x_range = (x_minRange, x_maxRange)
x_binSize = (x_maxRange - x_minRange) / x_bins
x_binCenters = np.linspace(x_minRange + x_binSize / 2,
x_maxRange - x_binSize / 2, x_bins)
# parameters for making the fake data...
nChunks = 10
nEvPerLoop = 10000
data_x = np.repeat(x_binCenters, nEvPerLoop)
# PARAMETER BOUNDARIES
min_e0, max_e0 = 750.0, 1200.0
min_sigma_0, max_sigma_0 = 0.02, 0.17
ddnXSinstance = ddnXSinterpolator()
beamTiming = beamTimingShape()
# stopping power model and parameters
stoppingMedia_Z = 1
stoppingMedia_A = 2
stoppingMedia_rho = 8.37e-5
incidentIon_charge = 1
stoppingMedia_meanExcitation = 19.2
stoppingModelParams = [
stoppingMedia_Z, stoppingMedia_A, stoppingMedia_rho, incidentIon_charge,
stoppingMedia_meanExcitation
]
stoppingModel = ionStopping.simpleBethe(stoppingModelParams)
eN_binCenters = getDDneutronEnergy(eD_binCenters)
def __init__(self,
chainFilename,
nSamplesFromTOF,
nBins_eD=100,
nBins_x=20,
nRuns=4):
"""Create a PPC tools object - reads in chain from file"""
self.chain, self.probs, self.nParams, self.nWalkers, self.nSteps = readChainFromFile(
chainFilename)
self.nRuns = nRuns
self.eD_bins = nBins_eD
self.eD_minRange = 200.0
self.eD_maxRange = 1200.0
self.eD_range = (self.eD_minRange, self.eD_maxRange)
self.eD_binSize = (self.eD_maxRange - self.eD_minRange) / self.eD_bins
self.eD_binCenters = np.linspace(
self.eD_minRange + self.eD_binSize / 2,
self.eD_maxRange - self.eD_binSize / 2, self.eD_bins)
self.eD_binMax = self.eD_bins - 1
self.x_bins = nBins_x
self.x_minRange = 0.0
self.x_maxRange = distances.tunlSSA_CsI.cellLength
self.x_range = (self.x_minRange, self.x_maxRange)
self.x_binSize = (self.x_maxRange - self.x_minRange) / self.x_bins
self.x_binCenters = np.linspace(self.x_minRange + self.x_binSize / 2,
self.x_maxRange - self.x_binSize / 2,
self.x_bins)
# parameters for making the fake data...
self.nEvPerLoop = nSamplesFromTOF
self.nSamplesFromTOF = nSamplesFromTOF
self.data_x = np.repeat(self.x_binCenters, self.nEvPerLoop)
self.ddnXSinstance = ddnXSinterpolator()
self.beamTiming = beamTimingShape()
self.zeroDegTimeSpreader = zeroDegreeTimingSpread()
# stopping power model and parameters
stoppingMedia_Z = 1
stoppingMedia_A = 2
stoppingMedia_rho = 8.565e-5 # from red notebook, p 157
incidentIon_charge = 1
stoppingMedia_meanExcitation = 19.2 * 1e-3
dgas_materialDef = [
stoppingMedia_Z, stoppingMedia_A, stoppingMedia_rho,
stoppingMedia_meanExcitation
]
#stoppingModel = ionStopping.simpleBethe( stoppingModelParams )
self.stoppingModel = ionStopping.simpleBethe([incidentIon_charge])
self.stoppingModel.addMaterial(dgas_materialDef)
self.eN_binCenters = getDDneutronEnergy(self.eD_binCenters)
tofWindowSettings = tofWindows()
tof_nBins = tofWindowSettings.nBins
self.tof_minRange = [
tofWindowSettings.minRange['mid'],
tofWindowSettings.minRange['close'],
tofWindowSettings.minRange['close'],
tofWindowSettings.minRange['far'],
tofWindowSettings.minRange['production']
]
self.tof_maxRange = [
tofWindowSettings.maxRange['mid'],
tofWindowSettings.maxRange['close'],
tofWindowSettings.maxRange['close'],
tofWindowSettings.maxRange['far'],
tofWindowSettings.maxRange['production']
]
self.tof_range = []
for minR, maxR in zip(self.tof_minRange, self.tof_maxRange):
self.tof_range.append((minR, maxR))
self.tofRunBins = [
tof_nBins['mid'], tof_nBins['close'], tof_nBins['close'],
tof_nBins['far'], tof_nBins['production']
]
self.standoffs = [
distances.tunlSSA_CsI.standoffMid,
distances.tunlSSA_CsI.standoffClose,
distances.tunlSSA_CsI.standoffClose,
distances.tunlSSA_CsI.standoffFar,
distances.tunlSSA_CsI.standoff_TUNLruns
]
self.tofData = None
self.neutronSpectra = None
self.paramNames = [
'$E_0$', '$f_1$', '$f_2$', '$f_3$', '$N_1$', '$N_2$', '$N_3$',
'$N_4$', '$N_5$'
]