def convertF0(self):
onlyfiles = [ f for f in os.listdir(self.csdir) if (os.path.isfile(os.path.join(self.csdir,f)) & f.endswith(self.ending)) ]
for datafile in onlyfiles:
print "====== Loading JENDL (alpha,n) File: " + datafile
endfeval = eva(os.path.join(self.csdir, datafile), verbose = False)
endfeval.read()
nuc = nucname.zzzaaa_to_id(endfeval.target['ZA'])
elname = elements[nucname.znum(nuc)].name
elname = elname.capitalize()
nuclidefilename = str(nucname.znum(nuc)) + "_" + str(nucname.anum(nuc)) + "_" + elname
if (endfeval.target['isomeric_state'] != 0):
print "Found nuclide in exited state, will add _<state> to filename"
nuclidefilename = nuclidefilename + "_" + str(endfeval.target['isomeric_state'])
mf3data = {}
fullfilename = "Inelastic/F01/" + nuclidefilename
fullfilename = self.outputdir + fullfilename
if not os.path.exists(os.path.dirname(fullfilename)):
os.makedirs(os.path.dirname(fullfilename))
print os.path.dirname(fullfilename) + " has been created (did not exist before)."
outf = open(fullfilename, 'w')
for mt in [4] + range(50, 92):
if(mt in endfeval.reactions):
mf3data[mt] = endfeval.reactions[mt]
if(mf3data[mt].xs.nbt[0] != len(mf3data[mt].xs.x)):
print("Problem with interpolation")
#print mf3data[mt].xs.interp
outf.write(" {:12d}".format(1))
outf.write("\n")
outf.write(" {:12d}".format(3))
outf.write("\n")
outf.write(" {:12d}".format(mt))
outf.write(" {:12d}".format(0))
outf.write("\n")
outf.write(" {:12d}".format(int(mf3data[mt].Q_reaction)))
outf.write(" {:12d}".format(0))
outf.write(" {:12d}".format(len(mf3data[mt].xs.x)))
outf.write("\n")
writeline = ""
zippedxs = zip(mf3data[mt].xs.x, mf3data[mt].xs.y)
count = 0
for se, sxs in zippedxs:
count += 1
writeline += " {:8.6e} {:8.6e}".format(se, sxs)
if (count == 3):
writeline += "\n"
count = 0
if(count != 0):
writeline += "\n"
outf.write(writeline)
outf.close()
def _copy_radio_abundances(self):
"""Copies the radioactive isotopes onto the corresponding stable
elements. This routine is intended for uses of the mapper during which
the decay is neglected
Notes
-----
Must be called after _remap_abundances.
Raises
------
AttributeError if called before _remap_abundances
"""
try:
self.radio_abundances_interp
except AttributeError:
logger.critical("Abundances must be remapped before radioactive:"
" isotopes are copied onto the stable elements")
raise AttributeError("no radio_abundances_interp; call"
" _remap_abundances first")
for ident in self.radio_abundances_interp.keys():
elemid = nucname.id(ident)
z = nucname.znum(elemid)
self.abundances_interp[z] = \
self.abundances_interp[z] + self.radio_abundances_interp[ident]
def _decay_abundances(self):
"""Determines the decay of all radioactive isotopes. Afterwards their
mass fractions are added to the stable elements.
Notes
-----
Must be called after _remap_abundances.
Raises
------
AttributeError if called before _remap_abundances
"""
try:
self.radio_abundances_interp
except AttributeError:
logger.critical("Abundances must be remapped before decay is"
" handled")
raise AttributeError("no radio_abundances_interp; call "
"_remap_abundances first")
for i in xrange(self.N_interp):
comp = {}
mass = 0
for ident in self.radio_abundances_interp.keys():
Xi = self.radio_abundances_interp[ident][i]
mass += Xi
comp[nucname.id(ident)] = Xi
inp = material.Material(comp, mass=mass)
res = inp.decay(
(self.t - self.orig.t).to("s").value).mult_by_mass()
for item in res.items():
z = nucname.znum(item[0])
self.abundances_interp[z][i] = \
self.abundances_interp[z][i] + item[1]
def _copy_radio_abundances(self):
"""Copies the radioactive isotopes onto the corresponding stable
elements. This routine is intended for uses of the mapper during which
the decay is neglected
Notes
-----
Must be called after _remap_abundances.
Raises
------
AttributeError if called before _remap_abundances
"""
try:
self.radio_abundances_interp
except AttributeError:
logger.critical("Abundances must be remapped before radioactive:"
" isotopes are copied onto the stable elements")
raise AttributeError("no radio_abundances_interp; call"
" _remap_abundances first")
for ident in self.radio_abundances_interp.keys():
elemid = nucname.id(ident)
z = nucname.znum(elemid)
self.abundances_interp[z] = \
self.abundances_interp[z] + self.radio_abundances_interp[ident]
def make_atomic_mass_table(nuc_data, build_dir=""):
"""Makes an atomic mass table in the nuc_data library.
Parameters
----------
nuc_data : str
Path to nuclide data file.
build_dir : str
Directory to place html files in.
"""
# Grab raw data
atomic_abund = get_isotopic_abundances()
atomic_masses = parse_atomic_mass_adjustment(build_dir)
A = {}
# Add normal isotopes to A
for nuc, mass, error in atomic_masses:
if nuc in atomic_abund:
A[nuc] = nuc, mass, error, atomic_abund[nuc]
else:
A[nuc] = nuc, mass, error, 0.0
# Add naturally occuring elements
for element in nucname.name_zz:
nuc = nucname.id(element)
A[nuc] = nuc, 0.0, 0.0, 0.0
for nuc, abund in atomic_abund.items():
zz = nucname.znum(nuc)
element_zz = nucname.id(zz)
element = nucname.zz_name[zz]
_nuc, nuc_mass, _error, _abund = A[nuc]
elem_zz, elem_mass, _error, _abund = A[element_zz]
new_elem_mass = elem_mass + (nuc_mass * abund)
A[element_zz] = element_zz, new_elem_mass, 0.0, float(
0.0 < new_elem_mass)
A = sorted(A.values(), key=lambda x: x[0])
# Open the HDF5 File
kdb = tb.open_file(nuc_data, "a", filters=BASIC_FILTERS)
# Make a new the table
Atable = kdb.create_table(
"/",
"atomic_mass",
atomic_mass_desc,
"Atomic Mass Data [amu]",
expectedrows=len(A),
)
Atable.append(A)
# Ensure that data was written to table
Atable.flush()
# Close the hdf5 file
kdb.close()
def genelemfuncs(
nucs,
short=1e-16,
small=1e-16,
sf=False,
debug=False,
):
idx = dict(zip(nucs, range(len(nucs))))
cases = {i: [-1, []] for i in elems(nucs)}
for nuc in nucs:
z = nucname.znum(nuc)
case, cases[z][0] = gencase(nuc,
idx,
cases[z][0],
short=short,
sf=sf,
debug=debug,
small=small)
cases[z][1] += case
funcs = []
for i, (b, kases) in cases.items():
kases[0] = kases[0][2:]
ctx = dict(nucs=nucs, elem=nucname.name(i), cases='\n'.join(kases))
funcs.append(ELEM_FUNC.render(ctx))
return "\n\n".join(funcs)
def _decay_abundances(self):
"""Determines the decay of all radioactive isotopes. Afterwards their
mass fractions are added to the stable elements.
