def OnExtractPSF(self, event):
if (len(self.PSFLocs) > 0):
from PYME.PSFEst import extractImages
chnum = self.chChannel.GetSelection()
psfROISize = [int(s) for s in self.tPSFROI.GetValue().split(',')]
psfBlur = [float(s) for s in self.tPSFBlur.GetValue().split(',')]
#print psfROISize
psf = extractImages.getPSF3D(self.image.data[:, :, :, chnum],
self.PSFLocs, psfROISize, psfBlur)
if self.chType.GetSelection() == 0:
#widefield image - do special background subtraction
psf = extractImages.backgroundCorrectPSFWF(psf)
# from pylab import *
# import cPickle
# imshow(psf.max(2))
from PYME.DSView.dsviewer_npy_nb import ImageStack, ViewIm3D
im = ImageStack(data=psf,
mdh=self.image.mdh,
titleStub='Extracted PSF')
im.defaultExt = '*.psf' #we want to save as PSF by default
ViewIm3D(im,
mode='psf',
parent=wx.GetTopLevelParent(self.dsviewer))
def OnExtractSplitPSF(self, event):
if (len(self.PSFLocs) > 0):
from PYME.PSFEst import extractImages
chnum = self.chChannel.GetSelection()
psfROISize = [int(s) for s in self.tPSFROI.GetValue().split(',')]
psfBlur = [float(s) for s in self.tPSFBlur.GetValue().split(',')]
#print psfROISize
psfs = []
for i in range(self.image.data.shape[3]):
psf = extractImages.getPSF3D(self.image.data[:,:,:,i], self.PSFLocs, psfROISize, psfBlur)
if self.chType.GetSelection() == 0:
#widefield image - do special background subtraction
psf = extractImages.backgroundCorrectPSFWF(psf)
psfs.append(psf)
psf = numpy.concatenate(psfs, 0)
# from pylab import *
# import cPickle
# imshow(psf.max(2))
from PYME.DSView.dsviewer_npy_nb import ImageStack, ViewIm3D
im = ImageStack(data = psf, mdh = self.image.mdh, titleStub = 'Extracted PSF')
im.defaultExt = '*.psf' #we want to save as PSF by default
ViewIm3D(im, mode='psf', parent=wx.GetTopLevelParent(self.dsviewer))
def OnSVMSegment(self, event):
from PYMEnf.Analysis import svmSegment
from PYME.DSView.image import ImageStack
from PYME.DSView import ViewIm3D
if not 'cf' in dir(self):
self.cf = svmSegment.svmClassifier(self.image)
#sp = self.image.data.shape[:3]
#if len(sp)
lab2 = self.cf.classify()
#self.vmax = 0
#self.image.labels = self.mask
im = ImageStack(lab2, titleStub='Segmentation')
im.mdh.copyEntriesFrom(self.image.mdh)
#im.mdh['Processing.CropROI'] = roi
if self.dsviewer.mode == 'visGUI':
mode = 'visGUI'
else:
mode = 'lite'
self.dv = ViewIm3D(im,
mode=mode,
glCanvas=self.dsviewer.glCanvas,
parent=wx.GetTopLevelParent(self.dsviewer))
self.rois = []
#set scaling to (0,1)
for i in range(im.data.shape[3]):
self.dv.do.Gains[i] = 1.0
def OnCreateMask(self, event=None):
#lx, ly, hx, hy = self.do.GetSliceSelection()
import numpy as np
#from scipy import ndimage
from PYME.DSView.image import ImageStack
from PYME.DSView import ViewIm3D
#sp = self.image.data.shape[:3]
#if len(sp)
self.mask = np.atleast_3d(np.zeros(self.image.data.shape[:3],
'uint16'))
#self.vmax = 0
self.image.labels = self.mask
im = ImageStack(self.mask, titleStub='Manual labels')
