def main(argv):
timeStepSize = 1
wfFileName = "most_recent_wf_pt.npz"
numWaveforms = 30
#wfFileName = "P42574A_512waveforms_%drisetimeculled.npz" % numWaveforms
if os.path.isfile(wfFileName):
data = np.load(wfFileName)
samples = data['samples']
else:
print "No saved waveform available."
tempGuess = 79.204603
gradGuess = 0.05
pcRadGuess = 2.5
pcLenGuess = 1.6
#Create a detector model
detName = "conf/P42574A_grad%0.2f_pcrad%0.2f_pclen%0.2f.conf" % (0.05, 2.5,
1.65)
det = Detector(
detName,
temperature=tempGuess,
timeStep=timeStepSize,
numSteps=fitSamples * 10. / timeStepSize,
)
det.LoadFields("P42574A_fields_v3.npz")
det.SetFields(pcRadGuess, pcLenGuess, gradGuess)
b_over_a = 0.107077
c = -0.817381
d = 0.825026
rc = 76.551780
det.SetTransferFunction(b_over_a, c, d, rc)
positionFig = plt.figure(5)
plt.clf()
xedges = np.linspace(0, np.around(det.detector_radius, 1),
np.around(det.detector_radius, 1) * 50 + 1)
yedges = np.linspace(0, np.around(det.detector_length, 1),
np.around(det.detector_length, 1) * 50 + 1)
z, xe, ye = np.histogram2d(samples[:, 0],
samples[:, 2],
bins=[xedges, yedges])
z /= z.sum()
n = 100
t = np.linspace(0, z.max(), n)
integral = ((z >= t[:, None, None]) * z).sum(axis=(1, 2))
from scipy import interpolate
f = interpolate.interp1d(integral, t)
t_contours = f(np.array([0.95, .68, .5, .2]))
cs = plt.contourf(z.T,
t_contours,
extent=[0, det.detector_radius, 0, det.detector_length],
alpha=0.7,
colors=("green", "r", "c", "b"),
extend='max')
cs.cmap.set_over('red')
proxy = [
plt.Rectangle((0, 0), 1, 1, fc=pc.get_facecolor()[0])
for pc in cs.collections
]
plt.legend(proxy, ["95% C.I.", "68% C.I.", "50% C.I.", "20% C.I."], loc=3)
plt.xlabel("r from Point Contact (mm)")
plt.ylabel("z from Point Contact (mm)")
plt.xlim(8, 14)
plt.ylim(14, 21)
plt.savefig("pt_credible_intervals.pdf")
print len(np.where(z >= t_contours[-1])[0]) * .02 * .02
plt.figure()
plt.imshow(z.T,
origin='lower',
extent=[0, det.detector_radius, 0, det.detector_length],
norm=LogNorm())
plt.xlabel("r from Point Contact (mm)")
plt.ylabel("z from Point Contact (mm)")
plt.savefig("pt_heat_map.pdf")
plt.figure()
wfPlotNumber = 100
simWfs = np.empty((wfPlotNumber, fitSamples))
for idx, (
r,
phi,
z,
scale,
t0,
smooth,
) in enumerate(samples[np.random.randint(len(samples),
size=wfPlotNumber)]):
simWfs[idx, :] = det.MakeSimWaveform(
r,
phi,
z,
scale,
t0,
fitSamples,
h_smoothing=smooth,
)
residFig = plt.figure(3)
helpers.plotResidual(simWfs, wf.windowedWf, figure=residFig)
plt.show()
def main(argv):
plt.ion()
numSamples = 4000000
burnin = 0.9 * numSamples
doInitPlot = False
fitSamples = 175
timeStepSize = 1. #ns
#Prepare detector
tempGuess = 79.071172
gradGuess = 0.05
pcRadGuess = 2.5
pcLenGuess = 1.6
#Create a detector model
detName = "conf/P42574A_grad%0.2f_pcrad%0.2f_pclen%0.2f.conf" % (0.05, 2.5,
1.65)
det = Detector(detName,
temperature=tempGuess,
timeStep=timeStepSize,
numSteps=fitSamples * 10)
det.LoadFields("P42574A_fields_v3.npz")
det.SetFields(pcRadGuess, pcLenGuess, gradGuess)
b_over_a = 3
c = -0.809211
d = 0.816583
rc1 = 74.4
rc2 = 1.79
rcfrac = 0.992
det.SetTransferFunction(b_over_a, c, d, rc1, rc2, rcfrac)
initializeDetector(det, )
# wfFileName = "P42574A_512waveforms_30risetimeculled.npz"
wfFileName = "P42574A_12_fastandslow_oldwfs.npz"
if os.path.isfile(wfFileName):
data = np.load(wfFileName)
results = data['results']
wfs = data['wfs']
numWaveforms = wfs.size
else:
print "No saved waveforms available. Go hard or go home."
exit(0)
#prep holders for each wf-specific param
r_arr = np.empty(numWaveforms)
phi_arr = np.empty(numWaveforms)
z_arr = np.empty(numWaveforms)
scale_arr = np.empty(numWaveforms)
t0_arr = np.empty(numWaveforms)
smooth_arr = np.ones(numWaveforms)
simWfArr = np.empty((1, numWaveforms, fitSamples))
nll = lambda *args: -lnlike_waveform(*args)
# for (idx, wf) in enumerate(wfs):
# wf.WindowWaveformTimepoint(fallPercentage=0.999, rmsMult=2)
#
# initializeWaveform(wf)
#
# r_arr[idx], phi_arr[idx], z_arr[idx], scale_arr[idx], t0_arr[idx], smooth_arr[idx]= results[idx]['x']
# startGuess = [r_arr[idx], phi_arr[idx], z_arr[idx], scale_arr[idx], t0_arr[idx], 10]
# result = op.minimize(nll, startGuess, method="Powell")
# r_arr[idx], phi_arr[idx], z_arr[idx], scale_arr[idx], t0_arr[idx], smooth_arr[idx]= result['x']
# t0_arr[idx] += 10 #because i had a different windowing offset back in the day
#smooth_arr[idx] /= 10.
# scale_arr[idx]*=100
#Plot the waveforms to take a look at the initial guesses
# if doInitPlot:
# plt.ion()
# fig = plt.figure(11)
# for (idx,wf) in enumerate(wfs):
#
# print "WF number %d:" % idx
# print " >>r: %f\n >>phi %f\n >>z %f\n >>e %f\n >>t0 %f, >>smooth:%f" % (r_arr[idx], phi_arr[idx], z_arr[idx], scale_arr[idx], t0_arr[idx], smooth_arr[idx])
# ml_wf = det.MakeSimWaveform(r_arr[idx], phi_arr[idx], z_arr[idx], scale_arr[idx], t0_arr[idx], fitSamples, h_smoothing=smooth_arr[idx])
# plt.plot(ml_wf, color="b")
# plt.plot(wf.windowedWf, color="r")
# value = raw_input(' --> Press q to quit, any other key to continue\n')
## plt.ioff()
# if value == 'q': exit(0)
for (wf_idx, wf) in enumerate(wfs):
if wf_idx <= 0: continue
wf.WindowWaveformTimepoint(fallPercentage=0.98, rmsMult=2)
initializeWaveform(wf)
minresult = None
minlike = np.inf
for r in np.linspace(4, np.floor(det.detector_radius) - 3, 6):
for z in np.linspace(4, np.floor(det.detector_length) - 3, 6):
if not det.IsInDetector(r, 0, z): continue
startGuess = [r, np.pi / 8, z, wf.wfMax, wf.t0Guess - 5, 10]
result = op.minimize(nll, startGuess, method="Nelder-Mead")
r, phi, z, scale, t0, smooth, = result["x"]
ml_wf = np.copy(
det.MakeSimWaveform(
r,
phi,
z,
scale,
t0,
fitSamples,
h_smoothing=smooth,
))
if ml_wf is None:
print r, z
continue
if result['fun'] < minlike:
minlike = result['fun']
minresult = result
r_arr[wf_idx], phi_arr[wf_idx], z_arr[wf_idx], scale_arr[
wf_idx], t0_arr[wf_idx], smooth_arr[wf_idx], = minresult["x"]
fig = plt.figure(11)
plt.clf()
ml_wf = det.MakeSimWaveform(r_arr[wf_idx],
phi_arr[wf_idx],
z_arr[wf_idx],
scale_arr[wf_idx],
t0_arr[wf_idx],
fitSamples,
h_smoothing=smooth_arr[wf_idx])
plt.plot(ml_wf, color="b")
plt.plot(wf.windowedWf, color="r")
value = raw_input(' --> Press q to quit, any other key to continue\n')
if value == 'q': exit(0)
new_r = np.sqrt(r_arr[wf_idx]**2 + z_arr[wf_idx]**2)
new_theta = np.arctan(z_arr[wf_idx] / r_arr[wf_idx])
print new_theta
startGuess = {
'radEst': new_r,
'thetaEst': new_theta,
'phiEst': phi_arr[wf_idx],
'wfScale': scale_arr[wf_idx],
'switchpoint': t0_arr[wf_idx],
'smooth': smooth_arr[wf_idx],
'b_over_a': b_over_a,
'c': c,
'd': d,
'rc1': rc1,
'rc2': rc2,
'rcfrac': rcfrac,
'temp': tempGuess
}
# for k, v in startGuess.iteritems():
# print k, v
model_locals = sm.CreateTFModel(det, wf, startGuess)
M = pymc.MCMC(model_locals, )
M.use_step_method(
pymc.AdaptiveMetropolis,
[M.radEst, M.phiEst, M.t0Est, M.thetaEst, M.b_over_a, M.tempEst],
scales={
M.radEst: 0.1,
M.t0Est: 0.1,
M.phiEst: np.pi / 10,
M.thetaEst: 0.01,
M.b_over_a: 0.5,
M.tempEst: 0.5
},
delay=10000,
interval=10000,
shrink_if_necessary=True,
)
M.use_step_method(
pymc.AdaptiveMetropolis,
[
M.c,
M.d,
],
scales={
M.c: 0.1,
M.d: 0.1,
},
delay=10000,
interval=10000,
shrink_if_necessary=True,
)
# M.use_step_method(pymc.Metropolis, M.radEst, proposal_sd=0.5, proposal_distribution='Normal')
# M.use_step_method(pymc.Metropolis, M.thetaEst, proposal_sd=np.pi/5, proposal_distribution='Normal', )
# M.use_step_method(pymc.Metropolis, M.phiEst, proposal_sd=np.pi/10, proposal_distribution='Normal')
M.use_step_method(pymc.Metropolis,
M.scaleEst,
proposal_sd=0.01 * startGuess['wfScale'],
proposal_distribution='Normal')
# M.use_step_method(pymc.Metropolis, M.t0Est, proposal_sd=0.05, proposal_distribution='Normal')
M.use_step_method(pymc.Metropolis,
M.sigEst,
proposal_sd=01,
proposal_distribution='Normal')
# M.use_step_method(pymc.Metropolis, M.tempEst, proposal_sd=1, proposal_distribution='Normal')
# M.use_step_method(pymc.Metropolis, M.b_over_a, proposal_sd=0.5, proposal_distribution='Normal')
# M.use_step_method(pymc.Metropolis, M.c, proposal_sd=0.1, proposal_distribution='Normal')
# M.use_step_method(pymc.Metropolis, M.d, proposal_sd=0.1, proposal_distribution='Normal')
M.use_step_method(pymc.Metropolis,
M.rc1,
proposal_sd=0.5,
proposal_distribution='Normal')
M.use_step_method(pymc.Metropolis,
M.rc2,
proposal_sd=0.1,
proposal_distribution='Normal')
M.use_step_method(pymc.Metropolis,
M.rcfrac,
proposal_sd=0.01,
proposal_distribution='Normal')
#
# M.use_step_method(pymc.AdaptiveMetropolis, [M.c, M.d,],
# scales={M.c: 0.1,
# M.d: 0.1,
# }, delay=1000, interval=1000,shrink_if_necessary=True, )
#
# M.use_step_method(pymc.AdaptiveMetropolis, [M.radEst, M.t0Est,],
# scales={M.radEst: 0.1,
# M.t0Est: 0.1,
# }, delay=1000, interval=1000,shrink_if_necessary=True, )
# M.use_step_method(pymc.Slicer, M.radEst)
# M.use_step_method(pymc.Slicer, M.thetaEst, )
# M.use_step_method(pymc.Slicer, M.phiEst, )
# M.use_step_method(pymc.Slicer, M.scaleEst, )
# M.use_step_method(pymc.Slicer, M.t0Est,)# proposal_sd=0.05, proposal_distribution='Normal')
# M.use_step_method(pymc.Slicer, M.sigEst,)# proposal_sd=01, proposal_distribution='Normal')
#
# M.use_step_method(pymc.Slicer, M.tempEst, )# proposal_sd=1, proposal_distribution='Normal')
# M.use_step_method(pymc.Slicer, M.b_over_a,)# proposal_sd=0.5, proposal_distribution='Normal')
# M.use_step_method(pymc.Slicer, M.c, )# proposal_sd=0.1, proposal_distribution='Normal')
# M.use_step_method(pymc.Slicer, M.d, )# proposal_sd=0.1, proposal_distribution='Normal')
#
# M.use_step_method(pymc.Slicer, M.rc1, )# proposal_sd=0.5, proposal_distribution='Normal')
# M.use_step_method(pymc.Slicer, M.rc2, )# proposal_sd=0.1, proposal_distribution='Normal')
# M.use_step_method(pymc.Slicer, M.rcfrac, )# proposal_sd=0.01, proposal_distribution='Normal')
print M.step_method_dict[M.radEst][0].C
