def convertToMultiFit(i3data, x_size, y_size, frame, nm_per_pixel, inverted=False):
"""
Create a 3D-DAOSTORM, sCMOS or Spliner analysis compatible peak array from I3 data.
Notes:
(1) This uses the non-drift corrected positions.
(2) This sets the initial fitting error to zero and the status to RUNNING.
"""
i3data = maskData(i3data, (i3data['fr'] == frame))
peaks = numpy.zeros((i3data.size, utilC.getNPeakPar()))
peaks[:,utilC.getBackgroundIndex()] = i3data['bg']
peaks[:,utilC.getHeightIndex()] = i3data['h']
peaks[:,utilC.getZCenterIndex()] = i3data['z'] * 0.001
if inverted:
peaks[:,utilC.getXCenterIndex()] = y_size - i3data['x']
peaks[:,utilC.getYCenterIndex()] = x_size - i3data['y']
ax = i3data['ax']
ww = i3data['w']
peaks[:,utilC.getYWidthIndex()] = 0.5*numpy.sqrt(ww*ww/ax)/nm_per_pixel
peaks[:,utilC.getXWidthIndex()] = 0.5*numpy.sqrt(ww*ww*ax)/nm_per_pixel
else:
peaks[:,utilC.getYCenterIndex()] = i3data['x'] - 1
peaks[:,utilC.getXCenterIndex()] = i3data['y'] - 1
ax = i3data['ax']
ww = i3data['w']
peaks[:,utilC.getXWidthIndex()] = 0.5*numpy.sqrt(ww*ww/ax)/nm_per_pixel
peaks[:,utilC.getYWidthIndex()] = 0.5*numpy.sqrt(ww*ww*ax)/nm_per_pixel
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 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, 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 __init__(self, parameters):
# Initialized from parameters.
self.find_max_radius = parameters.getAttr("find_max_radius", 5) # Radius (in pixels) over which the maxima is maximal.
self.iterations = parameters.getAttr("iterations") # Maximum number of cycles of peak finding, fitting and subtraction to perform.
self.sigma = parameters.getAttr("sigma") # Peak sigma (in pixels).
self.threshold = parameters.getAttr("threshold") # Peak minimum threshold (height, in camera units).
self.z_value = parameters.getAttr("z_value", 0.0) # The starting z value to use for peak fitting.
# Other member variables.
self.background = None # Current estimate of the image background.
self.image = None # The original image.
self.margin = PeakFinderFitter.margin # Size of the unanalyzed "edge" around the image.
self.neighborhood = PeakFinder.unconverged_dist * self.sigma # Radius for marking neighbors as unconverged.
self.new_peak_radius = PeakFinder.new_peak_dist # Minimum allowed distance between new peaks and current peaks.
self.parameters = parameters # Keep access to the parameters object.
self.peak_locations = None # Initial peak locations, as explained below.
self.peak_mask = None # Mask for limiting peak identification to a particular AOI.
self.taken = None # Spots in the image where a peak has already been added.
#
# This is for is you already know where your want fitting to happen, as
# for example in a bead calibration movie and you just want to use the
# approximate locations as inputs for fitting.
#
# peak_locations is a text file with the peak x, y, height and background
# values as white spaced columns (x and y positions are in pixels as
# determined using visualizer).
#
# 1.0 2.0 1000.0 100.0
# 10.0 5.0 2000.0 200.0
# ...
#
if parameters.hasAttr("peak_locations"):
print("Using peak starting locations specified in", parameters.getAttr("peak_locations"))
# Only do one cycle of peak finding as we'll always return the same locations.
if (self.iterations != 1):
print("WARNING: setting number of iterations to 1!")
