def main():
# get list of nuclides
data.atomic_mass('U235')
nucs = set(data.atomic_mass_map.keys())
for nuc in data.atomic_mass_map:
nucm = nuc + 1
if nucname.anum(nuc) == 0 or data.decay_const(nucm) < 1e-16 or \
data.decay_const(nuc) == data.decay_const(nucm):
continue
nucs.add(nucm)
nucs = [nuc for nuc in nucs if nucname.anum(nuc) > 0]
#and
# not np.isnan(data.decay_const(nuc)) and
# nuc < 200000000]
nucs.sort()
# get symbols
symbols = {}
channels = {}
for nuc in nucs:
add_child_decays(nuc, symbols)
add_child_xss(nuc, channels, nucs)
# print symbols
ns = []
for nuc in nucs:
try:
nname = nucname.name(nuc)
except RuntimeError:
continue
ns.append(nname)
nucs = ns
d = {'symbols': symbols, 'nucs': nucs, 'channels': channels}
s = json.dumps(d, indent=4, sort_keys=True)
print(s)
def evaluate_flux(isotopes, conc, DATA, fixed=False):
if fixed:
flux = 0
return flux
# Assume power/mass
power_per_mass = 35 # W/g or J/s/g
fission_E = 200 * 1e6 * EV2JOULE # J/fission
# macro_per_density = 0 # cm^2/g
macro = 0 # cm^-1
for i, iso in enumerate(isotopes):
try:
fis_xs = DATA[iso]['fis'] * 1e-24
# macro_per_density += fis_xs*MOL/nucname.anum(iso)
macro += fis_xs * conc[i] / nucname.anum(iso) * MOL
except:
fis_xs = 0
if macro > 0:
flux = power_per_mass / (fission_E * macro)
else:
flux = 0
# print('Flux!')
# print(flux)
# print(type(flux))
# print("{:.2e}".format(flux))
return flux
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 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 evaluate_kinf(isotopes, conc, DATA, reactor_type):
numerator = 0
denom = 0
for i, iso in enumerate(isotopes):
try:
fis_xs = DATA[iso]['fis'] * 1e-24
abs_xs = DATA[iso]['cap'] * 1e-24 + fis_xs
macro_abs = abs_xs * conc[i] / nucname.anum(iso) * MOL
denom += macro_abs
except:
continue
try:
fis_xs = DATA[iso]['fis'] * 1e-24
nubar = DATA[iso]['nubar']
macro_fis = fis_xs * conc[i] / nucname.anum(iso) * MOL
numerator += nubar * macro_fis
except:
continue
kinf = numerator / denom
return kinf
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 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 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_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 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 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
print(np.log(2)/data.decay_const('922340000')/3.15e7)
print('-------Np-239 to Pu-239 test--------')
print(data.decay_const('932390'))
print(data.decay_children('932390'))
print(data.decay_const('932390'))
print(data.decay_children('932390'))
print(data.branch_ratio(932390,942390))
print('-------U-240 decay test--------')
print(np.log(2)/data.decay_const('922400')/3600)
print(data.branch_ratio('922400','932400', use_metastable=False))
print('-----U234 Capture Test-----')
print(float('922350')-float('922340') == 10)
print('-----Mass Test-----')
print(nucname.anum('922350'))
print('-----Name Test-----')
print(nucname.serpent('922350'))
print('-------U-236 test--------')
print(data.decay_const('922360'))
print(data.half_life('922360')/(3.15e7))
print('-------Np-234 to U-234 test--------')
print(data.decay_const('932340'))
print(data.decay_children('932340'))
print(data.branch_ratio(932340,922340))
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 num_density_to_mesh(lines, time, m):
"""num_density_to_mesh(lines, time, m)
This function reads ALARA output containing number density information and
creates material objects which are then added to a supplied PyNE Mesh object.
The volumes within ALARA are assummed to appear in the same order as the
idx on the Mesh object.