im.mdh.copyEntriesFrom(self.image.mdh)
#im.mdh['Processing.CropROI'] = roi
if self.dsviewer.mode == 'visGUI':
mode = 'visGUI'
else:
mode = 'lite'
self.dv = ViewIm3D(im,
mode=mode,
glCanvas=self.dsviewer.glCanvas,
parent=wx.GetTopLevelParent(self.dsviewer))
self.rois = []
#set scaling to (0,1)
for i in range(im.data.shape[3]):
self.dv.do.Gains[i] = 1.0
def OnPlotProfile(self, event=None):
#lx, ly, hx, hy = self.do.GetSliceSelection()
pts = np.array(self.do.selection_trace)
w = int(np.floor(0.5 * self.do.selectionWidth))
try:
names = self.image.mdh.getEntry('ChannelNames')
except:
names = ['Channel %d' % d for d in range(self.image.data.shape[3])]
try:
voxx = self.image.mdh.getEntry('voxelsize.x')
except:
voxx = 1
plots = []
t = np.arange(np.ceil(len(pts)))
for chanNum in range(self.image.data.shape[3]):
p = ndimage.map_coordinates(
self.image.data[:, :, self.do.zp, chanNum].squeeze(), pts.T)
plots.append(p.reshape(-1, 1, 1))
#pylab.legend(names)
im = ImageStack(plots, titleStub='New Profile')
im.xvals = t * voxx
if not voxx == 1:
im.xlabel = 'Distance [um]'
else:
im.xlabel = 'Distance [pixels]'
im.ylabel = 'Intensity'
im.defaultExt = '.txt'
im.mdh['voxelsize.x'] = voxx
im.mdh['ChannelNames'] = names
im.mdh['Profile.XValues'] = im.xvals
im.mdh['Profile.XLabel'] = im.xlabel
im.mdh['Profile.YLabel'] = im.ylabel
#im.mdh['Profile.StartX'] = lx
#im.mdh['Profile.StartY'] = ly
#im.mdh['Profile.EndX'] = hx
#im.mdh['Profile.EndY'] = hy
#im.mdh['Profile.Width'] = 2*w + 1
im.mdh['OriginalImage'] = self.image.filename
ViewIm3D(im, mode='graph')
def OnPlotProfile(self, event=None):
#lx, ly, hx, hy = self.do.GetSliceSelection()
pts = np.array(self.do.selection_trace)
w = int(np.floor(0.5*self.do.selectionWidth))
try:
names = self.image.mdh.getEntry('ChannelNames')
except:
names = ['Channel %d' % d for d in range(self.image.data.shape[3])]
try:
voxx = self.image.mdh.getEntry('voxelsize.x')
except:
voxx=1
plots = []
t = np.arange(np.ceil(len(pts)))
for chanNum in range(self.image.data.shape[3]):
p = ndimage.map_coordinates(self.image.data[:,:, self.do.zp, chanNum].squeeze(), pts.T)
plots.append(p.reshape(-1, 1,1))
#pylab.legend(names)
im = ImageStack(plots, titleStub='New Profile')
im.xvals = t*voxx
if not voxx == 1:
im.xlabel = 'Distance [um]'
else:
im.xlabel = 'Distance [pixels]'
im.ylabel = 'Intensity'
im.defaultExt = '.txt'
im.mdh['voxelsize.x'] = voxx
im.mdh['ChannelNames'] = names
im.mdh['Profile.XValues'] = im.xvals
im.mdh['Profile.XLabel'] = im.xlabel
im.mdh['Profile.YLabel'] = im.ylabel
#im.mdh['Profile.StartX'] = lx
#im.mdh['Profile.StartY'] = ly
#im.mdh['Profile.EndX'] = hx
#im.mdh['Profile.EndY'] = hy
#im.mdh['Profile.Width'] = 2*w + 1
im.mdh['OriginalImage'] = self.image.filename
ViewIm3D(im, mode='graph')
def OnPlotProfile(self, event=None):
try:
names = self.image.mdh.getEntry('ChannelNames')
except:
names = ['Channel %d' % d for d in range(self.image.data.shape[3])]
#print lx, hx, ly, hy
#pylab.figure()
plots = []
