def test_pnccd_vs_geometry_access(self):
# ---- get the geometry Mikhail-style
try:
from PSCalib.GeometryAccess import GeometryAccess
ga = GeometryAccess('ref_files/pnccd.data')
xyz_old = ga.get_pixel_coords()
except:
# if that don't work, load a pre-saved answer
print 'could not use GeometryAccess, loading saved xyz'
xyz_old = np.load('ref_files/pnccd_saved.npy')
xyz_old = np.rollaxis(np.array(xyz_old), 0, 6) # send 0 --> end
xyz_old = np.squeeze(xyz_old)
geom = camera.CompoundAreaCamera.from_psana_file(
'ref_files/pnccd.data')
xyz_new = np.squeeze(geom.xyz)
assert xyz_new.shape == xyz_old.shape, 'shape mismatch'
err = np.sum(np.abs(xyz_new - xyz_old)) / float(
np.product(xyz_new.shape))
print 'Mean Absolute Error: %f um / px' % err
num_more_than_1px_err = np.sum(np.abs(xyz_new - xyz_old) > 75.0)
assert err < 10.0, 'error greater than 10um avg per px (%f)' % err
assert num_more_than_1px_err < 7500, '>7500 pix w err > 1 px'
def test_xyz_vs_old_implementation(self):
# ---- get the geometry Mikhail-style
try:
from PSCalib.GeometryAccess import GeometryAccess
ga = GeometryAccess('ref_files/refgeom_psana.data')
xyz_old = ga.get_pixel_coords()
except:
# if that don't work, load a pre-saved answer
print 'could not use GeometryAccess, loading saved xyz'
xyz_old = np.load('ref_files/GA_saved_1-end.npy')
# some np-foo to move the 3-d x,y,z axis from first dim to last
xyz_old = np.rollaxis(np.array(xyz_old), 0, 7) # send 0 --> 7
xyz_old = np.squeeze(xyz_old)
geom = camera.CompoundCamera.from_psana_file(
'ref_files/refgeom_psana.data')
xyz_new = np.squeeze(geom.xyz)
assert xyz_new.shape == xyz_old.shape, 'shape mismatch'
err = np.sum(np.abs(xyz_new - xyz_old)) / float(
np.product(xyz_new.shape))
print 'Mean Absolute Error: %f um / px' % err
num_more_than_1px_err = np.sum(np.abs(xyz_new - xyz_old) > 109.92)
assert err < 10.0, 'error greater than 10um avg per px (%f)' % err
assert num_more_than_1px_err < 7500, '>7500 pix w err > 1 px'
def data_geo(ntest) :
"""Returns test data numpy array and geometry object
"""
from time import time
from PSCalib.NDArrIO import save_txt, load_txt
from PSCalib.GeometryAccess import GeometryAccess
dir = '/reg/g/psdm/detector/alignment/cspad/calib-cxi-camera2-2016-02-05'
#fname_nda = '%s/nda-water-ring-cxij4716-r0022-e000001-CxiDs2-0-Cspad-0-ave.txt' % dir
fname_nda = '%s/nda-water-ring-cxij4716-r0022-e014636-CxiDs2-0-Cspad-0-ave.txt' % dir
fname_geo = '%s/calib/CsPad::CalibV1/CxiDs2.0:Cspad.0/geometry/geo-cxi01516-2016-02-18-Ag-behenate-tuned.data' % dir
#fname_geo = '%s/geo-cxi02416-r0010-2016-03-11.txt' % dir
fname_gain = '%s/calib/CsPad::CalibV1/CxiDs2.0:Cspad.0/pixel_gain/cxi01516-r0016-2016-02-18-FeKalpha.data' % dir
# load n-d array with averaged water ring
arr = load_txt(fname_nda)
#arr *= load_txt(fname_gain)
#print_ndarr(arr,'water ring')
arr.shape = (arr.size,) # (32*185*388,)
# retrieve geometry
t0_sec = time()
geo = GeometryAccess(fname_geo)
geo.move_geo('CSPAD:V1', 0, 1600, 0, 0)
geo.move_geo('QUAD:V1', 2, -100, 0, 0)
#geo.get_geo('QUAD:V1', 3).print_geo()
print 'Time to load geometry %.3f sec from file\n%s' % (time()-t0_sec, fname_geo)
return arr, geo
def test_xyz_vs_old_implementation(self):
# ---- get the geometry Mikhail-style
try:
from PSCalib.GeometryAccess import GeometryAccess
ga = GeometryAccess('ref_files/refgeom_psana.data')
xyz_old = ga.get_pixel_coords()
except:
# if that don't work, load a pre-saved answer
print('could not use GeometryAccess, loading saved xyz')
xyz_old = np.load('ref_files/GA_saved_1-end.npy')
# some np-foo to move the 3-d x,y,z axis from first dim to last
xyz_old = np.rollaxis(np.array(xyz_old), 0, 7) # send 0 --> 7
xyz_old = np.squeeze(xyz_old)
geom = camera.CompoundCamera.from_psana_file('ref_files/refgeom_psana.data')
xyz_new = np.squeeze(geom.xyz)
assert xyz_new.shape == xyz_old.shape, 'shape mismatch'
err = np.sum( np.abs(xyz_new - xyz_old) ) / float(np.product(xyz_new.shape))
print('Mean Absolute Error: %f um / px' % err)
num_more_than_1px_err = np.sum( np.abs(xyz_new - xyz_old) > 109.92 )
assert err < 10.0, 'error greater than 10um avg per px (%f)' % err
assert num_more_than_1px_err < 7500, '>7500 pix w err > 1 px'
def test_pnccd_vs_geometry_access(self):
# ---- get the geometry Mikhail-style
try:
from PSCalib.GeometryAccess import GeometryAccess
ga = GeometryAccess('ref_files/pnccd.data')
xyz_old = ga.get_pixel_coords()
except:
# if that don't work, load a pre-saved answer
print('could not use GeometryAccess, loading saved xyz')
xyz_old = np.load('ref_files/pnccd_saved.npy')
xyz_old = np.rollaxis(np.array(xyz_old), 0, 6) # send 0 --> end
xyz_old = np.squeeze(xyz_old)
geom = camera.CompoundAreaCamera.from_psana_file('ref_files/pnccd.data')
xyz_new = np.squeeze(geom.xyz)
assert xyz_new.shape == xyz_old.shape, 'shape mismatch'
err = np.sum( np.abs(xyz_new - xyz_old) ) / float(np.product(xyz_new.shape))
print('Mean Absolute Error: %f um / px' % err)
num_more_than_1px_err = np.sum( np.abs(xyz_new - xyz_old) > 75.0 )
assert err < 10.0, 'error greater than 10um avg per px (%f)' % err
assert num_more_than_1px_err < 7500, '>7500 pix w err > 1 px'
def __init__(self, geom_file):
"""
Initialize instance of Detector class.
:param geom_file: path to *-end.data geometry file
"""
from PSCalib.GeometryAccess import GeometryAccess
self._geometry = GeometryAccess(geom_file, 0)
self._compute_pixel_index_map()
def setupRadialBackground(self):
self.findPsanaGeometry()
if self.calibFile is not None:
self.geo = GeometryAccess(self.calibPath+'/'+self.calibFile)
self.xarr, self.yarr, self.zarr = self.geo.get_pixel_coords()
self.iX, self.iY = self.geo.get_pixel_coord_indexes()
self.mask = self.geo.get_pixel_mask(mbits=0377) # mask for 2x1 edges, two central columns, and unbound pixels with their neighbours
self.rb = RadialBkgd(self.xarr, self.yarr, mask=self.mask, radedges=None, nradbins=100, phiedges=(0, 360), nphibins=1)
else:
self.rb = None
def on_but_roi_convert(self):
self.setStatus(1, 'Convert image to ndarray')
mcbits = self.sensor_mask_cbits.value()
gfname = self.fname_geometry.value()
ifname = self.fname_roi_mask_img.value()
ofname = self.fname_roi_mask_nda.value()
tfname = self.fname_roi_mask_nda_tst.value()
msg = '\n Convert ROI mask image: %s\n to ndarray: %s\n using geometry: %s' % \
( ifname, ofname, gfname )
logger.info(msg, __name__)
geometry = GeometryAccess(gfname, 0)
iX, iY = geometry.get_pixel_coord_indexes()
msg = 'Pixel index array iX, iY shapes: %s, %s' % (str(
iX.shape), str(iY.shape))
logger.info(msg, __name__)
ifext = os.path.splitext(ifname)[1]
ofext = os.path.splitext(ofname)[1]
mask_roi = np.load(ifname) if ifext == '.npy' else np.loadtxt(
ifname, dtype=np.uint16)
mask_nda = np.array([mask_roi[r, c]
for r, c in zip(iX, iY)]) # 155 msec
if mcbits: mask_nda *= geometry.get_pixel_mask(mbits=mcbits)
img_mask_test = img_from_pixel_arrays(iX, iY, W=mask_nda)
if ofext == '.npy': np.save(ofname, mask_nda)
else:
mask_nda.shape = [iX.size / iX.shape[-1], iX.shape[-1]]
logger.info(
'Mask ndarray is re-shape for saving in txt to 2-d: %s' %
str(mask_nda.shape), __name__)
np.savetxt(ofname, mask_nda, fmt='%d', delimiter=' ')
logger.info('Mask ndarray is saved in the file %s' % ofname, __name__)
self.setStatus(1, 'Test: reconstruct image from mask ndarray...')
tfext = os.path.splitext(tfname)[1]
if tfext == '.npy': np.save(tfname, img_mask_test)
else: np.savetxt(tfname, img_mask_test, fmt='%d', delimiter=' ')
logger.info(
'Test-image generated from mask ndarray is saved in file %s' %
tfname, __name__)
self.setStatus(0)
def on_but_roi_convert (self):
self.setStatus(1, 'Convert image to ndarray')
mcbits = self.sensor_mask_cbits.value()
gfname = self.fname_geometry.value()
ifname = self.fname_roi_mask_img.value()
ofname = self.fname_roi_mask_nda.value()
tfname = self.fname_roi_mask_nda_tst.value()
msg = '\n Convert ROI mask image: %s\n to ndarray: %s\n using geometry: %s' % \
( ifname, ofname, gfname )
logger.info(msg, __name__)
geometry = GeometryAccess(gfname, 0)
iX, iY = geometry.get_pixel_coord_indexes()
msg = 'Pixel index array iX, iY shapes: %s, %s' % (str(iX.shape), str(iY.shape))
logger.info(msg, __name__)
ifext = os.path.splitext(ifname)[1]
ofext = os.path.splitext(ofname)[1]
mask_roi = np.load(ifname) if ifext == '.npy' else np.loadtxt(ifname, dtype=np.uint16)
mask_nda = np.array( [mask_roi[r,c] for r,c in zip(iX, iY)] ) # 155 msec
if mcbits : mask_nda *= geometry.get_pixel_mask(mbits=mcbits)
img_mask_test = img_from_pixel_arrays(iX, iY, W=mask_nda)
if ofext == '.npy' : np.save(ofname, mask_nda)
else :
mask_nda.shape = [iX.size/iX.shape[-1],iX.shape[-1]]
logger.info('Mask ndarray is re-shape for saving in txt to 2-d: %s' % str(mask_nda.shape), __name__)
np.savetxt(ofname, mask_nda, fmt='%d', delimiter=' ')
logger.info('Mask ndarray is saved in the file %s' % ofname, __name__)
self.setStatus(1, 'Test: reconstruct image from mask ndarray...')
