def OnPairwiseDistanceHistogram(self, event=None): from PYME.recipes import tablefilters, localisations, measurement from PYME.recipes.base import ModuleCollection import matplotlib.pyplot as plt # build a recipe programatically distogram = ModuleCollection() # split input according to colour channels selected distogram.add_module(localisations.ExtractTableChannel(distogram, inputName='input', outputName='chan0', channel='chan0')) distogram.add_module(localisations.ExtractTableChannel(distogram, inputName='input', outputName='chan1', channel='chan0')) # Histogram distogram.add_module(measurement.PairwiseDistanceHistogram(distogram, inputPositions='chan0', inputPositions2='chan1', outputName='output')) distogram.namespace['input'] = self.pipeline.output #do before configuring so that we already have the channel names populated #configure parameters if not distogram.configure_traits(view=distogram.pipeline_view, kind='modal'): return #handle cancel selectedChans = (distogram.modules[-1].inputPositions, distogram.modules[-1].inputPositions2) #run recipe distances = distogram.execute() binsz = (distances['bins'][1] - distances['bins'][0]) self.pairwiseDistances[selectedChans] = {'counts': np.array(distances['counts']), 'bins': np.array(distances['bins'] + 0.5*binsz)} plt.figure() plt.bar(self.pairwiseDistances[selectedChans]['bins'] - 0.5*binsz, self.pairwiseDistances[selectedChans]['counts'], width=binsz)
def OnClustersInTime(self, event=None): #FIXME - this would probably be better in an addon module outside of the core project from PYME.recipes import localisations from PYME.recipes.base import ModuleCollection import matplotlib.pyplot as plt # build a recipe programatically rec = ModuleCollection() # split input according to colour channel selected rec.add_module(localisations.ExtractTableChannel(rec, inputName='input', outputName='chan0', channel='chan0')) rec.add_module(localisations.ClusterCountVsImagingTime(rec, inputName='chan0', stepSize=3000, outputName='output')) rec.namespace['input'] = self.pipeline.output #do before configuring so that we already have the channel names populated #configure parameters if not rec.configure_traits(view=rec.pipeline_view, kind='modal'): return #handle cancel incrementedClumps = rec.execute() plt.figure() plt.scatter(incrementedClumps['t'], incrementedClumps['N_labelsWithLowMinPoints'], label=('clusters with Npoints > %i' % rec.modules[-1].lowerMinPtsPerCluster), c='b', marker='s') plt.scatter(incrementedClumps['t'], incrementedClumps['N_labelsWithHighMinPoints'], label=('clusters with Npoints > %i' % rec.modules[-1].higherMinPtsPerCluster), c='g', marker='o') plt.legend(loc=4, scatterpoints=1) plt.xlabel('Number of frames included') plt.ylabel('Number of Clusters')
def OnPairwiseDistanceHistogram(self, event=None): from PYME.recipes import tablefilters, localisations, measurement from PYME.recipes.base import ModuleCollection import matplotlib.pyplot as plt import wx import os # build a recipe programatically distogram = ModuleCollection() # split input according to colour channels selected distogram.add_module( localisations.ExtractTableChannel(distogram, inputName='input', outputName='chan0', channel='chan0')) distogram.add_module( localisations.ExtractTableChannel(distogram, inputName='input', outputName='chan1', channel='chan0')) # Histogram distogram.add_module( measurement.PairwiseDistanceHistogram(distogram, inputPositions='chan0', inputPositions2='chan1', outputName='output')) distogram.namespace[ 'input'] = self.pipeline.output #do before configuring so that we already have the channel names populated #configure parameters if not distogram.configure_traits(view=distogram.pipeline_view, kind='modal'): return #handle cancel selectedChans = (distogram.modules[-1].inputPositions, distogram.modules[-1].inputPositions2) #run recipe distances = distogram.execute() binsz = (distances['bins'][1] - distances['bins'][0]) self.pairwiseDistances[selectedChans] = { 'counts': np.array(distances['counts']), 'bins': np.array(distances['bins'] + 0.5 * binsz) } plt.figure() plt.bar(self.pairwiseDistances[selectedChans]['bins'] - 0.5 * binsz, self.pairwiseDistances[selectedChans]['counts'], width=binsz) hist_dlg = wx.FileDialog( None, message="Save histogram as csv...", # defaultDir=os.getcwd(), defaultFile='disthist_{}.csv'.format( os.path.basename(self.pipeline.filename)), wildcard='CSV (*.csv)|*.csv', style=wx.FD_SAVE | wx.FD_OVERWRITE_PROMPT) if hist_dlg.ShowModal() == wx.ID_OK: histfn = hist_dlg.GetPath() np.savetxt(histfn, np.vstack([ self.pairwiseDistances[selectedChans]['bins'] - 0.5 * binsz, self.pairwiseDistances[selectedChans]['counts'] ]).T, delimiter=',', header='Bins [nm],Counts')
