def overlayHRS(self, brain, trial, view=1):
if view == 1:
fun = self.vtkView.overlay
elif view == 2:
fun = self.vtkView2.overlay
else:
fun = self.vtkView.overlay
brain = brain[0]
trial = trial[0]
data1 = DataIO.LSMLoad(self.project.brain[brain].trial[trial].dataFile)
data2 = DataIO.LSMLoad(self.project.brain[brain].highResScan.dataFile)
data2 = [data2[0][::-1, :, :].copy(order='C')]
data1 = [
self.resampleData(data1[i], data2[0], data2[0].shape)
for i in range(len(data1))
]
transforms1 = self.project.brain[brain].trial[trial].transforms
transforms2 = self.project.brain[brain].highResScan.transforms
fun(data1=data1,
transforms1=transforms1,
data2=data2,
transforms2=transforms2,
colormap='OverlayHRS')
def overlayVOIHRS(self, brain, trial, view):
"""
camphorapp.overlayVOIHRS(brain, trial, view)
This function overlays the VOIs of a single target trial on top of the high-resolution scan
If the filter that has been used to extract the VOIs (the VOIfilter object of the target trialData object)
allows for a control widget, this also spawns the control widget and passes it to vtkView so that it
can be destroyed when new data is created
:param brain: brain index of the target trial (as list)
:param trial: trial index of the target trial (as list)
:param view: index of the vtkView in which to display the data
:return: nothing
"""
brain = brain[0]
trial = trial[0]
f = self.project.brain[brain].trial[trial].VOIfilter()
f._parameters = self.project.brain[brain].trial[trial].VOIfilterParams
if view == 1:
fun = self.vtkView.overlayVOIsOnStack
VOIPanel = self.vtkView.VOIPanel
elif view == 2:
fun = self.vtkView2.overlayVOIsOnStack
VOIPanel = self.vtkView2.VOIPanel
else:
fun = self.vtkView.overlayVOIsOnStack
VOIPanel = self.vtkView.VOIPanel
VOIdata = self.project.brain[brain].trial[trial].VOIdata.astype(
numpy.uint8)
VOIbase = self.project.brain[brain].trial[trial].VOIbase
stackData = DataIO.LSMLoad(
self.project.brain[brain].highResScan.dataFile)
stackData = [stackData[0][::-1, :, :].copy(order='C')]
VOIdata = [
self.resampleData(VOIdata, stackData[0], stackData[0].shape)
]
VOIbase = self.resampleData(VOIbase, stackData[0], stackData[0].shape)
stackTransforms = self.project.brain[brain].highResScan.transforms
fun(VOIdata=VOIdata,
stackData=stackData,
stackTransforms=stackTransforms,
colormap='Standard')
VOIPanel = f.controlWidget(
self,
VOIbase,
VOIdata[0],
message='[View {:d}] brain{:d}/trial{:d}'.format(
view, brain[i], trial[i]),
dockArea=self.VOIPanelDockArea)
if view == 1:
self.vtkView.VOIPanel = VOIPanel
elif view == 2:
self.vtkView2.VOIPanel = VOIPanel
else:
self.vtkView.VOIPanel = VOIPanel
def overlay(self, brain, trial, view=1):
if view == 1:
fun = self.vtkView.overlay
elif view == 2:
fun = self.vtkView2.overlay
else:
fun = self.vtkView.overlay
data1 = DataIO.LSMLoad(
self.project.brain[brain[0]].trial[trial[0]].dataFile)
data2 = DataIO.LSMLoad(
self.project.brain[brain[1]].trial[trial[1]].dataFile)
transforms1 = self.project.brain[brain[0]].trial[trial[0]].transforms
transforms2 = self.project.brain[brain[1]].trial[trial[1]].transforms
fun(data1=data1,
transforms1=transforms1,
data2=data2,
transforms2=transforms2)
def averageTrials(self, brain, trial, view):
"""
camphorapp.averageTrials(brain, trial, view)
This function displays the average of the selected trials in the specified view
For efficiency, the average is computed here and passed to the vtkView object as stack data
:param brain: brain index of the target trials (as list)
:param trial: trial index of the target trials (as list)
:param view: index of the vtkView in which to display the data
:return: nothing
"""
nTrials = len(brain)
stackData = DataIO.LSMLoad(self.project.brain[0].trial[0].dataFile)
transforms = self.project.brain[0].trial[0].transforms
for t in transforms:
if t.active:
stackData = t.apply(stackData)
averageData = [s.astype(numpy.double) for s in stackData]
nFrames = len(stackData)
for iTrial in range(1, nTrials):
stackData = DataIO.LSMLoad(self.project.brain[brain[iTrial]].trial[
trial[iTrial]].dataFile)