tfext = os.path.splitext(tfname)[1]
if tfext == '.npy' : np.save(tfname, img_mask_test)
else : np.savetxt(tfname, img_mask_test, fmt='%d', delimiter=' ')
logger.info('Test-image generated from mask ndarray is saved in file %s' % tfname, __name__)
self.setStatus(0)
def setupRadialBackground(self):
self.findPsanaGeometry()
if self.calibFile is not None:
self.geo = GeometryAccess(self.calibPath+'/'+self.calibFile)
self.xarr, self.yarr, self.zarr = self.geo.get_pixel_coords()
self.iX, self.iY = self.geo.get_pixel_coord_indexes()
self.mask = self.geo.get_pixel_mask(mbits=0377) # mask for 2x1 edges, two central columns, and unbound pixels with their neighbours
self.rb = RadialBkgd(self.xarr, self.yarr, mask=self.mask, radedges=None, nradbins=100, phiedges=(0, 360), nphibins=1)
else:
self.rb = None
def reco_image_from_ndarray (self, gfname, afname):
#mcbits = self.sensor_mask_cbits.value()
msg = 'Reconstruct image from\n geometry: %s\n and ndarray: %s' % \
( gfname, afname )
logger.info(msg, __name__)
geometry = GeometryAccess(gfname, 0)
iX, iY = geometry.get_pixel_coord_indexes()
afext = '' if afname is None else os.path.splitext(afname)[1]
nda = np.ones(iX.shape, dtype=np.uint16) if afname is None else \
np.load(afname) if afext == '.npy' else \
np.loadtxt(afname) #, dtype=np.uint16)
nda.shape = iX.shape
#if mcbits : nda *= geometry.get_pixel_mask(mbits=mcbits)
return img_from_pixel_arrays(iX, iY, W=nda)
def reco_image_from_ndarray(self, gfname, afname):
#mcbits = self.sensor_mask_cbits.value()
msg = 'Reconstruct image from\n geometry: %s\n and ndarray: %s' % \
( gfname, afname )
logger.info(msg, __name__)
geometry = GeometryAccess(gfname, 0)
iX, iY = geometry.get_pixel_coord_indexes()
afext = '' if afname is None else os.path.splitext(afname)[1]
nda = np.ones(iX.shape, dtype=np.uint16) if afname is None else \
np.load(afname) if afext == '.npy' else \
np.loadtxt(afname) #, dtype=np.uint16)
nda.shape = iX.shape
#if mcbits : nda *= geometry.get_pixel_mask(mbits=mcbits)
return img_from_pixel_arrays(iX, iY, W=nda)
def data_geo(ntest):
"""Returns test data numpy array and geometry object
"""
from time import time
from PSCalib.NDArrIO import save_txt, load_txt
from PSCalib.GeometryAccess import GeometryAccess
dir = '/reg/g/psdm/detector/alignment/cspad/calib-cxi-camera2-2016-02-05'
#fname_nda = '%s/nda-water-ring-cxij4716-r0022-e000001-CxiDs2-0-Cspad-0-ave.txt' % dir
fname_nda = '%s/nda-water-ring-cxij4716-r0022-e014636-CxiDs2-0-Cspad-0-ave.txt' % dir
fname_geo = '%s/calib/CsPad::CalibV1/CxiDs2.0:Cspad.0/geometry/geo-cxi01516-2016-02-18-Ag-behenate-tuned.data' % dir
#fname_geo = '%s/geo-cxi02416-r0010-2016-03-11.txt' % dir
fname_gain = '%s/calib/CsPad::CalibV1/CxiDs2.0:Cspad.0/pixel_gain/cxi01516-r0016-2016-02-18-FeKalpha.data' % dir
# load n-d array with averaged water ring
arr = load_txt(fname_nda)
#arr *= load_txt(fname_gain)
#print_ndarr(arr,'water ring')
arr.shape = (arr.size, ) # (32*185*388,)
# retrieve geometry
t0_sec = time()
geo = GeometryAccess(fname_geo)
geo.move_geo('CSPAD:V1', 0, 1600, 0, 0)
geo.move_geo('QUAD:V1', 2, -100, 0, 0)
#geo.get_geo('QUAD:V1', 3).print_geo()
print 'Time to load geometry %.3f sec from file\n%s' % (time() - t0_sec,
fname_geo)
return arr, geo
def read_slac_metrology(path = None, geometry = None, plot=False, include_asic_offset=False):
if path is None and geometry is None:
raise Sorry("Need to provide a geometry object or a path to a geometry file")
if path is not None and geometry is not None:
raise Sorry("Cannot provide a geometry object and a geometry file. Ambiguous")
if geometry is None:
try:
from PSCalib.GeometryAccess import GeometryAccess
geometry = GeometryAccess(path)
except Exception, e:
raise Sorry("Can't parse this metrology file")
class psanaWhisperer():
def __init__(self,
experimentName,
runNumber,
detInfo,
clen='',
aduPerPhoton=1,
localCalib=False):
self.experimentName = experimentName
self.runNumber = runNumber
self.detInfo = detInfo
self.clenStr = clen
self.aduPerPhoton = aduPerPhoton
self.localCalib = localCalib
def setupExperiment(self):
self.ds = psana.DataSource('exp=' + str(self.experimentName) +
':run=' + str(self.runNumber) + ':idx')
self.run = self.ds.runs().next()
self.times = self.run.times()
self.eventTotal = len(self.times)
self.env = self.ds.env()
self.evt = self.run.event(self.times[0])
self.det = psana.Detector(str(self.detInfo), self.env)
self.det.do_reshape_2d_to_3d(flag=True)
self.getDetInfoList()
self.detAlias = self.getDetectorAlias(str(self.detInfo))
self.updateClen() # Get epics variable, clen
def updateClen(self):
if 'cspad' in self.detInfo.lower() and 'cxi' in self.experimentName:
self.epics = self.ds.env().epicsStore()
self.clen = self.epics.value(self.clenStr)
elif 'rayonix' in self.detInfo.lower(
) and 'mfx' in self.experimentName:
self.epics = self.ds.env().epicsStore()
self.clen = self.epics.value(self.clenStr)
elif 'rayonix' in self.detInfo.lower(
) and 'xpp' in self.experimentName:
self.epics = self.ds.env().epicsStore()
self.clen = self.epics.value(self.clenStr)
def getDetectorAlias(self, srcOrAlias):
for i in self.detInfoList:
src, alias, _ = i
if srcOrAlias.lower() == src.lower() or srcOrAlias.lower(
) == alias.lower():
return alias
def getDetInfoList(self):
myAreaDetectors = []
self.detnames = psana.DetNames()
for k in self.detnames:
try:
if Detector.PyDetector.dettype(str(
k[0]), self.env) == Detector.AreaDetector.AreaDetector:
myAreaDetectors.append(k)
except ValueError:
continue
self.detInfoList = list(set(myAreaDetectors))
def getEvent(self, number):
self.evt = self.run.event(self.times[number])
def getImg(self, number):
self.getEvent(number)
img = self.det.image(self.evt, self.det.calib(self.evt))
return img
def getImg(self):
if self.evt is not None:
img = self.det.image(self.evt, self.det.calib(self.evt))
return img
return None
def getCheetahImg(self, calib=None):
"""Converts seg, row, col assuming (32,185,388)
to cheetah 2-d table row and col (8*185, 4*388)
"""
if 'cspad2x2' in self.detInfo.lower():
print "Not implemented yet: cspad2x2"
elif 'cspad' in self.detInfo.lower():
if calib is None: calib = self.det.calib(self.evt) # (32,185,388)
img = np.zeros((8 * 185, 4 * 388))
counter = 0
for quad in range(4):
for seg in range(8):
img[seg * 185:(seg + 1) * 185,
quad * 388:(quad + 1) * 388] = calib[counter, :, :]
counter += 1
elif 'rayonix' in self.detInfo.lower():
if calib is None:
img = np.squeeze(self.det.calib(self.evt)) # (1920,1920)
else:
img = np.squeeze(calib)
return img
def getCleanAssembledImg(self, backgroundEvent):
"""Returns psana assembled image
"""
backgroundEvt = self.run.event(self.times[backgroundEvent])
backgroundCalib = self.det.calib(backgroundEvt)
calib = self.det.calib(self.evt)
cleanCalib = calib - backgroundCalib
img = self.det.image(self.evt, cleanCalib)
return img
def getAssembledImg(self):
"""Returns psana assembled image
"""
img = self.det.image(self.evt)
return img
def getCalibImg(self):
"""Returns psana assembled image
"""
img = self.det.calib(self.evt)
return img
def getCleanAssembledPhotons(self, backgroundEvent):
"""Returns psana assembled image in photon counts
"""
backgroundEvt = self.run.event(self.times[backgroundEvent])
backgroundCalib = self.det.calib(backgroundEvt)
calib = self.det.calib(self.evt)
cleanCalib = calib - backgroundCalib
img = self.det.photons(self.evt,
nda_calib=cleanCalib,
adu_per_photon=self.aduPerPhoton)
phot = self.det.image(self.evt, img)
return phot
def getAssembledPhotons(self):
"""Returns psana assembled image in photon counts
"""
img = self.det.photons(self.evt, adu_per_photon=self.aduPerPhoton)
phot = self.det.image(self.evt, img)
return phot
def getPsanaEvent(self, cheetahFilename):
# Gets psana event given cheetahFilename, e.g. LCLS_2015_Jul26_r0014_035035_e820.h5
hrsMinSec = cheetahFilename.split('_')[-2]
fid = int(cheetahFilename.split('_')[-1].split('.')[0], 16)
for t in self.times:
if t.fiducial() == fid:
localtime = time.strftime('%H:%M:%S',
time.localtime(t.seconds()))
localtime = localtime.replace(':', '')
if localtime[0:3] == hrsMinSec[0:3]:
self.evt = self.run.event(t)
else:
self.evt = None
def getStartTime(self):
self.evt = self.run.event(self.times[0])
evtId = self.evt.get(psana.EventId)
sec = evtId.time()[0]
nsec = evtId.time()[1]
fid = evtId.fiducials()
return time.strftime('%FT%H:%M:%S-0800',
time.localtime(sec)) # Hard-coded pacific time
#####################################################################
# TODO: Functions below are not being used yet
#####################################################################
def findPsanaGeometry(self):
try:
self.source = psana.Detector.PyDetector.map_alias_to_source(
self.detInfo, self.ds.env()) # 'DetInfo(CxiDs2.0:Cspad.0)'
self.calibSource = self.source.split('(')[-1].split(')')[
0] # 'CxiDs2.0:Cspad.0'
self.detectorType = gu.det_type_from_source(self.source) # 1
self.calibGroup = gu.dic_det_type_to_calib_group[
self.detectorType] # 'CsPad::CalibV1'
self.detectorName = gu.dic_det_type_to_name[
self.detectorType].upper() # 'CSPAD'
if self.localCalib:
self.calibPath = "./calib/" + self.calibGroup + "/" + self.calibSource + "/geometry"
else:
self.calibPath = "/reg/d/psdm/" + self.parent.experimentName[0:3] + \
"/" + self.parent.experimentName + "/calib/" + \
self.calibGroup + "/" + self.calibSource + "/geometry"
# Determine which calib file to use
geometryFiles = os.listdir(self.calibPath)
self.calibFile = None
minDiff = -1e6
for fname in geometryFiles:
if fname.endswith('.data'):
endValid = False
startNum = int(fname.split('-')[0])
endNum = fname.split('-')[-1].split('.data')[0]
diff = startNum - self.parent.runNumber
# Make sure it's end number is valid too
if 'end' in endNum:
endValid = True
else:
try:
if self.parent.runNumber <= int(endNum):
endValid = True
except:
continue
if diff <= 0 and diff > minDiff and endValid is True:
minDiff = diff
self.calibFile = fname
except:
if self.parent.args.v >= 1: print "Couldn't find psana geometry"
self.calibFile = None
def setupRadialBackground(self):
self.findPsanaGeometry()
if self.calibFile is not None:
self.geo = GeometryAccess(self.calibPath + '/' + self.calibFile)
self.xarr, self.yarr, self.zarr = self.geo.get_pixel_coords()
self.iX, self.iY = self.geo.get_pixel_coord_indexes()
self.mask = self.geo.get_pixel_mask(
mbits=0377
) # mask for 2x1 edges, two central columns, and unbound pixels with their neighbours
self.rb = RadialBkgd(self.xarr,
self.yarr,
mask=self.mask,
radedges=None,
nradbins=100,
phiedges=(0, 360),
nphibins=1)
else:
self.rb = None
def updatePolarizationFactor(self, detectorDistance_in_m):
if self.rb is not None:
self.pf = polarization_factor(self.rb.pixel_rad(),
self.rb.pixel_phi(),
detectorDistance_in_m *
1e6) # convert to um
def getCalib(self, evtNumber):
if self.run is not None:
self.evt = self.getEvent(evtNumber)
if self.applyCommonMode: # play with different common mode
if self.commonMode[0] == 5: # Algorithm 5
calib = self.det.calib(self.evt,
cmpars=(self.commonMode[0],
self.commonMode[1]))
else: # Algorithms 1 to 4
print "### Overriding common mode: ", self.commonMode
calib = self.det.calib(
self.evt,
cmpars=(self.commonMode[0], self.commonMode[1],
self.commonMode[2], self.commonMode[3]))
else:
calib = self.det.calib(self.evt)
return calib
else:
return None
def getPreprocessedImage(self, evtNumber, image_property):
disp_medianCorrection = 19
disp_radialCorrection = 18
disp_gainMask = 17
disp_coordy = 16
disp_coordx = 15
disp_col = 14
disp_row = 13
disp_seg = 12
disp_quad = 11
disp_gain = 10
disp_commonMode = 9
disp_rms = 8
disp_status = 7
disp_pedestal = 6
disp_photons = 5
disp_raw = 4
disp_pedestalCorrected = 3
disp_commonModeCorrected = 2
disp_adu = 1
if image_property == disp_medianCorrection: # median subtraction
print "Sorry, this feature isn't available yet"
elif image_property == disp_radialCorrection: # radial subtraction + polarization corrected
self.getEvent(evtNumber)
calib = self.getCalib(evtNumber)
if calib:
self.pf.shape = self.parent.calib.shape
calib = self.rb.subtract_bkgd(calib * self.pf)
elif image_property == disp_adu: # gain and hybrid gain corrected
calib = self.getCalib(evtNumber)
elif image_property == disp_commonModeCorrected: # common mode corrected
calib = self.getCommonModeCorrected(evtNumber)
elif image_property == disp_pedestalCorrected: # pedestal corrected
calib = self.det.raw(self.evt).astype('float32')
if calib: calib -= self.det.pedestals(self.evt)
elif image_property == disp_raw: # raw
calib = self.det.raw(self.evt)
elif image_property == disp_photons: # photon counts
calib = self.det.photons(
self.evt,
mask=self.parent.mk.userMask,
adu_per_photon=self.parent.exp.aduPerPhoton)
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed,
dtype='int32')
elif image_property == disp_pedestal: # pedestal
calib = self.parent.det.pedestals(self.parent.evt)
elif image_property == disp_status: # status
calib = self.parent.det.status(self.parent.evt)
elif image_property == disp_rms: # rms
calib = self.parent.det.rms(self.parent.evt)
elif image_property == disp_commonMode: # common mode
calib = self.getCommonMode(evtNumber)
elif image_property == disp_gain: # gain
calib = self.parent.det.gain(self.parent.evt)
elif image_property == disp_gainMask: # gain_mask
calib = self.parent.det.gain_mask(self.parent.evt)
elif image_property == disp_coordx: # coords_x
calib = self.parent.det.coords_x(self.parent.evt)
elif image_property == disp_coordy: # coords_y
calib = self.parent.det.coords_y(self.parent.evt)
shape = self.parent.det.shape(self.parent.evt)
if len(shape) == 3:
if image_property == disp_quad: # quad ind
calib = np.zeros(shape)
for i in range(shape[0]):
# FIXME: handle detectors properly
if shape[0] == 32: # cspad
calib[i, :, :] = int(i) % 8
elif shape[0] == 2: # cspad2x2
calib[i, :, :] = int(i) % 2
elif shape[0] == 4: # pnccd
calib[i, :, :] = int(i) % 4
elif image_property == disp_seg: # seg ind
calib = np.zeros(shape)
if shape[0] == 32: # cspad
for i in range(32):
calib[i, :, :] = int(i) / 8
elif shape[0] == 2: # cspad2x2
for i in range(2):
calib[i, :, :] = int(i)
elif shape[0] == 4: # pnccd
for i in range(4):
calib[i, :, :] = int(i)
elif image_property == disp_row: # row ind
calib = np.zeros(shape)
if shape[0] == 32: # cspad
for i in range(185):
calib[:, i, :] = i
elif shape[0] == 2: # cspad2x2
for i in range(185):
calib[:, i, :] = i
elif shape[0] == 4: # pnccd
for i in range(512):
calib[:, i, :] = i
elif image_property == disp_col: # col ind
calib = np.zeros(shape)
if shape[0] == 32: # cspad
for i in range(388):
calib[:, :, i] = i
elif shape[0] == 2: # cspad2x2
for i in range(388):
calib[:, :, i] = i
elif shape[0] == 4: # pnccd
for i in range(512):
calib[:, :, i] = i
class psanaWhisperer():
def __init__(self, experimentName, runNumber, detInfo, clen='', aduPerPhoton=1, localCalib=False):
self.experimentName = experimentName
self.runNumber = runNumber
self.detInfo = detInfo
self.clenStr = clen
self.aduPerPhoton = aduPerPhoton
self.localCalib = localCalib
def setupExperiment(self):
self.ds = psana.DataSource('exp=' + str(self.experimentName) + ':run=' + str(self.runNumber) + ':idx')
self.run = self.ds.runs().next()
self.times = self.run.times()
self.eventTotal = len(self.times)
self.env = self.ds.env()
self.evt = self.run.event(self.times[0])
self.det = psana.Detector(str(self.detInfo), self.env)
self.det.do_reshape_2d_to_3d(flag=True)
self.getDetInfoList()
self.detAlias = self.getDetectorAlias(str(self.detInfo))
self.updateClen() # Get epics variable, clen
def updateClen(self):
if 'cspad' in self.detInfo.lower() and 'cxi' in self.experimentName:
self.epics = self.ds.env().epicsStore()
self.clen = self.epics.value(self.clenStr)
elif 'rayonix' in self.detInfo.lower() and 'mfx' in self.experimentName:
self.epics = self.ds.env().epicsStore()
self.clen = self.epics.value(self.clenStr)
elif 'rayonix' in self.detInfo.lower() and 'xpp' in self.experimentName:
self.epics = self.ds.env().epicsStore()
self.clen = self.epics.value(self.clenStr)
def getDetectorAlias(self, srcOrAlias):
for i in self.detInfoList:
src, alias, _ = i
if srcOrAlias.lower() == src.lower() or srcOrAlias.lower() == alias.lower():
return alias
def getDetInfoList(self):
myAreaDetectors = []
self.detnames = psana.DetNames()
for k in self.detnames:
try:
if Detector.PyDetector.dettype(str(k[0]), self.env) == Detector.AreaDetector.AreaDetector:
myAreaDetectors.append(k)
except ValueError:
continue
self.detInfoList = list(set(myAreaDetectors))
def getEvent(self, number):
self.evt = self.run.event(self.times[number])
def getImg(self, number):
self.getEvent(number)
img = self.det.image(self.evt, self.det.calib(self.evt))
return img
def getImg(self):
if self.evt is not None:
img = self.det.image(self.evt, self.det.calib(self.evt))
return img
return None
def getCheetahImg(self, calib=None):
"""Converts seg, row, col assuming (32,185,388)
to cheetah 2-d table row and col (8*185, 4*388)
"""
if 'cspad2x2' in self.detInfo.lower():
print "Not implemented yet: cspad2x2"
elif 'cspad' in self.detInfo.lower():
if calib is None: calib = self.det.calib(self.evt) # (32,185,388)
img = np.zeros((8 * 185, 4 * 388))
counter = 0
for quad in range(4):
for seg in range(8):
img[seg * 185:(seg + 1) * 185, quad * 388:(quad + 1) * 388] = calib[counter, :, :]
