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#!/usr/bin/env python3
# Copyright 2019-2022 Jean-Luc Vay, Maxence Thevenet, Remi Lehe, Axel Huebl
#
#
# This file is part of WarpX.
#
# License: BSD-3-Clause-LBNL
#
# This is a script that analyses the simulation results from
# the script `inputs.multi.rt`. This simulates a 3D periodic plasma wave.
# The electric field in the simulation is given (in theory) by:
# $$ E_x = \epsilon \,\frac{m_e c^2 k_x}{q_e}\sin(k_x x)\cos(k_y y)\cos(k_z z)\sin( \omega_p t)$$
# $$ E_y = \epsilon \,\frac{m_e c^2 k_y}{q_e}\cos(k_x x)\sin(k_y y)\cos(k_z z)\sin( \omega_p t)$$
# $$ E_z = \epsilon \,\frac{m_e c^2 k_z}{q_e}\cos(k_x x)\cos(k_y y)\sin(k_z z)\sin( \omega_p t)$$
import os
import re
import sys
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable
import yt
yt.funcs.mylog.setLevel(50)
import numpy as np
from scipy.constants import c, e, epsilon_0, m_e
sys.path.insert(1, '../../../../warpx/Regression/Checksum/')
import checksumAPI
# this will be the name of the plot file
fn = sys.argv[1]
# Parse test name and check if current correction (psatd.current_correction=1) is applied
current_correction = True if re.search( 'current_correction', fn ) else False
# Parse test name and check if Vay current deposition (algo.current_deposition=vay) is used
vay_deposition = True if re.search( 'Vay_deposition', fn ) else False
# Parse test name and check if div(E)/div(B) cleaning (warpx.do_div<e,b>_cleaning=1) is used
div_cleaning = True if re.search('div_cleaning', fn) else False
# Parameters (these parameters must match the parameters in `inputs.multi.rt`)
epsilon = 0.01
n = 4.e24
n_osc_x = 2
n_osc_y = 2
n_osc_z = 2
lo = [-20.e-6, -20.e-6, -20.e-6]
hi = [ 20.e-6, 20.e-6, 20.e-6]
Ncell = [64, 64, 64]
# Wave vector of the wave
kx = 2.*np.pi*n_osc_x/(hi[0]-lo[0])
ky = 2.*np.pi*n_osc_y/(hi[1]-lo[1])
kz = 2.*np.pi*n_osc_z/(hi[2]-lo[2])
# Plasma frequency
wp = np.sqrt((n*e**2)/(m_e*epsilon_0))
k = {'Ex':kx, 'Ey':ky, 'Ez':kz}
cos = {'Ex': (0,1,1), 'Ey':(1,0,1), 'Ez':(1,1,0)}
def get_contribution( is_cos, k, idim ):
du = (hi[idim]-lo[idim])/Ncell[idim]
u = lo[idim] + du*( 0.5 + np.arange(Ncell[idim]) )
if is_cos[idim] == 1:
return( np.cos(k*u) )
else:
return( np.sin(k*u) )
def get_theoretical_field( field, t ):
amplitude = epsilon * (m_e*c**2*k[field])/e * np.sin(wp*t)
cos_flag = cos[field]
x_contribution = get_contribution( cos_flag, kx, 0 )
y_contribution = get_contribution( cos_flag, ky, 1 )
z_contribution = get_contribution( cos_flag, kz, 2 )
E = amplitude * x_contribution[:, np.newaxis, np.newaxis] \
* y_contribution[np.newaxis, :, np.newaxis] \
* z_contribution[np.newaxis, np.newaxis, :]
return( E )
# Read the file
ds = yt.load(fn)
# Check that the particle selective output worked:
species = 'electrons'
print('ds.field_list', ds.field_list)
for field in ['particle_weight',
'particle_momentum_x']:
print('assert that this is in ds.field_list', (species, field))
assert (species, field) in ds.field_list
for field in ['particle_momentum_y',
'particle_momentum_z']:
print('assert that this is NOT in ds.field_list', (species, field))
assert (species, field) not in ds.field_list
species = 'positrons'
for field in ['particle_momentum_x',
'particle_momentum_y']:
print('assert that this is NOT in ds.field_list', (species, field))
assert (species, field) not in ds.field_list
t0 = ds.current_time.to_value()
data = ds.covering_grid(level = 0, left_edge = ds.domain_left_edge, dims = ds.domain_dimensions)