print M.step_method_dict[M.c][0].C
print M.step_method_dict[M.radEst][0].stochastics
print M.step_method_dict[M.c][0].stochastics
M.sample(iter=numSamples, burn=0, tune_interval=1000)
M.db.close()
print M.step_method_dict[M.radEst][0].C
# print M.step_method_dict[M.c][0].C
# totalIter = 0
# while totalIter < this_sample:
# M.sample(iter=10, verbose=0)
# totalIter += 10
# #pymc.Matplot.plot(M)
#
# ######### Plots for MC Steps
stepsFig = plt.figure(1, figsize=(20, 10))
plt.clf()
ax0 = stepsFig.add_subplot(611)
ax1 = stepsFig.add_subplot(612, sharex=ax0)
ax2 = stepsFig.add_subplot(613, sharex=ax0)
ax3 = stepsFig.add_subplot(614, sharex=ax0)
ax4 = stepsFig.add_subplot(615, sharex=ax0)
ax5 = stepsFig.add_subplot(616, sharex=ax0)
ax0.set_ylabel('r')
ax1.set_ylabel('theta')
ax2.set_ylabel('phi')
ax3.set_ylabel('e')
ax4.set_ylabel('t0')
ax5.set_ylabel('sig')
ax0.plot(M.trace('radEst')[:])
ax1.plot(M.trace('thetaEst')[:])
ax2.plot(M.trace('phiEst')[:])
ax3.plot(M.trace('wfScale')[:])
ax4.plot(M.trace('switchpoint')[:])
ax5.plot(M.trace('sigma')[:])
plt.savefig("pymc_wf_params.png")
stepsFig = plt.figure(2, figsize=(20, 10))
plt.clf()
ax0 = stepsFig.add_subplot(711)
ax1 = stepsFig.add_subplot(712, sharex=ax0)
ax2 = stepsFig.add_subplot(713, sharex=ax0)
ax3 = stepsFig.add_subplot(714, sharex=ax0)
ax4 = stepsFig.add_subplot(715, sharex=ax0)
ax5 = stepsFig.add_subplot(716, sharex=ax0)
ax6 = stepsFig.add_subplot(717, sharex=ax0)
ax0.set_ylabel('temp')
ax1.set_ylabel('b_over_a')
ax2.set_ylabel('c')
ax3.set_ylabel('d')
ax4.set_ylabel('rc1')
ax5.set_ylabel('rc2')
ax5.set_ylabel('rcfrac')
ax0.plot(M.trace('temp')[:])
ax1.plot(M.trace('b_over_a')[:])
ax2.plot(M.trace('c')[:])
ax3.plot(M.trace('d')[:])
ax4.plot(M.trace('rc1')[:])
ax5.plot(M.trace('rc2')[:])
ax6.plot(M.trace('rcfrac')[:])
plt.savefig("pymc_tf_params.png")
stepsFig = plt.figure(3, figsize=(20, 10))
plt.clf()
ax0 = stepsFig.add_subplot(321)
ax1 = stepsFig.add_subplot(322, )
ax2 = stepsFig.add_subplot(323, )
ax3 = stepsFig.add_subplot(324, )
ax4 = stepsFig.add_subplot(325, )
ax5 = stepsFig.add_subplot(326, )
ax0.set_ylabel('r')
ax1.set_ylabel('theta')
ax2.set_ylabel('phi')
ax3.set_ylabel('e')
ax4.set_ylabel('t0')
ax5.set_ylabel('sig')
hist, bins = np.histogram(M.trace('radEst')[burnin:])
ax0.plot(bins[:-1], hist, ls="steps-post")
hist, bins = np.histogram(M.trace('thetaEst')[burnin:])
ax1.plot(bins[:-1], hist, ls="steps-post")
hist, bins = np.histogram(M.trace('phiEst')[burnin:])
ax2.plot(bins[:-1], hist, ls="steps-post")
hist, bins = np.histogram(M.trace('wfScale')[burnin:])
ax3.plot(bins[:-1], hist, ls="steps-post")
hist, bins = np.histogram(M.trace('switchpoint')[burnin:])
ax4.plot(bins[:-1], hist, ls="steps-post")
hist, bins = np.histogram(M.trace('sigma')[burnin:])
ax5.plot(bins[:-1], hist, ls="steps-post")
plt.savefig("pymc_wf_params_hist.png")
wfPlotNumber = 5000
simWfs = np.empty((wfPlotNumber, fitSamples))
if burnin > len(M.trace('radEst')[:]):
burnin = len(M.trace('radEst')[:]) - wfPlotNumber
numSamples = len(M.trace('radEst')[:])
for (sim_idx, chain_idx) in enumerate(
np.random.randint(low=burnin,
high=numSamples,
size=wfPlotNumber)):
t0 = M.trace('switchpoint')[chain_idx]
rad = M.trace('radEst')[chain_idx]
theta = M.trace('thetaEst')[chain_idx]
phi = M.trace('phiEst')[chain_idx]
scale = M.trace('wfScale')[chain_idx]
sigma = M.trace('sigma')[chain_idx]
r = np.cos(theta) * rad
z = np.sin(theta) * rad
simWfs[sim_idx, :] = det.MakeSimWaveform(
r,
phi,
z,
scale,
t0,
fitSamples,
h_smoothing=sigma,
)
residFig = plt.figure(4, figsize=(20, 10))
helpers.plotResidual(simWfs, wf.windowedWf, figure=residFig)
plt.savefig("pymc_waveforms.png")
value = raw_input(' --> Press q to quit, any other key to continue\n')
if value == 'q': exit(0)
def main(argv):
plt.ion()
runRange = (13420,13429)
channel = 626
aeCutVal = 0.01425
fitSamples = 150
numWaveforms = 2
#get waveforms
cut = "trapECal>%f && trapECal<%f && TSCurrent100nsMax/trapECal > %f" % (1588,1594, aeCutVal)
wfs = helpers.GetWaveforms(runRange, channel, numWaveforms, cut)
#init wf sim
grad = 0.08
pcRad = 2.75#this is the actual starret value ish
detName = "conf/P42574A_grad%0.2f_pcrad%0.2f.conf" % (grad,pcRad)
num = [3.64e+09, 1.88e+17, 6.05e+15]
den = [1, 4.03e+07, 5.14e+14, 7.15e+18]
system = signal.lti(num, den)
det = Detector(detName, temperature=83., timeStep=1., numSteps=fitSamples*10, tfSystem=system)
# #plot to make sure you like what you see
# plt.figure()
# for wf in wfs:
# wf.WindowWaveform(fitSamples)
# plt.plot(wf.windowedWf, color="r")
#
# sim_wf = det.GetSimWaveform(10, 0, 10, wfs[0].wfMax, wfs[0].t0Guess, fitSamples)
# plt.plot(sim_wf, color="b")
#
# plt.show()
#ML as a start
nll = lambda *args: -lnlike(*args)
for (idx,wf) in enumerate(wfs):
# if idx == 0: continue
plt.figure(1)
wf.WindowWaveformTimepoint()
startGuess = [15., np.pi/8, 15., wf.wfMax, wf.t0Guess]
init_wf = det.GetSimWaveform(15, np.pi/8, 15, wf.wfMax, wf.t0Guess, fitSamples)
plt.plot(init_wf, color="g")
result = op.minimize(nll, startGuess, args=(wf.windowedWf, det, wf.baselineRMS), method="Powell")
r, phi, z, scale, t0= result["x"]
plt.plot(wf.windowedWf, color="r")
ml_wf = det.GetSimWaveform(r, phi, z, scale, t0, fitSamples)
ml_wf_inv = det.GetSimWaveform(z, phi, r, scale, t0, fitSamples)
plt.plot(ml_wf, color="b")
plt.plot(ml_wf_inv, "b:")
# print result["x"]
# plt.show()
# plt.figure(2)
# plt.scatter(r, z)
#Do the MCMC
ndim, nwalkers = 5, 20
mcmc_startguess = np.array([r, phi, z, scale, t0])
pos0 = [mcmc_startguess + 1e-2*np.random.randn(ndim) for i in range(nwalkers)]
sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob, args=(wf.windowedWf, det, wf.baselineRMS, wf.wfMax, wf.t0Guess))
# f = open("chain.dat", "w")
# f.close()
iter, burnIn = 1000, 500
bar = ProgressBar(widgets=[Percentage(), Bar()], maxval=iter).start()
start = timer()
for (idx,result) in enumerate(sampler.sample(pos0, iterations=iter, storechain=True)):
bar.update(idx+1)
# position = result[0]
# f = open("chain.dat", "a")
# for k in range(position.shape[0]):
# print " ".join(str(position[k]))
## f.write( "{0:4d} {1:s}\n".format(k, " ".join(position[k])))
# f.close()
# sampler.run_mcmc(pos, 500)
end = timer()
bar.finish()
print "Elapsed time: " + str(end-start)
#samples = sampler.chain[:, 50:, :].reshape((-1, ndim))
######### Plots for MC Steps
stepsFig = plt.figure(2)
plt.clf()
ax0 = stepsFig.add_subplot(511)
ax1 = stepsFig.add_subplot(512, sharex=ax0)
ax2 = stepsFig.add_subplot(513, sharex=ax0)
ax3 = stepsFig.add_subplot(514, sharex=ax0)
ax4 = stepsFig.add_subplot(515, sharex=ax0)
ax0.set_ylabel('r')
ax1.set_ylabel('phi')
ax2.set_ylabel('z')
ax3.set_ylabel('scale')
ax4.set_ylabel('t0')
for i in range(nwalkers):
ax0.plot(sampler.chain[i,:,0], "b", alpha=0.3)
ax1.plot(sampler.chain[i,:,1], "b", alpha=0.3)
ax2.plot(sampler.chain[i,:,2], "b", alpha=0.3)
ax3.plot(sampler.chain[i,:,3], "b", alpha=0.3)
ax4.plot(sampler.chain[i,:,4], "b", alpha=0.3)
#pull the samples after burn-in
samples = sampler.chain[:, 50:, :].reshape((-1, ndim))
wfPlotNumber = 100
simWfs = np.empty(wfPlotNumber, dtype=object)
for idx, (r, phi, z, scale, t0) in enumerate(samples[np.random.randint(len(samples), size=wfPlotNumber)]):
simWfs[idx] = det.GetSimWaveform(r, phi, z, scale, t0, fitSamples)
residFig = plt.figure(3)
helpers.plotResidual(simWfs, wf.windowedWf, figure=residFig)
# plt.show()
value = raw_input(' --> Press q to quit, any other key to continue\n')
if value == 'q':
exit(0)
def main(argv):
plt.ion()
runRange = (13420,13429)
channel = 626
aeCutVal = 0.01425
numThreads = 8
fitSamples = 250
timeStepSize = 1
# wfFileName = "fep_old_postpsa.npz"
wfFileName = "ms_event_set_runs11510-11530_mcmcfit.npz"
numWaveforms = 30
#wfFileName = "P42574A_512waveforms_%drisetimeculled.npz" % numWaveforms
if os.path.isfile(wfFileName):
data = np.load(wfFileName)
wfs = data['wfs']
numWaveforms = wfs.size
else:
print "No saved waveforms available. Loading from Data"
exit(0)
#Prepare detector
tempGuess = 79.204603
gradGuess = 0.05
pcRadGuess = 2.5
pcLenGuess = 1.6
#Create a detector model
detName = "conf/P42574A_grad%0.2f_pcrad%0.2f_pclen%0.2f.conf" % (0.05,2.5, 1.65)
det = Detector(detName, temperature=tempGuess, timeStep=timeStepSize, numSteps=fitSamples*10./timeStepSize,)
det.LoadFields("P42574A_fields_v3.npz")
det.SetFields(pcRadGuess, pcLenGuess, gradGuess, "full")
b_over_a = 0.107077
c = -0.817381
d = 0.825026
rc = 76.551780
det.SetTransferFunction(b_over_a, c, d, rc)
initializeDetector(det, )
#ML as a start
nll = lambda *args: -lnlike_waveform(*args)
for (idx,wf) in enumerate(wfs):
print "wf number %d" % idx
if idx < 130: continue
if wf.energy > 1800: continue
lnprob = -1*wf.lnprob/wf.wfLength
if lnprob < 5: continue
print " ln prob is %f" % lnprob
if wf.wfLength > fitSamples:
print "wtf?"
