def _tr_cspad(self, values, obj, evt_key):
"""Translates CsPad to hummingbird numpy array, quad by quad"""
n_quads = obj.quads_shape()[0]
for i in range(0, n_quads):
add_record(values, 'photonPixelDetectors',
'%sQuad%d' % (self._s2c[str(evt_key.src())], i),
obj.quads(i).data(), ureg.ADU)
def calculate_epoch_times(evt, time_sec, time_usec):
add_record(evt['ID'], 'ID', 'time', time_sec.data + 1.e-6 * time_usec.data)
#add_record(evt['ID'], 'ID', 'timeAgo', time.time() - (time_sec.data + 1.e-6*time_usec.data))
# Calculating timeAgo with 606 second offset due to miscalibration of pnCCD server clock
#add_record(evt['ID'], 'ID', 'timeAgo', -606. + time.time() - (time_sec.data + 1.e-6*time_usec.data))
add_record(evt['ID'], 'ID', 'timeAgo',
0. + time.time() - (time_sec.data + 1.e-6 * time_usec.data))
def _tr_pnccdFrames(self, values, obj, evt_key):
"""Translates pnCCD frames to hummingbird numpy array, frame by frame"""
n_frames = obj.frame_shape()[0]
for i in range(0, n_frames):
add_record(values, 'photonPixelDetectors',
'%sFrame%d' % (self._s2c[str(evt_key.src())], i),
obj.frame(i).data(), ureg.ADU)
def translate_object(self, evt, key):
values = {}
detid = self._c2id_detectors[key]
if detid in PNCCD_IDS:
det = self._detectors[detid]
obj = self._detectors[detid]['obj']
data_nda = det['data_method'](obj, evt)
if data_nda is None:
image = None
elif len(data_nda.shape) <= 2:
image = data_nda
elif len(data_nda.shape) == 3:
image = numpy.hstack([numpy.vstack([data_nda[0],data_nda[1][::-1,::-1]]),
numpy.vstack([data_nda[3],data_nda[2][::-1,::-1]])])
add_record(values, det['type'], det['key'], image, ureg.ADU)
elif detid in ACQ_IDS:
det = self._detectors[detid]
# waveforms are in Volts, times are in Seconds
obj = det['obj']
waveforms = obj.waveform(evt)
#print("waveforms", waveforms)
#times = obj.wftime(evt)
for i, wf in enumerate(waveforms):
add_record(values, det['type'], det['keys'][i], wf, ureg.V)
else:
raise RuntimeError('%s not yet supported' % key)
return values
def translate(self, evt, key):
"""Returns a dict of Records that match a given humminbird key"""
values = {}
if(key in self._c2n):
return self.translate_core(evt, key)
elif(key == 'parameters'):
return self._tr_epics()
elif(key == 'analysis'):
return {}
elif(key == 'stream'):
return {}
else:
# check if the key matches any of the existing keys in the event
event_keys = evt.keys()
values = {}
found = False
for event_key in event_keys:
if(event_key.key() == key):
obj = evt.get(event_key.type(), event_key.src(), event_key.key())
found = True
add_record(values, 'native', '%s[%s]' % (self._s2c[str(event_key.src())], key),
obj, ureg.ADU)
if(found):
return values
else:
print '%s not found in event' % (key)
def _tr_bld_data_ebeam(self, values, obj):
"""Translates BldDataEBeam to hummingbird photon energy"""
photon_energy_ev = -1
if(isinstance(obj, psana.Bld.BldDataEBeamV6)):
photon_energy_ev = obj.ebeamPhotonEnergy()
else:
peak_current = obj.ebeamPkCurrBC2()
dl2_energy_gev = 0.001*obj.ebeamL3Energy()
# If we don't have direct access to photonEnergy
# we need to calculate it
if(photon_energy_ev == -1):
ltu_wake_loss = 0.0016293*peak_current
# Spontaneous radiation loss per segment
sr_loss_per_segment = 0.63*dl2_energy_gev
# wakeloss in an undulator segment
wake_loss_per_segment = 0.0003*peak_current
# energy loss per segment
energy_loss_per_segment = (sr_loss_per_segment +
wake_loss_per_segment)
# energy in first active undulator segment [GeV]
energy_profile = (dl2_energy_gev - 0.001*ltu_wake_loss -
0.0005*energy_loss_per_segment)
# Calculate the resonant photon energy of the first active segment
photon_energy_ev = 44.42*energy_profile*energy_profile
add_record(values, 'photonEnergies', 'photonEnergy', photon_energy_ev, ureg.eV)
def _tr_bld_data_ebeam(self, values, obj):
"""Translates BldDataEBeam to hummingbird photon energy"""
photon_energy_ev = -1
if (isinstance(obj, psana.Bld.BldDataEBeamV6)):
photon_energy_ev = obj.ebeamPhotonEnergy()
else:
peak_current = obj.ebeamPkCurrBC2()
dl2_energy_gev = 0.001 * obj.ebeamL3Energy()
# If we don't have direct access to photonEnergy
# we need to calculate it
if (photon_energy_ev == -1):
ltu_wake_loss = 0.0016293 * peak_current
# Spontaneous radiation loss per segment
sr_loss_per_segment = 0.63 * dl2_energy_gev
# wakeloss in an undulator segment
wake_loss_per_segment = 0.0003 * peak_current
# energy loss per segment
energy_loss_per_segment = (sr_loss_per_segment +
wake_loss_per_segment)
# energy in first active undulator segment [GeV]
energy_profile = (dl2_energy_gev - 0.001 * ltu_wake_loss -
0.0005 * energy_loss_per_segment)
# Calculate the resonant photon energy of the first active segment
photon_energy_ev = 44.42 * energy_profile * energy_profile
add_record(values, 'photonEnergies', 'photonEnergy', photon_energy_ev,
ureg.eV)
def translate(self, evt, key):
"""Returns a dict of Records that match a given humminbird key"""
values = {}
if (key in self._c2n):
return self.translate_core(evt, key)
elif (key == 'parameters'):
return self._tr_epics()
elif (key == 'analysis'):
return {}
elif (key == 'stream'):
return {}
else:
# check if the key matches any of the existing keys in the event
event_keys = evt.keys()
values = {}
found = False
for event_key in event_keys:
if (event_key.key() == key):
obj = evt.get(event_key.type(), event_key.src(),
event_key.key())
found = True
add_record(
values, 'native',
'%s[%s]' % (self._s2c[str(event_key.src())], key), obj,
ureg.ADU)
if (found):
return values
else:
print '%s not found in event' % (key)
def _tr_cspad2x2(self, values, obj):
"""Translates CsPad2x2 to hummingbird numpy array"""
try:
add_record(values, 'photonPixelDetectors', 'CsPad2x2S', obj.data(),
ureg.ADU)
except AttributeError:
add_record(values, 'photonPixelDetectors', 'CsPad2x2',
obj.data16(), ureg.ADU)
def _tr_camera(self, values, obj):
"""Translates Camera frame to hummingbird numpy array"""
if obj.depth == 16:
data = obj.data16()
else:
data = obj.data8()
add_record(values, 'camera', 'opal', data, ureg.ADU)
def _tr_gmd_sqs_pnccd(self, values, obj, evt_key):
sase_3_pulse_index = 0
# print("obj.keys()", obj.keys())
sase3 = obj['data.intensitySa3TD'][sase_3_pulse_index]
add_record(values, 'GMD', 'SASE3', sase3, ureg.ADU)
sase_1_train_length = 176
sase1 = obj['data.intensitySa1TD'][:sase_1_train_length]
add_record(values, 'GMD', 'SASE1', sase1, ureg.ADU)
def _tr_bld_data_fee_gas_det_energy(self, values, obj):
"""Translates gas monitor detector to hummingbird pulse energy"""
# convert from mJ to J
add_record(values, 'pulseEnergies', 'f_11_ENRC', obj.f_11_ENRC(), ureg.mJ)
add_record(values, 'pulseEnergies', 'f_12_ENRC', obj.f_12_ENRC(), ureg.mJ)
add_record(values, 'pulseEnergies', 'f_21_ENRC', obj.f_21_ENRC(), ureg.mJ)
add_record(values, 'pulseEnergies', 'f_22_ENRC', obj.f_22_ENRC(), ureg.mJ)
def _tr_bld_data_fee_gas_det_energy(self, values, obj):
"""Translates gas monitor detector to hummingbird pulse energy"""
# convert from mJ to J
add_record(values, 'pulseEnergies', 'f_11_ENRC', obj.f_11_ENRC(), ureg.mJ)
add_record(values, 'pulseEnergies', 'f_12_ENRC', obj.f_12_ENRC(), ureg.mJ)
add_record(values, 'pulseEnergies', 'f_21_ENRC', obj.f_21_ENRC(), ureg.mJ)
add_record(values, 'pulseEnergies', 'f_22_ENRC', obj.f_22_ENRC(), ureg.mJ)
def onEvent(evt):
# Processing rate [Hz]
analysis.event.printProcessingRate()