Notes
-----
Must be called after _remap_abundances.
Raises
------
AttributeError if called before _remap_abundances
"""
try:
self.radio_abundances_interp
except AttributeError:
logger.critical("Abundances must be remapped before decay is"
" handled")
raise AttributeError("no radio_abundances_interp; call "
"_remap_abundances first")
for i in xrange(self.N_interp):
comp = {}
mass = 0
for ident in self.radio_abundances_interp.keys():
Xi = self.radio_abundances_interp[ident][i]
mass += Xi
comp[nucname.id(ident)] = Xi
inp = material.Material(comp, mass=mass)
res = inp.decay(
(self.t - self.orig.t).to("s").value).mult_by_mass()
for item in res.items():
z = nucname.znum(item[0])
self.abundances_interp[z][i] = \
self.abundances_interp[z][i] + item[1]
def write_metadata(self, nucs, libs, dirname):
track_actinides = [n for n in nucs if nucname.znum(n) in nucname.act]
with open(os.path.join(dirname, "manifest.txt"), "w") as f:
f.write("\n".join([str(nucname.zzaaam(act)) for act in track_actinides]))
f.write("\n")
with open(os.path.join(dirname, "params.txt"), "w") as f:
if self.rc.get("enrichment") is None:
enrichment = self.rc.initial_heavy_metal.get(922350)
else:
enrichment = self.rc.enrichment
if enrichment is not None:
f.write("ENRICHMENT {}\n".format(enrichment))
if self.rc.get("batches") is not None:
f.write("BATCHES {}\n".format(self.rc.batches))
if self.rc.get("pnl") is not None:
f.write("PNL {}\n".format(self.rc.pnl))
f.write("BURNUP {}\n".format(sum(libs["fuel"]["BUd"])))
f.write("FLUX {:.0E}\n".format(np.mean(libs["fuel"]["phi_tot"][1:])))
with open(os.path.join(dirname, "structural.txt"), "w") as f:
clad_linear_density = pi * self.rc.clad_density * \
(self.rc.clad_cell_radius ** 2 - self.rc.void_cell_radius ** 2)
fuel_linear_density = pi * self.rc.fuel_density * \
self.rc.fuel_cell_radius ** 2
clad_frac = float(clad_linear_density / fuel_linear_density)
cladrows = ["{} {:.8f}".format(nucname.zzaaam(n), f*clad_frac)
for n, f in self.rc.clad_material.comp.items()]
f.write("\n".join(cladrows))
f.write("\n")
shutil.copyfile("TAPE9.INP", os.path.join(dirname, "TAPE9.INP"))
def sss_meta_zzz(self, nuc_code):
"""Checks Serpent-specific meta stable-flag for zzaaam. For instance,
47310 instead of 471101 for `Ag-110m1`. Metastable isotopes represented
with `aaa` started with ``3``.
Parameters
----------
nuc_code : str
Name of nuclide in Serpent form. For instance, `47310`.
Returns
-------
int
Name of nuclide in `zzaaam` form (`471101`).
"""
zz = pyname.znum(nuc_code)
aa = pyname.anum(nuc_code)
if aa > 300:
if zz > 76:
aa_new = aa - 100
else:
aa_new = aa - 200
zzaaam = str(zz) + str(aa_new) + '1'
else:
zzaaam = nuc_code
return int(zzaaam)
def ScaleMaterialWriter(heu):
print "{matName} {materialNum} {mass} {numIso}".format(matName=heu.metadata['materialName'],
materialNum=heu.metadata['materialNumber'],
mass=heu.metadata['density_g_per_cc'],
numIso=len(heu))
for iso in heu:
print " %s%s %s"%(nucname.znum(iso),nucname.anum(iso), heu[iso]*100)
print " 1 300 end"
def make_atomic_mass_table(nuc_data, build_dir=""):
"""Makes an atomic mass table in the nuc_data library.
Parameters
----------
nuc_data : str
Path to nuclide data file.
build_dir : str
Directory to place html files in.
"""
# Grab raw data
atomic_abund = get_isotopic_abundances()
atomic_masses = parse_atomic_mass_adjustment(build_dir)
A = {}
# Add normal isotopes to A
for nuc, mass, error in atomic_masses:
if nuc in atomic_abund:
A[nuc] = nuc, mass, error, atomic_abund[nuc]
else:
A[nuc] = nuc, mass, error, 0.0
# Add naturally occuring elements
for element in nucname.name_zz:
nuc = nucname.id(element)
A[nuc] = nuc, 0.0, 0.0, 0.0
for nuc, abund in atomic_abund.items():
zz = nucname.znum(nuc)
element_zz = nucname.id(zz)
element = nucname.zz_name[zz]
_nuc, nuc_mass, _error, _abund = A[nuc]
elem_zz, elem_mass, _error, _abund = A[element_zz]
new_elem_mass = elem_mass + (nuc_mass * abund)
A[element_zz] = element_zz, new_elem_mass, 0.0, float(0.0 < new_elem_mass)
A = sorted(A.values(), key=lambda x: x[0])
# Open the HDF5 File
kdb = tb.openFile(nuc_data, 'a', filters=BASIC_FILTERS)
# Make a new the table
Atable = kdb.createTable("/", "atomic_mass", atomic_mass_desc,
"Atomic Mass Data [amu]", expectedrows=len(A))
Atable.append(A)
# Ensure that data was written to table
Atable.flush()
# Close the hdf5 file
kdb.close()
def genelemfuncs(nucs, short=1e-8, sf=False):
idx = dict(zip(nucs, range(len(nucs))))
cases = {i: [-1, []] for i in elems(nucs)}
for nuc in nucs:
z = nucname.znum(nuc)
case, cases[z][0] = gencase(nuc, idx, cases[z][0], short=short, sf=sf)
cases[z][1] += case
funcs = []
for i, (b, kases) in cases.items():
kases[0] = kases[0][2:]
ctx = dict(nucs=nucs, elem=nucname.name(i), cases='\n'.join(kases))
funcs.append(ELEM_FUNC.render(ctx))
return "\n\n".join(funcs)
def read_simple_isotope_abundances(fname, delimiter='\s+'):
"""
Reading an abundance file of the following structure (example; lines starting with hash will be ignored):
The first line of abundances describe the abundances in the center of the model and are not used.