self.iterations = 1
# Load peak x,y locations.
peak_locs = numpy.loadtxt(parameters.getAttr("peak_locations"), ndmin = 2)
print(peak_locs.shape)
# Create peak array.
self.peak_locations = numpy.zeros((peak_locs.shape[0],
utilC.getNPeakPar()))
self.peak_locations[:,utilC.getXCenterIndex()] = peak_locs[:,1] + self.margin
self.peak_locations[:,utilC.getYCenterIndex()] = peak_locs[:,0] + self.margin
self.peak_locations[:,utilC.getHeightIndex()] = peak_locs[:,2]
self.peak_locations[:,utilC.getBackgroundIndex()] = peak_locs[:,3]
self.peak_locations[:,utilC.getXWidthIndex()] = numpy.ones(peak_locs.shape[0]) * self.sigma
self.peak_locations[:,utilC.getYWidthIndex()] = numpy.ones(peak_locs.shape[0]) * self.sigma
def convertToMultiFit(i3data,
x_size,
y_size,
frame,
nm_per_pixel,
inverted=False):
"""
Create a 3D-DAOSTORM, sCMOS or Spliner analysis compatible peak array from I3 data.
Notes:
(1) This uses the non-drift corrected positions.
(2) This sets the initial fitting error to zero and the status to RUNNING.
"""
i3data = maskData(i3data, (i3data['fr'] == frame))
peaks = numpy.zeros((i3data.size, utilC.getNPeakPar()))
peaks[:, utilC.getBackgroundIndex()] = i3data['bg']
peaks[:, utilC.getHeightIndex()] = i3data['h']
peaks[:, utilC.getZCenterIndex()] = i3data['z'] * 0.001
if inverted:
peaks[:, utilC.getXCenterIndex()] = y_size - i3data['x']
peaks[:, utilC.getYCenterIndex()] = x_size - i3data['y']
ax = i3data['ax']
ww = i3data['w']
peaks[:, utilC.getYWidthIndex()] = 0.5 * numpy.sqrt(
ww * ww / ax) / nm_per_pixel
peaks[:, utilC.getXWidthIndex()] = 0.5 * numpy.sqrt(
ww * ww * ax) / nm_per_pixel
else:
peaks[:, utilC.getYCenterIndex()] = i3data['x'] - 1
peaks[:, utilC.getXCenterIndex()] = i3data['y'] - 1
ax = i3data['ax']
ww = i3data['w']
peaks[:, utilC.getXWidthIndex()] = 0.5 * numpy.sqrt(
ww * ww / ax) / nm_per_pixel
peaks[:, utilC.getYWidthIndex()] = 0.5 * numpy.sqrt(
ww * ww * ax) / nm_per_pixel
return peaks
def getPeaks(self, threshold, margin):
"""
Extract peaks from the deconvolved image and create
an array that can be used by a peak fitter.
FIXME: Need to compensate for up-sampling parameter in x,y.
"""
fx = self.getXVector()
# Get area, position, height.
fd_peaks = fdUtil.getPeaks(fx, threshold, margin)
num_peaks = fd_peaks.shape[0]
peaks = numpy.zeros((num_peaks, utilC.getNPeakPar()))
peaks[:, utilC.getXWidthIndex()] = numpy.ones(num_peaks)
peaks[:, utilC.getYWidthIndex()] = numpy.ones(num_peaks)
peaks[:, utilC.getXCenterIndex()] = fd_peaks[:, 2]
peaks[:, utilC.getYCenterIndex()] = fd_peaks[:, 1]
# Calculate height.
#
# FIXME: Typically the starting value for the peak height will be
# under-estimated unless a large enough number of FISTA
# iterations is performed to completely de-convolve the image.
#
h_index = utilC.getHeightIndex()
#peaks[:,h_index] = fd_peaks[:,0]
for i in range(num_peaks):
peaks[i, h_index] = fd_peaks[i, 0] * self.psf_heights[int(
round(fd_peaks[i, 3]))]
# Calculate z (0.0 - 1.0).
if (fx.shape[2] > 1):
peaks[:, utilC.getZCenterIndex()] = fd_peaks[:, 3] / (
float(fx.shape[2]) - 1.0)
# Background term calculation.
bg_index = utilC.getBackgroundIndex()
for i in range(num_peaks):
ix = int(round(fd_peaks[i, 1]))
iy = int(round(fd_peaks[i, 2]))
peaks[i, bg_index] = self.background[ix, iy]
return peaks
def getPeaks(self, threshold, margin):
fx = self.getXVector()
# Get area, position, height.
fd_peaks = fdUtil.getPeaks(fx, threshold, margin)
num_peaks = fd_peaks.shape[0]
peaks = numpy.zeros((num_peaks, utilC.getNResultsPar()))
peaks[:, utilC.getXWidthIndex()] = numpy.ones(num_peaks)
peaks[:, utilC.getYWidthIndex()] = numpy.ones(num_peaks)
peaks[:, utilC.getXCenterIndex()] = fd_peaks[:, 2]
peaks[:, utilC.getYCenterIndex()] = fd_peaks[:, 1]
