def test_1_pass_DnTcontrol(self):
"""Test that we can pass and access attributes of a DnTcontrol Python object
in C++.
"""
fncName = '('+__file__+') ' + sys._getframe().f_code.co_name + '():\n'
print('\ntest: ', fncName)
ctrl = DTcontrol_C()
# Run identifier
ctrl.title = "Test of argument-passing from Python to C++"
# Run author
ctrl.author = "tph"
ctrl.time = 0.0
ctrl.dt = 0.5
ctrl.n_timesteps = 19
# Pass a DT_control argument to C++:
test_so.function_with_DTcontrol_C_arg(ctrl)
return
### Physical constants m_e = 1.0 * MyPlasmaUnits_C.electron_mass q_e = 1.0 * MyPlasmaUnits_C.elem_charge #-# User input #-# numberOfParticles = 1000 numberOfTrajectories = 0 numberOfTrajectories = min(numberOfTrajectories, numberOfParticles) ctrl = DTcontrol_C() # Timestepping ctrl.n_timesteps = 100 # 100000 ctrl.dt = 4.0e-7 # # Initialize time counters ctrl.timeloop_count = 0 ctrl.time = 0.0 # Set random seed ctrl.random_seed = 1 np_m.random.seed(ctrl.random_seed) # Electric field control randomExternalElectricFieldAmplitude = 0.1 # Set the switches to empty dictionaries here if and only if they'll get set when the species are # defined below. The default is that all defined electric fields will be applied to all # species.
def test_1D_particle_source_region(self):
"""Set up a one-dimensional source region and display it.
"""
fncName = '(' + __file__ + ') ' + sys._getframe(
).f_code.co_name + '():\n'
print('\ntest: ', fncName, '(' + __file__ + ')')
## Set control variables
ctrl = DTcontrol_C()
ctrl.title = "test_ParticleGeneration.py:test_1D_particle_source_region"
ctrl.author = "tph"
ctrl.timeloop_count = 0
ctrl.time = 0.0
ctrl.dt = 1.0e-6
ctrl.n_timesteps = 1
ctrl.write_trajectory_files = False
pin = self.pin
pin.coordinate_system = 'cartesian_x'
pin.force_components = [
'x',
]
### Particle input
## Define electron species
charge = -1.0 * MyPlasmaUnits_C.elem_charge
mass = 1.0 * MyPlasmaUnits_C.electron_mass
dynamics = 'explicit'
electrons_S = ParticleSpecies_C('electrons', charge, mass, dynamics)
# Add the electrons to particle input
pin.particle_species = (electrons_S, )
## Make the particle storage array for all species.
particle_P = Particle_C(pin, print_flag=True)
## Give the name of the .py file containing special particle data (lists of
# particles, boundary conditions, source regions, etc.)
userParticlesModuleName = "UserParticles_1D"
# Import this module
userParticlesModule = im_m.import_module(userParticlesModuleName)
# particle_P.user_particles_module_name = userParticlesModuleName
particle_P.user_particles_class = userParticlesClass = userParticlesModule.UserParticleDistributions_C
### Mesh input for a 1D mesh
# Specify the mesh parameters
umi1D = UserMeshInput_C()
umi1D.pmin = df_m.Point(-10.0)
umi1D.pmax = df_m.Point(10.0)
umi1D.cells_on_side = (20, ) # Need the comma to indicate a tuple
### Create the 1D particle mesh and add to the Particle_C object
pmesh1D = UserMesh_C(umi1D,
compute_dictionaries=True,
compute_cpp_arrays=False,
compute_tree=True,
plot_flag=False)
particle_P.pmesh_M = pmesh1D
### Input for particle sources
# For each source:
# a. Define the source region
# b. Provide a particle-generating function
# c. Provide the species name and physical source parameters
# Create numpy storage needed below
driftVelocity = np_m.empty(particle_P.particle_dimension,
dtype=particle_P.precision)
## 1. Hot electron source on left side of the mesh
# a. Provide the species name and physical parameters
# for this source
speciesName = 'electrons'
# Check that this species has been defined
if speciesName in particle_P.species_names:
charge = particle_P.charge[speciesName]
mass = particle_P.mass[speciesName]
else:
print("The species", speciesName, "has not been defined")
sys.exit()
sourceDistributionType = 'functional'
# Compute a physical number-density creation rate for this source region
# The value should always be positive
chargeDensityRate = -1.0
# The timestep interval between calls to the creation function
timeStepInterval = 1
# Get number-density added per invocation
numberDensity = chargeDensityRate * timeStepInterval * ctrl.dt / charge
# Check for positivity
if numberDensity < 0:
print("Check number density for species", speciesName,
"is negative. Should be positive")
sys.exit()
# Compute a value for thermalSpeed from the temperature in eV
temperature_eV = 2.0
temp_joule = temperature_eV * MyPlasmaUnits_C.elem_charge
thermalSpeed = np_m.sqrt(2.0 * temp_joule / mass)
velocity_coordinate_system = 'x_y_z' # This sets the interpretation of the
# following values
# Set a drift velocity
vdrift_1 = 1.0
vdrift_2 = 2.0
vdrift_3 = 3.0
# driftVelocity = (vdrift_1, vdrift_2, vdrift_3)
driftVelocity[0] = vdrift_1
# The particle storage arrays have only a v[0] velocity component
# driftVelocity[1] = vdrift_2
# driftVelocity[2] = vdrift_3
# Set desired particles-per-cell
numberPerCell = 1
# Collect the parameters for this source in a dictionary
hotElectronParams = {
'species_name': speciesName,
'source_distribution_type': sourceDistributionType,
'number_density': numberDensity,
'thermal_speed': thermalSpeed,
'velocity_coordinate_system': velocity_coordinate_system,
'drift_velocity': driftVelocity,
'timestep_interval': timeStepInterval,
'number_per_cell': numberPerCell
}
# b. Name the particle-creation function.
maxwellianGenerator = particle_P.create_maxwellian_particles
# c. Source-region geometry
xmin = -9.0
xmax = -4.0
hotElectronsRegion = RectangularRegion_C(pmesh1D, xmin, xmax)
