def saveDetectorCalibration(master_loop,
detCalib,
config,
verbose=False,
beta=0):
# Concatenate the list of all calibration data
calibValues = np.concatenate(master_loop.calibValues, axis=0)
print 'calibValues', calibValues
# Check the data that is not NAN
#I = np.isfinite(calibValues.sum(axis=1))
#print 'mask', I
## average the finite values
#average = calibValues[I,:].mean(axis=0)
average = np.array([
np.nan if np.all(np.isnan(det)) else det[np.isfinite(det)].mean()
for det in calibValues.T
])
print 'average', average
#factors = average[config.boolFitMask].max()/average
params = cookie_box.initial_params()
params['A'].value = 1
params['beta'].value = beta
params['tilt'].value = 0
params['linear'].value = 1
phi = np.linspace(0, 2 * np.pi, 16, endpoint=False)
factors = cookie_box.model_function(params, phi) / average
factors /= factors[np.isfinite(factors)].max()
factors[~config.boolFitMask] = np.nan
if verbose:
print len(calibValues)
print master_loop.calibValues[0].shape
print calibValues.shape
print average
print 'Calibration factors:', factors
calibFile = (
detCalib.path + '/' + detCalib.name +
'{}.txt'.format(detCalib.fileNumber +
1 if np.isfinite(detCalib.fileNumber) else ''))
np.savetxt(calibFile, factors)
def saveDetectorCalibration(master_loop, detCalib, config, verbose=False, beta=0):
# Concatenate the list of all calibration data
calibValues = np.concatenate(master_loop.calibValues, axis=0)
print 'calibValues', calibValues
# Check the data that is not NAN
#I = np.isfinite(calibValues.sum(axis=1))
#print 'mask', I
## average the finite values
#average = calibValues[I,:].mean(axis=0)
average = np.array([np.nan if np.all(np.isnan(det)) else
det[np.isfinite(det)].mean()
for det in calibValues.T])
print 'average', average
#factors = average[config.boolFitMask].max()/average
params = cookie_box.initial_params()
params['A'].value = 1
params['beta'].value = beta
params['tilt'].value = 0
params['linear'].value = 1
phi = np.linspace(0, 2*np.pi, 16, endpoint=False)
factors = cookie_box.model_function(params, phi) / average
factors /= factors[np.isfinite(factors)].max()
factors[~config.boolFitMask] = np.nan
if verbose:
print len(calibValues)
print master_loop.calibValues[0].shape
print calibValues.shape
print average
print 'Calibration factors:', factors
calibFile = (detCalib.path + '/' + detCalib.name +
'{}.txt'.format( detCalib.fileNumber+1 if
np.isfinite(detCalib.fileNumber) else '' ) )
np.savetxt(calibFile, factors)
ax.set_xlim(220, 245)
plt.tight_layout()
# %%
try:
angular_plot = plt.figure('Angular')
angular_plot.clf()
except:
pass
angular_plot, angular_axis_array = plt.subplots(1, 2,
subplot_kw={'polar': True},
num='Angular')
phi = cookie_box.phi_rad
phi_line = np.linspace(0, 2*np.pi, 2**8)
norm_params = cookie_box.initial_params()
norm_params['A'].value = 1
norm_params['beta'].value = 2
norm_params['beta'].vary = False
norm_params['tilt'].value = 0
norm_params['tilt'].vary = False
norm_params['linear'].value = 1
norm_params['linear'].vary = False
lmfit.minimize(cookie_box.model_function, norm_params,
args=(phi,
photo_signals_average_corrected[285] *
auger_factors[285]))
beta2_factors = (cookie_box.model_function(norm_params, phi) /
(photo_signals_average_corrected[285] * auger_factors[285]))
def get_event_data(config, scales, detCalib, cb, args, epics, verbose=False):
if verbose:
print 'Rank {} grabbing data.'.format(rank)
data = np.zeros(dSize, dtype=float)
# Get the time amplitudes
time_amplitudes = cb.get_time_amplitudes_filtered()
# If there is a None in the data, some axqiris trace is missing
if None in time_amplitudes:
# Don't try to pull any more data from this event
print 'Rank {} detected None in time amplitude.'.format(rank)
return None
# This construction can handle traces of different length
data[dTraces] = np.nan
length = (dTraces.stop - dTraces.start) / 16
#print 'length =', length
#print data.shape, dTraces.start, dTraces.stop
start = dTraces.start
for t_sig in time_amplitudes:
if t_sig is None:
return None
#print len(t_sig)
#print start, start+len(t_sig)
#print data.shape, data[dTraces].shape
data[start : start + len(t_sig)] = t_sig
start += length
# Get the intensities
# Roi 0 base information (photoline)
data[dIntRoi0] = np.array([np.sum(trace) for trace
in cb.get_time_amplitudes_filtered(roi=0)])
