def frcCalc2d(mlist_name, results_name, show_plot):
pixel_size = 160.0
storm_scale = 8
# Load the data.
i3_grid = i3togrid.I3GData(mlist_name, scale = storm_scale)
# Split the data (approximately) in half & generate 2D histograms.
print("Searching for mid-point")
# For simulations the .dax file might not actually have as many
# frames as the molecule list so use a hack to get the number of
# frames in the molecule list.
max_f = int(numpy.max(i3_grid.i3data['fr'])) + 1
locs = round(numpy.sum(i3_grid.i3To2DGridAllChannelsMerged(fmax = max_f)))
start = 0
end = max_f
half_locs = locs/2
while ((end - start) > 1):
mid = (end - start)/2 + start
print(" ", start, mid, end)
grid1 = i3_grid.i3To2DGridAllChannelsMerged(fmin = 0, fmax = mid)
if (numpy.sum(grid1) < half_locs):
start = mid
else:
end = mid
print(" mid-point:", end)
grid1 = i3_grid.i3To2DGridAllChannelsMerged(fmin = 0, fmax = end)
grid2 = i3_grid.i3To2DGridAllChannelsMerged(fmin = end, fmax = max_f)
# Compute FFT
print("Calculating")
grid1_fft = numpy.fft.fftshift(numpy.fft.fft2(grid1))
grid2_fft = numpy.fft.fftshift(numpy.fft.fft2(grid2))
grid1_fft_sqr = grid1_fft * numpy.conj(grid1_fft)
grid2_fft_sqr = grid2_fft * numpy.conj(grid2_fft)
grid1_grid2 = grid1_fft * numpy.conj(grid2_fft)
if show_plot:
arraytoimage.singleColorImage(numpy.abs(grid1_fft), "grid1")
arraytoimage.singleColorImage(numpy.abs(grid2_fft), "grid2")
[frc, frc_counts] = frcC.frc(grid1_fft, grid2_fft)
# Plot results
for i in range(frc.size):
if (frc_counts[i] > 0):
frc[i] = frc[i]/float(frc_counts[i])
else:
frc[i] = 0.0
xvals = numpy.arange(frc.size)
xvals = xvals/(float(grid1_fft.shape[0]) * pixel_size * (1.0/float(storm_scale)))
frc = numpy.real(frc)
with open(results_name, "w") as fp:
for i in range(xvals.size):
fp.write(str(xvals[i]) + "," + str(frc[i]) + "\n")
if show_plot:
fig = pyplot.figure()
ax = fig.add_subplot(111)
ax.scatter(xvals, frc)
pyplot.xlim([xvals[0], xvals[-1]])
pyplot.ylim([-0.2,1.2])
pyplot.xlabel("Spatial Frequency (nm-1)")
pyplot.ylabel("Correlation")
pyplot.show()
if True:
import os
import storm_analysis.sa_library.arraytoimage as arraytoimage
import storm_analysis.sa_library.datawriter as datawriter
if (len(sys.argv) != 3):
print("usage: <in_hres> <out_img>")
exit()
hres = HResFile(sys.argv[1])
image = hres.sumFrames(verbose = True)
ext = os.path.splitext(sys.argv[2])[1]
if (ext == ".png"):
arraytoimage.singleColorImage(image, sys.argv[2])
elif (ext == ".dax"):
datawriter.singleFrameDax(sys.argv[2], image)
else:
print("unrecognized extension ", ext)
#
# The MIT License
#
# Copyright (c) 2012 Zhuang Lab, Harvard University
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
def frcCalc2d(mlist_name, results_name, show_plot):
pixel_size = 160.0
storm_scale = 8
# Load the data.
i3_grid = i3togrid.I3GData(mlist_name, scale = storm_scale)
# Split the data (approximately) in half & generate 2D histograms.
print("Searching for mid-point")
