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Cleanup openmc.data based on pylint

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This commit is contained in:
Paul Romano 2016-07-15 08:21:57 -05:00
parent ef7eb3cc64
commit 495556a2f5
13 changed files with 89 additions and 94 deletions
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65
openmc/data/ace.py
View file

@ -20,7 +20,6 @@ import io
from os import SEEK_CUR
import struct
import sys
from warnings import warn
import numpy as np
@ -170,7 +169,7 @@ class Library(object):
sb = b''.join([fh.readline() for i in range(10)])
# Try to decode it with ascii
sd = sb.decode('ascii')
sb.decode('ascii')
# No exception so proceed with ASCII - reopen in non-binary
fh.close()
@ -182,13 +181,13 @@ class Library(object):
fh = open(filename, 'rb')
self._read_binary(fh, table_names, verbose)
def _read_binary(self, fh, table_names, verbose=False,
def _read_binary(self, ace_file, table_names, verbose=False,
recl_length=4096, entries=512):
"""Read a binary (Type 2) ACE table.
Parameters
----------
fh : file
ace_file : file
Open ACE file
table_names : None, str, or iterable
Tables from the file to read in. If None, reads in all of the
@ -204,25 +203,25 @@ class Library(object):
"""
while True:
start_position = fh.tell()
start_position = ace_file.tell()
# Check for end-of-file
if len(fh.read(1)) == 0:
if len(ace_file.read(1)) == 0:
return
fh.seek(start_position)
ace_file.seek(start_position)
# Read name, atomic mass ratio, temperature, date, comment, and
# material
name, atomic_weight_ratio, temperature, date, comment, mat = \
struct.unpack(str('=10sdd10s70s10s'), fh.read(116))
struct.unpack(str('=10sdd10s70s10s'), ace_file.read(116))
name = name.decode().strip()
# Read ZAID/awr combinations
data = struct.unpack(str('=' + 16*'id'), fh.read(192))
data = struct.unpack(str('=' + 16*'id'), ace_file.read(192))
pairs = list(zip(data[::2], data[1::2]))
# Read NXS
nxs = list(struct.unpack(str('=16i'), fh.read(64)))
nxs = list(struct.unpack(str('=16i'), ace_file.read(64)))
# Determine length of XSS and number of records
length = nxs[0]
@ -230,20 +229,20 @@ class Library(object):
# verify that we are supposed to read this table in
if (table_names is not None) and (name not in table_names):
fh.seek(start_position + recl_length*(n_records + 1))
ace_file.seek(start_position + recl_length*(n_records + 1))
continue
if verbose:
temperature_in_K = round(temperature * 1e6 / 8.617342e-5)
print("Loading nuclide {0} at {1} K".format(name, temperature_in_K))
kelvin = round(temperature * 1e6 / 8.617342e-5)
print("Loading nuclide {0} at {1} K".format(name, kelvin))
# Read JXS
jxs = list(struct.unpack(str('=32i'), fh.read(128)))
jxs = list(struct.unpack(str('=32i'), ace_file.read(128)))
# Read XSS
fh.seek(start_position + recl_length)
ace_file.seek(start_position + recl_length)
xss = list(struct.unpack(str('={0}d'.format(length)),
fh.read(length*8)))
ace_file.read(length*8)))
# Insert zeros at beginning of NXS, JXS, and XSS arrays so that the
# indexing will be the same as Fortran. This makes it easier to
@ -263,14 +262,14 @@ class Library(object):
self.tables.append(table)
# Advance to next record
fh.seek(start_position + recl_length*(n_records + 1))
ace_file.seek(start_position + recl_length*(n_records + 1))
def _read_ascii(self, fh, table_names, verbose=False):
def _read_ascii(self, ace_file, table_names, verbose=False):
"""Read an ASCII (Type 1) ACE table.
Parameters
----------
fh : file
ace_file : file
Open ACE file
table_names : None, str, or iterable
Tables from the file to read in. If None, reads in all of the
@ -282,26 +281,23 @@ class Library(object):
tables_seen = set()
lines = [fh.readline() for i in range(13)]
lines = [ace_file.readline() for i in range(13)]
while (0 != len(lines)) and (lines[0] != ''):
while len(lines) != 0 and lines[0] != '':
# Read name of table, atomic mass ratio, and temperature. If first
# line is empty, we are at end of file
# check if it's a 2.0 style header
if lines[0].split()[0][1] == '.':
words = lines[0].split()
version = words[0]
name = words[1]
if len(words) == 3:
source = words[2]
words = lines[1].split()
atomic_weight_ratio = float(words[0])
temperature = float(words[1])
commentlines = int(words[3])
for i in range(commentlines):
lines.pop(0)
lines.append(fh.readline())
lines.append(ace_file.readline())
else:
words = lines[0].split()
name = words[0]
@ -325,24 +321,21 @@ class Library(object):
# verify that we are suppossed to read this table in
if (table_names is not None) and (name not in table_names):
fh.seek(n_bytes, SEEK_CUR)
fh.readline()
lines = [fh.readline() for i in range(13)]
ace_file.seek(n_bytes, SEEK_CUR)
ace_file.readline()
lines = [ace_file.readline() for i in range(13)]
continue
# read and fix over-shoot
lines += fh.readlines(n_bytes)
lines += ace_file.readlines(n_bytes)
if 12 + n_lines < len(lines):
goback = sum([len(line) for line in lines[12+n_lines:]])
lines = lines[:12+n_lines]
fh.seek(-goback, SEEK_CUR)
ace_file.seek(-goback, SEEK_CUR)
if verbose:
temperature_in_K = round(temperature * 1e6 / 8.617342e-5)
print("Loading nuclide {0} at {1} K".format(name, temperature_in_K))
# Read comment
comment = lines[1].strip()
kelvin = round(temperature * 1e6 / 8.617342e-5)
print("Loading nuclide {0} at {1} K".format(name, kelvin))
# Insert zeros at beginning of NXS, JXS, and XSS arrays so that the
# indexing will be the same as Fortran. This makes it easier to
@ -358,7 +351,7 @@ class Library(object):
self.tables.append(table)
# Read all data blocks
lines = [fh.readline() for i in range(13)]
lines = [ace_file.readline() for i in range(13)]
class Table(object):

