{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "\n",
    "This a notebook that inspects the results of a WarpX simulation.\n",
    "\n",
    "# Instruction\n",
    "\n",
    "Enter the path of the data you wish to visualize below. Then execute the cells one by one, by selecting them with your mouse and typing `Shift + Enter`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_path = '../Example/Langmuir/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import statements\n",
    "import os, glob\n",
    "import yt ; yt.funcs.mylog.setLevel(50)\n",
    "from IPython.display import clear_output\n",
    "import numpy as np\n",
    "from ipywidgets import interact, ToggleButtons, IntSlider\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# YT basic usage. Read this for practical examples\n",
    "# ------------------------------------------------\n",
    "\n",
    "# Create a dataset object\n",
    "ds = yt.load( './pml/plt00400' )\n",
    "# List all fields in the datasert\n",
    "ds.field_list\n",
    "# Get All Data from the dataset\n",
    "ad = ds.all_data()\n",
    "# Get some data. \".v\" converts arrays from units-aware yt arrays to numpy arrays.\n",
    "# Magnetic field\n",
    "b = ad['boxlib', 'Bx'].v\n",
    "# particle weight and z coordinate for species particle0\n",
    "w = ad['particle0', 'particle_momentum_z'].v\n",
    "z = ad['particle0', 'particle_weight'].v\n",
    "\n",
    "# Create a sliceplot object\n",
    "sl = yt.SlicePlot(ds, 2, 'Ex')\n",
    "# Set labels\n",
    "sl.set_xlabel(r'$x (\\mu m)$')\n",
    "sl.set_ylabel(r'$z (\\mu m)$')\n",
    "# Set figure size\n",
    "sl.figure_size = (9, 7)\n",
    "# Plot particles\n",
    "sl.annotate_particles(width=1., p_size=2, ptype='particle0', col='black')\n",
    "# Show grids\n",
    "sl.annotate_grids()\n",
    "# show the plot\n",
    "sl.show()\n",
    "# Save image\n",
    "# sl.save('./toto.pdf')\n",
    "# This returns the domain boundaries\n",
    "sl.bounds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions to plot the fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_field( iteration, field, slicing_direction='y', plotter='matplotlib' ):\n",
    "    ds = yt.load( os.path.join(data_path, 'plt%05d/' %iteration) )\n",
    "    all_data_level_0 = ds.covering_grid(level=0, \n",
    "                        left_edge=ds.domain_left_edge, \n",
    "                        dims=ds.domain_dimensions)\n",
    "    \n",
    "    if plotter == 'yt':\n",
    "        sl = yt.SlicePlot(ds, slicing_direction, field)\n",
    "        sl.set_log( field, False)\n",
    "        sl.annotate_grids()\n",
    "        # Show the new plot\n",
    "        clear_output()\n",
    "        sl.show()\n",
    "\n",
    "    elif plotter == 'matplotlib':\n",
    "\n",
    "        left_edge = ds.domain_left_edge.convert_to_mks()*1.e6\n",
    "        right_edge = ds.domain_right_edge.convert_to_mks()*1.e6\n",
    "        \n",
    "        if ds.dimensionality == 3:\n",
    "            if slicing_direction == 'x':\n",
    "                n = int( ds.domain_dimensions[0]//2 )\n",
    "                data2d = all_data_level_0[field][n, :, :]\n",
    "                extent = [ left_edge[2], right_edge[2], left_edge[1], right_edge[1] ]\n",
    "            elif slicing_direction == 'y':\n",
    "                n = int( ds.domain_dimensions[1]//2 )\n",
    "                data2d = all_data_level_0[field][:, n, :]\n",
    "                extent = [ left_edge[2], right_edge[2], left_edge[0], right_edge[0] ]\n",
    "            elif slicing_direction == 'z':\n",
    "                n = int( ds.domain_dimensions[2]//2 )\n",
    "                data2d = all_data_level_0[field][:, :, n]\n",
    "                extent = [ left_edge[1], right_edge[1], left_edge[0], right_edge[0] ]\n",
    "        elif ds.dimensionality == 2:\n",
    "            data2d = all_data_level_0[field][:,:,0].T\n",
    "            extent = [ left_edge[1], right_edge[1], left_edge[0], right_edge[0] ]\n",
    "\n",
    "        plt.clf()\n",
    "        plt.imshow( data2d.to_ndarray(), interpolation='nearest', cmap='viridis',\n",
    "                  origin='lower', extent=extent )\n",
    "        plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interactive viewer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51d3a2c04a684310a3e87ee33674f728"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_field>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find iterations\n",
    "file_list = glob.glob(os.path.join( data_path, 'plt?????') )\n",
    "iterations = [ int(file_name[-5:]) for file_name in file_list ]\n",
    "\n",
    "interact(plot_field, \n",
    "         iteration = IntSlider(min=min(iterations), max=max(iterations), step=iterations[1]-iterations[0]),\n",
    "         field = ToggleButtons( options=['jx', 'jy', 'jz', 'Ex', 'Ey', 'Ez', 'Bx', 'By', 'Bz'], value='jz'),\n",
    "         slicing_direction = ToggleButtons( options=[ 'x', 'y', 'z'], value='y'),\n",
    "         plotter = ToggleButtons( options=['matplotlib', 'yt'] ) )"
   ]
  }
 ],
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