{
 "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": [
      "10:25:26 [I] klustakwik KlustaKwik2 version 0.2.6\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 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')\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)\n",
    "\n",
    "\n",
    "def compute_clean_lfp(anas, width=500, threshold=2):\n",
    "    anas = np.array(anas)\n",
    "\n",
    "    for ch in range(anas.shape[1]):\n",
    "        idxs, = np.where(abs(anas[:, ch]) > threshold)\n",
    "        for idx in idxs:\n",
    "            anas[idx-width:idx+width, ch] = 0 # TODO AR model prediction\n",
    "    return anas"
   ]
  },
  {
   "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": [
    "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_spike_lfp(anas, sptr, t_start, t_stop, NFFT):\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",
    "    if t_start is not None and t_stop is not None:\n",
    "        t_stop = t_stop * pq.s\n",
    "        mask = (anas.times > t_start) & (anas.times < t_stop)\n",
    "        anas = np.array(anas)[mask,:]\n",
    "\n",
    "    cleaned_anas = zscore(compute_clean_lfp(anas))\n",
    "    \n",
    "    cleaned_anas = neo.AnalogSignal(\n",
    "        signal=cleaned_anas * units, sampling_rate=sampling_rate, t_start=t_start\n",
    "    )\n",
    "\n",
    "    sptr = neo.SpikeTrain(\n",
    "        sptr.times[(sptr.times > t_start) & (sptr.times < cleaned_anas.times[-1])],\n",
    "        t_start=t_start, t_stop=cleaned_anas.times[-1]\n",
    "    )\n",
    "\n",
    "    sigs, freqs = el.sta.spike_field_coherence(cleaned_anas, sptr, **{'nperseg': NFFT})\n",
    "    return sigs, freqs, cleaned_anas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# action_id_0 = '1833-200619-1'\n",
    "# action_id_1 = '1833-200619-2'\n",
    "# lfp_0 = data_loader.lfp(action_id_0, 6)\n",
    "# lfp_1 = data_loader.lfp(action_id_1, 6)\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, 6)\n",
    "# sptrs_1 = data_loader.spike_trains(action_id_1, 6)\n",
    "\n",
    "# coher_0, freq_0, clean_lfp_0 = compute_spike_lfp(lfp_0, sptrs_0[163], *lim_0, 4096)\n",
    "# coher_1, freq_1, clean_lfp_1 = compute_spike_lfp(lfp_1, sptrs_1[28], *lim_1, 4096)\n",
    "\n",
    "# best_channel_0 = np.argmax(signaltonoise(clean_lfp_0))\n",
    "# best_channel_1 = np.argmax(signaltonoise(clean_lfp_1))\n",
    "\n",
    "# plt.plot(freq_0, coher_0[:,best_channel_0])\n",
    "# plt.plot(freq_1, coher_1[:,best_channel_1])\n",
    "# plt.xlim(0,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "NFFT = 4096*2\n",
    "theta_band_f1, theta_band_f2 = 6, 10 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process(row):\n",
    "    action_id = row['action']\n",
    "    channel_group = row['channel_group']\n",
    "    unit_name = row['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",
    "    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",
    "    # 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",
    "    result = pd.Series({\n",
    "        'signal_to_noise': snl,\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",
    "        '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",
    "    })\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "564d1ef5339f46eebee42ec41cfcfe62",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1298), 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:1578: RuntimeWarning: invalid value encountered in true_divide\n",
      "  Cxy = np.abs(Pxy)**2 / Pxx / Pyy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "results = units.merge(\n",
    "    units.progress_apply(process, axis=1), \n",
    "    left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b20a9a26e0864f578e1a9aa0c021999b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1298), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'dict' object has no attribute 'to_feather'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-bf8a91a90c5a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcoher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_feather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'data'\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'coherence.feather'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mfreqs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_feather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'data'\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'freqs.feather'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'dict' object has no attribute 'to_feather'"
     ]
    }
   ],
   "source": [
    "coher.to_feather(output / 'data' / 'coherence.feather')\n",
    "freqs.to_feather(output / 'data' / 'freqs.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\")"
   ]
  },
  {
   "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.ipynb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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