examples/extracellular/extrafeats_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extracellular Features Extraction for MEA Data\n",
    "\n",
    "This example shows how to use extracellular features implemented in eFEL for recordings from Microelectrode Arrays (MEA). Also see [Buccino et al., 2024](https://doi.org/10.1162/neco_a_01672) and another [example](https://github.com/alejoe91/multimodalfitting/blob/main/notebooks/experimental/cell1/1-generate-experimental-features_cell1.ipynb) using these efeatures for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from efel.pyfeatures import extrafeats\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently, the following features are implemented in eFEL:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['peak_to_valley',\n",
       " 'halfwidth',\n",
       " 'peak_trough_ratio',\n",
       " 'repolarization_slope',\n",
       " 'recovery_slope',\n",
       " 'neg_peak_relative',\n",
       " 'pos_peak_relative',\n",
       " 'neg_peak_diff',\n",
       " 'pos_peak_diff',\n",
       " 'neg_image',\n",
       " 'pos_image']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extrafeats.all_1D_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These features are described as functions in extrafeats.py. You can find the docstrings of these functions by using the help function in Python. You can find more documentation of the features here: https://efel.readthedocs.io/en/latest/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function peak_to_valley in module efel.pyfeatures.extrafeats:\n",
      "\n",
      "peak_to_valley(waveforms, sampling_frequency)\n",
      "    Time between trough and peak. If the peak precedes the trough,\n",
      "    peak_to_valley is negative.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    waveforms  : numpy.ndarray (num_waveforms x num_samples)\n",
      "        waveforms to compute feature for\n",
      "    sampling_frequency  : float\n",
      "        rate at which the waveforms are sampled (Hz)\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    np.ndarray (num_waveforms)\n",
      "        peak_to_valley in seconds\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(extrafeats.peak_to_valley)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get the test MEA data and calculate the extracellular features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "waveforms_fpath = \"../../tests/testdata/extrafeats/mean_waveforms.dat\"\n",
    "\n",
    "waveforms = np.loadtxt(waveforms_fpath)\n",
    "waveform = np.array([waveforms[0]])\n",
    "sampling_freq = 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0013])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extrafeats.peak_to_valley(waveforms=waveform, sampling_frequency=sampling_freq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `waveform` should have a structure similar to the one in eFEL/tests/testdata/extrafeats/mean_waveforms.datmean_waveforms.dat. We will use the `calculate_features` function from extrafeats to calculate all the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak_to_valley [ 0.0013 -0.0001 -0.0016 -0.0013 -0.0011 -0.0013 -0.0012  0.0009 -0.0011\n",
      " -0.0002 -0.0004 -0.0008  0.0031 -0.0018  0.0016 -0.0007 -0.0009  0.0009\n",
      " -0.0007 -0.0018 -0.0013 -0.0005 -0.0015 -0.0011  0.0013 -0.0034  0.0018\n",
      "  0.001  -0.0019 -0.0015 -0.0014  0.0002 -0.0014 -0.0012 -0.0016  0.0008\n",
      "  0.0014 -0.0005 -0.0025  0.0009  0.0014 -0.0016 -0.0013  0.0009 -0.0008\n",