for chanNum in range(self.image.data.shape[3]):
tmin = self.do.Offs[chanNum]
tmax = tmin + 1. / self.do.Gains[chanNum]
d = self.image.data[:, :, :, chanNum].squeeze()
trange = np.linspace(tmin, tmax)
p = np.array([ndimage.label(d > t)[1] for t in trange])
plots.append(p.reshape(-1, 1, 1))
#pylab.legend(names)
im = ImageStack(plots, titleStub='New Threshold Range')
im.xvals = trange
im.xlabel = 'Threshold'
im.ylabel = 'Num Objects'
im.defaultExt = '.txt'
#im.mdh['voxelsize.x'] = voxx
im.mdh['ChannelNames'] = names
im.mdh['Profile.XValues'] = im.xvals
im.mdh['Profile.XLabel'] = im.xlabel
im.mdh['Profile.YLabel'] = im.ylabel
im.mdh['OriginalImage'] = self.image.filename
ViewIm3D(im, mode='graph', parent=wx.GetTopLevelParent(self.dsviewer))
def OnPlotProfile(self, event=None):
try:
names = self.image.mdh.getEntry('ChannelNames')
except:
names = ['Channel %d' % d for d in range(self.image.data.shape[3])]
#print lx, hx, ly, hy
#pylab.figure()
plots = []
for chanNum in range(self.image.data.shape[3]):
tmin = self.do.Offs[chanNum]
tmax = tmin + 1./self.do.Gains[chanNum]
d = self.image.data[:,:,:,chanNum].squeeze()
trange = np.linspace(tmin, tmax)
p = np.array([ndimage.label(d > t)[1] for t in trange])
plots.append(p.reshape(-1, 1,1))
#pylab.legend(names)
im = ImageStack(plots, titleStub='New Threshold Range')
im.xvals = trange
im.xlabel = 'Threshold'
im.ylabel = 'Num Objects'
im.defaultExt = '.txt'
#im.mdh['voxelsize.x'] = voxx
im.mdh['ChannelNames'] = names
im.mdh['Profile.XValues'] = im.xvals
im.mdh['Profile.XLabel'] = im.xlabel
im.mdh['Profile.YLabel'] = im.ylabel
im.mdh['OriginalImage'] = self.image.filename
ViewIm3D(im, mode='graph', parent=wx.GetTopLevelParent(self.dsviewer))
def OnColoc(self, event):
from PYME.Analysis.Colocalisation import correlationCoeffs, edtColoc
voxelsize = [1e3*self.image.mdh.getEntry('voxelsize.x') ,1e3*self.image.mdh.getEntry('voxelsize.y'), 1e3*self.image.mdh.getEntry('voxelsize.z')]
try:
names = self.image.mdh.getEntry('ChannelNames')
except:
names = ['Channel %d' % n for n in range(self.image.data.shape[3])]
dlg = ColocSettingsDialog(self.dsviewer, voxelsize[0], names)
dlg.ShowModal()
bins = dlg.GetBins()
chans = dlg.GetChans()
dlg.Destroy()
#assume we have exactly 2 channels #FIXME - add a selector
#grab image data
imA = self.image.data[:,:,:,chans[0]].squeeze()
imB = self.image.data[:,:,:,chans[1]].squeeze()
#assume threshold is half the colour bounds - good if using threshold mode
tA = self.do.Offs[chans[0]] + .5/self.do.Gains[chans[0]] #pylab.mean(self.ivps[0].clim)
tB = self.do.Offs[chans[1]] + .5/self.do.Gains[chans[1]] #pylab.mean(self.ivps[0].clim)
nameA = names[chans[0]]
nameB = names[chans[1]]
voxelsize = voxelsize[:imA.ndim] #trunctate to number of dimensions
print('Calculating Pearson and Manders coefficients ...')
pearson = correlationCoeffs.pearson(imA, imB)
MA, MB = correlationCoeffs.thresholdedManders(imA, imB, tA, tB)
print('Performing distance transform ...')
