def __init__(self,config_filepath=None,config=None,section="field parameters",
time_dependent_function=None, scale=1.0, normalize=None, **kwargs):
"""
Loads the config_filepath and puts the elements into the objects attributes.
Also loads and saves the fields.
Args:
config_filepath: filepath to the config file for the field. Either this or the
config object needs to be present.
config: A config parser object. Allows the reading of this data from an existent
config parser object.
section: The name of the section in the filepath with the field info. Default
is field parameters.
Attributes:
fields: A dict first pointing to the type of field (i.e. electric
or magnetic) then pointing to components of the field.
stepsize: A dict containing the stepsize of each of the coordinates key
by the coordinate.
number_of_steps: A dict containing the number of steps of each of the coordinates key
by the coordinate.
zmin: The minimum value of the z coordinate in the filepath.
zmax: The max z in the field.
zlen: The length over which the field is applied.
current_position: The position of the center of mass of the pulse
to be used if a distance needs to be calculated.
time_dependent_function: An option function callback (function is a function of top.time)
that can add time dependence to the field. Default is no such function.
scale: An option that provides a hook to scale the field(s) being loaded.
"""
if config_filepath is None and config is None:
raise Exception("Either the config_filepath needs to be specified or a config parser object " +
"needs to be passed to the init function.")
if config is None:
config = ConfigParser()
config.read(config_filepath)
self.config = config
self.section = section
self.xmin = config.get(section,"xmin")
self.ymin = config.get(section,"ymin")
self.zmin = config.get(section,"zmin")
self.zmax = config.get(section,"zmax")
self.zlen = config.get(section,"zlen")
self.stepsize = {}
self.number_of_steps = {}
self.fields = {}
for option in config.options(section):
if option.startswith("d"):
self.stepsize[option.replace("d","")] = config.get(section,option)
if option.startswith("n"):
self.number_of_steps[option.replace("n","")] = config.get(section,option)
if option.endswith("_pickled_field"):
field_type = option.replace("_pickled_field","")
self.fields[field_type] = pickle.load( open(config.get(section,option), "rb") )
for component in self.fields[field_type]:
self.fields[field_type][component] = scale*np.array(self.fields[field_type][component],order="FORTRAN")
self.time_dependent_function = time_dependent_function
def __init__(self,config_filepath=None,config=None,section="field parameters",
time_dependent_function=None, **kwargs):
"""
Loads the config_filepath and puts the elements into the objects attributes.
Also loads and saves the fields.
Args:
config_filepath: filepath to the config file for the field. Either this or the
config object needs to be present.
config: A config parser object. Allows the reading of this data from an existent
config parser object.
section: The name of the section in the filepath with the field info. Default
is field parameters.
Attributes:
fields: A dict first pointing to the type of field (i.e. electric
or magnetic) then pointing to components of the field.
stepsize: A dict containing the stepsize of each of the coordinates key
by the coordinate.
number_of_steps: A dict containing the number of steps of each of the coordinates key
by the coordinate.
zmin: The minimum value of the z coordinate in the filepath.
zmax: The max z in the field.
zlen: The length over which the field is applied.
current_position: The position of the center of mass of the pulse
to be used if a distance needs to be calculated.
time_dependent_function: An option function callback (function is a function of top.time)
that can add time dependence to the field. Default is no such function.
"""
if config_filepath is None and config is None:
raise Exception("Either the config_filepath needs to be specified or a config parser object " +
"needs to be passed to the init function.")
