{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 5 - Model validation\n",
    "\n",
    "Create the figure panels describing the SONIC model validation against the detailed NICE model by comparison of charge density profiles for regular-spiking and low-threshold spiking neurons across the LIFUS parameter space and for various sonophore radii."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import logging\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PySONIC.core import NeuronalBilayerSonophore, PulsedProtocol, AcousticDrive\n",
    "from PySONIC.postpro import computeSpikingMetrics\n",
    "from PySONIC.utils import logger, si_format\n",
    "from PySONIC.neurons import getPointNeuron\n",
    "from PySONIC.plt import CompTimeSeries, cm2inch\n",
    "from utils import saveFigsAsPDF, subdirectory, subthr, suprathr\n",
    "\n",
    "logger.setLevel(logging.INFO)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def altLabels(l):\n",
    "    ''' Add an empty label before each label on an iterable. '''\n",
    "    return sum([['', x] for x in l], [])\n",
    "\n",
    "def plotSpikingMetrics(xvar, xlabel, metrics_dict, logscale=False, spikeamp=True, colors=None,\n",
    "                       fs=8, lw=2, ps=4, figsize=cm2inch(7.25, 5.8), rf=10):\n",
    "    ''' Plot the evolution of spiking metrics as function of a specific stimulation parameter. '''\n",
    "\n",
    "    ls = {'full': 'o-', 'sonic': 'o--'}\n",
    "    cdefault = {'full': 'silver', 'sonic': 'k'}\n",
    "\n",
    "    # Create figure\n",
    "    fig, axes = plt.subplots(3, 1, figsize=figsize)\n",
    "    ibase = 0 if spikeamp else 1\n",
    "    axes[ibase].set_ylabel('Latency\\n (ms)', fontsize=fs, rotation=0, ha='right', va='center')\n",
    "    axes[ibase + 1].set_ylabel(\n",
    "        'Firing\\n rate (Hz)', fontsize=fs, rotation=0, ha='right', va='center')\n",
    "    if spikeamp:\n",
    "        axes[2].set_ylabel('Spike amp.\\n ($\\\\rm nC/cm^2$)', fontsize=fs, rotation=0, ha='right',\n",
    "                           va='center')\n",
    "    for ax in axes:\n",
    "        ax.spines['top'].set_visible(False)\n",
    "        ax.spines['right'].set_visible(False)\n",
    "        if logscale:\n",
    "            ax.set_xscale('log')\n",
    "        for item in ax.get_yticklabels():\n",
    "            item.set_fontsize(fs)\n",
    "    for ax in axes[:-1]:\n",
    "        ax.spines['bottom'].set_visible(False)\n",
    "        ax.set_xticks([])\n",
    "        plt.setp(ax.get_xticklabels(minor=True), visible=False)\n",
    "        ax.get_xaxis().set_tick_params(which='minor', size=0)\n",
    "        ax.get_xaxis().set_tick_params(which='minor', width=0)\n",
    "    axes[-1].set_xlabel(xlabel, fontsize=fs)\n",
    "    if not logscale:\n",
    "        axes[-1].set_xticks([min(xvar), max(xvar)])\n",
    "    for item in axes[-1].get_xticklabels():\n",
    "        item.set_fontsize(fs)\n",
    "\n",
    "    # Plot metrics for each neuron and method\n",
    "    for neuron, metrics_subdict in metrics_dict.items():\n",
    "        c = colors[neuron] if colors is not None else cdefault\n",
    "        for method, metrics in metrics_subdict.items():             \n",
    "            # Latency\n",
    "            axes[ibase].plot(\n",
    "                xvar, metrics['latencies (ms)'].values, ls[method], color=c[method],\n",
    "                linewidth=lw, markersize=ps, label=neuron if method == 'sonic' else '')\n",
    "\n",
    "            # Firing rate\n",
