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

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate spike-lfp coherence using other drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_loader = dp.Data()\n",
    "actions = data_loader.actions\n",
    "project = data_loader.project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = pathlib.Path('output/stimulus-spike-lfp-response-other-drive')\n",
    "(output / 'data').mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "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": 10,
   "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": 11,
   "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": 12,
   "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": 13,
   "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": 14,
   "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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(f, p)\n",
    "# plt.xlim(29.9,30.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 17,
   "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": 18,
   "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_clean_lfp(anas, width=500, 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",
    "    return anas, idxs\n",
    "\n",
    "\n",
    "def compute_clean_spikes(spikes, idxs, times, width=500):\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 = compute_clean_lfp(anas[:, best_channel])\n",
    "    \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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 100)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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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": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "drive_0_channel_groups = [0, 1, 2, 3]\n",
    "drive_1_channel_groups = [4, 5, 6, 7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f43cc322940>]"
      ]
     },
     "execution_count": 23,
     "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",
    "# select best channel among other drive\n",
    "if channel_0 in drive_0_channel_groups:\n",
    "    lfps = []\n",
    "    for ch in drive_1_channel_groups:\n",
    "        lfps.append(data_loader.lfp(action_id_0, ch))\n",
    "elif channel_0 in drive_1_channel_groups:\n",
    "    lfps = []\n",
    "    for ch in drive_0_channel_groups:\n",
    "        lfps.append(data_loader.lfp(action_id_0, ch))\n",
    "        \n",
    "# merge lfp of other drive into a single AnalogSignal\n",
    "lfp_arrays = np.hstack(lfps).as_array()\n",
    "lfp_0 = neo.AnalogSignal(signal=lfp_arrays * lfps[0].units, sampling_rate=lfps[0].sampling_rate,\n",
    "                         t_start=lfps[0].t_start)\n",
    "\n",
    "# select best channel among other drive\n",
    "if channel_1 in drive_0_channel_groups:\n",
    "    lfps = []\n",
    "    for ch in drive_1_channel_groups:\n",
    "        lfps.append(data_loader.lfp(action_id_1, ch))\n",
    "elif channel_1 in drive_1_channel_groups:\n",
    "    lfps = []\n",
    "    for ch in drive_0_channel_groups:\n",
    "        lfps.append(data_loader.lfp(action_id_1, ch))\n",
    "# merge lfp of other tetrodes into a signle AnalogSignal\n",
    "lfp_arrays = np.hstack(lfps).as_array()\n",
    "lfp_1 = neo.AnalogSignal(signal=lfp_arrays * lfps[0].units, sampling_rate=lfps[0].sampling_rate,\n",
    "                         t_start=lfps[0].t_start)\n",
    "\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 = prepare_spike_lfp(lfp_0, sptrs_0[unit_0], *lim_0)\n",
    "\n",
    "cleaned_lfps_1, sptr_1, best_channel_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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO fix artefact stuff from phase precession"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "NFFT = 8192\n",
    "theta_band_f1, theta_band_f2 = 6, 10 \n",
    "drive_0_channel_groups = [0, 1, 2, 3]\n",
    "drive_1_channel_groups = [4, 5, 6, 7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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 best channel among other drive\n",
    "    if channel_group in drive_0_channel_groups:\n",
    "        lfps = []\n",
    "        for ch in drive_1_channel_groups:\n",
    "            lfps.append(data_loader.lfp(action_id, ch))\n",
    "    elif channel_group in drive_1_channel_groups:\n",
    "        lfps = []\n",
    "        for ch in drive_0_channel_groups:\n",
    "            lfps.append(data_loader.lfp(action_id, ch))\n",
    "    \n",
    "    # merge lfp of other tetrodes into a signle AnalogSignal\n",
    "    lfp_arrays = np.hstack(lfps).as_array()\n",
    "    lfp = neo.AnalogSignal(signal=lfp_arrays * lfps[0].units, sampling_rate=lfps[0].sampling_rate,\n",
    "                           t_start=lfps[0].t_start)    \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 = 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",
    "        '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": "6e6aa8f543ce4316bb63d5c3b6761864",
       "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": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coher, freqs = {}, {}\n",
    "# for i, row in tqdm(units.iterrows(), total=len(units)):\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",
    "#     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",
    "#     p_xys, freq, clean_lfp = compute_spike_lfp(lfp, sptr, *lim, NFFT=NFFT)\n",
    "    \n",
    "#     snls = signaltonoise(clean_lfp)\n",
    "#     best_channel = np.argmax(snls)\n",
    "#     snl = snls[best_channel]\n",
    "#     p_xy = p_xys[:,best_channel].magnitude\n",
    "#     freq = freq.magnitude\n",
    "    \n",
    "#     coher.update({name: p_xy})\n",
    "#     freqs.update({name: freq})"
   ]
  },
  {
   "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-other-drive\")"
   ]
  },
  {
   "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-other-drive.ipynb\")"
   ]
  }
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