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
]
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)
# to the average psf
average_psf = numpy.zeros((4*aoi_size,4*aoi_size))
curf = 1
peaks_used = 0
total = 0.0
[dax_x, dax_y, dax_l] = dax_data.filmSize()
while (curf < dax_l) and (peaks_used < min_peaks):
# Select localizations in current frame & not near the edges.
mask = (i3_data['fr'] == curf) & (i3_data['x'] > aoi_size) & (i3_data['x'] < (dax_y - aoi_size - 1)) & (i3_data['y'] > aoi_size) & (i3_data['y'] < (dax_x - aoi_size - 1))
xr = i3_data['x'][mask]
yr = i3_data['y'][mask]
ht = i3_data['h'][mask]
# Remove localizations that are too close to each other.
in_peaks = numpy.zeros((xr.size,util_c.getNResultsPar()))
in_peaks[:,util_c.getXCenterIndex()] = xr
in_peaks[:,util_c.getYCenterIndex()] = yr
in_peaks[:,util_c.getHeightIndex()] = ht
out_peaks = util_c.removeNeighbors(in_peaks, aoi_size)
print curf, in_peaks.shape, out_peaks.shape
# Use remaining localizations to calculate spline.
image = dax_data.loadAFrame(curf-1).astype(numpy.float64)
xr = out_peaks[:,util_c.getXCenterIndex()]
yr = out_peaks[:,util_c.getYCenterIndex()]
ht = out_peaks[:,util_c.getHeightIndex()]
curf = 1
peaks_used = 0
total = 0.0
[dax_x, dax_y, dax_l] = dax_data.filmSize()
while (curf < dax_l) and (peaks_used < min_peaks):
# Select localizations in current frame & not near the edges.
mask = (i3_data['fr'] == curf) & (i3_data['x'] > aoi_size) & (
i3_data['x'] < (dax_x - aoi_size - 1)) & (i3_data['y'] > aoi_size) & (
i3_data['y'] < (dax_y - aoi_size - 1))
xr = i3_data['x'][mask]
yr = i3_data['y'][mask]
ht = i3_data['h'][mask]
# Remove localizations that are too close to each other.
in_peaks = numpy.zeros((xr.size, util_c.getNResultsPar()))
in_peaks[:, util_c.getXCenterIndex()] = xr
in_peaks[:, util_c.getYCenterIndex()] = yr
in_peaks[:, util_c.getHeightIndex()] = ht
out_peaks = util_c.removeNeighbors(in_peaks, aoi_size)
print curf, in_peaks.shape, out_peaks.shape
# Use remaining localizations to calculate spline.
image = dax_data.loadAFrame(curf - 1).astype(numpy.float64)
xr = out_peaks[:, util_c.getXCenterIndex()]
yr = out_peaks[:, util_c.getYCenterIndex()]
ht = out_peaks[:, util_c.getHeightIndex()]
ndpointer(dtype=numpy.float64),
c_double,
c_int,
c_int,
c_int,
c_int]
multi.initializeZParameters.argtypes = [ndpointer(dtype=numpy.float64),
ndpointer(dtype=numpy.float64),
c_double,
c_double]
# Globals
default_tol = 1.0e-6
peakpar_size = util_c.getNPeakPar()
resultspar_size = util_c.getNResultsPar()
##
# Helper functions.
##
def calcSxSy(wx_params, wy_params, z):
zx = (z - wx_params[1])/wx_params[2]
sx = 0.5 * wx_params[0] * math.sqrt(1.0 + zx*zx + wx_params[3]*zx*zx*zx + wx_params[4]*zx*zx*zx*zx)
zy = (z - wy_params[1])/wy_params[2]
sy = 0.5 * wy_params[0] * math.sqrt(1.0 + zy*zy + wy_params[3]*zy*zy*zy + wy_params[4]*zy*zy*zy*zy)
return [sx, sy]
def fitStats(results):
total = results.shape[0]
bad = numpy.count_nonzero(results[:,n_params] == 2.0)
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)
#
ndpointer(dtype=numpy.float64),
c_double,
c_int,
c_int,
c_int,
c_int]
multi.initializeZParameters.argtypes = [ndpointer(dtype=numpy.float64),
ndpointer(dtype=numpy.float64),
c_double,
c_double]
# Globals
default_tol = 1.0e-6
peakpar_size = util_c.getNPeakPar()
resultspar_size = util_c.getNResultsPar()
##
# Helper functions.
##
def calcSxSy(wx_params, wy_params, z):
zx = (z - wx_params[1])/wx_params[2]
sx = 0.5 * wx_params[0] * math.sqrt(1.0 + zx*zx + wx_params[3]*zx*zx*zx + wx_params[4]*zx*zx*zx*zx)
zy = (z - wy_params[1])/wy_params[2]
sy = 0.5 * wy_params[0] * math.sqrt(1.0 + zy*zy + wy_params[3]*zy*zy*zy + wy_params[4]*zy*zy*zy*zy)
return [sx, sy]
def fitStats(results):
total = results.shape[0]
bad = numpy.count_nonzero(results[:,n_params] == 2.0)