continue
fig1 = plt.figure(1)
plt.clf()
gs = gridspec.GridSpec(2, 1, height_ratios=[4, 1])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1], sharex=ax0)
ax1.set_xlabel("Digitizer Time [ns]")
ax0.set_ylabel("Voltage [Arb.]")
ax1.set_ylabel("Residual")
wf.WindowWaveformTimepoint(fallPercentage=.999, rmsMult=2)
initializeWaveform(wf)
dataLen = wf.wfLength
t_data = np.arange(dataLen) * 10
ax0.plot(t_data, wf.windowedWf, color="r")
value = raw_input(' --> Press q to quit, s to skip, any other key to continue\n')
if value == 'q':
exit(0)
if value == 's':
continue
minresult = None
minlike = np.inf
for r in np.linspace(4, np.floor(det.detector_radius)-3, 6):
for z in np.linspace(4, np.floor(det.detector_length)-3, 6):
# for t0_guess in np.linspace(wf.t0Guess-10, wf.t0Guess+5, 3):
if not det.IsInDetector(r,0,z): continue
startGuess = [r, np.pi/8, z, wf.wfMax, wf.t0Guess-5, 10]
result = op.minimize(nll, startGuess, method="Nelder-Mead")
r, phi, z, scale, t0, smooth, = result["x"]
ml_wf = np.copy(det.MakeSimWaveform(r, phi, z, scale, t0, fitSamples, h_smoothing=smooth, ))
if ml_wf is None:
print r, z
continue
try:
ax0.plot(t_data, ml_wf[:dataLen], color="b")
ax1.plot(t_data, ml_wf[:dataLen] - wf.windowedWf, color="b")
except ValueError:
print r, z
print ml_wf
if result['fun'] < minlike:
minlike = result['fun']
minresult = result
ax1.set_ylim(-20,20)
r, phi, z, scale, t0, smooth, = minresult["x"]
print r, phi, z, scale, t0, smooth
ml_wf = np.copy(det.MakeSimWaveform(r, phi, z, scale, t0, fitSamples, h_smoothing=smooth, ))
ax0.plot(t_data, ml_wf[:dataLen], color="g")
ax1.plot(t_data, ml_wf[:dataLen] - wf.windowedWf, color="g", lw=5)
mcmc_startguess = minresult["x"]
value = raw_input(' --> Press q to quit, s to skip, any other key to continue\n')
if value == 'q':
exit(0)
if value == 's':
continue
#
# startGuess = [det.detector_radius/2, np.pi/8, det.detector_length/2, wf.wfMax, wf.t0Guess-5, 10]
# #startGuess = [0.2, np.pi/8, det.detector_length/2, wf.wfMax, wf.t0Guess-5, 10]
#
# result = op.minimize(nll, startGuess, method="Nelder-Mead")
# #result = op.basinhopping(nll, startGuess, niter=100, T=10000, stepsize=10, minimizer_kwargs= {"method": "Nelder-Mead"})
# r, phi, z, scale, t0, smooth, = result["x"]
#
# r_new, z_new = det.ReflectPoint(r,z)
#
# print r, z
# print r_new, z_new
#
# r_new = np.amin( [z, np.floor(det.detector_radius)] )
# z_new = np.amin( [r, np.floor(det.detector_length)] )
# print "Initial LN like is %f" % (result['fun']/wf.wfLength)
#
## print r, phi, z, scale, t0, smooth
#
# ml_wf = np.copy(det.MakeSimWaveform(r, phi, z, scale, t0, fitSamples, h_smoothing=smooth, ))
# ax0.plot(t_data, ml_wf[:dataLen], color="b")
# ax1.plot(t_data, ml_wf[:dataLen] - wf.windowedWf, color="b")
#
# result2 = op.minimize(nll, [det.detector_radius/2, phi, 0.2, scale, wf.t0Guess-5,10], method="Powell")
# r, phi, z, scale, t0, smooth, = result2["x"]
# inv_ml_wf = det.MakeSimWaveform(r, phi, z, scale, t0, fitSamples, h_smoothing=smooth, )
# ax0.plot(t_data,inv_ml_wf[:dataLen], color="g")
## print r, phi, z, scale, t0, smooth
# ax1.plot(t_data,inv_ml_wf[:dataLen] - wf.windowedWf, color="g")
#
#
# ax1.set_ylim(-20,20)
#
#
# if result2['fun'] < result['fun']:
# mcmc_startguess = result2["x"]
# else:
# mcmc_startguess = result["x"]
#
# wf.prior = mcmc_startguess
## mcmc_startguess = startGuess
# print "Inverted LN like is %f" % (result2['fun']/wf.wfLength)
#
# value = raw_input(' --> Press q to quit, s to skip, any other key to continue\n')
# if value == 'q':
# exit(0)
# if value == 's':
# continue
#Do the MCMC
ndim, nwalkers = 6, 100
# mcmc_startguess = startGuess#np.array([r, phi, z, scale, t0, smooth])
pos0 = [mcmc_startguess + 1e-1*np.random.randn(ndim)*mcmc_startguess for i in range(nwalkers)]
p = Pool(numThreads, initializer=initializeWaveform, initargs=[ wf])
sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob_waveform, )
iter, burnIn = 1000, 900
bar = ProgressBar(widgets=[Percentage(), Bar(), ETA()], maxval=iter).start()
for (idx,result) in enumerate(sampler.sample(pos0, iterations=iter, storechain=True)):
bar.update(idx+1)
bar.finish()
p.close()
#samples = sampler.chain[:, 50:, :].reshape((-1, ndim))
######### Plots for MC Steps
stepsFig = plt.figure(2)
plt.clf()
ax0 = stepsFig.add_subplot(611)
ax1 = stepsFig.add_subplot(612, sharex=ax0)
ax2 = stepsFig.add_subplot(613, sharex=ax0)
ax3 = stepsFig.add_subplot(614, sharex=ax0)
ax4 = stepsFig.add_subplot(615, sharex=ax0)
ax5 = stepsFig.add_subplot(616, sharex=ax0)
# ax6 = stepsFig.add_subplot(717, sharex=ax0)
ax0.set_ylabel('r')
ax1.set_ylabel('phi')
ax2.set_ylabel('z')
ax3.set_ylabel('scale')
ax4.set_ylabel('t0')
ax5.set_ylabel('smooth')
# ax6.set_ylabel('esmooth')
for i in range(nwalkers):
ax0.plot(sampler.chain[i,:,0], "b", alpha=0.3)
ax1.plot(sampler.chain[i,:,1], "b", alpha=0.3)
ax2.plot(sampler.chain[i,:,2], "b", alpha=0.3)
ax3.plot(sampler.chain[i,:,3], "b", alpha=0.3)
ax4.plot(sampler.chain[i,:,4], "b", alpha=0.3)
ax5.plot(sampler.chain[i,:,5], "b", alpha=0.3)
# ax6.plot(sampler.chain[i,:,6], "b", alpha=0.3)
#pull the samples after burn-in
samples = sampler.chain[:, burnIn:, :].reshape((-1, ndim))
wfPlotNumber = 100
simWfs = np.empty((wfPlotNumber, fitSamples) )
for idx, (r, phi, z, scale, t0, smooth, ) in enumerate(samples[np.random.randint(len(samples), size=wfPlotNumber)]):
simWfs[idx,:] = det.MakeSimWaveform(r, phi, z, scale, t0, fitSamples, h_smoothing = smooth,)
residFig = plt.figure(3)
helpers.plotResidual(simWfs, wf.windowedWf, figure=residFig)
lnprobFig = plt.figure(4)
plt.clf()
lnprobs = sampler.lnprobability[:, burnIn:].reshape((-1))
median_prob = np.median(lnprobs)
print "median lnprob is %f (length normalized: %f)" % (median_prob, median_prob/wf.wfLength)
# print lnprobs
# lnprobs[np.where(np.isfinite(lnprobs) == 0)] = np.nan
#
# hist, bins = np.histogram(lnprobs/wf.wfLength, bins=np.linspace(-10,0,100))
# plt.plot(bins[:-1], hist, ls="steps-post")
positionFig = plt.figure(5)
plt.clf()
xedges = np.linspace(0, np.around(det.detector_radius,1), np.around(det.detector_radius,1)*10+1)
yedges = np.linspace(0, np.around(det.detector_length,1), np.around(det.detector_length,1)*10+1)
plt.hist2d(samples[:,0], samples[:,2], norm=LogNorm(), bins=[ xedges,yedges ])
plt.colorbar()
plt.xlabel("r from Point Contact (mm)")
plt.ylabel("z from Point Contact (mm)")
phiFig = plt.figure(6)
plt.clf()
plt.hist(samples[:,1], bins=np.linspace(0, np.pi/4, 100), )
plt.xlabel("Phi (0 to pi/4)")
plt.ylabel("Marginalized Posterior Probability")
# plt.xlim(0, det.detector_radius)
# plt.ylim(0, det.detector_length)
phiFig = plt.figure(7)
plt.clf()
plt.hist(samples[:,4]*10, bins=np.linspace(100, 299, 2000),)
plt.xlim(np.amin(samples[:,4]*10), np.amax(samples[:,4]*10))
plt.xlabel("t0 [ns]" )
plt.ylabel("Marginalized Posterior Probability")
print "1 sigma percentiles: "
print " r = %f (%f,%f)" % (np.median(samples[:,0]), np.percentile(samples[:,0], 50-34), np.percentile(samples[:,0], 50+34))
print " phi = %f (%f,%f)" % (np.median(samples[:,1]), np.percentile(samples[:,1], 50-34), np.percentile(samples[:,1], 50+34))
print " z = %f (%f,%f)" % (np.median(samples[:,2]), np.percentile(samples[:,2], 50-34), np.percentile(samples[:,2], 50+34))
print " t0 = %f (%f,%f)" % (np.median(samples[:,4]), np.percentile(samples[:,4], 50-34), np.percentile(samples[:,4], 50+34))
print np.median(samples[:,:6], axis=0)
# plt.show()
value = raw_input(' --> Press q to quit, any other key to continue\n')
if value == 'q':
exit(0)
def main(argv):
plt.ion()
runRange = (13420, 13429)
channel = 626
aeCutVal = 0.01425
fitSamples = 150
numWaveforms = 2
#get waveforms
wfFileName = "P42574A_32waveforms_risetimeculled.npz"
if os.path.isfile(wfFileName):
data = np.load(wfFileName)
wfs = data['wfs']
else:
exit(0)
# cut = "trapECal>%f && trapECal<%f && TSCurrent100nsMax/trapECal > %f" % (1588,1594, aeCutVal)
# wfs = helpers.GetWaveforms(runRange, channel, numWaveforms, cut)
num = [8772059139.7583485, 1.7795141663115218e+18, 17696700392300130.0]
den = [1, 50813678.171704151, 708456403501206.5, 1.4152530477835272e+19]
system = signal.lti(num, den)
tempGuess = 77
gradGuess = 0.0487
pcRadGuess = 2.535691
pcLenGuess = 1.655159
#Create a detector model
detName = "conf/P42574A_grad%0.2f_pcrad%0.2f_pclen%0.2f.conf" % (0.04, 2.5,
1.6)
det = Detector(detName,
temperature=tempGuess,
timeStep=1.,
numSteps=fitSamples * 10,
tfSystem=system)
det.LoadFields("P42574A_fields_len.npz")
det.SetFields(pcRadGuess, pcLenGuess, gradGuess)
# #plot to make sure you like what you see
# plt.figure()
# for wf in wfs:
# wf.WindowWaveform(fitSamples)
# plt.plot(wf.windowedWf, color="r")
#
# sim_wf = det.GetSimWaveform(10, 0, 10, wfs[0].wfMax, wfs[0].t0Guess, fitSamples)
# plt.plot(sim_wf, color="b")
#
# plt.show()
#ML as a start
nll = lambda *args: -lnlike(*args)
for (idx, wf) in enumerate(wfs):
# if idx == 0: continue
plt.figure(1)
wf.WindowWaveformTimepoint()
startGuess = [15., np.pi / 8, 15., wf.wfMax, wf.t0Guess, 3., 1.]