# For simulations the .dax file might not actually have as many
# frames as the molecule list so use a hack to get the number of
# frames in the molecule list.
max_f = int(numpy.max(i3_grid.i3data['fr'])) + 1
locs = round(numpy.sum(i3_grid.i3To2DGridAllChannelsMerged(fmax = max_f)))
start = 0
end = max_f
half_locs = locs/2
while ((end - start) > 1):
mid = (end - start)/2 + start
print(" ", start, mid, end)
grid1 = i3_grid.i3To2DGridAllChannelsMerged(fmin = 0, fmax = mid)
if (numpy.sum(grid1) < half_locs):
start = mid
else:
end = mid
print(" mid-point:", end)
grid1 = i3_grid.i3To2DGridAllChannelsMerged(fmin = 0, fmax = end)
grid2 = i3_grid.i3To2DGridAllChannelsMerged(fmin = end, fmax = max_f)
# Compute FFT
print("Calculating")
grid1_fft = numpy.fft.fftshift(numpy.fft.fft2(grid1))
grid2_fft = numpy.fft.fftshift(numpy.fft.fft2(grid2))
grid1_fft_sqr = grid1_fft * numpy.conj(grid1_fft)
grid2_fft_sqr = grid2_fft * numpy.conj(grid2_fft)
grid1_grid2 = grid1_fft * numpy.conj(grid2_fft)
if show_plot:
arraytoimage.singleColorImage(numpy.abs(grid1_fft), "grid1")
arraytoimage.singleColorImage(numpy.abs(grid2_fft), "grid2")
[frc, frc_counts] = frcC.frc(grid1_fft, grid2_fft)
# Plot results
for i in range(frc.size):
if (frc_counts[i] > 0):
frc[i] = frc[i]/float(frc_counts[i])
else:
frc[i] = 0.0
xvals = numpy.arange(frc.size)
xvals = xvals/(float(grid1_fft.shape[0]) * pixel_size * (1.0/float(storm_scale)))
frc = numpy.real(frc)
with open(results_name, "w") as fp:
for i in range(xvals.size):
fp.write(str(xvals[i]) + "," + str(frc[i]) + "\n")
if show_plot:
fig = pyplot.figure()
ax = fig.add_subplot(111)
ax.scatter(xvals, frc)
pyplot.xlim([xvals[0], xvals[-1]])
pyplot.ylim([-0.2,1.2])
pyplot.xlabel("Spatial Frequency (nm-1)")
pyplot.ylabel("Correlation")
pyplot.show()
if 1:
import os
import storm_analysis.sa_library.arraytoimage as arraytoimage
import storm_analysis.sa_library.daxwriter as daxwriter
if (len(sys.argv) != 3):
print("usage: <in_hres> <out_img>")
exit()
hres = HResFile(sys.argv[1])
image = hres.sumFrames(verbose=True)
ext = os.path.splitext(sys.argv[2])[1]
if (ext == ".png"):
arraytoimage.singleColorImage(image, sys.argv[2])
elif (ext == ".dax"):
daxwriter.singleFrameDax(sys.argv[2], image)
else:
print("unrecognized extension ", ext)
#
# The MIT License
#
# Copyright (c) 2012 Zhuang Lab, Harvard University
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
print(" mid-point:", end)
grid1 = i3_grid.i3To2DGridAllChannelsMerged(fmin=0, fmax=end)
grid2 = i3_grid.i3To2DGridAllChannelsMerged(fmin=end, fmax=max_f)
# Compute FFT
print("Calculating")
grid1_fft = numpy.fft.fftshift(numpy.fft.fft2(grid1))
grid2_fft = numpy.fft.fftshift(numpy.fft.fft2(grid2))
grid1_fft_sqr = grid1_fft * numpy.conj(grid1_fft)
grid2_fft_sqr = grid2_fft * numpy.conj(grid2_fft)
grid1_grid2 = grid1_fft * numpy.conj(grid2_fft)
if 1:
arraytoimage.singleColorImage(numpy.abs(grid1_fft), "grid1")
arraytoimage.singleColorImage(numpy.abs(grid2_fft), "grid2")
[frc, frc_counts] = frcC.frc(grid1_fft, grid2_fft)
# Plot results
for i in range(frc.size):
if (frc_counts[i] > 0):
frc[i] = frc[i] / float(frc_counts[i])
else:
frc[i] = 0.0
xvals = numpy.arange(frc.size)
xvals = xvals / (float(grid1_fft.shape[0]) * pixel_size *
(1.0 / float(storm_scale)))
frc = numpy.real(frc)
