfR2MYvUOBQozxn6O1/o9PfyryMjEN235AnzjHZl7ffwMFkU2LMcTAbuBY4DqgL3BZ5LuuCOgrL9LX5yKbSoFHga3AiEhfRRj3/0Wl1Nla64NxjHUAcFfkYy0Qz4x/Qn4nkXGkYFwjs79KDOG/FNiL8TyPxDjPE+MYoxf3Ai+HaDcS+H3kfQ3wfAe+M6HPmCAIHaS7FYq85CWvzn8BPwF+A3wVGBrZdhXtWEEAzrcdtwUodu1PwjDQzDb/8OmnP4ZRYba73qONAu62tXk0YFwjgfQYY58CNNj6O8mn3SO2Nq8DmR5tUjAMQLPdywm4T6diGJHfwhBGaZHt8a4gDMJY2fgB0M+nTQbOVZcN+KyqYBifc4D/B5wJ9I5sf4t2zO4CL9mOewRIcu0fHHm2zDZfDujL/nz8G8hw7c/HubrwnTjvyXOR45YCT9r6uaorfie29nfYfwdATkDbQr97mcgX8FvbmB7qYF8JfcbkJS95dezV7QOQl7zk1T0v2i8QltmOO9enTabLwDvGo80ttv3PB3xfEvBJpF0LMLKD5/0n2/fe7tNmq63NmIC+8jBm+TVQ1Yn3Kl6BkAZkh2iXAZTb+j8zznHFbbwB412Gc4ZPu3Nt7Zb4tBlAm7vNAWCAT7tjaHNfqQCSQ471PNtzdwJO15urYhybkN+Jbfzmc/ZSZz1ncd77ZAz3NHPsp3bS94hAkJe8uuElQcqCIIRGKTUSmBD5uE5r/ZpXO611PfCQbdNFHs2m2N4/6fedWutW4OnIxyTgktAD9maV7f1RPm0G2N6v8+tIa70Pw9UD4nM96VS01k1a69oQ7RqAV22bju28UVlcbHv/YGQMXvwbw8UE4ASl1DCPNjMxxBDAHK31Lq+OtNafAQsjH48CvhBrkEqpXhixKgD3a60/inWM7dhE/k4A/gdjxUpH3vcEzsFYqQBYrbV+rzsHIwhCYhGBIAhCPNgzy7wRo+3rtvfneOwfZHu/JkZfa23vz43RNhbDbe93+LTZaXvvG6cR8X/vH/lY2sFxdRc1tveZXfB9Z9vev+7XSGutcT5jXs9QqL489nv15eb3GLErW4FbQ7S3k7DfiVIqkzZR/K7WenOcY+ksrrG9/1u3jUIQhE5BBIIgCPFwjO39xzHaLsdwzQAY45Fmsr1pJ8e2N2WlUuoEjABliLhr+DR9xfb+roiR5u4rBbgHrGQP97RnTD0A+z3d0plfpJRKAsZEPh4EVsQ4xD5rf4zH/niex1h9WSilzsAIbgb4rta6Jqh9B8cV63dyPEbQORjB3Cilpiilno+kq22MpDp9Qyl1rVIqNc6xxo1Sqh9Gxi0w7uMTAc0FQTgEkSxGgiDEwyjb+81BDbXWB5VS24BiDPcbczbWZAdQYut3dcjvzQWOBrb5NVZKjbEdk4LhVnIGcAFt/+/9Qmu9zKeLn2PMAo+I/LtRKfU32rIYDacti1Er8DOt9eMB4++RRPLKnxX52IyRCrQzGUTbKsU2HTubkF2w2J8BU2yYq0EtOJ+tuPpy9ZuB4fajMPz9X/FrG0Aifycn2N5vVUrdB3zX1U0BxmrK2cBNSqkZWutN7Rh3WC4HTCHyb62130qcIAiHKCIQBEGIB3tRsN0h2ldhGD7msXbDZxFGthIw0lj+06uDiCF4mcc4fAUChvF+i8++FcCvtdZ/9ztYa71bKXUS8FcMUXEU8GOPpn8HfqM7nt60y4nMVD9A29+BB7XWVZ38te15fryOBSPrjTn2fSHERlBfdn6GYeDXADeGGKMXifyd2ONkboiMrQUj489CjKxcxwLfxEgJOhb4j1JqotZ6b7tGH5urbe8f7aTvEAShGxEXI0EQ4iHH9t4vuNROve29u2Lv3zBm4wG+qpS61qeP3xEdPNsrxHd7UYNRayGWawta6z0YIiPIv3om8GOlVGFAm57KrbT5vJcDt3XBdyby+UlkXwAopSZgpNkE+KnWOkiEBpHIsdnFxqhIf9O01ldorf+mtZ6jtf4JhuvWp5F2g4FfxznmUCiljgfMauS7cAa5C4JwmCACQRCEbkFrvRH4lW3TQ0qpfyulblBKfU0pdZNS6kMMg60SZzBta4y+f6S1VlprhZHKcxTGbPB+jNoAK5RSVwb1oZT6MYZL0bXAwxiuHlmR1wmRbWkYmWc+UEr5uq30NJRSFwO/iHxsAi6NCKIjFqVUMkZNhhRgCfDn7h2Rhfvv9B1a67fcjbRRQNAsLAdwdSQTU6Kxrx48GWLlRhCEQxBxMRIEIR4O2N5nhGhvD+71CvT8BYaR/SMMn+9ziM7kshOj6NS/bdtCu05orRsxDP11Sqmngf9irEg8ppSq9EpBqZT6FUZxOYDZWuv7XE0+Br6plPoUo65CMUYqVkcFW6XUzICh1Wmt54U9j0ShlPoyRlpZheGqcqnW+t0u+vpEPj+Jfha/DxyHsar1zUh63faSyLG5Pz+ED1rrT5VSHwCnAOnAaTh/Nx1CKZUOXGrbJO5FgnCYIgJBEIR42Gd73y9E+74+xwJWKsufKKWew/CvnowRyKowgjtfxqiUu482tyKNMw1paLTWe5VS3wbeiWz6GeAQCEqpgRirDGCkLr0/oMv7gOsxgq1PUEqdrLX+wLbfL0sSGEGzQ8KPvuMopaYBz2MEmLYCV2qtX+jCIST
|
|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
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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": {
|
|||
|
|
"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"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
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},
|
|||
|
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"output_type": "display_data"
|
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},
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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"text/plain": [
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|||
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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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"image/png": "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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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},
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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>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"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 results_with_data.query('pval_dist < 0.01 and RR_dist > .1 and flim_str==\"6_10\"').itertuples():\n",
|
|||
|
|
" compute_phase_precession(\n",
|
|||
|
|
" row, plot='all', flim=[6,10], crossing_type='rate'\n",
|
|||
|
|
" )"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Save to expipe"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 480,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"[autoreload of scipy.stats.stats failed: Traceback (most recent call last):\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 244, in check\n",
|
|||
|
|
