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#!/usr/bin/env python3
from pywarpx import picmi
# Number of time steps
max_steps = 3
# Number of cells
nx = 128
ny = 32
nz = 128
# Physical domain
xmin = 0.
xmax = 4.
ymin = 0.
ymax = 4.
zmin = 0.
zmax = 4.
# Create grid
grid = picmi.Cartesian3DGrid(
number_of_cells=[nx, ny, nz],
warpx_max_grid_size=32,
lower_bound=[xmin, ymin, zmin],
upper_bound=[xmax, ymax, zmax],
lower_boundary_conditions=['periodic', 'periodic', 'periodic'],
upper_boundary_conditions=['periodic', 'periodic', 'periodic']
)
# Electromagnetic solver
solver = picmi.ElectromagneticSolver(
grid=grid,
method='Yee',
cfl=0.99999
)
# Particles
electrons = picmi.Species(
particle_type='electron',
name='electrons',
initial_distribution=picmi.UniformDistribution(
density=1e14,
rms_velocity=[0]*3,
upper_bound=[xmax, ymax, 1.0]
)
)
layout = picmi.GriddedLayout(
n_macroparticle_per_cell=[1, 1, 1], grid=grid
)
# Reduced diagnostic
reduced_diags = []
reduced_diags.append(picmi.ReducedDiagnostic(
diag_type='LoadBalanceCosts',
period=1,
name='LBC'
))
reduced_diags.append(picmi.ReducedDiagnostic(
diag_type='FieldReduction',
period=1,
name='FR',
reduction_type='Maximum',
reduced_function = 'Ez'
))
reduced_diags.append(picmi.ReducedDiagnostic(
diag_type='ParticleHistogram',
period=1,
name='PH',
species = electrons,
bin_number = 10,
bin_min=0.,
bin_max = xmax,
normalization = 'unity_particle_weight',
histogram_function = 'x'
))
# Diagnostic
diag = picmi.FieldDiagnostic(
grid=grid,
period=3,
write_dir='.',
warpx_file_prefix='Python_reduced_diags_loadbalancecosts_timers_plt'
)
# Set up simulation
sim = picmi.Simulation(
solver=solver,
max_steps=max_steps,
verbose=1,
particle_shape=1,
warpx_current_deposition_algo='esirkepov',
warpx_field_gathering_algo='energy-conserving',
warpx_load_balance_intervals=2
)
# Add species
sim.add_species(electrons, layout=layout)
# Add reduced diagnostics
for reduced_diag in reduced_diags:
sim.add_diagnostic(reduced_diag)
# Add diagnostics
sim.add_diagnostic(diag)
# Advance simulation until last time step
# sim.write_input_file("test_input")
sim.step(max_steps)
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