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import numpy as np
from pywarpx import picmi
#from warp import picmi
constants = picmi.constants
nx = 32
ny = 32
nz = 32
xmin = -2.
xmax = +2.
ymin = -2.
ymax = +2.
zmin = -2.
zmax = +2.
number_sim_particles = 32768
total_charge = 8.010883097437485e-07
beam_rms_size = 0.25
electron_beam_divergence = -0.04*constants.c
em_order = 3
grid = picmi.Cartesian3DGrid(number_of_cells = [nx, ny, nz],
lower_bound = [xmin, ymin, zmin],
upper_bound = [xmax, ymax, zmax],
lower_boundary_conditions = ['periodic', 'periodic', 'open'],
upper_boundary_conditions = ['periodic', 'periodic', 'open'],
warpx_max_grid_size=16)
solver = picmi.ElectromagneticSolver(grid = grid,
cfl = 1.,
stencil_order=[em_order,em_order,em_order])
electron_beam = picmi.GaussianBunchDistribution(n_physical_particles = total_charge/constants.q_e,
rms_bunch_size = [beam_rms_size, beam_rms_size, beam_rms_size],
velocity_divergence = [electron_beam_divergence, electron_beam_divergence, electron_beam_divergence])
proton_beam = picmi.GaussianBunchDistribution(n_physical_particles = total_charge/constants.q_e,
rms_bunch_size = [beam_rms_size, beam_rms_size, beam_rms_size])
electrons = picmi.Species(particle_type='electron', name='electrons', initial_distribution=electron_beam)
protons = picmi.Species(particle_type='proton', name='protons', initial_distribution=proton_beam)
sim = picmi.Simulation(solver = solver,
max_steps = 1000,
verbose = 1,
warpx_plot_int = 8,
warpx_current_deposition_algo = 'direct')
sim.add_species(electrons, layout=picmi.PseudoRandomLayout(n_macroparticles=number_sim_particles))
sim.add_species(protons, layout=picmi.PseudoRandomLayout(n_macroparticles=number_sim_particles))
# write_inputs will create an inputs file that can be used to run
# with the compiled version.
sim.write_input_file(file_name = 'inputs_from_PICMI')
# Alternatively, sim.step will run WarpX, controlling it from Python
#sim.step()
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