def testFrames(detThresh = 0.9, offset = 0):
plt.close('all')
matplotlib.interactive(False)
plt.clf()
sq = min(mdh.getEntry('EstimatedLaserOnFrameNo') + 1000, dataSource.getNumSlices()/4)
zps = np.array(range(mdh.getEntry('EstimatedLaserOnFrameNo') + 20, mdh.getEntry('EstimatedLaserOnFrameNo') + 24) + range(sq, sq + 4) + range(dataSource.getNumSlices()/2,dataSource.getNumSlices() /2+4))
zps += offset
fitMod = cFitType.GetStringSelection()
#bgFrames = int(tBackgroundFrames.GetValue())
bgFrames = [int(v) for v in tBackgroundFrames.GetValue().split(':')]
for i in range(12):
#if 'Analysis.NumBGFrames' in md.getEntryNames():
#bgi = range(max(zps[i] - bgFrames,mdh.getEntry('EstimatedLaserOnFrameNo')), zps[i])
bgi = range(max(zps[i] + bgFrames[0],mdh.getEntry('EstimatedLaserOnFrameNo')), max(zps[i] + bgFrames[1],mdh.getEntry('EstimatedLaserOnFrameNo')))
#else:
# bgi = range(max(zps[i] - 10,md.EstimatedLaserOnFrameNo), zps[i])
if 'Splitter' in fitMod:
ft = remFitBuf.fitTask(seriesName, zps[i], detThresh, MetaDataHandler.NestedClassMDHandler(mdh), 'SplitterObjFindR', bgindices=bgi, SNThreshold=True)
else:
ft = remFitBuf.fitTask(seriesName, zps[i], detThresh, MetaDataHandler.NestedClassMDHandler(mdh), 'LatObjFindFR', bgindices=bgi, SNThreshold=True)
res = ft()
xp = np.floor(i/4)/3.
yp = (3 - i%4)/4.
#print xp, yp
plt.axes((xp,yp, 1./6,1./4.5))
#d = ds[zps[i], :,:].squeeze().T
d = dataSource.getSlice(zps[i]).T
plt.imshow(d, cmap=cm.hot, interpolation='nearest', hold=False, clim=(np.median(d.ravel()), d.max()))
plt.title('Frame %d' % zps[i])
plt.xlim(0, d.shape[1])
plt.ylim(0, d.shape[0])
plt.xticks([])
plt.yticks([])
#print 'i = %d, ft.index = %d' % (i, ft.index)
#subplot(4,6,2*i+13)
xp += 1./6
plt.axes((xp,yp, 1./6,1./4.5))
d = ft.ofd.filteredData.T
#d = ft.data.squeeze().T
plt.imshow(d, cmap=cm.hot, interpolation='nearest', hold=False, clim=(np.median(d.ravel()), d.max()))
plt.plot([p.x for p in ft.ofd], [p.y for p in ft.ofd], 'o', mew=2, mec='g', mfc='none', ms=9)
if ft.driftEst:
plt.plot([p.x for p in ft.ofdDr], [p.y for p in ft.ofdDr], 'o', mew=2, mec='b', mfc='none', ms=9)
if ft.fitModule in remFitBuf.splitterFitModules:
plt.plot([p.x for p in ft.ofd], [d.shape[0] - p.y for p in ft.ofd], 'o', mew=2, mec='g', mfc='none', ms=9)
#axis('tight')
plt.xlim(0, d.shape[1])
plt.ylim(0, d.shape[0])
plt.xticks([])
plt.yticks([])
plt.show()
matplotlib.interactive(True)
def pushImagesDS(self, startingAt=0, detThresh = .9, fitFcn = 'LatGaussFitFR'):
#global seriesName
#dataFilename = self.image.seriesName
resultsFilename = genResultFileName(self.image.seriesName)
while os.path.exists(resultsFilename):
di, fn = os.path.split(resultsFilename)
fdialog = wx.FileDialog(None, 'Analysis file already exists, please select a new filename',
wildcard='H5R files|*.h5r', defaultDir=di, defaultFile=os.path.splitext(fn)[0] + '_1.h5r', style=wx.SAVE)
succ = fdialog.ShowModal()
if (succ == wx.ID_OK):
resultsFilename = fdialog.GetPath().encode()
else:
raise RuntimeError('Invalid results file - not running')
#self.image.seriesName = resultsFilename
debugPrint('Results file = %s' % resultsFilename)
self.tq.createQueue('HDFResultsTaskQueue', resultsFilename, None)
debugPrint('Queue created')
mdhQ = MetaDataHandler.QueueMDHandler(self.tq, resultsFilename, self.image.mdh)
mdhQ.setEntry('Analysis.DetectionThreshold', detThresh)
mdhQ.setEntry('Analysis.FitModule', fitFcn)
mdhQ.setEntry('Analysis.DataFileID', fileID.genDataSourceID(self.image.dataSource))
debugPrint('Metadata transferred to queue')
evts = self.image.dataSource.getEvents()
if len(evts) > 0:
self.tq.addQueueEvents(resultsFilename, evts)
md = MetaDataHandler.NestedClassMDHandler(mdhQ)
mn = self.image.dataSource.moduleName
#if it's a buffered source, go back to underlying source