First 4 columns contain values related to velocity, density, electron_density and temperature.
From 5th column onwards, abundances of elements and isotopes begin.
The file consists of a header row and next row contains unit of the respective attributes
Since abundance fractions are unitless , its unit row is filled with ones
Example
velocity...temperature C O Ni56
km/s.........K 1 1 1
...................... 0.4 0.3 0.2
Parameters
----------
fname: str
filename or path with filename
Returns
-------
index: ~np.ndarray
abundances: ~pandas.DataFrame
isotope_abundance: ~pandas.MultiIndex
"""
df = pd.read_csv(fname, comment='#', delimiter=delimiter, skiprows=[0, 2])
df = df.transpose()
abundance = pd.DataFrame(columns=np.arange(df.shape[1] - 1),
index=pd.Index([], name='atomic_number'),
dtype=np.float64)
isotope_index = pd.MultiIndex([[]] * 2, [[]] * 2,
names=['atomic_number', 'mass_number'])
isotope_abundance = pd.DataFrame(columns=np.arange(df.shape[1] - 1),
index=isotope_index,
dtype=np.float64)
#First 4 columns related to density parser (e.g. velocity)
for element_symbol_string in df.index[4:]:
if element_symbol_string in nucname.name_zz:
z = nucname.name_zz[element_symbol_string]
abundance.loc[z, :] = df.loc[element_symbol_string].tolist()[1:]
else:
z = nucname.znum(element_symbol_string)
mass_no = nucname.anum(element_symbol_string)
isotope_abundance.loc[(
z, mass_no), :] = df.loc[element_symbol_string].tolist()[1:]
return abundance.index, abundance, isotope_abundance
def mesh_to_geom(mesh, geom_file, matlib_file):
"""This function reads the materials of a PyNE mesh object and prints the
geometry and materials portion of an ALARA input file, as well as a
corresponding matlib file. If the mesh is structured, xyz ordering is used
(z changing fastest). If the mesh is unstructured the mesh_iterate order is
used.
Parameters
----------
mesh : PyNE Mesh object
The Mesh object containing the materials to be printed.
geom_file : str
The name of the file to print the geometry and material blocks.
matlib_file : str
The name of the file to print the matlib.
"""
# Create geometry information header. Note that the shape of the geometry
# (rectangular) is actually inconsequential to the ALARA calculation so
# unstructured meshes are not adversely affected.
geometry = u"geometry rectangular\n\n"
# Create three strings in order to create all ALARA input blocks in a
# single mesh iteration.
volume = u"volume\n" # volume input block
mat_loading = u"mat_loading\n" # material loading input block
mixture = u"" # mixture blocks
matlib = u"" # ALARA material library string
for i, mat, ve in mesh:
volume += u" {0: 1.6E} zone_{1}\n".format(
mesh.elem_volume(ve), i)
mat_loading += u" zone_{0} mix_{0}\n".format(i)
matlib += u"mat_{0} {1: 1.6E} {2}\n".format(
i, mesh.density[i], len(mesh.comp[i]))
mixture += (u"mixture mix_{0}\n"
u" material mat_{0} 1 1\nend\n\n".format(i))
for nuc, comp in mesh.comp[i].items():
matlib += u"{0} {1: 1.6E} {2}\n".format(
alara(nuc), comp * 100.0, znum(nuc))
matlib += u"\n"
volume += u"end\n\n"
mat_loading += u"end\n\n"
with open(geom_file, 'w') as f:
f.write(geometry + volume + mat_loading + mixture)
with open(matlib_file, 'w') as f:
f.write(matlib)
def mesh_to_geom(mesh, geom_file, matlib_file):
"""This function reads the materials of a PyNE mesh object and prints the
geometry and materials portion of an ALARA input file, as well as a
corresponding matlib file. If the mesh is structured, xyz ordering is used
(z changing fastest). If the mesh is unstructured iMesh.iterate order is
used.
Parameters
----------
mesh : PyNE Mesh object
The Mesh object containing the materials to be printed.
geom_file : str
The name of the file to print the geometry and material blocks.
matlib_file : str
The name of the file to print the matlib.
"""
# Create geometry information header. Note that the shape of the geometry
# (rectangular) is actually inconsequential to the ALARA calculation so
# unstructured meshes are not adversely affected.
geometry = "geometry rectangular\n\n"
# Create three strings in order to create all ALARA input blocks in a
# single mesh iteration.
volume = "volume\n" # volume input block
mat_loading = "mat_loading\n" # material loading input block
mixture = "" # mixture blocks
matlib = "" # ALARA material library string
for i, mat, ve in mesh:
volume += " {0: 1.6E} zone_{1}\n".format(mesh.elem_volume(ve), i)
mat_loading += " zone_{0} mix_{0}\n".format(i)
matlib += "mat_{0} {1: 1.6E} {2}\n".format(i, mesh.density[i],
len(mesh.comp[i]))
mixture += ("mixture mix_{0}\n"
" material mat_{0} 1 1\nend\n\n".format(i))
for nuc, comp in mesh.comp[i].iteritems():
matlib += "{0} {1: 1.6E} {2}\n".format(alara(nuc), comp*100.0,
znum(nuc))
matlib += "\n"
volume += "end\n\n"
mat_loading += "end\n\n"
with open(geom_file, 'w') as f:
f.write(geometry + volume + mat_loading + mixture)
with open(matlib_file, 'w') as f:
f.write(matlib)
def parse_csv_abundances(csvy_data):
"""
A parser for the csv data part of a csvy model file. This function filters out columns that are not abundances.