## 2. Background electrons over the whole mesh
## Specify one or more species to be generated in the 2nd region.
# a. Provide the species name and physical parameters for this source
# Note that this is the same species as in ## 1. above. It's not necessary
# for each source region to have a different species name.
speciesName = 'electrons'
# Check that this species has been defined above
if speciesName in particle_P.species_names:
charge = particle_P.charge[speciesName]
mass = particle_P.mass[speciesName]
else:
print("The species", speciesName, "has not been defined")
sys.exit()
sourceDistributionType = 'functional'
# Compute a value for numberDensity, the physical number-density
# creation rate for this source region.
# The value should always be positive.
chargeDensityRate = -0.1
# The timestep interval between calls to the creation function
timeStepInterval = 1
# Get charge-density per invocation
numberDensity = chargeDensityRate * timeStepInterval * ctrl.dt / charge
# Check for positivity
if numberDensity < 0:
print("Check number density for species", speciesName,
"is negative. Should be positive")
sys.exit()
# Compute a value for thermalSpeed
temperature_eV = 2.0
temp_joule = temperature_eV * MyPlasmaUnits_C.elem_charge
thermalSpeed = np_m.sqrt(2.0 * temp_joule / mass)
velocity_coordinate_system = 'x_y_z' # This sets the interpretation of the
# following values
# Set a drift velocity
vdrift_1 = 0.5
vdrift_2 = 1.5
vdrift_3 = 2.5
# driftVelocity = (vdrift_1, vdrift_2, vdrift_3)
# The particle storage arrays have only a v[0] velocity component
driftVelocity[0] = vdrift_1
# Set desired particles-per-cell
numberPerCell = 10
# Collect the parameters for this source in a dictionary
backgroundElectronParams = {
'species_name': speciesName,
'source_distribution_type': sourceDistributionType,
'number_density': numberDensity,
'thermal_speed': thermalSpeed,
'velocity_coordinate_system': velocity_coordinate_system,
'drift_velocity': driftVelocity,
'timestep_interval': timeStepInterval,
'number_per_cell': numberPerCell
}
# b. Name the first function that will generate particles in the above region:
# Just use same maxwellianGenerator function as above.
# c. Source geometry: in this case, it's the whole mesh:
wholeMesh = WholeMesh_C(pmesh1D)
## Put all the particle source data into a dictionary
# The dictionary keys are mnemonic source names.
# The dictionary values are lists of tuples giving the
# a. the physical source parameters.
# b. particle creation function and
# c. source region
## Note: the names here are source-region names, NOT species names!
particleSourceDict = {
'hot_electron_source':
(hotElectronParams, maxwellianGenerator, hotElectronsRegion),
'background_electron_source':
(backgroundElectronParams, maxwellianGenerator, wholeMesh),
}
particle_P.particle_source_dict = particleSourceDict
### Create input for a particle trajectory object
# Use an input object to collect initialization data for the trajectory object
self.trajin = TrajectoryInput_C()
self.trajin.maxpoints = None # Set to None to get every point
self.trajin.extra_points = 1 # Set to 1 to make sure one boundary-crossing can be
# accommodated. Set to a larger value if there are
# multiple boundary reflections.
self.trajin.explicit_dict = {
'names': [
'x',
'ux',
],
'formats': [np_m.float32] * 2
}
## Create the trajectory object and attach it to the particle object.
# No trajectory storage is created until particles
# with TRAJECTORY_FLAG on are encountered.
p_P = particle_P
traj_T = Trajectory_C(self.trajin, ctrl, p_P.charged_species,
p_P.neutral_species, p_P.species_index, p_P.mass,
p_P.charge)
particle_P.traj_T = traj_T
## Invoke the source functions and write out the particles
### Select output for particles
ctrl.particle_output_file = "particleGeneration.h5part"
ctrl.particle_output_interval = 1
ctrl.particle_output_attributes = ('species_index', 'x', 'ux',
'weight')
# Check these values
particle_P.check_particle_output_parameters(ctrl)
particle_P.add_more_particles(ctrl)
# Dump the particle data to a file
particle_P.initialize_particle_output_file(ctrl)
# Write the particle attributes
particle_P.write_particles_to_file(ctrl)
return
def test_3D_particle_migration(self):
"""Test particle migration across cells on a 3D mesh.
"""
fncName = '('+__file__+') ' + sys._getframe().f_code.co_name + '():\n'
print('\ntest: ', fncName)
ctrl = DTcontrol_C()
ctrl.time = 0.0
ctrl.dt = 0.5
ctrl.n_timesteps = 19
ctrl.MAX_FACET_CROSS_COUNT = 100
# Run for 3D mesh
self.particle_P.pmesh_M = self.pmesh3D_M
self.particle_P.initialize_particle_integration()
# Get the initial cell index of each particle.
self.particle_P.compute_mesh_cell_indices()
# Save the initial conditions (p_ic) for plotting
p_ic = []
sp = self.particle_P.neutral_species[0]
for ip in [0, 1]:
(pseg, offset) = self.particle_P.sap_dict[sp].get_segment_and_offset(ip)
p = pseg[offset].copy() # Have to make a copy! Otherwise you overwrite the only copy of the particle
p_ic.append(p)
# The expected final position and cell
# First particle
xsp0 = -9.5; ysp0 = -9.5; zsp0 = 0.0
vxsp0 = -2.0; vysp0 = 0.0; vzsp0 = 0.0
weight0 = 2.0
bitflag0 = 2
cell_index0 = 98
unique_ID0 = 0
crossings = 0
psp0 = (xsp0,ysp0,zsp0, vxsp0,vysp0,vzsp0, weight0, bitflag0, cell_index0, unique_ID0, crossings)
# Second particle
xsp1 = -9.5; ysp1 = -9.5; zsp1 = -9.5
vxsp1 = -2.0; vysp1 = -2.0; vzsp1 = -2.0
weight1 = 3.0
bitflag1 = 2
cell_index1 = 0
unique_ID1 = 0
crossings = 0
psp1 = (xsp1,ysp1,zsp1, vxsp1,vysp1,vzsp1, weight1, bitflag1, cell_index1, unique_ID1, crossings)
p_expected = (psp0, psp1)
# Integrate for n_timesteps
print("Advancing", self.particle_P.get_total_particle_count(), "particles for", ctrl.n_timesteps, "timesteps on a 3D mesh")
for istep in range(ctrl.n_timesteps):
# Advance each species for 1 timestep
for sp in self.particle_P.species_names:
self.particle_P.integrators[sp](sp, ctrl)
# Create a mesh plotter to display the trajectory (just the
# first and last positions)
plotTitle = os.path.basename(__file__) + ": " + sys._getframe().f_code.co_name + ": First & last positions"
if self.plot_results is True:
plotter=df_m.plot(self.particle_P.pmesh_M.mesh, title=plotTitle)
# Check the results
ncoords = self.particle_P.particle_dimension # number of particle coordinates to check
for sp in self.particle_P.neutral_species:
for ip in [0, 1]:
(pseg, offset) = self.particle_P.sap_dict[sp].get_segment_and_offset(ip)
getparticle = pseg[offset] # Retrieve the particle from the SAP.
