3711 lines
16 MiB
Plaintext
3711 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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|||
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"output_type": "execute_result"
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}
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|||
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],
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|||
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"source": [
|
|||
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"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/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/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/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/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/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: 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 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/.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": 124,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def verbose_print(*args, verbose=False):\n",
|
|||
|
|
" if verbose:\n",
|
|||
|
|
" print(*args)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 125,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def compute_data(row, flim=[6,10], verbose=False):\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",
|
|||
|
|
" verbose_print('Wrong hemisphere?', row.action, row.channel, row.unit, verbose=verbose)\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",
|
|||
|
|
" verbose_print('Warning: using lfp from other hemisphere', verbose=verbose)\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": 49,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>action</th>\n",
|
|||
|
|
" <th>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>-0.054527</td>\n",
|
|||
|
|
" <td>0.857830</td>\n",
|
|||
|
|
" <td>-8.092555</td>\n",
|
|||
|
|
" <td>3.993843</td>\n",
|
|||
|
|
" <td>0.327572</td>\n",
|
|||
|
|
" <td>-0.036910</td>\n",
|
|||
|
|
" <td>0.919443</td>\n",
|
|||
|
|
" <td>-0.999996</td>\n",
|
|||
|
|
" <td>3.301318</td>\n",
|
|||
|
|
" <td>0.098497</td>\n",
|
|||
|
|
" <td>[0.03849912979355944, 0.05389839013232834, 0.0...</td>\n",
|
|||
|
|
" <td>[0.07601041666667196, 0.09857291666667223, 0.1...</td>\n",
|
|||
|
|
" <td>[2.1292424841744704, -0.9189764069010645, 1.79...</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>0.115357</td>\n",
|
|||
|
|
" <td>0.436121</td>\n",
|
|||
|
|
" <td>0.386165</td>\n",
|
|||
|
|
" <td>5.228073</td>\n",
|
|||
|
|
" <td>0.211678</td>\n",
|
|||
|
|
" <td>0.178153</td>\n",
|
|||
|
|
" <td>0.348566</td>\n",
|
|||
|
|
" <td>0.554219</td>\n",
|
|||
|
|
" <td>5.084648</td>\n",
|
|||
|
|
" <td>0.244201</td>\n",
|
|||
|
|
" <td>[0.03607067137879658, 0.040643127124849104, 0....</td>\n",
|
|||
|
|
" <td>[0.11083333333334355, 0.12627083333334355, 0.1...</td>\n",
|
|||
|
|
" <td>[2.5762489803310644, -1.2093023134938123, -0.4...</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>-0.163215</td>\n",
|
|||
|
|
" <td>0.658520</td>\n",
|
|||
|
|
" <td>-3.456314</td>\n",
|
|||
|
|
" <td>3.569008</td>\n",
|
|||
|
|
" <td>0.309684</td>\n",
|
|||
|
|
" <td>-0.129448</td>\n",
|
|||
|
|
" <td>0.728305</td>\n",
|
|||
|
|
" <td>-0.982799</td>\n",
|
|||
|
|
" <td>3.502924</td>\n",
|
|||
|
|
" <td>0.289831</td>\n",
|
|||
|
|
" <td>[0.08803958020496773, 0.09149457479106822, 0.1...</td>\n",
|
|||
|
|
" <td>[0.28919791666666406, 0.3115729166666643, 0.41...</td>\n",
|
|||
|
|
" <td>[1.706753592099695, -1.4574268368886827, 1.170...</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>-0.109122</td>\n",
|
|||
|
|
" <td>0.488839</td>\n",
|
|||
|
|
" <td>-3.449639</td>\n",
|
|||
|
|
" <td>4.881817</td>\n",
|
|||
|
|
" <td>0.165466</td>\n",
|
|||
|
|
" <td>-0.102577</td>\n",
|
|||
|
|
" <td>0.496014</td>\n",
|
|||
|
|
" <td>-0.609317</td>\n",
|
|||
|
|
" <td>5.375147</td>\n",
|
|||
|
|
" <td>0.223378</td>\n",
|
|||
|
|
" <td>[0.005908011363537866, 0.027713675942522074, 0...</td>\n",
|
|||
|
|
" <td>[0.037145833333333655, 0.1505624999999995, 0.2...</td>\n",
|
|||
|
|
" <td>[-1.4285741448421132, 0.10783296702320369, 2.4...</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>-0.201472</td>\n",
|
|||
|
|
" <td>0.243816</td>\n",
|
|||
|
|
" <td>-10.473140</td>\n",
|
|||
|
|
" <td>4.304799</td>\n",
|
|||
|
|
" <td>0.256146</td>\n",
|
|||
|
|
" <td>0.021936</td>\n",
|
|||
|
|
" <td>0.893819</td>\n",
|
|||
|
|
" <td>0.491381</td>\n",
|
|||
|
|
" <td>3.826136</td>\n",
|
|||
|
|
" <td>0.215420</td>\n",
|
|||
|
|
" <td>[0.01799016518138901, 0.0463087168126746, 0.05...</td>\n",
|
|||
|
|
" <td>[0.07531250000000966, 0.29167708333334375, 0.4...</td>\n",
|
|||
|
|
" <td>[2.57331536854798, 0.3473761422716517, 2.33059...</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>73</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>-0.274065</td>\n",
|
|||
|
|
" <td>0.084956</td>\n",
|
|||
|
|
" <td>-4.467064</td>\n",
|
|||
|
|
" <td>1.183937</td>\n",
|
|||
|
|
" <td>0.300758</td>\n",
|
|||
|
|
" <td>-0.205884</td>\n",
|
|||
|
|
" <td>0.190186</td>\n",
|
|||
|
|
" <td>-0.496057</td>\n",
|
|||
|
|
" <td>6.177468</td>\n",
|
|||
|
|
" <td>0.377936</td>\n",
|
|||
|
|
" <td>[0.040348306945471805, 0.041178115268193205, 0...</td>\n",
|
|||
|
|
" <td>[0.3555729166666879, 0.35905208333338123, 0.37...</td>\n",
|
|||
|
|
" <td>[-1.2153012113310853, -0.7138052093976789, 1.2...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>74</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>-0.575289</td>\n",
|
|||
|
|
" <td>0.122408</td>\n",
|
|||
|
|
" <td>-4.560163</td>\n",
|
|||
|
|
" <td>1.421935</td>\n",
|
|||
|
|
" <td>0.531196</td>\n",
|
|||
|
|
" <td>-0.598823</td>\n",
|
|||
|
|
" <td>0.076603</td>\n",
|
|||
|
|
" <td>-0.999996</td>\n",
|
|||
|
|
" <td>1.793454</td>\n",
|
|||
|
|
" <td>0.566809</td>\n",
|
|||
|
|
" <td>[0.006598896264123678, 0.011080755280351646, 0...</td>\n",
|
|||
|
|
" <td>[0.049062499999990905, 0.09893750000003365, 0....</td>\n",
|
|||
|
|
" <td>[1.7584700771650148, 2.3301057322039016, 2.872...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>75</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>-0.378982</td>\n",
|
|||
|
|
" <td>0.120418</td>\n",
|
|||
|
|
" <td>-4.055996</td>\n",
|
|||
|
|
" <td>6.006868</td>\n",
|
|||
|
|
" <td>0.465750</td>\n",
|
|||
|
|
" <td>-0.419793</td>\n",
|
|||
|
|
" <td>0.071791</td>\n",
|
|||
|
|
" <td>-0.999996</td>\n",
|
|||
|
|
" <td>5.190277</td>\n",
|
|||
|
|
" <td>0.356798</td>\n",
|
|||
|
|
" <td>[0.004652361943883722, 0.010680546341998885, 0...</td>\n",
|
|||
|
|
" <td>[0.029250000000047294, 0.06397916666674064, 0....</td>\n",
|
|||
|
|
" <td>[-0.6413796330329053, -1.3621045825936104, 0.4...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>76</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>0.369642</td>\n",
|
|||
|
|
" <td>0.347114</td>\n",
|
|||
|
|
" <td>15.737573</td>\n",
|
|||
|
|
" <td>3.560243</td>\n",
|
|||
|
|
" <td>0.408231</td>\n",
|
|||
|
|
" <td>0.323524</td>\n",
|
|||
|
|
" <td>0.393445</td>\n",
|
|||
|
|
" <td>0.874155</td>\n",
|
|||
|
|
" <td>3.889496</td>\n",
|
|||
|
|
" <td>0.396406</td>\n",
|
|||
|
|
" <td>[0.004190189746428242, 0.010425863958220637, 0...</td>\n",
|
|||
|
|
" <td>[0.034072916666673336, 0.08913541666663605, 0....</td>\n",
|
|||
|
|
" <td>[-2.6705845017336594, -1.294522747345118, 0.10...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>77</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>4.772857</td>\n",