if mn == 'BufferedDataSource':
mn = self.image.dataSource.dataSource.moduleName
for i in range(startingAt, self.image.dataSource.getNumSlices()):
debugPrint('Posting task %d' %i)
if 'Analysis.BGRange' in md.getEntryNames():
bgi = range(max(i + md.Analysis.BGRange[0],md.EstimatedLaserOnFrameNo), max(i + md.Analysis.BGRange[1],md.EstimatedLaserOnFrameNo))
elif 'Analysis.NumBGFrames' in md.getEntryNames():
bgi = range(max(i - md.Analysis.NumBGFrames, md.EstimatedLaserOnFrameNo), i)
else:
bgi = range(max(i - 10, md.EstimatedLaserOnFrameNo), i)
#task = fitTask(self.queueID, taskNum, self.metaData.Analysis.DetectionThreshold, self.metaData, self.metaData.Analysis.FitModule, 'TQDataSource', bgindices =bgi, SNThreshold = True)
self.tq.postTask(remFitBuf.fitTask(self.image.seriesName,i, detThresh, md, fitFcn, bgindices=bgi, SNThreshold=True, dataSourceModule=mn), queueName=resultsFilename)
self.image.seriesName = resultsFilename
def getTask(self, workerN=0, NWorkers=1):
"""get task from front of list, blocks"""
#print 'Task requested'
#self.getTaskLock.acquire()
while len(self.openTasks) < 1:
time.sleep(0.01)
#if self.metaDataStale:
# with self.dataFileLock.rlock:
# self.metaData = MetaDataHandler.NestedClassMDHandler(self.resultsMDH)
# self.metaDataStale = False
#patch up old data which doesn't have BGRange in metadata
if not 'Analysis.BGRange' in self.metaData.getEntryNames():
if 'Analysis.NumBGFrames' in self.metaData.getEntryNames():
nBGFrames = self.metaData.Analysis.NumBGFrames
else:
nBGFrames = 10
self.metaData.setEntry('Analysis.BGRange', (-nBGFrames, 0))
taskNum = self.openTasks.pop(
self.fTaskToPop(workerN, NWorkers, len(self.openTasks)))
#if 'Analysis.BGRange' in self.metaData.getEntryNames():
bgi = range(
max(taskNum + self.metaData.Analysis.BGRange[0],
self.metaData.EstimatedLaserOnFrameNo),
max(taskNum + self.metaData.Analysis.BGRange[1],
self.metaData.EstimatedLaserOnFrameNo))
#elif 'Analysis.NumBGFrames' in self.metaData.getEntryNames():
# bgi = range(max(taskNum - self.metaData.Analysis.NumBGFrames,self.metaData.EstimatedLaserOnFrameNo), taskNum)
#else:
# bgi = range(max(taskNum - 10,self.metaData.EstimatedLaserOnFrameNo), taskNum)
task = fitTask(self.queueID,
taskNum,
self.metaData.Analysis.DetectionThreshold,
self.metaData,
self.metaData.Analysis.FitModule,
'TQDataSource',
bgindices=bgi,
SNThreshold=True)
task.queueID = self.queueID
task.initializeWorkerTimeout(time.clock())
with self.inProgressLock:
self.tasksInProgress.append(task)
#self.inProgressLock.release()
#self.getTaskLock.release()
self.lastTaskTime = time.time()
return task
def testFrame(detThresh=0.9):
ft = remFitBuf.fitTask(seriesName,
vp.zp,
detThresh,
MetaDataHandler.NestedClassMDHandler(mdh),
cFitType.GetString(cFitType.GetSelection()),
bgindices=range(
max(vp.zp - 10,
mdh.getEntry('EstimatedLaserOnFrameNo')),
vp.zp),
SNThreshold=True)
return ft(True)
def testFrameD(detThresh=0.9):
ft = remFitBuf.fitTask(
seriesName,
vp.zp,
detThresh,
MetaDataHandler.NestedClassMDHandler(mdh),
'LatGaussFitFR',
bgindices=range(max(vp.zp - 10, md.EstimatedLaserOnFrameNo), vp.zp),
SNThreshold=True,
driftEstInd=range(max(vp.zp - 5, md.EstimatedLaserOnFrameNo),
min(vp.zp + 5, ds.shape[0])))
return ft(True)
def testFrameTQ(detThresh=0.9):
ft = remFitBuf.fitTask(seriesName,
vp.zp,
detThresh,
MetaDataHandler.NestedClassMDHandler(mdh),
'LatGaussFitFR',
'TQDataSource',
bgindices=range(
max(vp.zp - 10,
mdh.getEntry('EstimatedLaserOnFrameNo')),
vp.zp),
SNThreshold=True)
return ft(True, tq)
def getTask(self, workerN = 0, NWorkers = 1):
"""get task from front of list, blocks"""
#print 'Task requested'
#self.getTaskLock.acquire()
while len(self.openTasks) < 1:
time.sleep(0.01)
#if self.metaDataStale:
# with self.dataFileLock.rlock:
# self.metaData = MetaDataHandler.NestedClassMDHandler(self.resultsMDH)
# self.metaDataStale = False
#patch up old data which doesn't have BGRange in metadata
if not 'Analysis.BGRange' in self.metaData.getEntryNames():
if 'Analysis.NumBGFrames' in self.metaData.getEntryNames():
nBGFrames = self.metaData.Analysis.NumBGFrames
else:
nBGFrames = 10
self.metaData.setEntry('Analysis.BGRange', (-nBGFrames, 0))
taskNum = self.openTasks.pop(self.fTaskToPop(workerN, NWorkers, len(self.openTasks)))
#if 'Analysis.BGRange' in self.metaData.getEntryNames():
bgi = range(max(taskNum + self.metaData.Analysis.BGRange[0],self.metaData.EstimatedLaserOnFrameNo), max(taskNum + self.metaData.Analysis.BGRange[1],self.metaData.EstimatedLaserOnFrameNo))
#elif 'Analysis.NumBGFrames' in self.metaData.getEntryNames():
# bgi = range(max(taskNum - self.metaData.Analysis.NumBGFrames,self.metaData.EstimatedLaserOnFrameNo), taskNum)
#else:
# bgi = range(max(taskNum - 10,self.metaData.EstimatedLaserOnFrameNo), taskNum)
task = fitTask(self.queueID, taskNum, self.metaData.Analysis.DetectionThreshold, self.metaData, self.metaData.Analysis.FitModule, 'TQDataSource', bgindices = bgi, SNThreshold = True)
task.queueID = self.queueID
task.initializeWorkerTimeout(time.clock())
with self.inProgressLock:
self.tasksInProgress.append(task)
#self.inProgressLock.release()
#self.getTaskLock.release()
self.lastTaskTime = time.time()
return task
def pushImagesD(startingAt=0, detThresh=.9):
tq.createQueue('HDFResultsTaskQueue', seriesName, None)
mdhQ = MetaDataHandler.QueueMDHandler(tq, seriesName, mdh)
mdhQ.setEntry('Analysis.DetectionThreshold', detThresh)
for i in range(startingAt, ds.shape[0]):
tq.postTask(remFitBuf.fitTask(
seriesName,
i,
detThresh,
MetaDataHandler.NestedClassMDHandler(mdh),
'LatGaussFitFR',
bgindices=range(
max(i - 10, mdh.getEntry('EstimatedLaserOnFrameNo')), i),
SNThreshold=True,
driftEstInd=range(
max(i - 5, mdh.getEntry('EstimatedLaserOnFrameNo')),
min(i + 5, ds.shape[0])),
dataSourceModule=dataSource.moduleName),
queueName=seriesName)
def getTasks(self, workerN = 0, NWorkers = 1):
"""get task from front of list, blocks"""
print '> HDFTaskQueue: Task requested'
#self.getTaskLock.acquire()
while len(self.openTasks) < 1:
time.sleep(0.01)
#if self.metaDataStale:
# with self.dataFileLock.rlock:
# self.metaData = MetaDataHandler.NestedClassMDHandler(self.resultsMDH)
# self.metaDataStale = False
if not 'Analysis.BGRange' in self.metaData.getEntryNames():
if 'Analysis.NumBGFrames' in self.metaData.getEntryNames():
nBGFrames = self.metaData.Analysis.NumBGFrames
else:
nBGFrames = 10
self.metaData.setEntry('Analysis.BGRange', (-nBGFrames, 0))
tasks = []
if not 'Analysis.ChunkSize' in self.metaData.getEntryNames():
cs = min(max(CHUNKSIZE, min(MAXCHUNKSIZE, len(self.openTasks))),len(self.openTasks))
else:
cs = min(self.metaData['Analysis.ChunkSize'], len(self.openTasks))
for i in range(cs):
taskNum = self.openTasks.pop(self.fTaskToPop(workerN, NWorkers, len(self.openTasks)))
#if 'Analysis.BGRange' in self.metaData.getEntryNames():
bgi = range(max(taskNum + self.metaData.Analysis.BGRange[0],
self.metaData.EstimatedLaserOnFrameNo),
max(taskNum + self.metaData.Analysis.BGRange[1],
self.metaData.EstimatedLaserOnFrameNo))
#elif 'Analysis.NumBGFrames' in self.metaData.getEntryNames():
# bgi = range(max(taskNum - self.metaData.Analysis.NumBGFrames,self.metaData.EstimatedLaserOnFrameNo), taskNum)
#else:
# bgi = range(max(taskNum - 10,self.metaData.EstimatedLaserOnFrameNo), taskNum)
task = fitTask(self.queueID, # .h5 filename
taskNum,
self.metaData.Analysis.DetectionThreshold,
self.metaData,
self.metaData.Analysis.FitModule,
'TQDataSource',
bgindices =bgi,
SNThreshold = True)
task.queueID = self.queueID
task.initializeWorkerTimeout(time.clock())