if self.plot_results is True:
mplot_m.plot([p_ic[ip][0], getparticle[0]], [p_ic[ip][1], getparticle[1]], [p_ic[ip][2], getparticle[2]])
# print 'expected = ', p_expected[ip]
# print 'calculated = ', getparticle
# path = np_m.array([p_ic[ip][0], p_ic[ip][1], p_ic[ip][2], getparticle[0], getparticle[1], getparticle[2]])
# Replace this with a point plot:
# plotter.add_polygon(path)
for ic in range(ncoords):
self.assertAlmostEqual(p_expected[ip][ic], getparticle[ic], places=6, msg="Particle is not in correct position")
cell_index_position = -3
# print fncName, "expected cell =", p_expected[ip][cell_index_position], "computed cell =", getparticle[cell_index_position]
self.assertEqual(p_expected[ip][cell_index_position], getparticle[cell_index_position], msg="Particle is not in correct cell")
if self.plot_results is True:
mplot_m.show()
# yesno = raw_input("Just called show() in test_3D_particle_migration")
return
def test_1D_particle_migration(self):
"""Test particle migration across cells on a 1D mesh.
"""
fncName = '('+__file__+') ' + sys._getframe().f_code.co_name + '():\n'
print('\ntest: ', fncName)
# List all the possible spatial coordinates
# spatial_coordinates = ('x','y','z')
ctrl = DTcontrol_C()
# Run identifier
ctrl.title = "test_ParticleMigration.py:test_1D_particle_migration"
# Run author
ctrl.author = "tph"
ctrl.time = 0.0
ctrl.dt = 0.5
ctrl.n_timesteps = 19
ctrl.MAX_FACET_CROSS_COUNT = 100
# Run for 1D mesh
self.particle_P.pmesh_M = self.pmesh1D
self.particle_P.initialize_particle_integration()
# Get the initial cell index of each particle.
self.particle_P.compute_mesh_cell_indices()
### Put the expected ending results into the p_expected tuple ###
# First particle
xsp0 = -9.5; ysp0 = -9.5; zsp0 = 0.0
vxsp0 = -2.0; vysp0 = 0.0; vzsp0 = 0.0
weight0 = 2.0
bitflag0 = 2
cell_index0 = 0
unique_ID0 = 0
crossings = 0
psp0 = (xsp0,ysp0,zsp0, vxsp0,vysp0,vzsp0, weight0, bitflag0, cell_index0, unique_ID0, crossings)
# Second particle
xsp1 = -9.5; ysp1 = -9.5; zsp1 = -9.5
vxsp1 = -2.0; vysp1 = -2.0; vzsp1 = -2.0
weight1 = 3.0
bitflag1 = 2
cell_index1 = 0
unique_ID1 = 1
crossings = 0
psp1 = (xsp1,ysp1,zsp1, vxsp1,vysp1,vzsp1, weight1, bitflag1, cell_index1, unique_ID1, crossings)
p_expected = (psp0, psp1)
# Integrate for n_timesteps
print("Advancing", self.particle_P.get_total_particle_count(), "particles for", ctrl.n_timesteps, "timesteps on a 1D mesh")
for istep in range(ctrl.n_timesteps):
# Advance each species for 1 timestep
for sp in self.particle_P.neutral_species:
self.particle_P.integrators[sp](sp, ctrl)
# Check the results
ncoords = self.particle_P.particle_dimension # number of particle coordinates to check
for sp in self.particle_P.neutral_species:
for ip in [0, 1]:
(pseg, offset) = self.particle_P.sap_dict[sp].get_segment_and_offset(ip)
getparticle = pseg[offset] # Retrieve the particle from the SAP.
# print 'expected = ', p_expected[ip]
# print 'calculated = ', getparticle
for ic in range(ncoords):
self.assertAlmostEqual(p_expected[ip][ic], getparticle[ic], places=6, msg="Particle is not in correct position")
cell_index_position = -3
# print fncName, "expected cell =", p_expected[ip][cell_index_position], "computed cell =", getparticle[cell_index_position]
self.assertEqual(p_expected[ip][cell_index_position], getparticle[cell_index_position], msg="Particle is not in correct cell")
return
def test_1_electric_field_push_1step(self):
""" Check that the electric field push is correct.
Push test particles for 1 step on a 2D 1/4-circle mesh.
"""
fncName = '(' + __file__ + ') ' + sys._getframe(
).f_code.co_name + '():\n'
print('\ntest: ', fncName, '(' + __file__ + ')')
ctrl = DTcontrol_C()
ctrl.dt = 1.0e-5
ctrl.n_timesteps = 1
ctrl.MAX_FACET_CROSS_COUNT = 100
dt = ctrl.dt
# The expected results from ParticleNonuniformE.ods
# First electron
xp1 = 1.005609924
yp1 = 0.8131367827
zp1 = 0.0
vxp1 = -235314.728666394
vyp1 = -254562.042790713
vzp1 = 0.0
weight1 = 1.0
p1 = (xp1, yp1, vxp1, vyp1, weight1)
# Second electron
xp2 = -9.3684645671
yp2 = -10.1983686032
zp2 = 0.0
vxp2 = -1014628.2027
vyp2 = -1097618.606318
vzp2 = 0.0
weight2 = 3.0
p2 = (xp2, yp2, vxp2, vyp2, weight2)
p_expected = (p1, p2)
# species_names = self.particle_P.names
# first particle:
getp = self.particle_P.sap_dict['two_electrons'].get_item(0)
# print 'getp is a', type(getp)
# print('1st electron:', getp)
# second particle:
getparticle = self.particle_P.sap_dict['two_electrons'].get_item(1)
# print('2nd electron:', getparticle)
# Advance the particles one timestep
print("Moving", self.particle_P.get_total_particle_count(),
"particles for one timestep")
ctrl.time_step = 0
ctrl.time = 0.0
self.particle_P.advance_charged_particles_in_E_field(
ctrl, neg_E_field=self.neg_electric_field)
# Check the results
ncoords = self.particle_P.particle_dimension # number of particle coordinates to check
isp = 0
for sp in self.particle_P.species_names:
# print 'species count = ', self.particle_P.get_species_particle_count(sp)
if self.particle_P.get_species_particle_count(sp) == 0: continue
# Check that the first two particles in the array reaches the correct values
for ip in [0, 1]:
(pseg, offset
) = self.particle_P.sap_dict[sp].get_segment_and_offset(ip)
getparticle = pseg[offset]
# print('calculated = ', getparticle)
# print('expected = ', p_expected[ip])
for ic in range(ncoords):
# print "result:", getparticle[ic]/p_expected[ip][ic]
# Note: for different field solver, may have to reduce the places:
self.assertAlmostEqual(
getparticle[ic] / p_expected[ip][ic],
1.0,
places=6,
msg="Particle is not in correct position")
# print "ic", ic, "is OK"
isp += 1
return
def test_3_2D_r_theta_absorbing_boundary(self):
""" Check that particles are deleted correctly when they
cross a 2D absorbing boundary.
