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 __init__(self, parameters):
fitting.PeakFinder.__init__(self, parameters)
self.height_rescale = []
self.mfilter = []
self.mfilter_z = []
self.z_value = []
# Load the spline.
self.s_to_psf = splineToPSF.loadSpline(parameters.getAttr("spline"))
# Update margin based on the spline size.
old_margin = self.margin
self.margin = int((self.s_to_psf.getSize() + 1)/4 + 2)
self.mfilter_z = parameters.getAttr("z_value", [0.0])
for zval in self.mfilter_z:
self.z_value.append(self.s_to_psf.getScaledZ(zval))
if parameters.hasAttr("peak_locations"):
# Correct for any difference in the margins.
self.peak_locations[:,utilC.getXCenterIndex()] += self.margin - old_margin
self.peak_locations[:,utilC.getYCenterIndex()] += self.margin - old_margin
# Provide the "correct" starting z value.
self.peak_locations[:,utilC.getZCenterIndex()] = self.z_value[0]
def __init__(self, parameters):
self.fista_iterations = parameters.getAttr("fista_iterations")
self.fista_lambda = parameters.getAttr("fista_lambda")
self.fista_number_z = parameters.getAttr("fista_number_z")
self.fista_threshold = parameters.getAttr("fista_threshold")
self.fista_timestep = parameters.getAttr("fista_timestep")
self.fista_upsample = parameters.getAttr("fista_upsample")
self.spline_file = parameters.getAttr("spline")
self.rball = None
self.wbgr = None
# Load spline to get size.
self.spline_file = parameters.getAttr("spline")
s_to_psf = splineToPSF.loadSpline(self.spline_file)
# Update margin based on the spline size.
self.margin = int((s_to_psf.getSize() + 1) / 4 + 2)
if parameters.hasAttr("rb_radius"):
self.rball = rollingBall.RollingBall(
parameters.getAttr("rb_radius"),
parameters.getAttr("rb_sigma"))
else:
self.wbgr_iterations = parameters.getAttr("wbgr_iterations")
self.wbgr_threshold = parameters.getAttr("wbgr_threshold")
self.wbgr_wavelet_level = parameters.getAttr("wbgr_wavelet_level")
self.wbgr = waveletBGR.WaveletBGR()
self.fdecon = None
def __init__(self, parameters):
fitting.PeakFinder.__init__(self, parameters)
self.height_rescale = []
self.mfilter = []
self.mfilter_z = []
self.z_value = []
# Load the spline.
self.s_to_psf = splineToPSF.loadSpline(parameters.getAttr("spline"))
# Update margin based on the spline size.
old_margin = self.margin
self.margin = int((self.s_to_psf.getSize() + 1)/4 + 2)
self.mfilter_z = parameters.getAttr("z_value", [0.0])
for zval in self.mfilter_z:
self.z_value.append(self.s_to_psf.getScaledZ(zval))
if parameters.hasAttr("peak_locations"):
# Correct for any difference in the margins.
self.peak_locations[:,utilC.getXCenterIndex()] += self.margin - old_margin
self.peak_locations[:,utilC.getYCenterIndex()] += self.margin - old_margin
# Provide the "correct" starting z value.
self.peak_locations[:,utilC.getZCenterIndex()] = self.z_value[0]
def __init__(self, parameters):
self.fista_iterations = parameters.getAttr("fista_iterations")
self.fista_lambda = parameters.getAttr("fista_lambda")
self.fista_number_z = parameters.getAttr("fista_number_z")
self.fista_threshold = parameters.getAttr("fista_threshold")
self.fista_timestep = parameters.getAttr("fista_timestep")
self.fista_upsample = parameters.getAttr("fista_upsample")
self.spline_file = parameters.getAttr("spline")
self.rball = None
self.wbgr = None
# Load spline to get size.
self.spline_file = parameters.getAttr("spline")
s_to_psf = splineToPSF.loadSpline(self.spline_file)
# Update margin based on the spline size.
self.margin = int((s_to_psf.getSize() + 1)/4 + 2)
if parameters.hasAttr("rb_radius"):
self.rball = rollingBall.RollingBall(parameters.getAttr("rb_radius"),
parameters.getAttr("rb_sigma"))
else:
self.wbgr_iterations = parameters.getAttr("wbgr_iterations")
self.wbgr_threshold = parameters.getAttr("wbgr_threshold")
self.wbgr_wavelet_level = parameters.getAttr("wbgr_wavelet_level")
self.wbgr = waveletBGR.WaveletBGR()
self.fdecon = None
def initFindAndFit(parameters):
"""
Initialize and return a SplinerFISTAFinderFitter object.
"""
# Create spline object.
spline_fn = splineToPSF.loadSpline(parameters.getAttr("spline"))
# Create peak finder.
finder = SplinerFISTAPeakFinder(parameters = parameters,
psf_object = spline_fn)
# Create cubicFitC.CSplineFit object.
mfitter = findPeaksStd.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", "sum", "x", "y", "z"]
return fitting.PeakFinderFitter(peak_finder = finder,
peak_fitter = fitter,
properties = properties)
def __init__(self,
image_size,
spline_file,
number_zvals,
timestep,
upsample=1,
check_psf=True):
"""
Upsample is the multiplier to use for re-sizing the image,
for example upsample = 2 means to enlarge by 2x.