      " -0.0016 -0.0016 -0.0019 -0.0013 -0.0014 -0.0011  0.0003 -0.0014 -0.0014\n",
      " -0.001  -0.0013 -0.0006 -0.0024  0.0018 -0.0035 -0.0012 -0.0012  0.0002\n",
      " -0.0013 -0.0023 -0.0002 -0.0001  0.0032  0.0024 -0.0013  0.0029 -0.0006\n",
      "  0.001  -0.001   0.0013  0.0022  0.0009 -0.0014 -0.0014  0.0018  0.0007\n",
      "  0.0008 -0.0017  0.0014  0.0007 -0.0016 -0.0008  0.0027 -0.0021 -0.0012\n",
      " -0.0015 -0.0001 -0.0001  0.0009  0.0016 -0.0001  0.0006  0.0015  0.0011\n",
      " -0.0009  0.0012 -0.0012  0.0013 -0.0012  0.0007 -0.0023 -0.0009 -0.0013\n",
      " -0.0002 -0.001  -0.0011 -0.0012 -0.0015 -0.0011 -0.0012 -0.0014  0.0003\n",
      "  0.0009  0.0009  0.0006  0.0002  0.0008 -0.0006  0.0034  0.0014 -0.0013\n",
      " -0.0011 -0.0008  0.0007 -0.0011 -0.0013  0.0009 -0.0013  0.0009  0.0009\n",
      " -0.0018 -0.0012  0.0002 -0.0013 -0.0019 -0.0002 -0.0041  0.0009 -0.0009\n",
      " -0.0006 -0.001  -0.0015  0.0009 -0.0012 -0.002  -0.0008 -0.0032  0.0015\n",
      "  0.0021 -0.0002 -0.0005 -0.0009 -0.0008 -0.001  -0.0013  0.0014 -0.001\n",
      "  0.0009 -0.0013 -0.0017  0.0021 -0.0016 -0.0011 -0.0014 -0.0019  0.0011\n",
      " -0.0016 -0.0002 -0.001  -0.0018 -0.0015 -0.0001  0.002   0.0009 -0.0015\n",
      " -0.0014 -0.0017  0.0009 -0.0007  0.0013 -0.0024 -0.0013 -0.0037 -0.0002\n",
      " -0.0007 -0.0006 -0.0011 -0.0016 -0.0013 -0.0001 -0.001  -0.0014 -0.0015\n",
      " -0.0005 -0.0004 -0.0015 -0.0001 -0.0015 -0.0011  0.0002 -0.0013 -0.0012\n",
      " -0.0011 -0.0013]\n",
      "halfwidth [ 0.0015     nan -0.0028 -0.0093 -0.0025 -0.0017 -0.0018  0.0003 -0.0024\n",
      "     nan     nan -0.0055  0.0017 -0.003   0.0006 -0.0017 -0.0014     nan\n",
      " -0.0011     nan -0.0025 -0.0068 -0.003  -0.0024  0.0007 -0.0054  0.0007\n",
      "     nan -0.0035     nan -0.0023     nan -0.0026 -0.0019 -0.0029     nan\n",
      "     nan     nan -0.0039     nan     nan -0.0029 -0.0025     nan -0.0017\n",
      " -0.0026     nan -0.0035 -0.0023 -0.0018 -0.0017     nan -0.0028     nan\n",
      " -0.0016 -0.0023     nan -0.0042  0.0008 -0.0045 -0.0022 -0.0019     nan\n",
      " -0.0027 -0.0026 -0.0008     nan  0.0009  0.0008     nan  0.0014     nan\n",
      "  0.0005 -0.0017  0.0007  0.0015  0.0012 -0.002  -0.0019  0.0012  0.0004\n",
      "  0.0009 -0.0033  0.0006  0.0005 -0.0031 -0.0018  0.0006 -0.0038 -0.0024\n",
      " -0.0025     nan     nan  0.002   0.0006     nan     nan  0.0005  0.0006\n",
      " -0.0022     nan -0.0026     nan -0.0025     nan -0.0039 -0.0019 -0.0018\n",
      "     nan -0.0016 -0.0015 -0.0024 -0.003  -0.0023     nan -0.002      nan\n",
      "     nan     nan     nan     nan     nan     nan  0.0019     nan -0.0018\n",
      " -0.0017 -0.002   0.0005 -0.0017 -0.0023  0.002  -0.0067  0.0005     nan\n",
      "     nan -0.0019     nan -0.0022 -0.0036     nan -0.0063  0.0005 -0.0015\n",
      " -0.0018 -0.0023 -0.0019     nan -0.0026 -0.0037 -0.0019 -0.0051  0.0006\n",
      "  0.0008     nan     nan -0.0019 -0.002  -0.0016 -0.0027  0.0007 -0.0024\n",
      "     nan -0.002  -0.003   0.0007 -0.0031 -0.0024     nan -0.0036  0.0014\n",
      "     nan     nan -0.0016 -0.0035 -0.0081     nan  0.0005  0.0003 -0.0024\n",
      " -0.0028     nan  0.0007 -0.0018     nan -0.0046 -0.0024 -0.0059     nan\n",
      " -0.0019 -0.0009 -0.0018 -0.003  -0.0024     nan -0.0016 -0.0028 -0.0029\n",
      " -0.0073 -0.0044 -0.0025     nan -0.003      nan     nan -0.0019 -0.0018\n",
      " -0.0048 -0.0023]\n",