The particles are electrons and there's an applied Electric field
"""
fncName = '(' + __file__ + ') ' + sys._getframe(
).f_code.co_name + '():\n'
print('\ntest: ', fncName)
if os.environ.get('DISPLAY') is None:
plotFlag = False
else:
plotFlag = self.plot_results
## Set control variables
ctrl = DTcontrol_C()
# Run identifier
ctrl.title = "test_ParticleBoundaryConditions.py:test_3_2D_r_theta_absorbing_boundary"
# Run author
ctrl.author = "tph"
ctrl.timeloop_count = 0
ctrl.time = 0.0
# These are fast electrons, so the timestep is small
ctrl.dt = 1.0e-6
ctrl.n_timesteps = 14
ctrl.MAX_FACET_CROSS_COUNT = 100
ctrl.write_trajectory_files = False
### Particle species input
# Create an instance of the DTparticleInput class
pin = ParticleInput_C()
# Initialize particles
pin.precision = numpy.float64
pin.particle_integration_loop = 'loop-on-particles'
pin.coordinate_system = 'cartesian_xy'
pin.force_components = [
'x',
'y',
]
pin.force_precision = numpy.float64
pin.use_cpp_integrators = False # Use C++ code to advance particles.
# Specify the particle-species properties
# Define an electron species. Use this name later to initialize this species
# or to create a source of this species.
speciesName = 'trajelectrons'
charge = -1.0 * MyPlasmaUnits_C.elem_charge
mass = 1.0 * MyPlasmaUnits_C.electron_mass
dynamics = 'explicit'
trajelectrons_S = ParticleSpecies_C(speciesName, charge, mass,
dynamics)
# Add the electrons to particle input
pin.particle_species = (trajelectrons_S, )
## Make the particle storage array for all species.
particle_P = Particle_C(pin, print_flag=False)
## Give the name of the .py file containing special particle data (lists of
# particles, boundary conditions, source regions, etc.)
userParticlesModuleName = "UserParticles_2D_e"
# Import this module
userParticlesModule = im_m.import_module(userParticlesModuleName)
# particle_P.user_particles_module_name = userParticlesModuleName
particle_P.user_particles_class = userParticlesClass = userParticlesModule.UserParticleDistributions_C
### Add a ref to a Trajectory_C object to particle_P
# Create input object for trajectories
trajin = TrajectoryInput_C()
trajin.maxpoints = None # Set to None to get every point
trajin.extra_points = 1 # Set to 1 to make sure one boundary-crossing can be
# accommodated.
# Specify which particle variables to save ('step' and 't' are required). This has the
# form of a numpy dtype specification.
format_list_base = [int]
format_list = format_list_base + [numpy.float32] * 7
trajin.charged_dict = {
'names': ['step', 't', 'x', 'ux', 'y', 'uy', 'Ex', 'Ey'],
'formats': format_list
}
format_list = format_list_base + [numpy.float32] * 4
trajin.implicit_dict = {
'names': ['step', 't', 'x', 'ux', 'phi'],
'formats': format_list
}
format_list = format_list_base + [numpy.float32] * 5
trajin.neutral_dict = {
'names': ['step', 't', 'x', 'ux', 'y', 'uy'],
'formats': format_list
}
### Mesh and Fields input for the particle mesh.
## The mesh input
from UserMesh_y_Fields_FE2D_Module import UserMeshInput2DCirc_C
## The mesh to be created is in an existing file
umi = UserMeshInput2DCirc_C()
umi.mesh_file = 'mesh_quarter_circle_crossed.xml'
umi.particle_boundary_file = 'mesh_quarter_circle_crossed_Pbcs.xml'
# These are the boundary-name -> int pairs used to mark mesh facets:
rminIndx = 1
rmaxIndx = 2
thminIndx = 4
thmaxIndx = 8
particleBoundaryDict = {
'rmin': rminIndx,
'rmax': rmaxIndx,
'thmin': thminIndx,
'thmax': thmaxIndx,
}
umi.particle_boundary_dict = particleBoundaryDict
## Create the particle mesh
from UserMesh_y_Fields_FE2D_Module import UserMesh2DCirc_C
pmesh_M = UserMesh2DCirc_C(umi,
compute_dictionaries=True,
compute_cpp_arrays=False,
compute_tree=True,
plot_flag=self.plot_mesh)
# Add this to the particle object:
particle_P.pmesh_M = pmesh_M
## Get the electric field from an existing file
# The following value should correspond to the element degree
# used in the potential from which negE was obtained
phiElementDegree = 1
if phiElementDegree == 1:
# For linear elements, grad(phi) is discontinuous across
# elements. To represent this field, we need Discontinuous Galerkin
# elements.
electricFieldElementType = 'DG'
else:
electricFieldElementType = 'Lagrange'
# Create the negative electric field directly on the particle mesh
negElectricField = Field_C(particle_P.pmesh_M,
element_type=electricFieldElementType,
element_degree=phiElementDegree - 1,
field_type='vector')
file = df_m.File('negE_test_2_2D.xml')
file >> negElectricField.function
### Input for initial particles (i.e., particles present at t=0)
# Name the species (it should be in species_names above)
speciesName = 'trajelectrons'
# Check that this species has been defined above
if speciesName not in particle_P.species_names:
print("The species", speciesName, "has not been defined")
sys.exit()
initialDistributionType = 'listed'
# Check that there's a function listing the particles particles
printFlag = True
if hasattr(userParticlesClass, speciesName):
if printFlag:
print(fncName + "(DnT INFO) Initial distribution for",
speciesName, "is the function of that name in",
userParticlesClass)
# Write error message and exit if no distribution function exists
else:
errorMsg = fncName + "(DnT ERROR) Need to define a particle distribution function %s in UserParticle.py for species %s " % (
speciesName, speciesName)
sys.exit(errorMsg)
# Collect the parameters into a dictionary
# For a 'listed' type, there needs to be a function with the same name as the
# species in userParticlesClass
trajElectronParams = {
'species_name': speciesName,
'initial_distribution_type': initialDistributionType,
}
# The dictionary keys are mnemonics strings for identifying each set of
# initialized particles. They're not the names of particle species.
initialParticlesDict = {
'initial_trajelectrons': (trajElectronParams, ),
}
# Add the initialized particles to the Particle_C object
particle_P.initial_particles_dict = initialParticlesDict
## Particle boundary-conditions
# UserParticleBoundaryFunctions_C is where the facet-crossing callback
# functions are defined.
userPBndFnsClass = userParticlesModule.UserParticleBoundaryFunctions_C # abbreviation
# Make the particle-mesh boundary-conditions object and add it
# to the particle object.
spNames = particle_P.species_names
pmeshBCs = ParticleMeshBoundaryConditions_C(spNames,
pmesh_M,
userPBndFnsClass,
print_flag=False)
particle_P.pmesh_bcs = pmeshBCs
# The trajectory object can now be created and added to particle_P
p_P = particle_P
# p_P.traj_T = Trajectory_C(trajin, ctrl, p_P.explicit_species, p_P.implicit_species, p_P.neutral_species)
p_P.traj_T = Trajectory_C(trajin, ctrl, p_P.charged_species,
p_P.neutral_species, p_P.species_index,
p_P.mass, p_P.charge)
# Initialize the particles
printFlags = {}
for sp in p_P.species_names:
printFlags[sp] = False
p_P.initialize_particles(printFlags)
# Get the initial cell index of each particle.
p_P.compute_mesh_cell_indices()
p_P.initialize_particle_integration()
### Particle loop
print("Moving", p_P.get_total_particle_count(), "particles for",
ctrl.n_timesteps, "timesteps")
for istep in range(ctrl.n_timesteps):
# print("istep", istep)
if p_P.traj_T is not None:
# print 'p_P.traj_T.skip:', p_P.traj_T.skip
if istep % p_P.traj_T.skip == 0:
p_P.record_trajectory_data(ctrl.timeloop_count,
ctrl.time,
neg_E_field=negElectricField)
p_P.advance_charged_particles_in_E_field(
ctrl, neg_E_field=negElectricField)
ctrl.timeloop_count += 1
ctrl.time += ctrl.dt
# Record the LAST point on the particle trajectory
if p_P.traj_T is not None:
p_P.record_trajectory_data(ctrl.timeloop_count,
ctrl.time,
neg_E_field=negElectricField)
# Plot the trajectory onto the particle mesh
if self.plot_results is True:
mesh = p_P.pmesh_M.mesh
plotTitle = os.path.basename(
__file__) + ": " + sys._getframe().f_code.co_name + ": XY mesh"
holdPlot = True # Set to True to stop the plot from disappearing.
p_P.traj_T.plot_trajectories_on_mesh(
mesh, plotTitle, hold_plot=holdPlot
) # Plots trajectory spatial coordinates on top of the particle mesh
return
def test_1_2D_x_y_absorbing_boundary(self):
""" Check that particles are deleted correctly when they
cross an absorbing boundary on a 2D Cartesian mesh.
The particles are neutral H.
"""
fncName = '(' + __file__ + ') ' + sys._getframe(
).f_code.co_name + '():\n'
print('\ntest: ', fncName)
if os.environ.get('DISPLAY') is None:
plotFlag = False
else:
plotFlag = self.plot_mesh
ctrl = DTcontrol_C()
# Run identifier
ctrl.title = "test_ParticleBoundaryConditions.py:test_1_2D_x_y_absorbing_boundary"
# Run author
ctrl.author = "tph"
ctrl.timeloop_count = 0
ctrl.time = 0.0
ctrl.dt = 0.5
ctrl.n_timesteps = 100
ctrl.MAX_FACET_CROSS_COUNT = 100
ctrl.write_trajectory_files = False
# Create an instance of the DTparticleInput class
pin = ParticleInput_C()
# Settings common to all species
pin.precision = numpy.float64
pin.particle_integration_loop = 'loop-on-particles'
pin.coordinate_system = 'cartesian_xyz'
pin.force_precision = numpy.float64
pin.use_cpp_integrators = False # Use C++ code to advance particles.
# Specify the particle species properties
speciesName = 'neutral_H' # Use this name later to initialize this
# species or to create a source of this
# species.
charge = 0.0
mass = 1.0 * MyPlasmaUnits_C.AMU
dynamics = 'neutral'
neutralH_S = ParticleSpecies_C(speciesName, charge, mass, dynamics)
# Add the neutral hydrogen to particle input
pin.particle_species = (neutralH_S, )
## Make the particle storage array for all species.
particle_P = Particle_C(pin, print_flag=False)
# Provide the particle distributions for the above species
# This could be done more like how the mesh is specified:
# import UserParticles_3D as userParticlesModule
# Particles have a 3D/3D phase-space even though mesh is just 2D
userParticlesModuleName = 'UserParticles_3D'
# Import this module
infoMsg = "%s\tImporting %s" % (fncName, userParticlesModuleName)
print(infoMsg)
userParticlesModule = im_m.import_module(userParticlesModuleName)
# particle_P.user_particles_module_name = userParticlesModuleName
particle_P.user_particles_class = userParticlesClass = userParticlesModule.UserParticleDistributions_C
### Create a trajectory object and add it to particle_P
# Create input object for trajectories
trajin = TrajectoryInput_C()
