def getW_XYs(self):
from xData import axes as axesModule
from xData import XYs as XYsModule
from xData import multiD_XYs as multiD_XYsModule
xUnit, yUnit, zUnit = '', '', ''
if (self.xUnit is not None): xUnit = self.xUnit
if (self.yUnit is not None): yUnit = self.yUnit
if (self.zUnit is not None): zUnit = self.zUnit
axes_3d = axesModule.axes(3)
axes_3d[0] = axesModule.axis('z', 0, zUnit)
axes_3d[1] = axesModule.axis('y', 0, yUnit)
axes_3d[2] = axesModule.axis('x', 0, xUnit)
w_xys = multiD_XYsModule.multiD_XYs(axes=axes_3d)
axes_2d = axesModule.axes()
axes_2d[0] = axes_3d[0]
axes_2d[1] = axes_3d[1]
for ix, x in enumerate(self.x):
xys = [[y, self.z[iy][ix]] for iy, y in enumerate(self.y)]
w_xys[ix] = XYsModule.XYs(xys,
axes=axes_2d,
accuracy=1e-3,
value=x)
return (w_xys)
def defaultAxes(energyUnit):
axes = axesModule.axes(rank=3)
axes[0] = axesModule.axis('P(mu|energy_in)', 0, '')
axes[1] = axesModule.axis('mu', 1, '')
axes[2] = axesModule.axis('energy_in', 2, energyUnit)
return (axes)
def toEnergyFunctionalData(info,
dataLine,
MF5Data,
LF,
moniker,
unit,
xLabel='energy_in',
xUnit='eV'):
import fudge.gnd.productData.distributions.energy as energyModule
axes = axesModule.axes(labelsUnits={
0: (moniker, unit),
1: (xLabel, xUnit)
})
dataLine, TAB1, data = getTAB1Regions(dataLine,
MF5Data,
logFile=info.logs,
axes=axes)
EFclass = None
for tmp in (energyModule.a, energyModule.b, energyModule.theta,
energyModule.g, energyModule.T_M):
if (tmp.moniker == moniker):
EFclass = tmp
if (len(data) == 1):
oneD = data[0]
else:
oneD = regionsModule.regions1d(axes=data[0].axes)
for datum in data:
oneD.append(datum)
return (dataLine, TAB1, EFclass(oneD))
def toMultiGroup1d( cls, style, tempInfo, _axes, data, addLabel = True ) :
"""
This function takes 1-d multi-group data as a list of floats and return to 1-d gridded instance
containing the multi-group data. The list of data must contain n values where n is the
number of groups.
"""
reactionSuite = tempInfo['reactionSuite']
axes = axesModule.axes( rank = 2 )
axes[0] = axesModule.axis( _axes[0].label, 0, _axes[0].unit )
energyInGrid = style.transportables[reactionSuite.projectile].group.boundaries.values.copy( )
axes[1] = axesModule.grid( _axes[1].label, 1, _axes[1].unit, axesModule.boundariesGridToken, energyInGrid )
shape = [ len( data ) ]
data = valuesModule.values( data )
for start, datum in enumerate( data ) :
if( datum != 0 ) : break
end = 0
for i1, datum in enumerate( data ) :
if( datum != 0 ) : end = i1
if( start > end ) : # Only happens when all values are 0.
start = 0
else :
end += 1
data = data[start:end]
starts = valuesModule.values( [ start ], valueType = standardsModule.types.integer32Token )
lengths = valuesModule.values( [ len( data ) ], valueType = standardsModule.types.integer32Token )
flattened = arrayModule.flattened( shape = shape, data = data, starts = starts, lengths = lengths )
label = None
if( addLabel ) : label = style.label
return( cls( label = label, axes = axes, array = flattened ) )
def defaultAxes(energyUnit='eV', crossSectionUnit='b'):
axes = axesModule.axes(rank=2)
axes[0] = axesModule.axis(baseModule.crossSectionToken, 0,
crossSectionUnit)
axes[1] = axesModule.axis('energy_in', 1, energyUnit)
return (axes)
def defaultAxes( energyUnit ) :
axes = axesModule.axes( )
# BRB6 make 'energy_in' a token
axes[1] = axesModule.axis( 'energy_in', 0, energyUnit )
axes[0] = axesModule.axis( component.moniker, 1, energyUnit )
return( axes )
def calculateMomentumPoints( massInE, EMin, EMax, energyUnit, accuracy = 1e-4 ) :
"""
This is a temporary function (hopefully) to calculate momentum vs E.