# try:
# has_tof = True
# evt["DAQ"]["TOF"]
# print "We have TOF data!"
# except RuntimeError:
# has_tof = False
# #print "No TOF"
has_tof = False
detector_type = "photonPixelDetectors"
detector_key = "pnCCD"
if move_half:
detector = evt[detector_type][detector_key]
detector = analysis.pixel_detector.moveHalf(evt, detector, horizontal=int(gap_total/pixel_size), outkey='data_half-moved')
mask_center_s = analysis.pixel_detector.moveHalf(evt, add_record(evt["analysis"], "analysis", "mask", mask_center), horizontal=int(gap_total/pixel_size), outkey='mask_half-moved').data
detector_type = "analysis"
detector_key = "data_half-moved"
# Do basic hitfinding using lit pixels
analysis.hitfinding.countLitPixels(evt, evt[detector_type][detector_key],
aduThreshold=aduThreshold,
hitscoreThreshold=hitScoreThreshold, mask=mask_center_s)
hit = bool(evt["analysis"]["litpixel: isHit"].data)
strong_hit=evt["analysis"]["litpixel: hitscore"].data>strong_hit_threshold
plotting.line.plotHistory(add_record(evt["analysis"],"analysis","total ADUs", evt[detector_type][detector_key].data.sum()),
label='Total ADU', hline=hitScoreThreshold, group='Metric')
plotting.line.plotHistory(evt["analysis"]["litpixel: hitscore"],
label='Nr. of lit pixels', hline=hitScoreThreshold, group='Metric')
analysis.hitfinding.hitrate(evt, hit, history=50)
if hit and has_tof:
print evt["DAQ"]["TOF"].data
print evt["motorPositions"]["InjectorZ"].data
plotting.line.plotTrace(evt["DAQ"]["TOF"], label='TOF', history=100, tracelen=20000, name="TOF", group="TOF")
plotting.line.plotHistory(evt["motorPositions"]["InjectorZ"], label="InjectorZ (with TOF)", group="TOF")
plotting.image.plotImage(evt[detector_type][detector_key], name="pnCCD (Hits with TOF)", group='TOF', mask=mask_center_s)
D = {}
D['TOF'] = evt['DAQ']['TOF'].data
D['pnCCD'] = evt[detector_type][detector_key].data
D['InjectorZ'] = evt["motorPositions"]["InjectorZ"].data
if do_write:
W.write_slice(D)
def find_foci(evt, type,key,type2,key2,minPhase=-500000, maxPhase=500000, steps=101, field_of_view_rad=100, wavelength=1.053, CCD_S_DIST=0.375, PX_SIZE=75e-6):
img = evt[type][key].data
centroids = evt[type2][key2].data
Nfoci = centroids.shape[0]
Xrange, Yrange = img.shape
Npixel = field_of_view_rad
p = numpy.linspace(-Xrange/2, Xrange/2-1, Xrange)
q = numpy.linspace(-Yrange/2, Yrange/2-1, Yrange)
pp, qq = numpy.meshgrid(p, q)
phase_matrix = (2*numpy.pi/wavelength)*numpy.sqrt(1-((PX_SIZE/CCD_S_DIST)**2)*(qq**2 + pp**2))
prop_length = numpy.linspace(minPhase, maxPhase, steps)
variance = numpy.zeros([steps, Nfoci])
# shift stuff for performance reasons
img_shifted = fftshift(img)
phase_matrix_shifted = fftshift(phase_matrix)
for idx, phase in enumerate(prop_length):
img_propagated = img_shifted * numpy.exp(1.j*phase*phase_matrix_shifted)
recon = fftshift(ifft2(img_propagated))
for CC in numpy.arange(Nfoci):
centerx, centery = centroids[CC, :]
###print centerx, centery
reconcut = numpy.abs(recon[numpy.max([0, centerx-Npixel-1]).astype(int): numpy.min([Xrange-1, centerx+Npixel]).astype(int), numpy.max([0, centery-Npixel-1]).astype(int): numpy.min([Yrange-1, centery+Npixel]).astype(int)])
variance[idx, CC] = reconcut.var()
focus_distance = numpy.zeros(Nfoci)
CC_size = numpy.zeros(Nfoci)
focused_CC = numpy.zeros(4*Npixel**2 * Nfoci).reshape(Nfoci, 2*Npixel, 2*Npixel)
for CC in numpy.arange(Nfoci):
ind_max = numpy.argmax(variance[:, CC])
tmp = variance[:, CC]
# get max which is not at border
loc_max_bool = numpy.r_[True, tmp[1:] > tmp[:-1]] & numpy.r_[tmp[:-1] > tmp[1:], True]
loc_max_bool[0] = False
loc_max_bool[-1] = False
ind_max = numpy.argmax(tmp*loc_max_bool)
focus_distance[CC] = prop_length[ind_max]
img_propagated = img_shifted * numpy.exp(1.j * focus_distance[CC] * phase_matrix_shifted)
recon = fftshift(ifft2(img_propagated))
centerx, centery = centroids[CC, :]
reconcut = numpy.real(recon[numpy.max([0, centerx-Npixel]).astype(int): numpy.min([Xrange-1, centerx+Npixel]).astype(int), numpy.max([0, centery-Npixel]).astype(int): numpy.min([Yrange-1, centery+Npixel]).astype(int)])
focused_CC[CC, 0:reconcut.shape[0], 0:reconcut.shape[1]] = reconcut
CC_size[CC] = numpy.sum(get_CC_size(reconcut))
if len(focused_CC):
add_record(evt["analysis"], "analysis", "focused_CC", focused_CC[0])
add_record(evt["analysis"], "analysis", "focus distance", focus_distance)
add_record(evt["analysis"], "analysis", "CC_size", CC_size)
add_record(evt["analysis"], "analysis", "propagation length", prop_length)
def patterson(evt, type, key, mask=None, threshold=None, diameter_pix=None, crop=None, full_output=False, **params):
"""TODO: missing docstring
.. note:: This feature depends on the python package `libspimage <https://github.com/FilipeMaia/libspimage>`_.
"""
success, module = utils.io.load_spimage()
if not success:
print "Skipping analysis.patterson.patterson"
return
img = evt[type][key].data
if mask is None:
mask = numpy.ones(shape=img.shape, dtype="bool")
else:
mask = numpy.array(mask, dtype="bool")
if crop is not None:
img = module.crop(img, crop)
mask = module.crop(mask, crop)
out = module.patterson(numpy.float64(img), mask, full_output=full_output, normalize_median=True, **params)
v = evt["analysis"]
if full_output:
P = abs(out[0])
info = out[1]
add_record(v, "analysis", "patterson kernel", info["kernel"], unit='')
add_record(v, "analysis", "patterson intensities", info["intensities_times_kernel"], unit='')
else:
P = abs(out)
add_record(v, "analysis", "patterson", abs(P), unit='')
if threshold is not None:
M = P > threshold
if diameter_pix is not None:
Y,X = numpy.indices(P.shape)
X -= P.shape[1]/2
Y -= P.shape[0]/2
Rsq = X**2+Y**2
M *= Rsq > diameter_pix**2
if full_output:
add_record(v, "analysis", "patterson multiples", M, unit='')
multiple_score = M.sum()
add_record(v, "analysis", "multiple score", multiple_score, unit='')
def _tr_photon_detector(self, values, obj, evt_key):
"""Translates pixel detector into Humminbird ADU array"""
img = obj['image.data']
gain = obj['image.gain']
if self._sel_module is not None:
img = img[numpy.newaxis]
if self._sel_module is not None:
gain = gain[numpy.newaxis]
# If data is raw, add the gain reference along the 0th dimension
if self._data_format == 'Raw':
img = numpy.concatenate((img, gain), axis=0)
# If data is calibrated there is no need to look at the gain
elif self._data_format == 'Calib':
pass
else:
raise NotImplementedError("DataFormat should be 'Calib' or 'Raw''")
add_record(values, 'photonPixelDetectors', self._s2c[evt_key], img,
ureg.ADU)
def _tr_camera(self, values, obj):
"""Translates Camera frame to hummingbird numpy array"""
#if obj.depth == 16 or obj.depth() == 12:
# data = obj.data16()
# print data.shape
#else:
# data = obj.data8()
# print data.shape
data = obj.data16()
# off Axis cam at CXI
#if data.shape == (1024,1024):
# add_record(values, 'camera', 'offAxis', data, ureg.ADU)
# MCP (PNCCD replacement) at AMO (June 2016)
if data.shape == (1024,1024):
add_record(values, 'camera', 'mcp', data, ureg.ADU)
if data.shape == (1752,2336):
add_record(values, 'camera', 'onAxis', data, ureg.ADU)
def _tr_camera(self, values, obj):
"""Translates Camera frame to hummingbird numpy array"""
#if obj.depth == 16 or obj.depth() == 12:
# data = obj.data16()
# print data.shape
#else:
# data = obj.data8()
# print data.shape
data = obj.data16()
# off Axis cam at CXI
#if data.shape == (1024,1024):
# add_record(values, 'camera', 'offAxis', data, ureg.ADU)
# MCP (PNCCD replacement) at AMO (June 2016)
if data.shape == (1024, 1024):
add_record(values, 'camera', 'mcp', data, ureg.ADU)
if data.shape == (1752, 2336):
add_record(values, 'camera', 'onAxis', data, ureg.ADU)
def translate(self, evt, key):
"""Returns a dict of Records that match a given Humminbird key"""
values = {}
if('Dummy' not in self.state or
'Data Sources' not in self.state['Dummy']):
if(key == 'photonPixelDetectors'):
# Translate default CCD as default
add_record(values, key, 'CCD', evt['CCD'], ureg.ADU)
if(values == {}):
raise RuntimeError('%s not found in event' % (key))
return values
for ds in self.state['Dummy']['Data Sources']:
if self.state['Dummy']['Data Sources'][ds]['type'] == key:
# If unit is a string translate into PINT quantity
u = self.state['Dummy']['Data Sources'][ds]['unit']
if not isinstance(self.state['Dummy']['Data Sources'][ds]['unit'],ureg.Quantity):
u = ureg.parse_expression(u)
add_record(values, key, ds, evt[ds], u)
if(values == {} and not key == 'analysis'):
raise RuntimeError('%s not found in event' % (key))
return values
def translate(self, evt, key):
"""Returns a dict of Records that match a given Humminbird key"""
values = {}
if('Dummy' not in self.state or
'Data Sources' not in self.state['Dummy']):
if(key == 'photonPixelDetectors'):
# Translate default CCD as default
add_record(values, key, 'CCD', evt['CCD'], ureg.ADU)
if(values == {}):
raise RuntimeError('%s not found in event' % (key))
return values
for ds in self.state['Dummy']['Data Sources']:
if self.state['Dummy']['Data Sources'][ds]['type'] == key:
# If unit is a string translate into PINT quantity
u = self.state['Dummy']['Data Sources'][ds]['unit']
if not isinstance(self.state['Dummy']['Data Sources'][ds]['unit'],ureg.Unit):
u = ureg.parse_units(u)
add_record(values, key, ds, evt[ds], u)
if(values == {} and not key == 'analysis'):
raise RuntimeError('%s not found in event' % (key))
return values
def segment_holographic_hit(evt, type, key):
labeled = evt[type][key].data
if numpy.unique(labeled).shape[0] == 3:
return labeled
hitS = ((labeled >0)*1)
hitS = ndimage.morphology.binary_dilation(hitS)
for i in range(20):
hitS = ndimage.morphology.binary_dilation(hitS)
hh = gaussian_filter(hitS*4,sigma=15)
thresh = 5*numpy.median(hh)
hh[hh > thresh] = 1
hh[hh <= thresh] = 0
hitS = hh
labeled, nn = ndimage.measurements.label(hitS)
centroids = [[0 for a in numpy.arange(2)] for b in numpy.arange(nn)]
for CC in numpy.arange(nn):
tmp = (labeled == CC+1)*1.
centroid = center_of_mass(tmp)
#labeled[labeled == CC+1] = 0
if centroid[0] > 30 and centroid[0] < tmp.shape[0]-30 and centroid[1] > 30 and centroid[1] < tmp.shape[1]-30:
centroids[CC] = numpy.round(center_of_mass(tmp)).astype(int)
labeled[centroid[0]-4:centroid[0]+4,centroid[1]-4:centroid[1]+4] = 10
centroids = numpy.array(centroids, dtype=numpy.int64)
add_record(evt["analysis"], "analysis", "labeledHolograms", labeled)
add_record(evt["analysis"], "analysis", "centroids", centroids)
def _tr_photon_detector_spb(self, values, obj, evt_key):
"""Translates pixel detector into Humminbird ADU array"""
train_length = numpy.array(obj["image.pulseId"]).shape[-1]
cells = self._cell_filter[:train_length]
img = obj['image.data'][..., cells]
gain = obj['image.gain'][..., cells]
if self._sel_module is not None:
img = img[numpy.newaxis]
assert img.ndim == 4
if self._sel_module is not None:
gain = gain[numpy.newaxis]
# If data is raw, add the gain reference along the 0th dimension
if self._data_format == 'Raw':
assert gain.ndim == 4
img = numpy.concatenate((img, gain), axis=0)
# If data is calibrated there is no need to look at the gain
elif self._data_format == 'Calib':
pass
else:
raise NotImplementedError("DataFormat should be 'Calib' or 'Raw''")
add_record(values, 'photonPixelDetectors', self._s2c[evt_key], img,
ureg.ADU)
def translate(self, evt, key):
"""Returns a dict of Records that match a given hummingbird key"""
values = {}
if (key in self._c2n):
return self.translate_core(evt, key)
elif (key == 'analysis'):
return {}
elif (key == 'stream'):
return {}
else:
# check if the key matches any of the existing keys in the event
event_keys = evt.keys()
values = {}
found = False
if key in event_keys:
obj = evt[key]
for subkey in obj.keys():
add_record(values, 'native',
'%s[%s]' % (self._s2c[key], subkey),
obj[subkey], ureg.ADU)
return values
else:
print('%s not found in event' % (key))
def segment_holographic_hit(evt, type, key):
labeled = evt[type][key].data
if numpy.unique(labeled).shape[0] == 3:
return labeled
hitS = ((labeled > 0) * 1)
hitS = ndimage.morphology.binary_dilation(hitS)
for i in range(20):
hitS = ndimage.morphology.binary_dilation(hitS)
hh = gaussian_filter(hitS * 4, sigma=15)
thresh = 5 * numpy.median(hh)
hh[hh > thresh] = 1
hh[hh <= thresh] = 0
hitS = hh
labeled, nn = ndimage.measurements.label(hitS)
centroids = [[0 for a in numpy.arange(2)] for b in numpy.arange(nn)]
for CC in numpy.arange(nn):
tmp = (labeled == CC + 1) * 1.