6
openmc/data/angle_distribution.py
View file

@ -5,7 +5,7 @@ import numpy as np
import openmc.checkvalue as cv
from openmc.stats import Univariate, Tabular, Uniform
from .container import interpolation_scheme
from .container import INTERPOLATION_SCHEME
class AngleDistribution(object):
@ -125,7 +125,7 @@ class AngleDistribution(object):
else:
n = data.shape[1] - j
interp = interpolation_scheme[interpolation[i]]
interp = INTERPOLATION_SCHEME[interpolation[i]]
mu_i = Tabular(data[0, j:j+n], data[1, j:j+n], interp)
mu_i.c = data[2, j:j+n]
@ -189,7 +189,7 @@ class AngleDistribution(object):
data = ace.xss[idx + 2:idx + 2 + 3*n_points]
data.shape = (3, n_points)
mu_i = Tabular(data[0], data[1], interpolation_scheme[intt])
mu_i = Tabular(data[0], data[1], INTERPOLATION_SCHEME[intt])
mu_i.c = data[2]
else:
# Isotropic angular distribution

2
openmc/data/angle_energy.py
View file

@ -1,7 +1,5 @@
from abc import ABCMeta, abstractmethod
import numpy as np
import openmc.data

6
openmc/data/container.py
View file

@ -5,7 +5,7 @@ import numpy as np
import openmc.checkvalue as cv
interpolation_scheme = {1: 'histogram', 2: 'linear-linear', 3: 'linear-log',
INTERPOLATION_SCHEME = {1: 'histogram', 2: 'linear-linear', 3: 'linear-log',
4: 'log-linear', 5: 'log-log'}
@ -262,8 +262,8 @@ class Tabulated1D(object):
Function read from dataset
"""
x = dataset.value[0,:]
y = dataset.value[1,:]
x = dataset.value[0, :]
y = dataset.value[1, :]
breakpoints = dataset.attrs['breakpoints']
interpolation = dataset.attrs['interpolation']
return cls(x, y, breakpoints, interpolation)