      "peak_trough_ratio [ 0.53804035  1.21802672  2.36845269  0.13818893  2.01288236  0.67247375\n",
      "  0.71054337  0.51431686  1.2270848   6.12281799  0.16201052  0.52271024\n",
      "  0.29541054  2.10863154  0.16400563  0.71021801  0.82259676  0.80606722\n",
      "  0.59820375  1.67405533  1.22329924  0.19102443  2.6186373   1.35055906\n",
      "  0.42408852  6.55820515  0.16284988  0.93212375  3.95194239  0.61651983\n",
      "  4.12676067  0.7973423   0.82446904  0.63280487  0.94402227  0.83385778\n",
      "  0.31889983  0.40030984 13.01325066  0.41722616  1.08734046  1.28142004\n",
      "  1.31052961  0.65915915  0.59007358  8.24318384  1.05782205  3.37112047\n",
      "  0.54651881  0.73548706  1.13247942  2.20866198  2.01767528  0.97482789\n",
      "  0.47452749  0.6706796   6.99816194  5.11126778  0.18733708  6.95707896\n",
      "  0.48524825  0.60336188  0.9870102   2.79221104  9.70193971  0.82660372\n",
      "  1.19167412  0.21163596  0.20311777  0.72466183  0.14251774  1.44321506\n",
      "  0.53334603  0.90387643  0.41733713  0.24190003  0.53594822  0.73317705\n",
      "  0.69903345  0.31630318  0.64635683  0.97632033  2.84277294  0.57842661\n",
      "  0.67426552  3.12048394  0.65460616  0.18484049  4.40800015  3.50836316\n",
      "  2.37435616  2.72846297  0.81115791  0.57493701  0.39853579  1.71386828\n",
      "  5.21433688  0.16672223  0.49929396  1.67457084  1.5233474   2.58614124\n",
      "  0.66188939  2.5763163   1.11925818  4.77781519  0.53758802  0.36358919\n",
      "  3.00864117  0.96411726  0.45938812  2.93000602  2.33622968  1.09317424\n",
      "  0.49026222  1.13618701  1.50261237  1.095218    0.38294177  0.8881845\n",
      "  3.26153958  0.8232666   0.5709424   0.22638494  2.34391786  0.59851957\n",
      "  0.40645699  1.3843255   0.75217496  0.58369778  0.85124889  0.5570616\n",
      "  0.33895087  0.48671009  0.6832888   2.75731074  0.58309425  7.37870261\n",
      "  0.62792104  4.58416164  6.30390847  4.72034757  0.59310884  0.67547227\n",
      "  0.22271289  1.72432913  0.28557396  0.69169406  1.80447415  4.37127311\n",
      "  0.96403529  9.13984054  0.36332231  0.18233296  0.81281747  0.39035498\n",
      "  1.56471095  1.34022733  0.79437089  4.12955594  0.39527652  2.11227852\n",
      "  0.73075634  1.26226321  1.79751798  0.1394111   2.89838121  1.93914669\n",
      "  0.69080368  3.72053666  0.32107693  1.08166173  1.62173755  0.83715129\n",
      "  3.76741793  0.19781369  1.34602015  0.22818912  0.85238754  5.67776085\n",
      "  7.99932939  2.45151786  0.3190137   1.05551016  1.1821116   3.85214816\n",
      "  1.04723524  4.71443887  0.30886562  0.39164463  0.41260801  0.51914942\n",
      "  3.12387873  1.49623728  1.28426178  0.87490264  2.41248035  2.18445792\n",
      "  0.24783548  0.2482083   3.15501273  1.11067998  2.65673549  0.37181242\n",
      "  6.12408649  0.64401332  1.50091732  0.48633446  0.72905253]\n",
      "repolarization_slope [7.31257213e+01            nan 6.50182478e-01 4.99764537e+01\n",
      " 1.01266031e+00 1.22505405e+01 3.66794226e+00 3.96887490e+01\n",
      " 2.08781290e+00            nan 2.66397081e+01 3.32739630e+01\n",
      " 3.99171396e+01 1.18709472e+00 1.89150163e+02 5.33856699e+00\n",
      " 4.44666871e+00            nan 6.59520145e+00 1.35164366e+00\n",
      " 2.82988737e+00 2.03543510e+01 4.85872501e-01 1.38560311e+00\n",
      " 8.93961074e+01 1.15821548e-01 9.74608965e+01 4.35300936e+01\n",
      " 2.48036396e-01 5.35377718e+00 4.19533385e-01            nan\n",
      " 3.87647107e+00 2.49081159e+01 2.53626726e+00 3.08366795e+02\n",
