septum-mec/actions/stimulus-spike-lfp-response.../data/10-calculate-stimulus-spike...

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"16:15:36 [I] klustakwik KlustaKwik2 version 0.2.6\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:25: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
"Please use `tqdm.notebook.*` instead of `tqdm._tqdm_notebook.*`\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import spatial_maps as sp\n",
"import septum_mec.analysis.data_processing as dp\n",
"import septum_mec.analysis.registration\n",
"import expipe\n",
"import os\n",
"import pathlib\n",
"import scipy\n",
"import scipy.signal\n",
"import numpy as np\n",
"import exdir\n",
"import pandas as pd\n",
"import optogenetics as og\n",
"import quantities as pq\n",
"import shutil\n",
"from distutils.dir_util import copy_tree\n",
"import elephant as el\n",
"import neo\n",
"from scipy.signal import find_peaks\n",
"from scipy.interpolate import interp1d\n",
"from matplotlib import mlab\n",
"\n",
"from tqdm import tqdm_notebook as tqdm\n",
"from tqdm._tqdm_notebook import tqdm_notebook\n",
"tqdm_notebook.pandas()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"data_loader = dp.Data()\n",
"actions = data_loader.actions\n",
"project = data_loader.project"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"output = pathlib.Path('output/stimulus-spike-lfp-response-reduced-transient-cut')\n",
"(output / 'data').mkdir(parents=True, exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"identify_neurons = actions['identify-neurons']\n",
"# sessions = pd.read_csv(identify_neurons.data_path('sessions'))\n",
"units = pd.read_csv(identify_neurons.data_path('units'))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def get_lim(action_id):\n",
" stim_times = data_loader.stim_times(action_id)\n",
" if stim_times is None:\n",
" return [0, np.inf]\n",
" stim_times = np.array(stim_times)\n",
" return [stim_times.min(), stim_times.max()]\n",
"\n",
"def get_mask(lfp, lim):\n",
" return (lfp.times >= lim[0]) & (lfp.times <= lim[1])\n",
"\n",
"def zscore(a):\n",
" return (a - a.mean(0)) / a.std(0)\n",
"\n",
"def compute_stim_freq(action_id):\n",
" stim_times = data_loader.stim_times(action_id)\n",
" if stim_times is None:\n",
" return np.nan\n",
" stim_times = np.array(stim_times)\n",
" return 1 / np.mean(np.diff(stim_times))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def signaltonoise(a, axis=0, ddof=0):\n",
" a = np.asanyarray(a)\n",
" m = a.mean(axis)\n",
" sd = a.std(axis=axis, ddof=ddof)\n",
" return np.where(sd == 0, 0, m / sd)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def compute_energy(p, f, f1, f2):\n",
" if np.isnan(f1) or np.all(np.isnan(p)):\n",
" return np.nan\n",
" mask = (f > f1) & (f < f2)\n",
" df = f[1] - f[0]\n",
" return np.sum(p[mask]) * df"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def find_theta_peak(p, f, f1, f2):\n",
" if np.all(np.isnan(p)):\n",
" return np.nan, np.nan\n",
" mask = (f > f1) & (f < f2)\n",
" p_m = p[mask]\n",
" f_m = f[mask]\n",
" peaks, _ = find_peaks(p_m)\n",
" idx = np.argmax(p_m[peaks])\n",
" return f_m[peaks[idx]], p_m[peaks[idx]]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def compute_half_width(p, f, m_p, m_f):\n",
" if np.isnan(m_p):\n",
" return np.nan, np.nan\n",
" m_p_half = m_p / 2\n",
" half_p = p - m_p_half\n",
" idx_f = np.where(f <= m_f)[0].max()\n",
" idxs_p1, = np.where(np.diff(half_p[:idx_f + 1] > 0) == 1)\n",
" if len(idxs_p1) == 0:\n",
" return np.nan, np.nan\n",
" m1 = idxs_p1.max()\n",
" idxs_p2, = np.where(np.diff(half_p[idx_f:] > 0) == 1)\n",
" m2 = idxs_p2.min() + idx_f\n",
