def testingParameters():
"""
Create a Spliner parameters object for FISTA deconvolution.
"""
if settings.use_fista:
params = parameters.ParametersSplinerFISTA()
else:
params = parameters.ParametersSplinerSTD()
params.setAttr("max_frame", "int", -1)
params.setAttr("start_frame", "int", -1)
params.setAttr("background_sigma", "float", 8.0)
params.setAttr("camera_gain", "float", settings.camera_gain)
params.setAttr("camera_offset", "float", settings.camera_offset)
params.setAttr("find_max_radius", "int", 15)
params.setAttr("iterations", "int", settings.iterations)
params.setAttr("pixel_size", "float", settings.pixel_size)
params.setAttr("max_z", "float", 1.0)
params.setAttr("min_z", "float", -1.0)
params.setAttr("no_fitting", "int", 1)
params.setAttr("sigma", "float", 1.5)
params.setAttr("spline", "filename", "psf.spline")
params.setAttr("threshold", "float", 6.0)
# FISTA.
if settings.use_fista:
params.setAttr("use_fista", "int", settings.use_fista)
params.setAttr("fista_iterations", "int", 500)
params.setAttr("fista_lambda", "float", 20.0)
params.setAttr("fista_number_z", "int", 5)
params.setAttr("fista_threshold", "float", 500.0)
params.setAttr("fista_timestep", "float", 0.1)
params.setAttr("rb_radius", "float", 10.0)
params.setAttr("rb_sigma", "float", 1.0)
# Standard
else:
params.setAttr("z_value", "float-array", [-0.6, -0.3, 0.0, 0.3, 0.6])
# Don't do tracking.
params.setAttr("descriptor", "string", "1")
params.setAttr("radius", "float", "0.0")
# Don't do drift-correction.
params.setAttr("d_scale", "int", 2)
params.setAttr("drift_correction", "int", 0)
params.setAttr("frame_step", "int", 500)
params.setAttr("z_correction", "int", 0)
# 'peak_locations' testing.
if hasattr(settings, "peak_locations") and (settings.peak_locations
is not None):
params.setAttr("peak_locations", "filename", settings.peak_locations)
return params
def analyze(movie_name, mlist_name, settings_name):
parameters = params.ParametersSpliner().initFromFile(settings_name,
warnings=False)
if (parameters.getAttr("use_fista", 0) != 0):
parameters = params.ParametersSplinerFISTA().initFromFile(
settings_name)
finder = find_peaks_fista.SplinerFISTAFinderFitter(parameters)
else:
parameters = params.ParametersSplinerSTD().initFromFile(settings_name)
finder = find_peaks_std.SplinerFinderFitter(parameters)
std_analysis.standardAnalysis(finder, movie_name, mlist_name, parameters)
def analyze(movie_name, mlist_name, settings_name):
# Load parameters.
parameters = params.ParametersSpliner().initFromFile(settings_name,
warnings=False)
# Check for v1.0 parameters.
if not (parameters.hasAttr("camera_gain")
or parameters.hasAttr("camera_calibration")):
raise Exception(
"Camera parameters are missing. Version 1.0 parameters?")
# Create appropriate finding and fitting object.
if (parameters.getAttr("use_fista", 0) != 0):
parameters = params.ParametersSplinerFISTA().initFromFile(
settings_name)
finder = find_peaks_fista.initFindAndFit(parameters)
else:
parameters = params.ParametersSplinerSTD().initFromFile(settings_name)
finder = find_peaks_std.initFindAndFit(parameters)
# Create appropriate reader.
if parameters.hasAttr("camera_offset"):
frame_reader = analysisIO.FrameReaderStd(movie_file=movie_name,
parameters=parameters)
else:
frame_reader = analysisIO.FrameReaderSCMOS(movie_file=movie_name,
parameters=parameters)
# Create movie reader (uses frame reader).
movie_reader = analysisIO.MovieReader(frame_reader=frame_reader,
parameters=parameters)
# Create localization file writer.
data_writer = analysisIO.DataWriterHDF5(data_file=mlist_name,
parameters=parameters,
sa_type="Spliner")
std_analysis.standardAnalysis(finder, movie_reader, data_writer,
parameters)
import storm_analysis.rolling_ball_bgr.rolling_ball as rollingBall
import storm_analysis.sa_library.datareader as datareader
import storm_analysis.sa_library.daxwriter as daxwriter
import storm_analysis.sa_library.parameters as params
import storm_analysis.sa_library.writeinsight3 as writeinsight3
import storm_analysis.wavelet_bgr.wavelet_bgr as waveletBGR
import fista_decon_utilities_c as fdUtil
if (len(sys.argv) != 4):
print("usage: <movie, input> <parameters_file, input> <decon, output>")
exit()
# Load parameters
parameters = params.ParametersSplinerFISTA().initFromFile(sys.argv[2])
# Open movie and load the first frame.
movie_data = datareader.inferReader(sys.argv[1])
[x_size, y_size, z_size] = movie_data.filmSize()
image = movie_data.loadAFrame(0) - parameters.getAttr("baseline")
image = image.astype(numpy.float)
# Do FISTA deconvolution.
fdecon = FISTADecon(image.shape,
parameters.getAttr("spline"),
parameters.getAttr("fista_number_z"),
parameters.getAttr("fista_timestep"),
upsample=parameters.getAttr("fista_upsample"))
if 0:
import storm_analysis.spliner.spline_to_psf as splineToPSF
import storm_analysis.wavelet_bgr.wavelet_bgr as waveletBGR
parser = argparse.ArgumentParser(description = 'FISTA deconvolution - Beck and Teboulle, SIAM J. Imaging Sciences, 2009')
parser.add_argument('--movie', dest='movie', type=str, required=True,
help = "The name of the movie to deconvolve, can be .dax, .tiff or .spe format.")
parser.add_argument('--xml', dest='settings', type=str, required=True,
help = "The name of the settings xml file.")
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"))