with self.inProgressLock:
self.tasksInProgress.append(task)
tasks.append(task)
self.lastTaskTime = time.time()
return tasks
def getTasks(self, workerN=0, NWorkers=1):
"""get task from front of list, blocks"""
print '> HDFTaskQueue: Task requested'
#self.getTaskLock.acquire()
while len(self.openTasks) < 1:
time.sleep(0.01)
#if self.metaDataStale:
# with self.dataFileLock.rlock:
# self.metaData = MetaDataHandler.NestedClassMDHandler(self.resultsMDH)
# self.metaDataStale = False
if not 'Analysis.BGRange' in self.metaData.getEntryNames():
if 'Analysis.NumBGFrames' in self.metaData.getEntryNames():
nBGFrames = self.metaData.Analysis.NumBGFrames
else:
nBGFrames = 10
self.metaData.setEntry('Analysis.BGRange', (-nBGFrames, 0))
tasks = []
if not 'Analysis.ChunkSize' in self.metaData.getEntryNames():
cs = min(max(CHUNKSIZE, min(MAXCHUNKSIZE, len(self.openTasks))),
len(self.openTasks))
else:
cs = min(self.metaData['Analysis.ChunkSize'], len(self.openTasks))
for i in range(cs):
taskNum = self.openTasks.pop(
self.fTaskToPop(workerN, NWorkers, len(self.openTasks)))
#if 'Analysis.BGRange' in self.metaData.getEntryNames():
bgi = range(
max(taskNum + self.metaData.Analysis.BGRange[0],
self.metaData.EstimatedLaserOnFrameNo),
max(taskNum + self.metaData.Analysis.BGRange[1],
self.metaData.EstimatedLaserOnFrameNo))
#elif 'Analysis.NumBGFrames' in self.metaData.getEntryNames():
# bgi = range(max(taskNum - self.metaData.Analysis.NumBGFrames,self.metaData.EstimatedLaserOnFrameNo), taskNum)
#else:
# bgi = range(max(taskNum - 10,self.metaData.EstimatedLaserOnFrameNo), taskNum)
task = fitTask(
self.queueID, # .h5 filename
taskNum,
self.metaData.Analysis.DetectionThreshold,
self.metaData,
self.metaData.Analysis.FitModule,
'TQDataSource',
bgindices=bgi,
SNThreshold=True)
task.queueID = self.queueID
task.initializeWorkerTimeout(time.clock())
with self.inProgressLock:
self.tasksInProgress.append(task)
tasks.append(task)
self.lastTaskTime = time.time()
return tasks
def testFrame(self, detThresh = 0.9, offset = 0, gui=True):
from pylab import *
#close('all')
print '> testFrame Begin'
#print '> Try to print moduleName:'
#print(self.image.dataSource.moduleName)
if self.image.dataSource.moduleName == 'TQDataSource': # if .tif is loaded, module name will be buffered sth.
self.checkTQ()
if gui:
matplotlib.interactive(False)
figure()
#sq = min(self.image.mdh.getEntry('EstimatedLaserOnFrameNo') + 1000, self.image.dataSource.getNumSlices()/4)
#zps = array(range(self.image.mdh.getEntry('EstimatedLaserOnFrameNo') + 20, self.image.mdh.getEntry('EstimatedLaserOnFrameNo') + 24) + range(sq, sq + 4) + range(self.image.dataSource.getNumSlices()/2,self.image.dataSource.getNumSlices() /2+4))
#zps += offset
zp = self.do.zp
fitMod = self.fitFactories[self.cFitType.GetSelection()]
self.image.mdh.setEntry('Analysis.FitModule', fitMod)
#bgFrames = int(tBackgroundFrames.GetValue())
bgFrames = [int(v) for v in self.tBackgroundFrames.GetValue().split(':')]
#print zps
bgi = range(max(zp + bgFrames[0],self.image.mdh.getEntry('EstimatedLaserOnFrameNo')), max(zp + bgFrames[1],self.image.mdh.getEntry('EstimatedLaserOnFrameNo')))
#else:
# bgi = range(max(zps[i] - 10,md.EstimatedLaserOnFrameNo), zps[i])
mn = self.image.dataSource.moduleName
if mn == 'BufferedDataSource':
mn = self.image.dataSource.dataSource.moduleName
#if 'Splitter' in fitMod:
# ft = remFitBuf.fitTask(self.image.seriesName, zp, detThresh, MetaDataHandler.NestedClassMDHandler(self.image.mdh), 'SplitterObjFindR', bgindices=bgi, SNThreshold=True,dataSourceModule=mn)
#else:
# ft = remFitBuf.fitTask(self.image.seriesName, zp, detThresh, MetaDataHandler.NestedClassMDHandler(self.image.mdh), 'LatObjFindFR', bgindices=bgi, SNThreshold=True,dataSourceModule=mn)
ft = remFitBuf.fitTask(self.image.seriesName, zp, detThresh, MetaDataHandler.NestedClassMDHandler(self.image.mdh), fitMod, bgindices=bgi, SNThreshold=True,dataSourceModule=mn)