|
|||
|
|
" <td>0.286743</td>\n",
|
|||
|
|
" <td>0.345264</td>\n",
|
|||
|
|
" <td>0.048720</td>\n",
|
|||
|
|
" <td>0.183132</td>\n",
|
|||
|
|
" <td>5.784693</td>\n",
|
|||
|
|
" <td>0.284605</td>\n",
|
|||
|
|
" <td>[0.0334626952871276, 0.050424439064336794, 0.0...</td>\n",
|
|||
|
|
" <td>[0.1412083333333385, 0.20558333333337941, 0.22...</td>\n",
|
|||
|
|
" <td>[0.13522186477845663, 0.7241862965321739, -0.4...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>78 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",
|
|||
|
|
"73 11138-20040502 3 2 [-20, 20] [-1, 1] [20, 25] \n",
|
|||
|
|
"74 11138-20040502 3 2 [-20, 20] [-1, 1] [20, 25] \n",
|
|||
|
|
"75 11138-20040502 3 2 [-20, 20] [-1, 1] [20, 25] \n",
|
|||
|
|
"76 11138-20040502 3 2 [-20, 20] [-1, 1] [20, 25] \n",
|
|||
|
|
"77 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 -0.054527 0.857830 -8.092555 3.993843 0.327572 \n",
|
|||
|
|
"1 0.115357 0.436121 0.386165 5.228073 0.211678 \n",
|
|||
|
|
"2 -0.163215 0.658520 -3.456314 3.569008 0.309684 \n",
|
|||
|
|
"3 -0.109122 0.488839 -3.449639 4.881817 0.165466 \n",
|
|||
|
|
"4 -0.201472 0.243816 -10.473140 4.304799 0.256146 \n",
|
|||
|
|
".. ... ... ... ... ... \n",
|
|||
|
|
"73 -0.274065 0.084956 -4.467064 1.183937 0.300758 \n",
|
|||
|
|
"74 -0.575289 0.122408 -4.560163 1.421935 0.531196 \n",
|
|||
|
|
"75 -0.378982 0.120418 -4.055996 6.006868 0.465750 \n",
|
|||
|
|
"76 0.369642 0.347114 15.737573 3.560243 0.408231 \n",
|
|||
|
|
"77 0.237864 0.164136 6.027968 4.772857 0.286743 \n",
|
|||
|
|
"\n",
|
|||
|
|
" circ_lin_corr_dur pval_dur slope_dur phi0_dur RR_dur \\\n",
|
|||
|
|
"0 -0.036910 0.919443 -0.999996 3.301318 0.098497 \n",
|
|||
|
|
"1 0.178153 0.348566 0.554219 5.084648 0.244201 \n",
|
|||
|
|
"2 -0.129448 0.728305 -0.982799 3.502924 0.289831 \n",
|
|||
|
|
"3 -0.102577 0.496014 -0.609317 5.375147 0.223378 \n",
|
|||
|
|
"4 0.021936 0.893819 0.491381 3.826136 0.215420 \n",
|
|||
|
|
".. ... ... ... ... ... \n",
|
|||
|
|
"73 -0.205884 0.190186 -0.496057 6.177468 0.377936 \n",
|
|||
|
|
"74 -0.598823 0.076603 -0.999996 1.793454 0.566809 \n",
|
|||
|
|
"75 -0.419793 0.071791 -0.999996 5.190277 0.356798 \n",
|
|||
|
|
"76 0.323524 0.393445 0.874155 3.889496 0.396406 \n",
|
|||
|
|
"77 0.345264 0.048720 0.183132 5.784693 0.284605 \n",
|
|||
|
|
"\n",
|
|||
|
|
" spike_dist \\\n",
|
|||
|
|
"0 [0.03849912979355944, 0.05389839013232834, 0.0... \n",
|
|||
|
|
"1 [0.03607067137879658, 0.040643127124849104, 0.... \n",
|
|||
|
|
"2 [0.08803958020496773, 0.09149457479106822, 0.1... \n",
|
|||
|
|
"3 [0.005908011363537866, 0.027713675942522074, 0... \n",
|
|||
|
|
"4 [0.01799016518138901, 0.0463087168126746, 0.05... \n",
|
|||
|
|
".. ... \n",
|
|||
|
|
"73 [0.040348306945471805, 0.041178115268193205, 0... \n",
|
|||
|
|
"74 [0.006598896264123678, 0.011080755280351646, 0... \n",
|
|||
|
|
"75 [0.004652361943883722, 0.010680546341998885, 0... \n",
|
|||
|
|
"76 [0.004190189746428242, 0.010425863958220637, 0... \n",
|
|||
|
|
"77 [0.0334626952871276, 0.050424439064336794, 0.0... \n",
|
|||
|
|
"\n",
|
|||
|
|
" spike_dur \\\n",
|
|||
|
|
"0 [0.07601041666667196, 0.09857291666667223, 0.1... \n",
|
|||
|
|
"1 [0.11083333333334355, 0.12627083333334355, 0.1... \n",
|
|||
|
|
"2 [0.28919791666666406, 0.3115729166666643, 0.41... \n",
|
|||
|
|
"3 [0.037145833333333655, 0.1505624999999995, 0.2... \n",
|
|||
|
|
"4 [0.07531250000000966, 0.29167708333334375, 0.4... \n",
|
|||
|
|
".. ... \n",
|
|||
|
|
"73 [0.3555729166666879, 0.35905208333338123, 0.37... \n",
|
|||
|
|
"74 [0.049062499999990905, 0.09893750000003365, 0.... \n",
|
|||
|
|
"75 [0.029250000000047294, 0.06397916666674064, 0.... \n",
|
|||
|
|
"76 [0.034072916666673336, 0.08913541666663605, 0.... \n",
|
|||
|
|
"77 [0.1412083333333385, 0.20558333333337941, 0.22... \n",
|
|||
|
|
"\n",
|
|||
|
|
" spike_phase \n",
|
|||
|
|
"0 [2.1292424841744704, -0.9189764069010645, 1.79... \n",
|
|||
|
|
"1 [2.5762489803310644, -1.2093023134938123, -0.4... \n",
|
|||
|
|
"2 [1.706753592099695, -1.4574268368886827, 1.170... \n",
|
|||
|
|
"3 [-1.4285741448421132, 0.10783296702320369, 2.4... \n",
|
|||
|
|
"4 [2.57331536854798, 0.3473761422716517, 2.33059... \n",
|
|||
|
|
".. ... \n",
|
|||
|
|
"73 [-1.2153012113310853, -0.7138052093976789, 1.2... \n",
|
|||
|
|
"74 [1.7584700771650148, 2.3301057322039016, 2.872... \n",
|
|||
|
|
"75 [-0.6413796330329053, -1.3621045825936104, 0.4... \n",
|
|||
|
|
"76 [-2.6705845017336594, -1.294522747345118, 0.10... \n",
|
|||
|
|
"77 [0.13522186477845663, 0.7241862965321739, -0.4... \n",
|
|||
|
|
"\n",
|
|||
|
|
"[78 rows x 19 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 49,
|
|||
|
|
"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='field', \n",
|
|||
|
|
" field_num=None)\n",
|
|||
|
|
"res = pd.DataFrame(res)\n",
|
|||
|
|
"res"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 50,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.lines.Line2D at 0x7f30df93d208>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 50,
|
|||
|
|
"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": 58,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.lines.Line2D at 0x7f30df51f198>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 58,
|
|||
|
|
"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": 51,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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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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},
|
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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 600x600 with 1 Axes>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
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|
|
"needs_background": "light"
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},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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|
|
"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": [
|
|||
|
|
"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": 30,
|
|||
|
|
"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": 35,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"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': -0.08374602947048547,\n",
|
|||
|
|
" 'pval_dist': 0.0015423108086949355,\n",
|
|||
|
|
" 'slope_dist': -0.31571586724231304,\n",
|
|||
|
|
" 'phi0_dist': 0.3486401345869475,\n",
|
|||
|
|
" 'RR_dist': 0.2309806167543427,\n",
|
|||
|
|
" 'circ_lin_corr_dur': -0.05872284985402014,\n",
|
|||
|
|
" 'pval_dur': 0.025251434014016194,\n",
|
|||
|
|
" 'slope_dur': -0.14369252664492826,\n",
|
|||
|
|
" 'phi0_dur': 0.0003276522243226324,\n",
|
|||
|
|
" 'RR_dur': 0.22119033978122998}"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 35,
|
|||
|
|
"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": [
|
|||
|
|
"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='field')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 36,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7f30dfb5eac8>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 36,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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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": 126,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"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='field')\n",
|
|||
|
|
"results_theta = pd.DataFrame([b for a in results_theta for b in a])\n",
|
|||
|
|
"\n",
|
|||