# Use roi 2 for backgorud subtraction, could be commented out to not do
# subtraction...
data[dIntRoi0] -= (np.array([np.sum(trace) for trace
in cb.get_time_amplitudes_filtered(roi=2)]) *
scales.tRoi0BgFactors)
# Rescale the data for roi 0
data[dIntRoi0] *= config.nanFitMask * detCalib.factors
# Get roi 1 imformation (auger line)
data[dIntRoi1] = (np.array([np.sum(trace) for trace in
cb.get_time_amplitudes_filtered(roi=1)]) *
detCalib.factors * config.nanFitMask)
# Get the initial fit parameters
#params = cookie_box.initial_params(evt_data.intRoi0[-1])
params = cookie_box.initial_params()
params['A'].value, params['linear'].value, params['tilt'].value = \
cb.proj.solve(data[dIntRoi0], args.beta)
# Lock the beta parameter
params['beta'].value = args.beta
params['beta'].vary = False
#print params['A'].value, params['linear'].value, params['tilt'].value
# Perform the fit
#print scales.angles[config.boolFitMask]
#print evt_data.intRoi0[-1][config.boolFitMask]
res = lmfit.minimize(
cookie_box.model_function,
params,
args=(scales.angles[config.boolFitMask],
data[dIntRoi0][config.boolFitMask]),
method='leastsq')
#print params['A'].value, params['linear'].value, params['tilt'].value
#lmfit.report_fit(params)
# Store the values
data[dPol] = np.array([params['A'].value, params['A'].stderr,
params['beta'].value, params['beta'].stderr,
params['tilt'].value, params['tilt'].stderr,
params['linear'].value, params['linear'].stderr])
# Get the energy traces
data[d_energy_trace] = np.mean([trace for trace, test in
zip(cb.get_energy_amplitudes(), config.energy_spectrum_mask)
if test], axis=0)
# Get the photon energy center and width
if args.photonEnergy != 'no':
data[dEnergy] = cb.getPhotonEnergy(energyShift=args.energyShift)
# Get lcls parameters
data[dEL3] = lcls.getEBeamEnergyL3_MeV()
data[dFEE] = lcls.getPulseEnergy_mJ()
# timing information
data[dFiducials] = lcls.getEventFiducial()
data[dTime] = lcls.getEventTime()
data[dDeltaK] = epics.value('USEG:UND1:3350:KACT')
data[dDeltaEnc] = np.array(
[epics.value('USEG:UND1:3350:{}:ENC'.format(i)) for i in range(1,5)])
return data
plt.tight_layout()
# %%
try:
angular_plot = plt.figure('Angular')
angular_plot.clf()
except:
pass
angular_plot, angular_axis_array = plt.subplots(1,
2,
subplot_kw={'polar': True},
num='Angular')
phi = cookie_box.phi_rad
phi_line = np.linspace(0, 2 * np.pi, 2**8)
norm_params = cookie_box.initial_params()
norm_params['A'].value = 1
norm_params['beta'].value = 2
norm_params['beta'].vary = False
norm_params['tilt'].value = 0
norm_params['tilt'].vary = False
norm_params['linear'].value = 1
norm_params['linear'].vary = False
lmfit.minimize(cookie_box.model_function,
norm_params,
args=(phi, photo_signals_average_corrected[285]))
beta2_factors = (cookie_box.model_function(norm_params, phi) /
photo_signals_average_corrected[285])
I_fit = np.ones(16, dtype=bool)
def get_event_data(config, scales, detCalib, cb, args, epics, verbose=False):
if verbose:
print 'Rank {} grabbing data.'.format(rank)
data = np.zeros(dSize, dtype=float)
# Get the time amplitudes
time_amplitudes = cb.get_time_amplitudes_filtered()
# If there is a None in the data, some axqiris trace is missing
if None in time_amplitudes:
# Don't try to pull any more data from this event
print 'Rank {} detected None in time amplitude.'.format(rank)
return None
# This construction can handle traces of different length
data[dTraces] = np.nan
length = (dTraces.stop - dTraces.start) / 16
#print 'length =', length
#print data.shape, dTraces.start, dTraces.stop
start = dTraces.start
for t_sig in time_amplitudes:
if t_sig is None:
return None
#print len(t_sig)
#print start, start+len(t_sig)
#print data.shape, data[dTraces].shape
data[start:start + len(t_sig)] = t_sig
start += length
# Get the intensities
# Roi 0 base information (photoline)
data[dIntRoi0] = np.array(
[np.sum(trace) for trace in cb.get_time_amplitudes_filtered(roi=0)])
