def newBackground(self, background):
"""
background - a list of background estimates of length n_channels.
"""
if (len(background) != self.n_channels):
raise daoFitC.MultiFitterException("Number of images does not match number of channels.")
for i in range(self.n_channels):
if (background[i].shape[0] != self.im_shape[0]) or (background[i].shape[1] != self.im_shape[1]):
raise daoFitC.MultiFitterException("Background image shape and the original image shape are not the same.")
self.clib.mFitNewBackground(self.mfit.contents.fit_data[i],
numpy.ascontiguousarray(background[i], dtype = numpy.float64))
def newImage(self, image):
"""
image - a list of images of length n_channels.
"""
if (len(image) != self.n_channels):
raise daoFitC.MultiFitterException("Number of images does not match number of channels.")
for i in range(self.n_channels):
if (image[i].shape[0] != self.im_shape[0]) or (image[i].shape[1] != self.im_shape[1]):
raise daoFitC.MultiFitterException("Current image shape and the original image shape are not the same.")
self.clib.mFitNewImage(self.mfit.contents.fit_data[i],
numpy.ascontiguousarray(image[i], dtype = numpy.float64),
0)
def initializeChannel(self, rqe, variance, channel):
"""
Initializes the C fitter for a single image channel.
This will also initialize the C multi-fitter if this hasn't been done. The
C multi-fitter basically just coordinates the individual C fitters for each
channel. Without the C fitters for each channel it cannot do anything.
"""
if self.mfit is None:
self.initializeC(variance)
if (rqe.shape[0] != self.im_shape[0]) or (rqe.shape[1] != self.im_shape[1]):
raise daoFitC.MultiFitterException("Current RQE shape and the original variance shape are not the same.")
if (variance.shape[0] != self.im_shape[0]) or (variance.shape[1] != self.im_shape[1]):
raise daoFitC.MultiFitterException("Current variance shape and the original variance shape are not the same.")
def newImage(self, image, channel):
if (image.shape[0] != self.im_shape[0]) or (image.shape[1] !=
self.im_shape[1]):
raise daoFitC.MultiFitterException(
"Current image shape and the original image shape are not the same."
)
self.clib.mpNewImage(self.mfit, image, channel)
def setVariance(self, variance, channel):
#
# This is a little difference than the 3D-DAOSTORM, sCMOS and Spliner
# because this is the first thing that will be called with an array
# that is expected to have the same size of the images. So we initialize
# the C library now, rather than in newImage().
#
if self.mfit is None:
self.initializeC(variance)
else:
if (variance.shape[0] != self.im_shape[0]) or (variance.shape[1] !=
self.im_shape[1]):
raise daoFitC.MultiFitterException(
"Current variance shape and the original variance shape are not the same."
)
self.clib.mpInitializeChannel(self.mfit, self.c_splines[channel],
variance, channel)
def getPeakProperty(self, p_name, channel=0):
"""
Return a numpy array containing the requested property.
"""
if not p_name in self.peak_properties:
raise daoFitC.MultiFitterException("No such property '" + p_name +
"'")
# Properties that are calculated from other properties.
if (self.peak_properties[p_name] == "compound"):
# Return 0 length array if there are no localizations.
if (self.getNFit() == 0):
return numpy.zeros(0, dtype=numpy.float64)
# Peak significance calculation, calculated from the values for
# all of the individual peaks.
if (p_name == "significance"):
bg_sum = numpy.zeros(self.getNFit(), dtype=numpy.float64)
fg_sum = numpy.zeros(self.getNFit(), dtype=numpy.float64)
for i in range(self.n_channels):
bg_sum += self.getPeakProperty("bg_sum", channel=i)
fg_sum += self.getPeakProperty("fg_sum", channel=i)
return fg_sum / numpy.sqrt(bg_sum)
# Floating point properties.
elif (self.peak_properties[p_name] == "float"):
values = numpy.ascontiguousarray(
numpy.zeros(self.getNFit(), dtype=numpy.float64))
self.clib.mFitGetPeakPropertyDouble(
self.mfit.contents.fit_data[channel], values,
ctypes.c_char_p(p_name.encode()))
return values
# Integer properties.
elif (self.peak_properties[p_name] == "int"):
values = numpy.ascontiguousarray(
numpy.zeros(self.getNFit(), dtype=numpy.int32))
self.clib.mFitGetPeakPropertyInt(
self.mfit.contents.fit_data[channel], values,
ctypes.c_char_p(p_name.encode()))
return values
def initializeC(self, image):
"""
This initializes the C fitting library. You can call this directly, but
the idea is that it will get called automatically the first time that you
provide a new image for fitting.
"""
if self.scmos_cal is None:
self.scmos_cal = numpy.ascontiguousarray(numpy.zeros(image.shape))
else:
self.scmos_cal = numpy.ascontiguousarray(self.scmos_cal)
if (image.shape[0] != self.scmos_cal.shape[0]) or (
image.shape[1] != self.scmos_cal.shape[1]):
raise daoFitC.MultiFitterException(
"Image shape and sCMOS calibration shape do not match.")
self.im_shape = self.scmos_cal.shape
self.mfit = self.clib.cfInitialize(self.c_spline, self.scmos_cal,
numpy.ascontiguousarray(self.clamp),
self.default_tol,
self.scmos_cal.shape[1],
self.scmos_cal.shape[0])
def setVariance(self, variance, channel):
if self.mfit is None:
self.initializeC(variance)
else:
if (variance.shape[0] != self.im_shape[0]) or (variance.shape[1] != self.im_shape[1]):
raise daoFitC.MultiFitterException("Current variance shape and the original variance shape are not the same.")
