def getGoodPeaks(self, peaks, min_height, min_width):
"""
Create a new list from peaks containing only those peaks that meet
the specified criteria for minimum peak height and width.
"""
if(peaks.shape[0]>0):
min_width = 0.5 * min_width
status_index = utilC.getStatusIndex()
height_index = utilC.getHeightIndex()
xwidth_index = utilC.getXWidthIndex()
ywidth_index = utilC.getYWidthIndex()
if self.verbose:
tmp = numpy.ones(peaks.shape[0])
print("getGoodPeaks")
for i in range(peaks.shape[0]):
print(i, peaks[i,0], peaks[i,1], peaks[i,3], peaks[i,2], peaks[i,4], peaks[i,7])
print("Total peaks:", numpy.sum(tmp))
print(" fit error:", numpy.sum(tmp[(peaks[:,status_index] != 2.0)]))
print(" min height:", numpy.sum(tmp[(peaks[:,height_index] > min_height)]))
print(" min width:", numpy.sum(tmp[(peaks[:,xwidth_index] > min_width) & (peaks[:,ywidth_index] > min_width)]))
print("")
mask = (peaks[:,status_index] != 2.0) & (peaks[:,height_index] > min_height) & (peaks[:,xwidth_index] > min_width) & (peaks[:,ywidth_index] > min_width)
return peaks[mask,:]
else:
return peaks
def getGoodPeaks(self, peaks, threshold):
#
# FIXME: We'd like to have a minimum height threshold, but the
# threshold parameter is a relative value not an absolute.
# For now we are just rejecting ERROR peaks.
#
if (peaks.size > 0):
#
# In the C library, the peaks that represent a single object
# in multiple channels will all have the same status index
# and height, so we can filter here with some confidence that
# we'll keep or eliminate all the peaks in a particular group
# at the same time. We attempt to enforce this with an assertion
# statement, but it is an imperfect test as we could eliminate
# 1 peak from each of 4 groups and still pass, even though this
# would completely mess up the analysis.
#
status_index = utilC.getStatusIndex()
height_index = utilC.getHeightIndex()
#mask = (peaks[:,status_index] != 2.0) & (peaks[:,height_index] > min_height)
mask = (peaks[:, status_index] != 2.0)
if self.verbose:
print(" ", numpy.sum(mask), "were good out of", peaks.shape[0])
#
# For debugging.
#
if False:
xw_index = utilC.getXWidthIndex()
yw_index = utilC.getYWidthIndex()
for i in range(peaks.shape[0]):
if (peaks[i, xw_index] != peaks[i, yw_index]):
print("bad peak width detected", i, peaks[i, xw_index],
peaks[i, yw_index])
#
# Debugging check that the peak status markings are actually
# in sync.
#
if True:
n_peaks = int(peaks.shape[0] / self.n_channels)
not_bad = True
for i in range(n_peaks):
if (mask[i] != mask[i + n_peaks]):
print("Problem detected with peak", i)
print(" ", peaks[i, :])
print(" ", peaks[i + n_peaks, :])
not_bad = False
assert not_bad
masked_peaks = peaks[mask, :]
# The number of peaks should always be a multiple of the number of channels.
assert ((masked_peaks.shape[0] % self.n_channels) == 0)
return masked_peaks
else:
return peaks
def getGoodPeaks(self, peaks, min_height, min_width):
"""
Create a new list from peaks containing only those peaks that meet
the specified criteria for minimum peak height and width.
