def observe(self, intrinsicLC, noiseSeed = None, tnum = None): if tnum is None: tnum = 0 randSeed = np.zeros(1, dtype = 'uint32') if noiseSeed is None: rand.rdrand(randSeed) noiseSeed = randSeed[0] self._taskCython.add_ObservationNoise(intrinsicLC.numCadences, intrinsicLC.dt, intrinsicLC.fracNoiseToSignal, intrinsicLC.t, intrinsicLC.x, intrinsicLC.y, intrinsicLC.yerr, intrinsicLC.mask, noiseSeed, threadNum = tnum) count = int(np.sum(intrinsicLC.mask)) y_meanSum = 0.0 yerr_meanSum = 0.0 for i in xrange(intrinsicLC.numCadences): y_meanSum += intrinsicLC.mask[i]*intrinsicLC.y[i] yerr_meanSum += intrinsicLC.mask[i]*intrinsicLC.yerr[i] if count > 0.0: intrinsicLC._mean = y_meanSum/count intrinsicLC._meanerr = yerr_meanSum/count else: intrinsicLC._mean = 0.0 intrinsicLC._meanerr = 0.0 y_stdSum = 0.0 yerr_stdSum = 0.0 for i in xrange(intrinsicLC.numCadences): y_stdSum += math.pow(intrinsicLC.mask[i]*intrinsicLC.y[i] - intrinsicLC._mean, 2.0) yerr_stdSum += math.pow(intrinsicLC.mask[i]*intrinsicLC.yerr[i] - intrinsicLC._meanerr, 2.0) if count > 0.0: intrinsicLC._std = math.sqrt(y_stdSum/count) intrinsicLC._stderr = math.sqrt(yerr_stdSum/count) else: intrinsicLC._std = 0.0 intrinsicLC._stderr = 0.0
def sample(self, **kwargs):
returnLC = self.lcObj.copy()
probVal = kwargs.get('probability', 1.0)
sampleSeedVal = kwargs.get('sampleSeed', rand.rdrand(np.array([0], dtype='uint32')))
np.random.seed(seed=sampleSeedVal)
keepArray = spstats.bernoulli.rvs(probVal, size=self.lcObj.numCadences)
newNumCadences = np.sum(keepArray)
tNew = np.require(np.zeros(newNumCadences), requirements=['F', 'A', 'W', 'O', 'E'])
xNew = np.require(np.zeros(newNumCadences), requirements=['F', 'A', 'W', 'O', 'E'])
yNew = np.require(np.zeros(newNumCadences), requirements=['F', 'A', 'W', 'O', 'E'])
yerrNew = np.require(np.zeros(newNumCadences), requirements=['F', 'A', 'W', 'O', 'E'])
maskNew = np.require(np.zeros(newNumCadences), requirements=['F', 'A', 'W', 'O', 'E'])
counter = 0
for i in xrange(self.lcObj.numCadences):
if keepArray[i] == 1:
tNew[counter] = self.lcObj.t[i]
xNew[counter] = self.lcObj.x[i]
yNew[counter] = self.lcObj.y[i]
yerrNew[counter] = self.lcObj.yerr[i]
maskNew[counter] = self.lcObj.mask[i]
counter += 1
returnLC.t = tNew
returnLC.x = xNew
returnLC.y = yNew
returnLC.yerr = yerrNew
returnLC.mask = maskNew
returnLC._numCadences = newNumCadences
returnLC._checkIsRegular()
returnLC._times()
returnLC._statistics()
return returnLC
def sample(self, **kwargs):
returnLC = self.lcObj.copy()
widthVal = kwargs.get('width', returnLC.T/10.0)
centerVal = kwargs.get('center', returnLC.T/2.0)
sampleSeedVal = kwargs.get('sampleSeed', rand.rdrand(np.array([0], dtype='uint32')))
np.random.seed(seed=sampleSeedVal)
del returnLC.t
del returnLC.x
del returnLC.y
del returnLC.yerr
del returnLC.mask
tList = list()
xList = list()
yList = list()
yerrList = list()
maskList = list()
for i in xrange(self.lcObj.numCadences):
keepYN = np.random.binomial(1, self.normalizedSincSq(widthVal, centerVal, self.lcObj.t[i]))
