{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 10 - Effects of sonophore membrane coverage on neural responses"
   ]
  },
  {
   "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 pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from PySONIC.utils import logger, si_format\n",
    "from PySONIC.core import NeuronalBilayerSonophore, PulsedProtocol, AcousticDrive\n",
    "from PySONIC.neurons import getPointNeuron\n",
    "from MorphoSONIC.core import Node, surroundedSonophore, SectionAcousticSource\n",
    "from MorphoSONIC.plt import plotTimeseries0Dvs1D\n",
    "from MorphoSONIC.batches import CoverageTitrationBatch\n",
    "from utils import saveFigsAsPDF, subdirectory\n",
    "\n",
    "logger.setLevel(logging.INFO)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data sub-directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "subdir = subdirectory('coverage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "figindex = 10\n",
    "fs = 12\n",
    "tracefig_size = (8, 6)\n",
    "thrfig_size = (6, 5)\n",
    "figs = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulation parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pneuron = getPointNeuron('RS')\n",
    "a = 32e-9       # m\n",
    "Fdrive = 500e3  # Hz\n",
    "deff = 100e-9   # m\n",
    "rs = 1e2        # Ohm.cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Panel A: neural responses at 50% coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:10:18: Node(CorticalRS, a=32.0 nm, fs=50%): simulation @ f = 500kHz, A = 100.00kPa, tstim = 100ms, toffset = 50ms, tstart = 1ms\n",
      " 24/06/2020 21:10:18: RadialModel(CorticalRS, innerR32.0nm, outerR45.3nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=100.00kPa), tstim = 100ms, toffset = 50ms, tstart = 1ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=2.44724e+007\n",
      "IDA initialization failure, weighted norm of residual=7.18193e+006\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Adrive = 100e3   # kPa\n",
    "cov = 0.5\n",
    "drive = AcousticDrive(Fdrive, Adrive)\n",
    "pp = PulsedProtocol(100e-3, 50e-3, tstart=1e-3)\n",
    "figs['a'] = plotTimeseries0Dvs1D(pneuron, a, cov, rs, deff, drive, pp, figsize=tracefig_size, fs=fs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Panel B: threshold curves\n",
    "\n",
    "Comparison of excitation threshold amplitudes of the punctual and nanoscale spatially-extended SONIC models for a range of sonophore coverage fractions.**The rendering may take a few seconds...**\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:10:19: Computing fs-dependent thresholds for 0D model with AcousticDrive(f=500kHz)\n",
      " 24/06/2020 21:10:19: Computing fs-dependent thresholds for 1D model with SectionAcousticSource(sec_id=center, f=500kHz)\n",
      " 24/06/2020 21:10:19: RadialModel(CorticalRS, innerR32.0nm, outerR80.0nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=120.00kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=35.981\n",
      "IDA initialization failure, weighted norm of residual=6.21053e+006\n",
      "IDA initialization failure, weighted norm of residual=1.01569e+006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:10:24: RadialModel(CorticalRS, innerR32.0nm, outerR80.0nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=60.00kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=4.86861e+013\n",
      "IDA initialization failure, weighted norm of residual=2.709e+006\n",
      "IDA initialization failure, weighted norm of residual=1.36422e+006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:10:27: RadialModel(CorticalRS, innerR32.0nm, outerR80.0nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=30.00kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=6.7713e+013\n",
      "IDA initialization failure, weighted norm of residual=87574.6\n",
      "IDA initialization failure, weighted norm of residual=109528\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:10:28: RadialModel(CorticalRS, innerR32.0nm, outerR80.0nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=60.00kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=5.89307e+012\n",
      "IDA initialization failure, weighted norm of residual=2.709e+006\n",
      "IDA initialization failure, weighted norm of residual=1.3618e+006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:10:30: RadialModel(CorticalRS, innerR32.0nm, outerR80.0nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=45.00kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=6.758e+013\n",
      "IDA initialization failure, weighted norm of residual=1.22878e+006\n",
      "IDA initialization failure, weighted norm of residual=1.3454e+006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:10:32: RadialModel(CorticalRS, innerR32.0nm, outerR80.0nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=37.50kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=386513\n",
      "IDA initialization failure, weighted norm of residual=1.17966e+006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:10:33: RadialModel(CorticalRS, innerR32.0nm, outerR80.0nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=33.75kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=132221\n",
      "IDA initialization failure, weighted norm of residual=1.08892e+006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:10:34: RadialModel(CorticalRS, innerR32.0nm, outerR80.0nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=31.87kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=5.60813e+013\n",
      "IDA initialization failure, weighted norm of residual=109752\n",
      "IDA initialization failure, weighted norm of residual=966299\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:34:09: RadialModel(CorticalRS, innerR32.0nm, outerR80.0nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=32.81kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=5.00509e+013\n",
      "IDA initialization failure, weighted norm of residual=120950\n",
      "IDA initialization failure, weighted norm of residual=1.03975e+006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:39:42: RadialModel(CorticalRS, innerR32.0nm, outerR80.0nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=33.28kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=5.36232e+013\n",
      "IDA initialization failure, weighted norm of residual=126576\n",
      "IDA initialization failure, weighted norm of residual=1.07029e+006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:39:42: RadialModel(CorticalRS, innerR32.0nm, outerR80.0nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=33.52kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=5.50621e+013\n",
      "IDA initialization failure, weighted norm of residual=129396\n",
      "IDA initialization failure, weighted norm of residual=1.08518e+006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:39:43: RadialModel(CorticalRS, innerR32.0nm, outerR80.0nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=33.63kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=5.57282e+013\n",
