{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 8 - US frequency and sonophore radius dependent excitation thresholds\n",
    "\n",
    "Compute (DC x amplitude) threshold curves of RS and LTS neurons for various sonophore radii and US frequencies, from SONIC model predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import logging\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PySONIC.utils import logger\n",
    "from PySONIC.neurons import getPointNeuron\n",
    "from PySONIC.core import NeuronalBilayerSonophore, AcousticDrive, PulsedProtocol, Batch\n",
    "from PySONIC.utils import isIterable, si_format\n",
    "from PySONIC.plt import cm2inch\n",
    "from utils import saveFigsAsPDF\n",
    "\n",
    "logger.setLevel(logging.INFO)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getThresholds(nbls, drive, tstim, toffset, PRF, DCs):\n",
    "    ''' Get thresholds for a given DC range. '''\n",
    "    logger.info(f'Getting thresholds for {nbls} - {drive} combination')\n",
    "    queue = [[drive, PulsedProtocol(tstim, toffset, PRF, DC), cov, 'sonic', None] for DC in DCs]\n",
    "    batch = Batch(nbls.titrate, queue)\n",
    "    return np.array(batch.run(mpi=True, loglevel=logger.level))\n",
    "\n",
    "\n",
    "def plotThresholdAmps(pneurons, radii, freqs, tstim, toffset, PRF, DCs, cov,\n",
    "                      fs=10, lw=2, colors=None, figsize=cm2inch(10, 8)):\n",
    "    ''' Plot threshold excitation amplitudes of several neurons determined by titration procedures,\n",
    "        as a function of duty cycle, for various combinations of sonophore radius and US frequency.\n",
    "\n",
    "        :param neurons: list of neuron names\n",
    "        :param radii: list of sonophore radii (m)\n",
    "        :param freqs: list US frequencies (Hz)\n",
    "        :param PRF: pulse repetition frequency used for titration procedures (Hz)\n",
    "        :param tstim: stimulus duration used for titration procedures\n",
    "        :return: figure handle\n",
    "    '''\n",
    "    if isIterable(radii) and isIterable(freqs):\n",
    "        raise ValueError('cannot plot threshold curves for more than 1 varying condition')\n",
    "    if len(pneurons) > 3:\n",
    "        raise ValueError('cannot plot threshold curves for more than 3 neuron types')\n",
    "\n",
    "    if not isIterable(radii):\n",
    "        radii = [radii]\n",
    "    if not isIterable(freqs):\n",
    "        freqs = [freqs]\n",
    "    ncomb = len(pneurons) * len(freqs) * len(radii)\n",
    "    if colors is None:\n",
    "        colors = ['C{}'.format(i) for i in range(ncomb)]\n",
    "\n",
    "    linestyles = ['--', ':', '-.']\n",
    "    assert len(freqs) <= len(linestyles), 'too many frequencies'\n",
    "    fig, ax = plt.subplots(figsize=figsize)\n",
    "    ax.set_xlabel('Duty cycle (%)', fontsize=fs)\n",
    "    ax.set_ylabel('Amplitude (kPa)', fontsize=fs)\n",
    "    for item in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "        item.set_fontsize(fs)\n",
    "    ax.set_yscale('log')\n",
    "    ax.set_xlim([0, 100])\n",
    "    ax.set_ylim([10, 600])\n",
    "    linestyles = ['-', '--', '-.']\n",
    "    for pneuron, ls in zip(pneurons, linestyles):\n",
    "        icolor = 0\n",
    "        for i, a in enumerate(radii):\n",
    "            nbls = NeuronalBilayerSonophore(a, pneuron)\n",
    "            for j, Fdrive in enumerate(freqs):\n",
    "                Athrs = getThresholds(nbls, AcousticDrive(Fdrive), tstim, toffset, PRF, DCs)\n",
    "                Athrs = pd.Series(Athrs).interpolate().values\n",