|
|
"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='field')\n",
|
|||
|
|
"results_25 = pd.DataFrame([b for a in results_25 for b in a])\n",
|
|||
|
|
"\n",
|
|||
|
|
"results_theta_llim = cells.query('gridness > 0.3').parallel_apply(\n",
|
|||
|
|
" compute_phase_precession_runs, \n",
|
|||
|
|
" axis=1, \n",
|
|||
|
|
" slope_limit_dist=[-100, 100], \n",
|
|||
|
|
" slope_limit_dur=[-400, 400], \n",
|
|||
|
|
" flim=[6, 10],\n",
|
|||
|
|
" norm=False, \n",
|
|||
|
|
" store_runs=True, \n",
|
|||
|
|
" crossing_type='field')\n",
|
|||
|
|
"results_theta_llim = pd.DataFrame([b for a in results_theta_llim for b in a])\n",
|
|||
|
|
"\n",
|
|||
|
|
"results_25_llim = cells.query('gridness > 0.3').parallel_apply(\n",
|
|||
|
|
" compute_phase_precession_runs, \n",
|
|||
|
|
" axis=1, \n",
|
|||
|
|
" slope_limit_dist=[-100, 100], \n",
|
|||
|
|
" slope_limit_dur=[-400, 400], \n",
|
|||
|
|
" flim=[20, 25],\n",
|
|||
|
|
" norm=False, \n",
|
|||
|
|
" store_runs=True, \n",
|
|||
|
|
" crossing_type='field')\n",
|
|||
|
|
"results_25_llim = pd.DataFrame([b for a in results_25_llim for b in a])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 127,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"results_runs = pd.concat([results_theta, results_25, results_25_llim, results_theta_llim])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 128,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"results_runs['flim_str'] = results_runs.apply(lambda x: '_'.join(str(a) for a in x.flim), axis=1)\n",
|
|||
|
|
"results_runs['slim_dist_str'] = results_runs.apply(lambda x: '_'.join(str(a) for a in x.slope_limit_dist), axis=1)\n",
|
|||
|
|
"results_runs['slim_dur_str'] = results_runs.apply(lambda x: '_'.join(str(a) for a in x.slope_limit_dur), axis=1)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 132,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"'a-b'"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 132,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"'-'.join(('a','b'))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 138,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:8: FutureWarning: Interpreting tuple 'by' as a list of keys, rather than a single key. Use 'by=[...]' instead of 'by=(...)'. In the future, a tuple will always mean a single key.\n",
|
|||
|
|
" \n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
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"text/plain": [
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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 300x300 with 1 Axes>"
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|||
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]
|
|||
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},
|
|||
|
|
"metadata": {
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},
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|||
|
|
"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 300x300 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 300x300 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.rc('axes', titlesize=12)\n",
|
|||
|
|
"plt.rcParams.update({\n",
|
|||
|
|
" 'font.size': 12, \n",
|
|||
|
|
" 'figure.figsize': (2, 2), \n",
|
|||
|
|
" 'figure.dpi': 150\n",
|
|||
|
|
"})\n",
|
|||
|
|
"\n",
|
|||
|
|
"for i, d in results_runs.groupby(('flim_str', 'slim_dist_str')):\n",
|
|||
|
|
" fig = plt.figure()\n",
|
|||
|
|
" d.RR_dist.hist()\n",
|
|||
|
|
" plt.suptitle('-'.join(i))\n",
|
|||
|
|
" plt.xticks((0,.25,.5,.75,1))\n",
|
|||
|
|
" sns.despine()\n",
|
|||
|
|
" figname = 'hist-R' + '-'.join(i)\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": 115,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 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": 149,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWUAAAFQCAYAAABnMm+lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOy9eXzcVb3//zyTzJLJnqZtuqeFtrSlla0sZZeKIiCoIKCIoFzxqlxFCvd3v94rlatc2fTKogIqi+JFWaTKIkspi6xdKLQFStu0SZo0SbMvM5NkZs7vj8/MfM5nlmQmmUkmk/N8POaRz3Lm8znZXvP+vM97EVJKNBqNRpMd2MZ7AhqNRqMx0aKs0Wg0WYQWZY1Go8kitChrNBpNFqFFWaPRaLIILcoajUaTRWhR1mg0mixCi7JGo9FkEVqUNRqNJovQoqzRaDRZhBZljUajySK0KGs0Gk0WoUVZo9FosggtyhqNRpNFaFHWaDSaLCJnRFkI4RZCnCWE+E8hxBNCiFohhAy91o7y2muVaw31OjRN345Go5mk5I/3BNLIscAzGb7HINA+xHl/hu+v0WhynFwSZYAOYIvy+gVQlcbrvyGlPC2N19NoNBoLuSTKr0kpK9QDQoifjddkNBqNZiTkjE9ZShkY7zloNBrNaMkZUdZoNJpcQItyaiwTQmwXQniEEL1CiJ1CiPuEEEeO98Q0Gk1ukEs+5bGgEqgAOoESYFHo9Q0hxE1Syv9M5WJCiB0JTs0BXpZSfm40k9VoNBMPLcrJsQu4HlgH7JVSDgohHMBpwE3A0cAPhRAdUsrb03A/x9KlS88FZBqupRlHent7uf1240/i2muvpaioaJxnpEkTIlMX1qKcBFLKh+McGwCeF0K8CrwKrATWCiF+K6XsSvK6y+IdD1nQS0cxZY1GM0HRPuVRIqX0Af8vtFsEnDGO09FoNBMcLcrp4U1le8G4zUKj0Ux4tChrNBpNFqFFOT0cr2zvHbdZaDSaCY8W5WEQQgy5yiqEcAI/De32AeszPimNRpOz5JQoCyHKhRCV4Rfm9+dWjwshiqLep5bmrI667ClCiBeFEF8VQsxW3mMXQpwBvAYcFzp8o5SyMzPfnUajmQzkWkjcu8C8OMevC73CPAhcnuQ1BUZExRkAQggvhkVcCthDY4LAz6SUt6Q+ZY1m7OjyDrKjsYtlM0spLbAP/wbNmJNropwJtgFrgBOA5RhZfWWAB/gAw1K+V0q5bdxmqNEkQZd3kM/d9U9q2zwsnl7Mo/96AiUuLczZRk6JspSyeoTvWwusTXCuDUhHlp5GM648s+0AtW0eAHY29/C3rY1ceny8B0vNeJJTPmWNRpOYLbUdlv336vXyRzaiRVmjmSRsjRLhD5u6x2kmmqHQoqzRTAKklNS2eyzHPm7uJRjUNa+yDS3KGs0koK1vgAF/0HJswB+k3TMwTjPSJEKLskYzCWjs9MY93tTlG+OZaIZDi7JGMwnQojxx0KKsyU22PQb/92V45RYY1MLT0tMf9/iBbv2zyTZyKk5ZowFg0/3w1PeN7Z1Pw4H34KI/wtBlTHKajr7BuMcPJhBrzfihLWVNbjHQB+tvtB776Cn4+LnxmU+W0JFgQa9TL/RlHVqUNbnFrufB2x57/NXJXZakvc8UX0ee+W/f4YlvQWvGDy3Kmtyi5hVzu3Kxud2wGZomb3kS1VKeX1kY2daWcvahRVmTW+xVRPnEf4NZR5v7W/4w9vPJElRRXjBVFWVtKWcbWpQ1uUNnPbTXmPvzT4GjLjP3P/w7yMmZwaYu9KmWciJfs2b80KKsyR32vmpul8+Hsrlw2DkgQn/mPY3Q+G7Kl21ra+P+++/n0ksvZenSpRQWFuJ0Opk9ezbnn38+f/3rX5O6Tk9PD2vXrmX58uUUFRVRWlrKypUruf322xkYGJ04DjfH2i0bImOt7gtDrB944AGEEMO+XnzxxRHN77TTTkMIwWmnnTbsWHUu+/btG9H9JjI6JE6TO6iivOBU42thJcw5HureMPZ3PgOzjkrpslVVVfj9/si+y+XCbrfT0NBAQ0MD69at46yzzuKxxx7D7XbHvUZnZycnnHACtbW1ALjdbvr7+9m0aRObNm3i4YcfZv369ZSXl6c0t2TnCOtwLTiaqef/h8V90dvvt6Rf22w2pk6dmvA+TqdzRPPTJI+2lDW5gZRWf/L8U8ztRZ82t2vfSPnSfr+fY489ll/96lfs2bMHr9dLb28ve/fu5Rvf+AYAzz77LFdddVXc9wcCAf70pz9RW1vLjBkzeOGFF+jr68Pj8fDII49QXFzMu+++y6WXXpry3JKZ45e/ejkAvprNtD93N/MrLd3Q6PKaro05c+bQ1NSU8HXyySePeI6a5NCWsiY3aNsNPQfM/WpFlOetMrf3bwJ/P+Qnb/G99NJLnH766THHq6ur+e1vf0t+fj733HMPf/zjH7npppuYM2eOZdx7771HS0sLAI8//jgnnHACYFilF110EcFgkC9/+cs888wzrF+/njPOOCPpuSUzxxtuvYO/bWuid+s/6Nuxgd62Jhx5NgYChoWsIzCyC20pa3KDmpfN7WnLoEh5BJ9xBOS7jO1Av5HhlwLxxE4lbC0DbNq0Keb81q1bATjllFMigqxy8cUXM3/+fAAeeuihlOaWzBx7fH6KVpwZ2d+8eTOlbrMNVKd34kRgVFdXJ+X7TsZ3na1oUdbkBvH8yWHyHVC13Nxv3pHWW7tcrsh2IBCwnPN4PNTX1wPwqU99Ku77hRB85jOfAeD5559P69zA8BuLfIdljsUu8yG51+eP97asZOrUqUyfPj3hKz9/4j/8T/zvQDNipJR0T6B/yIQEg1CzCaSxyFZSfTIxVS6mLYX9G43tgx+l9fYvv/xyZHv58uWWczt37kSGwvCWLl2a8BqHH344AE1NTbS3t1NRUZG2+fX6/PjqzMSZ5cuXU9x0MLLf02/+DRw8eJCjjz6anTt3EggEmDFjBqtWreLKK6/MCutz48aNCc89++yznHvuuQCcffbZYzWltKNFeRLT7fPziR+n3zIbH26LbL03/XhKo09PUwSx5YO03bWzs5P/+Z//AeDkk09m8eLFlvMHDph+7pkzZya8zqxZsyLbjY2NaRXl5tZWut96FICphx7B4sWLKXrN7NenWsoej4ctW7ZQXl5OX18fe/fuZe/evTz88MNcccUV3HvvvaOyRt944w2qqqqGHOP1xi8zOhTvv/8+F110EYFAgMsvv5zrrrtupFMcd7T7QpN7uIpjj01bYm63pMdSDgaDfPWrX+XAgQO4XC7uuuuumDG9vb2R7YKCgoTXUkPpenp60jK/8Bx/+V/fJ9Dbjsh3cNLXrgegyKm4L/oHmTlzJjfccAPvvfcePp+P9vZ2PB4Pr7/+OqtXrwbg/vvv55prrhnVfAYHB2lubh7y1d2dWu/AAwcOcM4559DT08Opp57KPffcM6o5jjdalDWTgymHmNt9LTDgSTw2Sb73ve/x1FNPAXD33XezYsWKUV8z3Xzve9/j/TdeAqDiU9+ieqHx4VTkNBf6en1+zjzzTNauXcuKFSsisch5eXmsWrWK5557jvPOOw+AX/3qV+zatWvE8zn11FORUg75uv/++5O+nsfj4dxzz6W+vp5DDz2UJ554AofDMfwbsxjtvpjElLjyee+GM4cfOJ5sf8KsjQxwySPWEDdvB9xxJMhQAsTlT1HiivNnXTwDbPkQDD2qd+2HqYtGPK01a9ZELONf/OIXfP3rX487rqjIjAke6rHc4zE/JIqLTUt/5cqVkYVClVWrVvHEE08kPcfyT/4LRSvOpCj0s1EX+lSfcjxsNhu33XYb69atIxgM8ve//50f/OAHQ75nLAiHEm7evJny8nKefvrptLp9xgstypMYIQSlBfbhB44nHzwMQrFq37sPDlOiKz7eAPSCAAqnwbwj4xezt+VByUzorDP2u+pGLMrXX389t99+OwC33XYb3//+9xOOnTFjRmS7sbEx4Tgj685A9T0fPHiQ5ubmmPHt7XHKkyaY4+mX/YCaGZ8ETLeF6r7