      " 1.12289446e+02 7.72393222e+00 1.02580692e-01            nan\n",
      " 5.83851918e+00 1.57677921e+00 1.92203478e+00 6.57320993e+01\n",
      " 1.54010808e+01 1.55766806e-01 2.41955804e+00 3.11922659e-01\n",
      " 1.52125356e+01 3.20115632e+00 4.41163627e+00            nan\n",
      " 7.79157923e-01 2.62026144e+00 6.73181141e+01 7.50472345e+00\n",
      "            nan 1.36751428e-01 5.34852086e+01 2.24802060e-01\n",
      " 5.15398199e+01 2.30980102e+01            nan 5.65438604e-01\n",
      " 3.84641022e-01            nan            nan 1.90051711e+01\n",
      " 5.68442402e+01 6.28630703e+00 1.20718467e+01            nan\n",
      " 8.79612883e+01 3.09045447e+00 1.36219748e+02 1.07188996e+01\n",
      " 1.19465006e+02 3.74992327e+00 5.11446110e+00 3.21324376e+01\n",
      " 2.96762385e+02 2.87781921e+02 4.56945190e-01 1.36938154e+02\n",
      " 2.44544112e+02 3.82173278e-01 4.28763535e+00 1.25128169e+01\n",
      " 1.69948984e-01 4.05290769e-01 7.46124701e-01            nan\n",
      "            nan 6.40825688e+01 7.90614702e+01            nan\n",
      "            nan 5.41251231e+02 5.78885301e+01 1.34395745e+00\n",
      "            nan 7.11621115e-01 4.54818048e+01 6.68880108e-01\n",
      "            nan 1.63878756e-01 9.48684456e+00 1.27452569e+01\n",
      "            nan 7.83978570e+00 2.70935240e+01 5.88313421e-01\n",
      " 6.93437974e-01 2.65871059e+00 2.13100092e+01 2.53094985e+00\n",
      "            nan            nan 1.06479171e+02            nan\n",
      "            nan 1.97160273e+02 6.90330690e+00 4.15423798e+00\n",
      "            nan 7.32781106e+00 9.63477588e+01 1.89820897e+00\n",
      " 2.54461786e+02 6.19672821e+01 4.78810459e+00 1.05531879e+02\n",
      " 1.04548966e+01 1.24175790e+02 6.02423181e+01 9.37029728e-01\n",
      " 3.43643506e+01            nan 1.24964841e+01 1.56175850e-01\n",
      "            nan 1.77657282e-01 1.04539302e+02 2.10076218e+01\n",
      " 1.22608385e+01 1.41715843e+00 6.93772653e+00 1.63626246e+01\n",
      " 1.10691890e+00 1.69822463e-01 2.90615132e+00 9.16422355e-02\n",
      " 8.81610344e+01 3.49923843e+01            nan 1.45976613e+01\n",
      " 9.41618161e-01 1.76125600e+00 1.09905341e+01 3.63785992e-01\n",
      " 3.26088462e+01 9.29907719e-01 7.64039846e+01 2.86223544e+00\n",
      " 8.74881544e-01 4.62083858e+01 4.79718173e-01 1.10037652e+00\n",
      " 7.23525875e+00 2.33396674e-01 6.76706409e+01 2.92417470e+00\n",
      "            nan 1.39168207e+01 2.47310145e-01 2.75639683e+01\n",
      "            nan 1.20943344e+02 1.26141937e+01 3.12423625e-01\n",
      " 1.69656396e-01 1.80390321e+00 2.74981969e+01 2.43878552e+00\n",
      "            nan 2.14533667e-01 2.97934250e+00 1.55177673e-01\n",
      "            nan 6.74687462e+00 1.16212026e+01 6.70505162e+01\n",
      " 4.12664511e-01 2.44764617e+00            nan 9.28700444e+00\n",
      " 8.35177563e-01 7.86531878e-01 7.90299729e+00 2.12171085e+01\n",
      " 5.12327842e-01            nan 5.99487790e-01 2.87914657e+01\n",
      "            nan 5.07647764e+00 3.12094982e+00 5.37494106e+01\n",
      " 8.01730656e+00]\n",
      "recovery_slope [-3.63355521e+00  4.54342435e+00 -2.10494514e+00 -3.21612844e+01\n",
      " -1.59355549e+01 -2.91941227e+01 -4.48096741e+00 -7.79253365e+00\n",
      " -1.93142445e+01 -5.43590968e+00 -7.84073969e+01 -4.67504152e+01\n",
      " -1.24537129e+00  9.04851649e-01 -9.23155491e+00 -5.20729298e+01\n",
      " -2.88969544e+01 -1.92130265e+01 -4.31626734e+01  2.16413225e+00\n",
