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Fixed a lot of doc strings, jupyter example

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Isaac Meyer 2018-07-05 14:35:50 -05:00
parent a0182e12d6
commit 1845edb233
2 changed files with 383 additions and 113 deletions
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278
examples/jupyter/nuclear-data-resonance-covariance.ipynb Normal file
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218
openmc/data/resonance_covariance.py
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@ -14,7 +14,7 @@ from .resonance import Resonances
def file2contributions(file32params, file2params):
"""Function for aiding in adding resonance parameters from File 2 that are
not always present in file 32.
not always present in File 32. Uses already imported resonance data.
Paramateers
-----------
@ -25,7 +25,7 @@ def file2contributions(file32params, file2params):
Returns
-------
parameters: pandas.Dataframs
parameters: pandas.Dataframe
Complete set of parameters ordered by L-values and then energy
"""
#Use l-values and competitiveWidth from File 2 data
@ -56,8 +56,6 @@ class ResonanceCovariances(Resonances):
----------
ranges : list of openmc.data.ResonanceCovarianceRange
Distinct energy ranges for resonance data
resolved : openmc.data.ResonanceCovariance or None
Resolved resonance range
"""
def __init__(self, ranges):
@ -85,7 +83,7 @@ class ResonanceCovariances(Resonances):
----------
ev : openmc.data.endf.Evaluation
ENDF evaluation
resonances : Resonance object
resonances : openmc.data.Resonance object
Returns
-------
@ -133,35 +131,53 @@ class ResonanceCovariances(Resonances):
class ResonanceCovarianceRange(object):
"""Resonace covariance range
"""Resonace covariance range. Base class for different formalisms.
Parameters
----------
energy_min : float
Minimum energy of the resolved resonance range in eV
energy_max : float
Maximum energy of the resolved resonance range in eV
Attributes
----------
cov_parameters: list
The parameters that are included in the covariance matrix
covariance_matrix : array
energy_min : float
Minimum energy of the resolved resonance range in eV
energy_max : float
Maximum energy of the resolved resonance range in eV
parameters: pandas.DataFrame
Resonance parameters
covariance : numpy.array
The covariance matrix contained within the ENDF evaluation
lcomp : int
Flag indicating the format of the covariance matrix
Flag indicating the format of the covariance matrix within the ENDF file
mpar : int
Number of parameters in covariance matrix for each individual resonance
formalism : str
String descriptor of formalism
"""
def __init__(self, energy_min, energy_max):
self.energy_min = energy_min
self.energy_max = energy_max
def res_subset(self, parameter_str, bounds):
"""Produce a subset of resonance parameters and the covariance matrix
to an IncidentNeutron object.
"""Produce a subset of resonance parameters and the corresponding
covariance matrix to an IncidentNeutron object.
Parameters
----------
parameter_str: parameter to be discriminated
(i.e. 'energy','captureWidth','fissionWidthA'...)
bounds: np.array [low numerical bound, high numerical bound]
parameter_str: str
parameter to be discriminated
(i.e. 'energy','captureWidth','fissionWidthA'...)
bounds: np.array
[low numerical bound, high numerical bound]
Returns
-------
parameters_subset : Dataframe of a subset of parameters
(maintains indexing)
cov_subset: subset of covariance matrix (upper triangular)
parameters_subset : pandas.Dataframe
Subset of parameters (maintains indexing of original)
cov_subset: np.array
Subset of covariance matrix (upper triangular)
"""
parameters = self.parameters
@ -173,17 +189,17 @@ class ResonanceCovarianceRange(object):
parameters_subset=parameters[mask]
indices = parameters_subset.index.values
sub_cov_dim = len(indices)*mpar
oldvalues = []
cov_subset_vals = []
for index1 in indices:
for i in range(mpar):
for index2 in indices:
for j in range(mpar):
if index2*mpar+j >= index1*mpar+i:
oldvalues.append(cov[index1*mpar+i,index2*mpar+j])
cov_subset_vals.append(cov[index1*mpar+i,index2*mpar+j])
cov_subset = np.zeros([sub_cov_dim,sub_cov_dim])
tri_indices = np.triu_indices(sub_cov_dim)
cov_subset[tri_indices] = oldvalues
cov_subset[tri_indices] = cov_subset_vals
self.parameters_subset = parameters_subset
self.cov_subset = cov_subset
@ -193,13 +209,15 @@ class ResonanceCovarianceRange(object):
Parameters
----------
n_samples: int
n_samples : int
The number of samples to produce
use_subset: bool, optional
use_subset : bool, optional
Flag on whether to sample from an already produced subset
Returns
-------
samples : list of openmc.data.ResonanceCovarianceRange objects
List of samples size [n_samples]
"""
if use_subset==False:
@ -219,7 +237,7 @@ class ResonanceCovarianceRange(object):
samples = []
### Handling MLBW Sampling ###
# Handling MLBW Sampling
if formalism == 'mlbw' or formalism == 'slbw':
if mpar == 3:
