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#!/usr/bin/env python3
# Copyright 2019-2021 Luca Fedeli, Yinjian Zhao
#
# This file is part of WarpX.
#
# License: BSD-3-Clause-LBNL
# This script tests the reduced diagnostics.
# The setup is a uniform plasma with electrons, protons and photons.
# Various particle and field quantities are written to file using the reduced diagnostics
# and compared with the corresponding quantities computed from the data in the plotfiles.
import sys
import yt
import numpy as np
import os
from scipy.constants import c, m_e, m_p
from scipy.constants import mu_0 as mu0
from scipy.constants import epsilon_0 as eps0
sys.path.insert(1, '../../../../warpx/Regression/Checksum/')
import checksumAPI
# gamma threshold to switch between the relativistic expression of
# the kinetic energy and its Taylor expansion.
gamma_relativistic_threshold = 1.005
def do_analysis(single_precision = False):
fn = sys.argv[1]
ds = yt.load(fn)
ad = ds.all_data()
#--------------------------------------------------------------------------------------------------
# Part 1: get results from plotfiles (label '_yt')
#--------------------------------------------------------------------------------------------------
# Quantities computed from plotfiles
values_yt = dict()
# Electrons
px = ad['electrons', 'particle_momentum_x'].to_ndarray()
py = ad['electrons', 'particle_momentum_y'].to_ndarray()
pz = ad['electrons', 'particle_momentum_z'].to_ndarray()
w = ad['electrons', 'particle_weight'].to_ndarray()
p2 = px**2 + py**2 + pz**2
# Accumulate particle energy, store number of particles and sum of weights
e_u2 = p2/(m_e**2 * c**2)
e_gamma = np.sqrt(1 + e_u2)
e_energy = (m_e * c**2)*np.where(e_gamma > gamma_relativistic_threshold,
e_gamma-1,
(e_u2)/2 - (e_u2**2)/8 + (e_u2**3)/16 - (e_u2**4)*(5/128) + (e_u2**5)*(7/256))
values_yt['electrons: particle energy'] = np.sum(e_energy * w)
values_yt['electrons: particle momentum in x'] = np.sum(px * w)
values_yt['electrons: particle momentum in y'] = np.sum(py * w)
values_yt['electrons: particle momentum in z'] = np.sum(pz * w)
values_yt['electrons: number of particles'] = w.shape[0]
values_yt['electrons: sum of weights'] = np.sum(w)
# Protons
px = ad['protons', 'particle_momentum_x'].to_ndarray()
py = ad['protons', 'particle_momentum_y'].to_ndarray()
pz = ad['protons', 'particle_momentum_z'].to_ndarray()
w = ad['protons', 'particle_weight'].to_ndarray()
p2 = px**2 + py**2 + pz**2
# Accumulate particle energy, store number of particles and sum of weights
p_u2 = p2/(m_p**2 * c**2)
p_gamma = np.sqrt(1 + p_u2)
p_energy = (m_p * c**2)*np.where(p_gamma > gamma_relativistic_threshold,
p_gamma-1,
(p_u2)/2 - (p_u2**2)/8 + (p_u2**3)/16 - (p_u2**4)*(5/128) + (p_u2**5)*(7/256))
values_yt['protons: particle energy'] = np.sum(p_energy * w)
values_yt['protons: particle momentum in x'] = np.sum(px * w)
values_yt['protons: particle momentum in y'] = np.sum(py * w)
values_yt['protons: particle momentum in z'] = np.sum(pz * w)
values_yt['protons: number of particles'] = w.shape[0]
values_yt['protons: sum of weights'] = np.sum(w)
# Photons
px = ad['photons', 'particle_momentum_x'].to_ndarray()
py = ad['photons', 'particle_momentum_y'].to_ndarray()
pz = ad['photons', 'particle_momentum_z'].to_ndarray()
w = ad['photons', 'particle_weight'].to_ndarray()
p2 = px**2 + py**2 + pz**2
# Accumulate particle energy, store number of particles and sum of weights
values_yt['photons: particle energy'] = np.sum(np.sqrt(p2 * c**2) * w)
values_yt['photons: particle momentum in x'] = np.sum(px * w)
values_yt['photons: particle momentum in y'] = np.sum(py * w)
values_yt['photons: particle momentum in z'] = np.sum(pz * w)