counter += 1
elif 'rayonix' in self.detInfo.lower():
if calib is None:
img = np.squeeze(self.det.calib(self.evt)) # (1920,1920)
else:
img = np.squeeze(calib)
return img
def getCleanAssembledImg(self, backgroundEvent):
"""Returns psana assembled image
"""
backgroundEvt = self.run.event(self.times[backgroundEvent])
backgroundCalib = self.det.calib(backgroundEvt)
calib = self.det.calib(self.evt)
cleanCalib = calib - backgroundCalib
img = self.det.image(self.evt, cleanCalib)
return img
def getAssembledImg(self):
"""Returns psana assembled image
"""
img = self.det.image(self.evt)
return img
def getCalibImg(self):
"""Returns psana assembled image
"""
img = self.det.calib(self.evt)
return img
def getCleanAssembledPhotons(self, backgroundEvent):
"""Returns psana assembled image in photon counts
"""
backgroundEvt = self.run.event(self.times[backgroundEvent])
backgroundCalib = self.det.calib(backgroundEvt)
calib = self.det.calib(self.evt)
cleanCalib = calib - backgroundCalib
img = self.det.photons(self.evt, nda_calib=cleanCalib, adu_per_photon=self.aduPerPhoton)
phot = self.det.image(self.evt, img)
return phot
def getAssembledPhotons(self):
"""Returns psana assembled image in photon counts
"""
img = self.det.photons(self.evt, adu_per_photon=self.aduPerPhoton)
phot = self.det.image(self.evt, img)
return phot
def getPsanaEvent(self, cheetahFilename):
# Gets psana event given cheetahFilename, e.g. LCLS_2015_Jul26_r0014_035035_e820.h5
hrsMinSec = cheetahFilename.split('_')[-2]
fid = int(cheetahFilename.split('_')[-1].split('.')[0], 16)
for t in self.times:
if t.fiducial() == fid:
localtime = time.strftime('%H:%M:%S', time.localtime(t.seconds()))
localtime = localtime.replace(':', '')
if localtime[0:3] == hrsMinSec[0:3]:
self.evt = self.run.event(t)
else:
self.evt = None
def getStartTime(self):
self.evt = self.run.event(self.times[0])
evtId = self.evt.get(psana.EventId)
sec = evtId.time()[0]
nsec = evtId.time()[1]
fid = evtId.fiducials()
return time.strftime('%FT%H:%M:%S-0800', time.localtime(sec)) # Hard-coded pacific time
#####################################################################
# TODO: Functions below are not being used yet
#####################################################################
def findPsanaGeometry(self):
try:
self.source = psana.Detector.PyDetector.map_alias_to_source(self.detInfo,
self.ds.env()) # 'DetInfo(CxiDs2.0:Cspad.0)'
self.calibSource = self.source.split('(')[-1].split(')')[0] # 'CxiDs2.0:Cspad.0'
self.detectorType = gu.det_type_from_source(self.source) # 1
self.calibGroup = gu.dic_det_type_to_calib_group[self.detectorType] # 'CsPad::CalibV1'
self.detectorName = gu.dic_det_type_to_name[self.detectorType].upper() # 'CSPAD'
if self.localCalib:
self.calibPath = "./calib/" + self.calibGroup + "/" + self.calibSource + "/geometry"
else:
self.calibPath = "/reg/d/psdm/" + self.parent.experimentName[0:3] + \
"/" + self.parent.experimentName + "/calib/" + \
self.calibGroup + "/" + self.calibSource + "/geometry"
# Determine which calib file to use
geometryFiles = os.listdir(self.calibPath)
self.calibFile = None
minDiff = -1e6
for fname in geometryFiles:
if fname.endswith('.data'):
endValid = False
startNum = int(fname.split('-')[0])
endNum = fname.split('-')[-1].split('.data')[0]
diff = startNum - self.parent.runNumber
# Make sure it's end number is valid too
if 'end' in endNum:
endValid = True
else:
try:
if self.parent.runNumber <= int(endNum):
endValid = True
except:
continue
if diff <= 0 and diff > minDiff and endValid is True:
minDiff = diff
self.calibFile = fname
except:
if self.parent.args.v >= 1: print "Couldn't find psana geometry"
self.calibFile = None
def setupRadialBackground(self):
self.findPsanaGeometry()
if self.calibFile is not None:
self.geo = GeometryAccess(self.calibPath+'/'+self.calibFile)
self.xarr, self.yarr, self.zarr = self.geo.get_pixel_coords()
self.iX, self.iY = self.geo.get_pixel_coord_indexes()
self.mask = self.geo.get_pixel_mask(mbits=0377) # mask for 2x1 edges, two central columns, and unbound pixels with their neighbours
self.rb = RadialBkgd(self.xarr, self.yarr, mask=self.mask, radedges=None, nradbins=100, phiedges=(0, 360), nphibins=1)
else:
self.rb = None
def updatePolarizationFactor(self, detectorDistance_in_m):
if self.rb is not None:
self.pf = polarization_factor(self.rb.pixel_rad(), self.rb.pixel_phi(), detectorDistance_in_m*1e6) # convert to um
def getCalib(self, evtNumber):
if self.run is not None:
self.evt = self.getEvent(evtNumber)
if self.applyCommonMode: # play with different common mode
if self.commonMode[0] == 5: # Algorithm 5
calib = self.det.calib(self.evt,cmpars=(self.commonMode[0], self.commonMode[1]))
else: # Algorithms 1 to 4
print "### Overriding common mode: ", self.commonMode
calib = self.det.calib(self.evt,cmpars=(self.commonMode[0], self.commonMode[1],
self.commonMode[2], self.commonMode[3]))
else:
calib = self.det.calib(self.evt)
return calib
else:
return None
def getPreprocessedImage(self, evtNumber, image_property):
disp_medianCorrection = 19
disp_radialCorrection = 18
disp_gainMask = 17
disp_coordy= 16
disp_coordx= 15
disp_col= 14
disp_row= 13
disp_seg= 12
disp_quad= 11
disp_gain= 10
disp_commonMode= 9
disp_rms= 8
disp_status= 7
disp_pedestal= 6
disp_photons= 5
disp_raw= 4
disp_pedestalCorrected= 3
disp_commonModeCorrected= 2
disp_adu= 1
if image_property == disp_medianCorrection: # median subtraction
print "Sorry, this feature isn't available yet"
elif image_property == disp_radialCorrection: # radial subtraction + polarization corrected
self.getEvent(evtNumber)
calib = self.getCalib(evtNumber)
if calib:
self.pf.shape = self.parent.calib.shape
calib = self.rb.subtract_bkgd(calib * self.pf)
elif image_property == disp_adu: # gain and hybrid gain corrected
calib = self.getCalib(evtNumber)
elif image_property == disp_commonModeCorrected: # common mode corrected
calib = self.getCommonModeCorrected(evtNumber)
elif image_property == disp_pedestalCorrected: # pedestal corrected
calib = self.det.raw(self.evt).astype('float32')
if calib: calib -= self.det.pedestals(self.evt)
elif image_property == disp_raw: # raw
calib = self.det.raw(self.evt)
elif image_property == disp_photons: # photon counts
calib = self.det.photons(self.evt, mask=self.parent.mk.userMask,
adu_per_photon=self.parent.exp.aduPerPhoton)
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed, dtype='int32')
elif image_property == disp_pedestal: # pedestal
calib = self.parent.det.pedestals(self.parent.evt)
elif image_property == disp_status: # status
calib = self.parent.det.status(self.parent.evt)
elif image_property == disp_rms: # rms
calib = self.parent.det.rms(self.parent.evt)
elif image_property == disp_commonMode: # common mode
calib = self.getCommonMode(evtNumber)
elif image_property == disp_gain: # gain
calib = self.parent.det.gain(self.parent.evt)
elif image_property == disp_gainMask: # gain_mask
calib = self.parent.det.gain_mask(self.parent.evt)
elif image_property == disp_coordx: # coords_x
calib = self.parent.det.coords_x(self.parent.evt)
elif image_property == disp_coordy: # coords_y
calib = self.parent.det.coords_y(self.parent.evt)
shape = self.parent.det.shape(self.parent.evt)
if len(shape) == 3:
if image_property == disp_quad: # quad ind
calib = np.zeros(shape)
for i in range(shape[0]):
# FIXME: handle detectors properly
if shape[0] == 32: # cspad
calib[i, :, :] = int(i) % 8
elif shape[0] == 2: # cspad2x2
calib[i, :, :] = int(i) % 2
elif shape[0] == 4: # pnccd
calib[i, :, :] = int(i) % 4
elif image_property == disp_seg: # seg ind
calib = np.zeros(shape)
if shape[0] == 32: # cspad
for i in range(32):
calib[i, :, :] = int(i) / 8
elif shape[0] == 2: # cspad2x2
for i in range(2):
calib[i, :, :] = int(i)
elif shape[0] == 4: # pnccd
for i in range(4):
calib[i, :, :] = int(i)
elif image_property == disp_row: # row ind
calib = np.zeros(shape)
if shape[0] == 32: # cspad
for i in range(185):
calib[:, i, :] = i
elif shape[0] == 2: # cspad2x2
for i in range(185):
calib[:, i, :] = i
elif shape[0] == 4: # pnccd
for i in range(512):
calib[:, i, :] = i
elif image_property == disp_col: # col ind
calib = np.zeros(shape)
if shape[0] == 32: # cspad
for i in range(388):
calib[:, :, i] = i
elif shape[0] == 2: # cspad2x2
for i in range(388):
calib[:, :, i] = i
elif shape[0] == 4: # pnccd
for i in range(512):
calib[:, :, i] = i
def initialize(self, geom, run_num=0):
"""
Initialize the detector as pnccd
:param geom: The pnccd .data file which characterize the geometry profile.
:param run_num: The run_num containing the background, rms and gain and the other
pixel pixel properties.