      "IDA initialization failure, weighted norm of residual=130808\n",
      "IDA initialization failure, weighted norm of residual=1.09363e+006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:39:44: RadialModel(CorticalRS, innerR32.0nm, outerR80.0nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=33.69kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=5.60411e+013\n",
      "IDA initialization failure, weighted norm of residual=131515\n",
      "IDA initialization failure, weighted norm of residual=1.04834e+006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:39:44: RadialModel(CorticalRS, innerR32.0nm, outerR77.6nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=120.00kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=855557\n",
      "IDA initialization failure, weighted norm of residual=5.47417e+006\n",
      "IDA initialization failure, weighted norm of residual=1.04051e+006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:39:52: RadialModel(CorticalRS, innerR32.0nm, outerR77.6nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=60.00kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=2.39346e+006\n",
      "IDA initialization failure, weighted norm of residual=1.29381e+006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:39:56: RadialModel(CorticalRS, innerR32.0nm, outerR77.6nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=30.00kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=1.7618\n",
      "IDA initialization failure, weighted norm of residual=77634.9\n",
      "IDA initialization failure, weighted norm of residual=97088.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:39:56: RadialModel(CorticalRS, innerR32.0nm, outerR77.6nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=60.00kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=2.39346e+006\n",
      "IDA initialization failure, weighted norm of residual=1.29317e+006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:40:01: RadialModel(CorticalRS, innerR32.0nm, outerR77.6nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=45.00kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=1.08783e+006\n",
      "IDA initialization failure, weighted norm of residual=1.1503e+006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:40:03: RadialModel(CorticalRS, innerR32.0nm, outerR77.6nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=37.50kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=342527\n",
      "IDA initialization failure, weighted norm of residual=1.08037e+006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:40:04: RadialModel(CorticalRS, innerR32.0nm, outerR77.6nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=33.75kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=117208\n",
      "IDA initialization failure, weighted norm of residual=973468\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24/06/2020 21:40:04: RadialModel(CorticalRS, innerR32.0nm, outerR77.6nm, depth100nm, rs1.0e+02Ohm.cm, a=32.0 nm, fs=100%): simulation @ SectionAcousticSource(sec_id=center, f=500kHz, A=31.87kPa), tstim = 1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDA initialization failure, weighted norm of residual=97292.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NEURON: variable step integrator error\n",
      " near line 0\n",
      " objref hoc_obj_[2]\n",
      "                   ^\n",
      "        fadvance()\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "hoc error",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-4e0c5b67d11d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     18\u001b[0m     \u001b[1;32mlambda\u001b[0m \u001b[0mfs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0msurroundedSonophore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpneuron\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfs\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m1e-2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdepth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdeff\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m     source, pp, cov_range, root=subdir)\n\u001b[1;32m---> 20\u001b[1;33m \u001b[0mAthr1D\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch1D\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;31m# Plot threshold curves as a function of coverage fraction, for various sub-membrane depths\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\exsonic\\ExSONIC\\batches.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    300\u001b[0m         logger.info(\n\u001b[0;32m    301\u001b[0m             f'Computing fs-dependent thresholds for {self.model_key} model with {self.source}')\n\u001b[1;32m--> 302\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    303\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    304\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\pysonic\\PySONIC\\core\\batches.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, mpi)\u001b[0m\n\u001b[0;32m    324\u001b[0m             \u001b[0mbatch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcomputeAndLog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    325\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmpi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmpi\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 326\u001b[1;33m             \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmpi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloglevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    327\u001b[0m             \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    328\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmpi\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\pysonic\\PySONIC\\core\\batches.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, mpi, loglevel)\u001b[0m\n\u001b[0;32m    144\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mparams\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    145\u001b[0m                 \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresolve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 146\u001b[1;33m                 \u001b[0moutputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    147\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    148\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\pysonic\\PySONIC\\core\\batches.py\u001b[0m in \u001b[0;36mcomputeAndLog\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    305\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misEntry\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    306\u001b[0m             \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'entry not found: \"{x}\"'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 307\u001b[1;33m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    308\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misIterable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    309\u001b[0m                 \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\exsonic\\ExSONIC\\batches.py\u001b[0m