s_guess = 0.6
# stopping power model and parameters
stoppingMedia_Z = 1
stoppingMedia_A = 2
stoppingMedia_rho = 8.565e-5 # from red notebook, p 157
incidentIon_charge = 1
# IMPORTANT
# CHECK TO SEE IF THIS FACTOR OF 1e-3 IS NEEDED
stoppingMedia_meanExcitation = 19.2 * 1e-3 # FACTOR OF 1e-3 NEEDED?
# IMPORTANT
stoppingModelParams = [
stoppingMedia_Z, stoppingMedia_A, stoppingMedia_rho, incidentIon_charge,
stoppingMedia_meanExcitation
]
stoppingModel = ionStopping.simpleBethe(stoppingModelParams)
# hack shit
# just because i don't want to change code i copied and pasted below
params = [beamE_guess, eLoss_guess, scale_guess, s_guess, 1e4, 10]
beamE, eLoss, scale, s, scaleFactor, bgLevel = params
ddnXSfxn = ddnXSinstance
dedxfxn = stoppingModel.dEdx
eN_binCenters = getDDneutronEnergy(eD_binCenters)
dedxForODE = lambda x, y: dedxfxn(energy=y, x=x)
nSamples = 10000
nEvPerLoop = 1000
# 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
]
stoppingModelParams = [
stoppingMedia_Z, stoppingMedia_A, stoppingMedia_rho, incidentIon_charge,
stoppingMedia_meanExcitation
]
#stoppingModel = ionStopping.simpleBethe( stoppingModelParams )
stoppingModel = ionStopping.simpleBethe([1])
stoppingModel.addMaterial(dgas_materialDef)
eN_binCenters = getDDneutronEnergy(eD_binCenters)
def getTOF(mass, energy, distance):
"""
Compute time of flight, in nanoseconds, given\
mass of particle (in keV/c^2), the particle's energy (in keV),\
and the distance traveled (in cm).
Though simple enough to write inline, this will be used often.
"""
velocity = physics.speedOfLight * np.sqrt(2 * energy / mass)
ionCharge = 1 # little z in equation, in multiples of elementary charge
leadingTerm = (4 * np.pi * numDensity * ionCharge**2 /
(masses.electron * physics.speedOfLight**2 * velocity**2))
#squareTerm = (sciConsts.e**2/(4 * np.pi * physics.epsilon_0))**2
squareTerm = 1.67489e-14 # worked out on paper
iExcitationPot = 19.2 # from http://pdg.lbl.gov/2016/AtomicNuclearProperties/HTML/deuterium_gas.html
logArg = (2 * masses.electron /(physics.speedOfLight**2)* velocity**2 /
(iExcitationPot*1e-3))
stopping = -1 * leadingTerm * squareTerm * np.log(logArg)
# print('leading term {}\nmiddle term {}\nlog term {}'.format(np.log10(leadingTerm),
# squareTerm,
# np.log(logArg)))
return stopping
stoppingModel = ionStopping.simpleBethe([1, 2, 8.37e-5, 1, 19.2])
energies = np.linspace(900, 1100, 3)
#stoppingPowers = stoppingFxn(energies)
print('D energy\tstopping from fxn\tstopping from class')
for idx, energy in enumerate(energies):
print('{}\t{}\t{}'.format(energy,stoppingFxn(energy),stoppingModel.dEdx(energy)))
eZeros = np.random.normal(900.0, 50, 5000)
xLocations = np.linspace(0.0, distances.tunlSSA_CsI.cellLength, 50)
solutions = odeint( stoppingModel.dEdx, eZeros, xLocations)
for idx in range(0,solutions.shape[1]):
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 = [
'$E_0$', '$f_1$', '$f_2$', '$f_3$', '$N_1$', '$N_2$', '$N_3$',
'$N_4$', '$N_5$'
]