init_wf = det.GetSimWaveform(15, np.pi / 8, 15, wf.wfMax, wf.t0Guess,
fitSamples)
plt.plot(init_wf, color="g")
result = op.minimize(nll,
startGuess,
args=(wf.windowedWf, det, wf.baselineRMS),
method="Powell")
r, phi, z, scale, t0, smooth, esmooth = result["x"]
plt.plot(wf.windowedWf, color="r")
ml_wf = det.GetSimWaveform(r,
phi,
z,
scale,
t0,
fitSamples,
smoothing=smooth,
electron_smoothing=esmooth)
ml_wf_inv = det.GetSimWaveform(z,
phi,
r,
scale,
t0,
fitSamples,
smoothing=smooth,
electron_smoothing=esmooth)
plt.plot(ml_wf, color="b")
plt.plot(ml_wf_inv, "b:")
# print result["x"]
# plt.show()
# plt.figure(2)
# plt.scatter(r, z)
#Do the MCMC
ndim, nwalkers = 7, 100
mcmc_startguess = np.array([r, phi, z, scale, t0, smooth, esmooth])
pos0 = [
mcmc_startguess + 1e-1 * np.random.randn(ndim) * mcmc_startguess
for i in range(nwalkers)
]
sampler = emcee.EnsembleSampler(nwalkers,
ndim,
lnprob,
args=(wf.windowedWf, det,
wf.baselineRMS, wf.wfMax,
wf.t0Guess))
# f = open("chain.dat", "w")
# f.close()
iter, burnIn = 2000, 1500
bar = ProgressBar(widgets=[Percentage(), Bar()], maxval=iter).start()
start = timer()
for (idx, result) in enumerate(
sampler.sample(pos0, iterations=iter, storechain=True)):
bar.update(idx + 1)
# position = result[0]
# f = open("chain.dat", "a")
# for k in range(position.shape[0]):
# print " ".join(str(position[k]))
## f.write( "{0:4d} {1:s}\n".format(k, " ".join(position[k])))
# f.close()
# sampler.run_mcmc(pos, 500)
end = timer()
bar.finish()
print "Elapsed time: " + str(end - start)
#samples = sampler.chain[:, 50:, :].reshape((-1, ndim))
######### Plots for MC Steps
stepsFig = plt.figure(2)
plt.clf()
ax0 = stepsFig.add_subplot(711)
ax1 = stepsFig.add_subplot(712, sharex=ax0)
ax2 = stepsFig.add_subplot(713, sharex=ax0)
ax3 = stepsFig.add_subplot(714, sharex=ax0)
ax4 = stepsFig.add_subplot(715, sharex=ax0)
ax5 = stepsFig.add_subplot(716, sharex=ax0)
ax6 = stepsFig.add_subplot(717, sharex=ax0)
ax0.set_ylabel('r')
ax1.set_ylabel('phi')
ax2.set_ylabel('z')
ax3.set_ylabel('scale')
ax4.set_ylabel('t0')
ax5.set_ylabel('smooth')
ax6.set_ylabel('esmooth')
for i in range(nwalkers):
ax0.plot(sampler.chain[i, :, 0], "b", alpha=0.3)
ax1.plot(sampler.chain[i, :, 1], "b", alpha=0.3)
ax2.plot(sampler.chain[i, :, 2], "b", alpha=0.3)
ax3.plot(sampler.chain[i, :, 3], "b", alpha=0.3)
ax4.plot(sampler.chain[i, :, 4], "b", alpha=0.3)
ax5.plot(sampler.chain[i, :, 5], "b", alpha=0.3)
ax6.plot(sampler.chain[i, :, 6], "b", alpha=0.3)
#pull the samples after burn-in
samples = sampler.chain[:, burnIn:, :].reshape((-1, ndim))
wfPlotNumber = 100
simWfs = np.empty(wfPlotNumber, dtype=object)
for idx, (r, phi, z, scale, t0, smooth, e_smooth) in enumerate(
samples[np.random.randint(len(samples), size=wfPlotNumber)]):
simWfs[idx] = det.GetSimWaveform(r,
phi,
z,
scale,
t0,
fitSamples,
smoothing=smooth,
electron_smoothing=e_smooth)
residFig = plt.figure(3)
helpers.plotResidual(simWfs, wf.windowedWf, figure=residFig)
# plt.show()
value = raw_input(' --> Press q to quit, any other key to continue\n')
if value == 'q':
exit(0)
def main(argv):
plt.ion()
runRange = (13420,13429)
channel = 626
aeCutVal = 0.01425
fitSamples = 150
numWaveforms = 2
#get waveforms
wfFileName = "P42574A_32waveforms_risetimeculled.npz"
if os.path.isfile(wfFileName):
data = np.load(wfFileName)
wfs = data['wfs']
else:
exit(0)
# cut = "trapECal>%f && trapECal<%f && TSCurrent100nsMax/trapECal > %f" % (1588,1594, aeCutVal)
# wfs = helpers.GetWaveforms(runRange, channel, numWaveforms, cut)
num = [8772059139.7583485, 1.7795141663115218e+18, 17696700392300130.0]
den = [1, 50813678.171704151, 708456403501206.5, 1.4152530477835272e+19]
system = signal.lti(num, den)
tempGuess = 77
gradGuess = 0.0487
pcRadGuess = 2.535691
pcLenGuess = 1.655159
#Create a detector model
detName = "conf/P42574A_grad%0.2f_pcrad%0.2f_pclen%0.2f.conf" % (0.04,2.5, 1.6)
det = Detector(detName, temperature=tempGuess, timeStep=1., numSteps=fitSamples*10, tfSystem=system)
det.LoadFields("P42574A_fields_len.npz")
det.SetFields(pcRadGuess, pcLenGuess, gradGuess)
# #plot to make sure you like what you see
# plt.figure()
# for wf in wfs:
# wf.WindowWaveform(fitSamples)
# plt.plot(wf.windowedWf, color="r")
#
# sim_wf = det.GetSimWaveform(10, 0, 10, wfs[0].wfMax, wfs[0].t0Guess, fitSamples)
# plt.plot(sim_wf, color="b")
#
# plt.show()
#ML as a start
nll = lambda *args: -lnlike(*args)
for (idx,wf) in enumerate(wfs):
# if idx == 0: continue
plt.figure(1)
wf.WindowWaveformTimepoint()
startGuess = [15., np.pi/8, 15., wf.wfMax, wf.t0Guess, 3., 1.]
init_wf = det.GetSimWaveform(15, np.pi/8, 15, wf.wfMax, wf.t0Guess, fitSamples)
plt.plot(init_wf, color="g")
result = op.minimize(nll, startGuess, args=(wf.windowedWf, det, wf.baselineRMS), method="Powell")
r, phi, z, scale, t0, smooth, esmooth= result["x"]
plt.plot(wf.windowedWf, color="r")
ml_wf = det.GetSimWaveform(r, phi, z, scale, t0, fitSamples, smoothing=smooth, electron_smoothing = esmooth)
ml_wf_inv = det.GetSimWaveform(z, phi, r, scale, t0, fitSamples, smoothing=smooth, electron_smoothing = esmooth)
plt.plot(ml_wf, color="b")
plt.plot(ml_wf_inv, "b:")
# print result["x"]
# plt.show()
# plt.figure(2)
# plt.scatter(r, z)
#Do the MCMC
ndim, nwalkers = 7, 100
mcmc_startguess = np.array([r, phi, z, scale, t0, smooth, esmooth])
pos0 = [mcmc_startguess + 1e-1*np.random.randn(ndim)*mcmc_startguess for i in range(nwalkers)]
sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob, args=(wf.windowedWf, det, wf.baselineRMS, wf.wfMax, wf.t0Guess))
# f = open("chain.dat", "w")
# f.close()
iter, burnIn = 2000, 1500
bar = ProgressBar(widgets=[Percentage(), Bar()], maxval=iter).start()
start = timer()
for (idx,result) in enumerate(sampler.sample(pos0, iterations=iter, storechain=True)):
bar.update(idx+1)
# position = result[0]
# f = open("chain.dat", "a")
# for k in range(position.shape[0]):
# print " ".join(str(position[k]))
## f.write( "{0:4d} {1:s}\n".format(k, " ".join(position[k])))
# f.close()
# sampler.run_mcmc(pos, 500)
end = timer()
bar.finish()
print "Elapsed time: " + str(end-start)
#samples = sampler.chain[:, 50:, :].reshape((-1, ndim))
######### Plots for MC Steps
stepsFig = plt.figure(2)
plt.clf()
ax0 = stepsFig.add_subplot(711)
ax1 = stepsFig.add_subplot(712, sharex=ax0)
ax2 = stepsFig.add_subplot(713, sharex=ax0)
ax3 = stepsFig.add_subplot(714, sharex=ax0)
ax4 = stepsFig.add_subplot(715, sharex=ax0)
ax5 = stepsFig.add_subplot(716, sharex=ax0)
ax6 = stepsFig.add_subplot(717, sharex=ax0)
ax0.set_ylabel('r')
ax1.set_ylabel('phi')
ax2.set_ylabel('z')
ax3.set_ylabel('scale')
ax4.set_ylabel('t0')
ax5.set_ylabel('smooth')
ax6.set_ylabel('esmooth')
for i in range(nwalkers):
ax0.plot(sampler.chain[i,:,0], "b", alpha=0.3)
ax1.plot(sampler.chain[i,:,1], "b", alpha=0.3)
ax2.plot(sampler.chain[i,:,2], "b", alpha=0.3)
ax3.plot(sampler.chain[i,:,3], "b", alpha=0.3)
ax4.plot(sampler.chain[i,:,4], "b", alpha=0.3)
ax5.plot(sampler.chain[i,:,5], "b", alpha=0.3)
ax6.plot(sampler.chain[i,:,6], "b", alpha=0.3)
#pull the samples after burn-in
samples = sampler.chain[:, burnIn:, :].reshape((-1, ndim))
wfPlotNumber = 100
simWfs = np.empty(wfPlotNumber, dtype=object)
for idx, (r, phi, z, scale, t0, smooth, e_smooth) in enumerate(samples[np.random.randint(len(samples), size=wfPlotNumber)]):
simWfs[idx] = det.GetSimWaveform(r, phi, z, scale, t0, fitSamples, smoothing = smooth, electron_smoothing = e_smooth)
residFig = plt.figure(3)
helpers.plotResidual(simWfs, wf.windowedWf, figure=residFig)
# plt.show()
value = raw_input(' --> Press q to quit, any other key to continue\n')
if value == 'q':
exit(0)
def main(argv):
plt.ion()
numThreads = 8
fitSamples = 130
timeStepSize = 1
wfFileName = "P42574A_12_fastandslow_oldwfs.npz"
# wfFileName = "P42574A_pc_highe.npz"
#wfFileName = "fep_event_set_runs11531-11539.npz"
# numWaveforms = 16
# wfFileName = "P42574A_512waveforms_%drisetimeculled.npz" % numWaveforms
if os.path.isfile(wfFileName):
data = np.load(wfFileName)
wfs = data['wfs']
results = data['results']
numWaveforms = wfs.size
else:
print "No saved waveforms available. Loading from Data"
exit(0)
tempGuess = 79.071172
gradGuess = 0.04
pcRadGuess = 2.5
pcLenGuess = 1.6
#Create a detector model
detName = "conf/P42574A_grad%0.2f_pcrad%0.2f_pclen%0.2f.conf" % (0.05,2.5, 1.65)
det = Detector(detName, temperature=tempGuess, timeStep=timeStepSize, numSteps=fitSamples*10)
det.LoadFieldsGrad("fields_impgrad.npz", pcLen=pcLenGuess, pcRad=pcRadGuess)
det.SetFieldsGradInterp(gradGuess)
b_over_a = 0.107213
c = -0.815152
d = 0.822696
rc1 = 74.4
rc2 = 1.79
rcfrac = 0.992
trapping_rc = 120#us
det.SetTransferFunction(b_over_a, c, d, rc1, rc2, rcfrac)
# det.trapping_rc = trapping_rc #us
det.trapping_rc = trapping_rc
#do my own hole mobility model based on bruy
det.siggenInst.set_velocity_type(1)
h_100_mu0, h_100_beta, h_100_e0, h_111_mu0, h_111_beta, h_111_e0 = 66333., 0.744, 181., 107270., 0.580, 100.