res = ft(gui=gui,taskQueue=self.tq)
if gui:
figure()
try:
d = ft.ofd.filteredData.T
#d = ft.data.squeeze().T
imshow(d, cmap=cm.hot, interpolation='nearest', hold=False, clim=(median(d.ravel()), d.max()))
plot([p.x for p in ft.ofd], [p.y for p in ft.ofd], 'o', mew=2, mec='g', mfc='none', ms=9)
if ft.driftEst:
plot([p.x for p in ft.ofdDr], [p.y for p in ft.ofdDr], 'o', mew=2, mec='b', mfc='none', ms=9)
#if ft.fitModule in remFitBuf.splitterFitModules:
# plot([p.x for p in ft.ofd], [d.shape[0] - p.y for p in ft.ofd], 'o', mew=2, mec='g', mfc='none', ms=9)
#axis('tight')
xlim(0, d.shape[1])
ylim(d.shape[0], 0)
xticks([])
yticks([])
vx = 1e3*self.image.mdh['voxelsize.x']
vy = 1e3*self.image.mdh['voxelsize.y']
plot(res.results['fitResults']['x0']/vx, res.results['fitResults']['y0']/vy, '+b', mew=2)
if 'startParams' in res.results.dtype.names:
plot(res.results['startParams']['x0']/vx, res.results['startParams']['y0']/vy, 'xc', mew=2)
if 'tIm' in dir(ft.ofd):
figure()
imshow(ft.ofd.tIm.T, cmap=cm.hot, interpolation='nearest', hold=False)
#axis('tight')
xlim(0, d.shape[1])
ylim(d.shape[0], 0)
xticks([])
yticks([])
plot(res.results['fitResults']['x0']/vx, res.results['fitResults']['y0']/vy, '+b')
#figure()
#imshow()
except AttributeError:
#d = self.image.data[:,:,zp].squeeze().T
d = (ft.data.squeeze() - ft.bg.squeeze()).T
imshow(d, cmap=cm.jet, interpolation='nearest', clim = [0, d.max()])
xlim(0, d.shape[1])
ylim(d.shape[0], 0)
vx = 1e3*self.image.mdh['voxelsize.x']
vy = 1e3*self.image.mdh['voxelsize.y']
plot(res.results['fitResults']['x0']/vx, res.results['fitResults']['y0']/vy, 'ow')
pass
show()
matplotlib.interactive(True)
return ft, res
def testFrames(self, detThresh = 0.9, offset = 0):
from pylab import *
close('all')
if self.image.dataSource.moduleName == 'TQDataSource':
self.checkTQ()
matplotlib.interactive(False)
clf()
sq = min(self.image.mdh.getEntry('EstimatedLaserOnFrameNo') + 1000,
self.image.dataSource.getNumSlices()/4)
# sq = 1000
zps = array(range(self.image.mdh.getEntry('EstimatedLaserOnFrameNo') + 20,
self.image.mdh.getEntry('EstimatedLaserOnFrameNo') + 24) + range(sq, sq + 4) + range(self.image.dataSource.getNumSlices()/2,self.image.dataSource.getNumSlices() /2+4))
# zps = [20 21 22 23 1000 1001 1002 1003 2500 2501 2502 2503]
zps += offset
fitMod = self.fitFactories[self.cFitType.GetSelection()]
# e.g. fitMod = 'LatGaussFitFR'
#bgFrames = int(tBackgroundFrames.GetValue())
bgFrames = [int(v) for v in self.tBackgroundFrames.GetValue().split(':')]
# bgFrames = [-30, 0]
print(zps)
for i in range(12):
print(i)
#if 'Analysis.NumBGFrames' in md.getEntryNames():
#bgi = range(max(zps[i] - bgFrames,mdh.getEntry('EstimatedLaserOnFrameNo')), zps[i])
bgi = range(max(zps[i] + bgFrames[0],self.image.mdh.getEntry('EstimatedLaserOnFrameNo')),
max(zps[i] + bgFrames[1],self.image.mdh.getEntry('EstimatedLaserOnFrameNo')))
# bgi = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ..., 19]
#else:
# bgi = range(max(zps[i] - 10,md.EstimatedLaserOnFrameNo), zps[i])
mn = self.image.dataSource.moduleName
if mn == 'BufferedDataSource':
mn = self.image.dataSource.dataSource.moduleName
if 'Splitter' in fitMod:
ft = remFitBuf.fitTask(self.image.seriesName,
zps[i],
detThresh,
MetaDataHandler.NestedClassMDHandler(self.image.mdh),
'SplitterObjFindR',
bgindices = bgi,
SNThreshold = True,
dataSourceModule = mn)
else:
ft = remFitBuf.fitTask(self.image.seriesName,
zps[i],
detThresh,
MetaDataHandler.NestedClassMDHandler(self.image.mdh),
'LatObjFindFR',
bgindices = bgi,
SNThreshold = True,
dataSourceModule = mn)
res = ft(taskQueue=self.tq)
xp = floor(i/4)/3.