in \u001b[0;36mcompute\u001b[1;34m(self, fs)\u001b[0m\n\u001b[0;32m    304\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    305\u001b[0m         \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel_factory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 306\u001b[1;33m         \u001b[0mxthr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitrate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msource\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    307\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    308\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxthr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\exsonic\\ExSONIC\\core\\sonic.py\u001b[0m in \u001b[0;36mtitrate\u001b[1;34m(self, obj, pp, **kwargs)\u001b[0m\n\u001b[0;32m    186\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mAcousticDrive\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mAcousticSource\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    187\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetFuncTables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pre-loading lookups to have a defined Arange\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 188\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitrate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    189\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    190\u001b[0m     \u001b[1;32mclass\u001b[0m \u001b[0mSonicNode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSonicBase\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\exsonic\\ExSONIC\\core\\nmodel.py\u001b[0m in \u001b[0;36mtitrate\u001b[1;34m(self, source, pp)\u001b[0m\n\u001b[0;32m    849\u001b[0m             \u001b[0meps_thr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetAbsConvThr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mArange\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    850\u001b[0m             \u001b[0mrel_eps_thr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mREL_EPS_THR\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 851\u001b[1;33m             precheck=source.xvar_precheck)\n\u001b[0m\u001b[0;32m    852\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msource\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_cathodal\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    853\u001b[0m             \u001b[0mxthr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0mxthr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\pysonic\\PySONIC\\threshold.py\u001b[0m in \u001b[0;36mthreshold\u001b[1;34m(output_history, *args, **kwargs)\u001b[0m\n\u001b[0;32m    326\u001b[0m     '''\n\u001b[0;32m    327\u001b[0m     \u001b[0mth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mThresholder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 328\u001b[1;33m     \u001b[0mth\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    329\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0moutput_history\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    330\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mth\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx_history\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mth\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval_history\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\pysonic\\PySONIC\\threshold.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, output_history)\u001b[0m\n\u001b[0;32m    310\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfbound\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# Perform initial factor bounding if required\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    311\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpreCondition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 312\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbinSearch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# Run binary search until convergence\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    313\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhas_changed_eval\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# if feval has not changed output, evaluate at the bound\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    314\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheckAtBound\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\pysonic\\PySONIC\\threshold.py\u001b[0m in \u001b[0;36mbinSearch\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    286\u001b[0m             \u001b[1;31m# Set x to interval mid-point and re-evaluate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    287\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmidpoint\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 288\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    289\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    290\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mrefine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\pysonic\\PySONIC\\threshold.py\u001b[0m in \u001b[0;36meval\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    196\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    197\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 198\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_above\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfeval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    199\u001b[0m         \u001b[0misWithin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'x'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxbounds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_warning\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    200\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheckNiterations\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\exsonic\\ExSONIC\\core\\nmodel.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m    844\u001b[0m         xthr = threshold(\n\u001b[0;32m    845\u001b[0m             lambda x: self.titrationFunc(\n\u001b[1;32m--> 846\u001b[1;33m                 self.simulate(source.updatedX(-x if source.is_cathodal else x), pp)[0]),\n\u001b[0m\u001b[0;32m    847\u001b[0m             \u001b[0mArange\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    848\u001b[0m             \u001b[0mx0\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetStartPoint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mArange\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\pysonic\\PySONIC\\core\\model.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    212\u001b[0m             \u001b[1;31m# Execute simulation function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 213\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0msimfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\pysonic\\PySONIC\\core\\model.