# trapping_rc = 4
# det.trapping_rc = trapping_rc
initializeDetector(det, )
pmw.initializeDetector(det, )
#ML as a start
nll = lambda *args: -lnlike_waveform(*args)
nll_wf = lambda *args: -pmw.lnlike_waveform(*args)
# plt.ioff()
for (wf_idx,wf) in enumerate(wfs):
if wf_idx <= 2: continue
if wf.energy < 1700: continue
wf.WindowWaveformTimepoint(fallPercentage=.995, rmsMult=2)
initializeWaveform(wf)
pmw.initializeWaveform(wf)
dataLen = wf.wfLength
t_data = np.arange(dataLen) * 10
fig1 = plt.figure(1)
plt.clf()
gs = gridspec.GridSpec(2, 1, height_ratios=[4, 1])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1], sharex=ax0)
ax1.set_xlabel("Digitizer Time [ns]")
ax0.set_ylabel("Voltage [Arb.]")
ax1.set_ylabel("Residual")
ax0.plot(t_data, wf.windowedWf, color="r")
minresult = None
minlike = np.inf
#
for r in np.linspace(4, np.floor(det.detector_radius)-3, 3):
for z in np.linspace(4, np.floor(det.detector_length)-3, 3):
# for t0_guess in np.linspace(wf.t0Guess-10, wf.t0Guess+5, 3):
if not det.IsInDetector(r,0,z): continue
startGuess = [r, np.pi/8, z, wf.wfMax, wf.t0Guess-5, 10]
result = op.minimize(nll_wf, startGuess, method="Nelder-Mead")
r, phi, z, scale, t0, smooth, = result["x"]
ml_wf = np.copy(det.MakeSimWaveform(r, phi, z, scale, t0, fitSamples, h_smoothing=smooth, ))
if ml_wf is None:
print r, z
continue
if result['fun'] < minlike:
minlike = result['fun']
minresult = result
ax1.set_ylim(-20,20)
r, phi, z, scale, t0, smooth, = minresult["x"]
print r, phi, z, scale, t0, smooth
# r, phi, z, scale, t0, smooth = results[wf_idx]['x']
# startGuess = [r, phi, z, scale, t0, smooth]
# result = op.minimize(nll_wf, startGuess, method="Powell")
# r, phi, z, scale, t0, smooth = result['x']
ml_wf = np.copy(det.MakeSimWaveform(r, phi, z, scale, t0, fitSamples, h_smoothing=smooth, ))
ax0.plot(t_data, ml_wf[:dataLen], color="g")
ax1.plot(t_data, ml_wf[:dataLen] - wf.windowedWf, color="g",)
#
value = raw_input(' --> Press q to quit, s to skip, any other key to continue\n')
if value == 'q':
exit(0)
if value == 's': continue
mcmc_startguess = [r, phi, z, scale, t0, smooth, b_over_a, c, d, h_100_mu0, h_100_beta, h_100_e0, h_111_mu0, h_111_beta, h_111_e0,]# trapping_rc, ]#gradGuess, pcRadGuess, pcLenGuess ]
# mcmc_startguess = [r, phi, z, scale, t0, smooth, tempGuess, omega, decay, rc1, rc2, rcfrac, ]
#Do the MCMC
ndim = 15
nwalkers = ndim*10
# mcmc_startguess = startGuess#np.array([r, phi, z, scale, t0, smooth])
pos0 = [mcmc_startguess + 1e-2*np.random.randn(ndim)*mcmc_startguess for i in range(nwalkers)]
gradIdx = -1
mobIdx = 9
# pcRadIdx = -2
# pcLenIdx = -1
# rc1idx = -4
# rc2idx = -3
# rcfracidx = -2
# for pos in pos0:
## pos[rcfracidx] = np.clip(pos[rcfracidx], 0, 1)
## pos[rc2idx] = np.clip(pos[rc2idx], 0, np.inf)
## pos[rc1idx] = np.clip(pos[rc1idx], 0, np.inf)
#
# pos[gradIdx] = np.clip(pos[gradIdx], det.gradList[0], det.gradList[-1])
## pos[pcRadIdx] = np.clip(pos[pcRadIdx], det.pcRadList[0], det.pcRadList[-1])
## pos[pcLenIdx] = np.clip(pos[pcLenIdx], det.pcLenList[0], det.pcLenList[-1])
#
# prior = lnprob_waveform(pos,)
# if not np.isfinite(prior) :
# print "BAD PRIOR WITH START GUESS YOURE KILLING ME SMALLS"
# print pos
# exit(0)
p = Pool(numThreads, initializer=initializeWaveform, initargs=[ wf])
sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob_waveform, )
iter, burnIn = 10000, 9900
wfPlotNumber = 100
bar = ProgressBar(widgets=[Percentage(), Bar(), ETA()], maxval=iter).start()
for (i,result) in enumerate(sampler.sample(pos0, iterations=iter, storechain=True)):
bar.update(i+1)
bar.finish()
p.close()
#samples = sampler.chain[:, 50:, :].reshape((-1, ndim))
######### Plots for MC Steps
stepsFig = plt.figure(2, figsize= (20,10))
plt.clf()
plotnum = 600
ax0 = stepsFig.add_subplot(plotnum+11)
ax1 = stepsFig.add_subplot(plotnum+12, sharex=ax0)
ax2 = stepsFig.add_subplot(plotnum+13, sharex=ax0)
ax3 = stepsFig.add_subplot(plotnum+14, sharex=ax0)
ax4 = stepsFig.add_subplot(plotnum+15, sharex=ax0)
ax5 = stepsFig.add_subplot(plotnum+16, sharex=ax0)
ax0.set_ylabel('r')
ax1.set_ylabel('phi')
ax2.set_ylabel('z')
ax3.set_ylabel('scale')
ax4.set_ylabel('t0')
ax5.set_ylabel('smooth')
# ax6.set_ylabel('temp')
for i in range(nwalkers):
ax0.plot(sampler.chain[i,:,0], "b", alpha=0.3)
ax1.plot(sampler.chain[i,:,1], "b", alpha=0.3)
ax2.plot(sampler.chain[i,:,2], "b", alpha=0.3)
ax3.plot(sampler.chain[i,:,3], "b", alpha=0.3)
ax4.plot(sampler.chain[i,:,4], "b", alpha=0.3)
ax5.plot(sampler.chain[i,:,5], "b", alpha=0.3)
plt.savefig("hdxwf%d_wf_params.png"%wf_idx)
stepsFig = plt.figure(3, figsize= (20,10))
plt.clf()
plotnum = 600
ax0 = stepsFig.add_subplot(plotnum+11)
ax1 = stepsFig.add_subplot(plotnum+12, sharex=ax0)
ax2 = stepsFig.add_subplot(plotnum+13, sharex=ax0)
ax3 = stepsFig.add_subplot(plotnum+14, sharex=ax0)
ax4 = stepsFig.add_subplot(plotnum+15, sharex=ax0)
ax5 = stepsFig.add_subplot(plotnum+16, sharex=ax0)
ax0.set_ylabel('h_100_mu0')
ax1.set_ylabel('h_100_beta')
ax2.set_ylabel('h_100_e0')
ax3.set_ylabel('h_111_mu0')
ax4.set_ylabel('h_111_beta')
ax5.set_ylabel('h_111_e0')
# ax6.set_ylabel('temp')
for i in range(nwalkers):
ax0.plot(sampler.chain[i,:,mobIdx+0], "b", alpha=0.3)
ax1.plot(sampler.chain[i,:,mobIdx+1], "b", alpha=0.3)
ax2.plot(sampler.chain[i,:,mobIdx+2], "b", alpha=0.3)
ax3.plot(sampler.chain[i,:,mobIdx+3], "b", alpha=0.3)
ax4.plot(sampler.chain[i,:,mobIdx+4], "b", alpha=0.3)
ax5.plot(sampler.chain[i,:,mobIdx+5], "b", alpha=0.3)
plt.savefig("hdxwf%d_mobility_params.png"%wf_idx)
stepsFig2 = plt.figure(4, figsize = (20,10))
plotnum = 400
ax6 = stepsFig2.add_subplot(plotnum+11)
ax7 = stepsFig2.add_subplot(plotnum+12, sharex=ax6)
ax8 = stepsFig2.add_subplot(plotnum+13, sharex=ax6)
ax9 = stepsFig2.add_subplot(plotnum+14, sharex=ax6)
# ax10 = stepsFig2.add_subplot(plotnum+15, sharex=ax6)
# ax11 = stepsFig2.add_subplot(plotnum+16, sharex=ax6)
# ax12 = stepsFig2.add_subplot(plotnum+17, sharex=ax6)
# ax13 = stepsFig2.add_subplot(plotnum+18, sharex=ax6)
ax6.set_ylabel('b_ov_a')
ax7.set_ylabel('c')
ax8.set_ylabel('d')
ax9.set_ylabel('trapping')
# ax10.set_ylabel('rc1')
# ax11.set_ylabel('rc2')
# ax12.set_ylabel('rcfrac')
# ax8.set_ylabel('trapping')
# ax9.set_ylabel('grad')
for i in range(nwalkers):
ax6.plot(sampler.chain[i,:,6], "b", alpha=0.3)
ax7.plot(sampler.chain[i,:,7], "b", alpha=0.3)
ax8.plot(sampler.chain[i,:,8], "b", alpha=0.3)
# ax9.plot(sampler.chain[i,:,9], "b", alpha=0.3)
# ax10.plot(sampler.chain[i,:,10], "b", alpha=0.3)
# ax11.plot(sampler.chain[i,:,11], "b", alpha=0.3)
# ax12.plot(sampler.chain[i,:,12], "b", alpha=0.3)
# ax13.plot(sampler.chain[i,:,-1], "b", alpha=0.3)
plt.savefig("hdxwf%d_tf_params.png"%wf_idx)
samples = sampler.chain[:, burnIn:, :].reshape((-1, ndim))
# stepsFigTF = plt.figure(5, figsize = (20,10))
#
# tf0 = stepsFigTF.add_subplot(plotnum+11)
# tf1 = stepsFigTF.add_subplot(plotnum+12, )
# tf2 = stepsFigTF.add_subplot(plotnum+13, )
## tf3 = stepsFigTF.add_subplot(plotnum+14, )
## tf4 = stepsFigTF.add_subplot(815, )
## tf5 = stepsFigTF.add_subplot(816, )
## tf6 = stepsFigTF.add_subplot(817,)
## tf7 = stepsFigTF.add_subplot(818,)
#
# tf0.set_ylabel('temp')
## tf1.set_ylabel('c')
## tf2.set_ylabel('d')
## tf3.set_ylabel('rc1')
## tf4.set_ylabel('rc2')
## tf5.set_ylabel('rcfrac')
# tf1.set_ylabel('b_ov_a')
# tf2.set_ylabel('trapping')
## tf3.set_ylabel('grad')
#
# num_bins = 300
# [n, b, p] = tf0.hist(samples[:,6], bins=num_bins)
# print "b_over_a mode is %f" % b[np.argmax(n)]
#
# [n, b, p] = tf1.hist(samples[:,7],bins=num_bins)
# print "c mode is %f" % b[np.argmax(n)]
#
# [n, b, p] = tf2.hist(samples[:,8],bins=num_bins)
# print "d mode is %f" % b[np.argmax(n)]
# [n, b, p] = tf3.hist(samples[:,-1],bins=num_bins)
# print "rc_decay1 mode is %f" % b[np.argmax(n)]
#
# [n, b, p] = tf4.hist(samples[:,-3],bins=num_bins)
# print "rc_decay2 mode is %f" % b[np.argmax(n)]
#
# [n, b, p] = tf5.hist(samples[:,-2],bins=num_bins)
# print "rc_frac mode is %f" % b[np.argmax(n)]
#
# [n, b, p] = tf6.hist(samples[:,-8],bins=num_bins)
# print "temp mode is %f" % b[np.argmax(n)]
#
# [n, b, p] = tf6.hist(samples[:,-1],bins=num_bins)
# print "grad mode is %f" % b[np.argmax(n)]
plt.savefig("hdxwf%d_tf_hist.png"%wf_idx)