"""
if(peaks.shape[0]>0):
min_width = 0.5 * min_width
status_index = utilC.getStatusIndex()
height_index = utilC.getHeightIndex()
xwidth_index = utilC.getXWidthIndex()
ywidth_index = utilC.getYWidthIndex()
if self.verbose:
tmp = numpy.ones(peaks.shape[0])
print("getGoodPeaks")
for i in range(peaks.shape[0]):
print(i, peaks[i,0], peaks[i,1], peaks[i,3], peaks[i,2], peaks[i,4])
print("Total peaks:", numpy.sum(tmp))
print(" fit error:", numpy.sum(tmp[(peaks[:,status_index] != 2.0)]))
print(" min height:", numpy.sum(tmp[(peaks[:,height_index] > min_height)]))
print(" min width:", numpy.sum(tmp[(peaks[:,xwidth_index] > min_width) & (peaks[:,ywidth_index] > min_width)]))
print("")
mask = (peaks[:,7] != status_index) & (peaks[:,height_index] > min_height) & (peaks[:,xwidth_index] > min_width) & (peaks[:,ywidth_index] > min_width)
return peaks[mask,:]
else:
return peaks
def getConvergedPeaks(self, peaks, verbose = False):
if (peaks.shape[0] > 0):
status_index = utilC.getStatusIndex()
mask = (peaks[:,status_index] == 1.0) # 0.0 = running, 1.0 = converged.
if verbose:
print(" ", numpy.sum(mask), "converged out of", peaks.shape[0])
return self.peak_fitter.rescaleZ(peaks[mask,:])
else:
return peaks
def getConvergedPeaks(self, peaks, verbose=False):
if (peaks.shape[0] > 0):
status_index = utilC.getStatusIndex()
mask = (peaks[:, status_index] == 1.0
) # 0.0 = running, 1.0 = converged.
if verbose:
print(" ", numpy.sum(mask), "converged out of", peaks.shape[0])
return self.peak_fitter.rescaleZ(peaks[mask, :])
else:
return peaks
def createFromMultiFit(molecules,
x_size,
y_size,
frame,
nm_per_pixel,
inverted=False):
"""
Create an I3 data from the output of 3D-DAOSTORM, sCMOS or Spliner.
"""
n_molecules = molecules.shape[0]
h = molecules[:, 0]
if inverted:
xc = y_size - molecules[:, utilC.getXCenterIndex()]
yc = x_size - molecules[:, utilC.getYCenterIndex()]
wx = 2.0 * molecules[:, utilC.getXWidthIndex()] * nm_per_pixel
wy = 2.0 * molecules[:, utilC.getYWidthIndex()] * nm_per_pixel
else:
xc = molecules[:, utilC.getYCenterIndex()] + 1
yc = molecules[:, utilC.getXCenterIndex()] + 1
wx = 2.0 * molecules[:, utilC.getYWidthIndex()] * nm_per_pixel
wy = 2.0 * molecules[:, utilC.getXWidthIndex()] * nm_per_pixel
bg = molecules[:, utilC.getBackgroundIndex()]
zc = molecules[:, utilC.getZCenterIndex(
)] * 1000.0 # fitting is done in um, insight works in nm
st = numpy.round(molecules[:, utilC.getStatusIndex()])
err = molecules[:, utilC.getErrorIndex()]
