{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Minimal Example\n",
    "Testing basic functions of the ctwrap package using the minimal module \n",
    "(which does not use Cantera, but just 'sleeps' for a specified amount of seconds)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up Simulation object\n",
    "\n",
    "The `Simulation` object wraps the module, which can be run with default arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ctwrap as cw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = cw.Simulation.from_module(cw.modules.minimal)\n",
    "sim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up Simulation Handler object\n",
    "\n",
    "The `SimulationHandler` object uses a YAML file as input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fname = 'minimal.yaml'\n",
    "sh = cw.SimulationHandler.from_yaml(fname, strategy='sequence', verbosity=1)\n",
    "sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list simulation tasks\n",
    "sh.tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run serial simulation\n",
    "\n",
    "The `run_serial` function loops through simulation tasks one by one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "sh.run_serial(sim, verbosity=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run parallel simulation\n",
    "\n",
    "The `run_parallel` function uses Python's `multiprocessing` to process multiple single-threaded simulation jobs at the same time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "sh.run_parallel(sim, verbosity=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Batch Matrix: Set up Simulation Handler object\n",
    "\n",
    "The `SimulationHandler` object uses a YAML file as input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fname = 'minimal.yaml'\n",
    "sh = cw.SimulationHandler.from_yaml(fname, strategy='matrix', verbosity=1)\n",
    "sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list simulation tasks\n",
    "sh.tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run serial simulation\n",
    "\n",
    "The `run_serial` function loops through simulation tasks one by one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "sh.run_serial(sim, verbosity=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run parallel simulation\n",
    "\n",
    "The `run_parallel` function uses Python's `multiprocessing` to process multiple single-threaded simulation jobs at the same time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "sh.run_parallel(sim, verbosity=1)"
   ]
  }
 ],
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