# stepsFig3 = plt.figure(13)
# plotnum = 300
# ax13 = stepsFig3.add_subplot(plotnum+11)
# ax14 = stepsFig3.add_subplot(plotnum+12, sharex=ax13)
# ax15 = stepsFig3.add_subplot(plotnum+13, sharex=ax13)
# ax13.set_ylabel('grad')
# ax14.set_ylabel('pcrad')
# ax15.set_ylabel('pclem')
# for i in range(nwalkers):
# ax13.plot(sampler.chain[i,:,13], "b", alpha=0.3)
# ax14.plot(sampler.chain[i,:,14], "b", alpha=0.3)
# ax15.plot(sampler.chain[i,:,15], "b", alpha=0.3)
#
# plt.savefig("hdxwf%d_det_params.png"%wf_idx)
#pull the samples after burn-in
simWfs = np.empty((wfPlotNumber, fitSamples) )
for idx, (r, phi, z, scale, t0, smooth, b_over_a, c, d, h_100_mu0, h_100_beta, h_100_e0, h_111_mu0, h_111_beta, h_111_e0, ) in enumerate(samples[np.random.randint(len(samples), size=wfPlotNumber)]):
# for idx, (r, phi, z, scale, t0, smooth, temp, omega,decay, rc1, rc2, rcfrac, ) in enumerate(samples[np.random.randint(len(samples), size=wfPlotNumber)]):
det.SetTransferFunction(b_over_a, c, d, rc1, rc2, rcfrac)
det.siggenInst.set_hole_params(h_100_mu0, h_100_beta, h_100_e0, h_111_mu0, h_111_beta, h_111_e0)
# det.SetTemperature(temp)
# det.trapping_rc = trapping_rc
# det.SetFieldsGradInterp(grad)
# det.SetFields(pcRad, pcLen, impGrad)
# det.trapping_rc = trapping_rc
simWfs[idx,:] = det.MakeSimWaveform(r, phi, z, scale, t0, fitSamples, h_smoothing=smooth)
residFig = plt.figure(6)
helpers.plotResidual(simWfs, wf.windowedWf, figure=residFig)
print "Median values wf %d: " % wf_idx
for idx in range(ndim):
print " param %d: %f" % (idx, np.median(samples[:,idx]))
plt.savefig("hdxwf%d_resid.png"%wf_idx)
# plt.show()
value = raw_input(' --> Press q to quit, any other key to continue\n')
if value == 'q':
exit(0)
def main(argv):
plt.ion()
numThreads = 8
fitSamples = 130
timeStepSize = 1
wfFileName = "P42574A_12_fastandslow_oldwfs.npz"
# wfFileName = "P42574A_pc_highe.npz"
#wfFileName = "fep_event_set_runs11531-11539.npz"
# numWaveforms = 16
# wfFileName = "P42574A_512waveforms_%drisetimeculled.npz" % numWaveforms
if os.path.isfile(wfFileName):
data = np.load(wfFileName)
wfs = data['wfs']
results = data['results']
numWaveforms = wfs.size
else:
print "No saved waveforms available. Loading from Data"
exit(0)
tempGuess = 79.071172
gradGuess = 0.04
pcRadGuess = 2.5
pcLenGuess = 1.6
#Create a detector model
detName = "conf/P42574A_grad%0.2f_pcrad%0.2f_pclen%0.2f.conf" % (0.05, 2.5,
1.65)
det = Detector(detName,
temperature=tempGuess,
timeStep=timeStepSize,
numSteps=fitSamples * 10)
det.LoadFieldsGrad("fields_impgrad.npz",
pcLen=pcLenGuess,
pcRad=pcRadGuess)
det.SetFieldsGradInterp(gradGuess)
b_over_a = 0.107213
c = -0.815152
d = 0.822696
rc1 = 74.4
rc2 = 1.79
rcfrac = 0.992
trapping_rc = 120 #us
det.SetTransferFunction(b_over_a, c, d, rc1, rc2, rcfrac)
# det.trapping_rc = trapping_rc #us
det.trapping_rc = trapping_rc
#do my own hole mobility model based on bruy
det.siggenInst.set_velocity_type(1)
h_100_mu0, h_100_beta, h_100_e0, h_111_mu0, h_111_beta, h_111_e0 = 66333., 0.744, 181., 107270., 0.580, 100.
# trapping_rc = 4
# det.trapping_rc = trapping_rc
initializeDetector(det, )
pmw.initializeDetector(det, )
#ML as a start
nll = lambda *args: -lnlike_waveform(*args)
nll_wf = lambda *args: -pmw.lnlike_waveform(*args)
# plt.ioff()
for (wf_idx, wf) in enumerate(wfs):
if wf_idx <= 2: continue
if wf.energy < 1700: continue
wf.WindowWaveformTimepoint(fallPercentage=.995, rmsMult=2)
initializeWaveform(wf)
pmw.initializeWaveform(wf)
dataLen = wf.wfLength
t_data = np.arange(dataLen) * 10
fig1 = plt.figure(1)
plt.clf()
gs = gridspec.GridSpec(2, 1, height_ratios=[4, 1])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1], sharex=ax0)
ax1.set_xlabel("Digitizer Time [ns]")
ax0.set_ylabel("Voltage [Arb.]")
ax1.set_ylabel("Residual")
ax0.plot(t_data, wf.windowedWf, color="r")
minresult = None
minlike = np.inf
#
for r in np.linspace(4, np.floor(det.detector_radius) - 3, 3):
for z in np.linspace(4, np.floor(det.detector_length) - 3, 3):
# for t0_guess in np.linspace(wf.t0Guess-10, wf.t0Guess+5, 3):
if not det.IsInDetector(r, 0, z): continue
startGuess = [r, np.pi / 8, z, wf.wfMax, wf.t0Guess - 5, 10]
result = op.minimize(nll_wf, startGuess, method="Nelder-Mead")
r, phi, z, scale, t0, smooth, = result["x"]
ml_wf = np.copy(
det.MakeSimWaveform(
r,
phi,
z,
scale,
t0,
fitSamples,
h_smoothing=smooth,
))
if ml_wf is None:
print r, z
continue
if result['fun'] < minlike:
minlike = result['fun']
minresult = result
ax1.set_ylim(-20, 20)
r, phi, z, scale, t0, smooth, = minresult["x"]
print r, phi, z, scale, t0, smooth
# r, phi, z, scale, t0, smooth = results[wf_idx]['x']
# startGuess = [r, phi, z, scale, t0, smooth]
# result = op.minimize(nll_wf, startGuess, method="Powell")
# r, phi, z, scale, t0, smooth = result['x']
ml_wf = np.copy(
det.MakeSimWaveform(
r,
phi,
z,
scale,
t0,
fitSamples,
h_smoothing=smooth,
))
ax0.plot(t_data, ml_wf[:dataLen], color="g")
ax1.plot(
t_data,
ml_wf[:dataLen] - wf.windowedWf,
color="g",
)
#
value = raw_input(
' --> Press q to quit, s to skip, any other key to continue\n')
if value == 'q':
exit(0)
if value == 's': continue
mcmc_startguess = [
r,
phi,
z,
scale,
t0,
smooth,
b_over_a,
c,
d,
h_100_mu0,
h_100_beta,
h_100_e0,
h_111_mu0,
h_111_beta,
h_111_e0,
] # trapping_rc, ]#gradGuess, pcRadGuess, pcLenGuess ]
# mcmc_startguess = [r, phi, z, scale, t0, smooth, tempGuess, omega, decay, rc1, rc2, rcfrac, ]
#Do the MCMC
ndim = 15
nwalkers = ndim * 10
# mcmc_startguess = startGuess#np.array([r, phi, z, scale, t0, smooth])
pos0 = [
mcmc_startguess + 1e-2 * np.random.randn(ndim) * mcmc_startguess
for i in range(nwalkers)
]
gradIdx = -1
mobIdx = 9
# pcRadIdx = -2
# pcLenIdx = -1
# rc1idx = -4
# rc2idx = -3
# rcfracidx = -2
# for pos in pos0:
## pos[rcfracidx] = np.clip(pos[rcfracidx], 0, 1)
## pos[rc2idx] = np.clip(pos[rc2idx], 0, np.inf)
## pos[rc1idx] = np.clip(pos[rc1idx], 0, np.inf)
#
# pos[gradIdx] = np.clip(pos[gradIdx], det.gradList[0], det.gradList[-1])
## pos[pcRadIdx] = np.clip(pos[pcRadIdx], det.pcRadList[0], det.pcRadList[-1])
## pos[pcLenIdx] = np.clip(pos[pcLenIdx], det.pcLenList[0], det.pcLenList[-1])
#
# prior = lnprob_waveform(pos,)
# if not np.isfinite(prior) :
# print "BAD PRIOR WITH START GUESS YOURE KILLING ME SMALLS"
# print pos
# exit(0)
p = Pool(numThreads, initializer=initializeWaveform, initargs=[wf])
sampler = emcee.EnsembleSampler(
nwalkers,
ndim,
lnprob_waveform,
)
iter, burnIn = 10000, 9900
wfPlotNumber = 100
bar = ProgressBar(widgets=[Percentage(), Bar(),
ETA()], maxval=iter).start()
for (i, result) in enumerate(
sampler.sample(pos0, iterations=iter, storechain=True)):
bar.update(i + 1)
bar.finish()
p.close()
#samples = sampler.chain[:, 50:, :].reshape((-1, ndim))
######### Plots for MC Steps
stepsFig = plt.figure(2, figsize=(20, 10))
plt.clf()
plotnum = 600
ax0 = stepsFig.add_subplot(plotnum + 11)
ax1 = stepsFig.add_subplot(plotnum + 12, sharex=ax0)
