def initFindAndFit(parameters):
"""
Initialize and return a PSFFFTFinderFitter object.
"""
# Create psf fft function object.
psf_fn = psfFn.PSFFn(psf_filename=parameters.getAttr("psf"))
# Check that the PSF FFT and camera pixel sizes agree.
diff = abs(
parameters.getAttr("pixel_size") - psf_fn.getPixelSize() * 1.0e3)
assert (diff < 1.0e-6), "Wrong pixel size, incorrect PSF data?"
# Create peak finder.
finder = fitting.PeakFinderArbitraryPSF(parameters=parameters,
psf_object=psf_fn)
# Create fftFitC.CFFTFit object.
mfitter = initFitter(finder, parameters, psf_fn)
# Create peak fitter.
fitter = fitting.PeakFitterArbitraryPSF(mfitter=mfitter,
parameters=parameters)
# Specify which properties we want from the analysis.
properties = [
"background", "error", "height", "iterations", "significance", "sum",
"x", "y", "z"
]
return fitting.PeakFinderFitter(peak_finder=finder,
peak_fitter=fitter,
properties=properties)
def initFindAndFit(parameters):
"""
Initialize and return a SplinerFinderFitter object.
"""
# Create spline object.
spline_fn = splineToPSF.loadSpline(parameters.getAttr("spline"))
# Create peak finder.
finder = fitting.PeakFinderArbitraryPSF(parameters=parameters,
psf_object=spline_fn)
# Create cubicFitC.CSplineFit object.
mfitter = initFitter(finder, parameters, spline_fn)
# Create peak fitter.
fitter = fitting.PeakFitterArbitraryPSF(mfitter=mfitter,
parameters=parameters)
# Specify which properties we want from the analysis.
properties = [
"background", "error", "height", "iterations", "significance", "sum",
"x", "y", "z"
]
return fitting.PeakFinderFitter(peak_finder=finder,
peak_fitter=fitter,
properties=properties)
def initFindAndFit(parameters):
"""
Initialize and return a fitting.PeakFinderFitter object.
"""
# Create pupil function object.
[min_z, max_z] = parameters.getZRange()
pupil_fn = pupilFn.PupilFunction(pf_filename = parameters.getAttr("pupil_function"),
zmin = min_z * 1.0e+3,
zmax = max_z * 1.0e+3)
# PSF debugging.
if False:
tifffile.imsave("pupil_fn_psf.tif", pupil_fn.getPSF(0.1).astype(numpy.float32))
# Check that the PF and camera pixel sizes agree.
diff = abs(parameters.getAttr("pixel_size") - pupil_fn.getPixelSize()*1.0e3)
assert (diff < 1.0e-6), "Incorrect pupil function?"
# Create peak finder.
finder = fitting.PeakFinderArbitraryPSF(parameters = parameters,
psf_object = pupil_fn)
# Create cubicFitC.CSplineFit object.
mfitter = initFitter(finder, parameters, pupil_fn)
# Create peak fitter.
fitter = fitting.PeakFitterArbitraryPSF(mfitter = mfitter,
parameters = parameters)
# Specify which properties we want from the analysis.
properties = ["background", "error", "height", "iterations", "significance", "sum", "x", "y", "z"]
return fitting.PeakFinderFitter(peak_finder = finder,
peak_fitter = fitter,
properties = properties)