def alignZeroShiftImages(imagedata1,imagedata2,bin):
"""Align start-angle images for tilt series where data is collect in two halves"""
no_wx=True
bin = int(bin)
if not no_wx:
# Tilt correlator needs wx which is not available on the cluster
# This method is consistent with the rest of leginonxcorr and will
# revert correction channels is needed even though it is likely
# unnecessary
fakenode = node.Node('fake',imagedata1['session'])
correlator = tiltcorrelator.Correlator(fakenode, 0, bin,lpf=1.5)
for imagedata in (imagedata1,imagedata2):
correlator.correlate(imagedata, tiltcorrection=True, channel=None)
peak = correlator.getShift(False)
fakenode.die()
else:
# phase correlation
array1 = imagedata1['image']
array2 = imagedata2['image']
if bin != 1:
array1 = imagefun.bin(array1, bin)
array2 = imagefun.bin(array2, bin)
shift = simpleCorrelation(array1,array2)
peak = {'x':-shift[1]*bin,'y':shift[0]*bin}
# x (row) shift on image coordinate is of opposite sign
return {'shiftx':-peak['x'], 'shifty':peak['y']}
def alignZeroShiftImages(imagedata1, imagedata2, bin):
"""Align start-angle images for tilt series where data is collect in two halves"""
no_wx = True
bin = int(bin)
if not no_wx:
# Tilt correlator needs wx which is not available on the cluster
# This method is consistent with the rest of leginonxcorr and will
# revert correction channels is needed even though it is likely
# unnecessary
fakenode = node.Node('fake', imagedata1['session'])
correlator = tiltcorrelator.Correlator(fakenode, 0, bin, lpf=1.5)
for imagedata in (imagedata1, imagedata2):
correlator.correlate(imagedata, tiltcorrection=True, channel=None)
peak = correlator.getShift(False)
fakenode.die()
else:
# phase correlation
array1 = imagedata1['image']
array2 = imagedata2['image']
if bin != 1:
array1 = imagefun.bin(array1, bin)
array2 = imagefun.bin(array2, bin)
shift = simpleCorrelation(array1, array2)
peak = {'x': -shift[1] * bin, 'y': shift[0] * bin}
# x (row) shift on image coordinate is of opposite sign
return {'shiftx': -peak['x'], 'shifty': peak['y']}
def getCorrelationPeak(correlator, bin, tiltseries, tilt, imagedata, allpeaks,
second_group):
peak = getPredictionPeakForImage(imagedata)
start = tiltseries['tilt start']
if tilt < start:
peak['x'] = -peak['x']
peak['y'] = -peak['y']
if tilt < tiltseries['tilt start'] + 0.01 and tilt > tiltseries[
'tilt start'] - 0.01:
correlator.correlate(imagedata, tiltcorrection=True, channel=None)
if second_group:
allpeaks = [{'x': 0.0, 'y': 0.0}]
peak = correlator.getShift(False)
if tiltseries['tilt step'] > 0:
return (-peak['x'] / bin,
peak['y'] / bin), allpeaks, second_group
else:
return (peak['x'] / bin,
-peak['y'] / bin), allpeaks, second_group
else:
second_group = True
return None, allpeaks, second_group
allpeaks.append(peak)
return {
'x': (peak['x'] - allpeaks[-2]['x']) / bin,
'y': -(peak['y'] - allpeaks[-2]['y']) / bin
}, allpeaks, second_group
def getCorrelationPeak(correlator, bin, tiltseries, tilt, imagedata,allpeaks,second_group):
peak = getPredictionPeakForImage(imagedata)
start = tiltseries['tilt start']
if tilt < start:
peak['x']=-peak['x']
peak['y']=-peak['y']
if tilt < tiltseries['tilt start']+0.01 and tilt > tiltseries['tilt start']-0.01:
correlator.correlate(imagedata, tiltcorrection=True, channel=None)
if second_group:
allpeaks = [{'x':0.0,'y':0.0}]
peak = correlator.getShift(False)
if tiltseries['tilt step'] > 0:
return (-peak['x']/bin,peak['y']/bin),allpeaks,second_group
else:
return (peak['x']/bin,-peak['y']/bin),allpeaks,second_group
else:
second_group = True
return None,allpeaks,second_group
allpeaks.append(peak)
return {'x':(peak['x']-allpeaks[-2]['x'])/bin, 'y':-(peak['y']-allpeaks[-2]['y'])/bin},allpeaks,second_group
def getCorrelationPeak(correlator, bin, tiltseries, tilt, imagedata, allpeaks, second_group):
peak = getPredictionPeakForImage(imagedata)
start = tiltseries["tilt start"]
if tilt < start:
peak["x"] = -peak["x"]
peak["y"] = -peak["y"]
if tilt < tiltseries["tilt start"] + 0.01 and tilt > tiltseries["tilt start"] - 0.01:
correlator.correlate(imagedata, tiltcorrection=True, channel=None)
if second_group:
allpeaks = [{"x": 0.0, "y": 0.0}]
peak = correlator.getShift(False)
if tiltseries["tilt step"] > 0:
return (-peak["x"] / bin, peak["y"] / bin), allpeaks, second_group
else:
return (peak["x"] / bin, -peak["y"] / bin), allpeaks, second_group
else:
second_group = True
return None, allpeaks, second_group
allpeaks.append(peak)
return (
{"x": (peak["x"] - allpeaks[-2]["x"]) / bin, "y": -(peak["y"] - allpeaks[-2]["y"]) / bin},
allpeaks,
second_group,
)