Parameters
----------
csvy_data : pandas.DataFrame
Returns
-------
index : ~np.ndarray
abundances : ~pandas.DataFrame
isotope_abundance : ~pandas.MultiIndex
"""
abundance_col_names = [
name for name in csvy_data.columns
if nucname.iselement(name) or nucname.isnuclide(name)
]
df = csvy_data.loc[:, abundance_col_names]
df = df.transpose()
abundance = pd.DataFrame(
columns=np.arange(df.shape[1]),
index=pd.Index([], name="atomic_number"),
dtype=np.float64,
)
isotope_index = pd.MultiIndex([[]] * 2, [[]] * 2,
names=["atomic_number", "mass_number"])
isotope_abundance = pd.DataFrame(columns=np.arange(df.shape[1]),
index=isotope_index,
dtype=np.float64)
for element_symbol_string in df.index[0:]:
if element_symbol_string in nucname.name_zz:
z = nucname.name_zz[element_symbol_string]
abundance.loc[z, :] = df.loc[element_symbol_string].tolist()
else:
z = nucname.znum(element_symbol_string)
mass_no = nucname.anum(element_symbol_string)
isotope_abundance.loc[(
z, mass_no), :] = df.loc[element_symbol_string].tolist()
return abundance.index, abundance, isotope_abundance
def get_nuc_name(self, nuc_code):
"""Returns nuclide name in human-readable notation: chemical symbol
(one or two characters), dash, and the atomic weight. Lastly, if the
nuclide is in metastable state, the letter `m` is concatenated with
number of excited state. For example, `Am-242m1`.
Parameters
----------
nuc_code : str
Name of nuclide in Serpent2 form. For instance, `Am-242m`.
Returns
-------
nuc_name : str
Name of nuclide in human-readable notation (`Am-242m1`).
nuc_zzaaam : str
Name of nuclide in `zzaaam` form (`952421`).
"""
if '.' in str(nuc_code):
nuc_code = pyname.zzzaaa_to_id(int(nuc_code.split('.')[0]))
zz = pyname.znum(nuc_code)
aa = pyname.anum(nuc_code)
aa_str = str(aa)
# at_mass = pydata.atomic_mass(nuc_code_id)
if aa > 300:
if zz > 76:
aa_str = str(aa - 100) + 'm1'
aa = aa - 100
else:
aa_str = str(aa - 200) + 'm1'
aa = aa - 200
nuc_zzaaam = str(zz) + str(aa) + '1'
elif aa == 0:
aa_str = 'nat'
nuc_name = pyname.zz_name[zz] + aa_str
else:
meta_flag = pyname.snum(nuc_code)
if meta_flag:
nuc_name = pyname.name(nuc_code)[:-1] + 'm' + str(meta_flag)
else:
nuc_name = pyname.name(nuc_code)
nuc_zzaaam = \
self.convert_nuclide_name_serpent_to_zam(pyname.zzaaam(nuc_code))
return nuc_name, nuc_zzaaam
def read_uniform_abundances(abundances_section, no_of_shells):
"""
Parameters
----------
abundances_section: ~config.model.abundances
no_of_shells: int
Returns
-------
abundance: ~pandas.DataFrame
isotope_abundance: ~pandas.DataFrame
"""
abundance = pd.DataFrame(
columns=np.arange(no_of_shells),
index=pd.Index(np.arange(1, 120), name="atomic_number"),
dtype=np.float64,
)
isotope_index = pd.MultiIndex([[]] * 2, [[]] * 2,
names=["atomic_number", "mass_number"])
isotope_abundance = pd.DataFrame(columns=np.arange(no_of_shells),
index=isotope_index,
dtype=np.float64)
for element_symbol_string in abundances_section:
if element_symbol_string == "type":
continue
try:
if element_symbol_string in nucname.name_zz:
z = nucname.name_zz[element_symbol_string]
abundance.loc[z] = float(
abundances_section[element_symbol_string])
else:
mass_no = nucname.anum(element_symbol_string)
z = nucname.znum(element_symbol_string)
isotope_abundance.loc[(z, mass_no), :] = float(
abundances_section[element_symbol_string])
except RuntimeError as err:
raise RuntimeError(
"Abundances are not defined properly in config file : {}".
format(err.args))
return abundance, isotope_abundance
def plot_inv_para(fo, ts, param, low_limit=1e-10, save=False, sfile=""):
"""plots parameter as table of nuclides for a timestep
this is for stable parameters only
"""
atom_grid = np.zeros((113,173))
for nuc in fo.timestep_data[ts-1].inventory:
if nuc[0][-1] == "n":
nuc[0] = nuc[0][:-1]
a = nucname.anum(nuc[0])
z = nucname.znum(nuc[0])
n=a-z
atom_grid[z,n] = np.log10(float(nuc[param]))
atom_grid = np.ma.masked_array(atom_grid, atom_grid<low_limit)
title = fo.sumdat[0][ts-1]
make_plot(atom_grid, title, save, sfile)
def read_uniform_abundances(abundances_section, no_of_shells):
"""
Parameters
----------
abundances_section: ~config.model.abundances
no_of_shells: int
Returns
-------
abundance: ~pandas.DataFrame
isotope_abundance: ~pandas.DataFrame
"""
abundance = pd.DataFrame(columns=np.arange(no_of_shells),
index=pd.Index(np.arange(1, 120),
name='atomic_number'),
dtype=np.float64)
isotope_index = pd.MultiIndex(
[[]] * 2, [[]] * 2, names=['atomic_number', 'mass_number'])
isotope_abundance = pd.DataFrame(columns=np.arange(no_of_shells),
index=isotope_index,
dtype=np.float64)
for element_symbol_string in abundances_section:
if element_symbol_string == 'type':
continue
try:
if element_symbol_string in nucname.name_zz:
z = nucname.name_zz[element_symbol_string]
abundance.loc[z] = float(
abundances_section[element_symbol_string])
else:
mass_no = nucname.anum(element_symbol_string)
z = nucname.znum(element_symbol_string)
isotope_abundance.loc[(z, mass_no), :] = float(
abundances_section[element_symbol_string])
except RuntimeError as err:
raise RuntimeError(
"Abundances are not defined properly in config file : {}".format(err.args))
return abundance, isotope_abundance
def make_elements():
"""Make natural elemental materials based on isotopic abundances.
Returns
-------
eltsdict : dict from str to pyne.material.Material
Natural elements as materials.