" assert p[m1] < m_p_half < p[m1+1], (p[m1], m_p_half, p[m1+1])\n",
" assert p[m2] > m_p_half > p[m2+1], (p[m2], m_p_half, p[m2+1])\n",
" \n",
" f1 = interp1d([half_p[m1], half_p[m1 + 1]], [f[m1], f[m1 + 1]])(0)\n",
" f2 = interp1d([half_p[m2], half_p[m2 + 1]], [f[m2], f[m2 + 1]])(0)\n",
" return f1, f2"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# p = np.load('debug_p.npy')\n",
"# f = np.load('debug_f.npy')\n",
"# compute_half_width(p, f, 0.01038941, 30.30187709636872)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# plt.plot(f, p)\n",
"# plt.xlim(29.9,30.6)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"def compute_stim_peak(p, f, s_f):\n",
" if np.isnan(s_f):\n",
" return np.nan\n",
" return interp1d(f, p)(s_f)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def compute_spike_lfp_coherence(anas, sptr, NFFT):\n",
"\n",
" sigs, freqs = el.sta.spike_field_coherence(anas, sptr, **{'nperseg': NFFT})\n",
" return sigs, freqs"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def butter_bandpass(lowcut, highcut, fs, order=5):\n",
" nyq = 0.5 * fs\n",
" low = lowcut / nyq\n",
" high = highcut / nyq\n",
" b, a = scipy.signal.butter(order, [low, high], btype='band')\n",
" return b, a\n",
"\n",
"\n",
"def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):\n",
" b, a = butter_bandpass(lowcut, highcut, fs, order=order)\n",
" y = scipy.signal.filtfilt(b, a, data)\n",
" return y\n",
"\n",
"# def compute_spike_phase_func(lfp, times, return_degrees=False):\n",
"# x_a = hilbert(lfp)\n",
"# x_phase = np.angle(x_a)\n",
"# if return_degrees:\n",
"# x_phase = x_phase * 180 / np.pi\n",
"# return interp1d(times, x_phase)\n",
"\n",
"\n",
"def vonmises_kde(data, kappa=100, n_bins=100):\n",
" from scipy.special import i0\n",
" bins = np.linspace(-np.pi, np.pi, n_bins)\n",
" x = np.linspace(-np.pi, np.pi, n_bins)\n",
" # integrate vonmises kernels\n",
" kde = np.exp(kappa * np.cos(x[:, None] - data[None, :])).sum(1) / (2 * np.pi * i0(kappa))\n",
" kde /= np.trapz(kde, x=bins)\n",
" return bins, kde\n",
"\n",
"\n",
"def spike_phase_score(phase_bins, density):\n",
" import math\n",
" import pycircstat as pc\n",
" ang = pc.mean(phase_bins, w=density)\n",
" vec_len = pc.resultant_vector_length(phase_bins, w=density)\n",
" # ci_lim = pc.mean_ci_limits(head_angle_bins, w=rate)\n",
" return ang, vec_len"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def compute_clean_lfp(anas, width=50, threshold=2):\n",
" anas = np.array(anas)\n",
" idxs, = np.where(abs(anas) > threshold)\n",
" for idx in idxs:\n",
" anas[idx-width:idx+width] = 0 # TODO AR model prediction\n",
" percentage_removed = len(idxs) * width * 2 / len(anas)\n",
" return anas, idxs, percentage_removed\n",
"\n",
"\n",
"def compute_clean_spikes(spikes, idxs, times, width=50):\n",
"\n",
" for idx in idxs:\n",
" t0 = times[idx-width]\n",
" stop = idx + width\n",
" if stop > len(times) - 1:\n",
" stop = len(times) - 1 \n",
" t1 = times[stop]\n",
" mask = (spikes > t0) & (spikes < t1)\n",
" spikes = spikes[~mask]\n",
" spikes = spikes[spikes <= times[-1]]\n",
" return spikes\n",
"\n",
"\n",
"def prepare_spike_lfp(anas, sptr, t_start, t_stop):\n",
"\n",
" t_start = t_start * pq.s if t_start is not None else 0 * pq.s\n",
" sampling_rate = anas.sampling_rate\n",
" units = anas.units\n",
" times = anas.times\n",
" if t_start is not None and t_stop is not None:\n",
" t_stop = t_stop * pq.s\n",
" mask = (times > t_start) & (times < t_stop)\n",
" anas = np.array(anas)[mask,:]\n",
" times = times[mask]\n",
" \n",
" # take best channel from other drive\n",
" best_channel = np.argmax(signaltonoise(anas))\n",
