def compare_with_file(fname, dx, NA, lam, n):
u1 = read3dTiff(fname)
Nz, Ny, Nx = u1.shape
u2 = psf((Nx, Ny, Nz), (dx, ) * 3, lam, NA, n0=n)
u1 *= 1. / np.amax(u1)
u2 *= 1. / np.amax(u2)
x = dx * (np.arange(Nx) - Nx / 2)
z = dx * (np.arange(Nz) - Nz / 2)
pylab.clf()
pylab.subplot(2, 1, 1)
pylab.title("NA = %s n = %s" % (NA, n))
pylab.plot(x, u1[Nz / 2, Ny / 2, :], label="file_x")
pylab.plot(x, u2[Nz / 2, Ny / 2, :], label="u_x")
pylab.plot(x, theory_x(x, lam, NA, n), color="k", label="theor_x")
pylab.legend()
pylab.subplot(2, 1, 2)
pylab.plot(z, u1[:, Ny / 2, Nx / 2], label="file_z")
pylab.plot(z, u2[:, Ny / 2, Nx / 2], label="u_z")
pylab.plot(z, theory_z(z, lam, NA, n), color="k", label="theor_z")
#pylab.plot(z,theory_debye_z(z,lam,NA,n),"o",color="r",label="debye_z")
pylab.legend()
pylab.show()
return u1, u2
def compare_with_file(fname,dx, NA,lam,n):
u1 = read3dTiff(fname)
Nz,Ny,Nx = u1.shape
u2 = psf((Nx, Ny, Nz), (dx,) * 3, lam, NA, n0=n)
u1 *= 1./np.amax(u1)
u2 *= 1./np.amax(u2)
x = dx*(np.arange(Nx)-Nx/2)
z = dx*(np.arange(Nz)-Nz/2)
pylab.clf()
pylab.subplot(2,1,1)
pylab.title("NA = %s n = %s"%(NA,n))
pylab.plot(x,u1[Nz/2,Ny/2,:],label="file_x")
pylab.plot(x,u2[Nz/2,Ny/2,:],label="u_x")
pylab.plot(x,theory_x(x,lam,NA,n),color="k",label="theor_x")
pylab.legend()
pylab.subplot(2,1,2)
pylab.plot(z,u1[:,Ny/2,Nx/2],label="file_z")
pylab.plot(z,u2[:,Ny/2,Nx/2],label="u_z")
pylab.plot(z,theory_z(z,lam,NA,n),color="k",label="theor_z")
#pylab.plot(z,theory_debye_z(z,lam,NA,n),"o",color="r",label="debye_z")
pylab.legend()
pylab.show()
return u1, u2
y = myconvolve(x,h)+s*np.amax(x)*np.random.uniform(0,1,x.shape)
y2 = myconvolve(x,h2)+s*np.amax(x)*np.random.uniform(0,1,x.shape)
# u = deconv_tv_al([y,y2],[h,h2])
u = deconv_tv_al(y,h,10.,1.)
u2 = deconv_wiener(y,h,0.1)
#3d
from spimagine import read3dTiff
x = read3dTiff("data/usaf3d.tif")[100:228,100:228,100:228]
h = psf(x.shape,(3.,3.,11))
h2 = psf(x.shape,(11,3,3))
h3 = psf(x.shape,(3,11,3))
y = myconvolve(x,h)+.1*np.amax(x)*np.random.uniform(0,1,x.shape)
y2 = myconvolve(x,h2)+.1*np.amax(x)*np.random.uniform(0,1,x.shape)
y3 = myconvolve(x,h3)+.1*np.amax(x)*np.random.uniform(0,1,x.shape)
u = deconv_tv_al([y,y2,y3],[h,h2,h3],100.,1.)
u2 = deconv_wiener([y,y2,y3],[h,h2,h3],0.01)
R = np.sqrt(Z**2 + Y**2 + X**2)
noise = gputools.perlin3((N, ) * 3, scale=10)
dn = .1 * noise * (R < .4) * (1 - 7.j)
return dn
if __name__ == '__main__':
import sys
if len(sys.argv) > 1:
if sys.argv[1].isdigit():
dn = sphere_dn(int(sys.argv[1]))
else:
dn = read3dTiff(sys.argv[1])
else:
dn = sphere_dn(128)
app = QtWidgets.QApplication(sys.argv)
win = BeamGui(dn=dn, size=(100, ) * 3)
# win = BeamGui(dn = dn, size = (50,50,25))
win.show()
win.raise_()
win.propagate()
#sys.exit(app.exec_())
noise = gputools.perlin3((N,)*3,scale= 10)
dn = .1*noise*(R<.4)*(1-7.j)
return dn
if __name__=='__main__':
import sys
if len(sys.argv)>1:
if sys.argv[1].isdigit():
dn = sphere_dn(int(sys.argv[1]))
else:
dn = read3dTiff(sys.argv[1])
else:
dn = sphere_dn(128)
app = QtWidgets.QApplication(sys.argv)
win = BeamGui(dn=dn, size=(100,)*3)
# win = BeamGui(dn = dn, size = (50,50,25))
win.show()
win.raise_()
win.propagate()
def psf(dshape,sigmas = (2.,2.)):
Xs = np.meshgrid(*[np.arange(-_s/2,_s/2) for _s in dshape], indexing="ij")
h = np.exp(-np.sum([_X**2/2./_s**2 for _X,_s in zip(Xs,sigmas)],axis=0))
h *= 1./np.sum(h)
return np.fft.ifftshift(h)
if __name__ == '__main__':
from matplotlib.pyplot import imread
from spimagine import read3dTiff
im = read3dTiff("data/usaf3d.tif")[200:328,200:328,200:328]
# im = imread("data/usaf.png")[200:456,100:356]
im *= 255
np.random.seed(0)
# im*= 10
hx = (3.5,1.)
hs = [psf(im.shape,np.roll(hx,i)) for i in range(im.ndim)]
ys = [myconvolve(im ,h) for h in hs]
[email protected] """ from biobeam import SimLSM_Cylinder from spimagine import read3dTiff if __name__ == '__main__': #dn = read3dTiff("/Users/mweigert/python/bpm_projects/forward_model/data/sample_elegans_512.tif") #dn = dn.transpose(0,2,1).copy() #signal = 1.*dn.real dn = read3dTiff("/Users/mweigert/python/bpm_projects/forward_model/data/sample2_512_dn.tif")[128:-128] signal = read3dTiff("/Users/mweigert/python/bpm_projects/forward_model/data/sample2_512_signal.tif")[128:-128] #some point sources max_sig = np.amax(np.abs(signal)) np.random.seed(0) for _ in range(4000): k,j,i = np.random.randint(dn.shape[0]),np.random.randint(dn.shape[1]),np.random.randint(dn.shape[2]) signal[k,j,i] = 100.*max_sig #signal[146,::10,:] = 40.*max_dn