centroid = center_of_mass(tmp)
#labeled[labeled == CC+1] = 0
if centroid[0] > 30 and centroid[0] < tmp.shape[0] - 30 and centroid[
1] > 30 and centroid[1] < tmp.shape[1] - 30:
centroids[CC] = numpy.round(center_of_mass(tmp)).astype(int)
labeled[centroid[0] - 4:centroid[0] + 4,
centroid[1] - 4:centroid[1] + 4] = 10
centroids = numpy.array(centroids, dtype=numpy.int64)
add_record(evt["analysis"], "analysis", "labeledHolograms", labeled)
add_record(evt["analysis"], "analysis", "centroids", centroids)
def refocus_hologram_evt(evt, type, key, gain=30):
img = evt[type][key].data.copy()
img = img[513 - 256:513 + 256, 584 - 256:584 + 256]
image_to_show, centroidsmap, centroids, focal_distance, intensity = refocus_hologram(
img, gain=gain)
add_record(evt["analysis"], "analysis", "hologram_score", intensity.max())
if evt["analysis"]["hologram_score"]:
add_record(evt["analysis"], "analysis", "focused_CC", image_to_show)
add_record(evt["analysis"], "analysis", "focus distance",
focal_distance[intensity.argmax()] * 5E-7)
def _tr_lusi_ipm_fex(self, values, obj, evt_key):
"""Translates Ipm relative pulse energy monitor
to hummingbird pulse energy"""
add_record(values, 'pulseEnergies', 'IpmFex - '+str(evt_key.src()), obj.sum(), ureg.ADU)
print 'creating gaussian and center mask'
gMask = gaussian_mask(dimx,dimx,400,700,300)
centerMask = euclid(dimx,dimx,numpy.round(dimx/2),numpy.round(dimx/2),150)
return mask, gMask, centerMask
def double_hit_finder_evt(evt, type, key, mask, weighting_mask, center_mask, threshold_med)
img = evt[type][key].data
img *= mask
img *= weighting_mask
recon = fftshift(numpy.abs(fftshift(img)))
recon *= center_mask
recon = recon[recon>0]
med = numpy.median(recon)
lit_pix = recon > (med*threshold_med)
double_hitscore = lit_pix.sum()
add_record(evt["analysis"], "analysis", "hologram score", double_hitscore, unit='cats per doghnuts')
def double_hit_finder(pattern, mask, gMask, centerMask, threshold_med, imname=''):
hitData = numpy.array(pattern, dtype=numpy.float64)
hitData *= numpy.array(mask)
hitData *= numpy.array(gMask)
holoData = fftshift(numpy.abs(ifft2(hitData)))
# if imname == '':
# imsave('hit2.png',holoData*centerMask)
# else: imsave('%s.png' % imname[:-4],holoData*centerMask)
holoData *= centerMask
hData = holoData[holoData>0]
med = numpy.median(hData)
hitS = holoData > (med * threshold_med)
# if imname == '':
# imsave('hit.png',hitS)
def translate(self, evt, key):
"""Returns a dict of Records that match a given Humminbird key"""
values = {}
if key == 'photonPixelDetectors':
# Translate pnCCD
add_record(values, key, 'pnCCD', evt['pnCCD'], ureg.ADU)
elif key == 'motorPositions':
val = self.motors.get(self.get_bunch_time()[0])
if val is None:
raise RuntimeError('%s not found in event' % key)
for motorname, motorpos in val.iteritems():
add_record(values, key, motorname, motorpos, ureg.mm)
elif key == 'ID':
add_record(values, key, 'DataSetID',
self.reader.file_header.dataSetID.rstrip('\0'))
# Sometimes BunchID is missing
add_record(values, key, 'BunchID',
self.reader.frame_headers[-1].external_id)
add_record(values, key, 'tv_sec',
self.reader.frame_headers[-1].tv_sec, ureg.s)
add_record(values, key, 'tv_usec',
self.reader.frame_headers[-1].tv_usec, ureg.s)
add_record(values, key, 'bunch_sec',
self.get_bunch_time()[0], ureg.s)
elif key == "DAQ":
if self.daq is None:
self.daq = read_daq.DAQReader(self.state['FLASH/DAQBaseDir'])
if self._current_event_id is not None:
tof_trace = self.daq.get_tof(self._current_event_id)
if tof_trace is not None:
evt["TOF"] = tof_trace
#self.keys.add("DAQ")
add_record(values, key, "TOF", evt["TOF"], ureg.s)
else:
raise RuntimeError("{0} not found in event".format(key))
elif key == "FEL":
wl = self.get_wavelength(self.get_bunch_time()[1])
if wl is not None:
add_record(values, key, "wavelength", wl, ureg.nm)
else:
raise RuntimeError("%s not found in event" % key)
gmd = self.get_gmd(self.get_bunch_time()[1])
if gmd is not None:
add_record(values, key, "gmd", gmd, ureg.mJ)
else:
raise RuntimeError("%s not found in event" % key)
elif not key == 'analysis':
raise RuntimeError('%s not found in event' % key)
return values
def _tr_pnccdFrames(self, values, obj, evt_key):
"""Translates pnCCD frames to hummingbird numpy array, frame by frame"""
n_frames = obj.frame_shape()[0]
for i in range(0, n_frames):
add_record(values, 'photonPixelDetectors', '%sFrame%d' % (self._s2c[str(evt_key.src())], i),
obj.frame(i).data(), ureg.ADU)
def _tr_cspad(self, values, obj, evt_key):
"""Translates CsPad to hummingbird numpy array, quad by quad"""
n_quads = obj.quads_shape()[0]
for i in range(0, n_quads):
add_record(values, 'photonPixelDetectors', '%sQuad%d' % (self._s2c[str(evt_key.src())], i),
obj.quads(i).data(), ureg.ADU)
def onEvent(evt):
# ------------------- #
# INITIAL DIAGNOSTICS #
# ------------------- #
# Time measurement
analysis.event.printProcessingRate()
# Simple hitfinding (Count Nr. of lit pixels)
aduThreshold = 30*16
hitscoreThreshold = 50
hitscoreMax = 500000000
analysis.hitfinding.countLitPixels(evt, back_type, back_key, aduThreshold=aduThreshold, hitscoreThreshold=hitscoreThreshold, hitscoreMax=hitscoreMax, mask=mask_back)
analysis.hitfinding.countLitPixels(evt, back_type, back_key, aduThreshold=aduThreshold, hitscoreThreshold=9000, hitscoreMax=hitscoreMax, mask=mask_back,label="Golden ")
hit = evt["analysis"]["isHit - " + back_key].data
golden_hit = evt["analysis"]["Golden isHit - " + back_key].data
# -------------------- #
# DETECTOR CORRECTIONS #
# -------------------- #
back_type_s = back_type
back_key_s = back_key
front_type_s = front_type
front_key_s = front_key
mask_back_s = mask_back
if do_cmc:
# CMC
analysis.pixel_detector.cmc_pnccd(evt, back_type_s, back_key_s)
#analysis.pixel_detector.cmc_pnccd(evt, front_type_s, front_key_s)
back_type_s = "analysis"
back_key_s = "cmc_pnccd - " + back_key
#front_type_s = "analysis"
#front_key_s = "cmc_pnccd - " + front_key
# ----- #
# WRITE #
# ----- #
if hit and do_sizing:
# Binning
analysis.pixel_detector.bin(evt, back_type_s, back_key_s, binning, mask_back_s)
#analysis.pixel_detector.bin(evt, front_type_s, front_type_s)
#front_key_s = "binned image - " + front_key
mask_back_b = evt["analysis"]["binned mask - " + back_key_s].data
back_type_b = "analysis"
back_key_b = "binned image - " + back_key_s
#front_type_s = "analysis"
print "HIT (hit score %i > %i)" % (evt["analysis"]["hitscore - " + back_key].data, hitscoreThreshold)
# CENTER DETERMINATION
analysis.sizing.findCenter(evt, back_type_b, back_key_b, mask=mask_back_b, **centerParams)
# RADIAL AVERAGE
analysis.pixel_detector.radial(evt, back_type_b, back_key_b, mask=mask_back_b, cx=evt["analysis"]["cx"].data, cy=evt["analysis"]["cy"].data)
# FIT SPHERE MODEL
analysis.sizing.fitSphereRadial(evt, "analysis", "radial distance - " + back_key_b, "radial average - " + back_key_b, **dict(modelParams, **sizingParams))
# DIFFRACTION PATTERN FROM FIT
analysis.sizing.sphereModel(evt, "analysis", "offCenterX", "offCenterY", "diameter", "intensity", (ny_back/binning,nx_back/binning), poisson=False, **modelParams)
# RADIAL AVERAGE FIT
analysis.pixel_detector.radial(evt, "analysis", "fit", mask=mask_back_b, cx=evt["analysis"]["cx"].data, cy=evt["analysis"]["cy"].data)
# ERRORS
analysis.sizing.photon_error(evt, back_type_b, back_key_b, "analysis", "fit", 144.)
analysis.sizing.absolute_error(evt, back_type_b, back_key_b, "analysis", "fit", "absolute error")
# Image of fit
msg = "diameter: %.2f nm \nIntensity: %.2f mJ/um2\nError: %.2e" %(evt["analysis"]["diameter"].data, evt["analysis"]["intensity"].data, evt["analysis"]["photon error"].data)
# HYBRID PATTERN
hybrid = evt["analysis"]["fit"].data.copy()
hybrid[:,:520/binning] = evt[back_type_b][back_key_b].data[:,:520/binning]
add_record(evt["analysis"], "analysis", "Hybrid pattern", hybrid)
error = evt["analysis"]["photon error"].data
plotting.image.plotImage(evt["analysis"]["Hybrid pattern"], mask=mask_back_b, name="Hybrid pattern", msg=msg)
plotting.line.plotHistory(evt["analysis"]["photon error"], history=1000)
plotting.image.plotImage(evt["analysis"]["fit"], log=True, mask=mask_back_b, name="pnCCD: Fit result (radial sphere fit)", msg=msg)
# Plot measurement radial average
plotting.line.plotTrace(evt["analysis"]["radial average - "+back_key_b], evt["analysis"]["radial distance - "+back_key_b],tracelen=radial_tracelen)
# Plot fit radial average
plotting.line.plotTrace(evt["analysis"]["radial average - fit"], evt["analysis"]["radial distance - fit"], tracelen=radial_tracelen)
diameter_pix = evt["analysis"]["diameter"].data * 1E-9 / res
analysis.patterson.patterson(evt, back_type_b, back_key_b, mask_back_b, threshold=4. ,diameter_pix=diameter_pix)
plotting.image.plotImage(evt["analysis"]["patterson"], name="Patterson")
plotting.line.plotHistory(evt["analysis"]["multiple score"], history=1000)
def _tr_cspad2x2(self, values, obj):
"""Translates CsPad2x2 to hummingbird numpy array"""
add_record(values, 'photonPixelDetectors', 'CsPad2x2', obj.data16(), ureg.ADU)
def patterson(evt,
type,
key,
mask=None,
threshold=None,
diameter_pix=None,
crop=None,
full_output=False,
xgap_pix=None,
ygap_pix=None,
frame_pix=None,
**params):
"""TODO: missing docstring
.. note:: This feature depends on the python package `libspimage <https://github.com/FilipeMaia/libspimage>`_.