:return: None
"""
# Redirect the output stream
old_stdout = sys.stdout
f = six.StringIO()
# f = open('Detector_initialization.log', 'w')
sys.stdout = f
###########################################################################################
# Initialize the geometry configuration
############################################################################################
self.geometry = GeometryAccess(geom, 0o377)
self.run_num = run_num
# Set coordinate in real space
temp = self.geometry.get_pixel_coords()
temp_index = self.geometry.get_pixel_coord_indexes()
self.panel_num = temp[0].shape[1] * temp[0].shape[2]
self.distance = temp[2][0, 0, 0, 0, 0] * 1e-6 # Convert to m
self.pixel_position = np.zeros(
(self.panel_num, temp[0].shape[3], temp[0].shape[4], 3))
self.pixel_index_map = np.zeros(
(self.panel_num, temp[0].shape[3], temp[0].shape[4], 2))
for l in range(temp[0].shape[1]):
for m in range(temp[0].shape[2]):
for n in range(3):
self.pixel_position[m + l * temp[0].shape[2], :, :,
n] = temp[n][0, l, m]
for n in range(2):
self.pixel_index_map[m + l * temp[0].shape[2], :, :,
n] = temp_index[n][0, l, m]
self.pixel_index_map = self.pixel_index_map.astype(np.int64)
# Get the range of the pixel index
self.detector_pixel_num_x = np.max(self.pixel_index_map[:, :, :,
0]) + 1
self.detector_pixel_num_y = np.max(self.pixel_index_map[:, :, :,
1]) + 1
self.panel_pixel_num_x = np.array([
self.pixel_index_map.shape[1],
] * self.panel_num)
self.panel_pixel_num_y = np.array([
self.pixel_index_map.shape[2],
] * self.panel_num)
self.pixel_num_total = np.sum(
np.multiply(self.panel_pixel_num_x, self.panel_pixel_num_y))
tmp = float(self.geometry.get_pixel_scale_size() *
1e-6) # Convert to m
self.pixel_width = np.ones((self.panel_num, self.panel_pixel_num_x[0],
self.panel_pixel_num_y[0])) * tmp
self.pixel_height = np.ones((self.panel_num, self.panel_pixel_num_x[0],
self.panel_pixel_num_y[0])) * tmp
# Calculate the pixel area
self.pixel_area = np.multiply(self.pixel_height, self.pixel_width)
###########################################################################################
# Initialize the pixel effects
###########################################################################################
# first we should parse the path
parsed_path = geom.split('/')
source = parsed_path[-3]
if six.PY2:
cbase = CalibParsBasePnccdV1()
calibdir = '/'.join(parsed_path[:-4])
group = parsed_path[-4]
pbits = 255
gcp = GenericCalibPars(cbase, calibdir, group, source, run_num,
pbits)
self._pedestals = gcp.pedestals()
self._pixel_rms = gcp.pixel_rms()
self._pixel_mask = gcp.pixel_mask()
self._pixel_bkgd = gcp.pixel_bkgd()
self._pixel_status = gcp.pixel_status()
self._pixel_gain = gcp.pixel_gain()
else:
self.det = "pnccd_000" + source[-1]
self.exp = parsed_path[-5]
self._pedestals = None
self._pixel_rms = None
self._pixel_mask = None
self._pixel_bkgd = None
self._pixel_status = None
self._pixel_gain = None
# Redirect the output stream
sys.stdout = old_stdout
class PnccdDetector(DetectorBase):
"""
Class for lcls detectors.
"""
def __init__(self, geom, beam, run_num=0):
"""
Initialize a pnccd detector.
:param geom: The path to the geometry .data file.
:param beam: The beam object.
:param run_num: The run_num containing the background, rms and gain and the other pixel
pixel properties.
"""
super(PnccdDetector, self).__init__()
# Parse the path to extract the necessary information to use psana modules
parsed_path = geom.split('/')
# Notify the user that the path should be as deep as the geometry profile
if parsed_path[-2] != "geometry":
# print parsed_path[-1]
raise Exception(
" Sorry, at present, the package is not very smart. Please specify "
+
"the path of the detector as deep as the geometry profile. \n "
+ "And example would be like:" +
"/reg/d/psdm/amo/experiment_name/calib/group/source/geometry/0-end.data \n"
+
"where the '/calib/group/source/geometry/0-end.data' part is essential. \n"
+
"The address before that part is not essential and can be replaced with"
+ " your absolute address or relative address.\n"
"The experiment_name is also essential in Python 3.")
self.initialize(geom=geom, run_num=run_num)
# Initialize the pixel effects
self.initialize_pixels_with_beam(beam=beam)
def initialize(self, geom, run_num=0):
"""
Initialize the detector as pnccd
:param geom: The pnccd .data file which characterize the geometry profile.
:param run_num: The run_num containing the background, rms and gain and the other
pixel pixel properties.
:return: None
"""
# Redirect the output stream
old_stdout = sys.stdout
f = six.StringIO()
# f = open('Detector_initialization.log', 'w')
sys.stdout = f
###########################################################################################
# Initialize the geometry configuration
############################################################################################
self.geometry = GeometryAccess(geom, 0o377)
self.run_num = run_num
# Set coordinate in real space
temp = self.geometry.get_pixel_coords()
temp_index = self.geometry.get_pixel_coord_indexes()
self.panel_num = temp[0].shape[1] * temp[0].shape[2]
self.distance = temp[2][0, 0, 0, 0, 0] * 1e-6 # Convert to m
self.pixel_position = np.zeros(
(self.panel_num, temp[0].shape[3], temp[0].shape[4], 3))
self.pixel_index_map = np.zeros(
(self.panel_num, temp[0].shape[3], temp[0].shape[4], 2))
for l in range(temp[0].shape[1]):
for m in range(temp[0].shape[2]):
for n in range(3):
self.pixel_position[m + l * temp[0].shape[2], :, :,
n] = temp[n][0, l, m]
for n in range(2):
self.pixel_index_map[m + l * temp[0].shape[2], :, :,
n] = temp_index[n][0, l, m]
self.pixel_index_map = self.pixel_index_map.astype(np.int64)
# Get the range of the pixel index
self.detector_pixel_num_x = np.max(self.pixel_index_map[:, :, :,
0]) + 1
self.detector_pixel_num_y = np.max(self.pixel_index_map[:, :, :,
1]) + 1
self.panel_pixel_num_x = np.array([
self.pixel_index_map.shape[1],
] * self.panel_num)
self.panel_pixel_num_y = np.array([
self.pixel_index_map.shape[2],
] * self.panel_num)
self.pixel_num_total = np.sum(
np.multiply(self.panel_pixel_num_x, self.panel_pixel_num_y))
tmp = float(self.geometry.get_pixel_scale_size() *
1e-6) # Convert to m
self.pixel_width = np.ones((self.panel_num, self.panel_pixel_num_x[0],
self.panel_pixel_num_y[0])) * tmp
self.pixel_height = np.ones((self.panel_num, self.panel_pixel_num_x[0],
self.panel_pixel_num_y[0])) * tmp
# Calculate the pixel area
self.pixel_area = np.multiply(self.pixel_height, self.pixel_width)
###########################################################################################
# Initialize the pixel effects
###########################################################################################
# first we should parse the path
parsed_path = geom.split('/')
source = parsed_path[-3]
if six.PY2:
cbase = CalibParsBasePnccdV1()
calibdir = '/'.join(parsed_path[:-4])
group = parsed_path[-4]
pbits = 255
gcp = GenericCalibPars(cbase, calibdir, group, source, run_num,
pbits)
self._pedestals = gcp.pedestals()
self._pixel_rms = gcp.pixel_rms()
self._pixel_mask = gcp.pixel_mask()
self._pixel_bkgd = gcp.pixel_bkgd()
self._pixel_status = gcp.pixel_status()
self._pixel_gain = gcp.pixel_gain()
else:
self.det = "pnccd_000" + source[-1]
self.exp = parsed_path[-5]
self._pedestals = None
self._pixel_rms = None
self._pixel_mask = None
self._pixel_bkgd = None
self._pixel_status = None
self._pixel_gain = None
# Redirect the output stream
sys.stdout = old_stdout
# f.close()
# os.remove('./Detector_initialization.log')
@property
def pedestals(self):
if six.PY3 and not self._pedestals:
self._pedestals = calib_constants(self.det,
exp=self.exp,
ctype="pedestals",
run=self.run_num)[0]
return self._pedestals
@property
def pixel_rms(self):
if six.PY3 and not self._pixel_rms:
self._pixel_rms = calib_constants(self.det,
exp=self.exp,
ctype="pixel_rms",
run=self.run_num)[0]
return self._pixel_rms
@property
def pixel_mask(self):
if six.PY3 and not self._pixel_mask:
self._pixel_mask = calib_constants(self.det,
exp=self.exp,
ctype="pixel_mask",
run=self.run_num)[0]
return self._pixel_mask
@property
def pixel_bkgd(self):
if six.PY3 and not self._pixel_bkgd:
self._pixel_bkgd = calib_constants(self.det,
exp=self.exp,
ctype="pixel_bkgd",
run=self.run_num)[0]
return self._pixel_bkgd
@property
def pixel_status(self):
if six.PY3 and not self._pixel_status:
self._pixel_status = calib_constants(self.det,
exp=self.exp,
ctype="pixel_status",
run=self.run_num)[0]
return self._pixel_status
@property
def pixel_gain(self):
if six.PY3 and not self._pixel_gain:
self._pixel_gain = calib_constants(self.det,
exp=self.exp,
ctype="pixel_gain",
run=self.run_num)[0]
return self._pixel_gain
def assemble_image_stack(self, image_stack):
"""
Assemble the image stack into a 2D diffraction pattern.
For this specific object, since it only has one panel, the result is to remove the
first dimension.
:param image_stack: The [1, num_x, num_y] numpy array.
:return: The [num_x, num_y] numpy array.
"""
# construct the image holder:
image = np.zeros(
(self.detector_pixel_num_x, self.detector_pixel_num_y))
for l in range(self.panel_num):
image[self.pixel_index_map[l, :, :, 0],
self.pixel_index_map[l, :, :, 1]] = image_stack[l, :, :]
return image
def assemble_image_stack_batch(self, image_stack_batch):
"""
Assemble the image stack batch into a stack of 2D diffraction patterns.
For this specific object, since it has only one panel, the result is a simple reshape.