# Calculate height.
#
# FIXME: Typically the starting value for the peak height will be
# under-estimated unless a large enough number of FISTA
# iterations is performed to completely de-convolve the image.
#
h_index = utilC.getHeightIndex()
#peaks[:,h_index] = fd_peaks[:,0]
for i in range(num_peaks):
peaks[i, h_index] = fd_peaks[i, 0] * self.psf_heights[int(
round(fd_peaks[i, 3]))]
# Calculate z (0.0 - 1.0).
peaks[:,
utilC.getZCenterIndex()] = fd_peaks[:, 3] / (float(fx.shape[2]) -
1.0)
# Background term calculation.
bg_index = utilC.getBackgroundIndex()
for i in range(num_peaks):
ix = int(round(fd_peaks[i, 1]))
iy = int(round(fd_peaks[i, 2]))
peaks[i, bg_index] = self.background[ix, iy]
return peaks
def __init__(self, parameters):
# Member variables.
self.background = None # Current estimate of the image background.
self.image = None # The original image.
self.iterations = parameters.iterations # Maximum number of cycles of peak finding, fitting and subtraction to perform.
self.margin = PeakFinderFitter.margin # Size of the unanalyzed "edge" around the image.
self.neighborhood = PeakFinder.unconverged_dist * parameters.sigma # Radius for marking neighbors as unconverged.
self.new_peak_radius = PeakFinder.new_peak_dist # Minimum allowed distance between new peaks and current peaks.
self.parameters = parameters # Keep access to the parameters object.
self.peak_locations = None # Initial peak locations, as explained below.
self.peak_mask = None # Mask for limiting peak identification to a particular AOI.
self.sigma = parameters.sigma # Peak sigma (in pixels).
self.taken = None # Spots in the image where a peak has already been added.
self.threshold = parameters.threshold # Peak minimum threshold (height, in camera units).
self.z_value = 0.0 # The starting z value to use for peak fitting.
# Radius (in pixels) over which the maxima is maximal.
if hasattr(parameters, "find_max_radius"):
self.find_max_radius = float(parameters.find_max_radius)
else:
self.find_max_radius = 5
#
# This is for is you already know where your want fitting to happen, as
# for example in a bead calibration movie and you just want to use the
# approximate locations as inputs for fitting.
#
# peak_locations is a text file with the peak x, y, height and background
# values as white spaced columns (x and y positions are in pixels as
# determined using visualizer).
#
# 1.0 2.0 1000.0 100.0
# 10.0 5.0 2000.0 200.0
# ...
#
if hasattr(parameters, "peak_locations"):
print("Using peak starting locations specified in",
parameters.peak_locations)
# Only do one cycle of peak finding as we'll always return the same locations.
if (self.iterations != 1):
print("WARNING: setting number of iterations to 1!")
self.iterations = 1
# Load peak x,y locations.
peak_locs = numpy.loadtxt(parameters.peak_locations, ndmin=2)
print(peak_locs.shape)
# Create peak array.
self.peak_locations = numpy.zeros(
(peak_locs.shape[0], util_c.getNResultsPar()))
self.peak_locations[:, util_c.getXCenterIndex(
)] = peak_locs[:, 1] + self.margin
self.peak_locations[:, util_c.getYCenterIndex(
)] = peak_locs[:, 0] + self.margin
self.peak_locations[:, util_c.getHeightIndex()] = peak_locs[:, 2]
self.peak_locations[:, util_c.getBackgroundIndex()] = peak_locs[:,
3]
self.peak_locations[:, util_c.getXWidthIndex()] = numpy.ones(
peak_locs.shape[0]) * self.sigma
self.peak_locations[:, util_c.getYWidthIndex()] = numpy.ones(
peak_locs.shape[0]) * self.sigma