if config is None:
config = ConfigParser()
config.read(config_filepath)
self.config = config
self.section = section
self.xmin = config.get(section,"xmin")
self.ymin = config.get(section,"ymin")
self.zmin = config.get(section,"zmin")
self.zmax = config.get(section,"zmax")
self.zlen = config.get(section,"zlen")
self.stepsize = {}
self.number_of_steps = {}
self.fields = {}
for option in config.options(section):
if option.startswith("d"):
self.stepsize[option.replace("d","")] = config.get(section,option)
if option.startswith("n"):
self.number_of_steps[option.replace("n","")] = config.get(section,option)
if option.endswith("_pickled_field"):
field_type = option.replace("_pickled_field","")
self.fields[field_type] = pickle.load( open(config.get(section,option), "rb") )
for component in self.fields[field_type]:
self.fields[field_type][component] = np.array(self.fields[field_type][component],order="FORTRAN")
self.time_dependent_function = time_dependent_function
from diagnostics.phase_volume import dump_phase_volume
from diagnostics.steves_uem_diagnostics import steves_plots, electric_potential_plots
from discrete_fourspace.mesh import get_supremum_index
from injectors.injector_classes import ElectronInjector
from injectors.steves_uem_injection import steves_injectelectrons
from class_and_config_conversion import set_attributes_with_config_section
from moving_grid.moving_classes import SyncToCOM
from warp import *
# from histplot import *
# Invoke setup routine: needed to created a cgm file for plots
setup()
# Load the config file and parameters from config file.
config = MyConfigParser()
config.read(args.config_file)
adv_dt = array(config.get("Simulation parameters", "adv_dt")) # Numpy array of dts
adv_steps = array(config.get("Simulation parameters", "adv_steps")) # Number of steps for each dt
# Mesh size
dx = config.get("Simulation parameters", "dx")
dz = config.get("Simulation parameters", "dz")
xmax = config.get("Simulation parameters", "xmax")
zmin = config.get("Simulation parameters", "zmin")
zmax = config.get("Simulation parameters", "zmax")
# When to do diagnostics
diagnostic_time_interval = config.get("Simulation parameters", "diagnostic_time_interval")
# Load the parameters for the w3d and top objects from the config.
set_attributes_with_config_section(top, config, "top parameters", {",": parse_key_as_numpy_array})
from diagnostics.diagnostic_classes import DiagnosticsByTimes, DumpBySteps
from diagnostics.phase_volume import dump_phase_volume
from diagnostics.steves_uem_diagnostics import steves_plots, electric_potential_plots
from discrete_fourspace.mesh import get_supremum_index
from injectors.injector_classes import ElectronInjector
from injectors.steves_uem_injection import steves_injectelectrons
from injectors.io import photoemission_loader
from photoemission_support import photoemission_parameters, handle_cathode_and_anode
from warp import *
#from histplot import *
# Invoke setup routine: needed to created a cgm file for plots
setup()
#Load the config file and parameters from config file.
config = MyConfigParser()
config.read(args.config_file)
#Load the parameters into the warp objects and register solver
top, w3d, f3d, adv_dt, adv_steps = photoemission_parameters(
config, args, top, w3d, f3d)
solver = get_solver("wrz", top, w3d)
registersolver(solver)
#Install the conductor elements
conductor_elements = handle_cathode_and_anode(config, args.extraction_field,
args.potential)
for conductor_element in conductor_elements:
installconductor(
conductor_element) # install conductors into the warp framework
print "Installed " + str(conductor_element)
from injectors.steves_uem_injection import steves_injectelectrons
from class_and_config_conversion import set_attributes_with_config_section
from warp import *
#from histplot import *
# Invoke setup routine: needed to created a cgm file for plots
#Handle the output path and setup of the plots.
if args.prefix is None:
prefix, ext = os.path.splitext(args.config_file)
else:
prefix = args.prefix
setup(prefix=prefix, cgmlog=0)
#Load the config file and parameters from config file.
config = MyConfigParser()
config.read(args.config_file)
adv_dt = array(config.get("Simulation parameters",
"adv_dt")) # Numpy array of dts
adv_steps = array(config.get("Simulation parameters",
"adv_steps")) #Number of steps for each dt
#Mesh size
dx = config.get("Simulation parameters", "dx")
dz = config.get("Simulation parameters", "dz")
xmax = config.get("Simulation parameters", "xmax")
zmin = config.get("Simulation parameters", "zmin")
zmax = config.get("Simulation parameters", "zmax")
#When to do diagnostics
diagnostic_time_interval = config.get("Simulation parameters",
"diagnostic_time_interval")
