def RegenFilter(self):
if not self.selectedDataSource == None:
self.filter = inpFilt.resultsFilter(self.selectedDataSource, **self.filterKeys)
if self.mapping:
self.mapping.resultsSource = self.filter
else:
self.mapping = inpFilt.mappingFilter(self.filter)
if not self.colourFilter:
self.colourFilter = inpFilt.colourFilter(self.mapping, self)
self.edb = None
self.objects = None
self.GeneratedMeasures = {}
def RegenFilter(self):
if not self.selectedDataSource == None:
self.filter = inpFilt.resultsFilter(self.selectedDataSource,
**self.filterKeys)
if self.mapping:
self.mapping.resultsSource = self.filter
else:
self.mapping = inpFilt.mappingFilter(self.filter)
if not self.colourFilter:
self.colourFilter = inpFilt.colourFilter(self.mapping, self)
self.edb = None
self.objects = None
self.GeneratedMeasures = {}
def OnGenEvents(self, event):
from PYMEnf.Simulation import locify
#from PYME.Acquire.Hardware.Simulator import wormlike2
from PYME.Analysis.LMVis import inpFilt
from PYME.Analysis.LMVis.visHelpers import ImageBounds
import pylab
#wc = wormlike2.wormlikeChain(100)
pipeline = self.visFr.pipeline
pipeline.filename='Simulation'
pylab.figure()
pylab.plot(self.xp, self.yp, 'x') #, lw=2)
if isinstance(self.source, WormlikeSource):
pylab.plot(self.xp, self.yp, lw=2)
res = locify.eventify(self.xp, self.yp, self.meanIntensity, self.meanDuration, self.backgroundIntensity, self.meanEventNumber, self.scaleFactor, self.meanTime)
pylab.plot(res['fitResults']['x0'],res['fitResults']['y0'], '+')
pipeline.selectedDataSource = inpFilt.mappingFilter(inpFilt.fitResultsSource(res))
pipeline.imageBounds = ImageBounds.estimateFromSource(pipeline.selectedDataSource)
pipeline.dataSources.append(pipeline.selectedDataSource)
from PYME.Acquire.MetaDataHandler import NestedClassMDHandler
pipeline.mdh = NestedClassMDHandler()
pipeline.mdh['Camera.ElectronsPerCount'] = 1
pipeline.mdh['Camera.TrueEMGain'] = 1
pipeline.mdh['Camera.CycleTime'] = 1
pipeline.mdh['voxelsize.x'] = .110
try:
pipeline.filterKeys.pop('sig')
except:
pass
self.visFr.RegenFilter()
self.visFr.SetFit()
def generateThumbnail(inputFile, thumbSize):
f1 = inpFilt.h5rSource(inputFile)
threeD = False
stack = False
split = False
# print f1.keys()
if "fitResults_Ag" in f1.keys():
# if we used the splitter set up a mapping so we can filter on total amplitude and ratio
f1_ = inpFilt.mappingFilter(
f1, A="fitResults_Ag + fitResults_Ar", gFrac="fitResults_Ag/(fitResults_Ag + fitResults_Ar)"
)
# f2 = inpFilt.resultsFilter(f1_, error_x=[0,30], A=[5, 1e5], sig=[100/2.35, 350/2.35])
split = True
else:
f1_ = f1
if "fitResults_sigma" in f1.keys():
f2 = inpFilt.resultsFilter(f1_, error_x=[0, 30], A=[5, 1e5], sig=[100 / 2.35, 350 / 2.35])
else:
f2 = inpFilt.resultsFilter(f1_, error_x=[0, 30], A=[5, 1e5])
if "fitResults_z0" in f1_.keys():
threeD = True
if "Events" in dir(f1.h5f.root):
events = f1.h5f.root.Events[:]
evKeyNames = set()
for e in events:
evKeyNames.add(e["EventName"])
if "ProtocolFocus" in evKeyNames:
stack = True
xmax = f2["x"].max()
ymax = f2["y"].max()
if xmax > ymax:
step = xmax / thumbSize
else:
step = ymax / thumbSize
im, edx, edy = histogram2d(f2["x"], f2["y"], [arange(0, xmax, step), arange(0, ymax, step)])
f1.close()
im = minimum(2 * (255 * im) / im.max(), 255).T
im = concatenate((im[:, :, newaxis], im[:, :, newaxis], im[:, :, newaxis]), 2)
if stack:
im[-10:, -10:, 0] = 180
if threeD:
im[-10:, -10:, 1] = 180