      " -1.04642376e+01 -8.08408718e+01 -5.87632497e+00 -1.09565866e+01\n",
      " -5.85332555e+00 -1.16608216e+00 -3.21579887e+00 -2.17739809e+01\n",
      " -5.07126055e+00 -1.02362027e+01 -2.03299056e+00 -7.71686527e-01\n",
      " -2.51163221e+00 -2.95649418e+01 -8.15932912e-01 -5.13599982e+01\n",
      " -9.32113618e+00 -4.50290554e+01 -3.80578627e+00 -1.24285027e+01\n",
      " -9.27802447e-01 -2.00340018e+00 -6.20759074e+00 -2.66042436e+01\n",
      " -5.09533788e+01 -3.97681438e+00 -1.18503344e+00 -4.60929399e+00\n",
      " -6.92211691e+00 -6.16165160e+00 -1.12842409e+01 -1.75193125e+00\n",
      " -6.96007348e+00 -4.96107984e+00 -3.46614424e+01 -5.24159703e+00\n",
      " -8.08372841e+00 -2.71314048e+00 -2.65011956e+00  5.62134860e-01\n",
      " -1.78506949e+01 -1.81238510e+01  2.00340882e+00 -1.06992198e+01\n",
      " -3.91580166e+00 -4.59364500e-01  2.75542585e+00 -3.77812716e-01\n",
      " -1.39327173e+00 -1.32048883e+01 -1.86680577e-01 -3.93456185e+00\n",
      " -7.64656083e+00 -1.68954306e+01 -4.34881443e+00 -8.69229998e-01\n",
      " -1.19791230e+01 -6.67007742e+00 -1.23338715e+01 -3.92168564e+00\n",
      " -3.26267482e+01 -4.89466829e+01 -5.50323478e+00 -4.06361556e+01\n",
      " -2.04860872e+01 -6.46205930e+00 -2.42733005e+01 -3.29076079e-01\n",
      " -2.18624572e+00 -8.64471967e+00 -2.57717988e+00 -4.83779739e-01\n",
      "  5.09670643e+00 -2.81096643e+01 -1.24310724e+01 -2.41915951e+00\n",
      " -1.51126413e+00 -2.88979477e+01 -3.37945486e+00 -2.08947684e+01\n",
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      "pos_image [ 1.08501167e-01 -1.38519656e-02 -8.62022439e-03 -2.06040891e-01\n",
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      " -1.33672327e-02  1.63751749e-02 -1.39619823e-01  8.87501353e-03\n",
      "  3.79814657e-01 -1.30388820e-02  1.38912513e-02 -9.41480201e-02\n",
      " -2.86422267e-02 -8.54048672e-02 -1.08057469e-01 -2.75544036e-03\n",
      " -2.71497552e-03 -2.06840393e-02 -1.36119696e-01 -7.42773676e-02\n",
      " -1.03460912e-02 -3.48134643e-02  2.23164246e-02 -5.47293966e-03\n",
      " -7.61185814e-02  1.59368359e-02 -8.36007928e-03 -5.03633149e-02\n",
      "  1.37868462e-02 -1.25896227e-01  1.96586742e-02 -1.84120761e-01\n",
      "  1.03126190e-02  1.72570998e-01  1.82149623e-03 -2.23764069e-03\n",
      "  3.39241504e-03 -3.04666832e-02 -7.51461458e-02 -1.11952170e-01\n",
      "  2.42018668e-02  2.77979044e-02 -1.16808089e-01  2.04911006e-02\n",
      "  1.76164628e-02 -1.46040900e-01 -2.37644831e-01 -2.07194603e-01\n",
      "  1.82503388e-02 -4.13795071e-02  1.30805556e-02 -1.14840546e-01\n",
      "  2.49402162e-03  1.65120438e-02 -1.12337528e-01 -8.06768107e-02\n",
      " -5.84977379e-03 -4.73614935e-03  2.48863050e-02 -2.92409448e-01\n",
      "  3.56957519e-03 -6.70057126e-02 -1.06023022e-01 -9.88549097e-02\n",
      " -1.63652624e-01]\n"
     ]
    }
   ],
   "source": [
    "feats = extrafeats.calculate_features(\n",
    "        waveforms, sampling_freq\n",
    "    )\n",
    "for feature_name in extrafeats.all_1D_features:\n",
    "    print(feature_name, feats[feature_name])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another example of extraction of these features from the experimental data can be found in the [multimodalfitting](https://github.com/alejoe91/multimodalfitting/blob/main/notebooks/ground-truth/1-generate-ecode-protocols-and-features-hay_ais.ipynb) repository."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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