import sa_library.datareader as datareader
import sa_library.readinsight3 as readinsight3
if (len(sys.argv)!=4):
print "usage: homotopy_psf <dax_file, input> <bin_file, input> <npy_file, output>"
exit()
# Minimum number of peaks to calculate the PSF from.
min_peaks = 500
# Half width of the aoi size in pixels.
aoi_size = 8
# Load dax file and corresponding molecule list file.
dax_data = datareader.inferReader(sys.argv[1])
i3_data = readinsight3.loadI3File(sys.argv[2])
# Go through the frames identifying good peaks and adding them
# 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]
def peakFinding(find_peaks, movie_file, mlist_file, parameters):
# open files for input & output
movie_data = datareader.inferReader(movie_file)
[movie_x,movie_y,movie_l] = movie_data.filmSize()
# if the i3 file already exists, read it in,
# write it out & start the analysis from the
# end.
total_peaks = 0
if(os.path.exists(mlist_file)):
print "Found", mlist_file
i3data_in = readinsight3.loadI3File(mlist_file)
try:
curf = int(numpy.max(i3data_in['fr']))
except ValueError:
curf = 0
print " Starting analysis at frame:", curf
i3data = writeinsight3.I3Writer(mlist_file)
if (curf > 0):
i3data.addMolecules(i3data_in)
total_peaks = i3data_in['x'].size
else:
curf = 0
i3data = writeinsight3.I3Writer(mlist_file)
# process parameters
if hasattr(parameters, "start_frame"):
if (parameters.start_frame>=curf) and (parameters.start_frame<movie_l):
curf = parameters.start_frame
if hasattr(parameters, "max_frame"):
if (parameters.max_frame>0) and (parameters.max_frame<movie_l):
movie_l = parameters.max_frame
# analyze the movie
# catch keyboard interrupts & "gracefully" exit.
try:
while(curf<movie_l):
#for j in range(l):
# Set up the analysis.
image = movie_data.loadAFrame(curf) - parameters.baseline
mask = (image < 1.0)
if (numpy.sum(mask) > 0):
print " Removing negative values in frame", curf
image[mask] = 1.0
# Find and fit the peaks.
[peaks, residual] = find_peaks.analyzeImage(image)
# Save the peaks.
if (type(peaks) == type(numpy.array([]))):
# remove unconverged peaks
peaks = find_peaks.getConvergedPeaks(peaks)
# save results
if(parameters.orientation == "inverted"):
i3data.addMultiFitMolecules(peaks, movie_x, movie_y, curf+1, parameters.pixel_size, inverted = True)
else:
i3data.addMultiFitMolecules(peaks, movie_x, movie_y, curf+1, parameters.pixel_size, inverted = False)
total_peaks += peaks.shape[0]
print "Frame:", curf, peaks.shape[0], total_peaks
else:
print "Frame:", curf, 0, total_peaks
curf += 1
print ""
i3data.close()
find_peaks.cleanUp()
return 0
except KeyboardInterrupt:
print "Analysis stopped."
i3data.close()
find_peaks.cleanUp()
return 1
import sa_library.datareader as datareader
import sa_library.readinsight3 as readinsight3
if (len(sys.argv) != 4):
print "usage: homotopy_psf <dax_file, input> <bin_file, input> <npy_file, output>"
exit()
# Minimum number of peaks to calculate the PSF from.
min_peaks = 500
# Half width of the aoi size in pixels.
aoi_size = 8
# Load dax file and corresponding molecule list file.
dax_data = datareader.DaxReader(sys.argv[1])
i3_data = readinsight3.loadI3File(sys.argv[2])
# Go through the frames identifying good peaks and adding them
# 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_x - aoi_size - 1)) & (i3_data['y'] > aoi_size) & (
i3_data['y'] < (dax_y - aoi_size - 1))
xr = i3_data['x'][mask]
def peakFinding(find_peaks, movie_file, mlist_file, parameters):
# open files for input & output
movie_data = datareader.inferReader(movie_file)
[movie_x, movie_y, movie_l] = movie_data.filmSize()
# if the i3 file already exists, read it in,
# write it out & start the analysis from the
# end.
total_peaks = 0
if (os.path.exists(mlist_file)):
print "Found", mlist_file
i3data_in = readinsight3.loadI3File(mlist_file)
try:
curf = int(numpy.max(i3data_in['fr']))
except ValueError:
curf = 0
print " Starting analysis at frame:", curf
i3data = writeinsight3.I3Writer(mlist_file)
if (curf > 0):
i3data.addMolecules(i3data_in)
total_peaks = i3data_in['x'].size
else:
curf = 0
i3data = writeinsight3.I3Writer(mlist_file)
# process parameters
if hasattr(parameters, "start_frame"):
if (parameters.start_frame >= curf) and (parameters.start_frame <
movie_l):
curf = parameters.start_frame
if hasattr(parameters, "max_frame"):
if (parameters.max_frame > 0) and (parameters.max_frame < movie_l):
movie_l = parameters.max_frame
# analyze the movie
# catch keyboard interrupts & "gracefully" exit.
try:
while (curf < movie_l):
#for j in range(l):
# Set up the analysis.
image = movie_data.loadAFrame(curf) - parameters.baseline
mask = (image < 1.0)
if (numpy.sum(mask) > 0):
print " Removing negative values in frame", curf
image[mask] = 1.0
# Find and fit the peaks.
[peaks, residual] = find_peaks.analyzeImage(image)
# Save the peaks.
if (type(peaks) == type(numpy.array([]))):
# remove unconverged peaks
peaks = find_peaks.getConvergedPeaks(peaks)
# save results
if (parameters.orientation == "inverted"):
i3data.addMultiFitMolecules(peaks,
movie_x,
movie_y,
curf + 1,
parameters.pixel_size,
inverted=True)
else:
i3data.addMultiFitMolecules(peaks,
movie_x,
movie_y,
curf + 1,
parameters.pixel_size,
inverted=False)
total_peaks += peaks.shape[0]
print "Frame:", curf, peaks.shape[0], total_peaks
else:
print "Frame:", curf, 0, total_peaks
curf += 1
print ""
i3data.close()
find_peaks.cleanUp()
return 0
except KeyboardInterrupt:
print "Analysis stopped."
i3data.close()
find_peaks.cleanUp()
return 1