values_yt['photons: number of particles'] = w.shape[0]
values_yt['photons: sum of weights'] = np.sum(w)
# Accumulate total particle diagnostics
values_yt['particle energy'] = values_yt['electrons: particle energy'] \
+ values_yt['protons: particle energy'] \
+ values_yt['photons: particle energy']
values_yt['particle momentum in x'] = values_yt['electrons: particle momentum in x'] \
+ values_yt['protons: particle momentum in x'] \
+ values_yt['photons: particle momentum in x']
values_yt['particle momentum in y'] = values_yt['electrons: particle momentum in y'] \
+ values_yt['protons: particle momentum in y'] \
+ values_yt['photons: particle momentum in y']
values_yt['particle momentum in z'] = values_yt['electrons: particle momentum in z'] \
+ values_yt['protons: particle momentum in z'] \
+ values_yt['photons: particle momentum in z']
values_yt['number of particles'] = values_yt['electrons: number of particles'] \
+ values_yt['protons: number of particles'] \
+ values_yt['photons: number of particles']
values_yt['sum of weights'] = values_yt['electrons: sum of weights'] \
+ values_yt['protons: sum of weights'] \
+ values_yt['photons: sum of weights']
values_yt['mean particle energy'] = values_yt['particle energy'] / values_yt['sum of weights']
values_yt['mean particle momentum in x'] = values_yt['particle momentum in x'] / values_yt['sum of weights']
values_yt['mean particle momentum in y'] = values_yt['particle momentum in y'] / values_yt['sum of weights']
values_yt['mean particle momentum in z'] = values_yt['particle momentum in z'] / values_yt['sum of weights']
values_yt['electrons: mean particle energy'] = values_yt['electrons: particle energy'] \
/ values_yt['electrons: sum of weights']
values_yt['electrons: mean particle momentum in x'] = values_yt['electrons: particle momentum in x'] \
/ values_yt['electrons: sum of weights']
values_yt['electrons: mean particle momentum in y'] = values_yt['electrons: particle momentum in y'] \
/ values_yt['electrons: sum of weights']
values_yt['electrons: mean particle momentum in z'] = values_yt['electrons: particle momentum in z'] \
/ values_yt['electrons: sum of weights']
values_yt['protons: mean particle energy'] = values_yt['protons: particle energy'] \
/ values_yt['protons: sum of weights']
values_yt['protons: mean particle momentum in x'] = values_yt['protons: particle momentum in x'] \
/ values_yt['protons: sum of weights']
values_yt['protons: mean particle momentum in y'] = values_yt['protons: particle momentum in y'] \
/ values_yt['protons: sum of weights']
values_yt['protons: mean particle momentum in z'] = values_yt['protons: particle momentum in z'] \
/ values_yt['protons: sum of weights']
values_yt['photons: mean particle energy'] = values_yt['photons: particle energy'] \
/ values_yt['photons: sum of weights']
values_yt['photons: mean particle momentum in x'] = values_yt['photons: particle momentum in x'] \
/ values_yt['photons: sum of weights']
values_yt['photons: mean particle momentum in y'] = values_yt['photons: particle momentum in y'] \
/ values_yt['photons: sum of weights']
values_yt['photons: mean particle momentum in z'] = values_yt['photons: particle momentum in z'] \
/ values_yt['photons: sum of weights']
# Load 3D data from plotfiles
ad = ds.covering_grid(level=0, left_edge=ds.domain_left_edge, dims=ds.domain_dimensions)
Ex = ad[('mesh','Ex')].to_ndarray()
Ey = ad[('mesh','Ey')].to_ndarray()
Ez = ad[('mesh','Ez')].to_ndarray()
Bx = ad[('mesh','Bx')].to_ndarray()
By = ad[('mesh','By')].to_ndarray()
Bz = ad[('mesh','Bz')].to_ndarray()
rho = ad[('boxlib','rho')].to_ndarray()
rho_electrons = ad[('boxlib','rho_electrons')].to_ndarray()
