def test_get_cooling_step_name3(self):
"""Verify passing non-zero cooling step
"""
(coolingstep, numergbins) = read_alara_phtn.get_cooling_step_name( \
5, self.fr)
self.assertEqual(coolingstep, '1 y')
self.assertEqual(numergbins, 42)
def test_get_cooling_step_name1(self):
"""Verify passing 0 as cooling step
"""
(coolingstep, numergbins) = read_alara_phtn.get_cooling_step_name( \
0, self.fr)
self.assertEqual(coolingstep, 'shutdown')
self.assertEqual(numergbins, 42)
###########################
# Do step 2
print "Loading step one data file '{0}'".format(datafile)
smesh = ScdMesh.fromFile(datafile)
# Tagging mesh
print "Reading ALARA photon source '{0}'".format(phtn_src)
read_alara_phtn.read_to_h5m(phtn_src, smesh, isotope=opt_isotope, \
coolingstep=opt_cooling, retag=True, totals=True)
print "Saving photon source information to '{0}'".format(datafile)
smesh.imesh.save(datafile)
fr = open(phtn_src, 'r')
coolingstepstring = read_alara_phtn.get_cooling_step_name(opt_cooling, fr)[0]
fr.close()
print "Writing gammas file"
write_gammas.gen_gammas_file_from_h5m(smesh, sampling=opt_sampling, \
do_bias=opt_bias, cumulative=opt_cumulative, cust_ergbins=opt_ergs, \
coolingstep=coolingstepstring, isotope=opt_isotope)
# Create photon MCNP input file from neutron input if it doesn't exist already
if mcnp_p_problem:
if os.path.exists(mcnp_p_problem):
print "MCNP photon transport input file '{0}' already exists and will" \
" not be recreated.".format(mcnp_p_problem)
else:
def handle_phtn_data(datafile, phtn_src, opt_isotope, opt_cooling, \
opt_sampling, opt_bias, opt_cumulative, cust_ergbins,
resample, uni_resamp_all, gammas="gammas"):
"""Loads phtn_src data, tags this to mesh, and generates 'gammas' file.
Parameters
----------
datafile : string
Path to structured mesh file (e.g. .h5m file)
phtn_src : string
Path to phtn_src file
opt_isotope : string
The isotope identifier as listed in phtn_src file
opt_cooling : int or string
The cooling step, either as a numeric index (from 0) or a string
identifier as listed in phtn_src file
opt_sampling : ['v', 'u']
Type of sampling to generate the 'gammas' file for; v=voxel; u=uniform
opt_bias : boolean
If true, look for bias values on the mesh and include them in 'gammas'
opt_cumulative : boolean
If true, write energy bins' relative probabilities cumulatively
cust_ergbins : boolean
If true, look for custom energy bins on the mesh and include them in
'gammas'
resample : boolean
If true, 'r' flag is added to gammas, and resampling of particles
starting in void regions of voxels is enabled.
uni_resamp_all : boolean
If true, 'a' flag is added to gammas, and particles starting in void
regions of voxels, during uniform sampling, are resampled over the
entire problem, rather than resampling just the voxel. This has the
potential to result in an unfair game.
gammas : string (optional)
File name for 'gammas' file. Defaults to 'gammas'.
Returns
-------
mesh : ScdMesh object or iMesh.Mesh object
MOAB mesh object
Notes
-----
Only creates gammas file if mesh is an ScdMesh.
"""
print "Loading step one data file '{0}'".format(datafile)
try:
mesh = ScdMesh.fromFile(datafile)
except ScdMeshError:
mesh = iMesh.Mesh()
mesh.load(datafile)
# Tagging mesh
print "Reading ALARA photon source '{0}'".format(phtn_src)
read_alara_phtn.read_to_h5m(phtn_src, mesh, isotope=opt_isotope, \
coolingstep=opt_cooling, retag=True, totals=True)
print "Saving photon source information to '{0}'".format(datafile)
if isinstance(mesh, ScdMesh):
mesh.imesh.save(datafile)
with open(phtn_src, 'r') as fr:
try:
coolingstepstring = read_alara_phtn.get_cooling_step_name( \
opt_cooling, fr)[0]
except ValueError:
coolingstepstring = opt_cooling
print "Writing gammas file"
write_gammas.gen_gammas_file_from_h5m(mesh, outfile=gammas, \
sampling=opt_sampling, do_bias=opt_bias, \
cumulative=opt_cumulative, cust_ergbins=cust_ergbins, \
coolingstep=coolingstepstring, isotope=opt_isotope, \
resample=resample, uni_resamp_all=uni_resamp_all)
else:
mesh.save(datafile)
return mesh