if split:
im[-10:-5, :10, 1] = 210
im[-5:, :10, 0] = 210
return im.astype("uint8")
def OpenFile(self, filename):
self.dataSources = []
if 'zm' in dir(self):
del self.zm
self.filter = None
self.mapping = None
self.colourFilter = None
self.filename = filename
self.selectedDataSource = inpFilt.h5rSource(filename)
self.dataSources.append(self.selectedDataSource)
self.mdh = MetaDataHandler.HDFMDHandler(self.selectedDataSource.h5f)
if 'Camera.ROIWidth' in self.mdh.getEntryNames():
x0 = 0
y0 = 0
x1 = self.mdh.getEntry(
'Camera.ROIWidth') * 1e3 * self.mdh.getEntry('voxelsize.x')
y1 = self.mdh.getEntry(
'Camera.ROIHeight') * 1e3 * self.mdh.getEntry('voxelsize.y')
if 'Splitter' in self.mdh.getEntry('Analysis.FitModule'):
y1 = y1 / 2
self.imageBounds = ImageBounds(x0, y0, x1, y1)
else:
self.imageBounds = ImageBounds.estimateFromSource(
self.selectedDataSource)
if 'fitResults_Ag' in self.selectedDataSource.keys():
#if we used the splitter set up a mapping so we can filter on total amplitude and ratio
#if not 'fitError_Ag' in self.selectedDataSource.keys():
if 'fitError_Ag' in self.selectedDataSource.keys():
self.selectedDataSource = inpFilt.mappingFilter(
self.selectedDataSource,
A='fitResults_Ag + fitResults_Ar',
gFrac='fitResults_Ag/(fitResults_Ag + fitResults_Ar)',
error_gFrac=
'sqrt((fitError_Ag/fitResults_Ag)**2 + (fitError_Ag**2 + fitError_Ar**2)/(fitResults_Ag + fitResults_Ar)**2)*fitResults_Ag/(fitResults_Ag + fitResults_Ar)'
)
sg = self.selectedDataSource['fitError_Ag']
sr = self.selectedDataSource['fitError_Ar']
g = self.selectedDataSource['fitResults_Ag']
r = self.selectedDataSource['fitResults_Ar']
I = self.selectedDataSource['A']
self.selectedDataSource.colNorm = np.sqrt(
2 * np.pi) * sg * sr / (2 * np.sqrt(sg**2 + sr**2) * I) * (
scipy.special.erf(
(sg**2 * r + sr**2 * (I - g)) /
(np.sqrt(2) * sg * sr * np.sqrt(sg**2 + sr**2))) -
scipy.special.erf(
(sg**2 * (r - I) - sr**2 * g) /
(np.sqrt(2) * sg * sr * np.sqrt(sg**2 + sr**2))))
self.selectedDataSource.setMapping('ColourNorm', '1.0*colNorm')
else:
self.selectedDataSource = inpFilt.mappingFilter(
self.selectedDataSource,
A='fitResults_Ag + fitResults_Ar',
gFrac='fitResults_Ag/(fitResults_Ag + fitResults_Ar)',
error_gFrac='0*x + 0.01')
self.selectedDataSource.setMapping('fitError_Ag',
'1*sqrt(fitResults_Ag/1)')
self.selectedDataSource.setMapping('fitError_Ar',
'1*sqrt(fitResults_Ar/1)')
sg = self.selectedDataSource['fitError_Ag']
sr = self.selectedDataSource['fitError_Ar']
g = self.selectedDataSource['fitResults_Ag']
r = self.selectedDataSource['fitResults_Ar']
I = self.selectedDataSource['A']
self.selectedDataSource.colNorm = np.sqrt(
2 * np.pi) * sg * sr / (2 * np.sqrt(sg**2 + sr**2) * I) * (
scipy.special.erf(
(sg**2 * r + sr**2 * (I - g)) /
(np.sqrt(2) * sg * sr * np.sqrt(sg**2 + sr**2))) -
scipy.special.erf(
(sg**2 * (r - I) - sr**2 * g) /
(np.sqrt(2) * sg * sr * np.sqrt(sg**2 + sr**2))))
self.selectedDataSource.setMapping('ColourNorm', '1.0*colNorm')
self.dataSources.append(self.selectedDataSource)
elif 'fitResults_sigxl' in self.selectedDataSource.keys():
self.selectedDataSource = inpFilt.mappingFilter(
self.selectedDataSource)
self.dataSources.append(self.selectedDataSource)
self.selectedDataSource.setMapping(
'sig', 'fitResults_sigxl + fitResults_sigyu')
self.selectedDataSource.setMapping(
'sig_d', 'fitResults_sigxl - fitResults_sigyu')
self.selectedDataSource.dsigd_dz = -30.