rho_protons = ad[('boxlib','rho_protons')].to_ndarray()
x = ad[('boxlib','x')].to_ndarray()
y = ad[('boxlib','y')].to_ndarray()
z = ad[('boxlib','z')].to_ndarray()
# Field energy
E2 = np.sum(Ex**2) + np.sum(Ey**2) + np.sum(Ez**2)
B2 = np.sum(Bx**2) + np.sum(By**2) + np.sum(Bz**2)
N = np.array(ds.domain_width / ds.domain_dimensions)
dV = N[0] * N[1] * N[2]
values_yt['field energy'] = 0.5 * dV * (E2 * eps0 + B2 / mu0)
values_yt['field momentum in x'] = eps0 * np.sum(Ey * Bz - Ez * By) * dV
values_yt['field momentum in y'] = eps0 * np.sum(Ez * Bx - Ex * Bz) * dV
values_yt['field momentum in z'] = eps0 * np.sum(Ex * By - Ey * Bx) * dV
# Field energy in quarter of simulation domain
E2 = np.sum((Ex**2 + Ey**2 + Ez**2)*(y > 0)*(z < 0))
B2 = np.sum((Bx**2 + By**2 + Bz**2)*(y > 0)*(z < 0))
values_yt['field energy in quarter of simulation domain'] = 0.5 * dV * (E2 * eps0 + B2 / mu0)
# Max/min values of various grid quantities
values_yt['maximum of |Ex|'] = np.amax(np.abs(Ex))
values_yt['maximum of |Ey|'] = np.amax(np.abs(Ey))
values_yt['maximum of |Ez|'] = np.amax(np.abs(Ez))
values_yt['maximum of |Bx|'] = np.amax(np.abs(Bx))
values_yt['maximum of |By|'] = np.amax(np.abs(By))
values_yt['maximum of |Bz|'] = np.amax(np.abs(Bz))
values_yt['maximum of |E|'] = np.amax(np.sqrt(Ex**2 + Ey**2 + Ez**2))
values_yt['maximum of |B|'] = np.amax(np.sqrt(Bx**2 + By**2 + Bz**2))
values_yt['maximum of rho'] = np.amax(rho)
values_yt['minimum of rho'] = np.amin(rho)
values_yt['electrons: maximum of |rho|'] = np.amax(np.abs(rho_electrons))
values_yt['protons: maximum of |rho|'] = np.amax(np.abs(rho_protons))
values_yt['maximum of |B| from generic field reduction'] = np.amax(np.sqrt(Bx**2 + By**2 + Bz**2))
values_yt['minimum of x*Ey*Bz'] = np.amin(x*Ey*Bz)
#--------------------------------------------------------------------------------------------------
# Part 2: get results from reduced diagnostics (label '_rd')
#--------------------------------------------------------------------------------------------------
# Quantities computed from reduced diagnostics
values_rd = dict()
# Load data from output files
EFdata = np.genfromtxt('./diags/reducedfiles/EF.txt') # Field energy
EPdata = np.genfromtxt('./diags/reducedfiles/EP.txt') # Particle energy
PFdata = np.genfromtxt('./diags/reducedfiles/PF.txt') # Field momentum
PPdata = np.genfromtxt('./diags/reducedfiles/PP.txt') # Particle momentum
MFdata = np.genfromtxt('./diags/reducedfiles/MF.txt') # Field maximum
MRdata = np.genfromtxt('./diags/reducedfiles/MR.txt') # Rho maximum
NPdata = np.genfromtxt('./diags/reducedfiles/NP.txt') # Particle number
FR_Maxdata = np.genfromtxt('./diags/reducedfiles/FR_Max.txt') # Field Reduction using maximum
FR_Mindata = np.genfromtxt('./diags/reducedfiles/FR_Min.txt') # Field Reduction using minimum
FR_Integraldata = np.genfromtxt('./diags/reducedfiles/FR_Integral.txt') # Field Reduction using integral
# First index "1" points to the values written at the last time step
values_rd['field energy'] = EFdata[1][2]
values_rd['field energy in quarter of simulation domain'] = FR_Integraldata[1][2]
values_rd['particle energy'] = EPdata[1][2]
values_rd['electrons: particle energy'] = EPdata[1][3]
values_rd['protons: particle energy'] = EPdata[1][4]
values_rd['photons: particle energy'] = EPdata[1][5]
values_rd['mean particle energy'] = EPdata[1][6]
values_rd['electrons: mean particle energy'] = EPdata[1][7]
values_rd['protons: mean particle energy'] = EPdata[1][8]
values_rd['photons: mean particle energy'] = EPdata[1][9]
values_rd['field momentum in x'] = PFdata[1][2]
values_rd['field momentum in y'] = PFdata[1][3]
values_rd['field momentum in z'] = PFdata[1][4]
