415 lines
13 KiB
Plaintext
415 lines
13 KiB
Plaintext
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"cells": [
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"09:52:58 [I] klustakwik KlustaKwik2 version 0.2.6\n",
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"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
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" return f(*args, **kwds)\n",
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"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
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" return f(*args, **kwds)\n",
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"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
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" return f(*args, **kwds)\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import pathlib\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from matplotlib import colors\n",
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"import seaborn as sns\n",
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"import re\n",
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"import shutil\n",
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"import pandas as pd\n",
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"import scipy.stats\n",
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"\n",
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"import exdir\n",
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"import expipe\n",
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"from distutils.dir_util import copy_tree\n",
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"import septum_mec\n",
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"import spatial_maps as sp\n",
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"import head_direction.head as head\n",
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"import septum_mec.analysis.data_processing as dp\n",
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"import septum_mec.analysis.registration\n",
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"from septum_mec.analysis.plotting import violinplot, despine\n",
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"from spatial_maps.fields import (\n",
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" find_peaks, calculate_field_centers, separate_fields_by_laplace, \n",
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" map_pass_to_unit_circle, calculate_field_centers, distance_to_edge_function, \n",
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" which_field, compute_crossings)\n",
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"from phase_precession import cl_corr\n",
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"from spike_statistics.core import permutation_resampling\n",
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"import matplotlib.mlab as mlab\n",
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"import scipy.signal as ss\n",
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"from scipy.interpolate import interp1d\n",
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"from septum_mec.analysis.plotting import regplot\n",
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"from skimage import measure\n",
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"from tqdm.notebook import tqdm_notebook as tqdm\n",
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"tqdm.pandas()\n",
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"\n",
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"import pycwt"
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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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"max_speed = 1 # 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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"position_sampling_rate = 1000 # for interpolation\n",
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"position_low_pass_frequency = 6 # for low pass filtering of position\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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"stim_mask = True\n",
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"baseline_duration = 600"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"data_loader = dp.Data(\n",
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" position_sampling_rate=position_sampling_rate, \n",
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" position_low_pass_frequency=position_low_pass_frequency,\n",
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" box_size=box_size, bin_size=bin_size, \n",
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" stim_mask=stim_mask, baseline_duration=baseline_duration\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"project_path = dp.project_path()\n",
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"project = expipe.get_project(project_path)\n",
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"actions = project.actions\n",
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"\n",
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"output_path = pathlib.Path(\"output\") / \"lfp-speed-stim\"\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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"identify_neurons = actions['identify-neurons']\n",
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"sessions = pd.read_csv(identify_neurons.data_path('sessions'))"
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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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"channel_groups = []\n",
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"for i, row in sessions.iterrows():\n",
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" for ch in range(8):\n",
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" row['channel_group'] = ch\n",
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" channel_groups.append(row.to_dict())"
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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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"sessions = pd.DataFrame(channel_groups)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"def signaltonoise(a, axis=0, ddof=0):\n",
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" a = np.asanyarray(a)\n",
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" m = a.mean(axis)\n",
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" sd = a.std(axis=axis, ddof=ddof)\n",
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" return np.where(sd == 0, 0, m / sd)\n",
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"\n",
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"\n",
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"def remove_artifacts(anas, spikes=None, width=500, threshold=2, sampling_rate=None, fillval=0):\n",
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" sampling_rate = sampling_rate or anas.sampling_rate.magnitude\n",
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" times = np.arange(anas.shape[0]) / sampling_rate\n",
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" anas = np.array(anas)\n",
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" if anas.ndim == 1:\n",
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" anas = np.reshape(anas, (anas.size, 1))\n",
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" assert len(times) == anas.shape[0]\n",
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" nchan = anas.shape[1]\n",
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" if spikes is not None:\n",
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" spikes = np.array(spikes)\n",
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" for ch in range(nchan):\n",
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" idxs, = np.where(abs(anas[:, ch]) > threshold)\n",
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" for idx in idxs:\n",
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" if spikes is not None:\n",
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" t0 = times[idx-width]\n",
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" stop = idx+width\n",
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" if stop > len(times) - 1:\n",
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" stop = len(times) - 1 \n",
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" t1 = times[stop]\n",
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" mask = (spikes > t0) & (spikes < t1)\n",
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" spikes = spikes[~mask]\n",
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" anas[idx-width:idx+width, ch] = fillval\n",
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" if spikes is not None:\n",
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" spikes = spikes[spikes <= times[-1]]\n",
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" return anas, times, spikes\n",
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" else:\n",
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" return anas, times\n",
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" \n",
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"def find_theta_peak(p, f, f1, f2):\n",
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" if np.all(np.isnan(p)):\n",
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" return np.nan, np.nan\n",