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 300x300 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"fig = plt.figure(figsize=(2,2))\n",
|
|||
|
|
"for i, d in results_runs.query('slim_dist_str==\"-20_20\" and slim_dur_str==\"-5_5\"').groupby('flim_str'):\n",
|
|||
|
|
" q = 'pval_dist < 0.01 and RR_dist > .1'\n",
|
|||
|
|
" s = d.query(q).circ_lin_corr_dist\n",
|
|||
|
|
"# s = d.circ_lin_corr_dist\n",
|
|||
|
|
" s.plot.density(.08, 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.ylim(-.1, 1.5)\n",
|
|||
|
|
" plt.legend(loc=1, frameon=False)\n",
|
|||
|
|
" plt.xlabel('Correlation')\n",
|
|||
|
|
" sns.despine()\n",
|
|||
|
|
" \n",
|
|||
|
|
"figname = 'density-r-significant-runs'\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": 148,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 300x300 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"fig = plt.figure(figsize=(2,2))\n",
|
|||
|
|
"for i, d in results_runs.query('slim_dist_str==\"-20_20\" and slim_dur_str==\"-5_5\"').groupby('flim_str'):\n",
|
|||
|
|
" s = d.circ_lin_corr_dist\n",
|
|||
|
|
" s.plot.density(.08, 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.ylim(-.1, 1.5)\n",
|
|||
|
|
" plt.legend(loc=1, frameon=False)\n",
|
|||
|
|
" plt.xlabel('Correlation')\n",
|
|||
|
|
" sns.despine()\n",
|
|||
|
|
" \n",
|
|||
|
|
"figname = 'density-r-all-runs'\n",
|
|||
|
|
"fig.savefig(\n",
|
|||
|
|
" output_path / 'figures' / f'{figname}.png', \n",
|
|||
|
|
" bbox_inches='tight', transparent=True)\n",
|
|||
|
|
"fig.savefig(\n",
|
|||
|
|
" output_path / 'figures' / f'{figname}.svg', \n",
|
|||
|
|
" bbox_inches='tight', transparent=True)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Aggregated runs"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 64,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Wrong hemisphere? 11025-20050501 6 2\n",
|
|||
|
|
"Wrong hemisphere? 11016-31010502 6 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10884-03080402 4 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? 10938-08100401 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10884-03080402 4 1\n",
|
|||
|
|
"Wrong hemisphere? 11025-20050501 6 1\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? 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? 11016-31010502 8 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 8 1\n",
|
|||
|
|
"Wrong hemisphere? 11016-29010503 6 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10962-28110406 4 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-01020602 8 1\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 5 1\n",
|
|||
|
|
"Wrong hemisphere? 11265-13020601 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\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? 11025-19050503 6 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\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? 11025-19050503 6 1\n",
|
|||
|
|
"Wrong hemisphere? 11340-22110501 7 4\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 5 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? 10884-03080409 4 1\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 5 5\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11340-22110501 7 3\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 5 3\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? 11265-06020601 4 5\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? 11265-09020601 8 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? 11340-01120501 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? 11278-31080502 7 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? 11138-12110501 5 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? 11265-06020601 7 3\n",
|
|||
|
|
"Wrong hemisphere? 11138-11040509 5 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? 11265-09020601 4 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? 10938-12100406 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? 11265-09020601 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? 11265-07020602 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-11040501 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? 11207-21060503 8 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 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? 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"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"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='field')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 65,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Wrong hemisphere? 11265-07020602 5 3\n",
|
|||
|
|
"Wrong hemisphere? 11025-20050501 6 2\n",
|
|||
|
|
"Wrong hemisphere? 10938-08100401 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 10884-03080402 4 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11016-31010502 6 3\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",
|
|||
|
|
"Wrong hemisphere? 11138-26040501 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\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",
|
|||
|
|
"Wrong hemisphere? 11025-20050501 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\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? 11265-01020602 8 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? 11025-19050503 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-20040502 6 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-13020601 7 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? 11265-03020601 7 1\n",
|
|||
|
|
"Wrong hemisphere? 11025-19050503 6 2\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? 11025-19050503 6 1\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 4 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 5 1\n",
|
|||
|
|
"Wrong hemisphere? 11340-22110501 7 4\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? 11025-19050503 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-02020601 5 3\n",
|
|||
|
|
"Wrong hemisphere? 11340-22110501 7 3\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? 11265-09020601 5 5\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? 11265-02020601 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11340-01120501 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11278-31080502 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? 11265-06020601 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? 11138-11040509 5 1\n",
|
|||
|
|
"Wrong hemisphere? 11138-12110501 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 7 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 7 3\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-31010601 8 2\n",
|
|||
|
|
"Wrong hemisphere? 11265-07020602 7 3\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? 11265-09020601 4 2\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-07020602 7 2\n",
|
|||
|
|
"Wrong hemisphere? 11207-14060501 6 3\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-06020601 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-11040501 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? 11265-07020602 5 2\n",
|
|||
|
|