" superreload(m, reload, self.old_objects)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 378, in superreload\n",
|
|||
|
|
" module = reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/imp.py\", line 315, in reload\n",
|
|||
|
|
" return importlib.reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/__init__.py\", line 166, in reload\n",
|
|||
|
|
" _bootstrap._exec(spec, module)\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 618, in _exec\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/stats/stats.py\", line 180, in <module>\n",
|
|||
|
|
" import scipy.special as special\n",
|
|||
|
|
"AttributeError: module 'scipy' has no attribute 'special'\n",
|
|||
|
|
"]\n",
|
|||
|
|
"[autoreload of scipy.misc.doccer failed: Traceback (most recent call last):\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 244, in check\n",
|
|||
|
|
" superreload(m, reload, self.old_objects)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 394, in superreload\n",
|
|||
|
|
" update_generic(old_obj, new_obj)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 331, in update_generic\n",
|
|||
|
|
" update(a, b)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 265, in update_function\n",
|
|||
|
|
" setattr(old, name, getattr(new, name))\n",
|
|||
|
|
"ValueError: docformat() requires a code object with 0 free vars, not 2\n",
|
|||
|
|
"]\n",
|
|||
|
|
"[autoreload of scipy.interpolate.interpolate failed: Traceback (most recent call last):\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 244, in check\n",
|
|||
|
|
" superreload(m, reload, self.old_objects)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 378, in superreload\n",
|
|||
|
|
" module = reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/imp.py\", line 315, in reload\n",
|
|||
|
|
" return importlib.reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/__init__.py\", line 166, in reload\n",
|
|||
|
|
" _bootstrap._exec(spec, module)\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 618, in _exec\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/interpolate/interpolate.py\", line 15, in <module>\n",
|
|||
|
|
" import scipy.special as spec\n",
|
|||
|
|
"AttributeError: module 'scipy' has no attribute 'special'\n",
|
|||
|
|
"]\n",
|
|||
|
|
"[autoreload of scipy.sparse.base failed: Traceback (most recent call last):\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 244, in check\n",
|
|||
|
|
" superreload(m, reload, self.old_objects)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 378, in superreload\n",
|
|||
|
|
" module = reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/imp.py\", line 315, in reload\n",
|
|||
|
|
" return importlib.reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/__init__.py\", line 166, in reload\n",
|
|||
|
|
" _bootstrap._exec(spec, module)\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 618, in _exec\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/sparse/base.py\", line 8, in <module>\n",
|
|||
|
|
" from .sputils import (isdense, isscalarlike, isintlike,\n",
|
|||
|
|
"ImportError: cannot import name 'asmatrix'\n",
|
|||
|
|
"]\n",
|
|||
|
|
"[autoreload of scipy.sparse.compressed failed: Traceback (most recent call last):\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 244, in check\n",
|
|||
|
|
" superreload(m, reload, self.old_objects)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 378, in superreload\n",
|
|||
|
|
" module = reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/imp.py\", line 315, in reload\n",
|
|||
|
|
" return importlib.reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/__init__.py\", line 166, in reload\n",
|
|||
|
|
" _bootstrap._exec(spec, module)\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 618, in _exec\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/sparse/compressed.py\", line 17, in <module>\n",
|
|||
|
|
" from ._sparsetools import (get_csr_submatrix, csr_sample_offsets, csr_todense,\n",
|
|||
|
|
"ImportError: cannot import name 'csr_row_index'\n",
|
|||
|
|
"]\n",
|
|||
|
|
"[autoreload of scipy.sparse.linalg.isolve.iterative failed: Traceback (most recent call last):\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 244, in check\n",
|
|||
|
|
" superreload(m, reload, self.old_objects)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 378, in superreload\n",
|
|||
|
|