What is really needed is a function like rriint in mcfgen.
"""
import math
momentum = []
def insertPointIfNeeded( Ep1, Ep2 ) :
E1, p1 = Ep1
E2, p2 = Ep2
s = ( p2 - p1 ) / ( E2 - E1 )
Em = massInE / ( 2. * s * s )
pc = math.sqrt( 2 * massInE * Em )
pi = s * ( Em - E1 ) + p1
if( abs( pi / pc - 1 ) > accuracy ) :
m = [ Em, pc ]
momentum.append( m )
insertPointIfNeeded( Ep1, m )
insertPointIfNeeded( m, Ep2 )
if( massInE == 0 ) : # gammas
momentum.append( [ EMin, EMin ] )
momentum.append( [ EMax, EMax ] )
else :
momentum.append( [ EMin, math.sqrt( 2 * massInE * EMin ) ] )
momentum.append( [ EMax, math.sqrt( 2 * massInE * EMax ) ] )
insertPointIfNeeded( momentum[0], momentum[1] )
momentum.sort( )
axes = axesModule.axes( labelsUnits = { 0 : [ 'energy_in', energyUnit ], 1 : [ 'momentum', energyUnit + '/c' ] } )
return( XYsModule.XYs1d( data = momentum, axes = axes, accuracy = accuracy ) )
def defaultAxes(energyUnit, momentumDepositionUnit):
axes = axesModule.axes(rank=2)
axes[0] = axesModule.axis('averageProductMomentum', 0,
momentumDepositionUnit)
axes[1] = axesModule.axis('energy_in', 1, energyUnit)
return (axes)
def defaultAxes(energyUnit):
axes = axesModule.axes(rank=4)
axes[3] = axesModule.axis('l', 3, '')
axes[2] = axesModule.axis('energy_in', 2, energyUnit)
axes[1] = axesModule.axis('energy_out', 1, energyUnit)
axes[0] = axesModule.axis('c_l', 0, '1/' + energyUnit)
return (axes)
def defaultAxes( energyUnit, probabilityLabel = 'P(mu,energy_out|energy_in)' ) :
axes = axesModule.axes( rank = 4 )
axes[3] = axesModule.axis( 'energy_in', 3, energyUnit )
axes[2] = axesModule.axis( 'mu', 2, '' )
axes[1] = axesModule.axis( 'energy_out', 1, energyUnit )
axes[0] = axesModule.axis( probabilityLabel, 0, '1/' + energyUnit )
return( axes )
def getFluxAtLegendreOrder(self, order):
axes = axesModule.axes()
axes[1] = self.axes[2].copy([])
axes[0] = self.axes[0].copy([])
for LS in self:
if (len(LS) > 1): raise Exception('FIXME -- next line is wrong.')