# Use roi 2 for backgorud subtraction, could be commented out to not do
# subtraction...
data[dIntRoi0] -= (np.array(
[np.sum(trace) for trace in cb.get_time_amplitudes_filtered(roi=2)]) *
scales.tRoi0BgFactors)
# Rescale the data for roi 0
data[dIntRoi0] *= config.nanFitMask * detCalib.factors
# Get roi 1 imformation (auger line)
data[dIntRoi1] = (np.array(
[np.sum(trace) for trace in cb.get_time_amplitudes_filtered(roi=1)]) *
detCalib.factors * config.nanFitMask)
# Get the initial fit parameters
#params = cookie_box.initial_params(evt_data.intRoi0[-1])
params = cookie_box.initial_params()
params['A'].value, params['linear'].value, params['tilt'].value = \
cb.proj.solve(data[dIntRoi0], args.beta)
# Lock the beta parameter
params['beta'].value = args.beta
params['beta'].vary = False
#print params['A'].value, params['linear'].value, params['tilt'].value
# Perform the fit
#print scales.angles[config.boolFitMask]
#print evt_data.intRoi0[-1][config.boolFitMask]
res = lmfit.minimize(cookie_box.model_function,
params,
args=(scales.angles[config.boolFitMask],
data[dIntRoi0][config.boolFitMask]),
method='leastsq')
#print params['A'].value, params['linear'].value, params['tilt'].value
#lmfit.report_fit(params)
# Store the values
data[dPol] = np.array([
params['A'].value, params['A'].stderr, params['beta'].value,
params['beta'].stderr, params['tilt'].value, params['tilt'].stderr,
params['linear'].value, params['linear'].stderr
])
# Get the energy traces
data[d_energy_trace] = np.mean([
trace for trace, test in zip(cb.get_energy_amplitudes(),
config.energy_spectrum_mask) if test
],
axis=0)
# Get the photon energy center and width
if args.photonEnergy != 'no':
data[dEnergy] = cb.getPhotonEnergy(energyShift=args.energyShift)
# Get lcls parameters
data[dEL3] = lcls.getEBeamEnergyL3_MeV()
data[dFEE] = lcls.getPulseEnergy_mJ()
# timing information
data[dFiducials] = lcls.getEventFiducial()
data[dTime] = lcls.getEventTime()
data[dDeltaK] = epics.value('USEG:UND1:3350:KACT')
data[dDeltaEnc] = np.array(
[epics.value('USEG:UND1:3350:{}:ENC'.format(i)) for i in range(1, 5)])
return data
def updatePlot(figs):
socket.recv()
data = socket.recv_pyobj(zmq.NOBLOCK)
# we've got data, see if it is the most recent
while 1:
try:
socket.recv(zmq.NOBLOCK)
data = socket.recv_pyobj()
# if we get here with no exception there was
# more data
print '*** throw away data'
continue
except zmq.error.Again:
print '*** got last data'
break
if verbose:
print 'Data contains:'
for k, v in data.iteritems():
print '\n\t{} of type {}'.format(k, type(v)),
if isinstance(v, dict):
print 'with components:'
for k2, v2 in v.iteritems():
print '\t\t{} of type {}'.format(k2, type(v2)),
if isinstance(v2, list):
print 'of length {}.'.format(len(v2))
print '\t\t\tlengths = (',
for elem in v2:
try:
print '{},'.format(len(elem)),
except:
pass
print ')'
elif isinstance(v2, np.ndarray):
print 'of shape {}.'.format(v2.shape)
else:
print ''
else:
print ''
#try:
# print 'polar:roi0 =', data['polar']['roi0']
# print 'polar:roi1 =', data['polar']['roi1']
#except:
# pass
#try:
# print 'err_degree = ', data['strip'][1][:,7]
#except:
# pass
plotKey = 'polar'
if plotKey in data.keys():
d = data[plotKey]
ax = figs[1].axes[0]
lines = ax.lines
for l, roi in enumerate(['roi0', 'roi1']):
if roi in d.keys():
try:
lines[l].set_ydata(d[roi]/d[roi][ np.isfinite(d[roi])
].max())
except:
print 'Data error'
# The fit in figure 2
params = cookie_box.initial_params()
for k in params:
params[k].value = d[k]
try:
params['A'].value /= d['roi0'][ np.isfinite(d['roi0']) ].max()
except:
params['A'].value = 0
lines[2].set_ydata(cookie_box.model_function(params, angle))
if args.beta != None:
params['beta'].value = args.beta
params['linear'].value = 1.0
params['tilt'].value = 0
y = cookie_box.model_function(params, angle)
lines[3].set_ydata(y/y.max())
ax.relim()
ax.autoscale_view()
figs[1].canvas.draw()
plotKey = 'strip'
if plotKey in data.keys():
ax = figs[2].axes[0]
ax.cla()
ax.set_title('Degree of linear polarization.')