"""
self.background = numpy.zeros(image_size)
self.psf_heights = []
self.upsample = int(upsample)
im_size_x, im_size_y = image_size
size_x = im_size_x * self.upsample
size_y = im_size_y * self.upsample
# Load spline.
s_to_psf = splineToPSF.loadSpline(spline_file)
# Get size in X and Y.
self.spline_size_x = self.spline_size_y = s_to_psf.getSize()
# Calculate z values to use if 3D.
if (s_to_psf.getType() == "3D"):
self.z_min = s_to_psf.getZMin()
self.z_max = s_to_psf.getZMax()
z_step = (self.z_max - self.z_min) / float(number_zvals - 1.0)
self.zvals = []
for i in range(number_zvals):
self.zvals.append(self.z_min + float(i) * z_step)
else:
self.z_min = 0.0
self.z_max = 1.0
self.zvals = [0.0]
psfs = numpy.zeros((size_x, size_y, len(self.zvals)))
# Add PSFs.
for i in range(len(self.zvals)):
psfs[:, :, i] = s_to_psf.getPSF(self.zvals[i],
shape=(im_size_x, im_size_y),
up_sample=upsample,
normalize=True)
self.psf_heights.append(numpy.max(psfs[:, :, i]))
#print "fista_decon", i, numpy.max(psfs[:,:,i])
# Check PSFs.
if check_psf:
import os
import storm_analysis.sa_library.daxwriter as daxwriter
psf_data = daxwriter.DaxWriter(
os.path.join(os.path.dirname(spline_file),
"fista_decon_psf.dax"), psfs.shape[0],
psfs.shape[1])
for i in range(psfs.shape[2]):
temp = psfs[:, :, i]
psf_data.addFrame(1000.0 * temp / numpy.max(temp))
psf_data.close()
if 0:
# Python solver (useful for debugging).
print("Using Python solver.")
self.fsolver = fista_3d.FISTA(psfs, timestep)
else:
# C solver (about 4x faster).
print("Using C solver.")
self.fsolver = fistaFFTC.FISTA(psfs, timestep)
parser.add_argument('--output', dest='output', type=str, required=True,
help = "The name of the .tif file to save the results in.")
args = parser.parse_args()
# Load parameters
parameters = params.ParametersSplinerFISTA().initFromFile(args.settings)
# Open movie and load the first frame.
movie_data = datareader.inferReader(args.movie)
[x_size, y_size, z_size] = movie_data.filmSize()
image = (movie_data.loadAFrame(0) - parameters.getAttr("camera_offset"))/parameters.getAttr("camera_gain")
image = image.astype(numpy.float)
# Load spline.
psf_object = splineToPSF.loadSpline(parameters.getAttr("spline"))
# Do FISTA deconvolution.
fdecon = FISTADecon(image.shape,
psf_object,
parameters.getAttr("fista_number_z"),
parameters.getAttr("fista_timestep"))
if False:
# Wavelet background removal.
wbgr = waveletBGR.WaveletBGR()
background = wbgr.estimateBG(image,
parameters.getAttr("wbgr_iterations"),
parameters.getAttr("wbgr_threshold"),
parameters.getAttr("wbgr_wavelet_level"))
else:
def __init__(self, parameters):
super(MPPeakFinder, self).__init__(parameters)
self.atrans = [None]
self.backgrounds = []
self.bg_filter = None
self.bg_filter_sigma = parameters.getAttr("bg_filter_sigma")
self.check_mode = False
self.height_rescale = []
self.images = []
self.mapping_filename = None
self.mfilters = []
self.mfilters_z = []
self.mpu = None
self.n_channels = 0
self.s_to_psfs = []
self.variances = []
self.vfilters = []
self.xt = []
self.yt = []
self.z_values = []
# Print warning about check mode
if self.check_mode:
print("Warning: Running in check mode.")
# Load the splines.
for spline_attr in mpUtilC.getSplineAttrs(parameters):
self.s_to_psfs.append(
splineToPSF.loadSpline(parameters.getAttr(spline_attr)))
self.n_channels += 1
# Assert that all the splines are the same size.
for i in range(1, len(self.s_to_psfs)):
assert (self.s_to_psfs[0].getSize() == self.s_to_psfs[i].getSize())
#
# Update margin based on the spline size. Note the assumption
# that all of the splines are the same size, or at least smaller
# than the spline for plane 0.
#
old_margin = self.margin
self.margin = int((self.s_to_psfs[0].getSize() + 1) / 4 + 2)
# Load the plane to plane mapping data & create affine transform objects.
mappings = {}
if parameters.hasAttr("mapping"):
if os.path.exists(parameters.getAttr("mapping")):
self.mapping_filename = parameters.getAttr("mapping")
with open(parameters.getAttr("mapping"), 'rb') as fp:
mappings = pickle.load(fp)
# Use self.margin - 1, because we added 1 to the x,y coordinates when we saved them.
for i in range(self.n_channels - 1):
self.xt.append(
mpUtilC.marginCorrect(mappings["0_" + str(i + 1) + "_x"],
self.margin - 1))
self.yt.append(
mpUtilC.marginCorrect(mappings["0_" + str(i + 1) + "_y"],
self.margin - 1))
self.atrans.append(
affineTransformC.AffineTransform(xt=self.xt[i], yt=self.yt[i]))
#
# Note the assumption that the splines for each plane all use
# the same z scale / have the same z range.
#
self.mfilters_z = parameters.getAttr("z_value", [0.0])
for zval in self.mfilters_z:
self.z_values.append(self.s_to_psfs[0].getScaledZ(zval))
if parameters.hasAttr("peak_locations"):
# Correct for any difference in the margins.
self.peak_locations[:, utilC.getXCenterIndex(
)] += self.margin - old_margin
self.peak_locations[:, utilC.getYCenterIndex(
)] += self.margin - old_margin
# Provide the "correct" starting z value.
#
# FIXME: Should also allow the user to specify the peak z location
# as any fixed value could be so far off for some of the
# localizations that the fitting will fail.
#
self.peak_locations[:, utilC.getZCenterIndex()] = self.z_values[0]
