spynal_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# spynal tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Imports successful\n"
     ]
    }
   ],
   "source": [
    "# Standard PyData imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(\"Imports successful\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "trialInfo : pandas.DataFrame with columns[fixationTime,responseTime,sample,sampleOff,sampleOn,testOn,]\n\nspikeTimes : numpy.array( 40,  2,) of type object\n\nlfp : numpy.array(5001,  2, 40,) of type float64\n\nlfp_timepts : numpy.array(5001,) of type float64\n\n"
     ]
    }
   ],
   "source": [
    "### Data loading from mat file using matIO module ###\n",
    "from spynal.matIO import loadmat\n",
    "\n",
    "filename = r'./example_WM_data.mat'\n",
    "\n",
    "# Load some data from a mat file into Python variables. Load 'trialInfo' as a Pandas DataFrame\n",
    "trial_info, spike_times, lfp, lfp_timepts = \\\n",
    "    loadmat(filename, variables=['trialInfo','spikeTimes','lfp','lfp_timepts'],\n",
    "            typemap={'trialInfo':'DataFrame'}, verbose=True)\n",
    "\n",
    "# Alternatively, we could load all variables into a Python dictionary (similar to Matlab struct) \n",
    "# with variable names as keys (fields)\n",
    "data = loadmat(filename, variables=['trialInfo','spikeTimes','lfp','lfp_timepts'], asdict=True, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Binned spike rate (trials,units,time bins):  (40, 2, 296)\nSpike density (trials,units,timepoints):  (40, 2, 301)\n"
     ]
    }
   ],
   "source": [
    "### Spiking data analysis using spikes module ###\n",
    "from spynal.spikes import times_to_bool, rate\n",
    "\n",
    "# Compute spike rate in 50 ms bins (10 ms step) from -0.5 to 2.5 s\n",
    "# Can also set custom bins (bins=<bins>) or return spike counts (count=True)\n",
    "spike_rate, rate_bins = rate(spike_times, method='bin', lims=[-0.5,2.5], width=50e-3, step=10e-3)\n",
    "rate_bin_centers = np.mean(rate_bins, axis=1)\n",
    "print(\"Binned spike rate (trials,units,time bins): \", spike_rate.shape)\n",
    "\n",
    "# Compute spike density with 50 ms Gaussian kernel and downsample to 100 Hz (10 ms)\n",
    "spike_density, timepts = rate(spike_times, method='density', lims=[-0.5,2.5], kernel='gaussian', width=50e-3,\n",
    "                              step=10e-3)\n",
    "print(\"Spike density (trials,units,timepoints): \", spike_density.shape)\n",
    "\n",
    "# If you prefer working with binary (1/0) spike data, you could also do this\n",
    "# spike_bool, timepts = times_to_bool(spike_times, lims=[-0.5,2.5])\n",
    "# spike_density, timepts = rate(spike_bool, method='density', kernel='gaussian', width=50e-3, \n",
    "#                               t=timepts, downsmp=10, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Plot mean +/- SEM spike rate across trials (axis 0) for each single unit (unit 1)\n",
    "# Note: transform=plt.gca().transAxes makes text appear at normalized (0-1),\n",
    "#       rather than at location specified by plot axes\n",
    "plt.figure()\n",
    "for unit in range(2):\n",
    "    plt.subplot(2,1,unit+1) # Note: Subplots are numbered 1 - n_subplots\n",
    "    plt.plot(rate_bin_centers, np.mean(spike_rate[:,unit,:], axis=0), color='k')\n",
    "    plt.plot(timepts, np.mean(spike_density[:,unit,:], axis=0), color='b')    \n",
    "    plt.text(0.85, 0.9, 'binned', color='k', transform=plt.gca().transAxes)\n",
    "    plt.text(0.85, 0.8, 'density', color='b', transform=plt.gca().transAxes)\n",
    "    plt.title(\"Unit %d\" % unit)\n",
    "    plt.xlabel('Time (s)')\n",
    "    plt.ylabel('Spike rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Wavelet spectrogram (frequency,time,electrodes,trials):  (30, 501, 2, 40)\n",
      "Multitaper spectrogram (frequency,time,electrodes,trials):  (50, 46, 2, 40)\n",
      "Wavelet band-pooled (frequency bands,time,electrodes,trials):  (3, 501, 2, 40)\n",
      "Multitaper band-pooled (frequency bands,time,electrodes,trials):  (3, 46, 2, 40)\n"
     ]
    }
   ],
   "source": [
    "### Spectral analysis using spectra module ###\n",