    "            axes[ibase + 1].errorbar(\n",
    "                xvar, metrics['mean firing rates (Hz)'].values,\n",
    "                yerr=metrics['std firing rates (Hz)'].values,\n",
    "                fmt=ls[method], color=c[method], linewidth=lw, markersize=ps)\n",
    "\n",
    "            # Spike amplitudes\n",
    "            if spikeamp:\n",
    "                axes[2].errorbar(\n",
    "                    xvar, metrics['mean spike amplitudes (nC/cm2)'].values,\n",
    "                    yerr=metrics['std spike amplitudes (nC/cm2)'].values,\n",
    "                    fmt=ls[method], color=c[method], linewidth=lw, markersize=ps)\n",
    "\n",
    "    # Adapt axes y-limits\n",
    "    for ax in axes:\n",
    "        ax.set_ylim([np.floor(ax.get_ylim()[0] / rf) * rf, np.ceil(ax.get_ylim()[1] / rf) * rf])\n",
    "        ax.set_yticks([max(ax.get_ylim()[0], 0), ax.get_ylim()[1]])\n",
    "\n",
    "    # Legend\n",
    "    if len(metrics_dict.keys()) > 1:\n",
    "        leg = axes[0].legend(fontsize=fs, frameon=False, bbox_to_anchor=(0., 0.9, 1., .102),\n",
    "                             loc=8, ncol=2, borderaxespad=0.)\n",
    "        for l in leg.get_lines():\n",
    "            l.set_linestyle('-')\n",
    "\n",
    "    fig.subplots_adjust(hspace=.3, bottom=0.2, left=0.35, right=0.95, top=0.95)\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data sub-directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "subdir = subdirectory('comparisons')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "figindex = 5\n",
    "fs = 15\n",
    "lw = 2\n",
    "ps = 8\n",
    "Qtrace_figsize = cm2inch(25, 12)\n",
    "metrics_figsize = cm2inch(14.5, 11.6)\n",
    "figs = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulation parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 32e-9  # m\n",
    "Adrive = 100e3  # Pa\n",
    "Fdrive = 500e3  # Hz\n",
    "tstim = 150e-3  # s\n",
    "toffset = 100e-3  # s\n",
    "PRF = 100.  # Hz\n",
    "DC = 1.0\n",
    "cov = 1.\n",
    "\n",
    "CW_pp = PulsedProtocol(tstim, toffset)\n",
    "methods = ['full', 'sonic']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Panel A: comparison across US amplitudes (CW, RS neuron)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pneuron = getPointNeuron('RS')\n",
    "nbls = NeuronalBilayerSonophore(a, pneuron)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison of $Q_m$ traces for CW stimuli at sub-threshold, threshold and supra-threshold amplitudes. **The rendering may take a few seconds...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 01/05/2020 22:38:01: File \"ASTIM_RS_CW_32nm_f_500kHz_A_31.09kPa_tstim_150ms_toffset_100ms_full.pkl\" already present in directory \"C:\\Users\\lemaire\\Documents\\SONIC paper data\\comparisons\" -> preserving\n",
      " 01/05/2020 22:38:01: Loading data from \"ASTIM_RS_CW_32nm_f_500kHz_A_31.09kPa_tstim_150ms_toffset_100ms_full.pkl\"\n",
      " 01/05/2020 22:38:06: File \"ASTIM_RS_CW_32nm_f_500kHz_A_31.09kPa_tstim_150ms_toffset_100ms_sonic.pkl\" already present in directory \"C:\\Users\\lemaire\\Documents\\SONIC paper data\\comparisons\" -> preserving\n",
      " 01/05/2020 22:38:06: Loading data from \"ASTIM_RS_CW_32nm_f_500kHz_A_31.09kPa_tstim_150ms_toffset_100ms_sonic.pkl\"\n",
      " 01/05/2020 22:38:06: File \"ASTIM_RS_CW_32nm_f_500kHz_A_36.09kPa_tstim_150ms_toffset_100ms_full.pkl\" already present in directory \"C:\\Users\\lemaire\\Documents\\SONIC paper data\\comparisons\" -> preserving\n",
