x = numpy.arange(0.0, 2.001, 0.125)
y = numpy.array([[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]])
s = Spline2D(y)
surf = numpy.zeros((x.size, x.size))
dx_surf = numpy.zeros((x.size, x.size))
dy_surf = numpy.zeros((x.size, x.size))
for i in range(x.size):
for j in range(x.size):
surf[i, j] = s.f(x[i], x[j])
dx_surf[i, j] = s.dxf(x[i], x[j])
dy_surf[i, j] = s.dyf(x[i], x[j])
print(surf)
#print dx_surf
#print dy_surf
if 0:
surf = 100.0 + 100.0 * surf
dx_surf = 100.0 + 100.0 * dx_surf
dy_surf = 100.0 + 100.0 * dy_surf
daxwriter.singleFrameDax("surf.dax", surf)
daxwriter.singleFrameDax("dx_surf.dax", dx_surf)
daxwriter.singleFrameDax("dy_surf.dax", dy_surf)
#pw = pyqtgraph.plot()
#raw_input("return to continue")
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
# furnished to do so, subject to the following conditions:
#
def psfToSpline(psf_name, spline_name, s_size):
psf_data = pickle.load(open(psf_name, 'rb'))
np_psf = psf_data["psf"]
spline = False
start = np_psf.shape[1]/2.0 - s_size - 0.5
# 2D spline
if (len(np_psf.shape) == 2):
print("Generating 2D spline.")
s_size = 2*s_size
np_spline = numpy.zeros((s_size, s_size))
#np_psf = np_psf/numpy.max(np_psf)
xy_spline = spline2D.Spline2D(np_psf)
x = start
for i in range(s_size):
y = start
for j in range(s_size):
np_spline[j,i] = xy_spline.f(y,x)
y += 1.0
x += 1.0
print("Calculating spline coefficients.")
spline = spline2D.Spline2D(np_spline)
if True:
import storm_analysis.sa_library.daxwriter as daxwriter
daxwriter.singleFrameDax(os.path.join(os.path.dirname(spline_name), "spline.dax"), 1000.0*np_spline + 100)
# 3D spline
else:
print("Generating 3D spline.")
s_size = 2*s_size
np_spline = numpy.zeros((s_size, s_size, s_size))
xy_splines = []
print("Generating XY splines.")
for i in range(np_psf.shape[0]):
xy_splines.append(spline2D.Spline2D(np_psf[i,:,:]))
print("Generating fitting spline.")
x = start
for i in range(s_size):
y = start
for j in range(s_size):
zvals = numpy.zeros(np_psf.shape[0])
for k in range(np_psf.shape[0]):
zvals[k] = xy_splines[k].f(y,x)
z_spline = spline1D.Spline1D(zvals)
max_z = float(np_psf.shape[0]) - 1.0
inc = max_z/(float(s_size)-1.0)
for k in range(s_size):
z = float(k)*inc
if (z > max_z):
z = max_z
np_spline[k,j,i] = z_spline.f(z)
y += 1.0
x += 1.0
print("Calculating spline coefficients.")
spline = spline3D.Spline3D(np_spline)
if True:
import storm_analysis.sa_library.daxwriter as daxwriter
dxw = daxwriter.DaxWriter(os.path.join(os.path.dirname(spline_name), "spline.dax"),
np_spline.shape[1],
np_spline.shape[2])
for i in range(s_size):
dxw.addFrame(1000.0*np_spline[i,:,:] + 100)
dxw.close()
del psf_data["psf"]
psf_data["spline"] = np_spline
psf_data["coeff"] = spline.getCoeff()
pickle.dump(psf_data, open(spline_name, 'wb'))
def psfToSpline(psf_name, spline_name, s_size):
psf_data = pickle.load(open(psf_name, 'rb'))
np_psf = psf_data["psf"]
spline = False
start = np_psf.shape[1]/2.0 - s_size - 0.5
# 2D spline
if (len(np_psf.shape) == 2):
print("Generating 2D spline.")
s_size = 2*s_size
np_spline = numpy.zeros((s_size, s_size))
#np_psf = np_psf/numpy.max(np_psf)
xy_spline = spline2D.Spline2D(np_psf)
x = start
for i in range(s_size):
y = start
for j in range(s_size):
np_spline[j,i] = xy_spline.f(y,x)
y += 1.0
x += 1.0
print("Calculating spline coefficients.")
spline = spline2D.Spline2D(np_spline)
if True:
import storm_analysis.sa_library.daxwriter as daxwriter
daxwriter.singleFrameDax(os.path.join(os.path.dirname(spline_name), "spline.dax"), 1000.0*np_spline + 100)
# 3D spline
else:
print("Generating 3D spline.")
s_size = 2*s_size
np_spline = numpy.zeros((s_size, s_size, s_size))
xy_splines = []
print("Generating XY splines.")
for i in range(np_psf.shape[0]):
xy_splines.append(spline2D.Spline2D(np_psf[i,:,:]))
print("Generating fitting spline.")
x = start
for i in range(s_size):
y = start
for j in range(s_size):
zvals = numpy.zeros(np_psf.shape[0])
for k in range(np_psf.shape[0]):
zvals[k] = xy_splines[k].f(y,x)
z_spline = spline1D.Spline1D(zvals)
max_z = float(np_psf.shape[0]) - 1.0
inc = max_z/(float(s_size)-1.0)
for k in range(s_size):
z = float(k)*inc
if (z > max_z):
z = max_z
np_spline[k,j,i] = z_spline.f(z)
y += 1.0
x += 1.0
print("Calculating spline coefficients.")
spline = spline3D.Spline3D(np_spline)
if True:
import storm_analysis.sa_library.daxwriter as daxwriter
dxw = daxwriter.DaxWriter(os.path.join(os.path.dirname(spline_name), "spline.dax"),
np_spline.shape[1],
np_spline.shape[2])
for i in range(s_size):
dxw.addFrame(1000.0*np_spline[i,:,:] + 100)
dxw.close()
del psf_data["psf"]
psf_data["spline"] = np_spline
psf_data["coeff"] = spline.getCoeff()
pickle.dump(psf_data, open(spline_name, 'wb'))
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
# furnished to do so, subject to the following conditions:
#
x = numpy.arange(0.0, 2.001, 0.125)
y = numpy.array([[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]])
s = Spline2D(y)
surf = numpy.zeros((x.size, x.size))
dx_surf = numpy.zeros((x.size, x.size))
dy_surf = numpy.zeros((x.size, x.size))
for i in range(x.size):
for j in range(x.size):
surf[i, j] = s.f(x[i], x[j])
dx_surf[i, j] = s.dxf(x[i], x[j])
dy_surf[i, j] = s.dyf(x[i], x[j])
print(surf)
# print dx_surf
# print dy_surf
if 0:
surf = 100.0 + 100.0 * surf
dx_surf = 100.0 + 100.0 * dx_surf
dy_surf = 100.0 + 100.0 * dy_surf
daxwriter.singleFrameDax("surf.dax", surf)
daxwriter.singleFrameDax("dx_surf.dax", dx_surf)
daxwriter.singleFrameDax("dy_surf.dax", dy_surf)
# pw = pyqtgraph.plot()
# raw_input("return to continue")