transforms = self.project.brain[brain[iTrial]].trial[
trial[iTrial]].transforms
for t in transforms:
if t.active:
stackData = t.apply(stackData)
for iFrame in range(nFrames):
averageData[iFrame] += stackData[iFrame].astype(numpy.double)
if view == 1:
self.vtkView.assignData([(a / nTrials).astype(numpy.uint8)
for a in averageData])
elif view == 2:
self.vtkView2.assignData([(a / nTrials).astype(numpy.uint8)
for a in averageData])
else:
self.vtkView.assignData([(a / nTrials).astype(numpy.uint8)
for a in averageData])
def showtDiff(self, brain, trial, view=1):
if view == 1:
fun = self.vtkView.showtDiff
elif view == 2:
fun = self.vtkView2.showtDiff
else:
fun = self.vtkView.showtDiff
data = DataIO.LSMLoad(
self.project.brain[brain[0]].trial[trial[0]].dataFile)
transforms = self.project.brain[brain[0]].trial[trial[0]].transforms
fun(data=data, transforms=transforms)
def execute(self, camphor):
# Only first brain, for now
brain = range(camphor.project.nBrains)
brain = [0]
# Determines the total number of trials to do
nBrains = len(brain)
self.nTotal = 0
for b in brain:
self.nTotal += camphor.project.brain[b].nTrials
transformlist = []
self.nDone = 0
for b in brain:
nTrials = camphor.project.brain[b].nTrials
for i in range(nTrials):
# 1. Loads the data
dataFile = camphor.project.brain[b].trial[i].dataFile
data = DataIO.LSMLoad(dataFile)
#Applies the existing transforms
transforms = camphor.project.brain[b].trial[i].transforms
for t in transforms:
if(t.active):
data = t.apply(data)
# 2. Pre-registers
self.message('Pre-registering brain {:d}/{:d}, trial {:d}/{:d}'.format(b + 1, nBrains, i + 1, nTrials),
progress=100 * self.nDone / self.nTotal)
preTransform = self.preRegisterImage(data)
# Applies the pre-transforms in order to calculate the "improved" baseline
data = preTransform.apply(data)
# 2. calculate the mean baseline
baseline = self.calculateBaseline(data, endframe=camphor.ini['baseline_endframe'])
self.message('Registering brain {:d}/{:d}, trial {:d}/{:d}'.format(b+1, nBrains, i+1, nTrials),
progress=100 * (self.nDone+0.5) / self.nTotal)
# 3. Register each timeframe to the baseline
transformlist.append(self.registerImage(baseline, data, camphor.project.brain[b].trial[i], preTransform))
self.nDone += 1
if self.cancelled:
self.cancelled = False
self.message('Registration cancelled', progress=100)
return None
self.message('Registration completed', progress=100)
return transformlist
def overlayRawReg(self, brain, trial, view=1):
"""
Overlays the raw and registered version of the same trial
:param brain:
:param trial:
:param view:
:return:
"""
if view == 1:
fun = self.vtkView.overlay
elif view == 2:
fun = self.vtkView2.overlay
else:
fun = self.vtkView.overlay
data = DataIO.LSMLoad(self.project.brain[brain].trial[trial].dataFile)
transforms = self.project.brain[brain].trial[trial].transforms
fun(data1=data, transforms1=transforms, data2=data)
def execute(self, camphor):
# Only first brain, for now
brain = range(camphor.project.nBrains)
brain = [0]
# Determines the total number of trials to do
nBrains = len(brain)
self.nTotal = 0
for b in brain:
self.nTotal += camphor.project.brain[b].nTrials
transformlist = []
self.nDone = 0
for b in brain:
nTrials = camphor.project.brain[b].nTrials
for iTrial in range(nTrials):
# 1. Loads the data
dataFile = camphor.project.brain[b].trial[iTrial].dataFile
data = DataIO.LSMLoad(dataFile)
#Applies the existing transforms
transforms = camphor.project.brain[b].trial[iTrial].transforms
for t in transforms:
if (t.active):
data = t.apply(data)
# downscales the data
# f = utils.calculatedF(data)
#
# w = numpy.stack(f)
# s = numpy.std(w, 0)
# sm = numpy.median(s)
#
# mask = s > 0.5*sm
mask = None
# c = [ndimage.gaussian_filter(d, 5, order=0) for d in data]
# cf = utils.calculatedF(c)
# mask = cf[0]>5
# for i in range(1,len(cf)):
# mask = numpy.logical_or(mask,cf[i]>5)
# mask = 1-mask
# data = c
# 2. Pre-registers
self.message(
'Pre-registering brain {:d}/{:d}, trial {:d}/{:d}'.format(
b + 1, nBrains, iTrial + 1, nTrials),
progress=100 * self.nDone / self.nTotal)
transformlist.append(
