def alignSignalProfiles(self, signalChannel, sideChannels=[]):
"""Aligns signal profiles.
.. warning:: When we align here, then this is done per stack. Thus, single stacks might get aligned differently.
This is not a problem if there was a projection before, since then there is only a single stack.
Otherwise this part should be used with caution. Will need to fix this later.
Args:
signalChannel (str): Name of channel to align.
Keyword Args:
sideChannels (list): List of channel names that are supposed to be aligned the same way as signalChannel.
"""
#Collecting all results and aligning them
anglesAligned = []
signalsAligned = []
#Loop through all experiments
for exp in self.exps:
#Grab channels and set back their aligned lists
channel = exp.getChannel(signalChannel)
channel.anglesAligned = []
channel.signalsAligned = []
if len(sideChannels) > 0:
for sideChannel in sideChannels:
sideChannels[i] = exp.getChannel(sideChannel)
sideChannels[i].anglesAligned = []
sideChannels[i].signalsAligned = []
#Check some reqs
if self.checkMinPix(channel.angles):
angles, signals = self.filterNaNStacks(channel.angles,
channel.signals,
name=exp.series)
#Loop through all stacks
for j in range(len(channel.angles)):
#Build arrays for passing it to aligment function
anglesSub = channel.angles[j]
signalsSub = [channel.signals[j]]
if len(sideChannels) > 0:
for sideChannel in sideChannels:
signalsSub.append(sideChannel.signals[j])
else:
signalsSub.append(channel.signals[j])
signalsSub = np.asarray(signalsSub)
#Aligning
angles, signals = angleAligned, signalAligned = am.alignDorsal(
anglesSub, signalsSub, method=self.fitMethod)
#Dumping results in respecting channels
channel.anglesAligned.append(angles)
channel.signalsAligned.append(signals[0, :])
for i, sideChannel in enumerate(sideChannels):
channel.anglesAligned.append(angles)
channel.signalsAligned.append(signals[0, :])
return
for j in range(len(angles)):
if bool(np.isnan(angles[j]).sum()):
printWarning("Had to exclude dataset " +
os.path.basename(tifFiles[i]) +
" due to minPix requirement.")
continue
if bool(np.isnan(signals[j]).sum()):
printWarning("Had to exclude dataset " +
os.path.basename(tifFiles[i]) +
" due to minPix requirement.")
continue
#Align with maximum value
angleAligned, signalAligned = am.alignDorsal(
angles[j], np.asarray([signals[j], signals[j]]), method=fitMethod)
if len(signalAligned[0, :]) == 0:
printWarning("Had to exclude dataset " +
os.path.basename(tifFiles[i]) +
" due to empty list problem.")
else:
anglesAligned.append(angleAligned)
signalsAligned.append(signalAligned[0, :])
try:
x = len(signalsAligned[-1])
except:
printWarning("Had to exclude dataset " +
os.path.basename(tifFiles[i]) +
" due to empty list problem.")
#Loop through all probability files for i,fn in enumerate(probFiles): #Generate angular distributions angles,signals=am.createSignalProfileFromH5(images[i],fn,signalChannel=signalChannel,probThresh=probThresh,probIdx=probIdx, maxInt=maxInt,hist=hist,bins=bins,maskBkgd=maskBkgd,debug=debug) #Bookkeeping lists anglesAligned=[] signalsAligned=[] #Align distributions for j in range(len(angles)): #Align with maximum value angleAligned,signalAligned=am.alignDorsal(angles[j],np.asarray([signals[j],signals[j]])) anglesAligned.append(angleAligned) signalsAligned.append(signalAligned[0,:]) allAnglesAligned.append(anglesAligned) allSignalsAligned.append(signalsAligned) #Plot angular distributions figSeries=plt.figure() figSeries.show() figAll=plt.figure() figAll.show() #Create axis axAll=figAll.add_subplot(1,1,1)
#Align distributions
for j in range(len(angles)):
if bool(np.isnan(angles[j]).sum()):
printWarning("Had to exclude dataset " + os.path.basename(tifFiles[i]) + " due to minPix requirement.")