      " 01/05/2020 22:38:06: Loading data from \"ASTIM_RS_CW_32nm_f_500kHz_A_36.09kPa_tstim_150ms_toffset_100ms_full.pkl\"\n",
      " 01/05/2020 22:38:10: inconsistent differing inputs\n",
      " 01/05/2020 22:38:12: File \"ASTIM_RS_CW_32nm_f_500kHz_A_36.09kPa_tstim_150ms_toffset_100ms_sonic.pkl\" already present in directory \"C:\\Users\\lemaire\\Documents\\SONIC paper data\\comparisons\" -> preserving\n",
      " 01/05/2020 22:38:12: Loading data from \"ASTIM_RS_CW_32nm_f_500kHz_A_36.09kPa_tstim_150ms_toffset_100ms_sonic.pkl\"\n",
      " 01/05/2020 22:38:12: More than one differing inputs\n",
      " 01/05/2020 22:38:12: File \"ASTIM_RS_CW_32nm_f_500kHz_A_56.09kPa_tstim_150ms_toffset_100ms_full.pkl\" already present in directory \"C:\\Users\\lemaire\\Documents\\SONIC paper data\\comparisons\" -> preserving\n",
      " 01/05/2020 22:38:12: Loading data from \"ASTIM_RS_CW_32nm_f_500kHz_A_56.09kPa_tstim_150ms_toffset_100ms_full.pkl\"\n",
      " 01/05/2020 22:38:19: File \"ASTIM_RS_CW_32nm_f_500kHz_A_56.09kPa_tstim_150ms_toffset_100ms_sonic.pkl\" already present in directory \"C:\\Users\\lemaire\\Documents\\SONIC paper data\\comparisons\" -> preserving\n",
      " 01/05/2020 22:38:19: Loading data from \"ASTIM_RS_CW_32nm_f_500kHz_A_56.09kPa_tstim_150ms_toffset_100ms_sonic.pkl\"\n",
      " 01/05/2020 22:38:19: More than one differing inputs\n",
      " 01/05/2020 22:38:19: More than one differing inputs\n",
      " 01/05/2020 22:38:19: More than one differing inputs\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 708.661x340.157 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Amplitudes\n",
    "Athr = nbls.titrate(AcousticDrive(Fdrive), CW_pp)\n",
    "regime_amps = {\n",
    "    'AT - 5 kPa': subthr(Athr),\n",
    "    'AT': Athr,\n",
    "    'AT + 20 kPa': suprathr(Athr)\n",
    "}\n",
    "\n",
    "# Get outputs (using a generator expression to avoid overloading the memory)\n",
    "outputs = (\n",
    "    nbls.getOutput(AcousticDrive(Fdrive, x), CW_pp, cov, method, None, outputdir=subdir)\n",
    "    for x in regime_amps.values() for method in methods\n",
    ")\n",
    "\n",
    "# Plot\n",
    "figs['a_Qtraces'] = CompTimeSeries(outputs, 'Qm').render(\n",
    "    labels=altLabels(regime_amps.keys()),\n",
    "    lines=['-', '--'] * len(regime_amps),\n",
    "    colors=plt.get_cmap('Paired').colors[:2 * len(regime_amps)],\n",
    "    fs=fs, patches='one', figsize=Qtrace_figsize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison of spiking metrics for CW stimuli at various supra-threshold amplitudes. **The rendering may take a few seconds...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Amplitudes\n",
    "suprathr_amps = np.array([50, 100, 300, 600]) * 1e3  # Pa\n",
    "\n",
    "# Get outputs (using a generator expression to avoid overloading the memory)\n",
    "def gen(method):\n",
    "    return (nbls.getOutput(AcousticDrive(Fdrive, x), CW_pp, cov, method, None, outputdir=subdir)\n",
    "            for x in suprathr_amps)\n",
    "outputs = {method: gen(method) for method in methods}\n",
    "\n",
    "# Compute spiking metrics on them\n",
    "metrics = {pneuron.name: {k: computeSpikingMetrics(v) for k, v in outputs.items()}}\n",
    "\n",
    "# Plot\n",
    "figs['a_spikemetrics'] = plotSpikingMetrics(\n",
    "    suprathr_amps * 1e-3, 'Amplitude (kPa)', metrics,\n",
    "    logscale=True, fs=fs, ps=ps, figsize=metrics_figsize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Panel B: comparison across US frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pneuron = getPointNeuron('RS')\n",