self.selectedDataSource.setMapping('fitResults_z0',
'dsigd_dz*sig_d')
else:
self.selectedDataSource = inpFilt.mappingFilter(
self.selectedDataSource)
self.dataSources.append(self.selectedDataSource)
if 'Events' in self.selectedDataSource.resultsSource.h5f.root:
self.events = self.selectedDataSource.resultsSource.h5f.root.Events[:]
evKeyNames = set()
for e in self.events:
evKeyNames.add(e['EventName'])
if 'ProtocolFocus' in evKeyNames:
self.zm = piecewiseMapping.GeneratePMFromEventList(
self.events, self.mdh, self.mdh.getEntry('StartTime'),
self.mdh.getEntry('Protocol.PiezoStartPos'))
self.z_focus = 1.e3 * self.zm(self.selectedDataSource['t'])
#self.elv.SetCharts([('Focus [um]', self.zm, 'ProtocolFocus'),])
self.selectedDataSource.z_focus = self.z_focus
self.selectedDataSource.setMapping('focus', 'z_focus')
if 'ScannerXPos' in evKeyNames:
x0 = 0
if 'Positioning.Stage_X' in self.mdh.getEntryNames():
x0 = self.mdh.getEntry('Positioning.Stage_X')
self.xm = piecewiseMapping.GeneratePMFromEventList(
self.elv.eventSource, self.mdh,
self.mdh.getEntry('StartTime'), x0, 'ScannerXPos', 0)
self.selectedDataSource.scan_x = 1.e3 * self.xm(
self.selectedDataSource['t'] - .01)
self.selectedDataSource.setMapping('ScannerX', 'scan_x')
self.selectedDataSource.setMapping('x', 'x + scan_x')
if 'ScannerYPos' in evKeyNames:
y0 = 0
if 'Positioning.Stage_Y' in self.mdh.getEntryNames():
y0 = self.mdh.getEntry('Positioning.Stage_Y')
self.ym = piecewiseMapping.GeneratePMFromEventList(
self.elv.eventSource, self.mdh,
self.mdh.getEntry('StartTime'), y0, 'ScannerYPos', 0)
self.selectedDataSource.scan_y = 1.e3 * self.ym(
self.selectedDataSource['t'] - .01)
self.selectedDataSource.setMapping('ScannerY', 'scan_y')
self.selectedDataSource.setMapping('y', 'y + scan_y')
if 'ScannerXPos' in evKeyNames or 'ScannerYPos' in evKeyNames:
self.imageBounds = ImageBounds.estimateFromSource(
self.selectedDataSource)
if not 'foreShort' in dir(self.selectedDataSource):
self.selectedDataSource.foreShort = 1.
if not 'focus' in self.selectedDataSource.mappings.keys():
self.selectedDataSource.focus = np.zeros(
self.selectedDataSource['x'].shape)
if 'fitResults_z0' in self.selectedDataSource.keys():
self.selectedDataSource.setMapping(
'z', 'fitResults_z0 + foreShort*focus')
else:
self.selectedDataSource.setMapping('z', 'foreShort*focus')
#if we've done a 3d fit
#print self.selectedDataSource.keys()
for k in self.filterKeys.keys():
if not k in self.selectedDataSource.keys():
self.filterKeys.pop(k)
#print self.filterKeys
self.RegenFilter()
if 'Sample.Labelling' in self.mdh.getEntryNames():
self.SpecFromMetadata()
def OpenFile(self, filename= '', ds = None, **kwargs):
'''Open a file - accepts optional keyword arguments for use with files
saved as .txt and .mat. These are:
FieldNames: a list of names for the fields in the text file or
matlab variable.
VarName: the name of the variable in the .mat file which
contains the data.