:param image_stack_batch: The [stack_num, 1, num_x, num_y] numpy array
:return: The [stack_num, num_x, num_y] numpy array
"""
stack_num = image_stack_batch.shape[0]
# construct the image holder:
image = np.zeros(
(stack_num, self.detector_pixel_num_x, self.detector_pixel_num_y))
for l in range(self.panel_num):
idx_map_1 = self.pixel_index_map[l, :, :, 0]
idx_map_2 = self.pixel_index_map[l, :, :, 1]
image[:, idx_map_1, idx_map_2] = image_stack_batch[:, l]
return image
except IOError:
# See if it's a json file
from dxtbx.model.experiment_list import ExperimentListFactory
try:
experiments = ExperimentListFactory.from_json_file(params.metrology)
assert len(experiments) == 1
detector = experiments[0].detector
except Exception as e:
detector = None
if detector is None:
# see if it's a SLAC geometry file
from scitbx import matrix
try:
from PSCalib.GeometryAccess import GeometryAccess
geometry = GeometryAccess(params.metrology)
except Exception as e:
geometry = None
if geometry is None:
# see if this is a Ginn metrology file (see Helen Ginn et. al. (2015), Acta D Cryst)
panels = []
for line in open(params.metrology).readlines():
if len(line) > 0 and line[0] != '#' and len(line.strip().split()) == 10:
panels.append(line.strip())
if len(panels) != 64:
raise Sorry("Can't parse this metrology file")
print "Ginn metrology file"
for panel_id, panel in enumerate(panels):
def initialize(self, geom, run_num=0, cframe=0):
"""
Initialize the detector
:param geom: The *-end.data file which characterizes the geometry profile.
:param run_num: The run_num containing the background, rms and gain and the other
pixel pixel properties.
:param cframe: The desired coordinate frame, 0 for psana and 1 for lab conventions.
:return: None
"""
# Redirect the output stream
old_stdout = sys.stdout
f = six.StringIO()
# f = open('Detector_initialization.log', 'w')
sys.stdout = f
###########################################################################################
# Initialize the geometry configuration
############################################################################################
self.geometry = GeometryAccess(geom, cframe=cframe)
self.run_num = run_num
# Set coordinate in real space (convert to m)
temp = [
xp.asarray(t) * 1e-6
for t in self.geometry.get_pixel_coords(cframe=cframe)
]
temp_index = [
xp.asarray(t)
for t in self.geometry.get_pixel_coord_indexes(cframe=cframe)
]
self.panel_num = np.prod(temp[0].shape[:-2])
self._distance = float(temp[2].mean())
self._shape = (self.panel_num, temp[0].shape[-2], temp[0].shape[-1])
self.pixel_position = xp.zeros(self._shape + (3, ))
self.pixel_index_map = xp.zeros(self._shape + (2, ))
for n in range(3):
self.pixel_position[..., n] = temp[n].reshape(self._shape)
for n in range(2):
self.pixel_index_map[..., n] = temp_index[n].reshape(self._shape)
self.pixel_index_map = self.pixel_index_map.astype(xp.int64)
# Get the range of the pixel index
self.detector_pixel_num_x = asnumpy(
xp.max(self.pixel_index_map[..., 0]) + 1)
self.detector_pixel_num_y = asnumpy(
xp.max(self.pixel_index_map[..., 1]) + 1)
self.panel_pixel_num_x = np.array([
self.pixel_index_map.shape[1],
] * self.panel_num)
self.panel_pixel_num_y = np.array([
self.pixel_index_map.shape[2],
] * self.panel_num)
self.pixel_num_total = np.sum(
np.multiply(self.panel_pixel_num_x, self.panel_pixel_num_y))
tmp = float(self.geometry.get_pixel_scale_size() *
1e-6) # Convert to m
self.pixel_width = xp.ones((self.panel_num, self.panel_pixel_num_x[0],
self.panel_pixel_num_y[0])) * tmp
self.pixel_height = xp.ones((self.panel_num, self.panel_pixel_num_x[0],
self.panel_pixel_num_y[0])) * tmp
# Calculate the pixel area
self.pixel_area = xp.multiply(self.pixel_height, self.pixel_width)
###########################################################################################
# Initialize the pixel effects
###########################################################################################
# first we should parse the path
parsed_path = geom.split('/')
self.exp = parsed_path[-5]
if self.exp == 'calib':
self.exp = parsed_path[-6]
self.group = parsed_path[-4]
self.source = parsed_path[-3]
self._pedestals = None
self._pixel_rms = None
self._pixel_mask = None
self._pixel_bkgd = None
self._pixel_status = None
self._pixel_gain = None
if six.PY2:
try:
cbase = self._get_cbase()
self.calibdir = '/'.join(parsed_path[:-4])
pbits = 255
gcp = GenericCalibPars(cbase, self.calibdir, self.group,
self.source, run_num, pbits)
self._pedestals = gcp.pedestals()
self._pixel_rms = gcp.pixel_rms()
self._pixel_mask = gcp.pixel_mask()
self._pixel_bkgd = gcp.pixel_bkgd()
self._pixel_status = gcp.pixel_status()
self._pixel_gain = gcp.pixel_gain()
except NotImplementedError:
# No GenericCalibPars information.
pass
else:
try:
self.det = self._get_det_id(self.group)
except NotImplementedError:
# No GenericCalibPars information.
self.det = None
# Redirect the output stream
sys.stdout = old_stdout
class LCLSDetector(DetectorBase):
"""
Class for LCLS detectors.
"""
def __init__(self, geom, beam=None, run_num=0, cframe=0):
"""
Initialize a LCLS detector.
:param geom: The path to the geometry .data file.
:param beam: The beam object.
:param run_num: The run_num containing the background, rms and gain and the other pixel
pixel properties.
:param cframe: The desired coordinate frame, 0 for psana and 1 for lab conventions. The
default (psana) matches the convention of non-LCLS detectors. Lab frame yields the transpose.
"""
super(LCLSDetector, self).__init__()
# Parse the path to extract the necessary information to use psana modules
parsed_path = geom.split('/')
# Notify the user that the path should be as deep as the geometry profile
if parsed_path[-2] != "geometry":
# print parsed_path[-1]
raise Exception(
" Sorry, at present, the package is not very smart. Please specify "
+
"the path of the detector as deep as the geometry profile. \n "
+ "And example would be like:" +
"/reg/d/psdm/amo/experiment_name/calib/group/source/geometry/0-end.data \n"
+
"where the '/calib/group/source/geometry/0-end.data' part is essential. \n"
+
"The address before that part is not essential and can be replaced with"
+ " your absolute address or relative address.\n"
"The experiment_name is also essential in Python 3.")
self.initialize(geom=geom, run_num=run_num, cframe=cframe)
# Initialize the pixel effects, enforcing detector distance to be positive
if self.distance < 0:
self.distance *= -1
self.initialize_pixels_with_beam(beam=beam)
def initialize(self, geom, run_num=0, cframe=0):
"""
Initialize the detector
:param geom: The *-end.data file which characterizes the geometry profile.
:param run_num: The run_num containing the background, rms and gain and the other
pixel pixel properties.
:param cframe: The desired coordinate frame, 0 for psana and 1 for lab conventions.
:return: None
"""
# Redirect the output stream
old_stdout = sys.stdout
f = six.StringIO()
# f = open('Detector_initialization.log', 'w')
sys.stdout = f
###########################################################################################
# Initialize the geometry configuration
############################################################################################
self.geometry = GeometryAccess(geom, cframe=cframe)
self.run_num = run_num
# Set coordinate in real space (convert to m)
temp = [
xp.asarray(t) * 1e-6
for t in self.geometry.get_pixel_coords(cframe=cframe)
]
temp_index = [
xp.asarray(t)
for t in self.geometry.get_pixel_coord_indexes(cframe=cframe)
]
self.panel_num = np.prod(temp[0].shape[:-2])
self._distance = float(temp[2].mean())
self._shape = (self.panel_num, temp[0].shape[-2], temp[0].shape[-1])
self.pixel_position = xp.zeros(self._shape + (3, ))
self.pixel_index_map = xp.zeros(self._shape + (2, ))
for n in range(3):
self.pixel_position[..., n] = temp[n].reshape(self._shape)
for n in range(2):
self.pixel_index_map[..., n] = temp_index[n].reshape(self._shape)
self.pixel_index_map = self.pixel_index_map.astype(xp.int64)
# Get the range of the pixel index
self.detector_pixel_num_x = asnumpy(
xp.max(self.pixel_index_map[..., 0]) + 1)
self.detector_pixel_num_y = asnumpy(
xp.max(self.pixel_index_map[..., 1]) + 1)
self.panel_pixel_num_x = np.array([
self.pixel_index_map.shape[1],
] * self.panel_num)
self.panel_pixel_num_y = np.array([
self.pixel_index_map.shape[2],
] * self.panel_num)
self.pixel_num_total = np.sum(
np.multiply(self.panel_pixel_num_x, self.panel_pixel_num_y))
tmp = float(self.geometry.get_pixel_scale_size() *
1e-6) # Convert to m
self.pixel_width = xp.ones((self.panel_num, self.panel_pixel_num_x[0],
self.panel_pixel_num_y[0])) * tmp
self.pixel_height = xp.ones((self.panel_num, self.panel_pixel_num_x[0],
self.panel_pixel_num_y[0])) * tmp
# Calculate the pixel area
self.pixel_area = xp.multiply(self.pixel_height, self.pixel_width)
###########################################################################################
# Initialize the pixel effects
###########################################################################################
# first we should parse the path
parsed_path = geom.split('/')
self.exp = parsed_path[-5]
if self.exp == 'calib':
self.exp = parsed_path[-6]
self.group = parsed_path[-4]
self.source = parsed_path[-3]
self._pedestals = None
self._pixel_rms = None
self._pixel_mask = None
self._pixel_bkgd = None
self._pixel_status = None
self._pixel_gain = None
if six.PY2:
try:
cbase = self._get_cbase()
self.calibdir = '/'.join(parsed_path[:-4])
pbits = 255
gcp = GenericCalibPars(cbase, self.calibdir, self.group,
self.source, run_num, pbits)
self._pedestals = gcp.pedestals()
self._pixel_rms = gcp.pixel_rms()
self._pixel_mask = gcp.pixel_mask()
self._pixel_bkgd = gcp.pixel_bkgd()
self._pixel_status = gcp.pixel_status()
self._pixel_gain = gcp.pixel_gain()
except NotImplementedError:
# No GenericCalibPars information.
pass
else:
try:
self.det = self._get_det_id(self.group)
except NotImplementedError:
# No GenericCalibPars information.
self.det = None
# Redirect the output stream
sys.stdout = old_stdout
# f.close()
# os.remove('./Detector_initialization.log')
def _get_cbase(self):
"""Get detector calibration base object.
Psana 1 only.
"""
raise NotImplementedError()
def _get_det_id(self, group):
"""Get detector ID form source.
Psana 2 only.
