def __addSens(self, varName, vec, isEnergyIntegrated):
dest = (self.energyIntegratedSens
if isEnergyIntegrated else self.sensitivities)
newShape = [2, self.nPert, self.nZai, self.nMat]
if not isEnergyIntegrated:
newShape.insert(1, self.nEne)
try:
newName = convertVariableName(varName)
dest[newName] = reshapePermuteSensMat(vec, newShape)
except Exception as ee:
critical("The following error was raised attempting to "
"reshape matrix {}".format(varName))
raise ee
def _helpCompareGCDict(self, other, attrBase, sigma):
"""
Method that actually compare group constant dictionaries.
``attrBase`` is used to find dictionaries by appending
``'Exp'`` and ``'Unc'`` to ``attrBase``
"""
self._checkCompareObj(other)
valName = (attrBase + 'Exp') if attrBase != 'gc' else 'gc'
uncName = attrBase + 'Unc'
try:
myVals = getattr(self, valName)
myUncs = getattr(self, uncName)
otherVals = getattr(other, valName)
otherUncs = getattr(other, uncName)
except Exception as ee:
critical("The following error was raised extracting {} and "
"{} from universes {} and {}:\n\t{}".format(
valName, uncName, self, other, ee))
return False
keys = getKeyMatchingShapes(myVals, otherVals, valName)
similar = len(keys) == len(myVals) == len(otherVals)
for key in keys:
if key not in myUncs or key not in otherUncs:
loc = self if key in otherUncs else other
error("Uncertainty data for {} missing from {}".format(
key, loc))
similar = False
continue
myVal = myVals[key]
myUnc = myUncs[key]
otherVal = otherVals[key]
otherUnc = otherUncs[key]
similar &= getLogOverlaps(key,
myVal,
otherVal,
myUnc,
otherUnc,
sigma,
relative=True)
return similar
def test_depSamplerValidCalcs(self):
"""
Run through all variables and materials and check error propagation
and averaging.
"""
errMsg = "{varN} {qty} for material {matN}"
for name, material in self.sampler.iterMaterials():
for varName, varData in iteritems(material.data):
r0 = self.reader0.materials[name].data[varName]
r1 = self.reader1.materials[name].data[varName]
samplerUnc = material.uncertainties[varName]
expectedMean, expectedStd = computeMeansErrors(r0, r1)
for qty, actual, expected in zip(('mean', 'uncertainty'),
(varData, samplerUnc),
(expectedMean, expectedStd)):
msg = errMsg.format(qty=qty, varN=varName, matN=material)
try:
assert_allclose(actual,
expected,
err_msg=msg,
rtol=1E-5)
except AssertionError as ae:
critical("\nMaterial {} - value {}".format(
name, varName))
for aRow, eRow in zip(actual, expected):
critical('Actual: {}'.format(aRow))
critical('Expected: {}'.format(eRow))
den = aRow.copy()
if isinstance(den, ndarray):
den[where(den == 0)] = 1
else:
den = den or 1
rDiff = (fabs(aRow - eRow) / den)
critical('Relative: {}'.format(rDiff))
raise ae