" module = reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/imp.py\", line 315, in reload\n",
|
|||
|
|
" return importlib.reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/__init__.py\", line 166, in reload\n",
|
|||
|
|
" _bootstrap._exec(spec, module)\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 618, in _exec\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/sparse/linalg/isolve/iterative.py\", line 136, in <module>\n",
|
|||
|
|
" def bicg(A, b, x0=None, tol=1e-5, maxiter=None, M=None, callback=None, atol=None):\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/_lib/_threadsafety.py\", line 59, in decorator\n",
|
|||
|
|
" return lock.decorate(func)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/_lib/_threadsafety.py\", line 47, in decorate\n",
|
|||
|
|
" return scipy._lib.decorator.decorate(func, caller)\n",
|
|||
|
|
"AttributeError: module 'scipy' has no attribute '_lib'\n",
|
|||
|
|
"]\n",
|
|||
|
|
"[autoreload of scipy.optimize._differentiable_functions failed: Traceback (most recent call last):\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 244, in check\n",
|
|||
|
|
" superreload(m, reload, self.old_objects)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 378, in superreload\n",
|
|||
|
|
" module = reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/imp.py\", line 315, in reload\n",
|
|||
|
|
" return importlib.reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/__init__.py\", line 166, in reload\n",
|
|||
|
|
" _bootstrap._exec(spec, module)\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 618, in _exec\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py\", line 3, in <module>\n",
|
|||
|
|
" import scipy.sparse as sps\n",
|
|||
|
|
"AttributeError: module 'scipy' has no attribute 'sparse'\n",
|
|||
|
|
"]\n",
|
|||
|
|
"[autoreload of scipy.optimize._trustregion_constr.canonical_constraint failed: Traceback (most recent call last):\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 244, in check\n",
|
|||
|
|
" superreload(m, reload, self.old_objects)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 378, in superreload\n",
|
|||
|
|
" module = reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/imp.py\", line 315, in reload\n",
|
|||
|
|
" return importlib.reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/__init__.py\", line 166, in reload\n",
|
|||
|
|
" _bootstrap._exec(spec, module)\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 618, in _exec\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/optimize/_trustregion_constr/canonical_constraint.py\", line 2, in <module>\n",
|
|||
|
|
" import scipy.sparse as sps\n",
|
|||
|
|
"AttributeError: module 'scipy' has no attribute 'sparse'\n",
|
|||
|
|
"]\n",
|
|||
|
|
"[autoreload of scipy.optimize._trustregion_constr.tr_interior_point failed: Traceback (most recent call last):\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 244, in check\n",
|
|||
|
|
" superreload(m, reload, self.old_objects)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 378, in superreload\n",
|
|||
|
|
" module = reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/imp.py\", line 315, in reload\n",
|
|||
|
|
" return importlib.reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/__init__.py\", line 166, in reload\n",
|
|||
|
|
" _bootstrap._exec(spec, module)\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 618, in _exec\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/optimize/_trustregion_constr/tr_interior_point.py\", line 16, in <module>\n",
|
|||
|
|
" import scipy.sparse as sps\n",
|
|||
|
|
"AttributeError: module 'scipy' has no attribute 'sparse'\n",
|
|||
|
|
"]\n",
|
|||
|
|
"/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",
|
|||
|
|
" return f(*args, **kwds)\n",
|
|||
|
|
"[autoreload of scipy.optimize._linprog_ip failed: Traceback (most recent call last):\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 244, in check\n",
|
|||
|
|
" superreload(m, reload, self.old_objects)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 378, in superreload\n",
|
|||
|
|
" module = reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/imp.py\", line 315, in reload\n",
|
|||
|
|
" return importlib.reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/__init__.py\", line 166, in reload\n",
|
|||
|
|
" _bootstrap._exec(spec, module)\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 618, in _exec\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/optimize/_linprog_ip.py\", line 24, in <module>\n",
