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{
"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": {},
"outputs": [],
"source": [
"# Import statements\n",
"import yt ; yt.funcs.mylog.setLevel(50)\n",
"import numpy as np\n",
"import scipy.constants as scc\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read data in the simulation frame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot data with yt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds = yt.load( '../Examples/Modules/RigidInjection/diags/plotfiles/plt00318/' ) # Create a dataset object\n",
"sl = yt.SlicePlot(ds, 2, 'Ex', aspect=.2) # Create a sliceplot object\n",
"sl.annotate_particles(width=(10.e-6, 'm'), p_size=2, ptype='beam', col='black')\n",
"sl.annotate_grids() # Show grids\n",
"sl.show() # Show the plot\n",
"# sl.save('./toto.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Store quantities in numpy arrays, and plot with matplotlib"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Get field quantities\n",
"all_data_level_0 = ds.covering_grid(level=0,left_edge=ds.domain_left_edge, dims=ds.domain_dimensions)\n",
"Bx = all_data_level_0['boxlib', 'Ex'].v.squeeze()\n",
"Dx = ds.domain_width/ds.domain_dimensions\n",
"extent = [ds.domain_left_edge[ds.dimensionality-1], ds.domain_right_edge[ds.dimensionality-1],\n",
" ds.domain_left_edge[0], ds.domain_right_edge[0] ]\n",
"\n",
"# Get particle quantities\n",
"ad = ds.all_data()\n",
"x = ad['beam', 'particle_position_x'].v\n",
"z = ad['beam', 'particle_position_y'].v\n",
"\n",
"# Plot image\n",
"plt.figure()\n",
"plt.imshow(Bx, extent=extent)\n",
"plt.scatter(z,x,s=.1,c='k')\n",
"\n",
"# Print all available quantities\n",
"ds.field_list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read data back-transformed to the lab frame when the simulation runs in the boosted frame (example: 2D run)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# read_raw_data.py is located in warpx/Tools.\n",
"import os, glob\n",
"import read_raw_data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"species = 'beam'\n",
"iteration = 1\n",
"field = 'Ex'\n",
"\n",
"snapshot = './lab_frame_data/' + 'snapshot' + str(iteration).zfill(5)\n",
"header = './lab_frame_data/Header'\n",
"allrd, info = read_raw_data.read_lab_snapshot(snapshot, header) # Read field data\n",
"F = allrd[field]\n",
"print( \"Available info: \", *list(info.keys()) )\n",
"print(\"Available fields: \", info['field_names'])\n",
"nx = info['nx']\n",
"nz = info['nz']\n",
"x = info['x']\n",
"z = info['z']\n",
"xbo = read_raw_data.get_particle_field(snapshot, species, 'x') # Read particle data\n",
"ybo = read_raw_data.get_particle_field(snapshot, species, 'y')\n",
"zbo = read_raw_data.get_particle_field(snapshot, species, 'z')\n",
"uzbo = read_raw_data.get_particle_field(snapshot, species, 'uz')\n",
"\n",
"plt.figure(figsize=(6, 3))\n",
"extent = np.array([info['zmin'], info['zmax'], info['xmin'], info['xmax']])\n",
"plt.imshow(F, aspect='auto', extent=extent, cmap='seismic')\n",
"plt.colorbar()\n",
"plt.plot(zbo, xbo, 'g.', markersize=1.)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read back-transformed data with hdf5 format (example: 3D run)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import h5py\n",
"import matplotlib.pyplot as plt\n",
"f = h5py.File('HDF5_lab_frame_data/snapshot00003', 'r')\n",
"print( list(f.keys()) )\n",
"# plt.figure()\n",
"plt.imshow(f['Ey'][:,,:])"
]
}
],
"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": "3.7.1"
},
"widgets": {
"state": {
"11d243e9f5074fe1b115949d174d59de": {
"views": [
{
"cell_index": 6
}
]
}
},
"version": "1.2.0"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|