SkipRows: Number of header rows to skip for txt file data
PixelSize: Pixel size if not in nm
'''
#close any files we had open previously
while len(self.filesToClose) > 0:
self.filesToClose.pop().close()
#clear our state
self.dataSources = []
if 'zm' in dir(self):
del self.zm
self.filter = None
self.mapping = None
self.colourFilter = None
self.events = None
self.mdh = MetaDataHandler.NestedClassMDHandler()
self.filename = filename
if not ds is None:
self.selectedDataSource = ds
self.dataSources.append(ds)
elif os.path.splitext(filename)[1] == '.h5r':
try:
self.selectedDataSource = inpFilt.h5rSource(filename)
self.dataSources.append(self.selectedDataSource)
self.filesToClose.append(self.selectedDataSource.h5f)
if 'DriftResults' in self.selectedDataSource.h5f.root:
self.dataSources.append(inpFilt.h5rDSource(self.selectedDataSource.h5f))
if len(self.selectedDataSource['x']) == 0:
self.selectedDataSource = self.dataSources[-1]
except: #fallback to catch series that only have drift data
self.selectedDataSource = inpFilt.h5rDSource(filename)
self.dataSources.append(self.selectedDataSource)
self.filesToClose.append(self.selectedDataSource.h5f)
#catch really old files which don't have any metadata
if 'MetaData' in self.selectedDataSource.h5f.root:
self.mdh = MetaDataHandler.HDFMDHandler(self.selectedDataSource.h5f)
if ('Events' in self.selectedDataSource.h5f.root) and ('StartTime' in self.mdh.keys()):
self.events = self.selectedDataSource.h5f.root.Events[:]
elif os.path.splitext(filename)[1] == '.mat': #matlab file
ds = inpFilt.matfileSource(filename, kwargs['FieldNames'], kwargs['VarName'])
self.selectedDataSource = ds
self.dataSources.append(ds)
elif os.path.splitext(filename)[1] == '.csv':
#special case for csv files - tell np.loadtxt to use a comma rather than whitespace as a delimeter
if 'SkipRows' in kwargs.keys():
ds = inpFilt.textfileSource(filename, kwargs['FieldNames'], delimiter=',', skiprows=kwargs['SkipRows'])
else:
ds = inpFilt.textfileSource(filename, kwargs['FieldNames'], delimiter=',')
self.selectedDataSource = ds
self.dataSources.append(ds)
else: #assume it's a tab (or other whitespace) delimited text file
if 'SkipRows' in kwargs.keys():
ds = inpFilt.textfileSource(filename, kwargs['FieldNames'], skiprows=kwargs['SkipRows'])
else:
ds = inpFilt.textfileSource(filename, kwargs['FieldNames'])
self.selectedDataSource = ds
self.dataSources.append(ds)
#wrap the data source with a mapping so we can fiddle with things
#e.g. combining z position and focus
self.inputMapping = inpFilt.mappingFilter(self.selectedDataSource)
self.selectedDataSource = self.inputMapping
self.dataSources.append(self.inputMapping)
if 'PixelSize' in kwargs.keys():
self.selectedDataSource.pixelSize = kwargs['PixelSize']
self.selectedDataSource.setMapping('x', 'x*pixelSize')
self.selectedDataSource.setMapping('y', 'y*pixelSize')
#Retrieve or estimate image bounds
if 'Camera.ROIWidth' in self.mdh.getEntryNames():
x0 = 0
y0 = 0
x1 = self.mdh.getEntry('Camera.ROIWidth')*1e3*self.mdh.getEntry('voxelsize.x')
y1 = self.mdh.getEntry('Camera.ROIHeight')*1e3*self.mdh.getEntry('voxelsize.y')
if 'Splitter' in self.mdh.getEntry('Analysis.FitModule'):
if 'Splitter.Channel0ROI' in self.mdh.getEntryNames():
rx0, ry0, rw, rh = self.mdh['Splitter.Channel0ROI']
x1 = rw*1e3*self.mdh.getEntry('voxelsize.x')
x1 = rh*1e3*self.mdh.getEntry('voxelsize.x')
else:
y1 = y1/2
self.imageBounds = ImageBounds(x0, y0, x1, y1)
else:
self.imageBounds = ImageBounds.estimateFromSource(self.selectedDataSource)
#extract information from any events
self._processEvents()
#handle special cases which get detected by looking for the presence or
#absence of certain variables in the data.
if 'fitResults_Ag' in self.selectedDataSource.keys():
#if we used the splitter set up a number of mappings e.g. total amplitude and ratio
self._processSplitter()
if 'fitResults_ratio' in self.selectedDataSource.keys():
#if we used the splitter set up a number of mappings e.g. total amplitude and ratio
self._processPriSplit()
if 'fitResults_sigxl' in self.selectedDataSource.keys():
#fast, quickpalm like astigmatic fitting
self.selectedDataSource.setMapping('sig', 'fitResults_sigxl + fitResults_sigyu')
self.selectedDataSource.setMapping('sig_d', 'fitResults_sigxl - fitResults_sigyu')
self.selectedDataSource.dsigd_dz = -30.
self.selectedDataSource.setMapping('fitResults_z0', 'dsigd_dz*sig_d')
if not 'y' in self.selectedDataSource.keys():
self.selectedDataSource.setMapping('y', '10*t')
#set up correction for foreshortening and z focus stepping
if not 'foreShort' in dir(self.selectedDataSource):
self.selectedDataSource.foreShort = 1.
