return ws[np.argmin([mse(x, deconv_wiener(y, h, w)) for w in ws])]
if __name__ == "__main__":
np.random.seed(0)
# 2d
from matplotlib.pyplot import imread
s = 0.05
x = imread("data/usaf.png")
x *= 0
x[200:400, 200:400] = 1.0
mu = 10000.0
rho = 10.0
scale = 100
x *= 1.0 * scale
h = psf(x.shape, (6.0, 6.0))
y = myconvolve(x, h) + 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, mu, rho)
u2 = deconv_wiener(y, h, rho / mu)
h = psf(x.shape,(3.,11))
h = psf(x.shape,(5.,5.))
h2 = psf(x.shape,(11,3))
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)