return (XYsModule.XYs1d([[LS.value, LS[0]] for LS in self], axes=axes))
def defaultAxes():
axes = axesModule.axes(rank=4)
axes[0] = axesModule.axis('c_l', 0, '1/MeV')
axes[1] = axesModule.axis('energy_out', 1, 'MeV')
axes[2] = axesModule.axis('energy_in', 2, 'MeV')
axes[3] = axesModule.axis('l', 3, '')
return (axes)
def defaultAxes(energyUnit='eV'):
axes = axesModule.axes(rank=3)
axes[0] = axesModule.axis('P(energy_out|energy_in)', 0,
'1/' + energyUnit)
axes[1] = axesModule.axis('energy_out', 1, energyUnit)
axes[2] = axesModule.axis('energy_in', 2, energyUnit)
return (axes)
def energyFunctionToString( energyData, weight = None ) :
def getParameter( data, label ) :
_linlin = data.toPointwise_withLinearXYs( lowerEps = lowerEps, upperEps = upperEps )
sData = [ startOfNewData, "%s: n = %d" % ( label, len( _linlin ) ) ]
sData.append( 'Interpolation: %s' % linlin )
for x, y in _linlin : sData.append( '%s %s' % ( x, y ) )
return( sData )
sData = []
if( isinstance( energyData, ( energyModule.simpleMaxwellianFissionSpectrum, energyModule.evaporationSpectrum, energyModule.WattSpectrum ) ) ) :
sData.append( 'U: %s' % energyData.U.getValueAs( energyData.parameter1.data.axes[-1].unit ) )
parameter1 = energyData.parameter1.data.toPointwise_withLinearXYs( accuracy = 1e-5, lowerEps = lowerEps, upperEps = upperEps )
if( energyData.parameter2 is not None ) :
parameter2 = energyData.parameter2.data.toPointwise_withLinearXYs( accuracy = 1e-5, lowerEps = lowerEps, upperEps = upperEps )
if( weight is None ) :
axes = axesModule.axes( )
weight = XYsModule.XYs1d( data = [ [ parameter1.domainMin, 1. ], [ parameter1.domainMax, 1. ] ], axes = axes )
else :
weight = weight.weight
sData += twoDToString( "weight", weight, addExtraBlankLine = False )
if( isinstance( energyData, energyModule.generalEvaporationSpectrum ) ) :
sProcess = 'Process: general evaporation\n'
sSpecific = ''
sData += getParameter( parameter1, 'Theta' )
sData += getParameter( parameter2, 'g' )
elif( isinstance( energyData, energyModule.simpleMaxwellianFissionSpectrum ) ) :
sProcess = 'Process: Maxwell spectrum\n'
sSpecific = ''
sData += getParameter( parameter1, 'Theta' )
elif( isinstance( energyData, energyModule.evaporationSpectrum ) ) :
sProcess = 'Process: Evaporation spectrum\n'
sSpecific = 'Interpolate Eout integrals: false\n'
sSpecific += 'Quadrature method: Gauss6\n'
sData += getParameter( parameter1, 'Theta' )
elif( isinstance( energyData, energyModule.WattSpectrum ) ) :
sProcess = 'Process: Watt spectrum\n'
sSpecific = 'Interpolate Eout integrals: false\n'
sSpecific += 'Conserve: number\n'
sData += getParameter( parameter1, 'a' )
sData += getParameter( parameter2, 'b' )
elif( isinstance( energyData, energyModule.MadlandNix ) ) :
sProcess = 'Process: Madland-Nix spectrum\n'
sSpecific = 'Quadrature method: adaptive\n'
sSpecific += 'Interpolate Eout integrals: false\n'
sSpecific += 'Conserve: number\n'
sSpecific += 'EFL: %s\n' % energyData.EFL.getValueAs( energyData.parameter1.data.axes[-1].unit )
sSpecific += 'EFH: %s\n' % energyData.EFH.getValueAs( energyData.parameter1.data.axes[-1].unit )
sData += getParameter( parameter1, 'TM' )
else :
raise Exception( 'Unsupport energy functional = %s' % energyData.moniker )
sData.append( '' )
return( sProcess, sSpecific, startOfNewData + '\n'.join( sData ) )
def TMs2Form( style, tempInfo, TM_1, TM_E ) :
from fudge.processing import transportables as transportablesModule
from fudge.gnd.productData.distributions import multiGroup as multiGroupModule
reactionSuite = tempInfo['reactionSuite']
productName = tempInfo['productName']
transportable = style.transportables[productName]
conserve = transportable.conserve
energyUnit = tempInfo['incidentEnergyUnit']
# BRB hardwired.
crossSectionUnit = 'b' # ?????? 'b' should not be hardwired.