#
# Calculate peak area, which is saved in the "a" field.
#
# Note that this is assuming that the peak is a 2D gaussian. This
# will not calculate the correct area for a Spline..
#
parea = 2.0 * 3.14159 * h * molecules[:, utilC.getXWidthIndex(
)] * molecules[:, utilC.getYWidthIndex()]
ax = wy / wx
ww = numpy.sqrt(wx * wy)
i3data = createDefaultI3Data(xc.size)
posSet(i3data, 'x', xc)
posSet(i3data, 'y', yc)
posSet(i3data, 'z', zc)
setI3Field(i3data, 'h', h)
setI3Field(i3data, 'bg', bg)
setI3Field(i3data, 'fi', st)
setI3Field(i3data, 'a', parea)
setI3Field(i3data, 'w', ww)
setI3Field(i3data, 'ax', ax)
setI3Field(i3data, 'fr', frame)
setI3Field(i3data, 'i', err)
return i3data
def getGoodPeaks(self, peaks, min_height = 0.0):
if (peaks.size > 0):
status_index = utilC.getStatusIndex()
height_index = utilC.getHeightIndex()
mask = (peaks[:,status_index] != 2.0) & (peaks[:,height_index] > min_height)
if self.verbose:
print(" ", numpy.sum(mask), "were good out of", peaks.shape[0])
return peaks[mask,:]
else:
return peaks
def getGoodPeaks(self, peaks, min_height=0.0):
if (peaks.size > 0):
status_index = utilC.getStatusIndex()
height_index = utilC.getHeightIndex()
mask = (peaks[:, status_index] != 2.0) & (peaks[:, height_index] >
min_height)
if self.verbose:
print(" ", numpy.sum(mask), "were good out of", peaks.shape[0])
return peaks[mask, :]
else:
return peaks
def analyzeImage(self,
new_image,
bg_estimate=None,
save_residual=False,
verbose=False):
image = fitting.padArray(new_image, self.peak_finder.margin)
if bg_estimate is not None:
bg_estimate = fitting.padArray(bg_estimate,
self.peak_finder.margin)
self.peak_finder.newImage(image, bg_estimate)
self.peak_fitter.newImage(image)
#
# This is a lot simpler than 3D-DAOSTORM as we only do one pass,
# hopefully the compressed sensing (FISTA) deconvolution finds all the
# peaks and then we do a single pass of fitting.
#
if True:
peaks = self.peak_finder.findPeaks()
[fit_peaks, residual] = self.peak_fitter.fitPeaks(peaks)
#
# This is for testing if just using FISTA followed by the center
# of mass calculation is basically as good as also doing the
# additional MLE spline fitting step.
#
# The short answer is that it appears that it is not. It about
# 1.3x worse in XY and about 4x worse in Z.
#
else:
fit_peaks = self.peak_finder.findPeaks()
# Adjust z scale.
z_index = utilC.getZCenterIndex()
z_size = (self.peak_fitter.spline.shape[2] - 1.0)
status_index = utilC.getStatusIndex()
fit_peaks[:, z_index] = z_size * fit_peaks[:, z_index]
# Mark as converged.
fit_peaks[:, status_index] = 1.0
residual = None
#
# Subtract margin so that peaks are in the right
# place with respect to the original image.
#
fit_peaks[:, utilC.getXCenterIndex()] -= float(self.peak_finder.margin)
fit_peaks[:, utilC.getYCenterIndex()] -= float(self.peak_finder.margin)
return [fit_peaks, residual]
def getConvergedPeaks(self, peaks, verbose=False):
"""
peaks - A 1D numpy array containing the peaks.
return - A 1D numpy array containing only the converged peaks.
"""
if (peaks.shape[0] > 0):
status_index = utilC.getStatusIndex()
mask = (peaks[:, status_index] == 1.0
) # 0.0 = running, 1.0 = converged.
if verbose:
print(" ", numpy.sum(mask), "converged out of", peaks.shape[0])
return peaks[mask, :]
else:
return peaks
def createFromMultiFit(molecules, x_size, y_size, frame, nm_per_pixel, inverted=False):
"""
Create an I3 data from the output of 3D-DAOSTORM, sCMOS or Spliner.
"""
n_molecules = molecules.shape[0]
h = molecules[:,0]
if inverted:
xc = y_size - molecules[:,utilC.getXCenterIndex()]
yc = x_size - molecules[:,utilC.getYCenterIndex()]
wx = 2.0*molecules[:,utilC.getXWidthIndex()]*nm_per_pixel
wy = 2.0*molecules[:,utilC.getYWidthIndex()]*nm_per_pixel
else:
xc = molecules[:,utilC.getYCenterIndex()] + 1
yc = molecules[:,utilC.getXCenterIndex()] + 1
wx = 2.0*molecules[:,utilC.getYWidthIndex()]*nm_per_pixel
wy = 2.0*molecules[:,utilC.getXWidthIndex()]*nm_per_pixel
bg = molecules[:,utilC.getBackgroundIndex()]
zc = molecules[:,utilC.getZCenterIndex()] * 1000.0 # fitting is done in um, insight works in nm
st = numpy.round(molecules[:,utilC.getStatusIndex()])
err = molecules[:,utilC.getErrorIndex()]