yp = (3 - i%4)/4.
#print xp, yp
axes((xp,yp, 1./6,1./4.5))
#d = ds[zps[i], :,:].squeeze().T
d = self.image.dataSource.getSlice(zps[i]).T
imshow(d, cmap=cm.hot, interpolation='nearest', hold=False, clim=(median(d.ravel()), d.max()))
title('Frame %d' % zps[i])
xlim(0, d.shape[1])
ylim(0, d.shape[0])
xticks([])
yticks([])
#print 'i = %d, ft.index = %d' % (i, ft.index)
#subplot(4,6,2*i+13)
xp += 1./6
axes((xp,yp, 1./6,1./4.5))
d = ft.ofd.filteredData.T
#d = ft.data.squeeze().T
imshow(d, cmap=cm.hot, interpolation='nearest', hold=False, clim=(median(d.ravel()), d.max()))
plot([p.x for p in ft.ofd], [p.y for p in ft.ofd], 'o', mew=2, mec='g', mfc='none', ms=9)
if ft.driftEst:
plot([p.x for p in ft.ofdDr], [p.y for p in ft.ofdDr], 'o', mew=2, mec='b', mfc='none', ms=9)
if ft.fitModule in remFitBuf.splitterFitModules:
plot([p.x for p in ft.ofd], [d.shape[0] - p.y for p in ft.ofd], 'o', mew=2, mec='g', mfc='none', ms=9)
axis('tight')
xlim(0, d.shape[1])
ylim(0, d.shape[0])
xticks([])
yticks([])
show()
matplotlib.interactive(True)
def testFrames(self, detThresh=0.9, offset=0):
from pylab import *
close('all')
if self.image.dataSource.moduleName == 'TQDataSource':
self.checkTQ()
matplotlib.interactive(False)
clf()
sq = min(
self.image.mdh.getEntry('EstimatedLaserOnFrameNo') + 1000,
self.image.dataSource.getNumSlices() / 4)
# sq = 1000
zps = array(
range(
self.image.mdh.getEntry('EstimatedLaserOnFrameNo') + 20,
self.image.mdh.getEntry('EstimatedLaserOnFrameNo') + 24) +
range(sq, sq + 4) +
range(self.image.dataSource.getNumSlices() / 2,
self.image.dataSource.getNumSlices() / 2 + 4))
# zps = [20 21 22 23 1000 1001 1002 1003 2500 2501 2502 2503]
zps += offset
fitMod = self.fitFactories[self.cFitType.GetSelection()]
# e.g. fitMod = 'LatGaussFitFR'
#bgFrames = int(tBackgroundFrames.GetValue())
bgFrames = [
int(v) for v in self.tBackgroundFrames.GetValue().split(':')
]
# bgFrames = [-30, 0]
print(zps)
for i in range(12):
print(i)
#if 'Analysis.NumBGFrames' in md.getEntryNames():
#bgi = range(max(zps[i] - bgFrames,mdh.getEntry('EstimatedLaserOnFrameNo')), zps[i])
bgi = range(
max(zps[i] + bgFrames[0],
self.image.mdh.getEntry('EstimatedLaserOnFrameNo')),
max(zps[i] + bgFrames[1],
self.image.mdh.getEntry('EstimatedLaserOnFrameNo')))
# bgi = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ..., 19]
#else:
# bgi = range(max(zps[i] - 10,md.EstimatedLaserOnFrameNo), zps[i])
mn = self.image.dataSource.moduleName
if mn == 'BufferedDataSource':
mn = self.image.dataSource.dataSource.moduleName
if 'Splitter' in fitMod:
ft = remFitBuf.fitTask(self.image.seriesName,
zps[i],
detThresh,
MetaDataHandler.NestedClassMDHandler(
self.image.mdh),
'SplitterObjFindR',
bgindices=bgi,
SNThreshold=True,
dataSourceModule=mn)
else:
ft = remFitBuf.fitTask(self.image.seriesName,
zps[i],
detThresh,
MetaDataHandler.NestedClassMDHandler(
self.image.mdh),
'LatObjFindFR',
bgindices=bgi,
SNThreshold=True,
dataSourceModule=mn)
res = ft(taskQueue=self.tq)
xp = floor(i / 4) / 3.
yp = (3 - i % 4) / 4.
