def loadI(i,bitmasks,prinROI):
#refreshes the display on the GUI, declared here due to use in initialisation
if typ == 3:
#self.im.set_data(self.data[i])
self.ax_img.imshow(self.data[i],aspect='equal',cmap=plt.cm.gray,interpolation='nearest')
for j in range(len(bitmasks[i])):
self.ax_img.imshow(seg.overlay_labels(bitmasks[i][j]),aspect='equal',interpolation='nearest')
for prin in prinROI:
if prin == [i,j]:
self.ax_img.imshow(seg.overlay_full(bitmasks[i][j]),aspect='equal',interpolation='nearest')
if typ == 2:
self.ax_img.imshow(self.avg,aspect='equal',cmap=plt.cm.gray,interpolation='nearest')
for j in range(len(bitmasks)):
self.ax_img.imshow(seg.overlay_labels(bitmasks[j]),aspect='equal',interpolation='nearest')
"""
def __init__(self,x = None, m = None):
xMic,yMic = None,None
try:
path = sys.argv[1]
except:
print 'Please enter the path to the .xml metadata of your data or the single .tif or .npy file.'
path = raw_input('> ')
#handles multiple input types
self.data = loadData(path)
#self.data = np.load('sample_data/3Dtiffdata.npy')
#processes the data, this step takes longest
try:
typ = sys.argv[2]
except:
print 'Is the data 3D or 2D?'
typ = raw_input('> ')
if typ == None or typ == '3D' or typ == '3d':
self.bitmasks,self.prinROI = process3D(self.data,xMic,yMic)
typ = 3
elif typ == '2D' or typ == '2d':
self.bitmasks,self.prinROI,self.avg = process2D(self.data,xMic,yMic)
self.tc_data = self.data
typ = 2
self.t = 0
self.fig = plt.figure(1)
self.lenData,self.i = len(self.data),0
def loadI(i,bitmasks,prinROI):
#refreshes the display on the GUI, declared here due to use in initialisation
if typ == 3:
#self.im.set_data(self.data[i])
self.ax_img.imshow(self.data[i],aspect='equal',cmap=plt.cm.gray,interpolation='nearest')
for j in range(len(bitmasks[i])):
self.ax_img.imshow(seg.overlay_labels(bitmasks[i][j]),aspect='equal',interpolation='nearest')
for prin in prinROI:
if prin == [i,j]:
self.ax_img.imshow(seg.overlay_full(bitmasks[i][j]),aspect='equal',interpolation='nearest')
if typ == 2:
self.ax_img.imshow(self.avg,aspect='equal',cmap=plt.cm.gray,interpolation='nearest')
for j in range(len(bitmasks)):
self.ax_img.imshow(seg.overlay_labels(bitmasks[j]),aspect='equal',interpolation='nearest')
"""
for j in range(len(bitmasks[i])):
self.im.set_data(seg.overlay_labels(bitmasks[i][j]))
for prin in prinROI:
if prin == [i,j]:
self.im.set_data(seg.overlay_full(bitmasks[i][j]))
"""
self.ax_img = self.fig.add_axes([.1,.40,.8,.55])
self.ax_plot = self.fig.add_axes([.1,.1,.8,.25])
self.ax_but1 = self.fig.add_axes([.1,.05,.1,.04])
self.ax_but2 = self.fig.add_axes([.2,.05,.1,.04])
self.ax_but3 = self.fig.add_axes([.3,.05,.1,.04])
self.ax_but4 = self.fig.add_axes([.4,.05,.1,.04])
self.ax_but5 = self.fig.add_axes([.8,.05,.1,.04])
self.ax_but6 = self.fig.add_axes([.7,.05,.1,.04])
self.ax_but7 = self.fig.add_axes([.6,.05,.1,.04])
self.ax_but8 = self.fig.add_axes([.5,.05,.1,.04])
self.ax_but9 = self.fig.add_axes([.8,.01,.1,.04])
#self.ax_fullrange = self.fig.add_axes([.1,.275,.8,.15])
#self.ax_zoom = self.fig.add_axes([.1,.1,.8,.15])
if typ == 2:
self.im = self.ax_img.imshow(self.avg,aspect='equal',cmap=plt.cm.gray,interpolation='nearest')
for j in range(len(self.bitmasks)):
self.ax_img.imshow(seg.overlay_labels(self.bitmasks[j]),aspect='equal',interpolation='nearest')
if typ == 3:
self.im = self.ax_img.imshow(self.data[self.i],aspect='equal',cmap=plt.cm.gray,interpolation='nearest')
loadI(self.i,self.bitmasks,self.prinROI)
#self.LABELS = seg.watershed_segment(self.M)
#span = SpanSelector(self.ax_fullrange, self.onselect, 'horizontal', useblit=True,rectprops=dict(alpha=0.5, facecolor='red') )
#self.line1, = self.ax_fullrange.plot(self.x,self.y)