#
# Calculate peak area, which is saved in the "a" field.
#
# Note that this is assuming that the peak is a 2D gaussian. This
# will not calculate the correct area for a Spline..
#
parea = 2.0*3.14159*h*molecules[:,utilC.getXWidthIndex()]*molecules[:,utilC.getYWidthIndex()]
ax = wy/wx
ww = numpy.sqrt(wx*wy)
i3data = createDefaultI3Data(xc.size)
posSet(i3data, 'x', xc)
posSet(i3data, 'y', yc)
posSet(i3data, 'z', zc)
setI3Field(i3data, 'h', h)
setI3Field(i3data, 'bg', bg)
setI3Field(i3data, 'fi', st)
setI3Field(i3data, 'a', parea)
setI3Field(i3data, 'w', ww)
setI3Field(i3data, 'ax', ax)
setI3Field(i3data, 'fr', frame)
setI3Field(i3data, 'i', err)
return i3data
def analyzeImage(self, new_image, bg_estimate = None, save_residual = False, verbose = False):
image = fitting.padArray(new_image, self.peak_finder.margin)
if bg_estimate is not None:
bg_estimate = fitting.padArray(bg_estimate, self.peak_finder.margin)
self.peak_finder.newImage(image, bg_estimate)
self.peak_fitter.newImage(image)
#
# This is a lot simpler than 3D-DAOSTORM as we only do one pass,
# hopefully the compressed sensing (FISTA) deconvolution finds all the
# peaks and then we do a single pass of fitting.
#
if True:
peaks = self.peak_finder.findPeaks()
[fit_peaks, residual] = self.peak_fitter.fitPeaks(peaks)
#
# This is for testing if just using FISTA followed by the center
# of mass calculation is basically as good as also doing the
# additional MLE spline fitting step.
#
# The short answer is that it appears that it is not. It about
# 1.3x worse in XY and about 4x worse in Z.
#
else:
fit_peaks = self.peak_finder.findPeaks()
# Adjust z scale.
z_index = utilC.getZCenterIndex()
z_size = (self.peak_fitter.spline.shape[2] - 1.0)
status_index = utilC.getStatusIndex()
fit_peaks[:,z_index] = z_size*fit_peaks[:,z_index]
# Mark as converged.
fit_peaks[:,status_index] = 1.0
residual = None
#
# Subtract margin so that peaks are in the right
# place with respect to the original image.
#
fit_peaks[:,utilC.getXCenterIndex()] -= float(self.peak_finder.margin)
fit_peaks[:,utilC.getYCenterIndex()] -= float(self.peak_finder.margin)
return [fit_peaks, residual]
cubic_fit.initializeMultiFit.argtypes = [
ndpointer(dtype=numpy.float64), ctypes.c_double, ctypes.c_int, ctypes.c_int
]
cubic_fit.newImage.argtypes = [ndpointer(dtype=numpy.float64)]
cubic_fit.newPeaks2D.argtypes = [ndpointer(dtype=numpy.float64), ctypes.c_int]
cubic_fit.newPeaks3D.argtypes = [ndpointer(dtype=numpy.float64), ctypes.c_int]
# Globals
default_tol = 1.0e-6
height_index = utilC.getHeightIndex()
n_results_par = utilC.getNResultsPar()
status_index = utilC.getStatusIndex()
z_index = utilC.getZCenterIndex()
#
# Functions
#
def fSpline2D(x, y):
return cubic_fit.fSpline2D(x, y)
def fSpline3D(x, y, z):
return cubic_fit.fSpline3D(x, y, z)
#