if keepYN == 1:
tList.append(self.lcObj.t[i])
xList.append(self.lcObj.x[i])
yList.append(self.lcObj.y[i])
yerrList.append(self.lcObj.yerr[i])
maskList.append(self.lcObj.mask[i])
newNumCadences = len(tList)
returnLC.t = np.require(np.array(tList), requirements=['F', 'A', 'W', 'O', 'E'])
returnLC.x = np.require(np.array(xList), requirements=['F', 'A', 'W', 'O', 'E'])
returnLC.y = np.require(np.array(yList), requirements=['F', 'A', 'W', 'O', 'E'])
returnLC.yerr = np.require(np.array(yerrList), requirements=['F', 'A', 'W', 'O', 'E'])
returnLC.mask = np.require(np.array(maskList), requirements=['F', 'A', 'W', 'O', 'E'])
returnLC._numCadences = newNumCadences
returnLC._checkIsRegular()
returnLC._times()
returnLC._statistics()
return returnLC
def fit(self, observedLC, zSSeed=None, walkerSeed=None, moveSeed=None, xSeed=None):
randSeed = np.zeros(1, dtype='uint32')
if zSSeed is None:
rand.rdrand(randSeed)
zSSeed = randSeed[0]
if walkerSeed is None:
rand.rdrand(randSeed)
walkerSeed = randSeed[0]
if moveSeed is None:
rand.rdrand(randSeed)
moveSeed = randSeed[0]
if xSeed is None:
rand.rdrand(randSeed)
xSeed = randSeed[0]
xStart = np.require(np.zeros(self.ndims*self.nwalkers), requirements=['F', 'A', 'W', 'O', 'E'])
fluxEst, periodEst, eccentricityEst, omega1Est, tauEst, a2sinInclinationEst = self.estimate(
observedLC)
for walkerNum in xrange(self.nwalkers):
noSuccess = True
while noSuccess:
a1Guess, a2Guess, inclinationGuess = self.guess(a2sinInclinationEst)
ThetaGuess = np.array(
[a1Guess, a2Guess, periodEst, eccentricityEst, omega1Est, inclinationGuess, tauEst,
fluxEst])
res = self.set(ThetaGuess)
lnPrior = self.logPrior(observedLC)
if res == 0 and lnPrior == 0.0:
noSuccess = False
for dimNum in xrange(self.ndims):
xStart[dimNum + walkerNum*self.ndims] = ThetaGuess[dimNum]
lowestFlux = np.min(observedLC.y[np.where(observedLC.mask == 1.0)[0]])
highestFlux = np.max(observedLC.y[np.where(observedLC.mask == 1.0)[0]])
res = self._taskCython.fit_MBHBModel(
observedLC.numCadences, observedLC.dt, lowestFlux, highestFlux, observedLC.t, observedLC.x,
observedLC.y, observedLC.yerr, observedLC.mask, self.nwalkers, self.nsteps, self.maxEvals,
self.xTol, self.mcmcA, zSSeed, walkerSeed, moveSeed, xSeed, xStart, self._Chain,
self._LnPrior, self._LnLikelihood)
meanTheta = list()
for dimNum in xrange(self.ndims):
meanTheta.append(np.mean(self.Chain[dimNum, :, self.nsteps/2:]))
meanTheta = np.require(meanTheta, requirements=['F', 'A', 'W', 'O', 'E'])
self.set(meanTheta)
devianceThetaBar = -2.0*self.logLikelihood(observedLC)
barDeviance = np.mean(-2.0*self.LnLikelihood[:, self.nsteps/2:])
self._pDIC = barDeviance - devianceThetaBar
self._dic = devianceThetaBar + 2.0*self.pDIC
return res