continue
if bool(np.isnan(signals[j]).sum()):
printWarning("Had to exclude dataset " + os.path.basename(tifFiles[i]) + " due to minPix requirement.")
continue
#Align with maximum value
angleAligned,signalAligned=am.alignDorsal(angles[j],np.asarray([signals[j],signals[j]]),method=fitMethod)
if len(signalAligned[0,:])==0:
printWarning("Had to exclude dataset " + os.path.basename(tifFiles[i]) + " due to empty list problem.")
else:
anglesAligned.append(angleAligned)
signalsAligned.append(signalAligned[0,:])
try:
x=len(signalsAligned[-1])
except:
printWarning("Had to exclude dataset " + os.path.basename(tifFiles[i]) + " due to empty list problem.")
continue
allAnglesAligned.append(anglesAligned)
allSignalsAligned.append(signalsAligned)
def alignSignalProfiles(self,signalChannel,sideChannels=[]): """Aligns signal profiles. .. warning:: When we align here, then this is done per stack. Thus, single stacks might get aligned differently. This is not a problem if there was a projection before, since then there is only a single stack. Otherwise this part should be used with caution. Will need to fix this later. Args: signalChannel (str): Name of channel to align. Keyword Args: sideChannels (list): List of channel names that are supposed to be aligned the same way as signalChannel. """ #Collecting all results and aligning them anglesAligned=[] signalsAligned=[] #Loop through all experiments for exp in self.exps: #Grab channels and set back their aligned lists channel=exp.getChannel(signalChannel) channel.anglesAligned=[] channel.signalsAligned=[] if len(sideChannels)>0: for sideChannel in sideChannels: sideChannels[i]=exp.getChannel(sideChannel) sideChannels[i].anglesAligned=[] sideChannels[i].signalsAligned=[] #Check some reqs if self.checkMinPix(channel.angles): angles,signals=self.filterNaNStacks(channel.angles,channel.signals,name=exp.series) #Loop through all stacks for j in range(len(channel.angles)): #Build arrays for passing it to aligment function anglesSub=channel.angles[j] signalsSub=[channel.signals[j]] if len(sideChannels)>0: for sideChannel in sideChannels: signalsSub.append(sideChannel.signals[j]) else: signalsSub.append(channel.signals[j]) signalsSub=np.asarray(signalsSub) #Aligning angles,signals=angleAligned,signalAligned=am.alignDorsal(anglesSub,signalsSub,method=self.fitMethod) #Dumping results in respecting channels channel.anglesAligned.append(angles) channel.signalsAligned.append(signals[0,:]) for i,sideChannel in enumerate(sideChannels): channel.anglesAligned.append(angles) channel.signalsAligned.append(signals[0,:]) return
import matplotlib.mlab as mlab import math import ilastik_module as il import im_module as im import analysis_module as am import h5py #Pick random mu mu = np.random.rand()*4*math.pi-2*math.pi #Pick random variance variance = np.random.rand()*2*math.pi sigma = math.sqrt(variance) #Array from -pi to pi x = np.linspace(-2*math.pi, 2*math.pi, 100) # heap = mlab.normpdf(x,mu,sigma) intensity = np.array([heap,heap]) print(intensity.shape) aligned = am.alignDorsal(x,intensity) print(aligned[0]) plt.plot(aligned[0],aligned[1][0],'r') plt.plot(x,heap) plt.show()
probIdx=probIdx,
maxInt=maxInt,
hist=hist,
bins=bins,
maskBkgd=maskBkgd,
debug=debug)
#Bookkeeping lists
anglesAligned = []
signalsAligned = []
#Align distributions
for j in range(len(angles)):
#Align with maximum value
angleAligned, signalAligned = am.alignDorsal(
angles[j], np.asarray([signals[j], signals[j]]))
anglesAligned.append(angleAligned)
signalsAligned.append(signalAligned[0, :])
allAnglesAligned.append(anglesAligned)
allSignalsAligned.append(signalsAligned)
#Plot angular distributions
figSeries = plt.figure()
figSeries.show()
figAll = plt.figure()
figAll.show()
#Create axis
axAll = figAll.add_subplot(1, 1, 1)