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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Overview\n",
"\n",
"This a notebook that inspects the results of a WarpX simulation.\n",
"\n",
"# Instructions\n",
"\n",
"Execute the cells below one by one, by selecting them with your mouse and typing `Shift + Enter`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# General imports\n",
"import glob, sys\n",
"# Import numpy and matplotlib\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib\n",
"# Import yt\n",
"import yt\n",
"yt.funcs.mylog.setLevel(50) # Deactivate yt verbose\n",
"\n",
"# Import interactive notebook feature\n",
"#from IPython.display import clear_output\n",
"from ipywidgets import interact, IntSlider, RadioButtons\n",
"\n",
"# Find output iterations\n",
"file_list = glob.glob('plt?????')\n",
"iterations = [ int(file_name[3:]) for file_name in file_list ]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Functions to extract and plot the fields"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def extra_data( field, iteration ):\n",
" \"\"\"\n",
" Extract the 3D array that corresponds to `field` at the \n",
" requested iteration, and returns as a `YTarray`, which \n",
" can be used exactly like a numpy array.\n",
" \"\"\"\n",
" ds = yt.load( './plt%05d/' %iteration )\n",
" data = ds.covering_grid(level=0, \n",
" left_edge=ds.domain_left_edge, \n",
" dims=ds.domain_dimensions)\n",
" return( data[field] ) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def plot_field( iteration, field, slicing_direction='y' ):\n",
" # Get the data\n",
" field_array = extra_data( field, iteration )\n",
" \n",
" # Get the position of the edges\n",
" ds = yt.load( './plt%05d/' %iteration )\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 slicing_direction == 'x':\n",
" n = int( ds.domain_dimensions[0]//2 )\n",
" data2d = field_array[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 = field_array[:, 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 = field_array[:, :, n]\n",
" extent = [ left_edge[1], right_edge[1], left_edge[0], right_edge[0] ]\n",
" plt.clf()\n",
" plt.imshow( data2d, interpolation='nearest', cmap='viridis',\n",
" origin='lower', extent=extent )\n",
" plt.colorbar()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Interactive viewer"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"interact(plot_field, \n",
" iteration = IntSlider(min=min(iterations), max=max(iterations), step=iterations[1]-iterations[0]),\n",
" field = RadioButtons( options=['jx', 'jy', 'jz', 'Ex', 'Ey', 'Ez'], value='jz'),\n",
" slicing_direction = RadioButtons( options=[ 'x', 'y', 'z'], value='y') )"
]
}
],
"metadata": {
"anaconda-cloud": {},
"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": "3.6.0"
},
"widgets": {
"state": {
"5ca3ffc563434f7487da7b1bd657761b": {
"views": [
{
"cell_index": 5
}
]
}
},
"version": "1.2.0"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|
