def getPSFSlice(datasource, resultsSource, metadata, zm=None):
f1 = inpFilt.resultsFilter(resultsSource,
error_x=[1, 30],
A=[10, 500],
sig=(150 / 2.35, 900 / 2.35))
ims, pts, zvals, zis = extractIms(datasource, f1, metadata, zm)
return getPSF(ims, pts, zvals, zis)
def RegenFilter(self):
if not self.selectedDataSource == None:
self.filter = inpFilt.resultsFilter(self.selectedDataSource, **self.filterKeys)
if self.mapping:
self.mapping.resultsSource = self.filter
else:
self.mapping = inpFilt.mappingFilter(self.filter)
if not self.colourFilter:
self.colourFilter = inpFilt.colourFilter(self.mapping, self)
self.edb = None
self.objects = None
self.GeneratedMeasures = {}
def RegenFilter(self):
if not self.selectedDataSource == None:
self.filter = inpFilt.resultsFilter(self.selectedDataSource,
**self.filterKeys)
if self.mapping:
self.mapping.resultsSource = self.filter
else:
self.mapping = inpFilt.mappingFilter(self.filter)
if not self.colourFilter:
self.colourFilter = inpFilt.colourFilter(self.mapping, self)
self.edb = None
self.objects = None
self.GeneratedMeasures = {}
def generateThumbnail(inputFile, thumbSize):
f1 = inpFilt.h5rSource(inputFile)
threeD = False
stack = False
split = False
# print f1.keys()
if "fitResults_Ag" in f1.keys():
# if we used the splitter set up a mapping so we can filter on total amplitude and ratio
f1_ = inpFilt.mappingFilter(
f1, A="fitResults_Ag + fitResults_Ar", gFrac="fitResults_Ag/(fitResults_Ag + fitResults_Ar)"
)
# f2 = inpFilt.resultsFilter(f1_, error_x=[0,30], A=[5, 1e5], sig=[100/2.35, 350/2.35])
split = True
else:
f1_ = f1
if "fitResults_sigma" in f1.keys():
f2 = inpFilt.resultsFilter(f1_, error_x=[0, 30], A=[5, 1e5], sig=[100 / 2.35, 350 / 2.35])
else:
f2 = inpFilt.resultsFilter(f1_, error_x=[0, 30], A=[5, 1e5])
if "fitResults_z0" in f1_.keys():
threeD = True
if "Events" in dir(f1.h5f.root):
events = f1.h5f.root.Events[:]
evKeyNames = set()
for e in events:
evKeyNames.add(e["EventName"])
if "ProtocolFocus" in evKeyNames:
stack = True
xmax = f2["x"].max()
ymax = f2["y"].max()
if xmax > ymax:
step = xmax / thumbSize
else:
step = ymax / thumbSize
im, edx, edy = histogram2d(f2["x"], f2["y"], [arange(0, xmax, step), arange(0, ymax, step)])
f1.close()
im = minimum(2 * (255 * im) / im.max(), 255).T
im = concatenate((im[:, :, newaxis], im[:, :, newaxis], im[:, :, newaxis]), 2)
if stack:
im[-10:, -10:, 0] = 180
if threeD:
im[-10:, -10:, 1] = 180
if split:
im[-10:-5, :10, 1] = 210
im[-5:, :10, 0] = 210
return im.astype("uint8")
def generateThumbnail(inputFile, thumbSize):
f1 = inpFilt.h5rSource(inputFile)
threeD = False
stack = False
split = False
#print f1.keys()
if 'fitResults_Ag' in f1.keys():
#if we used the splitter set up a mapping so we can filter on total amplitude and ratio
f1_ = inpFilt.mappingFilter(
f1,
A='fitResults_Ag + fitResults_Ar',
gFrac='fitResults_Ag/(fitResults_Ag + fitResults_Ar)')
#f2 = inpFilt.resultsFilter(f1_, error_x=[0,30], A=[5, 1e5], sig=[100/2.35, 350/2.35])
split = True
else:
f1_ = f1
if 'fitResults_sigma' in f1.keys():
f2 = inpFilt.resultsFilter(f1_,
error_x=[0, 30],
A=[5, 1e5],
sig=[100 / 2.35, 350 / 2.35])
else:
f2 = inpFilt.resultsFilter(f1_, error_x=[0, 30], A=[5, 1e5])
if 'fitResults_z0' in f1_.keys():
threeD = True
if 'Events' in dir(f1.h5f.root):
events = f1.h5f.root.Events[:]
evKeyNames = set()
for e in events:
evKeyNames.add(e['EventName'])
if 'ProtocolFocus' in evKeyNames:
stack = True
xmax = f2['x'].max()
ymax = f2['y'].max()
if xmax > ymax:
step = xmax / thumbSize
else:
step = ymax / thumbSize
im, edx, edy = histogram2d(
f2['x'], f2['y'],
[arange(0, xmax, step), arange(0, ymax, step)])
f1.close()
im = minimum(2 * (255 * im) / im.max(), 255).T
im = concatenate((im[:, :, newaxis], im[:, :, newaxis], im[:, :, newaxis]),
2)
if stack:
im[-10:, -10:, 0] = 180
if threeD:
im[-10:, -10:, 1] = 180
if split:
im[-10:-5, :10, 1] = 210
im[-5:, :10, 0] = 210
return im.astype('uint8')
def getPSFSlice(datasource, resultsSource, metadata, zm=None):
f1 = inpFilt.resultsFilter(resultsSource, error_x=[1,30], A=[10, 500], sig=(150/2.35, 900/2.35))
ims, pts, zvals, zis = extractIms(datasource, f1, metadata, zm)
return getPSF(ims, pts, zvals, zis)