15
openmc/data/correlated.py
View file

@ -1,11 +1,12 @@
from collections import Iterable
from numbers import Real, Integral
from warnings import warn
import numpy as np
import openmc.checkvalue as cv
from openmc.stats import Tabular, Univariate, Discrete, Mixture
from .container import interpolation_scheme
from openmc.stats import Tabular, Univariate, Discrete, Mixture, Uniform
from .container import INTERPOLATION_SCHEME
from .angle_energy import AngleEnergy
@ -237,7 +238,7 @@ class CorrelatedAngleEnergy(AngleEnergy):
# Create continuous distribution
if m < n:
interp = interpolation_scheme[interpolation[i]]
interp = INTERPOLATION_SCHEME[interpolation[i]]
x = dset_eout[0, offset_e+m:offset_e+n]
p = dset_eout[1, offset_e+m:offset_e+n]
@ -275,7 +276,7 @@ class CorrelatedAngleEnergy(AngleEnergy):
if interp_code == 0:
mu_ij = Discrete(x, p)
else:
mu_ij = Tabular(x, p, interpolation_scheme[interp_code],
mu_ij = Tabular(x, p, INTERPOLATION_SCHEME[interp_code],
ignore_negative=True)
mu_ij.c = c
mu_i.append(mu_ij)
@ -285,8 +286,6 @@ class CorrelatedAngleEnergy(AngleEnergy):
energy_out.append(eout_i)
mu.append(mu_i)
j += n
return cls(energy_breakpoints, energy_interpolation,
energy, energy_out, mu)
@ -357,7 +356,7 @@ class CorrelatedAngleEnergy(AngleEnergy):
# Create continuous distribution
eout_continuous = Tabular(data[0][n_discrete_lines:],
data[1][n_discrete_lines:],
interpolation_scheme[intt],
INTERPOLATION_SCHEME[intt],
ignore_negative=True)
eout_continuous.c = data[2][n_discrete_lines:]
@ -390,7 +389,7 @@ class CorrelatedAngleEnergy(AngleEnergy):
data = ace.xss[idx + 2:idx + 2 + 3*n_cosine]
data.shape = (3, n_cosine)
mu_ij = Tabular(data[0], data[1], interpolation_scheme[intt])
mu_ij = Tabular(data[0], data[1], INTERPOLATION_SCHEME[intt])
mu_ij.c = data[2]
else:
# Isotropic distribution

12
openmc/data/data.py
View file

@ -150,9 +150,9 @@ reaction_name = {1: '(n,total)', 2: '(n,elastic)', 4: '(n,level)', 5: '(n,misc)'
198: '(n,n3p)', 199: '(n,3n2pa)', 200: '(n,5n2p)', 444: '(n,damage)',
649: '(n,pc)', 699: '(n,dc)', 749: '(n,tc)', 799: '(n,3Hec)',
849: '(n,ac)'}
reaction_name.update({i: '(n,n{})'.format(i-50) for i in range(50,91)})
reaction_name.update({i: '(n,p{})'.format(i-600) for i in range(600,649)})
reaction_name.update({i: '(n,d{})'.format(i-650) for i in range(650,699)})
reaction_name.update({i: '(n,t{})'.format(i-700) for i in range(700,749)})
reaction_name.update({i: '(n,3He{})'.format(i-750) for i in range(750,799)})
reaction_name.update({i: '(n,a{})'.format(i-800) for i in range(800,849)})
reaction_name.update({i: '(n,n{})'.format(i-50) for i in range(50, 91)})
reaction_name.update({i: '(n,p{})'.format(i-600) for i in range(600, 649)})
reaction_name.update({i: '(n,d{})'.format(i-650) for i in range(650, 699)})
reaction_name.update({i: '(n,t{})'.format(i-700) for i in range(700, 749)})
reaction_name.update({i: '(n,3He{})'.format(i-750) for i in range(750, 799)})
reaction_name.update({i: '(n,a{})'.format(i-800) for i in range(800, 849)})