edge = np.array([(ds.domain_left_edge[2]).item(), (ds.domain_right_edge[2]).item(), \
(ds.domain_left_edge[0]).item(), (ds.domain_right_edge[0]).item()])
# Check the validity of the fields
error_rel = 0
for field in ['Ex', 'Ey', 'Ez']:
E_sim = data[('mesh',field)].to_ndarray()
E_th = get_theoretical_field(field, t0)
max_error = abs(E_sim-E_th).max()/abs(E_th).max()
print('%s: Max error: %.2e' %(field,max_error))
error_rel = max( error_rel, max_error )
# Plot the last field from the loop (Ez at iteration 40)
fig, (ax1, ax2) = plt.subplots(1, 2, dpi = 100)
# First plot (slice at y=0)
E_plot = E_sim[:,Ncell[1]//2+1,:]
vmin = E_plot.min()
vmax = E_plot.max()
cax1 = make_axes_locatable(ax1).append_axes('right', size = '5%', pad = '5%')
im1 = ax1.imshow(E_plot, origin = 'lower', extent = edge, vmin = vmin, vmax = vmax)
cb1 = fig.colorbar(im1, cax = cax1)
ax1.set_xlabel(r'$z$')
ax1.set_ylabel(r'$x$')
ax1.set_title(r'$E_z$ (sim)')
# Second plot (slice at y=0)
E_plot = E_th[:,Ncell[1]//2+1,:]
vmin = E_plot.min()
vmax = E_plot.max()
cax2 = make_axes_locatable(ax2).append_axes('right', size = '5%', pad = '5%')
im2 = ax2.imshow(E_plot, origin = 'lower', extent = edge, vmin = vmin, vmax = vmax)
cb2 = fig.colorbar(im2, cax = cax2)
ax2.set_xlabel(r'$z$')
ax2.set_ylabel(r'$x$')
ax2.set_title(r'$E_z$ (theory)')
# Save figure
fig.tight_layout()
fig.savefig('Langmuir_multi_analysis.png', dpi = 200)
tolerance_rel = 5e-2
print("error_rel : " + str(error_rel))
print("tolerance_rel: " + str(tolerance_rel))
assert( error_rel < tolerance_rel )
# Check relative L-infinity spatial norm of rho/epsilon_0 - div(E)
# with current correction (and periodic single box option) or with Vay current deposition
if current_correction:
tolerance = 1e-9
elif vay_deposition:
tolerance = 1e-3
if current_correction or vay_deposition:
rho = data[('boxlib','rho')].to_ndarray()
divE = data[('boxlib','divE')].to_ndarray()
error_rel = np.amax( np.abs( divE - rho/epsilon_0 ) ) / np.amax( np.abs( rho/epsilon_0 ) )
print("Check charge conservation:")
print("error_rel = {}".format(error_rel))
print("tolerance = {}".format(tolerance))
assert( error_rel < tolerance )
if div_cleaning:
ds_old = yt.load('Langmuir_multi_psatd_div_cleaning_plt000038')
ds_mid = yt.load('Langmuir_multi_psatd_div_cleaning_plt000039')
ds_new = yt.load(fn) # this is the last plotfile
ad_old = ds_old.covering_grid(level = 0, left_edge = ds_old.domain_left_edge, dims = ds_old.domain_dimensions)
ad_mid = ds_mid.covering_grid(level = 0, left_edge = ds_mid.domain_left_edge, dims = ds_mid.domain_dimensions)
ad_new = ds_new.covering_grid(level = 0, left_edge = ds_new.domain_left_edge, dims = ds_new.domain_dimensions)
rho = ad_mid['rho'].v.squeeze()
divE = ad_mid['divE'].v.squeeze()
F_old = ad_old['F'].v.squeeze()
F_new = ad_new['F'].v.squeeze()
# Check max norm of error on dF/dt = div(E) - rho/epsilon_0
# (the time interval between the old and new data is 2*dt)
dt = 1.203645751e-15
x = F_new - F_old
y = (divE - rho/epsilon_0) * 2 * dt
error_rel = np.amax(np.abs(x - y)) / np.amax(np.abs(y))
tolerance = 1e-2
print("Check div(E) cleaning:")
print("error_rel = {}".format(error_rel))
print("tolerance = {}".format(tolerance))
assert(error_rel < tolerance)
test_name = os.path.split(os.getcwd())[1]
if re.search( 'single_precision', fn ):
checksumAPI.evaluate_checksum(test_name, fn, rtol=1.e-3)
else:
checksumAPI.evaluate_checksum(test_name, fn)
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