"""
success, module = utils.io.load_spimage()
if not success:
print("Skipping analysis.patterson.patterson")
return
img = evt[type][key].data
if mask is None:
mask = numpy.ones(shape=img.shape, dtype="bool")
else:
mask = numpy.array(mask, dtype="bool")
if crop is not None:
img = module.crop(img, crop)
mask = module.crop(mask, crop)
out = module.patterson(numpy.float64(img),
mask,
full_output=full_output,
normalize_median=False,
**params)
v = evt["analysis"]
if full_output:
P = abs(out[0])
info = out[1]
add_record(v, "analysis", "patterson kernel", info["kernel"], unit='')
add_record(v,
"analysis",
"patterson intensities",
info["intensities_times_kernel"],
unit='')
else:
P = abs(out)
m = numpy.median(P)
if not numpy.isclose(m, 0.):
P = P / m
add_record(v, "analysis", "patterson", P, unit='')
if threshold is not None:
Minf = ~numpy.isfinite(P)
if Minf.sum() > 0:
P[Minf] = 0
M = P > threshold
if diameter_pix is not None:
Y, X = numpy.indices(P.shape)
X -= P.shape[1] / 2
Y -= P.shape[0] / 2
Rsq = X**2 + Y**2
M *= Rsq > (diameter_pix / 2)**2
if xgap_pix is not None:
cy = M.shape[0] / 2
M[cy - xgap_pix / 2:cy + xgap_pix / 2, :] = False
if ygap_pix is not None:
cx = M.shape[1] / 2
M[:, cx - ygap_pix / 2:cx + ygap_pix / 2] = False
if frame_pix is not None:
M[:frame_pix, :] = False
M[-frame_pix:, :] = False
M[:, :frame_pix] = False
M[:, -frame_pix:] = False
if full_output:
add_record(v, "analysis", "patterson multiples", M, unit='')
multiple_score = M.sum()
add_record(v, "analysis", "multiple score", multiple_score, unit='')
def _tr_lusi_ipm_fex(self, values, obj, evt_key):
"""Translates Ipm relative pulse energy monitor
to hummingbird pulse energy"""
add_record(values, 'pulseEnergies', 'IpmFex - ' + str(evt_key.src()),
obj.sum(), ureg.ADU)
def holographic_hitfinder_evt(evt, type, key, maskt, gMask, centerMask, off_x, th=3,radius_smooth=70):
t = time.time()
img = evt[type][key].data
#img = gaussian_filter(img,sigma=3)
hitData = numpy.zeros((1024+off_x,1024+off_x))
hitData[:513,:] = img[:513,:]
hitData[-514:,:] = img[-514:,:]
#mask = numpy.zeros((1024+off_x,1024+off_x))
#mask[:513,:] = mask[:513,:]
#mask[-514:,:] = maskt[-514:,:]
hitData[hitData > 12000] = 0
hitData[hitData < 30] = 0
# hitData[:512,393] = 0
# hitData[-512:,395] = 0
dd = int(off_x/2)
cropsize = 256
pattern = hitData[dd+cropsize:-dd-cropsize,dd+cropsize:-dd-cropsize]
mask = maskt[dd+cropsize:-dd-cropsize,dd+cropsize:-dd-cropsize]
gMask = gMask[dd+cropsize:-dd-cropsize,dd+cropsize:-dd-cropsize]
centerMask = centerMask[dd+cropsize:-dd-cropsize,dd+cropsize:-dd-cropsize]
pattern[:512,393] = 0
pattern[-512:,395] = 0
hitData = pattern*mask
Ifilter = make_filter( pattern.shape[0],radius_smooth )[0]
hitData *= Ifilter
#holoData = autocorrelation(hitData,radius_smooth)
#holoData *= gMask
hitData *= gMask
holoData = fftshift(numpy.abs(ifft2(hitData)))
holoData *= centerMask
hData = holoData[holoData>0]
med = numpy.median(hData)
if holoData.max() > 0: holoData /= holoData.max()
med = numpy.median(holoData)
#hitS = 1.*(holoData > 0.05)
hitS = 1.*(holoData > med*4)
#hitS = crossremove(hitS)
hitScore = hitS.sum()
if hitScore > 2000:
#hitS = 1.*(holoData > 0.15)
hitS = 1.*(holoData > med*6)
#hitS = crossremove(hitS)
#hitScore = hitS.sum()
if hitScore > 5000:
hitS = binary_erosion(hitS)
hitS = binary_erosion(hitS)
hitS = binary_erosion(hitS)
hitS = binary_erosion(hitS)
hitS = binary_dilation(hitS)
#hitScore = hitS.sum()
#labeled = hitS
#labeled, n = ndimage.measurements.label(hitS)
#else:
#hitS[0][0] = 2
hitS = crossremove(hitS)
labeled, n = ndimage.measurements.label(hitS)
holoData[labeled == labeled[256][256]] = 0
labeled[labeled == labeled[256][256]] = 0
for i in range(1,n):
ss = ((labeled == i)*1.).sum()
if ss < 8:
labeled[labeled == i] = 0
hitS[labeled == i] = 0.
hitScore = ((labeled>0)*1).sum()
# print hitScore
#print holoData
add_record(evt["analysis"], "analysis", "hologramScore", hitScore)
add_record(evt["analysis"], "analysis", "holoData", holoData)
add_record(evt["analysis"], "analysis", "labeledHolograms", labeled)
add_record(evt["analysis"], "analysis", "croppedPattern", pattern*mask)
def make_propagation(evt, type, key_img, key_mask, imnr, minPhase=-120000, maxPhase=120000, steps=50, field_of_view_rad=50, wavelength=2.353, CCD_S_DIST=0.38, PX_SIZE=75e-6,Npixel=50):
img = evt[type][key_img].data
Xrange, Yrange = img.shape
p = numpy.linspace(-Xrange/2, Xrange/2-1, Xrange)
q = numpy.linspace(-Yrange/2, Yrange/2-1, Yrange)
pp, qq = numpy.meshgrid(p, q)
rr = numpy.sqrt(pp**2+qq**2)
phase_matrix = (2*numpy.pi/wavelength)*numpy.sqrt(1-((PX_SIZE/CCD_S_DIST)**2)*(qq**2 + pp**2))
img_shifted = fftshift(img)
phase_matrix_shifted = fftshift(phase_matrix)
r,rg = [],[]
steps = 10
rgt = numpy.zeros((steps,Xrange,Yrange))
stepsize = maxPhase/steps
from skimage.feature import canny
import numpy as np
sobt = numpy.zeros_like(img)
runname='r0043'
for step in range(steps):
phase = stepsize*step
img_propagated = img_shifted * numpy.exp(1.j*phase*phase_matrix_shifted)
img_propagated[:,Yrange/2-3:Yrange/2+3] = img_propagated.mean()
recon = fftshift(ifft2(img_propagated))
recon[rr < 30] = recon.mean()
cc = canny(abs(recon), sigma=9)
cc = binary_dilation(cc)
cc = binary_dilation(cc)
cc = binary_dilation(cc)
cc = binary_fill_holes(cc)
cc = binary_erosion(cc)
cc = binary_erosion(cc)
cc = binary_erosion(cc)
cc = binary_erosion(cc)
cc = binary_erosion(cc)
criterion0 = cc
sx = ndimage.sobel(abs(recon), axis=0, mode='constant')
sy = ndimage.sobel(abs(recon), axis=1, mode='constant')
sob = numpy.hypot(sx, sy)
sob[rr < 31] = sob.max()
sobt += (sob > 0.1*sob.max())*1.
