def execute(self, namespace):
from PYME.IO.FileUtils import readSpeckle
from PYME.IO import MetaDataHandler
import os
fileInfo = {'SEP': os.sep}
seriesLength = 100000
mdh = MetaDataHandler.NestedClassMDHandler()
mdh['voxelsize.x'] = .001 # default pixel size - FIXME
mdh['voxelsize.y'] = .001
#use a default sensor size of 512
#this gets over-ridden below if we supply an image
clip_region = [
self.edgeRejectionPixels, self.edgeRejectionPixels,
512 - self.edgeRejectionPixels, 512 - self.edgeRejectionPixels
]
if not self.inputImage == '':
inp = namespace[self.inputImage]
mdh.update(inp.mdh)
seriesLength = inp.data.shape[2]
clip_region = [
self.edgeRejectionPixels, self.edgeRejectionPixels,
inp.data.shape[0] - self.edgeRejectionPixels,
inp.data.shape[1] - self.edgeRejectionPixels
]
try:
fileInfo['DIRNAME'], fileInfo['IMAGENAME'] = os.path.split(
inp.filename)
fileInfo['IMAGESTUB'] = fileInfo['IMAGENAME'].split('MM')[0]
except:
pass
speckleFN = self.speckleFilename.format(**fileInfo)
specks = readSpeckle.readSpeckles(speckleFN)
traces = readSpeckle.gen_traces_from_speckles(
specks,
leadFrames=self.leadFrames,
followFrames=self.followFrames,
seriesLength=seriesLength,
clipRegion=clip_region)
#turn this into an inputFilter object
inp = tabular.RecArraySource(traces)
#create a mapping to covert the co-ordinates in pixels to co-ordinates in nm
vs = mdh.voxelsize_nm
map = tabular.MappingFilter(inp,
x='x_pixels*%3.2f' % vs.x,
y='y_pixels*%3.2f' % vs.y)
map.mdh = mdh
namespace[self.outputName] = map
def selectAndPlotEvents(pipeline, outputdir='/Users/david/FusionAnalysis', speckleFile = None):
import os
import pandas as pd
from PYME.IO.FileUtils.readSpeckle import readSpeckles
#now iterate through our clumps
clumpIndices = list(set(pipeline['clumpIndex']))
clumps = [pipeline.clumps[i] for i in clumpIndices]
if not speckleFile is None: #use speckle file to determine which tracks correspond to fusion events
vs = pipeline.mdh.voxelsize_nm.x
speckles = readSpeckles(speckleFile)
#print speckles
sp = np.array([s[0,:] for s in speckles])
#print sp.shape
#filteredClumps = [c for c in clumps if (((c['x'][0] - vs*sp[:,1])**2 + (c['y'][0] - vs*sp[:,0])**2 + (5*(c['t'][0] - sp[:,2]))**2).min() < 300**2)]
#find those clumps which are near (< 1um) to events identified in Joergs speckle file
filteredClumps = [c for c in clumps if (((c['x'][0] - vs*sp[:,1])**2 + (c['y'][0] - vs*sp[:,0])**2).min() < 1000**2)]
else:
#do another level of filtering - fusion events expand, so we're looking for larger than normal
#sigma in the lipid channel. We can also add a constraint on the mean intensity as proper docking
#and fusion events are brighter than a lot of the point-like rubbish
#We do this here, so we can filter on the aggregate behaviour of a track and be more resiliant against
#noise.
filteredClumps = [c for c in clumps if (c['Ag'].mean() > 2000) and (c['fitResults_sigma'].mean() > 300)]
outputDir = os.path.join(outputdir, os.path.split(pipeline.filename)[1])
if not os.path.exists(outputDir):
os.makedirs(outputDir)
for c in filteredClumps:
plotEvent(c, pipeline, plotRaw=True)
pl.savefig('%s/track%d.pdf' % (outputDir, c['clumpIndex'][0]))
r = fitIntensities(c)
pl.savefig('%s/track%d_fits.pdf' % (outputDir, c['clumpIndex'][0]))
r['filename'] = os.path.split(pipeline.filename)[1]
r['clumpIndex'] = c['clumpIndex'][0]
d = {}
d.update(c)
pd.DataFrame(d).to_csv('%s/track%d.csv' % (outputDir, c['clumpIndex'][0]))
#pd.DataFrame(r).to_csv('%s/track%d_fitResults.csv' % (outputDir, c['clumpIndex'][0]))
with open('%s/track%d_fitResults.json' % (outputDir, c['clumpIndex'][0]), 'w') as f:
json.dump(r, f)
return filteredClumps