self.preRegisterImage(
data,
camphor.project.brain[b].trial[iTrial],
mask=mask))
self.nDone += 1
if self.cancelled:
self.cancelled = False
self.message('Registration cancelled', progress=100)
return None
self.message('Registration completed', progress=100)
return transformlist
def tAverage(self, brain, trial, view):
"""
camphorapp.tAverage(brain, trial, view)
This function displays the temporal average of the selected trial in the specified view
For efficiency, the average is computed here and passed to the vtkView object as stack data
:param brain: brain index of the target trials (as list)
:param trial: trial index of the target trials (as list)
:param view: index of the vtkView in which to display the data
:return: nothing
"""
stackData = DataIO.LSMLoad(
self.project.brain[brain[0]].trial[trial[0]].dataFile)
transforms = self.project.brain[brain[0]].trial[trial[0]].transforms
for t in transforms:
if t.active:
stackData = t.apply(stackData)
averageData = [
numpy.mean(numpy.stack(stackData), 0).astype(numpy.uint8)
]
VOIdata = self.project.brain[brain[0]].trial[trial[0]].VOIdata
hasVOI = (VOIdata is not None)
if hasVOI:
VOIbase = self.project.brain[brain[0]].trial[trial[0]].VOIbase
f = self.project.brain[brain[0]].trial[trial[0]].VOIfilter()
f._parameters = self.project.brain[brain[0]].trial[
trial[0]].VOIfilterParams
VOIPanel = f.controlWidget(
self,
VOIbase,
VOIdata,
message='[View {:d}] brain{:d}/trial{:d}'.format(
view, brain[0], trial[0]),
dockArea=self.VOIPanelDockArea)
if view == 1:
self.vtkView.overlayVOIsOnStack(VOIdata=[VOIdata],
stackData=averageData,
colormap='Standard',
showVOIs=False)
self.vtkView.VOIPanel = VOIPanel
elif view == 2:
self.vtkView2.overlayVOIsOnStack(VOIdata=[VOIdata],
stackData=averageData,
colormap='Standard',
showVOIs=False)
self.vtkView2.VOIPanel = VOIPanel
else:
self.vtkView.overlayVOIsOnStack(VOIdata=[VOIdata],
stackData=averageData,
colormap='Standard',
showVOIs=False)
self.vtkView.VOIPanel = VOIPanel
else:
if view == 1:
self.vtkView.assignData(averageData)
elif view == 2:
self.vtkView2.assignData(averageData)
else:
self.vtkView.assignData(averageData)
def openFile(self, dub=None, file=None, view=0, transforms=(), flip=False):
"""
camphor.openFile()
This function summons a file dialog and calls the LSMLoad() function from the dataIO module to load the LSM data
:return:
"""
if file is None:
fname = os.path.normpath(
QtGui.QFileDialog.getOpenFileName(
self, 'Open file', os.path.normpath(self.ini['LOADDIR']),
'*.lsm;*.tif'))
else:
fname = file
self.Output("Opening {:s}".format(fname))
if fname != ".":
if view == 0:
self.rawData = DataIO.LSMLoad(fname)
if flip:
self.rawData = [
self.rawData[i][::-1, :, :].copy(order='C')
for i in range(len(self.rawData))
]
self.dataLoaded = True
self.fileName = fname
elif view == 1:
self.rawData1 = DataIO.LSMLoad(fname)
if flip:
self.rawData1 = [
self.rawData1[i][::-1, :, :].copy(order='C')
for i in range(len(self.rawData1))
]
self.dataLoaded1 = True
self.fileName = fname
# Renders the loaded data in the VTK plugin
self.vtkView.assignData(self.rawData1, transforms=transforms)
# Adjusts the window's title
if self.dataLoaded2:
self.setWindowTitle("{:s} - {:s} - {:s}".format(
self.ini['APPNAME'], os.path.basename(self.fileName),
os.path.basename(self.fileName2)))
else:
self.setWindowTitle("{:s} - {:s}".format(
self.ini['APPNAME'], os.path.basename(self.fileName)))
elif view == 2:
self.rawData2 = DataIO.LSMLoad(fname)
if flip:
self.rawData2 = [
self.rawData2[i][::-1, :, :].copy(order='C')
for i in range(len(self.rawData2))
]
self.dataLoaded2 = True
self.fileName2 = fname
# Renders the loaded data in the VTK plugin
self.vtkView2.assignData(self.rawData2, transforms=transforms)
# Adjusts the window's title
if self.dataLoaded:
self.setWindowTitle("{:s} - {:s} - {:s}".format(
self.ini['APPNAME'], os.path.basename(self.fileName),
os.path.basename(self.fileName2)))
else:
self.setWindowTitle("{:s} - {:s}".format(
self.ini['APPNAME'], os.path.basename(self.fileName2)))
else:
print("Error: view {:d} requested".format(view))