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    139\u001b[0m         \u001b[1;33m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msimfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 141\u001b[1;33m             \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtcomp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msimfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    142\u001b[0m             \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'completed in %ss'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msi_format\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtcomp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    143\u001b[0m             \u001b[0mmeta_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetMeta\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msimfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\pysonic\\PySONIC\\utils.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    390\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    391\u001b[0m         \u001b[0mstart_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mperf_counter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 392\u001b[1;33m         \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    393\u001b[0m         \u001b[0mend_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mperf_counter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    394\u001b[0m         \u001b[0mrun_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mend_time\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\pysonic\\PySONIC\\core\\model.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    179\u001b[0m             \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0malignWithMethodDef\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msimfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    180\u001b[0m             \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdesc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgetMeta\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msimfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 181\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0msimfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    182\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    183\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\exsonic\\ExSONIC\\core\\nmodel.py\u001b[0m in \u001b[0;36msimulate\u001b[1;34m(self, source, pp, dt, atol)\u001b[0m\n\u001b[0;32m    747\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    748\u001b[0m         \u001b[1;31m# Integrate model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 749\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mintegrate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0matol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    750\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    751\u001b[0m         \u001b[1;31m# Return output dataframe dictionary\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\exsonic\\ExSONIC\\core\\nmodel.py\u001b[0m in \u001b[0;36mintegrate\u001b[1;34m(self, pp, dt, atol)\u001b[0m\n\u001b[0;32m    377\u001b[0m         \u001b[1;31m# self.setDriveModulator(pp.stimEvents(), pp.tstop)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    378\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetTransitionEvents\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstimEvents\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtstop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 379\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mintegrateUntil\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtstop\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mS_TO_MS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    380\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    381\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\exsonic\\ExSONIC\\core\\nmodel.py\u001b[0m in \u001b[0;36mintegrateUntil\u001b[1;34m(self, tstop)\u001b[0m\n\u001b[0;32m    355\u001b[0m         \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'integrating system using {self.getIntegrationMethod()}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    356\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mtstop\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 357\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madvance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    358\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    359\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0madvance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lemaire\\documents\\repositories\\exsonic\\ExSONIC\\core\\nmodel.py\u001b[0m in \u001b[0;36madvance\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    359\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0madvance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    360\u001b[0m         \u001b[1;34m''' Advance simulation onto the next time step. '''\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 361\u001b[1;33m         \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfadvance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    362\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    363\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mintegrate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0matol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: hoc error"
     ]
    }
   ],
   "source": [
    "# Stimulation parameters\n",
    "pp = PulsedProtocol(1.0, 0.)\n",
    "drive = AcousticDrive(Fdrive)\n",
    "source = SectionAcousticSource('center', f=Fdrive)\n",
    "cov_range = np.linspace(1, 99, 99)\n",
    "out_key = 'Athr (Pa)'\n",
    "\n",
    "# Compute threshold amplitudes with point-neuron model\n",
    "batch0D = CoverageTitrationBatch(\n",
    "    out_key, '0D',\n",
    "    lambda fs: Node(pneuron, a=a, fs=fs * 1e-2),\n",
    "    drive, pp, cov_range, root=subdir)\n",
    "Athr0D = batch0D.run()\n",
    "\n",
    "# Compute threshold amplitudes with spatially-extended model\n",
    "batch1D = CoverageTitrationBatch(\n",
    "    out_key, '1D',\n",
    "    lambda fs: surroundedSonophore(pneuron, a, fs * 1e-2, rs, depth=deff),\n",
    "    source, pp, cov_range, root=subdir)\n",
    "Athr1D = batch1D.run()\n",
    "\n",
    "# Plot threshold curves as a function of coverage fraction, for various sub-membrane depths\n",
    "fig, ax = plt.subplots(figsize=thrfig_size)\n",
    "ax.set_xlabel('sonophore membrane coverage (%)', fontsize=fs)\n",
    "ax.set_ylabel('amplitude (kPa)', fontsize=fs)\n",
    "ax.plot(cov_range, Athr0D * 1e-3, c='dimgrey', label='punctual model')\n",
    "ax.plot(cov_range, Athr1D * 1e-3, c='C0', label='extended model')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlim(0, 100)\n",
    "ax.set_ylim(1e1, 6e2)\n",
    "for item in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "    item.set_fontsize(fs)\n",
    "ax.legend(frameon=False, fontsize=fs, bbox_to_anchor=(0., 1.02, 1., .102), loc=3,\n",
    "          ncol=2, mode='expand', borderaxespad=0.)\n",
    "\n",
    "figs['b'] = fig"
   ]
  },
  {
   "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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