"""
natural_abund("H1") # initialize natural_abund_map
# get rid of elemental total abundances and empty isotopic abundances
abunds_no_trivial = [abund for abund in natural_abund_map.items() if
nucname.anum(abund[0]) != 0 and abund[1] != 0]
sorted_abunds = sorted(abunds_no_trivial)
grouped_abunds = groupby(sorted_abunds, lambda abund: nucname.znum(abund[0]))
# filter out 111, 113, 115, 117, 118 - the ones with no names
elts = (Material(dict(abunds), metadata={"name": nucname.name(zz)})
for zz, abunds in grouped_abunds if zz in nucname.zz_name.keys())
eltsdict = dict(((elt.metadata["name"], elt) for elt in elts))
return eltsdict
def plot_inv_act_para(fo, ts, para, low_limit=1e-5, save=False, sfile=""):
"""plots parameter as table of nuclides form for a timestep
this is for active parameters
"""
act_grid = np.zeros((113,173))
for nuc in fo.timestep_data[ts-1].inventory:
if float(nuc[para]) > 0:
if nuc[0][-1] == "n":
nuc[0] = nuc[0][:-1]
a = nucname.anum(nuc[0])
z = nucname.znum(nuc[0])
n=a-z
act_grid[z,n] = np.log10(float(nuc[para]))
act_grid = np.ma.masked_array(act_grid, act_grid<low_limit)
make_plot(act_grid, save, sfile)
def write(self, libs, dirname):
if not os.path.isdir(dirname):
os.makedirs(dirname)
rownames = ["TIME", "NEUT_PROD", "NEUT_DEST", "BUd"]
for mat, matlib in libs.items():
if isinstance(mat, int):
fname = str(nucname.zzaaam(mat))
else:
fname = mat
lines = [row + " " + " ".join(map(str, matlib[row]))
for row in rownames]
nucs = matlib["tracked_nucs"]
lines.extend(sorted([n + " " + " ".
join(["{:.4g}".format(f) for f in nucs[n]])
for n in nucs]))
with open(os.path.join(dirname, fname + ".txt"), "w") as f:
f.write("\n".join(lines))
track_actinides = [n for n in nucs if nucname.znum(n) in nucname.act]
with open(os.path.join(dirname, "manifest.txt"), "w") as f:
f.write("\n".join([str(nucname.zzaaam(act)) for act in track_actinides]))
f.write("\n")
with open(os.path.join(dirname, "params.txt"), "w") as f:
f.write("ENRICHMENT {}\n".format(self.rc.enrichment))
f.write("BATCHES {}\n".format(self.rc.batches))
f.write("PNL {}\n".format(self.rc.pnl))
f.write("BURNUP {}\n".format(sum(libs["fuel"]["BUd"])))
f.write("FLUX {:.0E}\n".format(np.mean(libs["fuel"]["phi_tot"][1:])))
with open(os.path.join(dirname, "structural.txt"), "w") as f:
clad_linear_density = pi * self.rc.clad_density * \
(self.rc.clad_cell_radius ** 2 - self.rc.void_cell_radius ** 2)
fuel_linear_density = pi * self.rc.fuel_density * \
self.rc.fuel_cell_radius ** 2
clad_frac = float(clad_linear_density / fuel_linear_density)
cladrows = ["{} {:.8f}".format(nucname.zzaaam(n), f*clad_frac)
for n, f in self.rc.clad_material.comp.items()]
f.write("\n".join(cladrows))
f.write("\n")
shutil.copyfile("TAPE9.INP", os.path.join(dirname, "TAPE9.INP"))
def record_to_geom(mesh,
cell_fracs,
cell_mats,
geom_file,
matlib_file,
sig_figs=6,
sub_voxel=False):
"""This function preforms the same task as alara.mesh_to_geom, except the
geometry is on the basis of the stuctured array output of
dagmc.discretize_geom rather than a PyNE material object with materials.
This allows for more efficient ALARA runs by minimizing the number of
materials in the ALARA matlib. This is done by treating mixtures that are
equal up to <sig_figs> digits to be the same mixture within ALARA.
Parameters
----------
mesh : PyNE Mesh object
The Mesh object for which the geometry is discretized.
cell_fracs : structured array
The output from dagmc.discretize_geom(). A sorted, one dimensional
array, each entry containing the following fields:
:idx: int
The volume element index.
:cell: int
The geometry cell number.
:vol_frac: float
The volume fraction of the cell withing the mesh ve.
:rel_error: float
The relative error associated with the volume fraction.
cell_mats : dict
Maps geometry cell numbers to PyNE Material objects. Each PyNE material
object must have 'name' specified in Material.metadata.
geom_file : str
The name of the file to print the geometry and material blocks.
matlib_file : str
The name of the file to print the matlib.
sig_figs : int
The number of significant figures that two mixtures must have in common
to be treated as the same mixture within ALARA.
sub_voxel : bool
If sub_voxel is True, the sub-voxel r2s will be used.
"""
# Create geometry information header. Note that the shape of the geometry
# (rectangular) is actually inconsequential to the ALARA calculation so
# unstructured meshes are not adversely affected.
geometry = "geometry rectangular\n\n"
# Create three strings in order to create all ALARA input blocks in a
# single mesh iteration.
volume = "volume\n" # volume input block
mat_loading = "mat_loading\n" # material loading input block
mixture = "" # mixture blocks
unique_mixtures = []
if not sub_voxel:
for i, mat, ve in mesh:
volume += " {0: 1.6E} zone_{1}\n".format(
mesh.elem_volume(ve), i)
ve_mixture = {}
for row in cell_fracs[cell_fracs["idx"] == i]:
cell_mat = cell_mats[row["cell"]]
name = cell_mat.metadata["name"]
if _is_void(name):
name = "mat_void"
if name not in ve_mixture.keys():
ve_mixture[name] = np.round(row["vol_frac"], sig_figs)
else:
ve_mixture[name] += np.round(row["vol_frac"], sig_figs)
if ve_mixture not in unique_mixtures:
unique_mixtures.append(ve_mixture)
mixture += "mixture mix_{0}\n".format(
unique_mixtures.index(ve_mixture))
for key, value in ve_mixture.items():
mixture += " material {0} 1 {1}\n".format(key, value)
mixture += "end\n\n"
mat_loading += " zone_{0} mix_{1}\n".format(
i, unique_mixtures.index(ve_mixture))
else:
ves = list(mesh.iter_ve())
sve_count = 0
for row in cell_fracs:
if len(cell_mats[row["cell"]].comp) != 0:
volume += " {0: 1.6E} zone_{1}\n".format(
mesh.elem_volume(ves[row["idx"]]) * row["vol_frac"],
sve_count)
cell_mat = cell_mats[row["cell"]]
name = cell_mat.metadata["name"]
if name not in unique_mixtures:
unique_mixtures.append(name)
mixture += "mixture {0}\n".format(name)
mixture += " material {0} 1 1\n".format(name)
mixture += "end\n\n"
mat_loading += " zone_{0} {1}\n".format(sve_count, name)
sve_count += 1
volume += "end\n\n"
mat_loading += "end\n\n"
with open(geom_file, "w") as f:
f.write(geometry + volume + mat_loading + mixture)
matlib = "" # ALARA material library string
printed_mats = []
print_void = False
for mat in cell_mats.values():
name = mat.metadata["name"]
if _is_void(name):
print_void = True
continue
if name not in printed_mats:
printed_mats.append(name)
matlib += "{0} {1: 1.6E} {2}\n".format(
name, mat.density, len(mat.comp))
for nuc, comp in mat.comp.items():
matlib += "{0} {1: 1.6E} {2}\n".format(
alara(nuc), comp * 100.0, znum(nuc))
matlib += "\n"
if print_void:
matlib += "# void material\nmat_void 0.0 1\nhe 1 2\n"
with open(matlib_file, "w") as f:
f.write(matlib)
def id_to_tuple(atomic_id):
return nucname.znum(atomic_id), nucname.anum(atomic_id)
def write(self, libs, dirname):
"""Write out libraries to a directory.