Parameters
----------
lines : list or str
ALARA output from ALARA run with 'number_density' in the 'output' block
of the input file. Lines can either be a filename or the equivalent to
calling readlines() on an ALARA output file. If reading in ALARA output
from stdout, call split('\n') before passing it in as the lines parameter.
time : str
The decay time for which number densities are requested (e.g. '1 h',
'shutdown', etc.)
m : PyNE Mesh
Mesh object for which mats will be applied to.
"""
if isinstance(lines, basestring):
with open(lines) as f:
lines = f.readlines()
elif not isinstance(lines, collections.Sequence):
raise TypeError("Lines argument not a file or sequence.")
# Advance file to number density portion.
header = 'Number Density [atoms/cm3]'
line = ""
while line.rstrip() != header:
line = lines.pop(0)
# Get decay time index from next line (the column the decay time answers
# appear in.
line_strs = lines.pop(0).replace('\t', ' ')
time_index = [s.strip() for s in line_strs.split(' ')
if s.strip()].index(time)
# Create a dict of mats for the mesh.
mats = {}
count = 0
# Read through file until enough material objects are create to fill mesh.
while count != len(m):
# Pop lines to the start of the next material.
while (lines.pop(0) + " ")[0] != '=':
pass
# Create a new material object and add to mats dict.
line = lines.pop(0)
nucvec = {}
density = 0.0
# Read lines until '=' delimiter at the end of a material.
while line[0] != '=':
nuc = line.split()[0]
n = float(line.split()[time_index])
if n != 0.0:
nucvec[nuc] = n
density += n * anum(nuc) / N_A
line = lines.pop(0)
mat = from_atom_frac(nucvec, density=density, mass=0)
mats[count] = mat
count += 1
m.mats = mats
def num_density_to_mesh(lines, time, m):
"""num_density_to_mesh(lines, time, m)
This function reads ALARA output containing number density information and
creates material objects which are then added to a supplied PyNE Mesh object.
The volumes within ALARA are assummed to appear in the same order as the
idx on the Mesh object.
Parameters
----------
lines : list or str
ALARA output from ALARA run with 'number_density' in the 'output' block
of the input file. Lines can either be a filename or the equivalent to
calling readlines() on an ALARA output file. If reading in ALARA output
from stdout, call split('\n') before passing it in as the lines parameter.
time : str
The decay time for which number densities are requested (e.g. '1 h',
'shutdown', etc.)
m : PyNE Mesh
Mesh object for which mats will be applied to.
"""
if isinstance(lines, basestring):
with open(lines) as f:
lines = f.readlines()
elif not isinstance(lines, collections.Sequence):
raise TypeError("Lines argument not a file or sequence.")
# Advance file to number density portion.
header = 'Number Density [atoms/cm3]'
line = ""
while line.rstrip() != header:
line = lines.pop(0)
# Get decay time index from next line (the column the decay time answers
# appear in.
line_strs = lines.pop(0).replace('\t', ' ')
time_index = [s.strip() for s in line_strs.split(' ')
if s.strip()].index(time)
# Create a dict of mats for the mesh.
mats = {}
count = 0
# Read through file until enough material objects are create to fill mesh.
while count != len(m):
# Pop lines to the start of the next material.
while (lines.pop(0) + " " )[0] != '=':
pass
# Create a new material object and add to mats dict.
line = lines.pop(0)
nucvec = {}
density = 0.0
# Read lines until '=' delimiter at the end of a material.
while line[0] != '=':
nuc = line.split()[0]
n = float(line.split()[time_index])
if n != 0.0:
nucvec[nuc] = n
density += n * anum(nuc)/N_A
line = lines.pop(0)
mat = from_atom_frac(nucvec, density=density, mass=0)
mats[count] = mat
count += 1
m.mats = mats
def id_to_tuple(atomic_id):
return nucname.znum(atomic_id), nucname.anum(atomic_id)
def id_to_tuple(atomic_id):
return nucname.znum(atomic_id), nucname.anum(atomic_id)