trajin.maxpoints = None # Set to None to get every point
trajin.extra_points = 1 # Set to 1 to make sure one boundary-crossing can be
# accommodated.
# Specify which particle variables to save. This has the
# form of a numpy dtype specification.
name_list_base = ['step', 't'] # These are always recorded
format_list_base = [int, numpy.float32
] # Start off the format list with types for
# 'step' and 't'
charged_attributes = ['x', 'ux', 'y', 'uy', 'Ex', 'Ey']
format_list = format_list_base + [numpy.float32
] * len(charged_attributes)
trajin.charged_dict = {
'names': name_list_base + charged_attributes,
'formats': format_list
}
#implicit_attributes = ['x', 'ux', 'phi']
#format_list = format_list_base + [numpy.float32]*len(implicit_attributes)
# trajin.implicit_dict = {'names': name_list_base+implicit_attributes, 'formats': format_list}
neutral_attributes = ['x', 'ux', 'y', 'uy']
format_list = format_list_base + [numpy.float32
] * len(neutral_attributes)
trajin.neutral_dict = {
'names': name_list_base + neutral_attributes,
'formats': format_list
}
# Add a traj_T reference to the particle object
p_P = particle_P # abbreviation
#p_P.traj_T = Trajectory_C(trajin, ctrl, p_P.explicit_species, p_P.implicit_species, p_P.neutral_species)
p_P.traj_T = Trajectory_C(trajin, ctrl, p_P.charged_species,
p_P.neutral_species, p_P.species_index,
p_P.mass, p_P.charge)
## Mesh input for the particle mesh, including particle boundary conditions.
# Create a 2D Cartesian mesh to use for advancing the particles. The particles
# themselves are given 3D coordinates.
infoMsg = "%s\tImporting UserMeshInput_C from UserMesh_y_Fields_FE_XYZ_Module" % (
fncName)
print(infoMsg)
from UserMesh_y_Fields_FE_XYZ_Module import UserMeshInput_C
# 2D mesh input
umi2D = UserMeshInput_C()
(xmin, ymin) = (-10.0, -10.0)
(xmax, ymax) = (10.0, 10.0)
umi2D.pmin = df_m.Point(xmin, ymin)
umi2D.pmax = df_m.Point(xmax, ymax)
umi2D.cells_on_side = (4, 2)
# umi2D.diagonal = 'crossed'
# This could be automated, given a list of the boundary names: ['xmin', 'xmax', ...]
xminIndx = 1
xmaxIndx = 2
yminIndx = 4
ymaxIndx = 8
particleBoundaryDict = {
'xmin': xminIndx,
'xmax': xmaxIndx,
'ymin': yminIndx,
'ymax': ymaxIndx,
}
umi2D.particle_boundary_dict = particleBoundaryDict
## Create the 2D Cartesian mesh
infoMsg = "%s\tImporting UserMesh_C from UserMesh_y_Fields_FE_XYZ_Module" % (
fncName)
print(infoMsg)
from UserMesh_y_Fields_FE_XYZ_Module import UserMesh_C
plotTitle = os.path.basename(
__file__) + ": " + sys._getframe().f_code.co_name + ": XY mesh"
pmesh_M = UserMesh_C(umi2D,
compute_dictionaries=True,
compute_cpp_arrays=False,
compute_tree=True,
plot_flag=self.plot_mesh,
plot_title=plotTitle)
# Add this to the particle object:
# p_P.pmesh_M = pmesh_M
# 1. Attach the particle mesh to p_P.
# 2. Attach the Python particle movers.
# 3. Compute the cell-neighbors and facet-normals for the particle movers.
p_P.initialize_particle_mesh(pmesh_M)
### Input for initial particles (i.e., particles present at t=0)
# a. Name the species (it should be in species_names above)
speciesName = 'neutral_H'
# Check that this species has been defined above
if speciesName not in p_P.species_names:
print("The species", speciesName, "has not been defined")
sys.exit()
initialDistributionType = 'listed'
# Check that there's a function listing the particles particles
printFlag = True
if hasattr(userParticlesClass, speciesName):
if printFlag:
print(fncName + "(DnT INFO) Initial distribution for",
speciesName, "is the function of that name in",
userParticlesClass)
# Write error message and exit if no distribution function exists
else:
errorMsg = fncName + "(DnT ERROR) Need to define a particle distribution function %s in UserParticle.py for species %s " % (
speciesName, speciesName)
sys.exit(errorMsg)
# Collect the parameters into a dictionary
# The 'listed' type will expect a function with the same name as the species.
neutralHParams = {
'species_name': speciesName,
'initial_distribution_type': initialDistributionType,
}
# The dictionary keys are mnemonics for initialized particle distributions
initialParticlesDict = {
'initial_neutral_H': (neutralHParams, ),
}
# Add the initialized particles to the Particle_C object
p_P.initial_particles_dict = initialParticlesDict
# Particle boundary-conditions
# UserParticleBoundaryFunctions_C is where the facet-crossing callback
# functions are defined by the user.
# Make an instance of UserParticleBoundaryFunctions_C
userPBndFns = userParticlesModule.UserParticleBoundaryFunctions_C # abbreviation
# Make the particle-mesh boundary-conditions object and add it to the
# particle object. The user has to supply the facet-crossing callback
# functions in the UserParticleBoundaryFunctions_C object above.
spNames = p_P.species_names
pmeshBCs = ParticleMeshBoundaryConditions_C(spNames,
pmesh_M,
userPBndFns,
print_flag=False)
p_P.pmesh_bcs = pmeshBCs
# Create the initial particles
printFlags = {}
for sp in p_P.species_names:
printFlags[sp] = True
p_P.initialize_particles(printFlags)
# Get the initial cell index of each particle.
# Should this be something the pmesh computes? No: pmesh computes the
# index of a single particle. It doesn't know the particle storage
# infrastructure.
p_P.compute_mesh_cell_indices()
p_P.initialize_particle_integration()
# Advance the particles for n_timesteps
print("Moving", p_P.get_total_particle_count(), "particles for",
ctrl.n_timesteps, "timesteps")
for istep in range(ctrl.n_timesteps):
if p_P.traj_T is not None:
if istep % p_P.traj_T.skip == 0:
p_P.record_trajectory_data(ctrl.timeloop_count, ctrl.time)
p_P.advance_neutral_particles(ctrl)
ctrl.timeloop_count += 1
ctrl.time += ctrl.dt
# Record the LAST point on the particle trajectory
if p_P.traj_T is not None:
p_P.record_trajectory_data(ctrl.timeloop_count, ctrl.time)
# Plot the trajectory onto the particle mesh
if self.plot_results is True:
mesh = p_P.pmesh_M.mesh
plotTitle = os.path.basename(
__file__) + ": " + sys._getframe().f_code.co_name + ": XY mesh"
holdPlot = True # Set to True to stop the plot from disappearing.
p_P.traj_T.plot_trajectories_on_mesh(
mesh, plotTitle, hold_plot=holdPlot
) # Plots trajectory spatial coordinates on top of the particle mesh
return
def test_3D_particle_migration(self):
"""Test particle migration across cells on a 3D mesh.