diff = abs(sob-sob.max())
diff /= diff.max()
if phase == 0:
criterion1 = sob>sob.max()/2
sg = gaussian_filter(sobt,sigma=1)
labeled,nnn = ndimage.measurements.label(sg)
remove = (labeled == labeled[256][256])*1
remove[246:266,:] = 1
remove[206:306,246:266]= 1
sg[labeled == labeled[256][256]] = 0
sg = sg > 0
sg = binary_erosion(sg)
sg = binary_erosion(sg)
sg = binary_erosion(sg)
sg = binary_erosion(sg)
sg = binary_erosion(sg)
sg = binary_erosion(sg)
sg = binary_erosion(sg)
criterion2 = sg
FF = 1*(criterion0+criterion1+criterion2)#*abs(1-remove)
FF = binary_erosion(FF,numpy.ones((2,2)))
FF[remove > 0] = 1
labeled,nnn = ndimage.measurements.label(FF)
labeled[labeled == labeled[256][256]] = 0
dance = labeled > 0
dance = binary_dilation(dance)
if nnn-1 < 2:
return 0
num_holograms = nnn-1
images = []
variance = []
centroids = []
for structure in numpy.unique(labeled):
if structure < 1: continue
centroids.append( numpy.round(center_of_mass(labeled == structure)).astype(int) )
images.append([])
variance.append([])
cnt = 0
for centerx,centery in centroids:
reconcut = numpy.abs(recon[numpy.max([0, centerx-Npixel-1]).astype(int): numpy.min([Xrange-1, centerx+Npixel]).astype(int), numpy.max([0, centery-Npixel-1]).astype(int): numpy.min([Yrange-1, centery+Npixel]).astype(int)])
images[cnt].append(reconcut)
variance[cnt].append(reconcut.var())
cnt +=1
#imsave('hitScores/hollows/img_recon_%05d_%06d_3.png'%(imnr,phase),recon,cmap='gray')
#rg.append(recon)
#print variance
focused_CC = []
z_location = []
for i in range(num_holograms):
minn = numpy.argmax(numpy.array(variance[i]))
#imsave('hitScores/hollows/img_%s_%05d_%06d_%d.png'%(runname,imnr,phase,i), images[i][minn])
focused_CC.append(images[i][numpy.argmin(numpy.array(variance[i]))])
z_location.append(stepsize*minn)
if len(focused_CC) > 0:
add_record(evt["analysis"], "analysis", "focused_CC", focused_CC[0])
add_record(evt["analysis"], "analysis", "focus distance", z_location)
def onEvent(evt):
# Counter
global counter
counter += 1
# Option to stop after fixed number of frames
if args.nr_frames is not None:
#print counter, args.nr_frames/ipc.mpi.nr_event_readers()
if (counter == args.nr_frames / ipc.mpi.nr_event_readers()):
raise StopIteration
# Processing rate [Hz]
analysis.event.printProcessingRate()
# Read FEL parameters
try:
wavelength_nm = evt['FEL']['wavelength'].data
gmd = evt['FEL']['gmd'].data
except RuntimeError:
wavelength_nm = np.nan
gmd = np.nan
detector_type = detector_type_raw
detector_key = detector_key_raw
detector = evt[detector_type][detector_key]
if move_half:
detector_s = analysis.pixel_detector.moveHalf(
evt,
detector,
horizontal=int(gap_total / pixel_size),
outkey='data_half-moved')
mask_center_s = analysis.pixel_detector.moveHalf(
evt,
add_record(evt["analysis"], "analysis", "mask", mask_center),
horizontal=int(gap_total / pixel_size),
outkey='mask_half-moved').data
detector_type = "analysis"
detector_key = "data_half-moved"
detector = evt[detector_type][detector_key]
else:
mask_center_s = mask_center
# Do basic hitfinding using lit pixels
analysis.hitfinding.countLitPixels(evt,
detector,
aduThreshold=aduThreshold,
hitscoreThreshold=hitScoreThreshold,
mask=mask_center_s)
hit = bool(evt["analysis"]["litpixel: isHit"].data)
hitscore = evt['analysis']['litpixel: hitscore'].data
global hitscore_cache
hitscore_cache[counter % cache_length] = hitscore
# Find multiple hits based on patterson function
if hit:
analysis.patterson.patterson(evt,
"analysis",
"data_half-moved",
mask_center_s,
threshold=patterson_threshold,
diameter_pix=patterson_diameter,
xgap_pix=patterson_xgap_pix,
ygap_pix=patterson_ygap_pix,
frame_pix=patterson_frame_pix,
crop=512,
full_output=True,
**patterson_params)
#print evt['analysis'].keys()
multiple_hit = evt["analysis"][
"multiple score"].data > multiScoreThreshold
# Write to file
if save_anything:
if hit and (not args.only_save_multiples or multiple_hit):
D = {}
D['entry_1'] = {}
D['entry_1']['event'] = {}
D['entry_1']['motors'] = {}
D['entry_1']['FEL'] = {}
D['entry_1']['result_1'] = {}
if save_pnccd:
D['entry_1']['detector_1'] = {}
if save_tof:
D['entry_1']['detector_2'] = {}
# PNCCD
if save_pnccd:
D['entry_1']['detector_1']['data'] = np.asarray(
detector.data, dtype='float16')
if ipc.mpi.is_main_event_reader() and len(D_solo) == 0:
bitmask = np.array(mask_center_s, dtype='uint16')
bitmask[bitmask == 0] = 512
bitmask[bitmask == 1] = 0
D_solo["entry_1"] = {}
D_solo["entry_1"]["detector_1"] = {}
D_solo["entry_1"]["detector_1"]["mask"] = bitmask
# PATTERSON
if save_multiple:
D['entry_1']['detector_1']['patterson'] = np.asarray(
evt['analysis']['patterson'].data, dtype='float16')
D['entry_1']['detector_1']['patterson_mask'] = np.asarray(
evt['analysis']['patterson multiples'].data, dtype='bool')
# TOF
if save_tof:
# Read ToF traces
try:
tof = evt["DAQ"]["TOF"]
except RuntimeError:
logging.warning("Runtime error when reading TOF data.")
return
except KeyError:
logging.warning("Key error when reading TOF data.")
return
D['entry_1']['detector_2']['data'] = np.asarray(
tof.data, dtype='float16')
# HIT PARAMETERS
D['entry_1']['result_1']['hitscore_litpixel'] = evt['analysis'][
'litpixel: hitscore'].data
D['entry_1']['result_1'][
'hitscore_litpixel_threshold'] = hitScoreThreshold
D['entry_1']['result_1']['multiscore_patterson'] = evt['analysis'][
'multiple score'].data
D['entry_1']['result_1'][
'multiscore_patterson_threshold'] = multiScoreThreshold
try:
# FEL PARAMETERS
D['entry_1']['FEL']['gmd'] = gmd
D['entry_1']['FEL']['wavelength_nm'] = wavelength_nm
except KeyError:
logging.warning("Cannot find FEL data.")
try:
# EVENT IDENTIFIERS
D['entry_1']['event']['bunch_id'] = evt['ID']['BunchID'].data
D['entry_1']['event']['tv_sec'] = evt['ID']['tv_sec'].data
D['entry_1']['event']['tv_usec'] = evt['ID']['tv_usec'].data
D['entry_1']['event']['dataset_id'] = evt['ID'][
'DataSetID'].data
D['entry_1']['event']['bunch_sec'] = evt['ID'][
'bunch_sec'].data
except KeyError:
logging.warning("Cannot find event data.")
try:
# MOTORS
D['entry_1']['motors']['manualy'] = evt['motorPositions'][
'ManualY'].data
D['entry_1']['motors']['injectorx'] = evt['motorPositions'][
'InjectorX'].data
D['entry_1']['motors']['injectory'] = evt['motorPositions'][
'InjectorZ'].data
D['entry_1']['motors']['trigdelay'] = evt['motorPositions'][
'TrigDelay'].data
D['entry_1']['motors']['samplepress'] = evt['motorPositions'][
'InjectorSamplePressure'].data
D['entry_1']['motors']['nozzlepress'] = evt['motorPositions'][
'InjectorNozzlePressure'].data
D['entry_1']['motors']['posdownstream'] = evt[
'motorPositions']['PosDownstream'].data
D['entry_1']['motors']['posupstream'] = evt['motorPositions'][
'PosUpstream'].data
D['entry_1']['motors']['injectorpress'] = evt[
'motorPositions']['InjectorPressure'].data
D['entry_1']['motors']['focusinggas'] = evt['motorPositions'][
'InjectorFocusingGas'].data
except KeyError:
logging.warning("Cannot find motor data.")
# TODO: FEL
W.write_slice(D)
def _tr_cspad2x2(self, values, obj):
"""Translates CsPad2x2 to hummingbird numpy array"""
try:
add_record(values, 'photonPixelDetectors', 'CsPad2x2S', obj.data(), ureg.ADU)
except AttributeError:
add_record(values, 'photonPixelDetectors', 'CsPad2x2', obj.data16(), ureg.ADU)
def make_propagation(evt,
type,
key_img,
key_mask,
imnr,
minPhase=-120000,
maxPhase=120000,
steps=50,
field_of_view_rad=50,
wavelength=2.353,
CCD_S_DIST=0.38,
PX_SIZE=75e-6,
Npixel=50):
img = evt[type][key_img].data
Xrange, Yrange = img.shape
p = numpy.linspace(-Xrange / 2, Xrange / 2 - 1, Xrange)
q = numpy.linspace(-Yrange / 2, Yrange / 2 - 1, Yrange)
pp, qq = numpy.meshgrid(p, q)
rr = numpy.sqrt(pp**2 + qq**2)
phase_matrix = (2 * numpy.pi / wavelength) * numpy.sqrt(1 - (
(PX_SIZE / CCD_S_DIST)**2) * (qq**2 + pp**2))
img_shifted = fftshift(img)
phase_matrix_shifted = fftshift(phase_matrix)
r, rg = [], []
steps = 10
rgt = numpy.zeros((steps, Xrange, Yrange))
stepsize = maxPhase / steps
from skimage.feature import canny
import numpy as np
sobt = numpy.zeros_like(img)
runname = 'r0043'
for step in range(steps):
phase = stepsize * step
img_propagated = img_shifted * numpy.exp(
1.j * phase * phase_matrix_shifted)
img_propagated[:,
Yrange / 2 - 3:Yrange / 2 + 3] = img_propagated.mean()
recon = fftshift(ifft2(img_propagated))
recon[rr < 30] = recon.mean()
cc = canny(abs(recon), sigma=9)
cc = binary_dilation(cc)
cc = binary_dilation(cc)
cc = binary_dilation(cc)
cc = binary_fill_holes(cc)
cc = binary_erosion(cc)
cc = binary_erosion(cc)
cc = binary_erosion(cc)
cc = binary_erosion(cc)
cc = binary_erosion(cc)
criterion0 = cc
sx = ndimage.sobel(abs(recon), axis=0, mode='constant')
sy = ndimage.sobel(abs(recon), axis=1, mode='constant')
sob = numpy.hypot(sx, sy)
sob[rr < 31] = sob.max()
sobt += (sob > 0.1 * sob.max()) * 1.
diff = abs(sob - sob.max())
diff /= diff.max()
if phase == 0:
criterion1 = sob > sob.max() / 2
sg = gaussian_filter(sobt, sigma=1)
labeled, nnn = ndimage.measurements.label(sg)
remove = (labeled == labeled[256][256]) * 1
remove[246:266, :] = 1
remove[206:306, 246:266] = 1
sg[labeled == labeled[256][256]] = 0
sg = sg > 0
sg = binary_erosion(sg)
sg = binary_erosion(sg)
sg = binary_erosion(sg)
sg = binary_erosion(sg)
sg = binary_erosion(sg)
sg = binary_erosion(sg)
sg = binary_erosion(sg)
criterion2 = sg
FF = 1 * (criterion0 + criterion1 + criterion2) #*abs(1-remove)
FF = binary_erosion(FF, numpy.ones((2, 2)))
FF[remove > 0] = 1
labeled, nnn = ndimage.measurements.label(FF)
labeled[labeled == labeled[256][256]] = 0
dance = labeled > 0
dance = binary_dilation(dance)
if nnn - 1 < 2:
return 0
num_holograms = nnn - 1
images = []
variance = []
centroids = []
for structure in numpy.unique(labeled):
if structure < 1: continue
centroids.append(
numpy.round(
center_of_mass(labeled == structure)).astype(int))
images.append([])
variance.append([])
cnt = 0
for centerx, centery in centroids:
reconcut = numpy.abs(
recon[numpy.max([0, centerx - Npixel - 1]).astype(int):numpy.
min([Xrange - 1, centerx + Npixel]).astype(int),
numpy.max([0, centery - Npixel - 1]).astype(int):numpy.