param_list = ['energy','neutronWidth','captureWidth']
@ -263,29 +281,30 @@ class ResonanceCovarianceRange(object):
sample_params = pd.DataFrame.from_records(records, columns=columns)
samples.append(sample_params)
###FIXME doesn't look any different from mpar == 4
elif mpar == 5:
param_list = ['energy','neutronWidth','captureWidth','fissionWidth']
param_list = ['energy','neutronWidth','captureWidth',
'fissionWidth', 'competitiveWidth']
mean_array = pd.DataFrame.as_matrix(parameters[param_list])
spin = pd.DataFrame.as_matrix(parameters['J'])
mean = mean_array.flatten()
for i in range(n_samples):
sample = np.random.multivariate_normal(mean,cov)
energy = sample[0::4]
gn = sample[1::4]
gg = sample[2::4]
gf = sample[3::4]
energy = sample[0::5]
gn = sample[1::5]
gg = sample[2::5]
gf = sample[3::5]
gx = sample[4::5]
gt = gn + gg + gf
records = []
for j, E in enumerate(energy):
records.append([energy[j], spin[j], gt[j], gn[j],
gg[j], gf[j]])
gg[j], gf[j], gx[j]])
columns = ['energy', 'J', 'totalWidth', 'neutronWidth',
'captureWidth', 'fissionWidth']
'captureWidth', 'fissionWidth', 'competitveWidth']
sample_params = pd.DataFrame.from_records(records, columns=columns)
samples.append(sample_params)
### Handling RM Sampling ###
# Handling RM Sampling
if formalism == 'rm':
if mpar == 3:
param_list = ['energy','neutronWidth','captureWidth']
@ -333,29 +352,37 @@ class ResonanceCovarianceRange(object):
class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
"""Multi-level Breit-Wigner resolved resonance formalism covariance data.
Multi-level Breit-Wigner resolved resonance data is identified by LRF=2 in
the ENDF-6 format.
Parameters
----------
energy_min : float
Minimum energy of the resolved resonance range in eV
energy_max : float
Maximum energy of the resolved resonance range in eV
Attributes
----------
cov_parameters: list
The parameters that are included in the covariance matrix
covariance_matrix : array
energy_min : float
Minimum energy of the resolved resonance range in eV
energy_max : float
Maximum energy of the resolved resonance range in eV
parameters: pandas.DataFrame
Resonance parameters
covariance : numpy.array
The covariance matrix contained within the ENDF evaluation
lcomp : int
Flag indicating the format of the covariance matrix
Flag indicating the format of the covariance matrix within the ENDF file
mpar : int
Number of parameters in covariance matrix for each individual resonance
formalism : str
String descriptor of formalism
"""
def __init__(self, energy_min, energy_max):
super().__init__(energy_min, energy_max)
self.parameters = None
self.covariance = None
self.mpar = None
self.lcomp = None
self.num_parameters = None
self.formalism = 'mlbw'
@classmethod
@ -372,7 +399,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
items : list
Items from the CONT record at the start of the resonance range
subsection
resonances : openmc.data.IncidentNeutron.Resonance
resonances : openmc.data.IncidentNeutron.Resonance object
Returns
-------
@ -406,7 +433,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
records = []
for i in range(num_short_range):
items, values = get_list_record(file_obj)
num_parameters = items[2]
mpar = items[2]
num_res = items[5]
num_par_vals = num_res*6
res_values = values[:num_par_vals]
@ -424,7 +451,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
gg[i], gf[i]])
#Build the upper-triangular covariance matrix
cov_dim = num_parameters*num_res
cov_dim = mpar*num_res
cov = np.zeros([cov_dim,cov_dim])
indices = np.triu_indices(cov_dim)
cov[indices] = cov_values
@ -435,12 +462,6 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
'captureWidth', 'fissionWidth']
parameters = pd.DataFrame.from_records(records, columns=columns)
#Determine mpar (number of parameters for each resonance in
#covariance matrix)
nparams,params = parameters.shape
covsize = cov.shape[0]
mpar = int(covsize/nparams)
#Add parameters from File 2
parameters = file2contributions(parameters, file2params)
@ -450,7 +471,6 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
mlbw.covariance = cov
mlbw.mpar = mpar
mlbw.lcomp = LCOMP
mlbw.num_parameters = num_parameters
return mlbw
@ -498,7 +518,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
mpar = int(covsize/nparams)
#Add parameters from File 2
parameters = file2contributions(parameter, file2params)
parameters = file2contributions(parameters, file2params)
# Create instance of MultiLevelBreitWignerCovariance
mlbw = cls(energy_min, energy_max)
@ -557,7 +577,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
mpar = int(covsize/nparams)
#Add parameters from File 2
parameters = file2contributions(parameter, file2params)
parameters = file2contributions(parameters, file2params)
# Create instance of class
mlbw = cls(energy_min, energy_max)
@ -571,53 +591,36 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
class SingleLevelBreitWignerCovariance(MultiLevelBreitWignerCovariance):
"""Single-level Breit-Wigner resolved resonance formalism covariance data.