Parameters
----------
libs : dict
The reactor libraries gleaned from buk.
dirname : str
The output directory.
"""
if not os.path.isdir(dirname):
os.makedirs(dirname)
rownames = ["TIME", "phi_tot", "NEUT_PROD", "NEUT_DEST", "BUd"]
for mat, matlib in libs.items():
if isinstance(mat, int):
fname = str(nucname.zzaaam(mat))
elif mat == 'fuel':
fname = mat
else:
continue
lines = [row + " " + " ".join(map(str, matlib[row]))
for row in rownames]
trans_matrix = {}
i = 0
while i < len(matlib['material']):
for temp_nuc in matlib['material'][i].comp:
nuc_name = str(nucname.name(temp_nuc))
try:
trans_matrix[nuc_name].append(matlib['material'][i].comp[temp_nuc]*1000)
except KeyError:
if matlib['material'][i].comp[temp_nuc] > self.rc.track_nuc_threshold:
zero_array = [0.]*i
trans_matrix[nuc_name] = zero_array
trans_matrix[nuc_name].append(matlib['material'][i].comp[temp_nuc]*1000)
i+=1
#for nuc in trans_matrix:
# if len(trans_matrix[nuc]) < len(matlib['TIME']):
# zero_array = [0.]*(len(matlib['TIME'])-len(trans_matrix[nuc]))
# trans_matrix[nuc].append(zero_array)
nucs = matlib["tracked_nucs"]
lines.extend(sorted([n + " " + " ".
join(["{:.4g}".format(f) for f in trans_matrix[n]])
for n in trans_matrix]))
with open(os.path.join(dirname, fname + ".txt"), "w") as f:
f.write("\n".join(lines))
track_actinides = [n for n in nucs if nucname.znum(n) in nucname.act]
with open(os.path.join(dirname, "manifest.txt"), "w") as f:
f.write("\n".join([str(nucname.zzaaam(act)) for act in track_actinides]))
f.write("\n")
with open(os.path.join(dirname, "params.txt"), "w") as f:
if self.rc.get("enrichment") is None:
enrichment = self.rc.initial_heavy_metal.get(922350)
else:
enrichment = self.rc.enrichment
if enrichment is not None:
f.write("ENRICHMENT {}\n".format(enrichment))
if self.rc.get("batches") is not None:
f.write("BATCHES {}\n".format(self.rc.batches))
if self.rc.get("pnl") is not None:
f.write("PNL {}\n".format(self.rc.pnl))
f.write("BURNUP {}\n".format(sum(libs["fuel"]["BUd"])))
f.write("FLUX {:.0E}\n".format(np.mean(libs["fuel"]["phi_tot"][1:])))
with open(os.path.join(dirname, "structural.txt"), "w") as f:
clad_linear_density = pi * self.rc.clad_density * \
(self.rc.clad_cell_radius ** 2 - self.rc.void_cell_radius ** 2)
fuel_linear_density = pi * self.rc.fuel_density * \
self.rc.fuel_cell_radius ** 2
clad_frac = float(clad_linear_density / fuel_linear_density)
cladrows = ["{} {:.8f}".format(nucname.zzaaam(n), f*clad_frac)
for n, f in self.rc.clad_material.comp.items()]
f.write("\n".join(cladrows))
f.write("\n")
shutil.copyfile("TAPE9.INP", os.path.join(dirname, "TAPE9.INP"))
def readendf(self, datafile):
endfds = ENDFDataSource(os.path.join(self.csdir, datafile))
endfds.load()
# print endfds.library.intdict
print "++++ The File contains data for the following nuclides:"
self.lines = {}
for nuc, value in endfds.library.mat_dict.iteritems() :
cname = nucname.name(nuc)
print "+++ Data for nuclide " + cname
elname = elements[nucname.znum(nuc)].name
elname = elname.capitalize()
nuclidefilename = str(nucname.znum(nuc)) + "_" + str(nucname.anum(nuc)) + "_" + elname
if (nucname.snum(nuc) != 0):
print "Found nuclide in exited state, will add _<state> to filename"
nuclidefilename = nuclidefilename + "_" + str(nucname.snum(nuc))
for keyb, valueb in value['mfs'].iteritems() :
print "+++ The nuclide has data for MF=", keyb[0], " and MT=", keyb[1]
# MF=3 (cs)
if(keyb[0] == 3) :
print "Convert to geant compatible lines for MF=3"
reactiondata = endfds.reaction(nuc, keyb[1])
if (len(reactiondata['intschemes']) == 1):
if (reactiondata['intschemes'] == 2) :
writeline = ""
writeline += " {:12d}".format(len(reactiondata['xs']))
writeline += "\n"
zippedxs = zip(reactiondata['xs'], reactiondata['e_int'])
count = 0
for sxs, se in zippedxs:
count += 1
writeline += " {:8.6e} {:8.6e}".format(se, sxs)
if (count == 3):
writeline += "\n"
count = 0
if(count != 0):
writeline += "\n"
if(not keyb[1] in self.lines):
self.lines.update({keyb[1]: {}})
self.lines[keyb[1]].update({keyb[0]: writeline})
else:
print "Non-linear interpolation scheme (Type {}) is currently not supported.".format(reactiondata['intschemes'])
else:
print "Multiple interpolation schemes are currently not supprted."