min([Yrange - 1, centery + Npixel]).astype(int)])
images[cnt].append(reconcut)
variance[cnt].append(reconcut.var())
cnt += 1
#imsave('hitScores/hollows/img_recon_%05d_%06d_3.png'%(imnr,phase),recon,cmap='gray')
#rg.append(recon)
#print variance
focused_CC = []
z_location = []
for i in range(num_holograms):
minn = numpy.argmax(numpy.array(variance[i]))
#imsave('hitScores/hollows/img_%s_%05d_%06d_%d.png'%(runname,imnr,phase,i), images[i][minn])
focused_CC.append(images[i][numpy.argmin(numpy.array(variance[i]))])
z_location.append(stepsize * minn)
if len(focused_CC) > 0:
add_record(evt["analysis"], "analysis", "focused_CC", focused_CC[0])
add_record(evt["analysis"], "analysis", "focus distance", z_location)
def _tr_pnccdFullFrame(self, values, obj, evt_key):
"""Translates full pnCCD frame to hummingbird numpy array"""
add_record(values, 'photonPixelDetectors',
'%sfullFrame' % self._s2c[str(evt_key.src())], obj.data(),
ureg.ADU)
def _tr_pnccdFullFrame(self, values, obj, evt_key):
"""Translates full pnCCD frame to hummingbird numpy array"""
add_record(values, 'photonPixelDetectors', '%sfullFrame' % self._s2c[str(evt_key.src())], obj.data(), ureg.ADU)
def onEvent(evt):
# Processing rate [Hz]
#analysis.event.printProcessingRate()
# Calculate time and add to PlotHistory
# calculate_epoch_times(evt, evt["ID"]["tv_sec"], evt["ID"]["tv_usec"])
# plotting.line.plotHistory(evt['ID']['timeAgo'], label='Event Time (s)', group='ID')
# plotting.line.plotHistory(evt['ID']['tv_sec'], label='Epoch Time (s)', group='ID')
detector_type = "photonPixelDetectors"
detector_key = "pnCCD"
if move_half:
detector = evt[detector_type][detector_key]
detector = analysis.pixel_detector.moveHalf(evt,
detector,
horizontal=gap_total /
pixel_size,
outkey='data_half-moved')
mask_center_s = analysis.pixel_detector.moveHalf(
evt,
add_record(evt["analysis"], "analysis", "mask", mask_center),
horizontal=gap_total / pixel_size,
outkey='mask_half-moved').data
detector_type = "analysis"
detector_key = "data_half-moved"
# Do basic hitfinding using lit pixels
analysis.hitfinding.countLitPixels(evt,
evt[detector_type][detector_key],
aduThreshold=aduThreshold,
hitscoreThreshold=hitScoreThreshold)
hit = bool(evt["analysis"]["litpixel: isHit"].data)
plotting.line.plotHistory(evt["analysis"]["litpixel: hitscore"],
label='Nr. of lit pixels',
hline=hitScoreThreshold,
group='Metric')
analysis.hitfinding.hitrate(evt, hit, history=50)
if scanInjector:
plotting.histogram.plotNormalizedHistogram(
evt["motorPositions"]["InjectorX"],
float(1 if hit else 0),
hmin=scanXmin,
hmax=scanXmax,
bins=scanXbins,
name="Histogram: InjectorX x Hitrate",
group="Scan",
buffer_length=1000)
plotting.histogram.plotNormalizedHistogram(
evt["motorPositions"]["InjectorZ"],
float(1 if hit else 0),
hmin=scanZmin,
hmax=scanZmax,
bins=scanZbins,
name="Histogram: InjectorZ x Hitrate",
group="Scan",
buffer_length=1000)
plotting.correlation.plotScatter(evt["motorPositions"]["InjectorX"],
evt['analysis']['litpixel: hitscore'],
name='InjectorX vs Hitscore',
xlabel='InjectorX',
ylabel='Hit Score',
group='Scan')
plotting.correlation.plotScatter(evt["motorPositions"]["InjectorZ"],
evt['analysis']['litpixel: hitscore'],
name='InjectorZ vs Hitscore',
xlabel='InjectorZ',
ylabel='Hit Score',
group='Scan')
plotting.line.plotHistory(evt["motorPositions"]["InjectorX"],
label="Cluster delay",
group="Scan")
plotting.line.plotHistory(evt["motorPositions"]["InjectorZ"],
label="Nothing",
group="Scan")
# print("InjectorX = {0}".format(evt["motorPositions"]["InjectorX"].data))
if outputEveryImage:
plotting.image.plotImage(evt[detector_type][detector_key],
name="pnCCD (All)",
group='Images')
if ipc.mpi.is_main_worker():
plotting.line.plotHistory(evt["analysis"]["hitrate"],
label='Hit rate [%]',
group='Metric',
history=10000)
# plotting.correlation.plotMeanMap(evt['motorPositions']['nozzle_x'], evt['motorPositions']['nozzle_y'],
# #evt['analysis']['litpixel: hitscore'].data / 1e5,
# evt['analysis']['hitrate'].data,
# xmin=0.68, xmax=0.72, ymin=4.20, ymax=4.23,
# name='Hitscore mean map vs nozzle_xy',
# xlabel='nozzle_x (mm)',
# ylabel='nozzle_y (mm)',
# group='Metric')
if hit:
plotting.image.plotImage(evt[detector_type][detector_key],
name="pnCCD (Hits)",
group='Images',
mask=mask_center_s)
if do_sizing:
# Crop to 1024 x 1024
Nx, Ny = np.shape(evt[detector_type][detector_key].data)
diff_y = Ny - 1024
cropped_img = evt[detector_type][detector_key].data[:, diff_y /
2:-(diff_y /
2)]
add_record(evt["analysis"], "analysis", "data-cropped",
cropped_img)
detector_key = "data-cropped"
cropped_mask = mask_center_s[:, diff_y / 2:-(diff_y / 2)]
add_record(evt["analysis"], "analysis", "mask-cropped",
cropped_mask)
mask_center_fit_s = evt['analysis']['mask-cropped'].data
# Binning
analysis.pixel_detector.bin(evt, detector_type, detector_key,
binning, mask_center_fit_s)
mask_binned = evt["analysis"]["binned mask - " + detector_key].data
detector_type_b = "analysis"
detector_key_b = "binned image - " + detector_key
# CENTER DETERMINATION
analysis.sizing.findCenter(evt,
detector_type_b,
detector_key_b,
mask=mask_binned,
**centerParams)
# RADIAL AVERAGE
analysis.pixel_detector.radial(evt,
detector_type_b,
detector_key_b,
mask=mask_binned,
cx=evt["analysis"]["cx"].data,
cy=evt["analysis"]["cy"].data)
# FIT SPHERE MODEL
analysis.sizing.fitSphereRadial(
evt, "analysis", "radial distance - " + detector_key_b,
"radial average - " + detector_key_b,
**dict(modelParams, **sizingParams))
# DIFFRACTION PATTERN FROM FIT
analysis.sizing.sphereModel(evt,
"analysis",
"offCenterX",
"offCenterY",
"diameter",
"intensity",
(ny / binning, nx / binning),
poisson=False,
**modelParams)
# RADIAL AVERAGE FIT
analysis.pixel_detector.radial(evt,
"analysis",
"fit",
mask=mask_binned,
cx=evt["analysis"]["cx"].data,
cy=evt["analysis"]["cy"].data)
# ERRORS
#analysis.sizing.photon_error(evt, detector_type_b, detector_key_b, "analysis", "fit", adu_per_photon=144.)
#analysis.sizing.absolute_error(evt, detector_type_b, detector_key_b, "analysis", "fit", "absolute error")
msg = "diameter: %.2f nm \nIntensity: %.4f mJ/um2\nFit Error: %.2e" % (
evt["analysis"]["diameter"].data,
evt["analysis"]["intensity"].data,
evt["analysis"]["fit error"].data)
# Selection of good fits
small_fit_error = evt['analysis'][
'fit error'].data < fit_error_threshold
#small_photon_error = evt['analysis']['photon error'].data < photon_error_threshold
correctsized_hit = small_fit_error #and small_photon_error
# Select only events in a certain diameter window
diameter = evt['analysis']['diameter'].data
print diameter
plotting.histogram.plotHistogram(evt["analysis"]["diameter"],
bins=100,
name="Histogram: Particle size",
group="Sizing",
hmin=20,
hmax=100,
buffer_length=1000)
#correctsized_hit &= ((diameter > diameter_min) & (diameter < diameter_max))
# Plot errors
plotting.line.plotHistory(evt["analysis"]["fit error"],
history=1000,
hline=fit_error_threshold,
group='Sizing')
#plotting.line.plotHistory(evt["analysis"]["photon error"], history=1000, hline=photon_error_threshold, group='Sizing')
#plotting.line.plotHistory(evt["analysis"]["absolute error"], history=1000, group='Sizing')
#time.sleep(0.05)
if do_showhybrid:
# HYBRID PATTERN
hybrid = evt["analysis"]["fit"].data.copy()
hybrid[:, 512 / binning:] = evt[detector_type_b][
detector_key_b].data[:, 512 / binning:]
add_record(evt["analysis"], "analysis", "Hybrid pattern",
hybrid)
plotting.image.plotImage(evt["analysis"]["Hybrid pattern"],
name="Hybrid pattern ",
msg=msg,
group='Sizing')
plotting.image.plotImage(evt["analysis"]["Hybrid pattern"],
name="Hybrid pattern / log",
vmax=1e4,
log=True,
msg=msg,
group='Sizing')
if correctsized_hit:
# Plot Correct sized hits
plotting.image.plotImage(
evt[detector_type][detector_key],
group='Sizing',
msg=msg,
name="pnCCD front (correct hit)") #, mask=mask_front_s)
# Plot Intensity
plotting.line.plotHistory(evt["analysis"]["intensity"],
history=10000,
name='Intensity (from sizing)',
group='Results')
# Plot size (in nm)
plotting.line.plotHistory(evt["analysis"]["diameter"],
history=10000,
name='Size in nm (from sizing)',
group='Results')
# Normalizing intensity to pulse energy (assuming 1mJ on average)
#intensity_normalized = (evt['analysis']['intensity'].data / evt['analysis']['averagePulseEnergy'].data) * 1.0
#add_record(evt['analysis'], 'analysis', 'intensity_normalized', intensity_normalized)
# Plot Intensity (normalized)
plotting.line.plotHistory(
evt['analysis']['intensity'],
history=10000,
name='Intensity normalized (from sizing)',
group='Results')
def _tr_event_codes(self, values, obj):
"""Translates LCLS event codes into a hummingbird ones"""
codes = []
for fifo_event in obj.fifoEvents():
codes.append(fifo_event.eventCode())
add_record(values, 'eventCodes', 'EvrEventCodes', codes)
def _tr_event_codes(self, values, obj):
"""Translates LCLS event codes into a hummingbird ones"""
codes = []
for fifo_event in obj.fifoEvents():
codes.append(fifo_event.eventCode())
add_record(values, 'eventCodes', 'EvrEventCodes', codes)
def find_foci(evt,
type,
key,
type2,
key2,
minPhase=-500000,
maxPhase=500000,
steps=101,
field_of_view_rad=100,
wavelength=1.053,
CCD_S_DIST=0.375,
PX_SIZE=75e-6):
img = evt[type][key].data
centroids = evt[type2][key2].data
Nfoci = centroids.shape[0]
Xrange, Yrange = img.shape
Npixel = field_of_view_rad
p = numpy.linspace(-Xrange / 2, Xrange / 2 - 1, Xrange)
q = numpy.linspace(-Yrange / 2, Yrange / 2 - 1, Yrange)
pp, qq = numpy.meshgrid(p, q)
phase_matrix = (2 * numpy.pi / wavelength) * numpy.sqrt(1 - (
(PX_SIZE / CCD_S_DIST)**2) * (qq**2 + pp**2))
prop_length = numpy.linspace(minPhase, maxPhase, steps)
variance = numpy.zeros([steps, Nfoci])
# shift stuff for performance reasons
img_shifted = fftshift(img)
phase_matrix_shifted = fftshift(phase_matrix)
for idx, phase in enumerate(prop_length):
img_propagated = img_shifted * numpy.exp(
1.j * phase * phase_matrix_shifted)
recon = fftshift(ifft2(img_propagated))
for CC in numpy.arange(Nfoci):
centerx, centery = centroids[CC, :]
###print centerx, centery
reconcut = numpy.abs(
recon[numpy.max([0, centerx - Npixel - 1]).astype(int):numpy.