#self.line2, = self.ax_zoom.plot(self.x,self.y)
self.but1 = Button(self.ax_but1,"<---")
self.but2 = Button(self.ax_but2,"DELETE")
self.but3 = Button(self.ax_but3,"USE")
self.but4 = Button(self.ax_but4,"--->")
self.but5 = Button(self.ax_but5,"SHOW SPIKES")
self.but6 = Button(self.ax_but6,"CLEAR PLOT")
self.but7 = Button(self.ax_but7,"ALL SPIKES")
self.but8 = Button(self.ax_but8,"SINGLE SPIKE")
self.but9 = Button(self.ax_but9,"SAVE MASKS")
def onscroll(self,event):
if event.button == 'up':
prevS(event)
else:
nextS(event)
#self.ax_img.set_yticks([])
#self.ax_img.set_xticks([])
#self.but3 = Button(self.ax_but3,
self.roi_pts = None
def prevS(event):
if self.i-1 >= 0:
self.i-=1
print str(self.i)+'/'+str(self.lenData)
loadI(self.i,self.bitmasks,self.prinROI)
def nextS(event):
if self.i+1 <= self.lenData:
self.i+=1
print str(self.i)+'/'+str(self.lenData)
loadI(self.i,self.bitmasks,self.prinROI)
def delROI(event):
click = plt.ginput(1,300)
if typ == 2:
self.bitmasks = delete_roi_2D(click,self.bitmasks)
if typ == 3:
j = match_point(click,self.bitmasks)
self.prinROI.remove([self.i,j])
loadI(self.i,self.bitmasks,self.prinROI)
print 'ROI will not be used.'
def useROI(event):
click = plt.ginput(1,300)
j = match_point(click,self.bitmasks[self.i])
self.prinROI.append([self.i,j])
self.prinROI.sort()
loadI(self.i,self.bitmasks,self.prinROI)
#self.uniqueMasks[i] = vstack([self.bitmasks[si],addROI[newaxis,...]])
print 'ROI will be used.'
def returnROIs(event):
masks = []
for i in range(len(self.bitmasks)):
for roi in self.prinROI:
if roi[0] == i:
masks.append(bitmasks[i][roi[1]])
print 'What would you like to name this dataset?'
name = raw_input('> ')
np.save(name,masks)
print 'ROIs have been saved.'
def showSpikes(event):
click = plt.ginput(1,300)
if typ == 2:
j = match_point(click,self.bitmasks)
if self.t == 0:
train,spikes,self.fil = roip.spikeDet(self.tc_data,self.bitmasks[j])
self.t = 1
elif self.t == 1:
train,spikes = roip.spikeDetL(self.tc_data,self.bitmasks[j],self.fil)
else:
print 'Something is wrong.'
#self.plo = plt.figure(2)
self.ax_plot.plot(train)
self.ax_plot.plot(spikes,np.ones(np.size(spikes))*35000,'r.')
print 'Spiketimes for ROI '+str(j)+': ',spikes
if typ == 3:
print 'Not currently supported.'
def clearPlot(event):
self.ax_plot.clear()
def saveAllSpkTrains(event):
arch = []
if typ == 2:
for j in range(len(self.bitmasks)):
print 'Mask '+str(j+1)+' of '+str(len(self.bitmasks))
if self.t == 0:
train,spikes,self.fil = roip.spikeDet(self.tc_data,self.bitmasks[j])
self.t = 1
else:
train,spikes = roip.spikeDetL(self.tc_data,self.bitmasks[j],self.fil)
arch.append([train,spikes])
elif typ == 3:
for roi in self.prinROI:
if self.t == 0:
train,spikes,self.fil = roip.spikeDet(self.tc_data,self.bitmasks[roi[0]][roi[1]])
else:
train,spikes = roip.spikeDetL(self.tc_data,bitmasks[roi[0]][roi[1]],self.fil)
self.t = 1
arch.append([roi,train,spikes])
print 'What would you like to name this dataset?'
name = raw_input('> ')
np.save(name,arch)
def saveSingleSpkTrain(event):
click = plt.ginput(1,300)
if typ == 2:
j = match_point(click,self.bitmasks)
if self.t == 0:
train,spikes,self.fil = roip.spikeDet(self.tc_data,self.bitmasks[j])
self.t = 1
elif self.t == 1:
train,spikes = roip.spikeDetL(self.tc_data,self.bitmasks[j],self.fil)
arch = [train,spikes]
print 'What would you like to name the data from this ROI?'
name = raw_input('> ')
np.save(name,arch)
self.but1.on_clicked(prevS)
self.but2.on_clicked(delROI)
self.but3.on_clicked(useROI)
self.but4.on_clicked(nextS)
self.but5.on_clicked(showSpikes)
self.but6.on_clicked(clearPlot)
self.but7.on_clicked(saveAllSpkTrains)
self.but8.on_clicked(saveSingleSpkTrain)
self.but9.on_clicked(returnROIs)
self.fig.canvas.draw()