if not 'focus' in self.selectedDataSource.mappings.keys():
self.selectedDataSource.focus= np.zeros(self.selectedDataSource['x'].shape)
if 'fitResults_z0' in self.selectedDataSource.keys():
self.selectedDataSource.setMapping('z', 'fitResults_z0 + foreShort*focus')
elif not 'z' in self.selectedDataSource.keys():
self.selectedDataSource.setMapping('z', 'foreShort*focus')
#Fit module specific filter settings
if 'Analysis.FitModule' in self.mdh.getEntryNames():
fitModule = self.mdh['Analysis.FitModule']
print 'fitModule = %s' % fitModule
if 'Interp' in fitModule:
self.filterKeys['A'] = (5, 100000)
if 'LatGaussFitFR' in fitModule:
self.selectedDataSource.nPhot = getPhotonNums(self.selectedDataSource, self.mdh)
self.selectedDataSource.setMapping('nPhotons', 'nPhot')
if fitModule == 'SplitterShiftEstFR':
self.filterKeys['fitError_dx'] = (0,10)
self.filterKeys['fitError_dy'] = (0,10)
#remove any keys from the filter which are not present in the data
for k in self.filterKeys.keys():
if not k in self.selectedDataSource.keys():
self.filterKeys.pop(k)
self.Rebuild()
if 'Sample.Labelling' in self.mdh.getEntryNames() and 'gFrac' in self.selectedDataSource.keys():
self.SpecFromMetadata()
def generateThumbnail(inputFile, thumbSize):
f1 = inpFilt.h5rSource(inputFile)
threeD = False
stack = False
split = False
#print f1.keys()
if 'fitResults_Ag' in f1.keys():
#if we used the splitter set up a mapping so we can filter on total amplitude and ratio
f1_ = inpFilt.mappingFilter(
f1,
A='fitResults_Ag + fitResults_Ar',
gFrac='fitResults_Ag/(fitResults_Ag + fitResults_Ar)')
#f2 = inpFilt.resultsFilter(f1_, error_x=[0,30], A=[5, 1e5], sig=[100/2.35, 350/2.35])
split = True
else:
f1_ = f1
if 'fitResults_sigma' in f1.keys():
f2 = inpFilt.resultsFilter(f1_,
error_x=[0, 30],
A=[5, 1e5],
sig=[100 / 2.35, 350 / 2.35])
else:
f2 = inpFilt.resultsFilter(f1_, error_x=[0, 30], A=[5, 1e5])
if 'fitResults_z0' in f1_.keys():
threeD = True
if 'Events' in dir(f1.h5f.root):
events = f1.h5f.root.Events[:]
evKeyNames = set()
for e in events:
evKeyNames.add(e['EventName'])
if 'ProtocolFocus' in evKeyNames:
stack = True
xmax = f2['x'].max()
ymax = f2['y'].max()
if xmax > ymax:
step = xmax / thumbSize
else:
step = ymax / thumbSize
im, edx, edy = histogram2d(
f2['x'], f2['y'],
[arange(0, xmax, step), arange(0, ymax, step)])
f1.close()
im = minimum(2 * (255 * im) / im.max(), 255).T
im = concatenate((im[:, :, newaxis], im[:, :, newaxis], im[:, :, newaxis]),
2)
if stack:
im[-10:, -10:, 0] = 180
if threeD:
im[-10:, -10:, 1] = 180
if split:
im[-10:-5, :10, 1] = 210
im[-5:, :10, 0] = 210
return im.astype('uint8')
def OpenFile(self, filename='', ds=None, **kwargs):
'''Open a file - accepts optional keyword arguments for use with files
saved as .txt and .mat. These are:
FieldNames: a list of names for the fields in the text file or
matlab variable.
VarName: the name of the variable in the .mat file which
contains the data.