    "from spynal.spectra import remove_evoked, spectrogram, pool_freq_bands, one_over_f_norm\n",
    "\n",
    "# Remove evoked potentials from LFP data (separately for each sample object condition)\n",
    "lfp = remove_evoked(lfp, axis=-1, method='mean', design=trial_info['sample'])\n",
    "\n",
    "# Compute wavelet power spectrogram for each electrode (with logarithmic frequency sampling)\n",
    "spec_wave, f_wave, t_wave = spectrogram(lfp, smp_rate=1000, axis=0, method='wavelet', spec_type='power',\n",
    "                                        freqs=2**np.arange(1,7,0.2), downsmp=10)\n",
    "print(\"Wavelet spectrogram (frequency,time,electrodes,trials): \", spec_wave.shape)\n",
    "\n",
    "# Compute multitaper power spectrogram (sampling from 2-100 Hz, with 0.5 s time, 4 Hz freq bandwidth)\n",
    "spec_mt, f_mt, t_mt = spectrogram(lfp, smp_rate=1000, axis=0, method='multitaper', spec_type='power',\n",
    "                                  freq_range=[2,100], time_width=0.5, freq_width=4, n_tapers=3, spacing=0.1)\n",
    "print(\"Multitaper spectrogram (frequency,time,electrodes,trials): \", spec_mt.shape)\n",
    "\n",
    "# spectrogram returns time sampling referenced to start of input data. Convert to actual times.\n",
    "t_wave += lfp_timepts[0]\n",
    "t_mt += lfp_timepts[0]\n",
    "\n",
    "# Normalize time-frequency data to correct for 1/f baseline distribution of power\n",
    "spec_wave   = one_over_f_norm(spec_wave, axis=0, freqs=f_wave)\n",
    "spec_mt     = one_over_f_norm(spec_mt, axis=0, freqs=f_mt)\n",
    "\n",
    "# Pool wavelet and multitaper spectrograms by frequency band (theta, beta, and gamma)\n",
    "band_names = ['theta','beta','gamma']\n",
    "bands = np.asarray([[3,8],[12,30],[40,80]])\n",
    "spec_wave_band = pool_freq_bands(spec_wave, bands, axis=0, freqs=f_wave)\n",
    "spec_mt_band = pool_freq_bands(spec_mt, bands, axis=0, freqs=f_mt)\n",
    "\n",
    "print(\"Wavelet band-pooled (frequency bands,time,electrodes,trials): \", spec_wave_band.shape)\n",
    "print(\"Multitaper band-pooled (frequency bands,time,electrodes,trials): \", spec_mt_band.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from spynal.spectra import plot_spectrogram\n",
    "\n",
    "# Plot spectrograms averaged across trials (axis 3) and electrodes (axis 2)\n",
    "plt.figure()\n",
    "plt.subplot(2,2,1)\n",
    "plot_spectrogram(t_wave, f_wave, np.mean(spec_wave,axis=(2,3)))\n",
    "plt.title('Wavelet')\n",
    "plt.ylabel('Frequency (Hz)')\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plot_spectrogram(t_mt, f_mt, np.mean(spec_mt,axis=(2,3)))\n",
    "plt.title('Multitaper')\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "# Note: Selecting one freq band reduced spec_xxx_band variable to (time,electrodes,trials), \n",
    "# so electrodes is now axis 1 and trials axis 2\n",
    "for i_band in range(3):\n",
    "    # Note: colors 'C0','C1',... reference the standard plotting colors in matplotlib\n",
    "    plt.plot(t_wave, np.mean(spec_wave_band[i_band,:,:,:],axis=(1,2)), '-', color='C%d'%i_band)\n",
    "plt.xlabel('Time (s)')\n",
    "plt.ylabel('Power')\n",
    "\n",
    "ax = plt.subplot(2,2,4)\n",
    "for i_band in range(3):\n",
    "    plt.plot(t_mt, np.mean(spec_mt_band[i_band,:,:,:],axis=(1,2)), '-', color='C%d'%i_band)\n",
    "    plt.text(0.6, 0.9-i_band*0.1, band_names[i_band], color='C%d'%i_band, transform=ax.transAxes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Coherence (frequency,time):  (50, 46)\n",
      "PPC (frequency,time):  (30, 501)\n"
     ]
    }
   ],
   "source": [
    "### Synchrony analysis using sync module ###\n",
    "from spynal.sync import synchrony\n",
    "\n",
    "# Compute multitaper-based coherence between 1st and 2nd electrodes\n",
    "# Note: keepdims=False argument tells function to squeeze out singleton trial and taper axes\n",
    "coh, f_coh, t_coh = synchrony(lfp[:,0,:], lfp[:,1,:], axis=-1, method='coherence', spec_method='multitaper',\n",
    "                              smp_rate=1000, time_axis=0, freq_range=[2,100], spacing=0.1, keepdims=False)\n",
    "print(\"Coherence (frequency,time): \", coh.shape)\n",