bnA, bmA, binsA = edtColoc.imageDensityAtDistance(imB, imA > tA, voxelsize, bins)
print('Performing distance transform (reversed) ...')
bnB, bmB, binsB = edtColoc.imageDensityAtDistance(imA, imB > tB, voxelsize, bins)
#print binsB, bmB
plots = []
pnames = []
pylab.figure()
pylab.figtext(.1, .95, 'Pearson: %2.2f M1: %2.2f M2: %2.2f' % (pearson, MA, MB))
pylab.subplot(211)
p = bmA/bmA.sum()
#print p
pylab.bar(binsA[:-1], p, binsA[1] - binsA[0])
pylab.xlabel('Distance from edge of %s [nm]' % nameA)
pylab.ylabel('Density of %s' % nameB)
plots.append(p.reshape(-1, 1,1))
pnames.append('Dens. %s from %s' % (nameB, nameA))
pylab.subplot(212)
p = bmB/bmB.sum()
pylab.bar(binsB[:-1], p, binsB[1] - binsB[0])
pylab.xlabel('Distance from edge of %s [nm]' % nameB)
pylab.ylabel('Density of %s' % nameA)
plots.append(p.reshape(-1, 1,1))
pnames.append('Dens. %s from %s' % (nameA, nameB))
pylab.figure()
pylab.figtext(.1, .95, 'Pearson: %2.2f M1: %2.2f M2: %2.2f' % (pearson, MA, MB))
pylab.subplot(211)
fA = bmA*bnA
p = fA/fA.sum()
pylab.bar(binsA[:-1], p, binsA[1] - binsA[0])
pylab.xlabel('Distance from edge of %s [nm]' % nameA)
pylab.ylabel('Fraction of %s' % nameB)
plots.append(p.reshape(-1, 1,1))
pnames.append('Frac. %s from %s' % (nameB, nameA))
pylab.subplot(212)
fB = bmB*bnB
p = fB/fB.sum()
pylab.bar(binsB[:-1], p, binsB[1] - binsB[0])
pylab.xlabel('Distance from edge of %s [nm]' % nameB)
pylab.ylabel('Fraction of %s' % nameA)
plots.append(p.reshape(-1, 1,1))
pnames.append('Frac. %s from %s' % (nameA, nameB))
pylab.show()
im = ImageStack(plots, titleStub='Radial Distribution')
im.xvals = bins[:-1]
im.xlabel = 'Distance [nm]'
im.ylabel = 'Fraction'
im.defaultExt = '.txt'
im.mdh['voxelsize.x'] = (bins[1] - bins[0])*1e-3
im.mdh['ChannelNames'] = pnames
im.mdh['Profile.XValues'] = im.xvals
im.mdh['Profile.XLabel'] = im.xlabel
im.mdh['Profile.YLabel'] = im.ylabel
im.mdh['Colocalisation.Channels'] = names
im.mdh['Colocalisation.Thresholds'] = [tA, tB]
im.mdh['Colocalisation.Pearson'] = pearson
im.mdh['Colocalisation.Manders'] = [MA, MB]
im.mdh['OriginalImage'] = self.image.filename
ViewIm3D(im, mode='graph')
def OnPlotProfile(self, event=None):
lx, ly, hx, hy = self.do.GetSliceSelection()
w = int(np.floor(0.5*self.do.selectionWidth))
try:
names = self.image.mdh.getEntry('ChannelNames')
except:
names = ['Channel %d' % d for d in range(self.image.data.shape[3])]
try:
voxx = self.image.mdh.getEntry('voxelsize.x')
except:
voxx=1
Dx = hx - lx
Dy = hy - ly
l = np.sqrt((Dx**2 + Dy**2))
dx = Dx/l
dy = Dy/l
if Dx == 0 and Dy == 0: #special case - profile is orthogonal to current plane
d_x = w
d_y = w
else:
d_x = w*abs(dy)
d_y = w*abs(dx)
#print lx, hx, ly, hy
#pylab.figure()
plots = []
t = np.arange(np.ceil(l))
for chanNum in range(self.image.data.shape[3]):
