def getStatsChan(pipeline, chanName, file):
p = pipeline
p.colourFilter.setColour(chanName)
if chanName == 'Everything':
label = 'Everything'
else:
label = p.fluorSpeciesDyes[chanName]
if 'Camera.CycleTime' in p.mdh.getEntryNames():
t = p.colourFilter['t'].astype('f') * p.mdh.getEntry(
'Camera.CycleTime')
else:
t = p.colourFilter['t'].astype('f') * p.mdh.getEntry(
'Camera.IntegrationTime')
nEvents = t.size
tMax = t.max()
tMedian = np.median(t)
meanPhotons = getPhotonNums(p.colourFilter, p.mdh).mean()
sts = models.EventStats(fileID=file,
label=label,
nEvents=nEvents,
tMax=tMax,
tMedian=tMedian,
meanPhotons=meanPhotons)
sts.save()
return sts
def gphotons(pipeline):
colourFilter = pipeline.colourFilter
metadata = pipeline.mdh
chans = colourFilter.getColourChans()
channame = ''
if len(chans) == 0:
nph = kinModels.getPhotonNums(colourFilter, metadata)
merr = colourFilter['error_x']
return [channame, nph.mean(), merr.mean()]
ret = []
curcol = colourFilter.currentColour
for chan in chans:
channame = pipeline.fluorSpeciesDyes[chan]
colourFilter.setColour(chan)
nph = kinModels.getPhotonNums(colourFilter, metadata)
merr = colourFilter['error_x']
ret.append([channame, nph.mean(), merr.mean()])
colourFilter.setColour(curcol)
return ret
def analyseFile(filename):
print(filename)
seriesName = os.path.splitext(os.path.split(filename)[-1])[0]
PL.ExtendContext({'seriesName':seriesName})
try:
pipe = Pipeline(filename)
except RuntimeError:
print(('Error opening %s' % filename))
PL.PopContext()
return
#only look at first 7k frames
pipe.filterKeys['t'] = (0, 7000)
pipe.Rebuild()
trackUtils.findTracks(pipe, 'error_x', 2, 20)
pipe.Rebuild()
extraParams = {}
extraParams['cycleTime'] = pipe.mdh['Camera.CycleTime']
nPhot = kinModels.getPhotonNums(pipe.colourFilter, pipe.mdh)
extraParams['MedianPhotons'] = np.median(nPhot)
extraParams['MeanPhotons'] = np.mean(nPhot)
extraParams['NEvents'] = len(nPhot)
extraParams['MeanBackground'] = pipe['fitResults_background'].mean() - pipe.mdh['Camera.ADOffset']
extraParams['MedianBackground'] = np.median(pipe['fitResults_background']) - pipe.mdh['Camera.ADOffset']
extraParams['MeanClumpSize'] = pipe['clumpSize'].mean()
extraParams['MeanClumpPhotons'] = (pipe['clumpSize']*nPhot).mean()
PL.AddRecord('/Photophysics/ExtraParams', dictToRecarray(extraParams))
kinModels.fitDecay(pipe)
kinModels.fitFluorBrightness(pipe)
#kinModels.fitFluorBrightnessT(pipe)
#max_off_ts = [3,5,10,20,40]
#max_off_ts = [20]
#for ot in max_off_ts:
#PL.ExtendContext({'otMax':ot})
#find molecules appearing across multiple frames
kinModels.fitOnTimes(pipe)
#PL.PopContext()
pipe.CloseFiles()
PL.PopContext()
def plotphotons(pipeline, color='red'):
nph = km.getPhotonNums(pipeline.colourFilter, pipeline.mdh)
ph_range = 6 * nph.mean()
n, bins = np.histogram(nph, np.linspace(0, ph_range, 100))
plt.bar(bins[:-1], n, width=bins[1] - bins[0], alpha=0.4, color=color)
return nph
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.clear()
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 ds is None:
#load from file
ds = self._ds_from_file(filename, **kwargs)
#wrap the data source with a mapping so we can fiddle with things
#e.g. combining z position and focus
mapped_ds = tabular.MappingFilter(ds)
if 'PixelSize' in kwargs.keys():
mapped_ds.addVariable('pixelSize', kwargs['PixelSize'])
mapped_ds.setMapping('x', 'x*pixelSize')
mapped_ds.setMapping('y', 'y*pixelSize')
#extract information from any events
self.ev_mappings, self.eventCharts = _processEvents(mapped_ds, self.events, self.mdh)
#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:
mapped_ds.addColumn('nPhotons', getPhotonNums(mapped_ds, self.mdh))
if 'SplitterFitFNR' in fitModule:
mapped_ds.addColumn('nPhotonsg', getPhotonNums({'A': mapped_ds['fitResults_Ag'], 'sig': mapped_ds['fitResults_sigma']}, self.mdh))
mapped_ds.addColumn('nPhotonsr', getPhotonNums({'A': mapped_ds['fitResults_Ar'], 'sig': mapped_ds['fitResults_sigma']}, self.mdh))
mapped_ds.setMapping('nPhotons', 'nPhotonsg+nPhotonsr')
if fitModule == 'SplitterShiftEstFR':
self.filterKeys['fitError_dx'] = (0,10)
self.filterKeys['fitError_dy'] = (0,10)
#self._get_dye_ratios_from_metadata()
self.addDataSource('Localizations', mapped_ds)
# Retrieve or estimate image bounds
if False: # 'imgBounds' in kwargs.keys():
self.imageBounds = kwargs['imgBounds']
elif (not (
'scanx' in mapped_ds.keys() or 'scany' in mapped_ds.keys())) and 'Camera.ROIWidth' in self.mdh.getEntryNames():
self.imageBounds = ImageBounds.extractFromMetadata(self.mdh)
else:
self.imageBounds = ImageBounds.estimateFromSource(mapped_ds)
from PYME.recipes.localisations import ProcessColour
from PYME.recipes.tablefilters import FilterTable
self.colour_mapper = ProcessColour(self.recipe, input='Localizations', output='colour_mapped')
#we keep a copy of this so that the colour panel can find it.
self.recipe.add_module(self.colour_mapper)
self.recipe.add_module(FilterTable(self.recipe, inputName='colour_mapped', outputName='filtered_localizations', filters={k:list(v) for k, v in self.filterKeys.items() if k in mapped_ds.keys()}))
self.recipe.execute()
self.filterKeys = {}
self.selectDataSource('filtered_localizations') #NB - this rebuilds the pipeline
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='', 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()