def fit(self, observedLC, zSSeed = None, walkerSeed = None, moveSeed = None, xSeed = None): randSeed = np.zeros(1, dtype = 'uint32') if zSSeed is None: rand.rdrand(randSeed) zSSeed = randSeed[0] if walkerSeed is None: rand.rdrand(randSeed) walkerSeed = randSeed[0] if moveSeed is None: rand.rdrand(randSeed) moveSeed = randSeed[0] if xSeed is None: rand.rdrand(randSeed) xSeed = randSeed[0] xStart = np.require(np.zeros(self.ndims*self.nwalkers), requirements=['F', 'A', 'W', 'O', 'E']) intrinsicFluxEst, periodEst, eccentricityEst, omega1Est, tauEst, a2sinInclinationEst = self.estimate(observedLC) ''''ThetaGuess = np.array([0.001, 75.0, 10.0, 0.0, 0.0, 90.0, 0.0, 100.0, 0.5]) for dimNum in xrange(self.ndims): xStart[dimNum] = ThetaGuess[dimNum] for walkerNum in xrange(1, self.nwalkers):''' for walkerNum in xrange(self.nwalkers): noSuccess = True while noSuccess: sinInclinationGuess = random.uniform(-1.0, 1.0) inclinationGuess = (180.0/math.pi)*math.asin(sinInclinationGuess) m1Guess = math.pow(10.0, random.uniform(-1.0, 3.0)) # m1 in 1e6*SolarMass m2Guess = math.pow(10.0, random.uniform(-1.0, math.log10(m1Guess))) # m1 in 1e6SolarMass rS1Guess = ((2.0*self.G*m1Guess*1.0e6*self.SolarMass)/math.pow(self.c, 2.0)/self.Parsec) # rS1 in Parsec rS2Guess = ((2.0*self.G*m2Guess*1.0e6*self.SolarMass)/math.pow(self.c, 2.0)/self.Parsec) # rS2 in Parsec rPeriEst = math.pow(((self.G*math.pow(periodEst*self.Day, 2.0)*((m1Guess + m2Guess)*1.0e6*self.SolarMass)*math.pow(1.0 + eccentricityEst, 3.0))/math.pow(self.twoPi, 2.0)),1.0/3.0)/self.Parsec a1Est = (m2Guess*rPeriEst)/((m1Guess + m2Guess)*(1.0 - eccentricityEst)); a2Est = (m1Guess*rPeriEst)/((m1Guess + m2Guess)*(1.0 - eccentricityEst)); ThetaGuess = np.array([rPeriEst, m1Guess, m2Guess, eccentricityGuess, omega1Est, inclinationGuess, tauGuess, totalFluxGuess]) res = self.set(ThetaGuess) lnPrior = self.logPrior(observedLC) if res == 0 and lnPrior == 0.0: noSuccess = False for dimNum in xrange(self.ndims): xStart[dimNum + walkerNum*self.ndims] = ThetaGuess[dimNum] res = self._taskCython.fit_BinarySMBHModel(observedLC.numCadences, observedLC.dt, lowestFlux, highestFlux, observedLC.t, observedLC.x, observedLC.y, observedLC.yerr, observedLC.mask, self.nwalkers, self.nsteps, self.maxEvals, self.xTol, self.mcmcA, zSSeed, walkerSeed, moveSeed, xSeed, xStart, self._Chain, self._LnPosterior) return res
def sample(self, **kwargs):
returnLC = self.lcObj.copy()
widthVal = kwargs.get('width', returnLC.T / 10.0)
centerVal = kwargs.get('center', returnLC.T / 2.0)
sampleSeedVal = kwargs.get('sampleSeed',
rand.rdrand(np.array([0], dtype='uint32')))
np.random.seed(seed=sampleSeedVal)
del returnLC.t
del returnLC.x
del returnLC.y
del returnLC.yerr
del returnLC.mask
tList = list()
xList = list()
yList = list()
yerrList = list()
maskList = list()
for i in range(self.lcObj.numCadences):
keepYN = np.random.binomial(
1, self.normalizedSincSq(widthVal, centerVal, self.lcObj.t[i]))
if keepYN == 1:
tList.append(self.lcObj.t[i])
xList.append(self.lcObj.x[i])
yList.append(self.lcObj.y[i])
yerrList.append(self.lcObj.yerr[i])
maskList.append(self.lcObj.mask[i])
newNumCadences = len(tList)
returnLC.t = np.require(np.array(tList),