    "nbls = NeuronalBilayerSonophore(a, pneuron)\n",
    "freqs = np.array([20e3, 100e3, 500e3, 1e6, 2e6, 3e6, 4e6])  # Hz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison of $Q_m$ traces for supra-threshold CW stimuli at low and high US frequencies. **The rendering may take a few seconds...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Frequencies and corresponding supra-threshold amplitudes\n",
    "freqs_traces = [freqs.min(), freqs.max()]\n",
    "amps_traces = [suprathr(nbls.titrate(AcousticDrive(x), CW_pp)) for x in freqs_traces]  # Pa\n",
    "\n",
    "# Get outputs (using a generator expression to avoid overloading the memory)\n",
    "outputs = (\n",
    "    nbls.getOutput(AcousticDrive(x, y), CW_pp, cov, method, None, outputdir=subdir)\n",
    "    for x, y in zip(freqs_traces, amps_traces) for method in methods\n",
    ")\n",
    "\n",
    "# Plot\n",
    "figs['b_Qtraces'] = CompTimeSeries(outputs, 'Qm').render(\n",
    "    labels=altLabels([f'{si_format(f)}Hz' for f in freqs_traces]),\n",
    "    lines=['-', '--'] * len(freqs_traces),\n",
    "    colors=plt.get_cmap('Paired').colors[6:10],\n",
    "    fs=fs, patches='one', figsize=Qtrace_figsize,\n",
    "    inset={'xcoords': [5, 40], 'ycoords': [-35, 45], 'xlims': [57.5, 58.5], 'ylims': [10, 35]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison of spiking metrics for CW stimuli for supra-threshold amplitudes at various frequencies. **The rendering may take a few seconds...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Frequencies and corresponding supra-threshold amplitudes\n",
    "suprathr_amps = [suprathr(nbls.titrate(AcousticDrive(x), CW_pp)) for x in freqs]  # Pa\n",
    "\n",
    "# Get outputs (using a generator expression to avoid overloading the memory)\n",
    "def gen(method):\n",
    "    return (nbls.getOutput(AcousticDrive(x, y), CW_pp, cov, method, None, outputdir=subdir)\n",
    "            for x, y in zip(freqs, suprathr_amps))\n",
    "outputs = {method: gen(method) for method in methods}\n",
    "\n",
    "# Compute spiking metrics on them\n",
    "metrics = {pneuron.name: {k: computeSpikingMetrics(v) for k, v in outputs.items()}}\n",
    "\n",
    "# Plot\n",
    "figs['b_spikemetrics'] = plotSpikingMetrics(\n",
    "    freqs * 1e-3, 'Frequency (kHz)', metrics,\n",
    "    logscale=True, fs=fs, ps=ps, figsize=metrics_figsize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Panel C: comparison across sonophore radii"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pneuron = getPointNeuron('RS')\n",
    "nbls = NeuronalBilayerSonophore(a, pneuron)\n",
    "radii = np.array([16, 22.6, 32, 45.3, 64]) * 1e-9  # m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison of $Q_m$ traces for supra-threshold CW stimuli with small and large sonophore radii. **The rendering may take a few seconds...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Radii and corresponding supra-threshold amplitudes\n",
    "radii_traces = [radii.min(), radii.max()]\n",
    "amps_traces = [suprathr(NeuronalBilayerSonophore(x, pneuron).titrate(AcousticDrive(Fdrive), CW_pp))\n",
    "               for x in radii_traces]  # Pa\n",
    "\n",
    "# Get outputs (using a generator expression to avoid overloading the memory)\n",
    "outputs = (\n",
    "    NeuronalBilayerSonophore(x, pneuron).getOutput(AcousticDrive(Fdrive, y), CW_pp, cov, method, None, outputdir=subdir)\n",