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" mask = (f > f1) & (f < f2)\n",
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" p_m = p[mask]\n",
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" f_m = f[mask]\n",
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" peaks = find_peaks(p_m)\n",
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" idx = np.argmax(p_m[peaks])\n",
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" return f_m[peaks[idx]], p_m[peaks[idx]]"
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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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"def zscore(a):\n",
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" return (a - a.mean()) / a.std()\n",
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"# return a"
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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_stim_freq(action_id):\n",
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" stim_times = data_loader.stim_times(action_id)\n",
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" if stim_times is None:\n",
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" return\n",
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" stim_times = np.array(stim_times)\n",
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" return 1 / np.mean(np.diff(stim_times))"
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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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"output = exdir.File(output_path / 'results')\n",
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"\n",
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"mother = pycwt.Morlet(80)\n",
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"NFFT = 2056\n",
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"\n",
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"def process(row):\n",
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" name = row['action'] + '-' + str(row['channel_group'])\n",
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" stim_freq = compute_stim_freq(row['action'])\n",
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" if stim_freq is None:\n",
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" return\n",
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" \n",
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" flim = [stim_freq - 2, stim_freq + 2]\n",
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" \n",
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" lfp = data_loader.lfp(row.action, row.channel_group)\n",
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" sample_rate = lfp.sampling_rate.magnitude\n",
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" sampling_period = 1 / sample_rate\n",
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" x, y, t, speed = map(data_loader.tracking(row.action).get, ['x', 'y', 't', 'v'])\n",
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" cleaned_lfp, times = remove_artifacts(lfp)\n",
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" speed = interp1d(t, speed, bounds_error=False, fill_value='extrapolate')(times)\n",
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" peak_amp = {}\n",
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" for i, ch in enumerate(cleaned_lfp.T):\n",
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" pxx, freqs = mlab.psd(ch, Fs=lfp.sampling_rate.magnitude, NFFT=4000)\n",
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" f, p = find_theta_peak(pxx, freqs, 6, 10)\n",
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" peak_amp[i] = p\n",
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"\n",
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" theta_channel = max(peak_amp, key=lambda x: peak_amp[x])\n",
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" signal = zscore(cleaned_lfp[:,theta_channel])\n",
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" \n",
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" if name in output:\n",
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" return\n",
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" \n",
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" \n",
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" results = output.require_group(name)\n",
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" freqs = np.arange(*flim, .1)\n",
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" wave, scales, freqs, coi, fft, fftfreqs = pycwt.cwt(\n",
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" signal, sampling_period, freqs=freqs, wavelet=mother)\n",
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" \n",
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" power = (np.abs(wave)) ** 2\n",
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" power /= scales[:, None] #rectify the power spectrum according to the suggestions proposed by Liu et al. (2007)\n",
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" \n",
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" theta_freq = np.array([freqs[i] for i in np.argmax(power, axis=0)])\n",
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" theta_power = np.mean(power, axis=0)\n",
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"\n",
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" speed_bins = np.arange(min_speed, max_speed + speed_binsize, speed_binsize)\n",
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" ia = np.digitize(speed, bins=speed_bins, right=True)\n",
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" mean_freq = np.zeros_like(speed_bins)\n",
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" mean_power = np.zeros_like(speed_bins)\n",
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" for i in range(len(speed_bins)):\n",
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" mean_freq[i] = np.mean(theta_freq[ia==i])\n",
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" mean_power[i] = np.mean(theta_power[ia==i])\n",
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" \n",
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" freq_score = np.corrcoef(speed, theta_freq)[0,1]\n",
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" power_score = np.corrcoef(speed, theta_power)[0,1]\n",
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" \n",
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" results.attrs = {\n",
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" 'freq_score': float(freq_score),\n",
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" 'sample_rate': float(sample_rate),\n",
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" 'power_score': float(power_score),\n",
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" 'action': row['action'],\n",
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" 'channel_group': int(row['channel_group']),\n",
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" 'max_speed': max_speed,\n",
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" 'min_speed': min_speed,\n",
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" 'position_low_pass_frequency': position_low_pass_frequency\n",
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" }\n",
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" \n",
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" results.create_dataset('wavelet_power', data=power)\n",
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" results.create_dataset('wavelet_freqs', data=freqs)\n",
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" results.create_dataset('theta_freq', data=theta_freq)\n",
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" results.create_dataset('theta_power', data=theta_power)\n",
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" results.create_dataset('speed', data=speed)\n",
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" results.create_dataset('mean_freq', data=mean_freq)\n",
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" results.create_dataset('mean_power', data=mean_power)\n",
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" results.create_dataset('speed_bins', data=speed_bins)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "1f5b845ccb4c460780c75e4829109535",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"HBox(children=(IntProgress(value=0, max=696), HTML(value='')))"
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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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"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/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.\n",
|
||
|
|
" out=out, **kwargs)\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"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sessions.progress_apply(process, axis=1);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Store results in Expipe action"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"action = project.require_action(\"lfp_speed_stim\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"action.data[\"results\"] = \"results.exdir\"\n",
|
||
|
|
"copy_tree(output_path, str(action.data_path()))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"septum_mec.analysis.registration.store_notebook(action, \"10_lfp_speed_stim.ipynb\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"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
|
||
|
|
}
|