def OnColocBasic(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,
show_bins=False)
dlg.ShowModal()
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]]
print('Calculating Pearson and Manders coefficients ...')
pearson = correlationCoeffs.pearson(imA, imB)
MA, MB = correlationCoeffs.thresholdedManders(imA, imB, tA, tB)
mutual_information = correlationCoeffs.mutual_information(imA, imB)
I1 = imA.ravel()
I2 = imB.ravel()
h1 = np.histogram2d(np.clip(I1 / I1.mean(), 0, 100),
np.clip(I2 / I2.mean(), 0, 100), 200)
pylab.figure()
pylab.figtext(
.1, .95, 'Pearson: %2.2f M1: %2.2f M2: %2.2f MI: %2.3f' %
(pearson, MA, MB, mutual_information))
pylab.subplot(111)
pylab.imshow(np.log10(h1[0] + .1).T)
pylab.xlabel('%s' % nameA)
pylab.ylabel('%s' % nameB)
pylab.show()
def OnColocBasic(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)
I1 = imA.ravel()
I2 = imB.ravel()
h1 = np.histogram2d(np.clip(I1/I1.mean(), 0, 100), np.clip(I2/I2.mean(), 0, 100), 200)
pylab.figure()
pylab.figtext(.1, .95, 'Pearson: %2.2f M1: %2.2f M2: %2.2f' % (pearson, MA, MB))
pylab.subplot(111)
pylab.imshow(np.log10(h1[0] + .1).T)
pylab.xlabel('%s' % nameA)
pylab.ylabel('%s' % nameB)
pylab.show()
def OnColoc(self, event=None, restrict_z=False, coloc_vs_z = False):
from PYME.Analysis.Colocalisation import correlationCoeffs, edtColoc
from scipy import interpolate
voxelsize = self.image.voxelsize_nm
names = self.image.names
if not getattr(self.image, 'labels', None) is None:
have_mask = True
mask = (self.image.labels > 0.5).squeeze()
else:
have_mask = False
mask = None
if not restrict_z:
dlg = ColocSettingsDialog(self.dsviewer, voxelsize[0], names, have_mask=have_mask)
else:
dlg = ColocSettingsDialog(self.dsviewer, voxelsize[0], names, have_mask=have_mask, z_size=self.image.data.shape[2])
dlg.ShowModal()
bins = dlg.GetBins()
chans = dlg.GetChans()
use_mask = dlg.GetUseMask()
zs, ze = (0, self.image.data.shape[2])
if (self.image.data.shape[2] > 1) and restrict_z:
zs, ze = dlg.GetZrange()
zs,ze = (0,1)
if self.image.data.shape[2] > 1:
zs,ze = dlg.GetZrange()
dlg.Destroy()
print('Use mask: %s' % use_mask)
if not use_mask:
mask=None
#assume we have exactly 2 channels #FIXME - add a selector
#grab image data
imA = self.image.data[:,:,zs:ze,chans[0]].squeeze()
imB = self.image.data[:,:,zs:ze,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]] #plt.mean(self.ivps[0].clim)
tB = self.do.Offs[chans[1]] + .5/self.do.Gains[chans[1]] #plt.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, roi_mask=mask)
MA, MB = correlationCoeffs.thresholdedManders(imA, imB, tA, tB, roi_mask=mask)
if coloc_vs_z:
MAzs, MBzs = ([],[])
FAzs, FBzs = ([],[])
if self.image.data.shape[2] > 1:
for z in range(self.image.data.shape[2]):
imAz = self.image.data[:,:,z,chans[0]].squeeze()
imBz = self.image.data[:,:,z,chans[1]].squeeze()
MAz, MBz = correlationCoeffs.thresholdedManders(imAz, imBz, tA, tB)
FAz, FBz = correlationCoeffs.maskFractions(imAz, imBz, tA, tB)
MAzs.append(MAz)
MBzs.append(MBz)
FAzs.append(FAz)
FBzs.append(FBz)
print("M(A->B) %s" % MAzs)
print("M(B->A) %s" % MBzs)
print("Species A: %s, Species B: %s" %(nameA,nameB))
plt.figure()
plt.subplot(211)
if 'filename' in self.image.__dict__:
plt.title(self.image.filename)
# nameB with nameA
cAB, = plt.plot(MAzs,'o', label = '%s with %s' %(nameB,nameA))
# nameA with nameB
cBA, = plt.plot(MBzs,'*', label = '%s with %s' %(nameA,nameB))
plt.legend([cBA,cAB])
plt.xlabel('z slice level')
plt.ylabel('Manders coloc fraction')
plt.ylim(0,None)
plt.subplot(212)
if 'filename' in self.image.__dict__:
plt.title(self.image.filename)
# nameB with nameA
fA, = plt.plot(FAzs,'o', label = '%s mask fraction' %(nameA))
# nameA with nameB
fB, = plt.plot(FBzs,'*', label = '%s mask fraction' %(nameB))
plt.legend([fA,fB])
plt.xlabel('z slice level')
plt.ylabel('Mask fraction')
plt.ylim(0,None)
plt.show()
print('Performing distance transform ...')