#print xp, yp
axes((xp, yp, 1. / 6, 1. / 4.5))
#d = ds[zps[i], :,:].squeeze().T
d = self.image.dataSource.getSlice(zps[i]).T
imshow(d,
cmap=cm.hot,
interpolation='nearest',
hold=False,
clim=(median(d.ravel()), d.max()))
title('Frame %d' % zps[i])
xlim(0, d.shape[1])
ylim(0, d.shape[0])
xticks([])
yticks([])
#print 'i = %d, ft.index = %d' % (i, ft.index)
#subplot(4,6,2*i+13)
xp += 1. / 6
axes((xp, yp, 1. / 6, 1. / 4.5))
d = ft.ofd.filteredData.T
#d = ft.data.squeeze().T
imshow(d,
cmap=cm.hot,
interpolation='nearest',
hold=False,
clim=(median(d.ravel()), d.max()))
plot([p.x for p in ft.ofd], [p.y for p in ft.ofd],
'o',
mew=2,
mec='g',
mfc='none',
ms=9)
if ft.driftEst:
plot([p.x for p in ft.ofdDr], [p.y for p in ft.ofdDr],
'o',
mew=2,
mec='b',
mfc='none',
ms=9)
if ft.fitModule in remFitBuf.splitterFitModules:
plot([p.x for p in ft.ofd], [d.shape[0] - p.y for p in ft.ofd],
'o',
mew=2,
mec='g',
mfc='none',
ms=9)
axis('tight')
xlim(0, d.shape[1])
ylim(0, d.shape[0])
xticks([])
yticks([])
show()
matplotlib.interactive(True)
def pushImagesDS(self, startingAt=0, detThresh=.9, fitFcn='LatGaussFitFR'):
#global seriesName
#dataFilename = self.image.seriesName
resultsFilename = genResultFileName(self.image.seriesName)
while os.path.exists(resultsFilename):
di, fn = os.path.split(resultsFilename)
fdialog = wx.FileDialog(
None,
'Analysis file already exists, please select a new filename',
wildcard='H5R files|*.h5r',
defaultDir=di,
defaultFile=os.path.splitext(fn)[0] + '_1.h5r',
style=wx.SAVE)
succ = fdialog.ShowModal()
if (succ == wx.ID_OK):
resultsFilename = fdialog.GetPath().encode()
else:
raise RuntimeError('Invalid results file - not running')
#self.image.seriesName = resultsFilename
debugPrint('Results file = %s' % resultsFilename)
self.tq.createQueue('HDFResultsTaskQueue', resultsFilename, None)
debugPrint('Queue created')
mdhQ = MetaDataHandler.QueueMDHandler(self.tq, resultsFilename,
self.image.mdh)
mdhQ.setEntry('Analysis.DetectionThreshold', detThresh)
mdhQ.setEntry('Analysis.FitModule', fitFcn)
mdhQ.setEntry('Analysis.DataFileID',
fileID.genDataSourceID(self.image.dataSource))
debugPrint('Metadata transferred to queue')
evts = self.image.dataSource.getEvents()
if len(evts) > 0:
self.tq.addQueueEvents(resultsFilename, evts)
md = MetaDataHandler.NestedClassMDHandler(mdhQ)
mn = self.image.dataSource.moduleName
#if it's a buffered source, go back to underlying source
if mn == 'BufferedDataSource':
mn = self.image.dataSource.dataSource.moduleName
for i in range(startingAt, self.image.dataSource.getNumSlices()):
debugPrint('Posting task %d' % i)
if 'Analysis.BGRange' in md.getEntryNames():
bgi = range(
max(i + md.Analysis.BGRange[0],
md.EstimatedLaserOnFrameNo),
max(i + md.Analysis.BGRange[1],
md.EstimatedLaserOnFrameNo))
elif 'Analysis.NumBGFrames' in md.getEntryNames():
bgi = range(
max(i - md.Analysis.NumBGFrames,
md.EstimatedLaserOnFrameNo), i)
else:
bgi = range(max(i - 10, md.EstimatedLaserOnFrameNo), i)
#task = fitTask(self.queueID, taskNum, self.metaData.Analysis.DetectionThreshold, self.metaData, self.metaData.Analysis.FitModule, 'TQDataSource', bgindices =bgi, SNThreshold = True)
self.tq.postTask(remFitBuf.fitTask(self.image.seriesName,
i,
detThresh,
md,
fitFcn,
bgindices=bgi,
SNThreshold=True,
dataSourceModule=mn),
queueName=resultsFilename)
self.image.seriesName = resultsFilename
def testFrame(self, detThresh=0.9, offset=0, gui=True):
from pylab import *
#close('all')
print '> testFrame Begin'
#print '> Try to print moduleName:'
#print(self.image.dataSource.moduleName)
if self.image.dataSource.moduleName == 'TQDataSource': # if .tif is loaded, module name will be buffered sth.