"""
fncName = '(' + __file__ + ') ' + sys._getframe(
).f_code.co_name + '():\n'
print('\ntest: ', fncName)
ctrl = DTcontrol_C()
# Run identifier
ctrl.title = "test_ParticleMigration.py:test_3D_particle_migration"
# Run author
ctrl.author = "tph"
ctrl.time = 0.0
ctrl.timeloop_count = 0
ctrl.dt = 0.5
ctrl.n_timesteps = 19
ctrl.MAX_FACET_CROSS_COUNT = 100
# Run for 3D mesh
# 1. Attach the particle mesh to particle_P
# 2. Attach the C++ particle movers.
# 3. Compute the cell-neighbors and facet-normals for the particle movers.
self.particle_P.initialize_particle_mesh(self.pmesh3D)
# Attach the particle integrators
self.particle_P.initialize_particle_integration()
### Particle boundary-conditions
# See the TODO of 4apr20 for redoing this.
# Make a dictionary associating the above-named boundaries of the particle mesh with
# user-supplied call-back functions.
# First, we need to make a UserParticleBoundaryFunctions_... object, which
# contains the boundary-crossing callback functions.
spNames = self.particle_P.species_names
# Import C++ particle boundary-conditions
userParticleBoundaryFunctionsSOlibName = "user_particle_boundary_functions_cartesian_xyz_solib"
if userParticleBoundaryFunctionsSOlibName not in sys.modules:
infoMsg = "%s\t\"Importing %s\"" % (
fncName, userParticleBoundaryFunctionsSOlibName)
print(infoMsg)
userParticleBoundaryFunctionsSOlib = im_m.import_module(
userParticleBoundaryFunctionsSOlibName)
# Call the constructor to make a UserParticleBoundaryFunctions_... object
userPBndFns = userParticleBoundaryFunctionsSOlib.UserParticleBoundaryFunctions(
self.particle_P.position_coordinates)
# Create the map from mesh facets to particle callback functions:
pmeshBCs = self.particle_P.particle_solib.ParticleMeshBoundaryConditions(
spNames, self.particle_P.pmesh_M, userPBndFns, print_flag=False)
# Add pmeshBCs to the Particle_C object
self.particle_P.pmesh_bcs = pmeshBCs
# cell_dict{} is needed by the Python version of is_inside_cell(), which is
# used in compute_mesh_cell_indices() below.
self.particle_P.pmesh_M.compute_cell_dict()
# Get the initial cell index of each particle.
self.particle_P.compute_mesh_cell_indices()
# Save the initial conditions (p_ic) for plotting
p_ic = []
sp = self.particle_P.neutral_species[0]
for ip in [0, 1]:
(pseg,
offset) = self.particle_P.sap_dict[sp].get_segment_and_offset(ip)
p = pseg[offset].copy(
) # Have to make a copy! Otherwise you overwrite the only copy of the particle
p_ic.append(p)
# The expected final position and cell index
# First particle
xsp0 = -9.5
ysp0 = -9.5
zsp0 = 0.0
vxsp0 = -2.0
vysp0 = 0.0
vzsp0 = 0.0
weight0 = 2.0
bitflag0 = 2
cell_index0 = 98
unique_ID0 = 0
crossings = 0
psp0 = (xsp0, ysp0, zsp0, vxsp0, vysp0, vzsp0, weight0, bitflag0,
cell_index0, unique_ID0, crossings)
# Second particle
xsp1 = -9.5
ysp1 = -9.5
zsp1 = -9.5
vxsp1 = -2.0
vysp1 = -2.0
vzsp1 = -2.0
weight1 = 3.0
bitflag1 = 2
cell_index1 = 0
unique_ID1 = 0
crossings = 0
psp1 = (xsp1, ysp1, zsp1, vxsp1, vysp1, vzsp1, weight1, bitflag1,
cell_index1, unique_ID1, crossings)
p_expected = (psp0, psp1)
# Integrate for n_timesteps
print("Advancing", self.particle_P.get_total_particle_count(),
"particles for", ctrl.n_timesteps, "timesteps on a 3D mesh")
for istep in range(ctrl.n_timesteps):
self.particle_P.advance_neutral_particles(ctrl)
# Create a mesh plotter to display the trajectory (just the
# first and last positions)
if self.plot_results is True:
plotTitle = os.path.basename(__file__) + ": " + sys._getframe(
).f_code.co_name + ": First & last positions"
plotter = df_m.plot(self.particle_P.pmesh_M.mesh, title=plotTitle)
# Check the results
ncoords = self.particle_P.particle_dimension # number of particle coordinates to check
for sp in self.particle_P.neutral_species:
for ip in [0, 1]:
(pseg, offset
) = self.particle_P.sap_dict[sp].get_segment_and_offset(ip)
getparticle = pseg[
offset] # Retrieve the particle from the SAP.
if self.plot_results is True:
mplot_m.plot([p_ic[ip][0], getparticle[0]],
[p_ic[ip][1], getparticle[1]],
[p_ic[ip][2], getparticle[2]])
# print 'expected = ', p_expected[ip]
# print 'calculated = ', getparticle
# path = np_m.array([p_ic[ip][0], p_ic[ip][1], p_ic[ip][2], getparticle[0], getparticle[1], getparticle[2]])
# Replace this with a point plot:
# plotter.add_polygon(path)
for ic in range(ncoords):
self.assertAlmostEqual(
p_expected[ip][ic],
getparticle[ic],
places=6,
msg="Particle is not in correct position")
cell_index_position = -3
# print (fncName, "expected cell =", p_expected[ip][cell_index_position], ", computed cell =", getparticle[cell_index_position])
self.assertEqual(p_expected[ip][cell_index_position],
getparticle[cell_index_position],
msg="Particle is not in correct cell")
if self.plot_results is True:
mplot_m.show()
# yesno = raw_input("Just called show() in test_3D_particle_migration")
return
def test_1D_particle_migration(self):
"""Test that we can call a C++ function to push neutral particles.