    "    for x, y in zip(radii_traces, amps_traces) for method in methods\n",
    ")\n",
    "\n",
    "# Plot\n",
    "colors = plt.get_cmap('Paired').colors\n",
    "colors = colors[2:4] + colors[10:12]\n",
    "figs['c_Qtraces'] = CompTimeSeries(outputs, 'Qm').render(\n",
    "    labels=altLabels([f'{x * 1e9:.0f} nm' for x in radii_traces]),\n",
    "    lines=['-', '--'] * len(radii_traces), colors=colors,\n",
    "    fs=fs, patches='one', figsize=Qtrace_figsize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison of spiking metrics for CW stimuli for supra-threshold amplitudes for various sonophore radii. **The rendering may take a few seconds...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Radii and corresponding supra-threshold amplitudes\n",
    "suprathr_amps = [suprathr(NeuronalBilayerSonophore(x, pneuron).titrate(AcousticDrive(Fdrive), CW_pp))\n",
    "                 for x in radii]  # Pa\n",
    "\n",
    "# Get outputs (using a generator expression to avoid overloading the memory)\n",
    "def gen(method):\n",
    "    return (NeuronalBilayerSonophore(x, pneuron).getOutput(AcousticDrive(Fdrive, y), CW_pp, cov, method, None, outputdir=subdir)\n",
    "            for x, y in zip(radii, suprathr_amps))\n",
    "outputs = {method: gen(method) for method in methods}\n",
    "\n",
    "# Compute spiking metrics on them\n",
    "metrics = {pneuron.name: {k: computeSpikingMetrics(v) for k, v in outputs.items()}}\n",
    "\n",
    "# Plot\n",
    "figs['c_spikemetrics'] = plotSpikingMetrics(\n",
    "    radii * 1e9, 'Sonophore radius (nm)', metrics,\n",
    "    logscale=True, fs=fs, ps=ps, figsize=metrics_figsize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Panel D: comparison across duty cycles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neurons = ['RS', 'LTS']\n",
    "pneurons = [getPointNeuron(x) for x in neurons]\n",
    "colors = list(plt.get_cmap('Paired').colors[:6])\n",
    "del colors[2:4]\n",
    "DCs = np.array([5, 10, 25, 50, 75, 100]) * 1e-2\n",
    "DC_short = DCs.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison of $Q_m$ traces at for RS and LTS neurons at 100 kPa, 5% duty cycle. **The rendering may take a few seconds...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Short duty-cycle protocol\n",
    "short_pp = PulsedProtocol(tstim, toffset, PRF, DC_short)\n",
    "\n",
    "# Get outputs (using a generator expression to avoid overloading the memory)\n",
    "outputs = (\n",
    "    NeuronalBilayerSonophore(a, x).getOutput(AcousticDrive(Fdrive, Adrive), short_pp, cov, method, None, outputdir=subdir)\n",
    "    for x in pneurons for method in methods\n",
    ")\n",
    "\n",
    "# Plot\n",
    "figs['d_Qtraces'] = CompTimeSeries(outputs, 'Qm').render(\n",
    "    labels=altLabels([f'{x.name}, {short_pp.DC * 1e2:.0f}% DC' for x in pneurons]),\n",
    "    lines=['-', '--'] * len(pneurons), colors=colors,\n",
    "    fs=fs, patches='one', figsize=Qtrace_figsize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison of spiking metrics for PW stimuli at various duty cycles for RS and LTS neurons. **The rendering may take a few seconds...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get outputs (using a generator expression to avoid overloading the memory)\n",
    "def gen(pneuron, method):\n",
    "    return (\n",
    "        NeuronalBilayerSonophore(a, pneuron).getOutput(\n",
    "            AcousticDrive(Fdrive, Adrive), PulsedProtocol(tstim, toffset, PRF, x), cov, method, None, outputdir=subdir)\n",