storage['fiducial'].extend( data[plotKey][0] )
storage['degree'].extend( data[plotKey][1][:,6] )
storage['err_degree'].extend( data[plotKey][1][:,7] )
storage['tilt'].extend( data[plotKey][1][:,4] )
storage['err_tilt'].extend( data[plotKey][1][:,5] )
#storage['intFull'].extend(data[plotKey][2])
storage['intRoi0'].extend(data[plotKey][2])
#storage['intRoi1'].extend(data[plotKey][4])
#ax.plot(storage['fiducial'], storage['degree'],'.')
#plt.sca(ax)
if args.errors:
ax.errorbar(np.array(storage['fiducial']),
np.array(storage['degree']),
np.array(storage['err_degree']),
linestyle='', marker='.', capsize=0)
else:
ax.plot(np.array(storage['fiducial']),
np.array(storage['degree']),
linestyle='', marker='.')
if args.a:
ax.relim()
ax.autoscale_view()
else:
ax.set_ylim(-0.4, 1.4)
#ax.plot(data[plotKey][0], data[plotKey][1][:,6], '.b')
#ax.errorbar(data[plotKey][0], data[plotKey][1][:,6],
# data[plotKey][1][:,7], ls='None', color='b')
#ax.relim()
#ax.autoscale_view()
#ax.ylim
ax = figs[2].axes[1]
ax.cla()
ax.set_title('Polarization tilt')
if args.errors:
ax.errorbar(np.array(storage['fiducial']),
np.array(storage['tilt']),
np.array(storage['err_tilt']),
linestyle='', marker='.', capsize=0)
else:
ax.plot(np.array(storage['fiducial']),
np.array(storage['tilt'])*180/np.pi,
linestyle='', marker='.')
if args.a:
ax.relim()
ax.autoscale_view()
else:
ax.set_ylim(-90, 90)
#ax.errorbar(data[plotKey][0], data[plotKey][1][:,4],
# data[plotKey][1][:,5], ls='None', color='b')
ax = figs[2].axes[2]
ax.cla()
ax.set_title('Photoline intensity')
ax.plot(np.array(storage['fiducial']),
np.array(storage['intRoi0']),
'.')
ax.relim()
ax.autoscale_view()
min_value, _ = ax.get_ylim()
ax.set_ylim(bottom=np.minimum(min_value, 0))
figs[2].canvas.draw()
plotKey = 'traces'
if plotKey in data.keys():
plot = data[plotKey]
for i, ax in enumerate(figs[0].axes):
lines = ax.lines
if 'timeScale' in plot.keys():
lines[raw].set_xdata(data[plotKey]['timeScale'][i])
lines[filt].set_xdata(data[plotKey]['timeScale'][i])
if 'timeScaleRoi0' in plot.keys():
lines[roi0].set_xdata(data[plotKey]['timeScaleRoi0'][i])
if 'timeScaleRoi1' in plot.keys():
lines[roi1].set_xdata(data[plotKey]['timeScaleRoi1'][i])
if 'timeRaw' in plot.keys():
lines[raw].set_ydata(plot['timeRaw'][i])
if 'timeFiltered' in plot.keys():
lines[filt].set_ydata(plot['timeFiltered'][i])
if 'timeRoi0' in plot.keys():
lines[roi0].set_ydata(plot['timeRoi0'][i])
if 'timeRoi1' in plot.keys():
lines[roi1].set_ydata(plot['timeRoi1'][i])
ax.relim()
ax.autoscale_view()
figs[0].canvas.draw()
plotKey = 'spectrum'
if plotKey in data.keys():
plotData = data[plotKey]
ax = figs[3].axes[0]
l = ax.lines[0]
l.set_xdata(plotData['energyScale'])
l.set_ydata(plotData['energyAmplitude'])
l = ax.lines[1]
l.set_xdata(plotData['energyScaleRoi0'])
l.set_ydata(plotData['energyAmplitudeRoi0'])
ax.relim()
ax.autoscale_view()
ax.figure.canvas.draw()