axes = axesModule.axes( rank = 4 )
axes[0] = axesModule.axis( 'C_l(energy_in,energy_out)', 0, crossSectionUnit )
lMaxPlus1 = len( TM_1[0][0] )
lGrid = valuesModule.values( [ i1 for i1 in range( lMaxPlus1 ) ], valueType = standardsModule.types.integer32Token )
axes[1] = axesModule.grid( 'l', 1, '', axesModule.parametersGridToken, lGrid )
energyOutGrid = style.transportables[productName].group.boundaries.values.copy( )
axes[2] = axesModule.grid( 'energy_out', 2, energyUnit, axesModule.boundariesGridToken, energyOutGrid )
energyInGrid = style.transportables[reactionSuite.projectile].group.boundaries.values.copy( )
axes[3] = axesModule.grid( 'energy_in', 3, energyUnit, axesModule.boundariesGridToken, energyInGrid )
if( conserve == transportablesModule.conserve.number ) :
TM = TM_1
else :
raise NotImplementedError( 'Need to implement' )
n1 = len( TM )
n2 = len( TM[0] )
n3 = len( TM[0][0] )
data, starts, lengths = [], [], []
for i1 in sorted( TM.keys( ) ) :
TM_i1 = TM[i1]
start, length = None, 0
for i2 in sorted( TM_i1.keys( ) ) :
TM_i1_i2 = TM_i1[i2]
cellMin = min( TM_i1_i2 )
cellMax = max( TM_i1_i2 )
if( ( cellMin != 0 ) or ( cellMax != 0 ) ) :
if( start is None ) : start = n3 * ( n2 * i1 + i2 )
length += n3
data += TM_i1_i2
else :
if( start is not None ) :
starts.append( start )
lengths.append( length )
start, length = None, 0
if( start is not None ) :
starts.append( start )
lengths.append( length )
shape = [ n1, n2, n3 ]
data = valuesModule.values( data )
starts = valuesModule.values( starts, valueType = standardsModule.types.integer32Token )
lengths = valuesModule.values( lengths, valueType = standardsModule.types.integer32Token )
flattened = arrayModule.flattened( shape = shape, data = data, starts = starts, lengths = lengths,
dataToString = multiGroupModule.gridded3d.dataToString )
gridded3d = multiGroupModule.gridded3d( axes = axes, array = flattened )
return( multiGroupModule.form( style.label, standardsModule.frames.labToken, gridded3d ) )
def defaultAxes( moniker ) :
axes = axesModule.axes( rank = 3 )
axes[2] = axesModule.axis( 'energy_in', 2, 'eV' )
axes[1] = axesModule.axis( 'energy_out', 1, 'eV' )
x0Unit = ''
if( moniker == 'f' ) : x0Unit = '1/eV'
axes[0] = axesModule.axis( moniker, 0, x0Unit )
return( axes )
def defaultAxes(energyUnit='eV',
momentumDepositionName=averageProductMomentumToken,
momentumDepositionUnit='eV/c'):
axes = axesModule.axes(rank=2)
axes[0] = axesModule.axis(momentumDepositionName, 0,
momentumDepositionUnit)
axes[1] = axesModule.axis('energy_in', 1, energyUnit)
return (axes)
def defaultAxes(energyUnit='eV',
energyDepositionName=averageProductEnergyToken,
energyDepositionUnit=None):
if (energyDepositionUnit is None): energyDepositionUnit = energyUnit
axes = axesModule.axes(rank=2)
axes[0] = axesModule.axis(energyDepositionName, 0,
energyDepositionUnit)
axes[1] = axesModule.axis('energy_in', 1, energyUnit)
return (axes)
def calculateDepositionEnergyFromEpP(E, EpP):
"EpP is a list of [ E', P(E') ]"
axes = axesModule.axes()
axes[0] = axesModule.axis('a', 0, EpP.axes[0].unit)