values_rd['particle momentum in x'] = PPdata[1][2]
values_rd['particle momentum in y'] = PPdata[1][3]
values_rd['particle momentum in z'] = PPdata[1][4]
values_rd['electrons: particle momentum in x'] = PPdata[1][5]
values_rd['electrons: particle momentum in y'] = PPdata[1][6]
values_rd['electrons: particle momentum in z'] = PPdata[1][7]
values_rd['protons: particle momentum in x'] = PPdata[1][8]
values_rd['protons: particle momentum in y'] = PPdata[1][9]
values_rd['protons: particle momentum in z'] = PPdata[1][10]
values_rd['photons: particle momentum in x'] = PPdata[1][11]
values_rd['photons: particle momentum in y'] = PPdata[1][12]
values_rd['photons: particle momentum in z'] = PPdata[1][13]
values_rd['mean particle momentum in x'] = PPdata[1][14]
values_rd['mean particle momentum in y'] = PPdata[1][15]
values_rd['mean particle momentum in z'] = PPdata[1][16]
values_rd['electrons: mean particle momentum in x'] = PPdata[1][17]
values_rd['electrons: mean particle momentum in y'] = PPdata[1][18]
values_rd['electrons: mean particle momentum in z'] = PPdata[1][19]
values_rd['protons: mean particle momentum in x'] = PPdata[1][20]
values_rd['protons: mean particle momentum in y'] = PPdata[1][21]
values_rd['protons: mean particle momentum in z'] = PPdata[1][22]
values_rd['photons: mean particle momentum in x'] = PPdata[1][23]
values_rd['photons: mean particle momentum in y'] = PPdata[1][24]
values_rd['photons: mean particle momentum in z'] = PPdata[1][25]
values_rd['maximum of |Ex|'] = MFdata[1][2]
values_rd['maximum of |Ey|'] = MFdata[1][3]
values_rd['maximum of |Ez|'] = MFdata[1][4]
values_rd['maximum of |E|'] = MFdata[1][5]
values_rd['maximum of |Bx|'] = MFdata[1][6]
values_rd['maximum of |By|'] = MFdata[1][7]
values_rd['maximum of |Bz|'] = MFdata[1][8]
values_rd['maximum of |B|'] = MFdata[1][9]
values_rd['maximum of rho'] = MRdata[1][2]
values_rd['minimum of rho'] = MRdata[1][3]
values_rd['electrons: maximum of |rho|'] = MRdata[1][4]
values_rd['protons: maximum of |rho|'] = MRdata[1][5]
values_rd['number of particles'] = NPdata[1][2]
values_rd['electrons: number of particles'] = NPdata[1][3]
values_rd['protons: number of particles'] = NPdata[1][4]
values_rd['photons: number of particles'] = NPdata[1][5]
values_rd['sum of weights'] = NPdata[1][6]
values_rd['electrons: sum of weights'] = NPdata[1][7]
values_rd['protons: sum of weights'] = NPdata[1][8]
values_rd['photons: sum of weights'] = NPdata[1][9]
values_rd['maximum of |B| from generic field reduction'] = FR_Maxdata[1][2]
values_rd['minimum of x*Ey*Bz'] = FR_Mindata[1][2]
#--------------------------------------------------------------------------------------------------
# Part 3: compare values from plotfiles and reduced diagnostics and print output
#--------------------------------------------------------------------------------------------------
error = dict()
tolerance = 5e-3 if single_precision else 1e-12
field_energy_tolerance = 0.3
# The comparison of field energies requires a large tolerance,
# because the field energy from the plotfiles is computed from cell-centered data,
# while the field energy from the reduced diagnostics is computed from (Yee) staggered data.
for k in values_yt.keys():
print()
print('values_yt[' + k + '] = ', values_yt[k])
print('values_rd[' + k + '] = ', values_rd[k])
error[k] = abs(values_yt[k] - values_rd[k]) / abs(values_yt[k])
print('relative error = ', error[k])
tol = field_energy_tolerance if (k == 'field energy') else tolerance
assert(error[k] < tol)
print()
test_name = os.path.split(os.getcwd())[1]
checksumAPI.evaluate_checksum(test_name, fn)
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