"Wrong hemisphere? 10938-12100406 7 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11207-21060503 8 1\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? 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"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"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='field')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 66,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"results = pd.concat([results_theta, results_25])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 67,
|
|||
|
|
"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": 72,
|
|||
|
|
"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": 87,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"20_25 empty\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 600x600 with 0 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"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": 88,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"20_25 empty\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 600x600 with 0 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"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": 186,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# results.reset_index(drop=True).to_feather(output_path / 'results.feather')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 90,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 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": 79,
|
|||
|
|
"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": 80,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"results_with_data = cells.merge(results, on=['action', 'unit', 'channel'])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 81,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Wrong hemisphere? 11025-19050503 5 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? 11025-19050503 6 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? 11025-01060511 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? 11265-07020602 7 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"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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? 11265-01020602 8 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? 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 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? 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? 11138-20040502 6 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? 11265-09020601 5 5\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 8 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11265-09020601 5 1\n",
|
|||
|
|
"Warning: using lfp from other hemisphere\n",
|
|||
|
|
"Wrong hemisphere? 11138-26040501 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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]
|
|||
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|
},
|
|||
|
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"metadata": {
|
|||
|
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
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|
|
"needs_background": "light"
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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|
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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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|
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},
|
|||
|
|
"output_type": "display_data"
|
|||
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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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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAwkAAAMmCAYAAAC+Yc1vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydeXwV1fn/3yc3ELJBgLAJAVkEA8qmoohSZbNVcClWQVTcbWu1rbb9tXX5Wq2tVWtLtZuoFZWCC3XBFYw7LhEQUIiyQ9jCkgAhISHL+f0xd5KZyczcuUsW4Hm/XveVe+eeOXNmSfJ8znkWpbVGEARBEARBEATBJKm5ByAIgiAIgiAIQstCRIIgCIIgCIIgCDZEJAiCIAiCIAiCYENEgiAIgiAIgiAINkQkCIIgCIIgCIJgQ0SCIAiCIAiCIAg2RCQIgiAIgiAIgmBDRIIgCIIgCIIgCDZEJAiCIAiCIAiCYENEgiAIgiAIgiAINkQkCIIgCIIgCIJgQ0SCIAiCIAiCIAg2RCQIgiAIgiAIgmBDRIIgCIIgCIIgCDZEJAiCIAiCIAiCYENEgiAcBSilQkqpE5RSVymlHlFKfaqUKldK6fDr7ij6Ukqp/kqpy5RSf1ZKva+U2m/p66kYxzhSKfWkUmpdeGzFSqklSqk7lFLZAcc1Sin1f0qpt5RShUqpinBfm5RSLymlrlBKpQTo6yzL+QR5XRXLOXscu6NSaoJS6rdKqXnhsdcdK4b+QkqpaUqpl5VSW5RSleFru0wp9XulVM8o+0tXSt2qlFqklNoZvsablFLPKaXOCdjHxiiu7fsB+1RKqUuVUq9ZznO7UipPKXWdUio5QB9PRXPfg4wr3G+8z/axSqnLlVIzlFIfKqXWKKVKlFJVSqk9Sql8pdTDSqnBQccU4Xh3R/n8x/W7bzluWvha3Ry+F18rpaot/Z+ViPMTBCEgWmt5yUteR/gLmAdon9fdUfT15wh9PRXl2BTwMFDr0+cOYIxPH7nA1gjjMl/fAidFGNNZAfsyX1cl6D6dGOlYUfbXF1gWoc9S4IqA/Q0D1kXo71mgdYR+NkZxbd8PMK72QF6EfpYAPSP081Q0970pnu1wP3MDjqkW+AeQHOdzeHeUz7/5uivO4+6J0P9Zifg9k5e85BXsFXFmRRCEI4KQ43Mxxj/k4xLQVylQCAyMoS+APwI/D78vA54A8oEMYDIwHugCvKKUOlNrvcylj47AMZY+3gE+AbaEtw0Grgr30x/IU0qN0lqvDDC+5zCMND+WBugnCM5rWwN8A/QBUqPpSCnVDXgf6BHetBl4MtxfGnA2MBXjOj+llCrXWs/z6a8X8CbGNQTjHj0L7MYQNzdg3IdpGAbdFQGGuSu8nx+7/b5USrUGXgHODG8qBB4D1mKc+zUYInI48KZSaqTWen+Asd0I7AzQzo9EPNsm5cBiDLGzBuN3WAPdgXHA9zBEyY/C/V8Zx7jnYojLSLTDEFZgCJRZcRwTGj7/m4HWQNc4+xUEIRaaW6XIS17yavwX8FsMg+VioHd421XEtpJwA8bs6GXAAAzD5CxLX09F0dcw6mdZ9wKDXdrcbek7H1Aubc4ANmAYdpkex8rCMJrNvj7wGZf1fAJfmwTcp74YhtYtwOlAWnj7RnM8UfT1nOUc3gHSXdqMwjBeNYYxnuXT30uW/p4Akhzf9wI2Wdqc59OXeT4bE3DNfmo55hKgveP7NsBbljYP+vT1lKXdsXGOKyHPdrjdcUBKhOONASos/Z3aBM/rDy3HW5iA/mZh/K06B8h2uSdnNfY5yUte8qp/NfsA5CUveTXPixhFgkdfVqP6qSj2sxqeP/Zoo4DP/YxPjJnTVgGO19liFGvCginC+cR1bRJ0r6ISCUA3i4FaDnT2aftLy7ne69FmiKXNJqCNR7tzLe2+CHA+G+O8LskYs/2mq80gn/t+INyuAujo0S6RIiEhz3aUx5xh6eueJngu8y3Hu6yRjiEiQV7yaqaXBC4LgtAsKKUyMVwkAPZT77ZgQ2utgUcsmy51aXNAa10V6Zha653Ah5ZNJwYd72HGWRgGKMDb4fP24mnL+6kebazX/DGtdYVHuzcx3HwATlZK9Yk00DgZA3QKv8/THu5j4fM3XcZSgAsac1CJfLajZJXlfaO66CilBgGnhD/uBf7XmMcTBKHpEZEgCEJz8R0Mgw3gQ611uU/bty3vvxvncUst76Py8z+M6GF5/61fQ611EYYhC9BXKTXApdkEy/u3fPrSJPZeRSLQuFy+b+xxNdez3dfyfkecfUXiGsv7OT7CURCEwxQRCYIgNBcnWN4v8Wuotd6F4eYC0Ekp1TmO4w6yvN/k2aqeyUqpFUqpUqXUQWWkVp2vlPqhUqqligwVuYknttUVpVQS9UHp1cDyCPsvtrw/wbOVQUel1DvhVKqHlFK7wuk8/6SU6hthX2f/vs9QlOMCmKmU2hxOpbpXKbVKKTVTKTU6keNK1LOtlDoZI2gZDNecl2LpJ+CxkoHLLZuebKxjCYLQfEh2I0EQmov+lvcbA7TfhBEca+4bdeYZpdQZ1Bu8u4AvAuzmNCh7hF8TgbuUUtO01u9FO5ZGxjqL3N+zFaCU6gK0tWxyriT0oH7FZavWujrCsa3Cy/fYGLEkYy2fs8OvU4DblFIPAndorWs89o/mGdqCkS0qBBynlFLhlQ8vxlnet8bI5JMLXKeUeh24UmtdnIBxQRTPtlJqoKX/ZAy3otHARdT/T/+d1vrLAMeNlYkYcR4AK7TWi/0aC4JweCIiQRCE5iLL8t43zWWYPR77BiKcKvPvlk0P+BifEA6+Bd7DcNkpDR/3FAzf8XYYAcILlFLnaq0XRjumRmSR5f05SqlO4RlrN5ypSp3XtrHu0zYMF6BlQBGGId4P+D7GakYI+DXGNb7Ko4/AY9NaVyul9mPUVEgG0jGCmZ2UAgsxgnILMYRFDwzXJtO96Tzgg3AaXbd0qo35bF8J/D+P75YDf9BaPx/gmPFwteX9fxr5WIIgNBMiEgRBaC4yLO+D+DMftLzPjOF4/8ColwDwJfA3n7bfAgO01mtcvntcKfVrjBoB52L8HZ2jlOrjYTA2OVrrdUqp9zBqIaQBs5VSFzp945VSp2Gk4bTS1vG5Me7T5cAnWutal+/uVkr9CHgUwyV2ulJqodZ6tkvbWMbW3jI2p0h4BLhJa13msu+flVJnAi9izKKfgFFY8PoEjcsklmcbDHGzgMjuYHERXnk6N/zxEMbvgSAIRyASkyAIwhGPUuqXwLXhj/uAS7XWh7zaa623ewgE8/sSjGJYX4U3daTeH7ylcAtGulcwinatVErdqZS6RCl1lVLqKYxMT+nAest+boZ7QtFaf+whEMzv/wncadl0R2OPKXzcJR4Cwfz+I4yVDtNN6WqlVPemGJtlDL/WWiuttcKoAdEfuBnjuf4lsFwpNb0Rh3AF9ROM87XWQVZKBEE4DJGVBEEQmgvrLG6bAO2tQcKlnq0cKKVuAB4IfywDzvUTAEHRWlcopf4AzAlvOg/4k+PYEzBm8r36eDnecfj0/bVS6nvUz3wfC9zjbBbelkl9ZeASR5smuU8uPAz8CsOt6/jwSs16R5sD1K8MtMHdfSihY9NaL1JKLcAo+BUK/3QG7jbJNdNaV2JUX16jlJoNfIDhqvWUUmqX1vqNoH1FgdXVSAKWBeEIRkSCIAjNxV7L++wA7Tt67OuJUupK4F/hjweBSVrrT4INLxDvW94f7/L9Y9QHpLoRTxaiiGitP1JK9ceoRD0JI2g7EyNo+yPgkbDRO8uymzN1ZqPfJzfCIuwzDCMcjOvrFAl7qRcJ2fiIhHBGHtOVqor6VZZYeN8xLidNfs201iVKqR9j3FeA/wMSKhKUUqdSH/i/FXv6VkEQjjBEJAiC0Fystrw/NkB7q7G92rNVGKXUVIygSgVUAhc2QhaiuIKpmwKt9T6MlZQHfJoNtLx3ZnzagiGwUoHuSqnkCBmOorpPEYh0fVcDvcPvj8U/k1APjJl/gLURMhslYlwmxwboL1HXbBHGSkQmcIpSKt3PfSoGrLU
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