#print reactiondata
#exit()
if(keyb[0] == 1) :
# xsdata = endfds.library.get_rx(key, 1, keyb[1]).reshape(-1,6)
# print xsdata
print "Conversion for MF=1 not yet possible"
if(keyb[0] == 6) :
print "Convert to geant compatible lines for MF=6"
mf6data = newmf.mf6()
mf6data.setdatasource(endfds)
mf6data.loadmtenangdata(nuc, keyb[1])
enangdist = mf6data.getdata()
gdata = geantdata.geantdata()
gdata.importenangdist(enangdist)
gdata.writesection(6, keyb[1])
if(not gdata.producterror):
if(not keyb[1] in self.lines):
self.lines.update({keyb[1]: {}})
self.lines[keyb[1]].update({keyb[0]: gdata.getline()})
else:
print("No output for MT/MF combination because of errors")
return nuclidefilename
def write(self, libs, dirname):
"""Write out libraries to a directory.
Parameters
----------
libs : dict
The reactor libraries gleaned from buk.
dirname : str
The output directory.
"""
if not os.path.isdir(dirname):
os.makedirs(dirname)
rownames = ["TIME", "NEUT_PROD", "NEUT_DEST", "BUd"]
for mat, matlib in libs.items():
if isinstance(mat, int):
fname = str(nucname.zzaaam(mat))
elif mat == 'fuel':
fname = mat
else:
continue
lines = [row + " " + " ".join(map(str, matlib[row]))
for row in rownames]
nucs = matlib["tracked_nucs"]
lines.extend(sorted([n + " " + " ".
join(["{:.4g}".format(f) for f in nucs[n]])
for n in nucs]))
with open(os.path.join(dirname, fname + ".txt"), "w") as f:
f.write("\n".join(lines))
track_actinides = [n for n in nucs if nucname.znum(n) in nucname.act]
with open(os.path.join(dirname, "manifest.txt"), "w") as f:
f.write("\n".join([str(nucname.zzaaam(act)) for act in track_actinides]))
f.write("\n")
with open(os.path.join(dirname, "params.txt"), "w") as f:
if self.rc.get("enrichment") is None:
enrichment = self.rc.initial_heavy_metal.get(922350)
else:
enrichment = self.rc.enrichment
if enrichment is not None:
f.write("ENRICHMENT {}\n".format(enrichment))
if self.rc.get("batches") is not None:
f.write("BATCHES {}\n".format(self.rc.batches))
if self.rc.get("pnl") is not None:
f.write("PNL {}\n".format(self.rc.pnl))
f.write("BURNUP {}\n".format(sum(libs["fuel"]["BUd"])))
f.write("FLUX {:.0E}\n".format(np.mean(libs["fuel"]["phi_tot"][1:])))
with open(os.path.join(dirname, "structural.txt"), "w") as f:
clad_linear_density = pi * self.rc.clad_density * \
(self.rc.clad_cell_radius ** 2 - self.rc.void_cell_radius ** 2)
fuel_linear_density = pi * self.rc.fuel_density * \
self.rc.fuel_cell_radius ** 2
clad_frac = float(clad_linear_density / fuel_linear_density)
cladrows = ["{} {:.8f}".format(nucname.zzaaam(n), f*clad_frac)
for n, f in self.rc.clad_material.comp.items()]
f.write("\n".join(cladrows))
f.write("\n")
shutil.copyfile("TAPE9.INP", os.path.join(dirname, "TAPE9.INP"))
# write cross section json file
xsdata = [[[nucname.name(int(n)), rxname.name(int(r)), float(x)]
for n, r, x in lib] for lib in libs['xs']]
with open(os.path.join(dirname, 'xs.json'), 'w') as f:
json.dump(xsdata, f, sort_keys=True, indent=1,
separators=(', ', ': '))
# write flux json file
with open(os.path.join(dirname, 'phi_g.json'), 'w') as f:
json.dump(libs['phi_g'], f, sort_keys=True, indent=1,
separators=(', ', ': '))
def record_to_geom(mesh,
cell_fracs,
cell_mats,
geom_file,
matlib_file,
sig_figs=6):
"""This function preforms the same task as alara.mesh_to_geom, except the
geometry is on the basis of the stuctured array output of
dagmc.discretize_geom rather than a PyNE material object with materials.
This allows for more efficient ALARA runs by minimizing the number of
materials in the ALARA matlib. This is done by treating mixtures that are
equal up to <sig_figs> digits to be the same mixture within ALARA.
Parameters
----------
mesh : PyNE Mesh object
The Mesh object for which the geometry is discretized.
cell_fracs : structured array
The output from dagmc.discretize_geom(). A sorted, one dimensional
array, each entry containing the following fields:
:idx: int
The volume element index.
:cell: int
The geometry cell number.
:vol_frac: float
The volume fraction of the cell withing the mesh ve.
:rel_error: float
The relative error associated with the volume fraction.
cell_mats : dict
Maps geometry cell numbers to PyNE Material objects. Each PyNE material
object must have the 'mat_number' in Material.metadata.
geom_file : str
The name of the file to print the geometry and material blocks.
matlib_file : str
The name of the file to print the matlib.
sig_figs : int
The number of significant figures that two mixtures must have in common
to be treated as the same mixture within ALARA.
"""
# Create geometry information header. Note that the shape of the geometry
# (rectangular) is actually inconsequential to the ALARA calculation so
# unstructured meshes are not adversely affected.
geometry = "geometry rectangular\n\n"