def OnFindMixedClusters(self, event=None): """ FindMixedClusters first uses DBSCAN clustering on two color channels separately for denoising purposes, then after having removed noisy points, DBSCAN is run again on both channels combined, and the fraction of clumps containing both colors is determined. """ from PYME.recipes import tablefilters, localisations from PYME.recipes.base import ModuleCollection import wx chans = self.pipeline.colourFilter.getColourChans() nchan = len(chans) if nchan < 2: raise RuntimeError( 'FindMixedClusters requires at least two color channels') else: selectedChans = [0, 1] #rad_dlg = wx.NumberEntryDialog(None, 'Search Radius For Core Points', 'rad [nm]', 'rad [nm]', 125, 0, 9e9) #rad_dlg.ShowModal() searchRadius = 125.0 #rad_dlg.GetValue() #minPt_dlg = wx.NumberEntryDialog(None, 'Minimum Points To Be Core Point', 'min pts', 'min pts', 3, 0, 9e9) #minPt_dlg.ShowModal() minClumpSize = 3 #minPt_dlg.GetValue() #build a recipe programatically rec = ModuleCollection() #split input according to colour channels rec.add_module( localisations.ExtractTableChannel(rec, inputName='input', outputName='chan0', channel=chans[0])) rec.add_module( localisations.ExtractTableChannel(rec, inputName='input', outputName='chan1', channel=chans[1])) #clump each channel rec.add_module( localisations.DBSCANClustering(rec, inputName='chan0', outputName='chan0_clumped', searchRadius=searchRadius, minClumpSize=minClumpSize)) rec.add_module( localisations.DBSCANClustering(rec, inputName='chan1', outputName='chan1_clumped', searchRadius=searchRadius, minClumpSize=minClumpSize)) #filter unclumped points rec.add_module( tablefilters.FilterTable( rec, inputName='chan0_clumped', outputName='chan0_cleaned', filters={'dbscanClumpID': [.5, sys.maxsize]})) rec.add_module( tablefilters.FilterTable( rec, inputName='chan1_clumped', outputName='chan1_cleaned', filters={'dbscanClumpID': [.5, sys.maxsize]})) #rejoin cleaned datasets rec.add_module( tablefilters.ConcatenateTables(rec, inputName0='chan0_cleaned', inputName1='chan1_cleaned', outputName='joined')) #clump on cleaded and rejoined data rec.add_module( localisations.DBSCANClustering(rec, inputName='joined', outputName='output', searchRadius=searchRadius, minClumpSize=minClumpSize)) rec.namespace[ 'input'] = self.pipeline.output #do it before configuring so that we already have the channe; names populated if not rec.configure_traits(view=rec.pipeline_view, kind='modal'): return #handle cancel #run recipe joined_clumps = rec.execute() joined_clump_IDs = np.unique(joined_clumps['dbscanClumpID']) joined_clump_IDs = joined_clump_IDs[joined_clump_IDs > .5] #reject unclumped points chan0_clump_IDs = np.unique( joined_clumps['dbscanClumpID'][joined_clumps['concatSource'] < .5]) chan0_clump_IDs = chan0_clump_IDs[chan0_clump_IDs > .5] chan1_clump_IDs = np.unique( joined_clumps['dbscanClumpID'][joined_clumps['concatSource'] > .5]) chan1_clump_IDs = chan1_clump_IDs[chan1_clump_IDs > .5] both_chans_IDS = [c for c in chan0_clump_IDs if c in chan1_clump_IDs] n_total_clumps = len(joined_clump_IDs) print('Total clumps: %i' % n_total_clumps) c0Ratio = float(len(chan0_clump_IDs)) / n_total_clumps print('fraction clumps with channel %i present: %f' % (selectedChans[0], c0Ratio)) self.colocalizationRatios['Channel%iin%i%i' % (selectedChans[0], selectedChans[0], selectedChans[1])] = c0Ratio c1Ratio = float(len(chan1_clump_IDs)) / n_total_clumps print('fraction clumps with channel %i present: %f' % (selectedChans[1], c1Ratio)) self.colocalizationRatios['Channel%iin%i%i' % (selectedChans[1], selectedChans[0], selectedChans[1])] = c1Ratio bothChanRatio = float(len(both_chans_IDS)) / n_total_clumps print('fraction of clumps with both channel %i and %i present: %f' % (selectedChans[0], selectedChans[1], bothChanRatio)) self.colocalizationRatios['mixedClumps%i%i' % tuple(selectedChans)] = bothChanRatio self._rec = rec