"""
raise NotImplementedError()
def _get_calib_constants(self, name):
_name = "_" + name
attribute = getattr(self, _name)
if six.PY3 and attribute is None and self.det is not None:
# We haven't tried to get the calib_constant yet.
attribute = calib_constants(self.det,
exp=self.exp,
ctype=name,
run=self.run_num)[0]
if attribute is None:
# We still don't have it
raise RuntimeError("No {} available for this detector"
"".format(name))
setattr(self, _name, attribute)
return attribute
@property
def pedestals(self):
return self._get_calib_constants("pedestals")
@property
def pixel_rms(self):
return self._get_calib_constants("pixel_rms")
@property
def pixel_mask(self):
return self._get_calib_constants("pixel_mask")
@property
def pixel_bkgd(self):
return self._get_calib_constants("pixel_bkgd")
@property
def pixel_status(self):
return self._get_calib_constants("pixel_status")
@property
def pixel_gain(self):
return self._get_calib_constants("pixel_gain")
@pedestals.setter
def pedestals(self, value):
self._pedestals = value
@pixel_rms.setter
def pixel_rms(self, value):
self._pixel_rms = value
@pixel_mask.setter
def pixel_mask(self, value):
self._pixel_mask = value
@pixel_bkgd.setter
def pixel_bkgd(self, value):
self._pixel_bkgd = value
@pixel_status.setter
def pixel_status(self, value):
self._pixel_status = value
@pixel_gain.setter
def pixel_gain(self, value):
self._pixel_gain = value
def reset_calib(self, run_num):
"""
Update calibration pixel effects based on new run number.
"""
old_stdout = sys.stdout
f = six.StringIO()
sys.stdout = f
self.run_num = run_num
if six.PY2:
try:
pbits = 255
gcp = GenericCalibPars(self._get_cbase(), self.calibdir,
self.group, self.source, self.run_num,
pbits)
self._pedestals = gcp.pedestals()
self._pixel_rms = gcp.pixel_rms()
self._pixel_mask = gcp.pixel_mask()
self._pixel_bkgd = gcp.pixel_bkgd()
self._pixel_status = gcp.pixel_status()
self._pixel_gain = gcp.pixel_gain()
except NotImplementedError:
pass
else:
self._pedestals = calib_constants(self.det,
exp=self.exp,
ctype='pedestals',
run=self.run_num)[0]
self._pixel_rms = calib_constants(self.det,
exp=self.exp,
ctype='pixel_rms',
run=self.run_num)[0]
self._pixel_mask = calib_constants(self.det,
exp=self.exp,
ctype='pixel_mask',
run=self.run_num)[0]
self._pixel_bkgd = calib_constants(self.det,
exp=self.exp,
ctype='pixel_bkgd',
run=self.run_num)[0]
self._pixel_status = calib_constants(self.det,
exp=self.exp,
ctype='pixel_status',
run=self.run_num)[0]
self._pixel_gain = calib_constants(self.det,
exp=self.exp,
ctype='pixel_gain',
run=self.run_num)[0]
sys.stdout = old_stdout
return
###########################################################################################
# Functionality for adding dark noise
###########################################################################################
def _calibrate_evt(self, evt):
"""
Retrieve calibrated data from psana event object. Applied corrections are
pedestal, common mode, gain mask, gain, and pixel status mask, performed
by the psana.Detector class.
:param evt: psana event object
:return data: calibrated image
"""
import psana
# retrieve psana.Source alias
det_type = self.__class__.__name__.split("Detector")[0].lower()
alias = None
for key in evt.keys():
if det_type in key.alias().lower():
alias = key.alias()
break
else:
srcname = key.src()
if srcname.__class__.__name__ == 'DetInfo':
if det_type in srcname.devName().lower():
alias = str(srcname)
break
# retrieve calibrated shot
det = psana.Detector(alias)
return det.calib(evt)
def _retrieve_batch_evt(self, num_shots):
"""
Retrieve num_shots patterns from a run of the experiment.
:param num_shots: number of patterns to retrieve
:return data: array of patterns in shape (num_shots, n_pedestals, ped_x, ped_y)
"""
# set up psana1 DataSource object
from psana import DataSource
ds = DataSource('exp=%s:run=%i' % (self.exp, self.run_num))
# set up storage array
if self.pedestals.ndim == 4:
pshape = self.pedestals.shape[1:]
else:
pshape = self.pedestals.shape
data = np.zeros((num_shots, pshape[0], pshape[1], pshape[2]))
# retrieve multiple events (shots)
counter = 0
for num, evt in enumerate(ds.events()):
if counter < num_shots:
data[counter] = np.array(self._calibrate_evt(evt))
counter += 1
else:
break
# if run is shorter than num_shots, fill in remainder by linear combination
if counter < num_shots:
for i in range(counter, num_shots):
indices = np.random.randint(0, high=counter, size=2)
weights = np.random.dirichlet(np.ones(2))
data[i] = weights[0] * data[indices[0]] + weights[1] * data[
indices[1]]
return data
def _random_dark_index(self):
"""
Return the run index of random dark run, assuming that the indices of dark
runs can be inferred from the pedestal nomenclature.
:return dark_idx: index of random dark run, -1 if no dark runs available
"""
import glob
# list of available pedestals
pnames = glob.glob(
"/reg/d/psdm/%s/%s/calib/%s/%s/pedestals/*-end.data" %
(self.exp[:3].upper(), self.exp, self.group, self.source))
# add run indices from pedestals list if associated XTC files exist
dark_indices = list()
for pn in pnames:
temp_str = pn.split("/")[-1]
temp_idx = int(temp_str.split("-")[0])
fnames = glob.glob("/reg/d/psdm/%s/%s/xtc/*-r%04d-*.xtc" %
(self.exp[:3].upper(), self.exp, temp_idx))
if len(fnames) > 0:
dark_indices.append(temp_idx)
# return random dark run or -1 if none available
if len(dark_indices) != 0:
return np.random.choice(np.array(dark_indices))
else:
return -1
def add_dark_noise(self,
num_shots,
det_shape=True,
dark_idx=None,
mask_neg=True):
"""
Retrieve calibrated images from dark runs.
:param num_shots: number of calibrated dark shots to retreive
:param det_shape: boolean, if True reassemble panels into detector's shape
:param dark_idx: index of dark run; if None, a run number will be chosen randomly
:param mask_neg: boolean, if True set negative-valued pixels to zero
:return dark_data: array of calibrated dark shots with shape
(num_shots, det_x, det_y) if det_shape is True
(num_shots, n_panels, panel_x, panel_y) if det_shape is False
None if pedestals and/or XTC files for a dark run are unavailable
"""
if six.PY3:
raise NotImplementedError(
'Currently only implemented for psana2/python3.')
return
# grab index of random dark run and reset calibration attributes to match
if dark_idx == None:
dark_idx = self._random_dark_index()
if dark_idx == -1:
print("Pedestals and/or XTC data are unavailable.")
return
self.reset_calib(dark_idx)
# retrieve dark data
dark_data = self._retrieve_batch_evt(num_shots)
# floor: set negative intensities to zero
if mask_neg:
dark_data[dark_data < 0] = 0
# optionally reshape to match detector's shape
if det_shape:
dark_data = self.assemble_image_stack_batch(dark_data)
return dark_data
class LCLSDetector(DetectorBase):
"""
Class for lcls detectors.
"""
def __init__(self, geom, beam=None, run_num=0):
"""
Initialize a pnccd detector.
:param geom: The path to the geometry .data file.
:param beam: The beam object.
:param run_num: The run_num containing the background, rms and gain and the other pixel
pixel properties.
"""
super(LCLSDetector, self).__init__()
# Parse the path to extract the necessary information to use psana modules
parsed_path = geom.split('/')
# Notify the user that the path should be as deep as the geometry profile
if parsed_path[-2] != "geometry":
# print parsed_path[-1]
raise Exception(
" Sorry, at present, the package is not very smart. Please specify " +
"the path of the detector as deep as the geometry profile. \n " +
"And example would be like:" +
"/reg/d/psdm/amo/experiment_name/calib/group/source/geometry/0-end.data \n" +
"where the '/calib/group/source/geometry/0-end.data' part is essential. \n" +
"The address before that part is not essential and can be replaced with" +
" your absolute address or relative address.\n"
"The experiment_name is also essential in Python 3.")
self.initialize(geom=geom, run_num=run_num)
# Initialize the pixel effects
self.initialize_pixels_with_beam(beam=beam)
def initialize(self, geom, run_num=0):
"""
Initialize the detector as pnccd
:param geom: The pnccd .data file which characterize the geometry profile.
:param run_num: The run_num containing the background, rms and gain and the other
pixel pixel properties.
:return: None
"""
# Redirect the output stream
old_stdout = sys.stdout
f = six.StringIO()
# f = open('Detector_initialization.log', 'w')
sys.stdout = f
###########################################################################################
# Initialize the geometry configuration
############################################################################################
self.geometry = GeometryAccess(geom, 0)
self.run_num = run_num
# Set coordinate in real space (convert to m)
temp = [xp.asarray(t) * 1e-6 for t in self.geometry.get_pixel_coords()]
temp_index = [xp.asarray(t)
for t in self.geometry.get_pixel_coord_indexes()]
self.panel_num = np.prod(temp[0].shape[:-2])
self._distance = float(temp[2].mean())
self._shape = (self.panel_num, temp[0].shape[-2], temp[0].shape[-1])
self.pixel_position = xp.zeros(self._shape + (3,))
self.pixel_index_map = xp.zeros(self._shape + (2,))
for n in range(3):
self.pixel_position[..., n] = temp[n].reshape(self._shape)
for n in range(2):
self.pixel_index_map[..., n] = temp_index[n].reshape(self._shape)
self.pixel_index_map = self.pixel_index_map.astype(xp.int64)
# Get the range of the pixel index
self.detector_pixel_num_x = asnumpy(
xp.max(self.pixel_index_map[..., 0]) + 1)
self.detector_pixel_num_y = asnumpy(
xp.max(self.pixel_index_map[..., 1]) + 1)
self.panel_pixel_num_x = np.array([self.pixel_index_map.shape[1], ] * self.panel_num)
self.panel_pixel_num_y = np.array([self.pixel_index_map.shape[2], ] * self.panel_num)
self.pixel_num_total = np.sum(np.multiply(self.panel_pixel_num_x, self.panel_pixel_num_y))
tmp = float(self.geometry.get_pixel_scale_size() * 1e-6) # Convert to m
self.pixel_width = xp.ones(
(self.panel_num, self.panel_pixel_num_x[0], self.panel_pixel_num_y[0])) * tmp
self.pixel_height = xp.ones(
(self.panel_num, self.panel_pixel_num_x[0], self.panel_pixel_num_y[0])) * tmp
# Calculate the pixel area
self.pixel_area = xp.multiply(self.pixel_height, self.pixel_width)
###########################################################################################
# Initialize the pixel effects
###########################################################################################
# first we should parse the path
parsed_path = geom.split('/')
group = parsed_path[-4]
source = parsed_path[-3]
self._pedestals = None
self._pixel_rms = None
self._pixel_mask = None
self._pixel_bkgd = None
self._pixel_status = None
self._pixel_gain = None
if six.PY2:
try:
cbase = self._get_cbase()
calibdir = '/'.join(parsed_path[:-4])
pbits = 255
gcp = GenericCalibPars(cbase, calibdir, group, source, run_num, pbits)
self._pedestals = gcp.pedestals()
self._pixel_rms = gcp.pixel_rms()
self._pixel_mask = gcp.pixel_mask()
self._pixel_bkgd = gcp.pixel_bkgd()
self._pixel_status = gcp.pixel_status()
self._pixel_gain = gcp.pixel_gain()
except NotImplementedError:
# No GenericCalibPars information.
pass
else:
try:
self.det = self._get_det_id(source)
except NotImplementedError:
# No GenericCalibPars information.
self.det = None
self.exp = parsed_path[-5]
# Redirect the output stream
sys.stdout = old_stdout
# f.close()
# os.remove('./Detector_initialization.log')
def _get_cbase(self):
"""Get detector calibration base object.