|
|||
|
|
" import scipy.sparse as sps\n",
|
|||
|
|
"AttributeError: module 'scipy' has no attribute 'sparse'\n",
|
|||
|
|
"]\n",
|
|||
|
|
"[autoreload of scipy.optimize._linprog_util failed: Traceback (most recent call last):\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 244, in check\n",
|
|||
|
|
" superreload(m, reload, self.old_objects)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 378, in superreload\n",
|
|||
|
|
" module = reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/imp.py\", line 315, in reload\n",
|
|||
|
|
" return importlib.reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/__init__.py\", line 166, in reload\n",
|
|||
|
|
" _bootstrap._exec(spec, module)\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 618, in _exec\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/optimize/_linprog_util.py\", line 6, in <module>\n",
|
|||
|
|
" import scipy.sparse as sps\n",
|
|||
|
|
"AttributeError: module 'scipy' has no attribute 'sparse'\n",
|
|||
|
|
"]\n",
|
|||
|
|
"[autoreload of scipy.integrate._ivp.rk failed: Traceback (most recent call last):\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 244, in check\n",
|
|||
|
|
" superreload(m, reload, self.old_objects)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 394, in superreload\n",
|
|||
|
|
" update_generic(old_obj, new_obj)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 331, in update_generic\n",
|
|||
|
|
" update(a, b)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 279, in update_class\n",
|
|||
|
|
" if (old_obj == new_obj) is True:\n",
|
|||
|
|
"ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()\n",
|
|||
|
|
"]\n",
|
|||
|
|
"[autoreload of scipy.stats._continuous_distns failed: Traceback (most recent call last):\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 244, in check\n",
|
|||
|
|
" superreload(m, reload, self.old_objects)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 378, in superreload\n",
|
|||
|
|
" module = reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/imp.py\", line 315, in reload\n",
|
|||
|
|
" return importlib.reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/__init__.py\", line 166, in reload\n",
|
|||
|
|
" _bootstrap._exec(spec, module)\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 618, in _exec\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/stats/_continuous_distns.py\", line 17, in <module>\n",
|
|||
|
|
" import scipy.special as sc\n",
|
|||
|
|
"AttributeError: module 'scipy' has no attribute 'special'\n",
|
|||
|
|
"]\n",
|
|||
|
|
"[autoreload of scipy.stats.mstats_basic failed: Traceback (most recent call last):\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 244, in check\n",
|
|||
|
|
" superreload(m, reload, self.old_objects)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 378, in superreload\n",
|
|||
|
|
" module = reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/imp.py\", line 315, in reload\n",
|
|||
|
|
" return importlib.reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/__init__.py\", line 166, in reload\n",
|
|||
|
|
" _bootstrap._exec(spec, module)\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 618, in _exec\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/stats/mstats_basic.py\", line 50, in <module>\n",
|
|||
|
|
" import scipy.special as special\n",
|
|||
|
|
"AttributeError: module 'scipy' has no attribute 'special'\n",
|
|||
|
|
"]\n",
|
|||
|
|
"[autoreload of scipy.cluster.hierarchy failed: Traceback (most recent call last):\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 244, in check\n",
|
|||
|
|
" superreload(m, reload, self.old_objects)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 378, in superreload\n",
|
|||
|
|
" module = reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/imp.py\", line 315, in reload\n",
|
|||
|
|
" return importlib.reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/__init__.py\", line 166, in reload\n",
|
|||
|
|
" _bootstrap._exec(spec, module)\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 618, in _exec\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/cluster/hierarchy.py\", line 131, in <module>\n",
|
|||
|
|
" import scipy.spatial.distance as distance\n",
|
|||
|
|
"AttributeError: module 'scipy' has no attribute 'spatial'\n",
|
|||
|
|
"]\n",
|
|||
|
|