21
openmc/data/energy_distribution.py
View file

@ -3,10 +3,9 @@ from collections import Iterable
from numbers import Integral, Real
from warnings import warn
import h5py
import numpy as np
from openmc.data.container import Tabulated1D, interpolation_scheme
from openmc.data.container import Tabulated1D, INTERPOLATION_SCHEME
from openmc.stats.univariate import Univariate, Tabular, Discrete, Mixture
import openmc.checkvalue as cv
@ -78,11 +77,12 @@ class ArbitraryTabulated(EnergyDistribution):
"""
def __init__(self, energy, pdf):
super(ArbitraryTabulated, self).__init__()
self.energy = energy
self.pdf = pdf
def to_hdf5(self, group):
NotImplementedError
raise NotImplementedError
class GeneralEvaporation(EnergyDistribution):
@ -114,6 +114,7 @@ class GeneralEvaporation(EnergyDistribution):
"""
def __init__(self, theta, g, u):
super(GeneralEvaporation, self).__init__()
self.theta = theta
self.g = g
self.u = u
@ -121,6 +122,10 @@ class GeneralEvaporation(EnergyDistribution):
def to_hdf5(self, group):
raise NotImplementedError
@classmethod
def from_ace(cls, ace, idx=0):
raise NotImplementedError
class MaxwellEnergy(EnergyDistribution):
r"""Simple Maxwellian fission spectrum represented as
@ -147,6 +152,7 @@ class MaxwellEnergy(EnergyDistribution):
"""
def __init__(self, theta, u):
super(MaxwellEnergy, self).__init__()
self.theta = theta
self.u = u
@ -254,6 +260,7 @@ class Evaporation(EnergyDistribution):
"""
def __init__(self, theta, u):
super(Evaporation, self).__init__()
self.theta = theta
self.u = u
@ -364,6 +371,7 @@ class WattEnergy(EnergyDistribution):
"""
def __init__(self, a, b, u):
super(WattEnergy, self).__init__()
self.a = a
self.b = b
self.u = u
@ -504,6 +512,7 @@ class MadlandNix(EnergyDistribution):
"""
def __init__(self, efl, efh, tm):
super(MadlandNix, self).__init__()
self.efl = efl
self.efh = efh
self.tm = tm
@ -966,7 +975,7 @@ class ContinuousTabular(EnergyDistribution):
# Create continuous distribution
if m < n:
interp = interpolation_scheme[interpolation[i]]
interp = INTERPOLATION_SCHEME[interpolation[i]]
eout_continuous = Tabular(data[0, j+m:j+n], data[1, j+m:j+n], interp)
eout_continuous.c = data[2, j+m:j+n]
@ -1048,8 +1057,8 @@ class ContinuousTabular(EnergyDistribution):
# Create continuous distribution
eout_continuous = Tabular(data[0][n_discrete_lines:],
data[1][n_discrete_lines:],
interpolation_scheme[intt])
data[1][n_discrete_lines:],
INTERPOLATION_SCHEME[intt])
eout_continuous.c = data[2][n_discrete_lines:]
# If discrete lines are present, create a mixture distribution

9
openmc/data/kalbach_mann.py
View file

@ -1,11 +1,12 @@
from collections import Iterable
from numbers import Real, Integral
from warnings import warn
import numpy as np
import openmc.checkvalue as cv
from openmc.stats import Tabular, Univariate, Discrete, Mixture
from .container import Tabulated1D, interpolation_scheme
from .container import Tabulated1D, INTERPOLATION_SCHEME
from .angle_energy import AngleEnergy
@ -228,7 +229,7 @@ class KalbachMann(AngleEnergy):
# Create continuous distribution
if m < n:
interp = interpolation_scheme[interpolation[i]]
interp = INTERPOLATION_SCHEME[interpolation[i]]
eout_continuous = Tabular(data[0, j+m:j+n], data[1, j+m:j+n], interp)
eout_continuous.c = data[2, j+m:j+n]
@ -318,8 +319,8 @@ class KalbachMann(AngleEnergy):
# Create continuous distribution
eout_continuous = Tabular(data[0][n_discrete_lines:],
data[1][n_discrete_lines:],
interpolation_scheme[intt])
data[1][n_discrete_lines:],
INTERPOLATION_SCHEME[intt])
eout_continuous.c = data[2][n_discrete_lines:]
# If discrete lines are present, create a mixture distribution

2
openmc/data/library.py
View file

@ -13,7 +13,7 @@ class DataLibrary(object):
h5file = h5py.File(filename, 'r')
materials = []
for name, group in h5file.items():
for name in h5file:
materials.append(name)
library = {'path': filename, 'type': filetype, 'materials': materials}

22
openmc/data/neutron.py
View file

@ -1,25 +1,17 @@
from __future__ import division, unicode_literals
import io
import sys
from warnings import warn
from collections import OrderedDict, Iterable, Mapping
from copy import deepcopy
from numbers import Integral, Real
import sys
import numpy as np
from numpy.polynomial import Polynomial
import h5py
from . import atomic_number, atomic_symbol
from . import atomic_symbol
from .ace import Table, get_table
from .container import Tabulated1D
from .energy_distribution import *
from .product import Product
from .reaction import Reaction, _get_photon_products
from .thermal import CoherentElastic
from .urr import ProbabilityTables
from openmc.stats import Tabular, Discrete, Uniform, Mixture
import openmc.checkvalue as cv
if sys.version_info[0] >= 3:
@ -243,7 +235,7 @@ class IncidentNeutron(object):
f.close()
@classmethod
def from_hdf5(self, group_or_filename):
def from_hdf5(cls, group_or_filename):
"""Generate continuous-energy neutron interaction data from HDF5 group
Parameters
@ -255,7 +247,7 @@ class IncidentNeutron(object):
Returns
-------
openmc.data.ace.IncidentNeutron
openmc.data.IncidentNeutron
Continuous-energy neutron interaction data
"""
@ -272,8 +264,8 @@ class IncidentNeutron(object):
atomic_weight_ratio = group.attrs['atomic_weight_ratio']
temperature = group.attrs['temperature']
data = IncidentNeutron(name, atomic_number, mass_number, metastable,
atomic_weight_ratio, temperature)
data = cls(name, atomic_number, mass_number, metastable,
atomic_weight_ratio, temperature)
# Read energy grid
data.energy = group['energy'].value
@ -362,8 +354,8 @@ class IncidentNeutron(object):
else:
name = '{}{}.{}'.format(element, mass_number, xs)
data = IncidentNeutron(name, Z, mass_number, metastable,
ace.atomic_weight_ratio, ace.temperature)
data = cls(name, Z, mass_number, metastable,
ace.atomic_weight_ratio, ace.temperature)
# Read energy grid
n_energy = ace.nxs[3]

