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#!/usr/bin/env python3
# Copyright 2022
# Authors: Revathi Jambunathan, Remi Lehe
#
# This tests checks the backtransformed diagnostics by emitting a laser
# (with the antenna) in the boosted-frame and then checking that the
# fields recorded by the backtransformed diagnostics have the right amplitude,
# wavelength, and envelope (i.e. gaussian envelope with the right duration.
import os
import sys
import numpy as np
from openpmd_viewer import OpenPMDTimeSeries
from scipy.constants import c, e, m_e
from scipy.optimize import curve_fit
sys.path.insert(1, '../../../../warpx/Regression/Checksum/')
import checksumAPI
def gaussian_laser( z, a0, z0_phase, z0_prop, ctau, lambda0 ):
"""
Returns a Gaussian laser profile
"""
k0 = 2*np.pi/lambda0
E0 = a0*m_e*c**2*k0/e
return( E0*np.exp( - (z-z0_prop)**2/ctau**2 ) \
*np.cos( k0*(z-z0_phase) ) )
# Fit the on-axis profile to extract the phase (a.k.a. CEP)
def fit_function(z, z0_phase):
return( gaussian_laser( z, a0, z0_phase,
z0_b+Lprop_b, ctau0, lambda0 ) )
plotfile = sys.argv[1]
# The values must be consistent with the values provided in the simulation input
t_current = 80e-15 # Time of the snapshot1
c = 299792458;
z0_antenna = -1.e-6 # position of laser
lambda0 = 0.8e-6 # wavelength of the signal
tau0 = 10e-15 # duration of the signal
ctau0 = tau0 * c
a0 = 15 # amplitude
t_peak = 20e-15 # Time at which laser reaches its peak
Lprop_b = c*t_current
z0_b = z0_antenna - c * t_peak
ts = OpenPMDTimeSeries('./diags/back_rz')
Ex, info = ts.get_field('E', 'x', iteration=1, slice_across='r')
fit_result = curve_fit( fit_function, info.z, Ex,
p0=np.array([z0_b+Lprop_b]) )
z0_fit = fit_result[0]
Ex_fit = gaussian_laser( info.z, a0, z0_fit, z0_b+Lprop_b, ctau0, lambda0)
## Check that the a0 agrees within 5% of the predicted value
assert np.allclose( Ex, Ex_fit, atol=0.18*Ex.max() )
# Checksum regression analysis
test_name = os.path.split(os.getcwd())[1]
checksumAPI.evaluate_checksum(test_name, plotfile)
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