SkipRows: Number of header rows to skip for txt file data
PixelSize: Pixel size if not in nm
'''
#close any files we had open previously
while len(self.filesToClose) > 0:
self.filesToClose.pop().close()
#clear our state
self.dataSources = []
if 'zm' in dir(self):
del self.zm
self.filter = None
self.mapping = None
self.colourFilter = None
self.events = None
self.mdh = MetaDataHandler.NestedClassMDHandler()
self.filename = filename
if not ds is None:
self.selectedDataSource = ds
self.dataSources.append(ds)
elif os.path.splitext(filename)[1] == '.h5r':
try:
self.selectedDataSource = inpFilt.h5rSource(filename)
self.dataSources.append(self.selectedDataSource)
self.filesToClose.append(self.selectedDataSource.h5f)
if 'DriftResults' in self.selectedDataSource.h5f.root:
self.dataSources.append(
inpFilt.h5rDSource(self.selectedDataSource.h5f))
if len(self.selectedDataSource['x']) == 0:
self.selectedDataSource = self.dataSources[-1]
except: #fallback to catch series that only have drift data
self.selectedDataSource = inpFilt.h5rDSource(filename)
self.dataSources.append(self.selectedDataSource)
self.filesToClose.append(self.selectedDataSource.h5f)
#catch really old files which don't have any metadata
if 'MetaData' in self.selectedDataSource.h5f.root:
self.mdh = MetaDataHandler.HDFMDHandler(
self.selectedDataSource.h5f)
if ('Events' in self.selectedDataSource.h5f.root) and (
'StartTime' in self.mdh.keys()):
self.events = self.selectedDataSource.h5f.root.Events[:]
elif os.path.splitext(filename)[1] == '.mat': #matlab file
ds = inpFilt.matfileSource(filename, kwargs['FieldNames'],
kwargs['VarName'])
self.selectedDataSource = ds
self.dataSources.append(ds)
elif os.path.splitext(filename)[1] == '.csv':
#special case for csv files - tell np.loadtxt to use a comma rather than whitespace as a delimeter
if 'SkipRows' in kwargs.keys():
ds = inpFilt.textfileSource(filename,
kwargs['FieldNames'],
delimiter=',',
skiprows=kwargs['SkipRows'])
else:
ds = inpFilt.textfileSource(filename,
kwargs['FieldNames'],
delimiter=',')
self.selectedDataSource = ds
self.dataSources.append(ds)
else: #assume it's a tab (or other whitespace) delimited text file
if 'SkipRows' in kwargs.keys():
ds = inpFilt.textfileSource(filename,
kwargs['FieldNames'],
skiprows=kwargs['SkipRows'])
else:
ds = inpFilt.textfileSource(filename, kwargs['FieldNames'])
self.selectedDataSource = ds
self.dataSources.append(ds)
#wrap the data source with a mapping so we can fiddle with things
#e.g. combining z position and focus
self.inputMapping = inpFilt.mappingFilter(self.selectedDataSource)
self.selectedDataSource = self.inputMapping
self.dataSources.append(self.inputMapping)
if 'PixelSize' in kwargs.keys():
self.selectedDataSource.pixelSize = kwargs['PixelSize']
self.selectedDataSource.setMapping('x', 'x*pixelSize')
self.selectedDataSource.setMapping('y', 'y*pixelSize')
#Retrieve or estimate image bounds
if 'Camera.ROIWidth' in self.mdh.getEntryNames():
x0 = 0
y0 = 0
x1 = self.mdh.getEntry(
'Camera.ROIWidth') * 1e3 * self.mdh.getEntry('voxelsize.x')
y1 = self.mdh.getEntry(
'Camera.ROIHeight') * 1e3 * self.mdh.getEntry('voxelsize.y')
if 'Splitter' in self.mdh.getEntry('Analysis.FitModule'):
if 'Splitter.Channel0ROI' in self.mdh.getEntryNames():
rx0, ry0, rw, rh = self.mdh['Splitter.Channel0ROI']
x1 = rw * 1e3 * self.mdh.getEntry('voxelsize.x')
x1 = rh * 1e3 * self.mdh.getEntry('voxelsize.x')
else:
y1 = y1 / 2
self.imageBounds = ImageBounds(x0, y0, x1, y1)
else:
self.imageBounds = ImageBounds.estimateFromSource(
self.selectedDataSource)
#extract information from any events
self._processEvents()
#handle special cases which get detected by looking for the presence or
#absence of certain variables in the data.
if 'fitResults_Ag' in self.selectedDataSource.keys():
#if we used the splitter set up a number of mappings e.g. total amplitude and ratio
self._processSplitter()
if 'fitResults_ratio' in self.selectedDataSource.keys():
#if we used the splitter set up a number of mappings e.g. total amplitude and ratio
self._processPriSplit()
if 'fitResults_sigxl' in self.selectedDataSource.keys():
#fast, quickpalm like astigmatic fitting
self.selectedDataSource.setMapping(
'sig', 'fitResults_sigxl + fitResults_sigyu')
self.selectedDataSource.setMapping(
'sig_d', 'fitResults_sigxl - fitResults_sigyu')
self.selectedDataSource.dsigd_dz = -30.
self.selectedDataSource.setMapping('fitResults_z0',
'dsigd_dz*sig_d')
if not 'y' in self.selectedDataSource.keys():
self.selectedDataSource.setMapping('y', '10*t')
#set up correction for foreshortening and z focus stepping
if not 'foreShort' in dir(self.selectedDataSource):
self.selectedDataSource.foreShort = 1.