axes[1] = axesModule.axis('b', 1, EpP.axes[1].unit)
Ep = XYsModule.XYs1d(data=[[EpP[0][0], EpP[0][0]],
[EpP[-1][0], EpP[-1][0]]],
axes=axes)
return (float(EpP.integrateTwoFunctions(Ep)))
def defaultAxes(moniker, energyUnit):
axes = axesModule.axes(rank=3)
axes[2] = axesModule.axis('energy_in', 2, energyUnit)
axes[1] = axesModule.axis('energy_out', 1, energyUnit)
if (moniker == 'f'):
axes[0] = axesModule.axis(moniker, 0, '1/' + energyUnit)
else:
axes[0] = axesModule.axis(moniker, 0, '')
return (axes)
def defaultAxes(energyUnit='eV',
energy_outUnit='eV',
probabilityUnit='1/eV'):
axes = axesModule.axes(rank=4)
axes[0] = axesModule.axis('P(mu,energy_out|energy_in)', 0,
probabilityUnit)
axes[1] = axesModule.axis('mu', 1, '')
axes[2] = axesModule.axis('energy_out', 2, energy_outUnit)
axes[3] = axesModule.axis('energy_in', 3, energyUnit)
return (axes)
def defaultAxes(energyUnit='eV', probabilityUnit='', asLegendre=False):
axes = axesModule.axes(rank=3)
if (asLegendre):
axes[0] = axesModule.axis('C_l(energy_in)', 0, '')
axes[1] = axesModule.axis('l', 1, '')
else:
axes[0] = axesModule.axis('P(mu|energy_in)', 0, probabilityUnit)
axes[1] = axesModule.axis('mu', 1, '')
axes[2] = axesModule.axis('energy_in', 2, energyUnit)
return (axes)
def toPointwise_withLinearXYs(self, accuracy=None, lowerEps=0, upperEps=0):
from numericalFunctions import specialFunctions
def MadlandNixFunc(Ep, parameters):
def g(Ep, E_F, T_M):
u1 = (math.sqrt(Ep) - math.sqrt(E_F))
u1 *= u1 / T_M
u2 = (math.sqrt(Ep) + math.sqrt(E_F))
u2 *= u2 / T_M
E1 = 0 # u1^3/2 * E1 is zero for u1 = 0. but E1 is infinity, whence, the next test.
if (u1 != 0): E1 = specialFunctions.exponentialIntegral(1, u1)
E2 = specialFunctions.exponentialIntegral(1, u2)
complementary = (u1 > 2.)
gamma1 = specialFunctions.incompleteGamma(
1.5, u1, complementary)
gamma2 = specialFunctions.incompleteGamma(
1.5, u2, complementary)
signG = 1
if (complementary): signG = -1
return ((u2 * math.sqrt(u2) * E2 - u1 * math.sqrt(u1) * E1 +
signG * (gamma2 - gamma1)) /
(3 * math.sqrt(E_F * T_M)))
EFL, EFH, T_M = parameters
return (0.5 * (g(Ep, EFL, T_M) + g(Ep, EFH, T_M)))
if (accuracy is None): accuracy = 1e-3
axes = XYs2d.defaultAxes(energyUnit=self.parameter1.data.axes[0].unit)
pwl = XYs2d(axes=axes)
E_in_unit = self.parameter1.data.axes[-1].unit
EFL, EFH = self.EFL.getValueAs(E_in_unit), self.EFH.getValueAs(
E_in_unit)
factor = PQU.PQU(1, 'eV').getValueAs(E_in_unit)
xs_ = [1e-5, 1e-3, 1e-1, 1e1, 1e3, 1e5, 3e7]
xs = [factor * x for x in xs_]
axes1d = axesModule.axes()
axes1d[0] = axes[0]
axes1d[1] = axes[1]
for E, T_M in self.parameter1.data: # This logic ignores the interpolation of parameter1 as the only two subforms in ENDF/B-VII shows
parameters = [
EFL, EFH, T_M
] # that linear-linear is better than the 'log-log' given in the ENDF/B-VII/data.
g_Ep = XYs1d.createFromFunction(axes1d,
xs,
MadlandNixFunc,
parameters,
accuracy,
biSectionMax=12)
g_Ep.value = E # ????????? Class XYs1d does not the a proper setValue method. One should be added.