Initial particle positions are in UserParticles_3D.py.
The first particle moves in -x only, from 9.5 to -9.5.
The first particle starts at:
(x0, y0, z0) = (9.5, -9.5, 0.0), with velocity:
(ux0, uy0, uz0) = (-2.0, 0.0, 0.0)
It moves to (-9.5, -9.5, 0.0)
The second particle starts at
(x1, y1, z1) = (9.5, 9.5, 9.5)
(ux1, uy1, uz1) = (-2.0, -2.0, -2.0)
It moves in -x, -y, -z to the opposite corner.
"""
fncName = '(' + __file__ + ') ' + sys._getframe(
).f_code.co_name + '():\n'
print('\ntest: ', fncName)
ctrl = DTcontrol_C()
# Run identifier
ctrl.title = "1D particle advance using C++"
# Run author
ctrl.author = "tph"
ctrl.time = 0.0
ctrl.timeloop_count = 0
ctrl.dt = 0.5
ctrl.n_timesteps = 19
ctrl.MAX_FACET_CROSS_COUNT = 100
# Run on a 1D mesh
# 1. Attach the particle mesh to particle_P
# 2. Attach the C++ particle movers.
# 3. Compute the cell-neighbors and facet-normals for the particle movers.
self.particle_P.initialize_particle_mesh(self.pmesh1D)
# Attach the particle integrators
self.particle_P.initialize_particle_integration()
### Particle boundary-conditions
# See the TODO of 4apr20 for redoing this.
# Make a dictionary associating the above-named boundaries of the particle mesh with
# user-supplied call-back functions.
# First, we need to make a UserParticleBoundaryFunctions_... object, which
# contains the boundary-crossing callback functions.
spNames = self.particle_P.species_names
# Import C++ particle boundary-conditions
userParticleBoundaryFunctionsSOlibName = "user_particle_boundary_functions_cartesian_xyz_solib"
if userParticleBoundaryFunctionsSOlibName not in sys.modules:
infoMsg = "%s\t\"Importing %s\"" % (
fncName, userParticleBoundaryFunctionsSOlibName)
print(infoMsg)
userParticleBoundaryFunctionsSOlib = im_m.import_module(
userParticleBoundaryFunctionsSOlibName)
# Call the constructor to make a UserParticleBoundaryFunctions_... object
userPBndFns = userParticleBoundaryFunctionsSOlib.UserParticleBoundaryFunctions(
self.particle_P.position_coordinates)
# Create the map from mesh facets to particle callback functions:
pmeshBCs = self.particle_P.particle_solib.ParticleMeshBoundaryConditions(
spNames, self.particle_P.pmesh_M, userPBndFns, print_flag=False)
# Add pmeshBCs to the Particle_C object
self.particle_P.pmesh_bcs = pmeshBCs
# Get the initial cell index of each particle.
# Note: this could be moved down closer to the beginning of the
# particle-advance loop.
# cell_dict{} is needed by the Python version of is_inside_cell(), which is
# used in compute_mesh_cell_indices() below.
self.particle_P.pmesh_M.compute_cell_dict()
self.particle_P.compute_mesh_cell_indices()
### Put the expected ending results for the two particles into the p_expected tuple ###
# First particle
xsp0 = -9.5
ysp0 = -9.5
zsp0 = 0.0
xsp00 = -8.5
ysp00 = -9.5
zsp00 = 0.0
vxsp0 = -2.0
vysp0 = 0.0
vzsp0 = 0.0
weight0 = 2.0
bitflag0 = 2
cell_index0 = 0
unique_ID0 = 0
crossings = 0
psp0 = (xsp0, ysp0, zsp0, xsp00, ysp00, zsp00, vxsp0, vysp0, vzsp0,
weight0, bitflag0, cell_index0, unique_ID0, crossings)
# Second particle
xsp1 = -9.5
ysp1 = -9.5
zsp1 = -9.5
xsp10 = -8.5
ysp10 = -8.5
zsp10 = -8.5
vxsp1 = -2.0
vysp1 = -2.0
vzsp1 = -2.0
weight1 = 3.0
bitflag1 = 2
cell_index1 = 0
unique_ID1 = 1
crossings = 0
psp1 = (xsp1, ysp1, zsp1, xsp10, ysp10, zsp10, vxsp1, vysp1, vzsp1,
weight1, bitflag1, cell_index1, unique_ID1, crossings)
p_expected = (psp0, psp1)
#
# Move the particles and check the final positions
#
speciesName = 'neutral_H'
sap = self.particle_P.sap_dict[
speciesName] # segmented array for this species
# Print the cell indices
# print("cell_vertices_dict = ", self.particle_P.pmesh_M.cell_vertices_dict)
# Integrate for n_timesteps
print("Advancing", self.particle_P.get_total_particle_count(),
"particles for", ctrl.n_timesteps, "timesteps on a 1D mesh")
for istep in range(ctrl.n_timesteps):
# print(fncName, "istep:", istep)
self.particle_P.advance_neutral_particles(ctrl)
# Check the results
ncoords = self.particle_P.particle_dimension # number of particle coordinates to check
for sp in self.particle_P.neutral_species:
for ip in [0, 1]:
# getparticle = self.particle_P.sap_dict[sp].get(ip)
# Instead of .get(), retrieve the particle structure using the returned Numpy array it's in.
(pseg, offset
) = self.particle_P.sap_dict[sp].get_segment_and_offset(ip)
getparticle = pseg[
offset] # Retrieve the particle from the SAP.
# print('expected = ', p_expected[ip])
# print('calculated = ', getparticle)
for ic in range(2 * ncoords):
self.assertAlmostEqual(
p_expected[ip][ic],
getparticle[ic],
places=6,
msg="Particle is not in correct position")
cell_index_position = -3
# print fncName, "expected cell =", p_expected[ip][cell_index_position], "computed cell =", getparticle[cell_index_position]
self.assertEqual(p_expected[ip][cell_index_position],
getparticle[cell_index_position],
msg="Particle is not in correct cell")
return