"[autoreload of scipy.signal.ltisys failed: Traceback (most recent call last):\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 244, in check\n",
|
|||
|
|
" superreload(m, reload, self.old_objects)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 378, in superreload\n",
|
|||
|
|
" module = reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/imp.py\", line 315, in reload\n",
|
|||
|
|
" return importlib.reload(module)\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/__init__.py\", line 166, in reload\n",
|
|||
|
|
" _bootstrap._exec(spec, module)\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 618, in _exec\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
|
|||
|
|
" File \"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/signal/ltisys.py\", line 29, in <module>\n",
|
|||
|
|
" import scipy._lib.six as six\n",
|
|||
|
|
"AttributeError: module 'scipy' has no attribute '_lib'\n",
|
|||
|
|
"]\n",
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/misc/doccer.py:225: DeprecationWarning: `docformat` is deprecated!\n",
|
|||
|
|
"scipy.misc.docformat is deprecated in Scipy 1.3.0\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"action = project.require_action(\"phase-precession\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 481,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"['/media/storage/expipe/septum-mec/actions/phase-precession/data/results.feather',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-120619-2 4 168.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-260619-2_0_32_f10-12.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-260619-2_0_32_f6-10.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/phase-precession-quantification.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-010719-1 3 121.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-120619-2_4_168_f10-12.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-010719-2 3 121.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-120619-2_4_168_f10-12.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-010719-1 3 121.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-260619-1_0_32_f6-10.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-120619-2 4 168.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-010719-2_3_121_f6-10.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-010719-1_3_121_f6-10.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-010719-1_3_121_f6-10.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-260619-1 0 32.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-010719-2_3_121_f10-12.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-010719-2_3_121_f6-10.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-120619-1_4_168_f6-10.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-120619-2_4_168_f6-10.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-260619-2_0_32_f6-10.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-010719-2_3_121_f10-12.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-260619-2 0 32.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/phase-precession-quantification.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-120619-2_4_168_f6-10.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-120619-1 4 168.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-260619-1 0 32.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-120619-1 4 168.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-260619-2_0_32_f10-12.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-260619-2 0 32.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-010719-2 3 121.svg',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-120619-1_4_168_f6-10.png',\n",
|
|||
|
|
" '/media/storage/expipe/septum-mec/actions/phase-precession/data/figures/1833-260619-1_0_32_f6-10.svg']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 481,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"copy_tree(output_path, str(action.data_path()))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 482,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"septum_mec.analysis.registration.store_notebook(action, \"20_phase-precession.ipynb\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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)\n",
|
|||
|
|
"rate_map = data_loader.rate_map(row.action, row.channel_group, row.unit, 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": 4
|
|||
|
|
}
|