g_Ep.normalize(insitu=True)
pwl.append(g_Ep)
return (pwl)
def uncorrelated_EMuP_EEpP_TransferMatrix( style, tempInfo, crossSection, productFrame, angularData, energyData,
multiplicity, comment = None, weight = None ) :
logFile = tempInfo['logFile']
workDir = tempInfo['workDir']
sSpecific = ''
if( isinstance( energyData, energyModule.functionalBase ) ) :
sProcess, sSpecific, sData = energyFunctionToString( energyData, weight = weight )
weight = None
elif( isinstance( energyData, energyModule.weightedFunctionals ) ) :
TM1s, TMEs = [], []
for weight in energyData :
TM1, TME = uncorrelated_EMuP_EEpP_TransferMatrix( style, tempInfo, crossSection, productFrame, angularData,
weight.functional, multiplicity, comment = comment, weight = weight )
TM1s.append( TM1 )
TMEs.append( TME )
TM1 = addTMs( TM1s )
TME = addTMs( TMEs )
return( TM1, TME )
elif( isinstance( energyData, energyModule.regions2d ) ) :
TM1s, TMEs = [], []
axes = axesModule.axes( )
for i1, region in enumerate( energyData ) :
weight = XYsModule.XYs1d( data = [ [ region.domainMin, 1. ], [ region.domainMax, 1. ] ], axes = axes )
tempInfo['workFile'].append( 'r%s' % i1 )
try :
TM1, TME = uncorrelated_EMuP_EEpP_TransferMatrix( style, tempInfo, crossSection, productFrame, angularData,
region, multiplicity, comment = comment, weight = weight )
except :
del tempInfo['workFile'][-1]
raise
del tempInfo['workFile'][-1]
TM1s.append( TM1 )
TMEs.append( TME )
TM1 = addTMs( TM1s )
TME = addTMs( TMEs )
return( TM1, TME )
elif( isinstance( energyData, energyModule.primaryGamma ) ) :
return( primaryGammaAngularData( style, tempInfo, crossSection, energyData, angularData, multiplicity = multiplicity, comment = comment ) )
else :
if( angularData.isIsotropic() ) :
sProcess = "Process: isotropic table\n"
sData = EEpPDataToString( energyData )
else:
sProcess = "Process: 'Uncorrelated energy-angle data transfer matrix'\n"
sData = angularToString( angularData, crossSection )
sData += EEpPDataToString( energyData )
sCommon = commonDataToString( comment, style, tempInfo, crossSection, productFrame, multiplicity = multiplicity, weight = weight )
s = versionStr + '\n' + sProcess + sSpecific + sCommon + sData
return( executeCommand( logFile, transferMatrixExecute, s, workDir, tempInfo['workFile'] ) )
def getUncertaintyVector( self, theData=None, relative=True ):
"""
Get an XYs1d object containing uncertainty for this matrix.
Convert relative/absolute if requested (if so, must also pass central values as theData)
Examples:
- if the covariance matrix is relative and we want relative uncertainty vector, just do:
> matrix.getUncertaintyVector()
- if we want the absolute matrix instead:
> matrix.getUncertaintyVector( theData=<XYs1d instance>, relative=False )
"""
if not self.isSymmetric():
raise ValueError("getUncertaintyVector only applies to symmetric matrices!")
energies = list( self.matrix.axes[-1].values )
diag = numpy.diagonal( self.matrix.array.constructArray() ).copy()
diag[ diag < 0.0 ] = 0.0
diag = list( numpy.sqrt( diag ) )
diag.append( diag[-1] ) # point corresponding to final energy bin
yunit = self.matrix.axes[0].unit
if yunit != '': # get square root of the unit
yunit = PQU.PQU(1,yunit).sqrt().getUnitSymbol()
axes_ = axesModule.axes(
labelsUnits={1:('energy_in',self.matrix.axes[2].unit),
0:('uncertainty',yunit)} )
uncert = XYsModule.XYs1d( zip(energies,copy.deepcopy(diag)), axes=axes_, interpolation='flat',
accuracy=0.0001, ) # what should accuracy be set to?