min([Xrange - 1, centerx + Npixel]).astype(int),
numpy.max([0, centery - Npixel - 1]).astype(int):numpy.
min([Yrange - 1, centery + Npixel]).astype(int)])
variance[idx, CC] = reconcut.var()
focus_distance = numpy.zeros(Nfoci)
CC_size = numpy.zeros(Nfoci)
focused_CC = numpy.zeros(4 * Npixel**2 * Nfoci).reshape(
Nfoci, 2 * Npixel, 2 * Npixel)
for CC in numpy.arange(Nfoci):
ind_max = numpy.argmax(variance[:, CC])
tmp = variance[:, CC]
# get max which is not at border
loc_max_bool = numpy.r_[True, tmp[1:] > tmp[:-1]] & numpy.r_[
tmp[:-1] > tmp[1:], True]
loc_max_bool[0] = False
loc_max_bool[-1] = False
ind_max = numpy.argmax(tmp * loc_max_bool)
focus_distance[CC] = prop_length[ind_max]
img_propagated = img_shifted * numpy.exp(
1.j * focus_distance[CC] * phase_matrix_shifted)
recon = fftshift(ifft2(img_propagated))
centerx, centery = centroids[CC, :]
reconcut = numpy.real(
recon[numpy.max([0, centerx - Npixel]).astype(int):numpy.
min([Xrange - 1, centerx + Npixel]).astype(int),
numpy.max([0, centery - Npixel]).astype(int):numpy.
min([Yrange - 1, centery + Npixel]).astype(int)])
focused_CC[CC, 0:reconcut.shape[0], 0:reconcut.shape[1]] = reconcut
CC_size[CC] = numpy.sum(get_CC_size(reconcut))
if len(focused_CC):
add_record(evt["analysis"], "analysis", "focused_CC", focused_CC[0])
add_record(evt["analysis"], "analysis", "focus distance",
focus_distance)
add_record(evt["analysis"], "analysis", "CC_size", CC_size)
add_record(evt["analysis"], "analysis", "propagation length",
prop_length)
def _tr_bld_data_ebeam(self, values, obj):
"""Translates BldDataEBeam to hummingbird photon energy and other beam properties"""
try:
photon_energy_ev = obj.ebeamPhotonEnergy()
except AttributeError:
peak_current = obj.ebeamPkCurrBC2()
dl2_energy_gev = 0.001*obj.ebeamL3Energy()
ltu_wake_loss = 0.0016293*peak_current
# Spontaneous radiation loss per segment
sr_loss_per_segment = 0.63*dl2_energy_gev
# wakeloss in an undulator segment
wake_loss_per_segment = 0.0003*peak_current
# energy loss per segment
energy_loss_per_segment = (sr_loss_per_segment +
wake_loss_per_segment)
# energy in first active undulator segment [GeV]
energy_profile = (dl2_energy_gev - 0.001*ltu_wake_loss -
0.0005*energy_loss_per_segment)
# Calculate the resonant photon energy of the first active segment
photon_energy_ev = 44.42*energy_profile*energy_profile
add_record(values, 'photonEnergies', 'photonEnergy', photon_energy_ev, ureg.eV)
try:
ebeam_ang_x = obj.ebeamLTUAngX()
ebeam_ang_y = obj.ebeamLTUAngY()
ebeam_pos_x = obj.ebeamLTUPosX()
ebeam_pos_y = obj.ebeamLTUPosY()
ebeam_charge = obj.ebeamCharge()
add_record(values, 'photonEnergies', 'angX', ebeam_ang_x)
add_record(values, 'photonEnergies', 'angY', ebeam_ang_y)
add_record(values, 'photonEnergies', 'posX', ebeam_pos_x)
add_record(values, 'photonEnergies', 'posY', ebeam_pos_y)
add_record(values, 'photonEnergies', 'charge', ebeam_charge)
except AttributeError:
print("Couldn't translate electron beam properties from BldDataEBeam")
def patterson(evt,
type,
key,
mask=None,
threshold=None,
diameter_pix=None,
crop=None,
full_output=False,
**params):
"""TODO: missing docstring
.. note:: This feature depends on the python package `libspimage <https://github.com/FilipeMaia/libspimage>`_.
"""
success, module = utils.io.load_spimage()
if not success:
print "Skipping analysis.patterson.patterson"
return
img = evt[type][key].data
if mask is None:
mask = numpy.ones(shape=img.shape, dtype="bool")
else:
mask = numpy.array(mask, dtype="bool")
if crop is not None:
img = module.crop(img, crop)
mask = module.crop(mask, crop)
out = module.patterson(numpy.float64(img),
mask,
full_output=full_output,
normalize_median=True,
**params)
v = evt["analysis"]
if full_output:
P = abs(out[0])
info = out[1]
add_record(v, "analysis", "patterson kernel", info["kernel"], unit='')
add_record(v,
"analysis",
"patterson intensities",
info["intensities_times_kernel"],
unit='')
else:
P = abs(out)
add_record(v, "analysis", "patterson", abs(P), unit='')
if threshold is not None:
M = P > threshold
if diameter_pix is not None:
Y, X = numpy.indices(P.shape)
X -= P.shape[1] / 2
Y -= P.shape[0] / 2
Rsq = X**2 + Y**2
M *= Rsq > diameter_pix**2
if full_output:
add_record(v, "analysis", "patterson multiples", M, unit='')
multiple_score = M.sum()
add_record(v, "analysis", "multiple score", multiple_score, unit='')
def onEvent(evt):
# Processing rate [Hz]
#analysis.event.printProcessingRate()
# Calculate time and add to PlotHistory
# calculate_epoch_times(evt, evt["ID"]["tv_sec"], evt["ID"]["tv_usec"])
# plotting.line.plotHistory(evt['ID']['timeAgo'], label='Event Time (s)', group='ID')
# plotting.line.plotHistory(evt['ID']['tv_sec'], label='Epoch Time (s)', group='ID')
detector_type = "photonPixelDetectors"
detector_key = "pnCCD"
if move_half:
detector = evt[detector_type][detector_key]
detector = analysis.pixel_detector.moveHalf(evt,
detector,
horizontal=int(gap_total /
pixel_size),
outkey='data_half-moved')
mask_center_s = analysis.pixel_detector.moveHalf(
evt,
add_record(evt["analysis"], "analysis", "mask", mask_center),
horizontal=int(gap_total / pixel_size),
outkey='mask_half-moved').data
detector_type = "analysis"
detector_key = "data_half-moved"
# Do basic hitfinding using lit pixels
analysis.hitfinding.countLitPixels(evt,
evt[detector_type][detector_key],
aduThreshold=aduThreshold,
hitscoreThreshold=hitScoreThreshold,
mask=mask_center_s)
hit = bool(evt["analysis"]["litpixel: isHit"].data)
strong_hit = evt["analysis"][
"litpixel: hitscore"].data > strong_hit_threshold
plotting.line.plotHistory(add_record(
evt["analysis"], "analysis", "total ADUs",
evt[detector_type][detector_key].data.sum()),
label='Total ADU',
hline=hitScoreThreshold,
group='Metric',
history=1000)
plotting.line.plotHistory(evt["analysis"]["litpixel: hitscore"],
label='Nr. of lit pixels',
hline=hitScoreThreshold,
group='Metric',
history=1000)
analysis.hitfinding.hitrate(evt, hit, history=50)
plotting.line.plotHistory(add_record(evt["analysis"], "analysis", "is_hit",
hit),
label='Is hit',
group='Metric',
history=1000)
is_strong_hit = evt["analysis"]["total ADUs"].data > 10e6
if hit and is_strong_hit:
plotting.image.plotImage(evt[detector_type][detector_key],
name="pnCCD (Very Strong)",
group='Images',
mask=mask_center_s)
if scanInjector:
plotting.histogram.plotNormalizedHistogram(
evt["motorPositions"]["InjectorX"],
float(1 if hit else 0),
hmin=scanXmin,
hmax=scanXmax,
bins=scanXbins,
name="Histogram: InjectorX x Hitrate",
group="Scan injector pos",
buffer_length=1000)
plotting.histogram.plotNormalizedHistogram(
evt["motorPositions"]["InjectorZ"],
float(1 if hit else 0),
hmin=scanZmin,
hmax=scanZmax,
bins=scanZbins,
name="Histogram: InjectorZ x Hitrate",
group="Scan injector pos",
buffer_length=1000)
plotting.histogram.plotNormalizedHistogram(
evt["motorPositions"]["ManualY"],
float(1 if hit else 0),
hmin=scanYmin,
hmax=scanYmax,
bins=scanYbins,
name="Histogram: ManualY x Hitrate",
group="Scan injector pos",
buffer_length=1000)
plotting.histogram.plotNormalizedHistogram(
evt["motorPositions"]["InjectorSamplePressure"],
float(1 if hit else 0),
hmin=50,
hmax=300,
bins=50,
name="Histogram: InjectorSamplePressure x Hitrate",
group="Scan injector pressure",
buffer_length=1000)
plotting.histogram.plotNormalizedHistogram(
evt["motorPositions"]["InjectorNozzlePressure"],
float(1 if hit else 0),
hmin=50,
hmax=300,
bins=50,
name="Histogram: InjectorNozzlePressure x Hitrate",
group="Scan injector pressure",
buffer_length=1000)
plotting.histogram.plotNormalizedHistogram(
evt["motorPositions"]["InjectorFocusingGas"],
float(1 if hit else 0),
hmin=50,
hmax=300,
bins=50,
name="Histogram: InjectorFocusingGas x Hitrate",
group="Scan injector pressure",
buffer_length=1000)
plotting.histogram.plotNormalizedHistogram(
evt["motorPositions"]["InjectorPressure"],
float(1 if hit else 0),
hmin=50,
hmax=300,
bins=50,
name="Histogram: InjectorPressure x Hitrate",
group="Scan",
buffer_length=1000)
plotting.correlation.plotScatter(evt["motorPositions"]["InjectorX"],
evt['analysis']['litpixel: hitscore'],
name='InjectorX vs Hitscore',
xlabel='InjectorX',
ylabel='Hit Score',
group='Scan injector pos')
plotting.correlation.plotScatter(evt["motorPositions"]["InjectorZ"],
evt['analysis']['litpixel: hitscore'],
name='InjectorZ vs Hitscore',
xlabel='InjectorZ',
ylabel='Hit Score',
group='Scan injector pos')
plotting.correlation.plotScatter(evt["motorPositions"]["ManualY"],
evt['analysis']['litpixel: hitscore'],
name='ManualY vs Hitscore',
xlabel='ManualY',
ylabel='Hit Score',
group='Scan injector pos')
plotting.correlation.plotScatter(
evt["motorPositions"]["InjectorSamplePressure"],
evt['analysis']['litpixel: hitscore'],
name='InjectorSamplePressure vs Hitscore',
xlabel='InjectorSamplePressure',
ylabel='Hit Score',
group='Scan injector pressure')
plotting.correlation.plotScatter(
evt["motorPositions"]["InjectorNozzlePressure"],
evt['analysis']['litpixel: hitscore'],
name='InjectorNozzlePressure vs Hitscore',
xlabel='InjectorNozzlePressure',
ylabel='Hit Score',
