def OnMeasureClusters(self, event=None):
"""
Calculates various measures for clusters using PYME.recipes.localisations.MeasureClusters
Parameters
----------
labelsKey: pipeline key to access array of label assignments. Measurements will be calculated for each label.
"""
from PYME.recipes import localisations
from PYME.recipes import Recipe
# build a recipe programatically
measrec = Recipe()
measrec.add_module(
localisations.MeasureClusters3D(measrec,
inputName='input',
labelsKey='dbscanClustered',
outputName='output'))
measrec.namespace['input'] = self.pipeline.output
#configure parameters
if not measrec.configure_traits(view=measrec.pipeline_view,
kind='modal'):
return # handle cancel
# run recipe
meas = measrec.execute()
# For now, don't make this a data source, as that requires (for multicolor) clearing the pipeline mappings.
self.clusterMeasures.append(meas)
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 import Recipe
import matplotlib.pyplot as plt
# build a recipe programatically
rec = Recipe()
# 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 import Recipe
import matplotlib.pyplot as plt
import wx
import os
# build a recipe programatically
distogram = Recipe()
# 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 import Recipe
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 = Recipe()
#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