uncert = uncert.changeInterpolation('lin-lin',None,1e-8,1e-8)
# do we need to convert absolute->relative or vice versa?
if (relative and self.type==tokens.absoluteToken) or (not relative and self.type==tokens.relativeToken):
if theData is None:
theData = self.ancestor.rowData.link.toPointwise_withLinearXYs(1e-8,1e-8)
try:
theData = theData.toPointwise_withLinearXYs(1e-8,1e-8)
uncert, theData = uncert.mutualify(1e-8, 1e-8, False, theData, 1e-8, 1e-8, False)
if relative: #convert absolute to relative
uncert /= theData
else: #relative to absolute
uncert *= theData
except Exception as err:
print len( uncert ), uncert.copyDataToXYs()[0], uncert.copyDataToXYs()[-1]
print len( theData ), theData.copyDataToXYs()[0], theData.copyDataToXYs()[-1]
raise Exception( err.message )
return uncert
def multiGroupInverseSpeed(style, tempInfo):
_groupBoundaries, flux = miscellaneousModule._groupFunctionsAndFluxInit(
style, tempInfo, None)
groupBoundaries = _groupBoundaries.values.values
# cm/sh
const = 2. / (3. * math.sqrt(2) * 2.99792458e+02
) # BRB - speed-of-light should not be hardwired.
const *= math.sqrt(
float(PQUModule.PQU(1, 'MeV').convertToUnit(_groupBoundaries.unit)))
const *= math.sqrt(
tempInfo['projectileMass']) # Mass is in energy unit / c^2.
numberOfGroups = len(groupBoundaries) - 1
E1 = groupBoundaries[0]
inverseSpeeds = []
for i1 in range(numberOfGroups):
E2 = groupBoundaries[i1 + 1]
fluxSlice = flux.domainSlice(domainMin=E1, domainMax=E2, fill=True)
fluxIntegral = 0
inverseSpeedIntegral = 0
_E1 = None
for _E2, _flux2 in fluxSlice:
if (_E1 is not None):
fluxIntegral += (_E2 - _E1) * (_flux2 + _flux1)
x1 = math.sqrt(_E1)
x2 = math.sqrt(_E2)
inverseSpeedIntegral += (x2 -
x1) * (_flux1 + _flux2 +
(_flux1 * x2 + _flux2 * x1) /
(x1 + x2))
_E1 = _E2
_flux1 = _flux2
inverseSpeed = 0
if (fluxIntegral != 0):
inverseSpeed = const * inverseSpeedIntegral / (fluxIntegral / 2)
inverseSpeeds.append(inverseSpeed)
E1 = E2
axes = axesModule.axes(rank=2)
axes[0] = axesModule.axis(label="inverse speed", unit="sh/cm", index=0)
axes[1] = flux.axes[1]
return (groupModule.toMultiGroup1d(gridded1d,
style,
tempInfo,
axes,
inverseSpeeds,
addLabel=False))
def readT_effective(line, mf7):
line, dat = endfFileToGNDMisc.getTAB1(line, mf7)
e_interp = dat['interpolationInfo']
if len(e_interp) > 1:
raise ValueError(
"only one interpolation region allowed for T_eff data")
e_interp = endfFileToGNDMisc.ENDFInterpolationToGND1d(e_interp[0][1])
t_axes = axesModule.axes(labelsUnits={
1: ('temperature', 'K'),
0: ('t_effective', 'K')
})
return line, TS.T_effective(dat['data'],
axes=t_axes,
interpolation=e_interp)
def makeGrouped(self, processInfo, tempInfo):
projectile, target = processInfo.getProjectileName(
), processInfo.getTargetName()
groups = processInfo.getParticleGroups(projectile)
energyUnit = tempInfo['crossSection'].domainUnit()
axes = axesModule.axes(labelsUnits={
0: ['energy_in', energyUnit],
1: ['energy', energyUnit]
})
domainMin, domainMax = tempInfo['crossSection'].domain()
energy = XYsModule.XYs1d(data=[[domainMin, domainMin],
[domainMax, domainMax]],
axes=axes,
accuracy=1e-8)
axes, grouped_ = miscellaneousModule.makeGrouped(