group='Scan injector pressure')
plotting.correlation.plotScatter(
evt["motorPositions"]["InjectorFocusingGas"],
evt['analysis']['litpixel: hitscore'],
name='InjectorFocusingGas vs Hitscore',
xlabel='InjectorFocusingGas',
ylabel='Hit Score',
group='Scan injector presssure')
plotting.correlation.plotScatter(
evt["motorPositions"]["InjectorPressure"],
evt['analysis']['litpixel: hitscore'],
name='InjectorPressure vs Hitscore',
xlabel='InjectorPressure',
ylabel='Hit Score',
group='Scan injector pressure')
plotting.line.plotHistory(evt["motorPositions"]["InjectorX"],
label="InjectorX",
group="Scan injector pos")
plotting.line.plotHistory(evt["motorPositions"]["InjectorZ"],
label="InjectorZ",
group="Scan injector pos")
if outputEveryImage:
plotting.image.plotImage(evt[detector_type][detector_key],
name="pnCCD (All)",
group='Images',
mask=mask_center_s)
if ipc.mpi.is_main_worker():
plotting.line.plotHistory(evt["analysis"]["hitrate"],
label='Hit rate [%]',
group='Metric',
history=10000)
# plotting.correlation.plotMeanMap(evt['motorPositions']['nozzle_x'], evt['motorPositions']['nozzle_y'],
# #evt['analysis']['litpixel: hitscore'].data / 1e5,
# evt['analysis']['hitrate'].data,
# xmin=0.68, xmax=0.72, ymin=4.20, ymax=4.23,
# name='Hitscore mean map vs nozzle_xy',
# xlabel='nozzle_x (mm)',
# ylabel='nozzle_y (mm)',
# group='Metric')
if hit:
plotting.image.plotImage(evt[detector_type][detector_key],
name="pnCCD (log Hits)",
group='Images',
mask=mask_center_s,
log=True)
plotting.image.plotImage(evt[detector_type][detector_key],
name="pnCCD (Hits)",
group='Images',
mask=mask_center_s,
log=False)
if do_sizing:
# Crop to 1024 x 1024
Nx, Ny = np.shape(evt[detector_type][detector_key].data)
diff_y = Ny - 1024
cropped_img = evt[detector_type][detector_key].data[:, diff_y /
2:-(diff_y /
2)]
add_record(evt["analysis"], "analysis", "data-cropped",
cropped_img)
detector_key = "data-cropped"
cropped_mask = mask_center_s[:, diff_y / 2:-(diff_y / 2)]
add_record(evt["analysis"], "analysis", "mask-cropped",
cropped_mask)
mask_center_fit_s = evt['analysis']['mask-cropped'].data
# Binning
analysis.pixel_detector.bin(evt, detector_type, detector_key,
binning, mask_center_fit_s)
mask_binned = evt["analysis"]["binned mask - " + detector_key].data
detector_type_b = "analysis"
detector_key_b = "binned image - " + detector_key
# CENTER DETERMINATION
analysis.sizing.findCenter(evt,
detector_type_b,
detector_key_b,
mask=mask_binned,
**centerParams)
# RADIAL AVERAGE
analysis.pixel_detector.radial(evt,
detector_type_b,
detector_key_b,
mask=mask_binned,
cx=evt["analysis"]["cx"].data,
cy=evt["analysis"]["cy"].data)
# FIT SPHERE MODEL
analysis.sizing.fitSphereRadial(
evt, "analysis", "radial distance - " + detector_key_b,
"radial average - " + detector_key_b,
**dict(modelParams, **sizingParams))
# DIFFRACTION PATTERN FROM FIT
analysis.sizing.sphereModel(evt,
"analysis",
"offCenterX",
"offCenterY",
"diameter",
"intensity",
(ny / binning, nx / binning),
poisson=False,
**modelParams)
# RADIAL AVERAGE FIT
analysis.pixel_detector.radial(evt,
"analysis",
"fit",
mask=mask_binned,
cx=evt["analysis"]["cx"].data,
cy=evt["analysis"]["cy"].data)
# ERRORS
#analysis.sizing.photon_error(evt, detector_type_b, detector_key_b, "analysis", "fit", adu_per_photon=144.)
#analysis.sizing.absolute_error(evt, detector_type_b, detector_key_b, "analysis", "fit", "absolute error")
msg = "diameter: %.2f nm \nIntensity: %.4f mJ/um2\nFit Error: %.2e" % (
evt["analysis"]["diameter"].data,
evt["analysis"]["intensity"].data,
evt["analysis"]["fit error"].data)
# Selection of good fits
small_fit_error = evt['analysis'][
'fit error'].data < fit_error_threshold
#small_photon_error = evt['analysis']['photon error'].data < photon_error_threshold
correctsized_hit = small_fit_error #and small_photon_error
# Select only events in a certain diameter window
diameter = evt['analysis']['diameter'].data
#print diameter
plotting.histogram.plotHistogram(evt["analysis"]["diameter"],
bins=100,
name="Histogram: Particle size",
group="Sizing",
hmin=20,
hmax=100,
buffer_length=1000)
correctsized_hit &= ((diameter > diameter_min) &
(diameter < diameter_max))
# Plot errors
plotting.line.plotHistory(evt["analysis"]["fit error"],
history=1000,
hline=fit_error_threshold,
group='Sizing')
#plotting.line.plotHistory(evt["analysis"]["photon error"], history=1000, hline=photon_error_threshold, group='Sizing')
#plotting.line.plotHistory(evt["analysis"]["absolute error"], history=1000, group='Sizing')
#time.sleep(0.05)
if do_showhybrid:
# HYBRID PATTERN
hybrid = evt["analysis"]["fit"].data.copy()
hybrid[:, 512 / binning:] = evt[detector_type_b][
detector_key_b].data[:, 512 / binning:]
add_record(evt["analysis"], "analysis", "Hybrid pattern",
hybrid)
plotting.image.plotImage(evt["analysis"]["Hybrid pattern"],
name="Hybrid pattern ",
msg=msg,
group='Sizing')
plotting.image.plotImage(evt["analysis"]["Hybrid pattern"],
name="Hybrid pattern / log",
vmax=1e4,
log=True,
msg=msg,
group='Sizing')
if correctsized_hit:
# Plot Correct sized hits
plotting.image.plotImage(evt[detector_type][detector_key],
group='Sizing',
msg=msg,
name="pnCCD front (correct hit)",
mask=mask_center_fit_s)
if strong_hit:
plotting.image.plotImage(
evt[detector_type][detector_key],
group='Sizing',
msg=msg,
name="pnCCD front (correct and strong hit)",
mask=mask_center_fit_s)
# Plot Intensity
plotting.line.plotHistory(evt["analysis"]["intensity"],
history=10000,
name='Intensity (from sizing)',
group='Results')
# Plot size (in nm)
plotting.line.plotHistory(evt["analysis"]["diameter"],
history=10000,
name='Size in nm (from sizing)',
group='Results')
# Normalizing intensity to pulse energy (assuming 1mJ on average)
#intensity_normalized = (evt['analysis']['intensity'].data / evt['analysis']['averagePulseEnergy'].data) * 1.0
#add_record(evt['analysis'], 'analysis', 'intensity_normalized', intensity_normalized)
# Plot Intensity (normalized)
plotting.line.plotHistory(
evt['analysis']['intensity'],
history=10000,
name='Intensity normalized (from sizing)',
group='Results')
# Center position
#plotting.correlation.plotMeanMap(evt["analysis"]["cx"], evt["analysis"]["cy"], intensity_normalized, group='Results',name='Wavefront (center vs. intensity)', xmin=-10, xmax=10, xbins=21, ymin=-10, ymax=10, ybins=21, xlabel='Center position in x', ylabel='Center position in y')
if hit and do_patterson:
analysis.patterson.patterson(evt,
detector_type,
detector_key,
mask_center_s,
threshold=patterson_threshold,
diameter_pix=patterson_diameter,
xgap_pix=patterson_xgap_pix,
ygap_pix=patterson_ygap_pix,
frame_pix=patterson_frame_pix,
crop=512,
full_output=True,
**patterson_params)
plotting.line.plotHistory(evt["analysis"]["multiple score"],
history=1000,
name='Multiscore',
group='Holography',
hline=multiScoreThreshold)
#print evt["analysis"]["multiple score"].data, multiScoreThreshold
multiple_hit = evt["analysis"][
"multiple score"].data > multiScoreThreshold
if multiple_hit:
plotting.image.plotImage(evt["analysis"]["patterson"],
group="Holography",
name="Patterson (multiple hits)")
plotting.image.plotImage(evt["analysis"]["patterson multiples"],
group="Holography",
name="Patterson mask (multiple hits)")
plotting.image.plotImage(evt[detector_type][detector_key],
group="Holography",
name="Multiple hits (image)",
mask=mask_center_s)
analysis.refocus_hologram.refocus_hologram_evt(
evt, detector_type, detector_key)
if do_hologram and evt["analysis"]["hologram_score"]:
plotting.image.plotImage(evt["analysis"]["focused_CC"],
group="Holography",
name="refocused Hologram (image)")
else:
plotting.image.plotImage(evt["analysis"]["patterson"],
group="Holography",
name="Patterson (non-multiple hits)")
if not hit and do_patterson:
multiple_hit = False
if do_patterson:
analysis.hitfinding.hitrate(evt,
multiple_hit,
history=50,
outkey='multiple_hitrate')
if scanInjector:
plotting.histogram.plotNormalizedHistogram(
evt["motorPositions"]["InjectorX"],
float(1 if multiple_hit else 0),
hmin=scanXmin,
hmax=scanXmax,
bins=scanXbins,
name="Histogram: InjectorX x Multiple hitrate",
group="Scan injector pos",
buffer_length=1000)
plotting.histogram.plotNormalizedHistogram(
evt["motorPositions"]["InjectorZ"],
float(1 if multiple_hit else 0),
hmin=scanZmin,
hmax=scanZmax,
bins=scanZbins,
name="Histogram: InjectorZ x Multiple hitrate",
group="Scan injector pos",
buffer_length=1000)
if ipc.mpi.is_main_worker():
plotting.line.plotHistory(evt["analysis"]["multiple_hitrate"],
label='Multiple Hit rate [%]',
group='Metric',
history=10000)
non_hitrate = max(1. - (evt["analysis"]["hitrate"].data / 100.),
0.01)
multi_hitrate = (evt["analysis"]["multiple_hitrate"].data / 100.)
hitrate_corrected_poisson = multi_hitrate / (
0.25 * np.exp(-2.) * non_hitrate * np.log(non_hitrate)**2)
#print "%f/%f/%.4f" %(non_hitrate,multi_hitrate,hitrate_corrected_poisson)
e = add_record(evt['analysis'], "analysis",
"multiple hitrate (corrected)",
hitrate_corrected_poisson)
plotting.line.plotHistory(
e,
label='Multiple hitrate (poisson corrected)',
group='Holography',
history=10000)