"# best_channel = np.random.choice(anas.shape[1])\n",
" \n",
" cleaned_anas, idxs, percentage_removed = compute_clean_lfp(anas[:, best_channel])\n",
" cleaned_anas = neo.AnalogSignal(\n",
" signal=cleaned_anas * units, sampling_rate=sampling_rate, t_start=t_start\n",
" )\n",
" \n",
" spike_units = sptr.units\n",
" spike_times = sptr.times\n",
" spike_times = compute_clean_spikes(spike_times, idxs, times)\n",
"\n",
" sptr = neo.SpikeTrain(\n",
" spike_times[(spike_times > t_start) & (spike_times < times[-1])], units=spike_units,\n",
" t_start=t_start, t_stop=times[-1]\n",
" )\n",
"\n",
" return cleaned_anas, sptr, best_channel, percentage_removed"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"def compute_spike_phase_func(lfp, times, return_degrees=False):\n",
" from scipy.fftpack import next_fast_len\n",
" x_a = scipy.signal.hilbert(\n",
" lfp, next_fast_len(len(lfp)))[:len(lfp)]\n",
"# x_a = hilbert(lfp)\n",
" x_phase = np.angle(x_a, deg=return_degrees)\n",
" return interp1d(times, x_phase)\n",
"\n",
"\n",
"def compute_spike_phase(lfp, spikes, flim=[6,10]):\n",
" \n",
" sample_rate = lfp.sampling_rate.magnitude\n",
" \n",
" # sometimes the position is recorded after LFP recording is ended\n",
" times = np.arange(lfp.shape[0]) / sample_rate\n",
" \n",
" spikes = np.array(spikes)\n",
" spikes = spikes[(spikes > times.min()) & (spikes < times.max())]\n",
" \n",
" filtered_lfp = butter_bandpass_filter(\n",
" lfp.magnitude.ravel(), *flim, fs=sample_rate, order=3)\n",
"\n",
" spike_phase_func = compute_spike_phase_func(filtered_lfp, times)\n",
" \n",
" return spike_phase_func(spikes), filtered_lfp"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0, 100)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1152x648 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x648 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(16,9))\n",
"lfp = data_loader.lfp('1833-200619-2', 6)\n",
"# lfp = data_loader.lfp('1834-220319-3', 6)\n",
"# lfp = data_loader.lfp('1849-010319-4', 6)\n",
"times = np.arange(lfp.shape[0]) / lfp.sampling_rate.magnitude\n",
"clean_lfp, _, _ = compute_clean_lfp(lfp.magnitude[:, 0], threshold=2)\n",
"plt.plot(times,lfp[:,0])\n",
"plt.plot(times,clean_lfp)\n",
"\n",
"plt.figure(figsize=(16,9))\n",
"plt.psd(lfp[:,0].ravel(), Fs=1000, NFFT=10000)\n",
"plt.psd(clean_lfp, Fs=1000, NFFT=10000)\n",
"plt.xlim(0,100)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# plt.figure(figsize=(16,9))\n",
"\n",
"# plt.plot(times,lfp[:,0])\n",
"# # plt.plot(clean_lfp*100)\n",
"# plt.plot(times[:-1], np.diff(lfp[:,0].magnitude.ravel()))\n",
"# plt.xlim(64.5,65.5)\n",
"# # plt.ylim(-250,250)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fd29009a710>]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# action_id_0, channel_0, unit_0 = '1833-200619-1', 6, 163\n",
"# action_id_1, channel_1, unit_1 = '1833-200619-2', 6, 28\n",
"action_id_0, channel_0, unit_0 = '1834-220319-3', 2, 46\n",
"action_id_1, channel_1, unit_1 = '1834-220319-4', 2, 60\n",
"\n",
"# change data loader to get all LFPs and then selecte the best form the other\n",
"lfp_0 = data_loader.lfp(action_id_0, channel_0)\n",
"lfp_1 = data_loader.lfp(action_id_1, channel_1)\n",
"\n",
"sample_rate_0 = lfp_0.sampling_rate\n",
"sample_rate_1 = lfp_1.sampling_rate\n",
"\n",
"lim_0 = get_lim(action_id_0)\n",
"lim_1 = get_lim(action_id_1)\n",
"\n",
"sptrs_0 = data_loader.spike_trains(action_id_0, channel_0)\n",
"\n",
"sptrs_1 = data_loader.spike_trains(action_id_1, channel_1)\n",
"\n",
"cleaned_lfps_0, sptr_0, best_channel_0, percentage_removed_0 = prepare_spike_lfp(lfp_0, sptrs_0[unit_0], *lim_0)\n",