6
openmc/data/reaction.py
View file

@ -103,7 +103,7 @@ def _get_fission_products(ace):
idx = ace.jxs[25]
n_group = ace.nxs[8]
total_group_probability = 0.
for i, group in enumerate(range(n_group)):
for group in range(n_group):
delayed_neutron = Product('neutron')
delayed_neutron.emission_mode = 'delayed'
delayed_neutron.decay_rate = ace.xss[idx]
@ -201,14 +201,14 @@ def _get_photon_products(ace, mt):
n_energy = int(ace.xss[idx + 1])
photon._xs = ace.xss[idx + 2:idx + 2 + n_energy]
# Determine yield based on ratio of cross sections
# TODO: Determine yield based on ratio of cross sections
energy = ace.xss[ace.jxs[1] + threshold_idx:
ace.jxs[1] + threshold_idx + n_energy]
photon.yield_ = Tabulated1D(energy, photon._xs)
else:
raise ValueError("MFTYPE must be 12, 13, 16. Got {0}".format(
mftype))
mftype))
# ==================================================================
# Photon energy distribution

11
openmc/data/thermal.py
View file

@ -8,7 +8,10 @@ import h5py
import openmc.checkvalue as cv
from .ace import Table, get_table
from .angle_energy import AngleEnergy
from .container import Tabulated1D
from .correlated import CorrelatedAngleEnergy
from openmc.stats import Discrete, Tabular
_THERMAL_NAMES = {'al': 'c_Al27', 'al27': 'c_Al27',
@ -37,7 +40,7 @@ _THERMAL_NAMES = {'al': 'c_Al27', 'al27': 'c_Al27',
class CoherentElastic(object):
"""Coherent elastic scattering data from a crystalline material
r"""Coherent elastic scattering data from a crystalline material
Parameters
----------
@ -212,7 +215,7 @@ class ThermalScattering(object):
self.inelastic_dist.to_hdf5(inelastic_group)
@classmethod
def from_hdf5(self, group):
def from_hdf5(cls, group):
"""Generate thermal scattering data from HDF5 group
Parameters
@ -229,7 +232,7 @@ class ThermalScattering(object):
name = group.name[1:]
atomic_weight_ratio = group.attrs['atomic_weight_ratio']
temperature = group.attrs['temperature']
table = ThermalScattering(name, atomic_weight_ratio, temperature)
table = cls(name, atomic_weight_ratio, temperature)
table.zaids = group.attrs['zaids']
# Read thermal elastic scattering
@ -363,7 +366,7 @@ class ThermalScattering(object):
# Create correlated angle-energy distribution
breakpoints = [n_energy]
interpolation = [2]
energy = inelastic_xs.x
energy = table.inelastic_xs.x
table.inelastic_dist = CorrelatedAngleEnergy(
breakpoints, interpolation, energy, energy_out, mu_out)

6
openmc/data/urr.py
View file

@ -7,7 +7,7 @@ import openmc.checkvalue as cv
class ProbabilityTables(object):
"""Unresolved resonance region probability tables.
r"""Unresolved resonance region probability tables.
Parameters
----------
@ -18,7 +18,7 @@ class ProbabilityTables(object):
where N is the number of energies and M is the number of bands. The
second dimension indicates whether the value is for the cumulative
probability (0), total (1), elastic (2), fission (3), :math:`(n,\gamma)`
(4), or heating number (6).
(4), or heating number (5).
interpolation : {2, 5}
Interpolation scheme between tables
inelastic_flag : int
@ -45,7 +45,7 @@ class ProbabilityTables(object):
where N is the number of energies and M is the number of bands. The
second dimension indicates whether the value is for the cumulative
probability (0), total (1), elastic (2), fission (3), :math:`(n,\gamma)`
(4), or heating number (6).
(4), or heating number (5).
interpolation : {2, 5}
Interpolation scheme between tables
inelastic_flag : int