コード例 #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')
コード例 #2
0
ファイル: squiggleTools.py プロジェクト: RuralCat/CLipPYME
    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')
コード例 #3
0
ファイル: multithreshold.py プロジェクト: RuralCat/CLipPYME
    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))
コード例 #4
0
    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))
コード例 #5
0
ファイル: coloc.py プロジェクト: RuralCat/CLipPYME
    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')
コード例 #6
0
    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))
コード例 #7
0
    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')