# Create three strings in order to create all ALARA input blocks in a
# single mesh iteration.
volume = "volume\n" # volume input block
mat_loading = "mat_loading\n" # material loading input block
mixture = "" # mixture blocks
unique_mixtures = []
for i, mat, ve in mesh:
volume += " {0: 1.6E} zone_{1}\n".format(mesh.elem_volume(ve), i)
ve_mixture = {}
for row in cell_fracs[cell_fracs['idx'] == i]:
if cell_mats[row['cell']].metadata['mat_number'] \
not in ve_mixture.keys():
ve_mixture[cell_mats[row['cell']].metadata['mat_number']] = \
round(row['vol_frac'], sig_figs)
else:
ve_mixture[cell_mats[row['cell']].metadata['mat_number']] += \
round(row['vol_frac'], sig_figs)
if ve_mixture not in unique_mixtures:
unique_mixtures.append(ve_mixture)
mixture += "mixture mix_{0}\n".format(
unique_mixtures.index(ve_mixture))
for key, value in ve_mixture.items():
mixture += " material mat_{0} 1 {1}\n".format(key, value)
mixture += "end\n\n"
mat_loading += " zone_{0} mix_{1}\n".format(
i, unique_mixtures.index(ve_mixture))
volume += "end\n\n"
mat_loading += "end\n\n"
with open(geom_file, 'w') as f:
f.write(geometry + volume + mat_loading + mixture)
matlib = "" # ALARA material library string
printed_mats = []
for mat in cell_mats.values():
mat_num = mat.metadata['mat_number']
if mat_num not in printed_mats:
printed_mats.append(mat_num)
matlib += "mat_{0} {1: 1.6E} {2}\n".format(
mat.metadata['mat_number'], mat.density, len(mat.comp))
for nuc, comp in mat.comp.iteritems():
matlib += "{0} {1: 1.6E} {2}\n".format(
alara(nuc), comp * 100.0, znum(nuc))
matlib += "\n"
with open(matlib_file, 'w') as f:
f.write(matlib)
def id_to_tuple(atomic_id):
return nucname.znum(atomic_id), nucname.anum(atomic_id)
def read_csv_isotope_abundances(fname, delimiter='\s+', skip_columns=0,
skip_rows=[1]):
"""
A generic parser for a TARDIS composition stored as a CSV file
The parser can read in both elemental and isotopic abundances. The first
column is always expected to contain a running index, labelling the grid
cells. The parser also allows for additional information to be stored in
the first skip_columns columns. These will be ignored if skip_columns > 0.
Note that the first column, containing the cell index is not taken into
account here.
Specific header lines can be skipped by the skip_rows keyword argument
It is expected that the first row of the date block (after skipping the
rows specified in skip_rows) specifies the different elements and isotopes.
Each row after contains the composition in the corresponding grid shell.
The first composition row describes the composition of the photosphere and
is essentially ignored (for the default value of skip_rows).
Example:
Index C O Ni56
0 1 1 1
1 0.4 0.3 0.2
Parameters
----------
fname: str
filename or path with filename
Returns
-------
index: ~np.ndarray
abundances: ~pandas.DataFrame
isotope_abundance: ~pandas.MultiIndex
"""
df = pd.read_csv(fname, comment='#',
sep=delimiter, skiprows=skip_rows, index_col=0)
df = df.transpose()
abundance = pd.DataFrame(columns=np.arange(df.shape[1]),
index=pd.Index([],
name='atomic_number'),
dtype=np.float64)
isotope_index = pd.MultiIndex(
[[]] * 2, [[]] * 2, names=['atomic_number', 'mass_number'])
isotope_abundance = pd.DataFrame(columns=np.arange(df.shape[1]),
index=isotope_index,
dtype=np.float64)
for element_symbol_string in df.index[skip_columns:]:
if element_symbol_string in nucname.name_zz:
z = nucname.name_zz[element_symbol_string]
abundance.loc[z, :] = df.loc[element_symbol_string].tolist()
else:
z = nucname.znum(element_symbol_string)
mass_no = nucname.anum(element_symbol_string)
isotope_abundance.loc[(
z, mass_no), :] = df.loc[element_symbol_string].tolist()
return abundance.index, abundance, isotope_abundance
def record_to_geom(mesh, cell_fracs, cell_mats, geom_file, matlib_file,
sig_figs=6):
"""This function preforms the same task as alara.mesh_to_geom, except the
geometry is on the basis of the stuctured array output of
dagmc.discretize_geom rather than a PyNE material object with materials.
This allows for more efficient ALARA runs by minimizing the number of
materials in the ALARA matlib. This is done by treating mixtures that are
equal up to <sig_figs> digits to be the same mixture within ALARA.
Parameters
----------
mesh : PyNE Mesh object
The Mesh object for which the geometry is discretized.
cell_fracs : structured array
The output from dagmc.discretize_geom(). A sorted, one dimensional
array, each entry containing the following fields:
:idx: int
The volume element index.
:cell: int
The geometry cell number.
:vol_frac: float
The volume fraction of the cell withing the mesh ve.
:rel_error: float
The relative error associated with the volume fraction.
cell_mats : dict
Maps geometry cell numbers to PyNE Material objects. Each PyNE material
object must have the 'mat_number' in Material.metadata.
geom_file : str
The name of the file to print the geometry and material blocks.
matlib_file : str
The name of the file to print the matlib.
sig_figs : int
The number of significant figures that two mixtures must have in common
to be treated as the same mixture within ALARA.
"""
# Create geometry information header. Note that the shape of the geometry
# (rectangular) is actually inconsequential to the ALARA calculation so
# unstructured meshes are not adversely affected.
geometry = "geometry rectangular\n\n"
# Create three strings in order to create all ALARA input blocks in a
# single mesh iteration.
volume = "volume\n" # volume input block
mat_loading = "mat_loading\n" # material loading input block
mixture = "" # mixture blocks
unique_mixtures = []
for i, mat, ve in mesh:
volume += " {0: 1.6E} zone_{1}\n".format(mesh.elem_volume(ve), i)
ve_mixture = {}
for row in cell_fracs[cell_fracs['idx'] == i]:
if cell_mats[row['cell']].metadata['mat_number'] \
not in ve_mixture.keys():
ve_mixture[cell_mats[row['cell']].metadata['mat_number']] = \
round(row['vol_frac'], sig_figs)
else:
ve_mixture[cell_mats[row['cell']].metadata['mat_number']] += \
round(row['vol_frac'], sig_figs)
if ve_mixture not in unique_mixtures:
unique_mixtures.append(ve_mixture)
mixture += "mixture mix_{0}\n".format(
unique_mixtures.index(ve_mixture))
for key, value in ve_mixture.items():
mixture += " material mat_{0} 1 {1}\n".format(key, value)
mixture += "end\n\n"
mat_loading += " zone_{0} mix_{1}\n".format(i,
unique_mixtures.index(ve_mixture))
volume += "end\n\n"
mat_loading += "end\n\n"
with open(geom_file, 'w') as f:
f.write(geometry + volume + mat_loading + mixture)
matlib = "" # ALARA material library string
printed_mats = []
for mat in cell_mats.values():
mat_num = mat.metadata['mat_number']
if mat_num not in printed_mats:
printed_mats.append(mat_num)
matlib += "mat_{0} {1: 1.6E} {2}\n".format(
mat.metadata['mat_number'], mat.density, len(mat.comp))
for nuc, comp in mat.comp.iteritems():
matlib += "{0} {1: 1.6E} {2}\n".format(alara(nuc),
comp*100.0, znum(nuc))
matlib += "\n"
with open(matlib_file, 'w') as f:
f.write(matlib)