self, processInfo, tempInfo, energy)
self.addForm(grouped(axes, grouped_))
axes, grouped_ = miscellaneousModule.makeGrouped(
self, processInfo, tempInfo, energy, normType='groupedFlux')
self.addForm(groupedWithCrossSection(axes, grouped_))
def check(self, info):
from fudge.gnd import warning
from pqu import PQU
warnings = []
axes = axesModule.axes()
axes[0] = self.axes[-2]
axes[1] = self.axes[-1]
for index, energy_in in enumerate(self):
integral = float(
XYsModule.XYs1d([(eout.value, eout.coefficients[0])
for eout in energy_in],
axes=axes,
accuracy=0.001).integrate())
if abs(integral - 1.0) > info['normTolerance']:
warnings.append(
warning.unnormalizedDistribution(
PQU.PQU(energy_in.value, axes[0].unit), index,
integral, self.toXLink()))
minProb = min([
xy.toPointwise_withLinearXYs(0.001).rangeMin()
for xy in energy_in
])
if minProb < 0:
warnings.append(
warning.negativeProbability(PQU.PQU(
energy_in.value, axes[0].unit),
value=minProb,
obj=energy_in))
if self.interpolationQualifier is standardsModule.interpolation.unitBaseToken:
# check for more than one outgoing distribution integrating to 0 at high end of incident energies
integrals = [eout.integrate() for eout in energy_in]
for idx, integral in enumerate(integrals[::-1]):
if integral != 0: break
if idx > 1:
warnings.append(
warning.extraOutgoingEnergy(PQU.PQU(
energy_in.value, axes[-1].unit),
obj=energy_in))
return warnings
def twoBodyTransferMatrix( style, tempInfo, productFrame, crossSection, angularData, Q, weight = None, comment = None ) :
"""
Generate input and call processing code to generate a transfer matrix for two-body angular distribution.
If the distribution is actually made up of two different forms in different energy regions, this function
calls itself in the two regions and sums the result.
"""
if( isinstance( angularData, angularModule.isotropic ) ) : angularData = angularData.toPointwise_withLinearXYs( )
if( isinstance( angularData, angularModule.XYs2d ) ) :
TM1, TME = twoBodyTransferMatrix2( style, tempInfo, crossSection, angularData, Q, productFrame, comment = comment )
elif( isinstance( angularData, angularModule.regions2d ) ) :
TM1s, TMEs = [], []
lowestBound, highestBound = angularData[0].domainMin, angularData[-1].domainMax
weightAxes = axesModule.axes( )
for iRegion, region in enumerate( angularData ) :
if( iRegion == 0 ) :
weightData = [ [ lowestBound, 1 ], [ region.domainMax, 0 ], [ highestBound, 0 ] ]
elif( iRegion == len( angularData ) - 1 ) :
weightData = [ [ lowestBound, 0 ], [ region.domainMin, 1 ], [ highestBound, 1 ] ]
else :
weightData = [ [ lowestBound, 0 ], [ region.domainMin, 1 ], [ region.domainMax, 0 ], [ highestBound, 0 ] ]
_weight = XYsModule.XYs1d( data = weightData, axes = weightAxes )
tempInfo['workFile'].append( 'r%s' % iRegion )
try :
TM1, TME = twoBodyTransferMatrix2( style, tempInfo, crossSection, region, Q,
productFrame, weight = _weight, comment = comment )
except :
del tempInfo['workFile'][-1]
raise
del tempInfo['workFile'][-1]
TM1s.append( TM1 )
TMEs.append( TME )
TM1 = addTMs( TM1s )
TME = addTMs( TMEs )
else :
raise Exception( 'Unsupported P(mu|E) = %s' % angularData.moniker )
return( TM1, TME )