if not 'focus' in self.selectedDataSource.mappings.keys():
self.selectedDataSource.focus = np.zeros(
self.selectedDataSource['x'].shape)
if 'fitResults_z0' in self.selectedDataSource.keys():
self.selectedDataSource.setMapping(
'z', 'fitResults_z0 + foreShort*focus')
elif not 'z' in self.selectedDataSource.keys():
self.selectedDataSource.setMapping('z', 'foreShort*focus')
#Fit module specific filter settings
if 'Analysis.FitModule' in self.mdh.getEntryNames():
fitModule = self.mdh['Analysis.FitModule']
print 'fitModule = %s' % fitModule
if 'Interp' in fitModule:
self.filterKeys['A'] = (5, 100000)
if 'LatGaussFitFR' in fitModule:
self.selectedDataSource.nPhot = getPhotonNums(
self.selectedDataSource, self.mdh)
self.selectedDataSource.setMapping('nPhotons', 'nPhot')
if fitModule == 'SplitterShiftEstFR':
self.filterKeys['fitError_dx'] = (0, 10)
self.filterKeys['fitError_dy'] = (0, 10)
#remove any keys from the filter which are not present in the data
for k in self.filterKeys.keys():
if not k in self.selectedDataSource.keys():
self.filterKeys.pop(k)
self.Rebuild()
if 'Sample.Labelling' in self.mdh.getEntryNames(
) and 'gFrac' in self.selectedDataSource.keys():
self.SpecFromMetadata()
def OpenFile(self, filename):
self.dataSources = []
if 'zm' in dir(self):
del self.zm
self.filter = None
self.mapping = None
self.colourFilter = None
self.filename = filename
self.selectedDataSource = inpFilt.h5rSource(filename)
self.dataSources.append(self.selectedDataSource)
self.mdh = MetaDataHandler.HDFMDHandler(self.selectedDataSource.h5f)
if 'Camera.ROIWidth' in self.mdh.getEntryNames():
x0 = 0
y0 = 0
x1 = self.mdh.getEntry('Camera.ROIWidth')*1e3*self.mdh.getEntry('voxelsize.x')
y1 = self.mdh.getEntry('Camera.ROIHeight')*1e3*self.mdh.getEntry('voxelsize.y')
if 'Splitter' in self.mdh.getEntry('Analysis.FitModule'):
y1 = y1/2
self.imageBounds = ImageBounds(x0, y0, x1, y1)
else:
self.imageBounds = ImageBounds.estimateFromSource(self.selectedDataSource)
if 'fitResults_Ag' in self.selectedDataSource.keys():
#if we used the splitter set up a mapping so we can filter on total amplitude and ratio
#if not 'fitError_Ag' in self.selectedDataSource.keys():
if 'fitError_Ag' in self.selectedDataSource.keys():
self.selectedDataSource = inpFilt.mappingFilter(self.selectedDataSource, A='fitResults_Ag + fitResults_Ar', gFrac='fitResults_Ag/(fitResults_Ag + fitResults_Ar)', error_gFrac = 'sqrt((fitError_Ag/fitResults_Ag)**2 + (fitError_Ag**2 + fitError_Ar**2)/(fitResults_Ag + fitResults_Ar)**2)*fitResults_Ag/(fitResults_Ag + fitResults_Ar)')
sg = self.selectedDataSource['fitError_Ag']
sr = self.selectedDataSource['fitError_Ar']
g = self.selectedDataSource['fitResults_Ag']
r = self.selectedDataSource['fitResults_Ar']
I = self.selectedDataSource['A']
self.selectedDataSource.colNorm = np.sqrt(2*np.pi)*sg*sr/(2*np.sqrt(sg**2 + sr**2)*I)*(
scipy.special.erf((sg**2*r + sr**2*(I-g))/(np.sqrt(2)*sg*sr*np.sqrt(sg**2+sr**2)))
- scipy.special.erf((sg**2*(r-I) - sr**2*g)/(np.sqrt(2)*sg*sr*np.sqrt(sg**2+sr**2))))
self.selectedDataSource.setMapping('ColourNorm', '1.0*colNorm')
else:
self.selectedDataSource = inpFilt.mappingFilter(self.selectedDataSource, A='fitResults_Ag + fitResults_Ar', gFrac='fitResults_Ag/(fitResults_Ag + fitResults_Ar)', error_gFrac = '0*x + 0.01')
self.selectedDataSource.setMapping('fitError_Ag', '1*sqrt(fitResults_Ag/1)')
self.selectedDataSource.setMapping('fitError_Ar', '1*sqrt(fitResults_Ar/1)')
sg = self.selectedDataSource['fitError_Ag']
sr = self.selectedDataSource['fitError_Ar']
g = self.selectedDataSource['fitResults_Ag']
r = self.selectedDataSource['fitResults_Ar']
I = self.selectedDataSource['A']
self.selectedDataSource.colNorm = np.sqrt(2*np.pi)*sg*sr/(2*np.sqrt(sg**2 + sr**2)*I)*(
scipy.special.erf((sg**2*r + sr**2*(I-g))/(np.sqrt(2)*sg*sr*np.sqrt(sg**2+sr**2)))
- scipy.special.erf((sg**2*(r-I) - sr**2*g)/(np.sqrt(2)*sg*sr*np.sqrt(sg**2+sr**2))))
self.selectedDataSource.setMapping('ColourNorm', '1.0*colNorm')
self.dataSources.append(self.selectedDataSource)
elif 'fitResults_sigxl' in self.selectedDataSource.keys():
self.selectedDataSource = inpFilt.mappingFilter(self.selectedDataSource)
self.dataSources.append(self.selectedDataSource)
self.selectedDataSource.setMapping('sig', 'fitResults_sigxl + fitResults_sigyu')
self.selectedDataSource.setMapping('sig_d', 'fitResults_sigxl - fitResults_sigyu')
self.selectedDataSource.dsigd_dz = -30.