"\n",
"cleaned_lfps_1, sptr_1, best_channel_1, percentage_removed_1 = prepare_spike_lfp(lfp_1, sptrs_1[unit_1], *lim_1)\n",
"\n",
"coher_0, freq_0 = compute_spike_lfp_coherence(cleaned_lfps_0, sptr_0, 4096)\n",
"\n",
"coher_1, freq_1 = compute_spike_lfp_coherence(cleaned_lfps_1, sptr_1, 4096)\n",
"\n",
"spike_phase_0, filtered_lfp_0 = compute_spike_phase(cleaned_lfps_0, sptrs_0[unit_0], flim=[6,10])\n",
"\n",
"spike_phase_1, filtered_lfp_1 = compute_spike_phase(cleaned_lfps_1, sptrs_1[unit_1], flim=[6,10])\n",
"\n",
"# spike_phase_1_stim, filtered_lfp_1_stim = compute_spike_phase(cleaned_lfps_1, sptrs_1[unit_1], flim=[10.5,11.5])\n",
"spike_phase_1_stim, filtered_lfp_1_stim = compute_spike_phase(cleaned_lfps_1, sptrs_1[unit_1], flim=[29.5,30.5])\n",
"\n",
"plt.figure()\n",
"plt.plot(freq_0, coher_0.ravel())\n",
"plt.plot(freq_1, coher_1.ravel())\n",
"plt.xlim(0,20)\n",
"\n",
"plt.figure()\n",
"bins_0, kde_0 = vonmises_kde(spike_phase_0, 100)\n",
"ang_0, vec_len_0 = spike_phase_score(bins_0, kde_0)\n",
"plt.polar(bins_0, kde_0, color='b')\n",
"plt.polar([ang_0, ang_0], [0, vec_len_0], color='b')\n",
"\n",
"bins_1, kde_1 = vonmises_kde(spike_phase_1, 100)\n",
"ang_1, vec_len_1 = spike_phase_score(bins_1, kde_1)\n",
"plt.polar(bins_1, kde_1, color='r')\n",
"plt.polar([ang_1, ang_1], [0, vec_len_1], color='r')\n",
"\n",
"bins_1_stim, kde_1_stim = vonmises_kde(spike_phase_1_stim, 100)\n",
"ang_1_stim, vec_len_1_stim = spike_phase_score(bins_1_stim, kde_1_stim)\n",
"plt.polar(bins_1_stim, kde_1_stim, color='k')\n",
"plt.polar([ang_1_stim, ang_1_stim], [0, vec_len_1_stim], color='k')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"percentage_removed_1"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"# TODO fix artefact stuff from phase precession"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"NFFT = 8192\n",
"theta_band_f1, theta_band_f2 = 6, 10 "
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"coherence_data, freqency_data = {}, {}\n",
"theta_kde_data, theta_bins_data = {}, {}\n",
"stim_kde_data, stim_bins_data = {}, {}\n",
"\n",
"def process(row):\n",
" action_id = row['action']\n",
" channel_group = row['channel_group']\n",
" unit_name = row['unit_name']\n",
" \n",
" name = f'{action_id}_{channel_group}_{unit_name}'\n",
" \n",
" # select \n",
" lfp = data_loader.lfp(action_id, channel_group) # TODO consider choosing strongest stim response\n",
" \n",
" sptr = data_loader.spike_train(action_id, channel_group, unit_name)\n",
" \n",
" lim = get_lim(action_id)\n",
" \n",
" cleaned_lfp, sptr, best_channel, percentage_removed = prepare_spike_lfp(lfp, sptr, *lim)\n",
" \n",
" p_xys, freq = compute_spike_lfp_coherence(cleaned_lfp, sptr, NFFT=NFFT)\n",
" \n",
" p_xy = p_xys.magnitude.ravel()\n",
" freq = freq.magnitude\n",
" \n",
" theta_f, theta_p_max = find_theta_peak(p_xy, freq, theta_band_f1, theta_band_f2)\n",
" \n",
" theta_energy = compute_energy(p_xy, freq, theta_band_f1, theta_band_f2) # theta band 6 - 10 Hz\n",
" \n",
" theta_half_f1, theta_half_f2 = compute_half_width(p_xy, freq, theta_p_max, theta_f)\n",
" \n",
" theta_half_width = theta_half_f2 - theta_half_f1\n",
" \n",
" theta_half_energy = compute_energy(p_xy, freq, theta_half_f1, theta_half_f2) # theta band 6 - 10 Hz\n",
" \n",
" theta_spike_phase, _ = compute_spike_phase(cleaned_lfp, sptr, flim=[theta_band_f1, theta_band_f2])\n",
" theta_bins, theta_kde = vonmises_kde(theta_spike_phase)\n",
" theta_ang, theta_vec_len = spike_phase_score(theta_bins, theta_kde)\n",
" theta_kde_data.update({name: theta_kde})\n",
" theta_bins_data.update({name: theta_bins})\n",
"\n",