ax2 = stepsFig.add_subplot(plotnum + 13, sharex=ax0)
ax3 = stepsFig.add_subplot(plotnum + 14, sharex=ax0)
ax4 = stepsFig.add_subplot(plotnum + 15, sharex=ax0)
ax5 = stepsFig.add_subplot(plotnum + 16, sharex=ax0)
ax0.set_ylabel('r')
ax1.set_ylabel('phi')
ax2.set_ylabel('z')
ax3.set_ylabel('scale')
ax4.set_ylabel('t0')
ax5.set_ylabel('smooth')
# ax6.set_ylabel('temp')
for i in range(nwalkers):
ax0.plot(sampler.chain[i, :, 0], "b", alpha=0.3)
ax1.plot(sampler.chain[i, :, 1], "b", alpha=0.3)
ax2.plot(sampler.chain[i, :, 2], "b", alpha=0.3)
ax3.plot(sampler.chain[i, :, 3], "b", alpha=0.3)
ax4.plot(sampler.chain[i, :, 4], "b", alpha=0.3)
ax5.plot(sampler.chain[i, :, 5], "b", alpha=0.3)
plt.savefig("hdxwf%d_wf_params.png" % wf_idx)
stepsFig = plt.figure(3, figsize=(20, 10))
plt.clf()
plotnum = 600
ax0 = stepsFig.add_subplot(plotnum + 11)
ax1 = stepsFig.add_subplot(plotnum + 12, sharex=ax0)
ax2 = stepsFig.add_subplot(plotnum + 13, sharex=ax0)
ax3 = stepsFig.add_subplot(plotnum + 14, sharex=ax0)
ax4 = stepsFig.add_subplot(plotnum + 15, sharex=ax0)
ax5 = stepsFig.add_subplot(plotnum + 16, sharex=ax0)
ax0.set_ylabel('h_100_mu0')
ax1.set_ylabel('h_100_beta')
ax2.set_ylabel('h_100_e0')
ax3.set_ylabel('h_111_mu0')
ax4.set_ylabel('h_111_beta')
ax5.set_ylabel('h_111_e0')
# ax6.set_ylabel('temp')
for i in range(nwalkers):
ax0.plot(sampler.chain[i, :, mobIdx + 0], "b", alpha=0.3)
ax1.plot(sampler.chain[i, :, mobIdx + 1], "b", alpha=0.3)
ax2.plot(sampler.chain[i, :, mobIdx + 2], "b", alpha=0.3)
ax3.plot(sampler.chain[i, :, mobIdx + 3], "b", alpha=0.3)
ax4.plot(sampler.chain[i, :, mobIdx + 4], "b", alpha=0.3)
ax5.plot(sampler.chain[i, :, mobIdx + 5], "b", alpha=0.3)
plt.savefig("hdxwf%d_mobility_params.png" % wf_idx)
stepsFig2 = plt.figure(4, figsize=(20, 10))
plotnum = 400
ax6 = stepsFig2.add_subplot(plotnum + 11)
ax7 = stepsFig2.add_subplot(plotnum + 12, sharex=ax6)
ax8 = stepsFig2.add_subplot(plotnum + 13, sharex=ax6)
ax9 = stepsFig2.add_subplot(plotnum + 14, sharex=ax6)
# ax10 = stepsFig2.add_subplot(plotnum+15, sharex=ax6)
# ax11 = stepsFig2.add_subplot(plotnum+16, sharex=ax6)
# ax12 = stepsFig2.add_subplot(plotnum+17, sharex=ax6)
# ax13 = stepsFig2.add_subplot(plotnum+18, sharex=ax6)
ax6.set_ylabel('b_ov_a')
ax7.set_ylabel('c')
ax8.set_ylabel('d')
ax9.set_ylabel('trapping')
# ax10.set_ylabel('rc1')
# ax11.set_ylabel('rc2')
# ax12.set_ylabel('rcfrac')
# ax8.set_ylabel('trapping')
# ax9.set_ylabel('grad')
for i in range(nwalkers):
ax6.plot(sampler.chain[i, :, 6], "b", alpha=0.3)
ax7.plot(sampler.chain[i, :, 7], "b", alpha=0.3)
ax8.plot(sampler.chain[i, :, 8], "b", alpha=0.3)
# ax9.plot(sampler.chain[i,:,9], "b", alpha=0.3)
# ax10.plot(sampler.chain[i,:,10], "b", alpha=0.3)
# ax11.plot(sampler.chain[i,:,11], "b", alpha=0.3)
# ax12.plot(sampler.chain[i,:,12], "b", alpha=0.3)
# ax13.plot(sampler.chain[i,:,-1], "b", alpha=0.3)
plt.savefig("hdxwf%d_tf_params.png" % wf_idx)
samples = sampler.chain[:, burnIn:, :].reshape((-1, ndim))
# stepsFigTF = plt.figure(5, figsize = (20,10))
#
# tf0 = stepsFigTF.add_subplot(plotnum+11)
# tf1 = stepsFigTF.add_subplot(plotnum+12, )
# tf2 = stepsFigTF.add_subplot(plotnum+13, )
## tf3 = stepsFigTF.add_subplot(plotnum+14, )
## tf4 = stepsFigTF.add_subplot(815, )
## tf5 = stepsFigTF.add_subplot(816, )
## tf6 = stepsFigTF.add_subplot(817,)
## tf7 = stepsFigTF.add_subplot(818,)
#
# tf0.set_ylabel('temp')
## tf1.set_ylabel('c')
## tf2.set_ylabel('d')
## tf3.set_ylabel('rc1')
## tf4.set_ylabel('rc2')
## tf5.set_ylabel('rcfrac')
# tf1.set_ylabel('b_ov_a')
# tf2.set_ylabel('trapping')
## tf3.set_ylabel('grad')
#
# num_bins = 300
# [n, b, p] = tf0.hist(samples[:,6], bins=num_bins)
# print "b_over_a mode is %f" % b[np.argmax(n)]
#
# [n, b, p] = tf1.hist(samples[:,7],bins=num_bins)
# print "c mode is %f" % b[np.argmax(n)]
#
# [n, b, p] = tf2.hist(samples[:,8],bins=num_bins)
# print "d mode is %f" % b[np.argmax(n)]
# [n, b, p] = tf3.hist(samples[:,-1],bins=num_bins)
# print "rc_decay1 mode is %f" % b[np.argmax(n)]
#
# [n, b, p] = tf4.hist(samples[:,-3],bins=num_bins)
# print "rc_decay2 mode is %f" % b[np.argmax(n)]
#
# [n, b, p] = tf5.hist(samples[:,-2],bins=num_bins)
# print "rc_frac mode is %f" % b[np.argmax(n)]
#
# [n, b, p] = tf6.hist(samples[:,-8],bins=num_bins)
# print "temp mode is %f" % b[np.argmax(n)]
#
# [n, b, p] = tf6.hist(samples[:,-1],bins=num_bins)
# print "grad mode is %f" % b[np.argmax(n)]
plt.savefig("hdxwf%d_tf_hist.png" % wf_idx)
# stepsFig3 = plt.figure(13)
# plotnum = 300
# ax13 = stepsFig3.add_subplot(plotnum+11)
# ax14 = stepsFig3.add_subplot(plotnum+12, sharex=ax13)
# ax15 = stepsFig3.add_subplot(plotnum+13, sharex=ax13)
# ax13.set_ylabel('grad')
# ax14.set_ylabel('pcrad')
# ax15.set_ylabel('pclem')
# for i in range(nwalkers):
# ax13.plot(sampler.chain[i,:,13], "b", alpha=0.3)
# ax14.plot(sampler.chain[i,:,14], "b", alpha=0.3)
# ax15.plot(sampler.chain[i,:,15], "b", alpha=0.3)
#
# plt.savefig("hdxwf%d_det_params.png"%wf_idx)
#pull the samples after burn-in
simWfs = np.empty((wfPlotNumber, fitSamples))
for idx, (
r,
phi,
z,
scale,
t0,
smooth,
b_over_a,
c,
d,
h_100_mu0,
h_100_beta,
h_100_e0,
h_111_mu0,
h_111_beta,
h_111_e0,
) in enumerate(samples[np.random.randint(len(samples),
size=wfPlotNumber)]):
# for idx, (r, phi, z, scale, t0, smooth, temp, omega,decay, rc1, rc2, rcfrac, ) in enumerate(samples[np.random.randint(len(samples), size=wfPlotNumber)]):
det.SetTransferFunction(b_over_a, c, d, rc1, rc2, rcfrac)
det.siggenInst.set_hole_params(h_100_mu0, h_100_beta, h_100_e0,
h_111_mu0, h_111_beta, h_111_e0)
# det.SetTemperature(temp)
# det.trapping_rc = trapping_rc
# det.SetFieldsGradInterp(grad)
# det.SetFields(pcRad, pcLen, impGrad)
# det.trapping_rc = trapping_rc
simWfs[idx, :] = det.MakeSimWaveform(r,
phi,
z,
scale,
t0,
fitSamples,
h_smoothing=smooth)
residFig = plt.figure(6)
helpers.plotResidual(simWfs, wf.windowedWf, figure=residFig)
print "Median values wf %d: " % wf_idx
for idx in range(ndim):
print " param %d: %f" % (idx, np.median(samples[:, idx]))
plt.savefig("hdxwf%d_resid.png" % wf_idx)
# plt.show()
value = raw_input(' --> Press q to quit, any other key to continue\n')
if value == 'q':
exit(0)
def main(argv):
plt.ion()
runRange = (13420,13429)
channel = 626
aeCutVal = 0.01425
numThreads = 8
fitSamples = 200
timeStepSize = 1
wfFileName = "fep_old_postpsa.npz"
numWaveforms = 30
#wfFileName = "P42574A_512waveforms_%drisetimeculled.npz" % numWaveforms
if os.path.isfile(wfFileName):
data = np.load(wfFileName)
wfs = data['wfs']
numWaveforms = wfs.size
else:
print "No saved waveforms available. Loading from Data"
exit(0)
#Prepare detector
tempGuess = 79.310080
gradGuess = 0.051005
pcRadGuess = 2.499387
pcLenGuess = 1.553464
#Create a detector model
detName = "conf/P42574A_grad%0.2f_pcrad%0.2f_pclen%0.2f.conf" % (0.05,2.5, 1.65)
det = Detector(detName, temperature=tempGuess, timeStep=timeStepSize, numSteps=fitSamples*10./timeStepSize,)
det.LoadFields("P42574A_fields_v3.npz")
det.SetFields(pcRadGuess, pcLenGuess, gradGuess)
b_over_a = 0.107213
c = -0.821158
d = 0.828957
rc = 76.710043
det.SetTransferFunction(b_over_a, c, d, rc)
initializeDetector(det, )
#ML as a start
nll = lambda *args: -lnlike_waveform(*args)
for (idx,wf) in enumerate(wfs):
print "Waveform number %d" % idx
if idx < 22: continue
fig1 = plt.figure(1)
plt.clf()
gs = gridspec.GridSpec(2, 1, height_ratios=[4, 1])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1], sharex=ax0)
ax1.set_xlabel("Digitizer Time [ns]")
ax0.set_ylabel("Voltage [Arb.]")