    "\n",
    "# Compute wavelet-based pairwise phase consistency between 1st and 2nd electrodes\n",
    "ppc, f_ppc, t_ppc = synchrony(lfp[:,0,:], lfp[:,1,:], axis=-1, method='ppc', spec_method='wavelet',\n",
    "                              smp_rate=1000, time_axis=0, freqs=2**np.arange(1,7,0.2), downsmp=10, keepdims=False)\n",
    "print(\"PPC (frequency,time): \", ppc.shape)\n",
    "\n",
    "t_coh += lfp_timepts[0]\n",
    "t_ppc += lfp_timepts[0]\n",
    "\n",
    "# Alternatively, you could pre-compute complex-valued spectrograms, and input those to synchrony()\n",
    "# spec_wave, f_wave, t_wave = spectrogram(lfp[:,0:2,:], smp_rate=1000, axis=0, method='wavelet', spec_type='complex',\n",
    "#                                         freqs=2**np.arange(1,7,0.2), downsmp=10)\n",
    "# ppc, _, _ = synchrony(spec_wave[:,:,0,:], spec_wave[:,:,1,:], axis=-1, method='ppc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Plot time-frequency synchrony\n",
    "plt.figure()\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plot_spectrogram(t_coh, f_coh, coh)\n",
    "plt.title('Coherence')\n",
    "plt.ylabel('Frequency (Hz)')\n",
    "plt.xlabel('Time (s)')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plot_spectrogram(t_ppc, f_ppc, ppc)\n",
    "plt.title('Pairwise phase consistency')\n",
    "plt.xlabel('Time (s)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Accuracy (1,1,time):  (1, 1, 296)\nMutual information (1,units,time):  (1, 2, 296)\n"
     ]
    }
   ],
   "source": [
    "### Information analysis using info module ###\n",
    "from spynal.info import neural_info\n",
    "\n",
    "labels = trial_info['sample']\n",
    "\n",
    "# Compute neural information about sample object in spike rates using population decoding (classification)\n",
    "# Note: Info functions return trial axis as singleton for consistency with multi-factor designs and with input data\n",
    "accuracy = neural_info(spike_rate, labels, axis=0, feature_axis=1, method='decode')\n",
    "print(\"Accuracy (1,1,time): \", accuracy.shape)\n",
    "\n",
    "# # Compute neural information about sample object in spike rates using percent explained variance\n",
    "# # Note: Info functions return trial axis as singleton for consistency with multi-factor designs and with input data\n",
    "# pev = neural_info(spike_rate, labels, axis=0, method='pev', model='anova1')\n",
    "# print(\"PEV (1,units,time): \", pev.shape)\n",
    "\n",
    "# Alternatively, compute neural information about sample object using Shannon mutual information\n",
    "mut_info = neural_info(spike_rate, labels, axis=0, method='mutual_information')\n",
    "print(\"Mutual information (1,units,time): \", mut_info.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time (s)')"
      ]
     },
     "metadata": {},
     "execution_count": 10
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Plot neural information for each method\n",
    "plt.figure()\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(rate_bin_centers, np.squeeze(accuracy))   # Squeeze out singleton axes\n",
    "plt.ylabel('Decoding accuracy')\n",
    "plt.xlabel('Time (s)')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(rate_bin_centers, np.squeeze(np.mean(mut_info,axis=1)))  # Compute mean across units\n",
    "plt.ylabel('Mutual info')\n",
    "plt.xlabel('Time (s)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "p(sample vs baseline) (1,units):  (1, 2)\n",
      "t(sample vs baseline) (1,units):  (1, 2)\n",
      "t_shuffle(sample vs baseline) (resamples,units):  (9999, 2)\n",
      "p(sample condition) (1,units):  (1, 2)\n",
      "F(sample condition) (1,units):  (1, 2)\n",
      "F_shuffle(sample condition) (resamples,units):  (9999, 2)\n",
      "confints(mean rate) (2=[lower,upper],units):  (2, 2, 296)\n"
     ]
    }
   ],
   "source": [
    "### Error estimation and significance tests using randstats ###\n",
    "from spynal.randstats import paired_sample_test, one_way_test, one_sample_confints\n",
    "\n",
    "# Paired-sample permutation test of difference in overall mean rate during stimulus vs baseline using t-statistic \n",