    "        for x in DCs)\n",
    "outputs = {\n",
    "    pneuron.name: {method: gen(pneuron, method) for method in methods}\n",
    "    for pneuron in pneurons\n",
    "}\n",
    "\n",
    "# Compute spiking metrics on them\n",
    "metrics = {\n",
    "    key: {k: computeSpikingMetrics(v) for k, v in value.items()}\n",
    "    for key, value in outputs.items()\n",
    "}\n",
    "\n",
    "# Plot\n",
    "colors_dict = {pneuron.name: {method: colors[2 * i + j] for j, method in enumerate(methods)}\n",
    "               for i, pneuron in enumerate(pneurons)}\n",
    "figs['d_spikemetrics'] = plotSpikingMetrics(\n",
    "    DCs * 1e2, 'Duty cycle (%)', metrics,\n",
    "    spikeamp=False, colors=colors_dict,\n",
    "    fs=fs, ps=ps, figsize=metrics_figsize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Panel E: comparison across pulse repetition frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pneuron = getPointNeuron('LTS')\n",
    "nbls = NeuronalBilayerSonophore(a, pneuron)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison of $Q_m$ traces for PW stimuli at 5% duty cycle with different pulse repetition frequencies. **The rendering may take a few seconds...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pulse-repetition frequencies\n",
    "PRFs_traces = np.array([1e1, 1e2, 1e3, 1e4])  # Hz\n",
    "\n",
    "# Get outputs (using a generator expression to avoid overloading the memory)\n",
    "outputs = (\n",
    "    nbls.getOutput(AcousticDrive(Fdrive, Adrive), PulsedProtocol(tstim, toffset, x, DC_short), cov, method, None, outputdir=subdir)\n",
    "    for x in PRFs_traces for method in methods)\n",
    "\n",
    "# Plot\n",
    "patches = [False, True] * len(PRFs_traces)\n",
    "patches[-1] = False\n",
    "figs['e_Qtraces'] = CompTimeSeries(outputs, 'Qm').render(\n",
    "    labels=altLabels([f'{si_format(x)}Hz PRF' for x in PRFs_traces]),\n",
    "    lines=['-', '--'] * len(PRFs_traces),\n",
    "    colors=plt.get_cmap('Paired').colors[4:12],\n",
    "    fs=fs, patches=patches, figsize=Qtrace_figsize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison of spiking metrics for PW stimuli at 5% duty cycle with different pulse repetition frequencies. **The rendering may take a few seconds...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pulse-repetition frequencies\n",
    "PRFs_dense = sum([[x, 2 * x, 5 * x] for x in PRFs_traces[:-1]], []) + [PRFs_traces[-1]]  # Hz\n",
    "\n",
    "# Get outputs (using a generator expression to avoid overloading the memory)\n",
    "def gen(method):\n",
    "    return (nbls.getOutput(\n",
    "        AcousticDrive(Fdrive, Adrive), PulsedProtocol(tstim, toffset, x, DC_short), cov, method, None, outputdir=subdir)\n",
    "        for x in PRFs_dense)\n",
    "outputs = {method: gen(method) for method in methods}\n",
    "\n",
    "# Compute spiking metrics on them\n",
    "metrics = {pneuron.name: {k: computeSpikingMetrics(v) for k, v in outputs.items()}}\n",
    "\n",
    "# Plot\n",
    "figs['e_spikemetrics'] = plotSpikingMetrics(\n",
    "    PRFs_dense, 'PRF (Hz)', metrics,\n",
    "    spikeamp=False, logscale=True,\n",
    "    fs=fs, ps=ps, figsize=metrics_figsize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save figure panels\n",
    "\n",
    "Save figure panels as **pdf** in the *figs* sub-folder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "saveFigsAsPDF(figs, figindex)"
   ]
  }
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