self.checkTQ()
if gui:
matplotlib.interactive(False)
figure()
#sq = min(self.image.mdh.getEntry('EstimatedLaserOnFrameNo') + 1000, self.image.dataSource.getNumSlices()/4)
#zps = array(range(self.image.mdh.getEntry('EstimatedLaserOnFrameNo') + 20, self.image.mdh.getEntry('EstimatedLaserOnFrameNo') + 24) + range(sq, sq + 4) + range(self.image.dataSource.getNumSlices()/2,self.image.dataSource.getNumSlices() /2+4))
#zps += offset
zp = self.do.zp
fitMod = self.fitFactories[self.cFitType.GetSelection()]
self.image.mdh.setEntry('Analysis.FitModule', fitMod)
#bgFrames = int(tBackgroundFrames.GetValue())
bgFrames = [
int(v) for v in self.tBackgroundFrames.GetValue().split(':')
]
#print zps
bgi = range(
max(zp + bgFrames[0],
self.image.mdh.getEntry('EstimatedLaserOnFrameNo')),
max(zp + bgFrames[1],
self.image.mdh.getEntry('EstimatedLaserOnFrameNo')))
#else:
# bgi = range(max(zps[i] - 10,md.EstimatedLaserOnFrameNo), zps[i])
mn = self.image.dataSource.moduleName
if mn == 'BufferedDataSource':
mn = self.image.dataSource.dataSource.moduleName
#if 'Splitter' in fitMod:
# ft = remFitBuf.fitTask(self.image.seriesName, zp, detThresh, MetaDataHandler.NestedClassMDHandler(self.image.mdh), 'SplitterObjFindR', bgindices=bgi, SNThreshold=True,dataSourceModule=mn)
#else:
# ft = remFitBuf.fitTask(self.image.seriesName, zp, detThresh, MetaDataHandler.NestedClassMDHandler(self.image.mdh), 'LatObjFindFR', bgindices=bgi, SNThreshold=True,dataSourceModule=mn)
ft = remFitBuf.fitTask(self.image.seriesName,
zp,
detThresh,
MetaDataHandler.NestedClassMDHandler(
self.image.mdh),
fitMod,
bgindices=bgi,
SNThreshold=True,
dataSourceModule=mn)
res = ft(gui=gui, taskQueue=self.tq)
if gui:
figure()
try:
d = ft.ofd.filteredData.T
#d = ft.data.squeeze().T
imshow(d,
cmap=cm.hot,
interpolation='nearest',
hold=False,
clim=(median(d.ravel()), d.max()))
plot([p.x for p in ft.ofd], [p.y for p in ft.ofd],
'o',
mew=2,
mec='g',
mfc='none',
ms=9)
if ft.driftEst:
plot([p.x for p in ft.ofdDr], [p.y for p in ft.ofdDr],
'o',
mew=2,
mec='b',
mfc='none',
ms=9)
#if ft.fitModule in remFitBuf.splitterFitModules:
# plot([p.x for p in ft.ofd], [d.shape[0] - p.y for p in ft.ofd], 'o', mew=2, mec='g', mfc='none', ms=9)
#axis('tight')
xlim(0, d.shape[1])
ylim(d.shape[0], 0)
xticks([])
yticks([])
vx = 1e3 * self.image.mdh['voxelsize.x']
vy = 1e3 * self.image.mdh['voxelsize.y']
plot(res.results['fitResults']['x0'] / vx,
res.results['fitResults']['y0'] / vy,
'+b',
mew=2)
if 'startParams' in res.results.dtype.names:
plot(res.results['startParams']['x0'] / vx,
res.results['startParams']['y0'] / vy,
'xc',
mew=2)
if 'tIm' in dir(ft.ofd):
figure()
imshow(ft.ofd.tIm.T,
cmap=cm.hot,
interpolation='nearest',
hold=False)
#axis('tight')
xlim(0, d.shape[1])
ylim(d.shape[0], 0)
xticks([])
yticks([])
plot(res.results['fitResults']['x0'] / vx,
res.results['fitResults']['y0'] / vy, '+b')
#figure()
#imshow()
except AttributeError:
#d = self.image.data[:,:,zp].squeeze().T
d = (ft.data.squeeze() - ft.bg.squeeze()).T
imshow(d,
cmap=cm.jet,
interpolation='nearest',
clim=[0, d.max()])
xlim(0, d.shape[1])
ylim(d.shape[0], 0)
vx = 1e3 * self.image.mdh['voxelsize.x']
vy = 1e3 * self.image.mdh['voxelsize.y']
plot(res.results['fitResults']['x0'] / vx,
res.results['fitResults']['y0'] / vy, 'ow')
pass
show()
matplotlib.interactive(True)
return ft, res