x_0 = min(lx, hx)
y_0 = min(ly, hy)
d__x = abs(d_x) + 1
d__y = abs(d_y) + 1
print((dx, dy, d__x, d__y, w))
if(self.do.slice == self.do.SLICE_XY):
ims = self.image.data[(min(lx, hx) - d__x):(max(lx,hx)+d__x+1), (min(ly, hy)-d__y):(max(ly,hy)+d__y+1), self.do.zp, chanNum].squeeze()
splf = ndimage.spline_filter(ims)
p = np.zeros(len(t))
x_c = t*dx + lx - x_0
y_c = t*dy + ly - y_0
print((splf.shape))
for i in range(-w, w+1):
#print np.vstack([x_c + d__x +i*dy, y_c + d__y + i*dx])
p += ndimage.map_coordinates(splf, np.vstack([x_c + d__x +i*dy, y_c + d__y - i*dx]), prefilter=False)
p = p/(2*w + 1)
plots.append(p.reshape(-1, 1,1))
#pylab.legend(names)
im = ImageStack(plots, titleStub='New Profile')
im.xvals = t*voxx
if not voxx == 1:
im.xlabel = 'Distance [um]'
else:
im.xlabel = 'Distance [pixels]'
im.ylabel = 'Intensity'
im.defaultExt = '.txt'
im.mdh['voxelsize.x'] = voxx
im.mdh['ChannelNames'] = names
im.mdh['Profile.XValues'] = im.xvals
im.mdh['Profile.XLabel'] = im.xlabel
im.mdh['Profile.YLabel'] = im.ylabel
im.mdh['Profile.StartX'] = lx
im.mdh['Profile.StartY'] = ly
im.mdh['Profile.EndX'] = hx
im.mdh['Profile.EndY'] = hy
im.mdh['Profile.Width'] = 2*w + 1
im.mdh['OriginalImage'] = self.image.filename
ViewIm3D(im, mode='graph', parent=wx.GetTopLevelParent(self.dsviewer))
def OnPlotAxialProfile(self, event=None):
lx, ly, hx, hy = self.do.GetSliceSelection()
try:
names = self.image.mdh.getEntry('ChannelNames')
except:
names = ['Channel %d' % d for d in range(self.image.data.shape[3])]
plots = []
t = np.arange(self.image.data.shape[2])
try:
stack = (self.image.mdh['AcquisitionType'] == 'Stack')
except:
stack = False
if stack:
dt = self.image.mdh['voxelsize.z']
else:
try:
dt = self.image.mdh['Camera.CycleTime']
except:
dt = 1
for chanNum in range(self.image.data.shape[3]):
plots.append(np.zeros((len(t), 1, 1)))
dlg = wx.ProgressDialog('Extracting Axial Profile', 'Progress', len(t) - 1)
for i in t:
for chanNum in range(self.image.data.shape[3]):
plots[chanNum][i] = self.image.data[lx:hx, ly:hy, i, chanNum].mean()
if (i % 10) == 0:
dlg.Update(i, '%d of %d frames' % (i, t.size))
dlg.Destroy()
#pylab.legend(names)
im = ImageStack(plots, titleStub='New Profile')
im.xvals = t*dt
if stack:
im.xlabel = 'Position [um]'
elif not dt == 1:
im.xlabel = 'Time [s]'
else:
im.xlabel = 'Time [frames]'
im.ylabel = 'Intensity'
im.defaultExt = '.txt'
im.mdh['voxelsize.x'] = dt
im.mdh['ChannelNames'] = names
im.mdh['Profile.XValues'] = im.xvals
im.mdh['Profile.XLabel'] = im.xlabel
im.mdh['Profile.YLabel'] = im.ylabel
im.mdh['Profile.X1'] = lx
im.mdh['Profile.Y1'] = ly
im.mdh['Profile.X2'] = hx
im.mdh['Profile.Y2'] = hy
im.mdh['OriginalImage'] = self.image.filename
im.parent = self.image
ViewIm3D(im, mode='graph')