ax1.set_ylabel("Residual")
wf.WindowWaveformTimepoint(fallPercentage=.999, rmsMult=2)
initializeWaveform(wf)
dataLen = wf.wfLength
t_data = np.arange(dataLen) * 10
ax0.plot(t_data, wf.windowedWf, color="r")
startGuess = [15., np.pi/8, 15., wf.wfMax, wf.t0Guess-5, 10]
init_wf = det.MakeSimWaveform(15, np.pi/8, 15, wf.wfMax, wf.t0Guess, fitSamples, h_smoothing=10)
result = op.minimize(nll, startGuess, method="Powell")
#result = op.basinhopping(nll, startGuess, T=1000,stepsize=15, minimizer_kwargs= {"method": "Nelder-Mead", "args":(wf)})
r, phi, z, scale, t0, smooth, = result["x"]
r_new, z_new = det.ReflectPoint(r,z)
print "Initial LN like is %f" % (result['fun'])
# print r, phi, z, scale, t0, smooth
ml_wf = np.copy(det.MakeSimWaveform(r, phi, z, scale, t0, fitSamples, h_smoothing=smooth, ))
ax0.plot(t_data, ml_wf[:dataLen], color="b")
ax1.plot(t_data, ml_wf[:dataLen] - wf.windowedWf, color="b")
result2 = op.minimize(nll, [r_new, phi, z_new, scale, wf.t0Guess-5,10], method="Powell")
r, phi, z, scale, t0, smooth, = result2["x"]
inv_ml_wf = det.MakeSimWaveform(r, phi, z, scale, t0, fitSamples, h_smoothing=smooth, )
ax0.plot(t_data,inv_ml_wf[:dataLen], color="g")
# print r, phi, z, scale, t0, smooth
ax1.plot(t_data,inv_ml_wf[:dataLen] - wf.windowedWf, color="g")
ax1.set_ylim(-20,20)
if result2['fun'] < result['fun']:
mcmc_startguess = result2["x"]
else:
mcmc_startguess = result["x"]
wf.prior = mcmc_startguess
print "Inverted LN like is %f" % (result2['fun'])
value = raw_input(' --> Press q to quit, s to skip, any other key to continue\n')
if value == 'q':
exit(0)
if value == 's':
continue
#Do the MCMC
ndim, nwalkers = 6, 12
mcmc_startguess = startGuess#np.array([r, phi, z, scale, t0, smooth])
ntemps = 512
pos0 = np.random.uniform(low=0.99, high=1.01, size=(ntemps, nwalkers, ndim))
for tidx in range(ntemps):
for widx in range(nwalkers):
pos0[tidx, widx, :] = pos0[tidx, widx, :] * mcmc_startguess
#sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob_waveform, args=([wf]))
p = Pool(numThreads, initializer=initializeWaveform, initargs=[ wf])
sampler = emcee.PTSampler(ntemps, nwalkers, ndim, lnlike_waveform, lnprob_waveform, pool=p, )
iter, burnIn = 1000, 900
bar = ProgressBar(widgets=[Percentage(), Bar(), ETA()], maxval=iter).start()
for (idx,result) in enumerate(sampler.sample(pos0, iterations=iter, storechain=True)):
bar.update(idx+1)
bar.finish()
p.close()
#samples = sampler.chain[:, 50:, :].reshape((-1, ndim))
# ######### Plots for MC Steps
# stepsFig = plt.figure(2)
# plt.clf()
# ax0 = stepsFig.add_subplot(611)
# ax1 = stepsFig.add_subplot(612, sharex=ax0)
# ax2 = stepsFig.add_subplot(613, sharex=ax0)
# ax3 = stepsFig.add_subplot(614, sharex=ax0)
# ax4 = stepsFig.add_subplot(615, sharex=ax0)
# ax5 = stepsFig.add_subplot(616, sharex=ax0)
## ax6 = stepsFig.add_subplot(717, sharex=ax0)
#
# ax0.set_ylabel('r')
# ax1.set_ylabel('phi')
# ax2.set_ylabel('z')
# ax3.set_ylabel('scale')
# ax4.set_ylabel('t0')
# ax5.set_ylabel('smooth')
## ax6.set_ylabel('esmooth')
#
# for k in range(ntemps):
# for i in range(nwalkers):
# ax0.plot(sampler.chain[k,i,:,0], "b", alpha=0.3)
# ax1.plot(sampler.chain[k,i,:,1], "b", alpha=0.3)
# ax2.plot(sampler.chain[k,i,:,2], "b", alpha=0.3)
# ax3.plot(sampler.chain[k,i,:,3], "b", alpha=0.3)
# ax4.plot(sampler.chain[k,i,:,4], "b", alpha=0.3)
# ax5.plot(sampler.chain[k,i,:,5], "b", alpha=0.3)
## ax6.plot(sampler.chain[i,:,6], "b", alpha=0.3)
#
# #pull the samples after burn-in
samples = sampler.chain[:,:, burnIn:, :].reshape((-1, ndim))
wfPlotNumber = 100
simWfs = np.empty((wfPlotNumber, fitSamples) )
for idx, (r, phi, z, scale, t0, smooth, ) in enumerate(samples[np.random.randint(len(samples), size=wfPlotNumber)]):
simWfs[idx,:] = det.MakeSimWaveform(r, phi, z, scale, t0, fitSamples, h_smoothing = smooth,)
# plt.ioff()
plt.ion()
residFig = plt.figure(3)
helpers.plotResidual(simWfs, wf.windowedWf, figure=residFig)
plt.savefig("waveform_fig.png")
plt.figure(4)
plt.hist(samples[:,0], bins=40)
plt.xlabel("radius [mm]")
plt.figure(5)
plt.hist(samples[:,2], bins=40)
plt.ylabel("height [mm]")
# lnprobFig = plt.figure(4)
# plt.clf()
# lnprobs = sampler.lnprobability[:, burnIn:].reshape((-1))
# median_prob = np.median(lnprobs)
# print "median lnprob is %f (length normalized: %f)" % (median_prob, median_prob/wf.wfLength)
# plt.hist(lnprobs)
#
positionFig = plt.figure(6)
plt.hist2d(samples[:,0], samples[:,2], norm=LogNorm(), bins=[ np.around(det.detector_radius,1)*10,np.around(det.detector_length,1)*10 ])
plt.xlim(0, det.detector_radius)
plt.ylim(0, det.detector_length)
plt.colorbar()
lnprobFig = plt.figure(7)
plt.clf()
lnprobs = sampler.lnprobability[:, :, burnIn:].reshape((-1))
median_prob = np.median(lnprobs)
print "median lnprob is %f (length normalized: %f)" % (median_prob, median_prob/wf.wfLength)
hist, bins = np.histogram(lnprobs/wf.wfLength, bins=np.linspace(-10,0,1000))
plt.plot(bins[:-1], hist, ls="steps-post")
# plt.show()
value = raw_input(' --> Press q to quit, any other key to continue\n')
if value == 'q':
exit(0)
def main(argv):
plt.ion()
runRange = (13420, 13429)
channel = 626
aeCutVal = 0.01425
numThreads = 8
fitSamples = 200
timeStepSize = 1
wfFileName = "fep_old_postpsa.npz"
numWaveforms = 30
#wfFileName = "P42574A_512waveforms_%drisetimeculled.npz" % numWaveforms
if os.path.isfile(wfFileName):
data = np.load(wfFileName)
wfs = data['wfs']
numWaveforms = wfs.size
else:
print "No saved waveforms available. Loading from Data"
exit(0)
#Prepare detector
tempGuess = 79.310080
gradGuess = 0.051005
pcRadGuess = 2.499387
pcLenGuess = 1.553464
#Create a detector model
detName = "conf/P42574A_grad%0.2f_pcrad%0.2f_pclen%0.2f.conf" % (0.05, 2.5,
1.65)
det = Detector(
detName,
temperature=tempGuess,
timeStep=timeStepSize,
numSteps=fitSamples * 10. / timeStepSize,
)
det.LoadFields("P42574A_fields_v3.npz")
det.SetFields(pcRadGuess, pcLenGuess, gradGuess)
b_over_a = 0.107213
c = -0.821158
d = 0.828957
rc = 76.710043
det.SetTransferFunction(b_over_a, c, d, rc)
initializeDetector(det, )
#ML as a start
nll = lambda *args: -lnlike_waveform(*args)
for (idx, wf) in enumerate(wfs):
print "Waveform number %d" % idx
if idx < 22: continue
fig1 = plt.figure(1)
plt.clf()
gs = gridspec.GridSpec(2, 1, height_ratios=[4, 1])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1], sharex=ax0)
ax1.set_xlabel("Digitizer Time [ns]")
ax0.set_ylabel("Voltage [Arb.]")
ax1.set_ylabel("Residual")
wf.WindowWaveformTimepoint(fallPercentage=.999, rmsMult=2)
initializeWaveform(wf)
dataLen = wf.wfLength
t_data = np.arange(dataLen) * 10
ax0.plot(t_data, wf.windowedWf, color="r")
startGuess = [15., np.pi / 8, 15., wf.wfMax, wf.t0Guess - 5, 10]
init_wf = det.MakeSimWaveform(15,
np.pi / 8,
15,
wf.wfMax,
wf.t0Guess,
fitSamples,
h_smoothing=10)
result = op.minimize(nll, startGuess, method="Powell")
#result = op.basinhopping(nll, startGuess, T=1000,stepsize=15, minimizer_kwargs= {"method": "Nelder-Mead", "args":(wf)})
r, phi, z, scale, t0, smooth, = result["x"]
r_new, z_new = det.ReflectPoint(r, z)
print "Initial LN like is %f" % (result['fun'])
# print r, phi, z, scale, t0, smooth
ml_wf = np.copy(
det.MakeSimWaveform(
r,
phi,
z,
scale,
t0,
fitSamples,
h_smoothing=smooth,
))
ax0.plot(t_data, ml_wf[:dataLen], color="b")
ax1.plot(t_data, ml_wf[:dataLen] - wf.windowedWf, color="b")
result2 = op.minimize(nll,
[r_new, phi, z_new, scale, wf.t0Guess - 5, 10],
method="Powell")
r, phi, z, scale, t0, smooth, = result2["x"]
inv_ml_wf = det.MakeSimWaveform(
r,
phi,
z,
scale,
t0,
fitSamples,
h_smoothing=smooth,
)
ax0.plot(t_data, inv_ml_wf[:dataLen], color="g")
# print r, phi, z, scale, t0, smooth
ax1.plot(t_data, inv_ml_wf[:dataLen] - wf.windowedWf, color="g")
ax1.set_ylim(-20, 20)
if result2['fun'] < result['fun']:
mcmc_startguess = result2["x"]
else:
mcmc_startguess = result["x"]
wf.prior = mcmc_startguess
print "Inverted LN like is %f" % (result2['fun'])
value = raw_input(
' --> Press q to quit, s to skip, any other key to continue\n')
if value == 'q':
exit(0)
if value == 's':
continue
#Do the MCMC
ndim, nwalkers = 6, 12
mcmc_startguess = startGuess #np.array([r, phi, z, scale, t0, smooth])
ntemps = 512
pos0 = np.random.uniform(low=0.99,
high=1.01,
size=(ntemps, nwalkers, ndim))
for tidx in range(ntemps):
for widx in range(nwalkers):
pos0[tidx, widx, :] = pos0[tidx, widx, :] * mcmc_startguess
#sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob_waveform, args=([wf]))
p = Pool(numThreads, initializer=initializeWaveform, initargs=[wf])
sampler = emcee.PTSampler(
ntemps,
nwalkers,
ndim,
lnlike_waveform,
lnprob_waveform,
pool=p,
)
iter, burnIn = 1000, 900
bar = ProgressBar(widgets=[Percentage(), Bar(),
ETA()], maxval=iter).start()
for (idx, result) in enumerate(
sampler.sample(pos0, iterations=iter, storechain=True)):
bar.update(idx + 1)
bar.finish()
p.close()
#samples = sampler.chain[:, 50:, :].reshape((-1, ndim))
# ######### Plots for MC Steps
# stepsFig = plt.figure(2)
# plt.clf()
# ax0 = stepsFig.add_subplot(611)
# ax1 = stepsFig.add_subplot(612, sharex=ax0)
# ax2 = stepsFig.add_subplot(613, sharex=ax0)
# ax3 = stepsFig.add_subplot(614, sharex=ax0)
# ax4 = stepsFig.add_subplot(615, sharex=ax0)
# ax5 = stepsFig.add_subplot(616, sharex=ax0)
## ax6 = stepsFig.add_subplot(717, sharex=ax0)
#
# ax0.set_ylabel('r')
# ax1.set_ylabel('phi')
# ax2.set_ylabel('z')
# ax3.set_ylabel('scale')
# ax4.set_ylabel('t0')
# ax5.set_ylabel('smooth')
## ax6.set_ylabel('esmooth')
#
# for k in range(ntemps):
# for i in range(nwalkers):
# ax0.plot(sampler.chain[k,i,:,0], "b", alpha=0.3)
# ax1.plot(sampler.chain[k,i,:,1], "b", alpha=0.3)
# ax2.plot(sampler.chain[k,i,:,2], "b", alpha=0.3)
# ax3.plot(sampler.chain[k,i,:,3], "b", alpha=0.3)
# ax4.plot(sampler.chain[k,i,:,4], "b", alpha=0.3)
# ax5.plot(sampler.chain[k,i,:,5], "b", alpha=0.3)
## ax6.plot(sampler.chain[i,:,6], "b", alpha=0.3)
#
# #pull the samples after burn-in
samples = sampler.chain[:, :, burnIn:, :].reshape((-1, ndim))
wfPlotNumber = 100
simWfs = np.empty((wfPlotNumber, fitSamples))
for idx, (
r,
phi,
z,
scale,
t0,
smooth,
) in enumerate(samples[np.random.randint(len(samples),
size=wfPlotNumber)]):
simWfs[idx, :] = det.MakeSimWaveform(
r,
phi,
z,
scale,
t0,
fitSamples,
h_smoothing=smooth,
)
# plt.ioff()
plt.ion()
residFig = plt.figure(3)
helpers.plotResidual(simWfs, wf.windowedWf, figure=residFig)
plt.savefig("waveform_fig.png")
plt.figure(4)
plt.hist(samples[:, 0], bins=40)
plt.xlabel("radius [mm]")
plt.figure(5)
plt.hist(samples[:, 2], bins=40)
plt.ylabel("height [mm]")
# lnprobFig = plt.figure(4)
# plt.clf()
# lnprobs = sampler.lnprobability[:, burnIn:].reshape((-1))
# median_prob = np.median(lnprobs)
# print "median lnprob is %f (length normalized: %f)" % (median_prob, median_prob/wf.wfLength)
# plt.hist(lnprobs)
#
positionFig = plt.figure(6)
plt.hist2d(samples[:, 0],
samples[:, 2],
norm=LogNorm(),
bins=[
np.around(det.detector_radius, 1) * 10,
np.around(det.detector_length, 1) * 10
])
plt.xlim(0, det.detector_radius)
plt.ylim(0, det.detector_length)
plt.colorbar()
lnprobFig = plt.figure(7)
plt.clf()
lnprobs = sampler.lnprobability[:, :, burnIn:].reshape((-1))
median_prob = np.median(lnprobs)
print "median lnprob is %f (length normalized: %f)" % (
median_prob, median_prob / wf.wfLength)
hist, bins = np.histogram(lnprobs / wf.wfLength,
bins=np.linspace(-10, 0, 1000))
plt.plot(bins[:-1], hist, ls="steps-post")
# plt.show()
value = raw_input(' --> Press q to quit, any other key to continue\n')
if value == 'q':
exit(0)