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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Overview\n",
"\n",
"This a notebook that inspects the results of a WarpX simulation and a Warp simulation at the same time. Typically used to compare the results of both codes for the same set of parameters.\n",
"\n",
"# Instructions\n",
"\n",
"- Enter the paths for the WarpX and the Warp simulations\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
},
"outputs": [],
"source": [
"# Import statements\n",
"import sys\n",
"from tqdm import tqdm\n",
"import yt, glob\n",
"yt.funcs.mylog.setLevel(50)\n",
"from IPython.display import clear_output\n",
"import numpy as np\n",
"from ipywidgets import interact, RadioButtons, IntSlider\n",
"from openpmd_viewer import OpenPMDTimeSeries\n",
"from yt.units import volt\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Define path way for WarpX results and find iterations\n",
"path_warpx = '/Users/mthevenet/warpX_directory/res_warpx/valid_pwfa/'\n",
"file_list_warpx = glob.glob(path_warpx + 'plt?????')\n",
"iterations_warpx = [ int(file_name[len(file_name)-5:]) for file_name in file_list_warpx ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Define path way for Warp results and find iterations\n",
"path_warp = '/Users/mthevenet/warp_results/valid_pwfa/'\n",
"file_list_warp = glob.glob(path_warp + 'diags/hdf5/data????????.h5')\n",
"iterations_warp = [ int(file_name[len(file_name)-11:len(file_name)-3]) for file_name in file_list_warp ]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functions to plot the fields\n",
"\n",
"Note that the data are loaded with yt and plotted using matplotlib"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def plot_field( iteration, field, slicing_direction='y'):\n",
" \n",
" # First dataset\n",
" ds = yt.load( path_warpx + '/plt%05d/' %iteration )\n",
" all_data_level_0 = ds.covering_grid(level=0, \n",
" left_edge=ds.domain_left_edge, dims=ds.domain_dimensions)\n",
" \n",
" # Second dataset\n",
"# ts = OpenPMDTimeSeries(path_warp + 'diags/hdf5/') \n",
" ds2 = yt.load( path_warp + 'diags/hdf5/data%08d.h5' %iteration )\n",
" all_data_level_02 = ds2.covering_grid(level=0, \n",
" left_edge=ds2.domain_left_edge, dims=ds2.domain_dimensions)\n",
"\n",
" # first dataset loaded via yt\n",
" left_edge = ds.domain_left_edge.convert_to_mks()*1e6\n",
" right_edge = ds.domain_right_edge.convert_to_mks()*1e6\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",
"\n",
" # second dataset loaded via yt\n",
" left_edge = ds2.domain_left_edge.convert_to_mks()*1e6\n",
" right_edge = ds2.domain_right_edge.convert_to_mks()*1e6\n",
" field_reformat = field[0].upper() + '_' + field[1]\n",
" if slicing_direction == 'x':\n",
" n = int( ds2.domain_dimensions[0]//2 )\n",
" data2d2 = all_data_level_02[field_reformat][n, :, :]\n",
" extent2 = [ left_edge[2], right_edge[2], left_edge[1], right_edge[1] ]\n",
" elif slicing_direction == 'y':\n",
" n = int( ds2.domain_dimensions[1]//2 )\n",
" data2d2 = all_data_level_02[field_reformat][:, n, :]\n",
" extent2 = [ left_edge[2], right_edge[2], left_edge[0], right_edge[0] ]\n",
" elif slicing_direction == 'z':\n",
" n = int( ds2.domain_dimensions[2]//2 )\n",
" data2d2 = all_data_level_02[field_reformat][:, :, n]\n",
" extent2 = [ left_edge[1], right_edge[1], left_edge[0], right_edge[0] ]\n",
"\n",
" if slicing_direction == 'x':\n",
" yaxis_label = 'y'\n",
" if slicing_direction == 'y':\n",
" yaxis_label = 'x'\n",
" if slicing_direction == 'z':\n",
" yaxis_label = 'y'\n",
"\n",
"# xlim = [5,25]\n",
"# ylim = [-20,20]\n",
"\n",
" plt.clf()\n",
" # first dataset\n",
" plt.subplot(2,1,1)\n",
" plt.title(\"WarpX %s at iteration %d\" %(field, iteration) )\n",
" plt.imshow( data2d, interpolation='nearest', cmap='viridis',\n",
" origin='lower', extent=extent, aspect='auto')\n",
" plt.ylabel(yaxis_label + ' (um)')\n",
" vmin, vmax = plt.gci().get_clim()\n",
" plt.colorbar()\n",
" if 'xlim' in locals():\n",
" plt.xlim(xlim)\n",
" if 'ylim' in locals():\n",
" plt.ylim(ylim)\n",
" plt.xticks([])\n",
" \n",
" # second dataset\n",
" plt.subplot(2,1,2)\n",
" plt.title(\"Warp %s at iteration %d\" %(field, iteration) )\n",
" plt.imshow( data2d2, interpolation='nearest', cmap='viridis',\n",
" origin='lower', extent=extent2, aspect='auto',vmin=vmin,vmax=vmax)\n",
" plt.colorbar()\n",
" if 'xlim' in locals():\n",
" plt.xlim(xlim)\n",
" if 'ylim' in locals():\n",
" plt.ylim(ylim)\n",
" plt.xlabel('z (um)')\n",
" plt.ylabel(yaxis_label + ' (um)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Interactive viewer"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iterations = iterations_warp\n",
"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='jx'),\n",
" slicing_direction = RadioButtons( options=[ 'x', 'y', 'z'], value='x'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|
