def read_output(self):
"""!
Reads the STAYSL output and saves as a DataFrame.
Calculated the norm of the uncertainty.
@param self: <em> IterativeSTYASL pointer </em> \n
The IterativeSTYASL pointer. \n
"""
self._df = pd.read_table(self.path + 'stayslin.out',
engine='python',
sep='\s+',
skiprows=self._dataStart,
skipfooter=649,
header=None,
names=[
'lowE', 'adjFlux', 'unadjFlux',
'fluxRatio', 'adjStd', 'unadjStd',
'uncertRatio', 'integralFlux',
'intFluxUncert'
])
self._df.apply(pd.to_numeric)
self._df['adjFlux'] = bin_integration(self._df['lowE'].tolist(),
self._df['adjFlux'].tolist(),
'low')
self._df['adjStd'] = self._df['adjStd'] / 100
self._stdNorm = np.linalg.norm(self._df['adjStd'].tolist(), self.norm)
def read_output(self):
"""!
Reads the STAYSL output and saves as a DataFrame.
Calculated the norm of the uncertainty.
@param self: <em> IterativeSTYASL pointer </em> \n
The IterativeSTYASL pointer. \n
"""
self._df = pd.read_table(self.path + 'stayslin.out',
engine='python',
sep='\s+',
skiprows=self._dataStart,
skipfooter=649,
header=None,
names=[
'lowE', 'adjFlux', 'unadjFlux',
'fluxRatio', 'adjStd', 'unadjStd',
'uncertRatio', 'integralFlux',
'intFluxUncert'
])
self._df.apply(pd.to_numeric)
self._df['adjFlux'] = bin_integration(self._df['lowE'].tolist(),
self._df['adjFlux'].tolist(),
'low')
self._df['adjStd'] = self._df['adjStd'] / 100
self._stdNorm = np.linalg.norm(self._df['adjStd'].tolist(), self.norm)
# Add in data for groups if first group is not 1.
dfi = pd.DataFrame(columns=[
'lowE', 'adjFlux', 'unadjFlux', 'fluxRatio', 'adjStd', 'unadjStd',
'uncertRatio', 'integralFlux', 'intFluxUncert'
])
# print self.FirstGroup
for a in range(self.FirstGroup - 1):
# print 'hi'
dfi = dfi.append(pd.Series(
(1E-11 * float(a + 1), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0),
index=[
'lowE', 'adjFlux', 'unadjFlux', 'fluxRatio', 'adjStd',
'unadjStd', 'uncertRatio', 'integralFlux', 'intFluxUncert'
]),
ignore_index=True)
self._df = pd.concat([dfi, self._df])
def integralXSecEst(firPath,
numRx,
firName='stayslin.out',
xSecName='xsectlib_140.dat'):
"""
@ingroup STAYSL
Calculates the integral cross section values for the SigPhiCalculator.
The results are printed to the screen to be enetered into the
SigPhiCalculator.
Parameters
==========
@param firPath: \e string \n
Absolute path to the directory containing the FIR output and cross
section file. \n
@param numRx: \e integer \n
The number of reactions used for the FIR calculation. \n
@param firName: \e string \n
Name of the FIR output file. \n
@param xSecName: \e string \n
Name of the cross section file. \n
"""
# Open FIR output file
try:
f = open(firPath + firName, 'r')
# Skip the header
for i in range(0, 4):
f.next()
# Read in the RX names
rxNames = []
for i in range(0, numRx):
splt = f.next().strip().split()
rxNames.append(splt[0])
# Read in flux
start = []
flux = []
ebin = []
for line in f:
splt = line.strip().split()
if len(splt) > 0:
if splt[0] == 'GRP':
splt = f.next().strip().split()
while len(splt) > 3:
ebin.append(float(splt[1]))
flux.append(float(splt[2]))
if (float(splt[1]) >= 5.500E-07 and len(start) == 0) \
or (float(splt[1]) >= 0.1 and len(start) == 1) \
or (float(splt[1]) >= 1 and len(start) == 2):
start.append(int(splt[0]) - 1)
splt = f.next().strip().split()
# Close the file
f.close()
except IOError as e:
print "I/O error({0}): {1}".format(e.errno, e.strerror)
# Integrate out per MeV and normalize
flux = bin_integration(ebin, flux, 'low')
print "The epithermal/thermal ratio is: {}\n".format(
sum(flux[start[0]:start[1]])\
/sum(flux[:start[0]]))
flux = np.asarray(flux) / sum(flux)
# Open XSec file
try:
f = open(firPath + xSecName, 'r')
for line in f:
splt = line.strip().split()
# Store cross section
if splt[0] in rxNames:
print splt[0]
xSec = []
for i in range(0, 20):
splt = f.next().strip().split()
for xs in splt:
xSec.append(float(xs))
#Calculate integrals
print 'The > 0.1 flux weighted sigma is: {}'.format(
sum(np.asarray(xSec[start[1]:]) \
*np.asarray(flux[start[1]:])))
print 'The > 1 flux weighted sigma is: {}'.format(
sum(np.asarray(xSec[start[2]:]) \
*np.asarray(flux[start[2]:])))
# Close the file
f.close()
except IOError as e:
print "I/O error({0}): {1}".format(e.errno, e.strerror)
# Get data from result without updating standard deviation until the end.
path = 'C:/Users/nickq/Documents/AFIT_Masters/NENG612/NIF_Unfold/Unfold/STAYSL/Pin_N120405_iter/Iteration1/stayslin.out'
df = pd.read_table(path,
engine='python',
sep='\s+',
skiprows=99,
skipfooter=649,
header=None,
names=[
'lowE', 'adjFlux', 'unadjFlux', 'fluxRatio', 'adjStd',
'unadjStd', 'uncertRatio', 'integralFlux',
'intFluxUncert'
])
df.apply(pd.to_numeric)
df['adjFlux'] = bin_integration(df['lowE'].tolist(), df['adjFlux'].tolist(),
'low')
df['adjStd'] = df['adjStd'] * df['adjFlux'] / 100
df1 = df
adjHisto = Histogram()
adjHisto.build_histo(df['lowE'].tolist(),
df['adjFlux'].tolist(),
uncert=df['adjStd'].tolist(),
edgeLoc='low',
name='\\textbf{$\sigma$ updated $\chi^{2}$/v = 1.86}')
#adjHisto.plot(pinGuessHisto,xMin=1E-6,yMax=4e12,logX=False, logY=False,
# xLabel='Energy [MeV]', yLabel='Neutron Fluence [n/cm$^2$]',
# savePath=outpath+'Pin.png',includeMarkers=False,
# legendLoc=2)
#adjHisto.plot(pinGuessHisto,xMin=1E-6, yMin=1, logX=False, logY=True,