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|
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"needs_background": "light"
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},
|
|||
|
|
"output_type": "display_data"
|
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|
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},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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"text/plain": [
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|||
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"<Figure size 900x900 with 4 Axes>"
|
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]
|
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},
|
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|
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"metadata": {
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|
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"output_type": "display_data"
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{
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"data": {
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"image/png": "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|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
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]
|
|||
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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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|
|
"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": {
|
|||
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|
"needs_background": "light"
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},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
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{
|
|||
|
|
"data": {
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
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},
|
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|
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{
|
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|
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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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|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
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|
]
|
|||
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},
|
|||
|
|
"metadata": {
|
|||
|
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"needs_background": "light"
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},
|
|||
|
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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"text/plain": [
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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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
|
|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
|
{
|
|||
|
|
"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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},
|
|||
|
|
"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": [
|
|||
|
|
"<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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|
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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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|
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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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|
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"needs_background": "light"
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},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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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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},
|
|||
|
|
"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": {
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|
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"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>"
|
|||
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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>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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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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"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": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
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},
|
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|
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{
|
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|
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAwgAAAMmCAYAAABRo6ZRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdeZwcdZ3/8dd37syR+74vjoQbYhAQF5NwqImKLEcQAUU8QKOyrrL7Q5SI17qggjdyyyGKKwYUA2FRObNAAiEZCEFCQo6Z3JOZydzf3x/V3dNdU9Vd3V19TOb9fDz6QVd11be+Vd0TPp+q72GstYiIiIiIiACUFLoCIiIiIiJSPJQgiIiIiIhIjBIEERERERGJUYIgIiIiIiIxShBERERERCRGCYKIiIiIiMQoQRARERERkRglCCIiIiIiEqMEQUREREREYpQgiIiIiIhIjBIEERERERGJUYIgIiIiIiIxShBERERERCRGCYKIiIiIiMQoQRARERERkRglCCIDgDGm1BhzpDHmUmPMzcaYZ40xrcYYG3l9M42yjDHmUGPMhcaYG4wxTxpjmuLKuiPNuk01xpxjjPmuMWa5MWZXXFlPplnORcaYHxtj/m6MecMYs8cY0xkpc6Ux5kZjzNFplFltjLncGPOIMeYdY0xb5LU1UtcvGmOGpnO+aRz7yMh39ZoxptkYs88Ys8YY8z1jzJQMyhtijLkyUu9NkfPYZYxZZ4y5L3KewwOUM8oY83VjzIvGmN2R39GbxpjbjDHvzuxsY2UPM8Zsj/v+rTFmasB9yyPnsMIYs80Y0x75zpYZY843xpgAZTzpOnay18Y0zussY8xvjTFvR657ozHmaWPMl40xNQHLCOXvJI06VxhjTjDGfNoY86vI990Rd8xLc3DM44wxX4t8Z29Fflttxpgtxpg/R36/dWEfV0Q8WGv10kuvg/wFPAjYJK9vplHWDSnKuiONsr6Qoqwn0yjr/hRlRV89wM+AshTlzQH+GaC8BuCMkL+vrwAdSY7ZBFyQRnnnA9sDnMtHUpSzIHK+ya7tD7I479s9ypwaYL+pwEspzu0xYGiKcp4M+BuywMYA9aoE7ktRzgbg6Hz9naTxXbyY4piXhnis4ZHrEOS6bw37700vvfTq+ypDRAaCUtfybmAXcEgIZe0HNgOzQyjrAPAGEPguv0sr8AJOcPMGznlaYAJOcPt+wACfA2qBi70KidyhfxwYElm1HbgjUmYXMD2y7zRgNLDMGHOitXZ1hvWOP/ZngR9EFjuBu4G/AeXAmcC/AnXA3caYvdbaR1OU9wXgpshiB/BH4CmcQL8MJ7g+BZifopwTgIeA6siqx3ASz2ZgLnAZUAN8xRjTbq29JtgZx8o/HbgUJ8noAKoC7jcU+AtweGRVPXAb8A4wE/g0MAnn+/+DMeYMa21XgKLPTvF5a4Ay7sRJzsD5e/sVsAYYCVyEc91mAI9Gfj+bfcoJ++8kCPcxtwPtQNpPrwKoxrkO4Hz3/4vzG90UWT4MuATn720czt/bWdba/81BXUQE9ARBL70Gwgv4T+C7OMHltMi6S8nsCcKngRuBC3H+x22A0+LKuiONsj4C/AT4BE6wU4oTsGbyBOEQoDLFNvOAtrjyT/TZ7ta4bR4FBnlsU4YTAEa3+2MI39M4oCVSXiewwGOb+O9tE1CVpLwFcdv+H0nuxuMkTMN8PjMk3lH+hsc2xwD7Ip93A0elcd7V9D6tuQnYGHcs3zpH9v1h3LZ/cV8PnLvT8U8XrkxS1pPR7UL4Lj8cd8y3gcmuz0twEpnoNr/Lx99JGvW/EbgO+BAwPrLum3HHvDTEY03ESVj/HRjps00ViU8J3yTFU0C99NIr81fBK6CXXnoV5kWGCYJPWafFlXVHlmXlOvD5cVz5S322eSdum9lJyhqKE8hbYFcIdYsPdv8ryXYPpAp4cZq3RAPtt4HBWdQrPth9DjA+210RJOD12O/GyD7v4Dwd2RhXztQk+43GuattcZ5kjPbZ7kicJxMW2AaU+mz3ZPS4IXyXq+LO4QM+2wyKfDfR7Y5Mo/yc/p34HDNXCUIFUBNguyqcp5XROrwvH+etl14D8aVOyiIy0KyLez/WZ5vRce/f8CvIWrsX2BFZDNTZ1E+kE+250aKBm5NsflPc+/N9tjmP3uYgX7fWNmVRvfhj3GyttT7b3YHTPwLgg0E64Bpj5gJfjCx+wVq7P416fQQnuAS4z1rb6LWRtfZV4InI4ljgX9I4RtqMMYcAx0YW37DW/tmnXgeAW+JWnZfLehUra22HtbYlwHZtwMNxq47KXa1EBjYlCCIy0MyIe7/dZ5uGuPe+/TQi7d9HRRbrs6zXETh9JQDWWv/26ADP0BuIn+IzsssnIv9tB36XZd3OiHv/V7+NrLWtwD8ii4NIEYgbY8qBX+P8v+gha+3/ZFGvpH0xXJ+fleZx0nVm3Hvf6xWRz3odDOITyEEFq4XIQU4JgogMGMaYOTgdlMG5S+8XkD4U9/5GY0yfQMQYUwb8CGKDPfwoy+odGff+xWQbWmt7cJqwgPPv+CxX3cqB6JCjr1prDxhnaNqfGmM2xA1x+rwx5hvJhjc1xowFRkQW37bW7kxxHi/4nJOXq3HuAu8HPp9iWy+Br1ma9cI4Q9tuiwztucsYszoy7OyxqfZNs16rcfpsAMwOMhzrABd/bd8uWC1EDnIaxUhEDjrGmNnAoZHFMpxmJe/FGZkm+u/eddbaVR67g9PW