{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "789a1930",
   "metadata": {},
   "source": [
    "# Generic Notebook for NeuroML Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20c97415",
   "metadata": {},
   "source": [
    "## *Source path and filename*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce7cc8dd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import ipywidgets\n",
    "import ui_widget\n",
    "from importlib.machinery import SourceFileLoader\n",
    "%matplotlib widget\n",
    "\n",
    "#widget to read input files\n",
    "display(ui_widget.header,ui_widget.loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f2d35a3",
   "metadata": {},
   "source": [
    "## *Read NeuroML files and build dashboard*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "079c703a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# get path and filename from above widget----------------------------------------------------#\n",
    "path2source = ui_widget.loader.result[0]\n",
    "fname_LEMS  = ui_widget.loader.result[1]\n",
    "fname_net   = ui_widget.loader.result[2]\n",
    "\n",
    "# imports the python module-----------------------------------------------------------------#\n",
    "nmlPython = SourceFileLoader(\"nml2jupyter_ver3.py\",\"nml2jupyter_ver3.py\").load_module()\n",
    "runner = nmlPython.nml2jupyter(path2source, fname_LEMS, fname_net)\n",
    "\n",
    "nml_doc=runner.loadnml()\n",
    "runner.createTabWithAccordions(nml_doc)      #create GUI with tabs (including LEMS) and nested accordions\n",
    "#display(runner.createAccordions(nml_doc,'NML Document'))  #create only nested accordions\n",
    "runner.loadGUI(nml_doc)                                    #load buttons and log/plot window"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa709aa7",
   "metadata": {},
   "source": [
    "## *INFO method output*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7989feba",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#for key,values in nml_doc.cells[0].info(True,'dict').items():\n",
    "#    if values['members'] is None or (isinstance(values['members'], list) and len(values['members']) == 0): continue\n",
    "#    print(key,' = ', values['members'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "219b7931",
   "metadata": {},
   "source": [
    "## *Exploring sub-model (examples)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02d4dbdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#with first tab as LEMS simulation parameters (default)\n",
    "#runner.createTabWithAccordions(nml_doc.networks[0]) #pass NeuroML class object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79f7b597",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#only NeuroML model (no LEMS details included)\n",
    "#runner.createAccordions(nml_doc.networks[0],'Networks') #pass NeuroML class object and a title for parent accordion"
   ]
  }
 ],
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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