Single-level Breit-Wigner resolved resonance data is is identified by LRF=1
in the ENDF-6 format.
Parameters
----------
target_spin : float
Intrinsic spin, :math:`I`, of the target nuclide
energy_min : float
Minimum energy of the resolved resonance range in eV
energy_max : float
Maximum energy of the resolved resonance range in eV
channel : dict
Dictionary whose keys are l-values and values are channel radii as a
function of energy
scattering : dict
Dictionary whose keys are l-values and values are scattering radii as a
function of energy
Attributes
----------
atomic_weight_ratio : float
Atomic weight ratio of the target nuclide given as a function of
l-value. Note that this may be different than the value for the
evaluation as a whole.
channel_radius : dict
Dictionary whose keys are l-values and values are channel radii as a
function of energy
energy_max : float
Maximum energy of the resolved resonance range in eV
energy_min : float
Minimum energy of the resolved resonance range in eV
parameters : pandas.DataFrame
Energies, spins, and resonances widths for each resonance
q_value : dict
Q-value to be added to incident particle's center-of-mass energy to
determine the channel energy for use in the penetrability factor. The
keys of the dictionary are l-values.
scattering_radius : dict
Dictionary whose keys are l-values and values are scattering radii as a
function of energy
target_spin : float
Intrinsic spin, :math:`I`, of the target nuclide
energy_max : float
Maximum energy of the resolved resonance range in eV
parameters: pandas.DataFrame
Resonance parameters
covariance : numpy.array
The covariance matrix contained within the ENDF evaluation
lcomp : int
Flag indicating the format of the covariance matrix within the ENDF file
mpar : int
Number of parameters in covariance matrix for each individual resonance
formalism : str
String descriptor of formalism
"""
def __init__(self, energy_min, energy_max):
super().__init__(energy_min,energy_max)
self.formalism = 'slbw'
class ReichMooreCovariance(ResonanceCovarianceRange):
@ -626,39 +629,35 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
Reich-Moore resolved resonance data is identified by LRF=3 in the ENDF-6
format.
Parameters
----------
target_spin : float
Intrinsic spin, :math:`I`, of the target nuclide
energy_min : float
Minimum energy of the resolved resonance range in eV
energy_max : float
Maximum energy of the resolved resonance range in eV
channel : dict
Dictionary whose keys are l-values and values are channel radii as a
function of energy
scattering : dict
Dictionary whose keys are l-values and values are scattering radii as a
function of energy
Attributes
----------
num_parameters: list
Number of parameters used in each subsection
cov_parameters: list
The parameters that are included in the covariance matrix
covariance_matrix : array
energy_min : float
Minimum energy of the resolved resonance range in eV
energy_max : float
Maximum energy of the resolved resonance range in eV
parameters: pandas.DataFrame
Resonance parameters
covariance : numpy.array
The covariance matrix contained within the ENDF evaluation
lcomp : int
Flag indicating the format of the covariance matrix within the ENDF file
mpar : int
Number of parameters in covariance matrix for each individual resonance
formalism : str
String descriptor of formalism
"""
def __init__(self, energy_min, energy_max):
self.num_parameters = None
super().__init__(energy_min, energy_max)
self.parameters = None
self.covariance = None
self.num_parameters = None
self.formalism = 'rm'
@classmethod
@ -711,7 +710,7 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
records = []
for i in range(num_short_range):
items, values = get_list_record(file_obj)
num_parameters = items[2]
mpar = items[2]
num_res = items[5]
num_par_vals = num_res*6
res_values = values[:num_par_vals]
@ -729,7 +728,7 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
gfa[i], gfb[i]])
#Build the upper-triangular covariance matrix
cov_dim = num_parameters*num_res
cov_dim = mpar*num_res
cov = np.zeros([cov_dim,cov_dim])
indices = np.triu_indices(cov_dim)
cov[indices] = cov_values
@ -739,14 +738,8 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
'fissionWidthA', 'fissionWidthB']
parameters = pd.DataFrame.from_records(records, columns=columns)
#Determine mpar (number of parameters for each resonance in
#covariance matrix)
nparams,params = parameters.shape
covsize = cov.shape[0]
mpar = int(covsize/nparams)
#Add parameters from File 2
parameters = file2contributions(parameter, file2params)
parameters = file2contributions(parameters, file2params)
# Create instance of ReichMooreCovariance
rmc = cls(energy_min, energy_max)
@ -754,7 +747,6 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
rmc.covariance = cov
rmc.mpar = mpar
rmc.lcomp = LCOMP
rmc.num_parameters = num_parameters
return rmc
@ -795,7 +787,7 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
mpar = int(covsize/nparams)
#Add parameters from File 2
parameters = file2contributions(parameter, file2params)
parameters = file2contributions(parameters, file2params)
# Create instance of ReichMooreCovariance
rmc = cls(energy_min, energy_max)