#bnA, bmA, binsA = edtColoc.imageDensityAtDistance(imB, imA > tA, voxelsize, bins, roi_mask=mask)
#bnAA, bmAA, binsA = edtColoc.imageDensityAtDistance(imA, imA > tA, voxelsize, bins, roi_mask=mask)
bins_, enrichment_BA, enclosed_BA, enclosed_area_A = edtColoc.image_enrichment_and_fraction_at_distance(imB, imA > tA, voxelsize,
bins, roi_mask=mask)
bins_, enrichment_AA, enclosed_AA, _ = edtColoc.image_enrichment_and_fraction_at_distance(imA, imA > tA, voxelsize,
bins, roi_mask=mask)
print('Performing distance transform (reversed) ...')
#bnB, bmB, binsB = edtColoc.imageDensityAtDistance(imA, imB > tB, voxelsize, bins, roi_mask=mask)
#bnBB, bmBB, binsB = edtColoc.imageDensityAtDistance(imB, imB > tB, voxelsize, bins, roi_mask=mask)
bins_, enrichment_AB, enclosed_AB, enclosed_area_B = edtColoc.image_enrichment_and_fraction_at_distance(imA, imB > tB, voxelsize,
bins, roi_mask=mask)
bins_, enrichment_BB, enclosed_BB, _ = edtColoc.image_enrichment_and_fraction_at_distance(imB, imB > tB, voxelsize,
bins, roi_mask=mask)
#print binsB, bmB
plots = []
pnames = []
# B from mA
####################
plots_ = {}
#plots_['Frac. %s from mask(%s)' % (nameB, nameA)] =
plots.append(enclosed_BA.reshape(-1, 1, 1))
pnames.append('Frac. %s from mask(%s)' % (nameB, nameA))
plots.append(enrichment_BA.reshape(-1, 1, 1))
pnames.append('Enrichment of %s at distance from mask(%s)' % (nameB, nameA))
edtColoc.plot_image_dist_coloc_figure(bins_, enrichment_BA, enrichment_AA, enclosed_BA, enclosed_AA, enclosed_area_A, pearson, MA, MB, nameA, nameB)
plots.append(enclosed_AB.reshape(-1, 1, 1))
pnames.append('Frac. %s from mask(%s)' % (nameA, nameB))
plots.append(enrichment_AB.reshape(-1, 1, 1))
pnames.append('Enrichment of %s at distance from mask(%s)' % (nameA, nameB))
edtColoc.plot_image_dist_coloc_figure(bins_, enrichment_AB, enrichment_BB, enclosed_AB, enclosed_BB, enclosed_area_B, pearson,
MA, MB, nameB, nameA)
plt.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]
try:
im.mdh['Colocalisation.ThresholdMode'] = self.do.ThreshMode
except:
pass
im.mdh['OriginalImage'] = self.image.filename
ViewIm3D(im, mode='graph')
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')