Psana 1 only.
"""
raise NotImplementedError()
def _get_det_id(self, source):
"""Get detector ID form source.
Psana 2 only.
"""
raise NotImplementedError()
def _get_calib_constants(self, name):
_name = "_" + name
attribute = getattr(self, _name)
if six.PY3 and attribute is None and self.det is not None:
# We haven't tried to get the calib_constant yet.
attribute = calib_constants(
self.det, exp=self.exp, ctype=name,
run=self.run_num)[0]
if attribute is None:
# We still don't have it
raise RuntimeError("No {} available for this detector"
"".format(name))
setattr(self, _name, attribute)
return attribute
@property
def pedestals(self):
return self._get_calib_constants("pedestals")
@property
def pixel_rms(self):
return self._get_calib_constants("pixel_rms")
@property
def pixel_mask(self):
return self._get_calib_constants("pixel_mask")
@property
def pixel_bkgd(self):
return self._get_calib_constants("pixel_bkgd")
@property
def pixel_status(self):
return self._get_calib_constants("pixel_status")
@property
def pixel_gain(self):
return self._get_calib_constants("pixel_gain")
def read_slac_metrology(path = None, geometry = None, plot=False, include_asic_offset=False):
if path is None and geometry is None:
raise Sorry("Need to provide a geometry object or a path to a geometry file")
if path is not None and geometry is not None:
raise Sorry("Cannot provide a geometry object and a geometry file. Ambiguous")
if geometry is None:
try:
from PSCalib.GeometryAccess import GeometryAccess
geometry = GeometryAccess(path)
except Exception as e:
raise Sorry("Can't parse this metrology file")
metro = {}
pixel_size = geometry.get_pixel_scale_size()/1000
null_ori = matrix.col((0,0,1)).axis_and_angle_as_unit_quaternion(0, deg=True)
# collapse any transformations above those of the quadrants into one X/Y offset,
# but don't keep Z transformations, as those come from the XTC stream
root = geometry.get_top_geo()
root_basis = basis_from_geo(root, use_z=False)
while len(root.get_list_of_children()) != 4 and len(root.get_list_of_children()) != 32:
assert len(root.get_list_of_children()) == 1
root = root.get_list_of_children()[0]
root_basis *= basis_from_geo(root, use_z=False)
metro[(0,)] = root_basis
def add_sensor(quad_id, sensor_id, sensor):
metro[(0,quad_id,sensor_id)] = basis_from_geo(sensor)
x, y, z = sensor.get_pixel_coords()
x/=1000; y/=1000; z/=1000
assert x.shape == y.shape == z.shape
sensor_px_slow = x.shape[0]
sensor_px_fast = x.shape[1]
assert sensor_px_fast % 2 == 0
a0ul = sul = matrix.col((x[0,0],y[0,0],z[0,0]))
a1ur = sur = matrix.col((x[0,sensor_px_fast-1],y[0,sensor_px_fast-1],z[0,sensor_px_fast-1]))
a1lr = slr = matrix.col((x[sensor_px_slow-1,sensor_px_fast-1],y[sensor_px_slow-1,sensor_px_fast-1],z[sensor_px_slow-1,sensor_px_fast-1]))
a0ll = sll = matrix.col((x[sensor_px_slow-1,0],y[sensor_px_slow-1,0],z[sensor_px_slow-1,0]))
a0ur = matrix.col((x[0,sensor_px_fast//2-1],y[0,sensor_px_fast//2-1],z[0,sensor_px_fast//2-1]))
a0lr = matrix.col((x[sensor_px_slow-1,sensor_px_fast//2-1],y[sensor_px_slow-1,sensor_px_fast//2-1],z[sensor_px_slow-1,sensor_px_fast//2-1]))
a1ul = matrix.col((x[0,sensor_px_fast//2],y[0,sensor_px_fast//2],z[0,sensor_px_fast//2]))
a1ll = matrix.col((x[sensor_px_slow-1,sensor_px_fast//2],y[sensor_px_slow-1,sensor_px_fast//2],z[sensor_px_slow-1,sensor_px_fast//2]))
sensor_center = center([sul,sur,slr,sll])
asic0_center = center([a0ul,a0ur,a0lr,a0ll])
asic1_center = center([a1ul,a1ur,a1lr,a1ll])
asic_trans0 = (asic0_center-sensor_center).length()
asic_trans1 = (asic1_center-sensor_center).length()
if include_asic_offset:
rotated_ori = matrix.col((1,0,0)).axis_and_angle_as_unit_quaternion(180.0, deg=True)
offset_fast = -pixel_size*((sensor_px_fast) / 4) # 4 because sensor_px_fast is for sensor
offset_slow = +pixel_size*((sensor_px_slow) / 2) # Sensor is divided into 2 only in fast direction
metro[(0,quad_id,sensor_id,0)] = basis(orientation=rotated_ori,translation=matrix.col((-asic_trans0,0,0)))
metro[(0,quad_id,sensor_id,1)] = basis(orientation=rotated_ori,translation=matrix.col((+asic_trans1,0,0)))
metro[(0,quad_id,sensor_id,0)].translation += matrix.col((offset_fast, offset_slow, 0))
metro[(0,quad_id,sensor_id,1)].translation += matrix.col((offset_fast, offset_slow, 0))
else:
metro[(0,quad_id,sensor_id,0)] = basis(orientation=null_ori,translation=matrix.col((-asic_trans0,0,0)))
metro[(0,quad_id,sensor_id,1)] = basis(orientation=null_ori,translation=matrix.col((+asic_trans1,0,0)))
if len(root.get_list_of_children()) == 4:
for quad_id, quad in enumerate(root.get_list_of_children()):
metro[(0,quad_id)] = basis_from_geo(quad)
for sensor_id, sensor in enumerate(quad.get_list_of_children()):
add_sensor(quad_id, sensor_id, sensor)
elif len(root.get_list_of_children()) == 32:
for quad_id in range(4):
metro[(0,quad_id)] = basis(orientation = null_ori, translation = matrix.col((0,0,0)))
sensors = root.get_list_of_children()
for sensor_id in range(8):
add_sensor(quad_id, sensor_id, sensors[quad_id*4+sensor_id])
else:
assert False
return metro
class Detector:
"""
Detector object useful for reshaping data from XTC into detector shape. Stripped
down version of Detector class from pysingfel when that library isn't available.
"""
def __init__(self, geom_file):
"""
Initialize instance of Detector class.
:param geom_file: path to *-end.data geometry file
"""
from PSCalib.GeometryAccess import GeometryAccess
self._geometry = GeometryAccess(geom_file, 0)
self._compute_pixel_index_map()
def _compute_pixel_index_map(self):
"""
Compute various parameters related to detector dimensions and pixel indices
and store as class variables.
"""
# Set coordinate in real space (convert to m)
temp = [
np.asarray(t) * 1e-6 for t in self._geometry.get_pixel_coords()
]
temp_index = [
np.asarray(t) for t in self._geometry.get_pixel_coord_indexes()
]
self._panel_num = np.prod(temp[0].shape[:-2])
self._shape = (self._panel_num, temp[0].shape[-2], temp[0].shape[-1])
pixel_index_map = np.zeros(self._shape + (2, ))
for n in range(2):
pixel_index_map[..., n] = temp_index[n].reshape(self._shape)
self._pixel_index_map = pixel_index_map.astype(np.int64)
self._detector_pixel_num_x = int(
np.max(self._pixel_index_map[..., 0]) + 1)
self._detector_pixel_num_y = int(
np.max(self._pixel_index_map[..., 1]) + 1)
self._panel_index = np.zeros(
(self._detector_pixel_num_x, self._detector_pixel_num_y))
for l in range(self._panel_num):
self._panel_index[self._pixel_index_map[l, :, :, 0],
self._pixel_index_map[l, :, :, 1]] = l + 1
return
def assemble_image_stack(self, data, dtype=np.int64):
"""
Reassemble data retrieved from XTC file into shape of detector.
:param data: list of stacked data from each quad of panels
:param dtype: type of the returned image
:return image: data in shape of detector
"""
data = np.array(data).reshape(self._shape)
image = np.zeros((self._detector_pixel_num_x,
self._detector_pixel_num_y)).astype(dtype)
for l in range(self._panel_num):
image[self._pixel_index_map[l, :, :, 0],
self._pixel_index_map[l, :, :, 1]] = data[l, :, :]
return image
ax = fig.add_subplot(111, aspect='equal')
if os.path.isfile(params.metrology):
# Try dxtbx first to see if this is a regular diffraction image
from dxtbx.format.Registry import Registry
try:
reader = Registry.find(params.metrology)
except IOError:
reader = None
if reader is None:
# see if it's a SLAC geometry file
from PSCalib.GeometryAccess import GeometryAccess
from scitbx import matrix
try:
geometry = GeometryAccess(params.metrology)
except Exception, e:
geometry = None
if geometry is None:
# see if this is a Ginn metrology file (see Helen Ginn et. al. (2015), Acta D Cryst)
panels = []
for line in open(params.metrology).readlines():
if len(line) > 0 and line[0] != '#' and len(line.strip().split()) == 10:
panels.append(line.strip())
if len(panels) != 64:
raise Sorry("Can't parse this metrology file")
print "Ginn metrology file"
for panel_id, panel in enumerate(panels):