self.selectedDataSource.setMapping('fitResults_z0', 'dsigd_dz*sig_d')
else:
self.selectedDataSource = inpFilt.mappingFilter(self.selectedDataSource)
self.dataSources.append(self.selectedDataSource)
if 'Events' in self.selectedDataSource.resultsSource.h5f.root:
self.events = self.selectedDataSource.resultsSource.h5f.root.Events[:]
evKeyNames = set()
for e in self.events:
evKeyNames.add(e['EventName'])
if 'ProtocolFocus' in evKeyNames:
self.zm = piecewiseMapping.GeneratePMFromEventList(self.events, self.mdh, self.mdh.getEntry('StartTime'), self.mdh.getEntry('Protocol.PiezoStartPos'))
self.z_focus = 1.e3*self.zm(self.selectedDataSource['t'])
#self.elv.SetCharts([('Focus [um]', self.zm, 'ProtocolFocus'),])
self.selectedDataSource.z_focus = self.z_focus
self.selectedDataSource.setMapping('focus', 'z_focus')
if 'ScannerXPos' in evKeyNames:
x0 = 0
if 'Positioning.Stage_X' in self.mdh.getEntryNames():
x0 = self.mdh.getEntry('Positioning.Stage_X')
self.xm = piecewiseMapping.GeneratePMFromEventList(self.elv.eventSource, self.mdh, self.mdh.getEntry('StartTime'), x0, 'ScannerXPos', 0)
self.selectedDataSource.scan_x = 1.e3*self.xm(self.selectedDataSource['t']-.01)
self.selectedDataSource.setMapping('ScannerX', 'scan_x')
self.selectedDataSource.setMapping('x', 'x + scan_x')
if 'ScannerYPos' in evKeyNames:
y0 = 0
if 'Positioning.Stage_Y' in self.mdh.getEntryNames():
y0 = self.mdh.getEntry('Positioning.Stage_Y')
self.ym = piecewiseMapping.GeneratePMFromEventList(self.elv.eventSource, self.mdh, self.mdh.getEntry('StartTime'), y0, 'ScannerYPos', 0)
self.selectedDataSource.scan_y = 1.e3*self.ym(self.selectedDataSource['t']-.01)
self.selectedDataSource.setMapping('ScannerY', 'scan_y')
self.selectedDataSource.setMapping('y', 'y + scan_y')
if 'ScannerXPos' in evKeyNames or 'ScannerYPos' in evKeyNames:
self.imageBounds = ImageBounds.estimateFromSource(self.selectedDataSource)
if not 'foreShort' in dir(self.selectedDataSource):
self.selectedDataSource.foreShort = 1.
if not 'focus' in self.selectedDataSource.mappings.keys():
self.selectedDataSource.focus= np.zeros(self.selectedDataSource['x'].shape)
if 'fitResults_z0' in self.selectedDataSource.keys():
self.selectedDataSource.setMapping('z', 'fitResults_z0 + foreShort*focus')
else:
self.selectedDataSource.setMapping('z', 'foreShort*focus')
#if we've done a 3d fit
#print self.selectedDataSource.keys()
for k in self.filterKeys.keys():
if not k in self.selectedDataSource.keys():
self.filterKeys.pop(k)
#print self.filterKeys
self.RegenFilter()
if 'Sample.Labelling' in self.mdh.getEntryNames():
self.SpecFromMetadata()