    "# (nonparametric analog of paired-sample t-test)\n",
    "base_bool = (rate_bin_centers > -500e-3) & (rate_bin_centers <= 0)\n",
    "smp_bool = (rate_bin_centers > 0) & (rate_bin_centers <= 500e-3)\n",
    "p_mean, t_obs, t_shuf = paired_sample_test(np.mean(spike_rate[:,:,smp_bool],2), np.mean(spike_rate[:,:,base_bool],2),\n",
    "                                           axis=0, method='permutation', stat='t', tail='both', return_stats=True)\n",
    "print(\"p(sample vs baseline) (1,units): \", p_mean.shape)\n",
    "print(\"t(sample vs baseline) (1,units): \", t_obs.shape)\n",
    "print(\"t_shuffle(sample vs baseline) (resamples,units): \", t_shuf.shape)\n",
    "\n",
    "# One-way permutation test of difference in mean rate between stimulus conditions using F-statistic\n",
    "# (nonparametric analog of 1-way ANOVA F-test). Note: For 2 conditions, we could've also just used two_sample_test().\n",
    "p_cond, F_obs, F_shuf = one_way_test(np.mean(spike_rate[:,:,base_bool],2), labels, axis=0, method='permutation',\n",
    "                                           stat='F', tail='right', return_stats=True)\n",
    "print(\"p(sample condition) (1,units): \", p_cond.shape)\n",
    "print(\"F(sample condition) (1,units): \", F_obs.shape)\n",
    "print(\"F_shuffle(sample condition) (resamples,units): \", F_shuf.shape)\n",
    "\n",
    "# Bootstrap 95% confidence intervals on mean rate\n",
    "rate_confints = one_sample_confints(spike_rate, axis=0, stat='mean', confint=0.95, return_stats=False)\n",
    "print(\"confints(mean rate) (2=[lower,upper],units): \", rate_confints.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
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     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from spynal.plots import plot_line_with_error_fill\n",
    "\n",
    "# Plot results of randomization tests/stats for each unit\n",
    "plt.figure()\n",
    "\n",
    "for unit in range(2):\n",
    "    plt.subplot(2,3,unit*3+1)\n",
    "    plt.hist(t_shuf[:,unit])                               # Histogram of shuffled stat values\n",
    "    plt.plot([t_obs[0,unit],t_obs[0,unit]], plt.ylim(), 'r-')   # Line showing stat value for actual data\n",
    "    plt.title(\"p = %.2f\" % p_mean[0,unit])\n",
    "    plt.ylabel(\"Histogram count\")\n",
    "    if unit == 1: plt.xlabel(\"t(mean)\")\n",
    "\n",
    "    plt.subplot(2,3,unit*3+2)\n",
    "    plt.hist(F_shuf[:,unit])                               # Histogram of shuffled stat values\n",
    "    plt.plot([F_obs[0,unit],F_obs[0,unit]], plt.ylim(), 'r-') # Line showing stat value for actual data\n",
    "    plt.title(\"p = %.2f\" % p_cond[0,unit])\n",
    "    if unit == 1: plt.xlabel(\"t(cond)\")\n",
    "\n",
    "    plt.subplot(2,3,unit*3+3)\n",
    "    # Plot confidence intervals as a transparent fill around mean line\n",
    "    plot_line_with_error_fill(rate_bin_centers, np.mean(spike_rate[:,unit,:],axis=0),\n",
    "                              err=rate_confints[:,unit,:], color='b')\n",
    "    plt.ylabel(\"Spike rate\")\n",
    "    if unit == 1: plt.xlabel(\"Time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Variables saved to Matlab .mat datafile\n"
     ]
    }
   ],
   "source": [
    "### Save results back out to mat file using matIO ### \n",
    "from spynal.matIO import savemat\n",
    "\n",
    "# Create a dictionary holding variables to save, where \"keys\" are variable names and \"values\" are\n",
    "# the variables themselves (Analogous to Matlab struct, where keys=fieldnames)\n",
    "variables = dict(accuracy=accuracy, F_obs=F_obs, p_cond=p_cond)\n",
    "# Save variables to mat file that can be opened in Matlab\n",
    "savemat('my_example_results.mat', variables)\n",
    "print(\"Variables saved to Matlab .mat datafile\")\n",
    "\n",
    "# Alternatively, can save each Numpy array variable in Python Numpy format file\n",
    "# np.save('pev.npy', pev)\n",
    "# np.save('F_obs.npy', F_obs)\n",
    "# np.save('p_cond.npy', p_cond)\n",
    "# print(\"Variables saved to Numpy datafiles\")"
   ]
  }
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