MultiPSD_B_python_fooofing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mat->Py->Mat Multi PSD Example\n",
    "\n",
    "This notebook is an example, with multiple power spectra, of integrating Python FOOOF into a Matlab workflow. \n",
    "\n",
    "It is part of a trio of files that must be run in order:\n",
    "- `MultiPSD_A_*\n",
    "- `MultiPSD_B_*\n",
    "- `MultiPSD_C_*\n",
    "\n",
    "This code must be run after `MultiPSD_A_matlab_preprocessing.m` has been run. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "from scipy.io import loadmat, savemat\n",
    "\n",
    "from fooof import FOOOFGroup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the mat file \n",
    "data = loadmat('power_spectra.mat')\n",
    "\n",
    "# Unpack data from dictionary, and squeeze numpy arrays\n",
    "freqs = np.squeeze(data['freqs']).astype('float')\n",
    "psds = np.squeeze(data['psds']).astype('float')\n",
    "# ^Note: this also explicitly enforces type as float (type casts to float64, instead of float32)\n",
    "#  This is not strictly necessary for fitting, but is for saving out as json from FOOOF, if you want to do that\n",
    "\n",
    "# Transpose power spectra, to have the expected orientation for FOOOF\n",
    "psds = psds.T  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize FOOOFGroup object\n",
    "fg = FOOOFGroup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FOOOFGroup across 2 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.98 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "==================================================================================================\n",
      "                                                                                                  \n",
      "                                       FOOOF - GROUP RESULTS                                      \n",
      "                                                                                                  \n",
      "                             Number of power spectra in the Group: 2                              \n",
      "                                                                                                  \n",
      "                        The model was run on the frequency range 1 - 30 Hz                        \n",
      "                                 Frequency Resolution is 0.98 Hz                                  \n",
      "                                                                                                  \n",
      "                              Power spectra were fit without a knee.                              \n",
      "                                                                                                  \n",
      "                                      Aperiodic Fit Values:                                       \n",
      "                        Exponents - Min:  1.637, Max:  1.769, Mean: 1.703                         \n",
      "                                                                                                  \n",
      "                          In total 5 peaks were extracted from the group                          \n",
      "                                                                                                  \n",
      "                                     Goodness of fit metrics:                                     \n",
      "                            R2s -  Min:  0.979, Max:  0.988, Mean: 0.984                          \n",
      "                         Errors -  Min:  0.066, Max:  0.078, Mean: 0.072                          \n",
      "                                                                                                  \n",
      "==================================================================================================\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fit the FOOOF model on all PSDs, and report\n",
    "fg.report(freqs, psds, [1, 30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save out a specific FOOOF measure of interest - for example, slopes\n",
    "exps = fg.get_params('aperiodic_params', 'exponent')\n",
    "savemat('exps.mat', {'exps' : exps})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save out fooof results to json file\n",
    "#  There is a utility file to load this json file directly into Matlab\n",
    "fg.save('fooof_results', save_results=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alternatively: save out full FOOOFresults for each PSD fit separately to mat files\n",
    "#  This might be useful if you are using Matlab < r2016B, and don't have json support\n",
    "for ind, f_res in enumerate(fg):\n",
    "    savemat('f_results_' + str(ind) + '.mat', f_res._asdict())"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}