    "                lbl = f'{pneuron.name}, {a * 1e9:.0f} nm, {si_format(Fdrive)}Hz'\n",
    "                ax.plot(DCs * 1e2, Athrs * 1e-3, ls, c=colors[icolor], label=lbl, linewidth=lw)\n",
    "                icolor += 1\n",
    "    ax.legend(fontsize=fs - 2, frameon=False)\n",
    "    fig.tight_layout()\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "figindex = 8\n",
    "figsize=cm2inch(15, 12)\n",
    "fs = 14\n",
    "cmap = plt.get_cmap('tab20c').colors\n",
    "figs = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulation parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 32e-9       # m\n",
    "Fdrive = 500e3  # Hz\n",
    "tstim = 1.0     # s\n",
    "toffset = 0.0   # s\n",
    "PRF = 100.0     # s\n",
    "cov = 1.0\n",
    "DCs = np.arange(1, 101) * 1e-2\n",
    "\n",
    "neurons = ['RS', 'LTS']\n",
    "pneurons = [getPointNeuron(x) for x in neurons]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Panel A: sonophore radius-dependent threshold curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 02/05/2020 12:30:31: Getting thresholds for NeuronalBilayerSonophore(16.0 nm, CorticalRS) - AcousticDrive(f=500kHz) combination\n",
      " 02/05/2020 12:30:35: Getting thresholds for NeuronalBilayerSonophore(32.0 nm, CorticalRS) - AcousticDrive(f=500kHz) combination\n",
      " 02/05/2020 12:30:42: Getting thresholds for NeuronalBilayerSonophore(64.0 nm, CorticalRS) - AcousticDrive(f=500kHz) combination\n",
      " 02/05/2020 12:30:50: Getting thresholds for NeuronalBilayerSonophore(16.0 nm, CorticalLTS) - AcousticDrive(f=500kHz) combination\n",
      " 02/05/2020 12:30:57: Getting thresholds for NeuronalBilayerSonophore(32.0 nm, CorticalLTS) - AcousticDrive(f=500kHz) combination\n",
      " 02/05/2020 12:31:04: Getting thresholds for NeuronalBilayerSonophore(64.0 nm, CorticalLTS) - AcousticDrive(f=500kHz) combination\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 425.197x340.157 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "radii = np.array([16, 32, 64]) * 1e-9  # m\n",
    "figs['a'] = plotThresholdAmps(pneurons, radii, Fdrive, tstim, toffset, PRF, DCs, cov, colors=cmap[:3][::-1], fs=fs, figsize=figsize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Panel B: frequency-dependent threshold curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 02/05/2020 12:31:12: Getting thresholds for NeuronalBilayerSonophore(32.0 nm, CorticalRS) - AcousticDrive(f=20kHz) combination\n",
      " 02/05/2020 12:31:19: Getting thresholds for NeuronalBilayerSonophore(32.0 nm, CorticalRS) - AcousticDrive(f=500kHz) combination\n",
      " 02/05/2020 12:31:25: Getting thresholds for NeuronalBilayerSonophore(32.0 nm, CorticalRS) - AcousticDrive(f=4MHz) combination\n",
      " 02/05/2020 12:31:32: Getting thresholds for NeuronalBilayerSonophore(32.0 nm, CorticalLTS) - AcousticDrive(f=20kHz) combination\n",
      " 02/05/2020 12:31:39: Getting thresholds for NeuronalBilayerSonophore(32.0 nm, CorticalLTS) - AcousticDrive(f=500kHz) combination\n",
      " 02/05/2020 12:31:46: Getting thresholds for NeuronalBilayerSonophore(32.0 nm, CorticalLTS) - AcousticDrive(f=4MHz) combination\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 425.197x340.157 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "freqs = np.array([20, 500, 4000]) * 1e3  # Hz\n",
    "figs['b'] = plotThresholdAmps(pneurons, a, freqs, tstim, toffset, PRF, DCs, cov, colors=cmap[8:11][::-1], fs=fs, figsize=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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "saveFigsAsPDF(figs, figindex)"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}