" # stim\n",
" \n",
" stim_freq = compute_stim_freq(action_id)\n",
" \n",
" stim_p_max = compute_stim_peak(p_xy, freq, stim_freq)\n",
" \n",
" stim_half_f1, stim_half_f2 = compute_half_width(p_xy, freq, stim_p_max, stim_freq)\n",
" stim_half_width = stim_half_f2 - stim_half_f1\n",
" \n",
" stim_energy = compute_energy(p_xy, freq, stim_half_f1, stim_half_f2)\n",
" \n",
" if np.isnan(stim_freq):\n",
" stim_spike_phase, stim_bins, stim_kde, stim_ang, stim_vec_len = [np.nan] * 5\n",
" else:\n",
" stim_spike_phase, _ = compute_spike_phase(cleaned_lfp, sptr, flim=[stim_freq - .5, stim_freq + .5])\n",
" stim_bins, stim_kde = vonmises_kde(stim_spike_phase)\n",
" stim_ang, stim_vec_len = spike_phase_score(stim_bins, stim_kde)\n",
" stim_kde_data.update({name: stim_kde})\n",
" stim_bins_data.update({name: stim_bins})\n",
" \n",
" coherence_data.update({name: p_xy})\n",
" freqency_data.update({name: freq})\n",
" \n",
" result = pd.Series({\n",
" 'percentage_removed': percentage_removed,\n",
" 'best_channel': best_channel,\n",
" 'theta_freq': theta_f,\n",
" 'theta_peak': theta_p_max,\n",
" 'theta_energy': theta_energy,\n",
" 'theta_half_f1': theta_half_f1, \n",
" 'theta_half_f2': theta_half_f2,\n",
" 'theta_half_width': theta_half_width,\n",
" 'theta_half_energy': theta_half_energy,\n",
" 'theta_ang': theta_ang, \n",
" 'theta_vec_len': theta_vec_len,\n",
" 'stim_freq': stim_freq,\n",
" 'stim_p_max': stim_p_max,\n",
" 'stim_half_f1': stim_half_f1, \n",
" 'stim_half_f2': stim_half_f2,\n",
" 'stim_half_width': stim_half_width,\n",
" 'stim_energy': stim_energy,\n",
" 'stim_ang': stim_ang, \n",
" 'stim_vec_len': stim_vec_len\n",
" })\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6dfdc93cb0b546f28867b3134d582205",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, max=1284), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/signal/spectral.py:1577: RuntimeWarning: invalid value encountered in true_divide\n",
" Cxy = np.abs(Pxy)**2 / Pxx / Pyy\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:28: RuntimeWarning: invalid value encountered in true_divide\n"
]
}
],
"source": [
"results = units.merge(\n",
" units.progress_apply(process, axis=1), \n",
" left_index=True, right_index=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pd.DataFrame(coherence_data).to_feather(output / 'data' / 'coherence.feather')\n",
"pd.DataFrame(freqency_data).to_feather(output / 'data' / 'freqs.feather')\n",
"pd.DataFrame(theta_kde_data).to_feather(output / 'data' / 'theta_kde.feather')\n",
"pd.DataFrame(theta_bins_data).to_feather(output / 'data' / 'theta_bins.feather')\n",
"pd.DataFrame(stim_kde_data).to_feather(output / 'data' / 'stim_kde.feather')\n",
"pd.DataFrame(stim_bins_data).to_feather(output / 'data' / 'stim_bins.feather')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Save to expipe"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"action = project.require_action(\"stimulus-spike-lfp-response-reduced-transient-cut\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"action.modules['parameters'] = {\n",
" 'NFFT': NFFT,\n",
" 'theta_band_f1': theta_band_f1,\n",
" 'theta_band_f2': theta_band_f2\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"action.data['results'] = 'results.csv'\n",
"results.to_csv(action.data_path('results'), index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"copy_tree(output, str(action.data_path()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"septum_mec.analysis.registration.store_notebook(action, \"10-calculate-stimulus-spike-lfp-response-reduced-transient-cut.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
}