+kycUXDOBP5pjLmd3lGMZtA7ilEPTqfdO7Os9qFx7zcG2D4+ODoUWBm3fAS9d1c3G2M+DvySxDuulTij6MwFvmSMOd9auzwH9fJkjJkF/L/I4jXW2ncClB2/fwm9T4O6cfovZF2vOB+Iez888joG+Hzkt3BlpImQl8DXzFrbZYzZAkzGaaY2gdTnMiBF5sQ4PbLYiTOSlojkgBIEETkYXQx8zeezl4HvWGsf8NvZWrvTGHMiTlB9Nk6C8R8emz4AfNeGMLwpTofnqFR36cEZNtNrX0jsW3EUsAhn5Jsngd/j9JuYinOdjojs/7Ax5lRr7fM5rBcQC+5/jZOkvIAzQk+6aun9f9hem3ro0pT1itvurzh3/rfijOA0FVgInBzZ5hPA5MhQm17HzeSaTY7bVwmCS+TJys/p/c5/Za3dlWQXEcmCEgQRGUj2A8txkoSkrLW7jTFfA/YCn/LZ7CMAxpgvWWu3ZVm32rj3bQG2j7977e6DEB+gRu+yX22t/X78RsaYG3E6Fn8MZ56F240xR7g6IYdZr6grcILtbuDTkSZT6cpFvf4DeMFa2+nx2XeNMWcDv8EZlnU+ThL67TzVbaC7ht4+GpuBawtYF5GDnvogiMhBx1p7tbXWWGsNztCIh+LMRrsPZ6z1l40xlyQrwxjzHzhNij6Fc7d7Dk5gWB15/2ucEXTOA54zxgRptpIv7n/bH3cnB+A0bwEup/eO9Sx6m3DkhDFmMs6cHAA/StLMK++stc/6JAfRz/8H53pF/bsxpjL3NRvYjDHn48zJAM7EaRdaa3cXsEoiBz09QRCRg5q1th0n0H/DGHMPzqzERwF3GGN2eA1BaYz5Ns7kcgBLrLXuIUdfBC43xqzBmVdhMnAP8C5XOR9JUrVWV5v/5rj3QWYRju9P4B4a1L38K79CIh2Y76a3CdV8nKcsuagXOM1EanH6BHwjQHl+wq5XINbae40x1+JMEjgEZxbqJ1ybFaRuByNjzAdxZhM3OE+cLrTWPlXYWokc/JQgiMiAYa3dY4y5gt6hOL8BJCQIxpgJOE8ZwBm6NFn7+JuBz+LceZ9jjHm3tfa5uM+TDdv5Nk7b9qi9ce9HJtkvakTc+72uz9zLqUbSif98huuz0OpljPkYvZ1/rwgy9n0SzTgdxsuAocaYshT9EJJdr3Q9iZMgABxO3wQhzO9ywDLGLMDpM1OOMxjAJdbaBwtbK5GBQQmCiAw0T+Pcpa0D3mWMqXEFqmfgBCQAK5JMCoa11hpjnqB3mNG5ODMNZ2J93PupAbafEvd+veuz113L+1KUFf/5ENdnYdYr2pd
|
|||
|
|
"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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},
|
|||
|
|
"metadata": {
|
|||
|
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},
|
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"output_type": "display_data"
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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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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},
|
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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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},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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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"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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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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|
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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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|||
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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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|
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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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|
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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>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
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|
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},
|
|||
|
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{
|
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|
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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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]
|
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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": "iVBORw0KGgoAAAANSUhEUgAAAwgAAAMmCAYAAABRo6ZRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydeXgV1dnAfyc7WQh72AKCAQwgoCwqFhcWtQqt1pVqq1W72FZq7fr5Vduirdr207q02lq3VgWtVi22KhC1CiqICliI7BJACEsSskD28/0xd27mzp2ZO3fLDfj+nmee3Jk5c86ZM3Nv3vecd1FaawRBEARBEARBEADSUt0BQRAEQRAEQRC6DqIgCIIgCIIgCIIQRBQEQRAEQRAEQRCCiIIgCIIgCIIgCEIQURAEQRAEQRAEQQgiCoIgCIIgCIIgCEFEQRAEQRAEQRAEIYgoCIIgCIIgCIIgBBEFQRAEQRAEQRCEIKIgCIIgCIIgCIIQRBQEQRAEQRAEQRCCiIIgCIIgCIIgCEIQURAEQRAEQRAEQQgiCoIgCIIgCIIgCEFEQRAEQRAEQRAEIYgoCILwGUApla6UGquUukopdZ9S6h2l1CGllA5sv4iiLqWUGqmU+rJS6v+UUm8opWotdT0WRV0ZSqmZSqk7lFKvKaV2K6WalFL1SqktSqmFSqnzlVLpUd7v2MB9fhyo66BS6qNAO0N9XK9j3I6Jpp8ubY9SSn1PKfWsUmpjoP9NSqlKpVSZUuonSqk+Pup5I8Z7uCpCvUopdalS6iWl1M5A33YH+natUiojhnsep5T6jVLqQ6XUvkCdO5VSK5RSdymlPu+znlOUUo8E3p1DSqkqpdT7Sqmf+Ryzq6IcqzN89mto4N37KPAu1gfezfuUUmN81tFdKXWGUuoHSqkFgXejPZHvnq29UqXUNUqpB5Xxe7E18D1vVkrtVUq9pZS6VSk1PIFt9lNKfTXwDD9UStUopVqUUgeUUu8p4/emNFHtCYLggdZaNtlkO8o34DlAe2y/iKKu/4tQ12M+6zkTOBChLnNbCRzrs94fAs0eddUCl0Wow0+f7FsdkBvnc/rQZ1sHgSsi1PVGjPdxmkedPYGyCNe/Dwzxeb+5wINAW4Q6ayLUo4C7gHaPOvYA0yPUc1WUY3WGj3u8PPBuuNXRBHw/Qh2FEe5NA8ck+DfjXZ9j0AzckoD27gVafbTXBvwOSE/k/comm2yhW9QzPYIgHJHYZ+CrMITzEQmoqw7YAYyOsp5BQC9Lf5ZiCCW7gUxgCvBVoDswGXhdKTVFa73HrUKl1LeA3wZ2W4C/Af8J1Hc2cBFQAPxNKVWjtX7FpaoLfN7DN4FzAp+f0Vof8nmdG8cH/rYDyzD6vhU4BAwDvgyMwxiTvyql0Fo/4VLXz4CIs+bAqRhKFcAW4C2nQkqpLOBFYFrg0A7gz8BmYDBwNVAKnAi8rJQ6RWtd69aoUiofeAk4PXCoAkOR/S+GElcIHIcxvoMj3MPtwPcDnxuAhzGUynzgQmAWUAS8qJSaprVeHaE+gPuA1yKU+a/XSaXUecDjGN8ZDTwLvIrxbp4OfAXIAu5SStVprf/iVlVgM9EY494HQ2lLFgcxxvFDjHejGuNehgLnAqdhfLd+qZTK0FrfEkdbo+n4bVmHMfYfATVAP+A84PMYlg8/wHg/vh5He4IgeJFqDUU22WRL/gbchCFEXQQMCxy7ithWEL6BMVv7ZWAUhuByhqWux3zWcwWwFpgLZLuUGYQhhJl1P+5R3wAM4VBjCGAzHcpY77kCyIljTNOBXZb6Tk3Ac6oCfgUUu5xPI3QFpxroFWebCy31/a9Hue9Zyr0P9LSdzwFesZT5bYR2n7CU/RWQ5VHWcTwC506gY3a9BhjnUOYXlrZWAsqlLuv7cVWc45prez+udCgzK/CuagxFu8ilrnxgAYYidyZQGDj+hqX+Y+J9/2xtlgIZEcpcbhn7FmBQHO29GngnJnqUucgyXpoIK0KyySZb7FvKOyCbbLKlZiNGBcGlrjMsdT3m85oeboKardxYS92HcDHjAe62lPuNR33PWMp9J457PtdSz4YEPZOePsqogJBrtv21eNoDGukw3RjsUi4D2Bso1w6McSnXD6gPlGsEeruUO8fS/9/HOWbPW+r6tseYrbCUO8+lXCIVBKtC9YxHud9YynkqVQ7XJk1BiKIPL1r6cHU876LPcr+ztOc6YSCbbLLFt4mTsiAIKUFrXaO11j7K/Rf4OLDbDSixl1FKKeBi8xIM8xA37rV8vtRfbx35muXzo3HUE0RrXe2jjMYwxTE53q2sD74MZAc+L9Fa73QpNx3oG/hcprVe59K3vRgrEgTq/aJLfT8K/K3DMIWKCaVUAYbZCRhmSY+59Mv+TsTz3P1ibeMej3L3YbyzAJckrztJY73lc/9YK/Hz7gf4u+VzPO++IAgeiIIgCMKRQJ3lczeH82MwzJEA1mmtd3jU9TaGMAlwakDIjAqlVG/gC4HdNgw7884k0nj45WrL50c8yp1l+ezmt+F0/hz7SWVEkTozsPuC1ro+Qn1enE6HgvOm9vYBedWrX4lEKdUdODmwexB4x61s4F01hewhSqlofXlSzbGWz67+QQkkUe++IAgeiIIgCEKXJuAca3Wm3u5QbKzl8/te9Wmt2zGcLsH4DYwlbOLlGM6lAK9orXfHUEc8WO/XaTwiopQah+FQDIbvw4s+2/McX2CVy3Um0+hwuF0Z6MuXlFIvK6X2KKUalVK7lFIvKKUuCawOxd0vrfU+Osaqr1Kqn/dt8O1AKNKGwPaJUurvSqnLfYRyHU3HPa4OvHNeRBqzLknACftLgd1G4N+d0Gzc774gCJGRKEaCIHR1LsHwVwD4QDtHMRpp+fyJjzqtgsVIAoJqFFjNi7xm3hOOUqqQUPOVf8VYlXX14EmtdZNH2WjGdyfGqko6MEIppWymZJMsn/cqpZ6jQ8g0GYhhnvRF4LtKqS9prffH2S8wnvtQy7V7PcpOtu0PDWwXATcrpS7RWq91uTbe97FLoZSaREckqSyM1bpZdJh3tWH483TGCsI3LJ9jffcFQYiAKAiCIHRZlFI9MZw4TW53KdrD8tlJkLRzwOVaP306AZgQ2N0HLIrm+gRwJx3hYf+ltf4o2gqUUpkYqyAmkZQc3+OrtW5VStViOEBnAHkYjssmVjv1WzEE4kYMP453MJygJwPXBq6dhhE29VStdXOs/Qrg57m3AsuBNzFCiR7C8L+YiqEc5GBE71qmlPqci5LQae9jJ/FD3P02lmPkQYgUEjZulFKXATMCu5V0snIuCJ8lREEQBKFLoozsyQsxwpeCIQw/61I83/K50Uf1hy2fo/VBsM68P6G1bony+phRSn0FI/cCGH4U34uxqi/QkSPhQx05L0As42vG5y8gVEGwCsAjMQToMwPO6CZPKqXux4jSMwhj1eH7GMpRvP0ycXruy4ChWutPHc49oJS6CcNBfHLg+qeVUmO11m1J7ldXpRJYDGxMdkMB34w/Ww5dr7VuSHa7gvBZRXwQBEHoqtxDh3NsBUYIypSilMrGiPxj0mkzmEqpaXQISBr4utZ6S4zVpcxEivD/OzfYlAMAtNabgW9ZDs1Laq8CbbooB+b5HRhmNaYpzXEYqwpHNVrry7TWSmutMFZ1xgL/g2Fu9EvgI6XU2clqXynVH2OlzlSe/qi1/rvHJYIgxImsIAiC0OVQSv0K+E5gtxKY5WKDbmKdoc7x0YQ1+kmda6lwvkiHec97ToKtFaXU5/DOZrw4QuQds55JGFmHzXv7vtb6GR/9daprAB1RfJqAJ31cVk/HikAOoePthNf4WvcPAk971PMv4FMMn4SBSqlSrXW5rV8myXzuQbTWB5RS99Bh7nYe4ffQ6f3qLALv6zpgnVJqAYaJ0SBgkVJqstZ6TSLbU0r1wlilGB449CydoCwKwmcdURAEQehSKKV+hpH5GQzzk5la60gmDDWWz14CuUlvl2sjEe3M+20YoTjdGEYEJ9ZAtKFXge6BQ/+jtfaKqx+Jr2I4EIMRZtRP/PkaOhSEPngoCIE
|
|||
|
|