requirements=['F', 'A', 'W', 'O', 'E'])
returnLC.x = np.require(np.array(xList),
requirements=['F', 'A', 'W', 'O', 'E'])
returnLC.y = np.require(np.array(yList),
requirements=['F', 'A', 'W', 'O', 'E'])
returnLC.yerr = np.require(np.array(yerrList),
requirements=['F', 'A', 'W', 'O', 'E'])
returnLC.mask = np.require(np.array(maskList),
requirements=['F', 'A', 'W', 'O', 'E'])
returnLC._numCadences = newNumCadences
returnLC._checkIsRegular()
returnLC._times()
returnLC._statistics()
return returnLC
def sample(self, **kwargs):
returnLC = self.lcObj.copy()
probVal = kwargs.get('probability', 1.0)
sampleSeedVal = kwargs.get('sampleSeed',
rand.rdrand(np.array([0], dtype='uint32')))
np.random.seed(seed=sampleSeedVal)
keepArray = spstats.bernoulli.rvs(probVal, size=self.lcObj.numCadences)
newNumCadences = np.sum(keepArray)
tNew = np.require(np.zeros(newNumCadences),
requirements=['F', 'A', 'W', 'O', 'E'])
xNew = np.require(np.zeros(newNumCadences),
requirements=['F', 'A', 'W', 'O', 'E'])
yNew = np.require(np.zeros(newNumCadences),
requirements=['F', 'A', 'W', 'O', 'E'])
yerrNew = np.require(np.zeros(newNumCadences),
requirements=['F', 'A', 'W', 'O', 'E'])
maskNew = np.require(np.zeros(newNumCadences),
requirements=['F', 'A', 'W', 'O', 'E'])
counter = 0
for i in range(self.lcObj.numCadences):
if keepArray[i] == 1:
tNew[counter] = self.lcObj.t[i]
xNew[counter] = self.lcObj.x[i]
yNew[counter] = self.lcObj.y[i]
yerrNew[counter] = self.lcObj.yerr[i]
maskNew[counter] = self.lcObj.mask[i]
counter += 1
returnLC.t = tNew
returnLC.x = xNew
returnLC.y = yNew
returnLC.yerr = yerrNew
returnLC.mask = maskNew
returnLC._numCadences = newNumCadences
returnLC._checkIsRegular()
returnLC._times()
returnLC._statistics()
return returnLC
#!/usr/bin/env python
""" Module to draw hardware random numbers.
For a demonstration of the module, please run the module as a command line program eg.
bash-prompt$ python randDemo.py --help
and
bash-prompt$ python randDemo.py -n 100
"""
import numpy as np
import rand
if __name__ == '__main__':
import argparse as argparse
parser = argparse.ArgumentParser()
parser.add_argument(
'-n',
'--numRand',
type=int,
default=10,
help=r'Number of hardware random numbers to generate...')
args = parser.parse_args()
A = np.zeros(args.numRand, dtype='uint32')
success = rand.rdrand(A)
for i in xrange(args.numRand):
print '%s' % (str(A[i]))
#!/usr/bin/env python
""" Module to draw hardware random numbers.
For a demonstration of the module, please run the module as a command line program eg.
bash-prompt$ python randDemo.py --help
and
bash-prompt$ python randDemo.py -n 100
"""
import numpy as np
import rand
if __name__ == '__main__':
import argparse as argparse
parser = argparse.ArgumentParser()
parser.add_argument('-n', '--numRand', type=int, default=10,
help=r'Number of hardware random numbers to generate...')
args = parser.parse_args()
A = np.zeros(args.numRand, dtype='uint32')
success = rand.rdrand(A)
for i in xrange(args.numRand):
print '%s'%(str(A[i]))