"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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|
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"text/plain": [
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|||
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"<Figure size 900x900 with 4 Axes>"
|
|||
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]
|
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},
|
|||
|
|
"metadata": {
|
|||
|
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},
|
|||
|
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"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"
|
|||
|
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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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},
|
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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"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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"text/plain": [
|
|||
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"<Figure size 900x900 with 4 Axes>"
|
|||
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]
|
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},
|
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|
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"image/png": "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|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
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},
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"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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"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"
|
|||
|
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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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|
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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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{
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"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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},
|
|||
|
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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"
|
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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>"
|
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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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|
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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": "iVBORw0KGgoAAAANSUhEUgAAAwwAAAMmCAYAAABYSAYrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdd5xU9b3/8dd3l91lC3Vhl7aAFJEiqCgRiQ2EaAR77Ikt3uQm0SSm3hRvrmn+YmISk5tck4gaNVYSETXRiAUFFRCRthaQXpZetrD1+/vjzOyemZ0zbaft7Pv5eMzDOe17vufMWfx+zrcZay0iIiIiIiKh5KQ7AyIiIiIikrkUMIiIiIiIiCcFDCIiIiIi4kkBg4iIiIiIeFLAICIiIiIinhQwiIiIiIiIJwUMIiIiIiLiSQGDiIiIiIh4UsAgIiIiIiKeFDCIiIiIiIgnBQwiIiIiIuJJAYOIiIiIiHhSwCAiIiIiIp4UMIiIiIiIiCcFDCIiIiIi4kkBg0gXYIzJNcZMMMZcb4z5nTHmTWNMrTHG+j4/iiEtY4w51hhztTHmV8aYV40xh11pPRBDWt2MMecYY+40xrxsjNlpjKk3xlQbYzYYYx4zxlxkjMmN45oHGGO+ZYxZZIzZ7kt3tzFmlTHmfmPMZ40xRR7HnuW6nmg+18eavyjyP8x3X1YbYw757sn7vt9vfAzp5BpjrjHGPG2M2ea7D/uNMSuNMT8xxgyNMV/FxpjbjDGLfffzqDFmszHmcWPMp6JMo8wY8zljzFxjzLvGmIPGmEZjzD5jzDLfczU2xnwZY8wVxphnXde50xiz0BjzeWNMt1jS86U50RjzC18e9/jS3GaMedsYc7cx5rwo05nqu9YNvr+7/caYd4wxPzDG9IsyjYHGmDnGmB/5rnGn6/nbFOu1RcsYc4LvmfP/Tk2+/64yxvzJGPPJBJ6r1BgzyxjzPWPMPN9z1fp3lqjziEgcrLX66KNPln+AeYAN8/lRDGn9KkJaD0SZztnAvghp+T9LgZEx5PEW4HAU6Z7gcfxZUebL/7k+wb/XNcCRMOerB74eRTojgZUR8n4E+GyU+ToR2BAhvYeB/DBp3AM0RXFPm4FfArlR5KsPsDBCeu8AQ6O8ziLg/3x5CJfmwQjpGOBuoCVMGruA6RHSmRMhH5uS8G9GDvDbCHn3fx4FunfwfMdHOk+ir1EfffSJ/hPzGxcR6ZSC39Dvxymsj05AWkeArcC4GNMZDPR15ecl4C1gJ5AHTAE+B/QETgFeMcZMsdbuCpeoMeaXwDdcefu7L919QHdgBE5AEO2b0ceBxyLssyLKtCIyxpwPPIhzny3wFPAC0AicCXwWyAfuNsYcsdb+xSOdgcCrwBDfqi3AXOB9nALx2cBVQAnwgDGm1lo7L0y+hgH/BMp9q5biBAd7cQp7/wGU4gQ71pfPUMbR9gytBV4GVgMHgTLgfOA8nALrN4BewM1h8pUPzAdO963aCvwJWO+79huBscBJwD+NMVOttYfDpFcCPItzr8G5b/OANThBaC/gOOBc2u6tl58DX/d9rwHuw7lvJcClwEyc+znfGHO6tXalRzrBf3ONvvycGOH8HXE3cKtreQHO87QD53eaCnzGl7crff+9vAPnC77GZpxndQRQ2IF0RSQR0h2x6KOPPsn/AN/DKbxcBhzjW3c98dUw/AdOYeJqYAzOW9SzXGk9EGU61wKrcAqtBR77DMYpGPnTfjBCmp937fsc0D/Mvn2BYo9t7uuJ+t4k4HcqAra7zn1diH1m4hQY/bUD5R5pPe5K56VQ1wpMwynIWpyCf+8wefuHK737gJyg7cOAza59zvdI5wWcQGNymHNd5rpGS5g38MBXXfu9A/QJ2t4d+Jdrn7si/AYPu/b9KeFrSyrCbDuRtrfzB4GJIfb5ketcSwHjkdZpOEHQF4CT/XlyHbspwc/hcNpqV5qAWR77nURgTVjIGrsozzkSJ1C+1Xe9Rb71m/zpJ/Ia9dFHn9g+ac+APvrok54PcQYMHmm5C9gPRHlMb68CUtB+E1xp1/oLEiH2KwcO+fZbBnRL0PV06N7EeF534feJMPv9IlwBGBjoKqzWAmVh0vqWK60fe+wzybXPZjyanwCfdu23zGOfPl55Cdrvl660QgaKQDdgt2+fFmC8x35lQLVvv6NAqcd+57rO+ZsO/pbuAOtLHvsY4G3XfiGDrDDnSFbA4A68PZ/DEL/TLYnMhy99BQz66JMBH3V6FpG0sNYetNbaKPZbg9M0AZymCaM8dv0PnOZLAN+w1jZ1PJcpd4Xr+2/D7Pc7nEIUhG4GchZOYRTgBWvt7jBp/dX1/aoo8vUna+1Rj/3+idMUCOBkY8yI4B2stQfC5MXtSdf34z32mQ70931faK1dG2on3/X7m5UVABd6pPct33+PAD+IMp/tGGN64DSrAqcZ0wMe+bI4v6XfFaH2S4My1/ePIuz7oet7cRLyIiIZQAGDiHQGR1zfvdoz3+D771Zr7aIk5yfhjDE9gVN9i4eAN732tdZuBdb5FocaY4L7j7jb1n8Q7rzW2iqcQi3ASGPMmBC7zXJ9/1eYtCxOkyO/c8OdO4JofvOo8hVie7t8+fponO1bfNpaWx0xh97OxAlMABZZa2vD7Juo+5VIVa7vkfo5ubdXJiEvIpIBFDCISEbzdWp1F0o2h9hnCHCMb3Gpb91kY8yDvqEZ633DYr7mGxI05HCqHi71DSF5xBhTZ4zZaoxZYIz5ojEmkZ0xx9FWK7DSWtsSYf/lru8TgrYZ4hfwNt8Yk0Nbh/Ym4L0O5CsW7mPb/eYh9nknQnqR8nU6bffN/wxdYoz5pzFml2/42O2+4WkvN8aEu8dR58tau4e26+tvjCkLt3+K/BNo8H2/xBgzM9ROxpiTcPpVgFMT8XwK8iYiaaCAQUQy3eU4/R0AVtjQoySd7Pq+1RjzHZy24Z8DhuKMKtQPOANnWNgPfIWdaEzAKUSX4HSgHQLMBv4IbDDGnB3m2Fgc6/q+KYr93YXoY4O27QqzLYAxppy2plzgdGR3G0LbG/7tUTT1CpevWPyH6/tzHvvEcs+24XTkBRgdosDvfoZ2G2Pm4YyOdC5O/5gCYBBOc6bHgdfCzKGQyN8y5ay1O4Dv+BZzgReNMc8YY77um+viFmPMoziBVQ+c2q7zrbWNacqyiCSZhlUVkYxljOmD08HX7+ceuw5wfT+PtkLvfJzC5mHfuhtxRvMZArxsjDnJWvuxR5r+ztOv4DTrOYITuJyC09a8F07n4heNMZ+21v47tqtrp7fr+94o9t/ncSzAYtf3Txlj+vveZIcSPPxpcFqJzFdUjDFXAjN8i1U4w8GGEnXerLVNxpjDOHM2dMNpb+9uduR+hn6MU3A/CtyP0zysBee3/7zv2NNxhmmdZq1tIFDK71miWWt/Y4zZBfw/nKB7ju/jtgf4PvBIhGZXItLJKWAQkYxknNmdH8MplAM8Z619ymN3dyFrDE5h/xpr7aNBaf4KeAans2wv4H9p65zq9gEwxlobqsPnX4wx38UZfvPTOP+OPmqMGWHDjO8fhRLXd69OxW51ru893BustRuMMa/gtMkvAh4xxlwUXKgzxpyKM7SnW8+g5YTlKxq+/hh/cq26xVpb47F7PHnr48qbO2BwP0PH4hT0z/Z1uvd7xBjze5z5CAbj1Ep8HadQ3dF8+cV8z5JoHs7wtr/Fud5g/YFv4zRVuz+F+RKRFFOTJBHJVL+lrVPrFpxhYL0E/1t2X3CwAOAreF6NM9QowLnGmHZNQKy1Oz2CBf/2AzgTb632rSoF/jNM/tLhVpw5FsCZu2GtMeaHvvb31xtjHgAW4bwtd9eyROo7kTTGmAE4E4T5C81/sNY+GeaQRAp+hr4WFCwAYK1dD3zRterW4H2ygTHGP0v4Uzj9GT6HE7zn+/77OWAjzqhlc40xXrV/IpIFVMMgIhnHGPNT4Mu+xSpgprU2XNOOI0HLfwq5F86oQMaY+bQNITqDwKEho2KtPWqM+RngD0zOJ+hNszFmFs4bfq80nnYtut92d48iC+4O18HXj7V
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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|||
|
|
"text/plain": [
|
|||
|
|
"<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"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
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]
|
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},
|
|||
|
|
"metadata": {
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},
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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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},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 900x900 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"for row in 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='field'\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
|
|||
|
|
}
|