def display(im, slice=80, threshold=None):
pylab.figure()
pylab.pink()
array = im.data
if threshold==None:
pylab.imshow(array[:,slice,:])
else:
pylab.imshow(array[:,slice,:]>threshold)
N = 10000 # lato della matrice di punti
ITER = 200 # iterazioni della mappa, dovrebbe essere \infty
xmin = -5 #-1.6 # -1.5
xmax = 5 #-1.36 # 0.55
ymin = -5 #-0.10 #-1.0
ymax = 5 #0.10 # 1.0
M = ones([N, N], float) # parte con colore uniforme
for k in xrange(N):
print "Running map on column", k, "of", N, "Re(x)=", xmin + (xmax -
xmin) * k / N
for l in xrange(N):
z = 0
for i in xrange(ITER):
z = z * z + complex(xmin + (xmax - xmin) * k / N, ymin +
(ymax - ymin) * l / N)
if abs(z) > 2:
M[k, l] = float(i) / ITER # quanto sto in mandel?
#M[k, l] = 0 # Modo Manicheo
break
#savetxt("Man_mat.dat", M, delimiter = ' ', newline = "\n")
imshow(zip(*M), extent=[xmin, xmax, ymin, ymax]) # density map di M
xlabel("Re(c)")
ylabel("Im(c)")
pink()
colorbar()
show()
from numpy import ones, savetxt
from pylab import imshow, show, colorbar, gray, xlabel, ylabel, autumn, bone, cool, copper, flag, hsv, jet, pink, prism, spring, summer, winter
N = 10000 # lato della matrice di punti
ITER = 200 # iterazioni della mappa, dovrebbe essere \infty
xmin = -5#-1.6 # -1.5
xmax = 5 #-1.36 # 0.55
ymin = -5 #-0.10 #-1.0
ymax = 5 #0.10 # 1.0
M = ones([N, N], float) # parte con colore uniforme
for k in xrange(N):
print "Running map on column", k, "of", N, "Re(x)=", xmin+(xmax-xmin)*k/N
for l in xrange(N):
z = 0
for i in xrange(ITER):
z = z*z + complex(xmin+(xmax-xmin)*k/N, ymin+(ymax-ymin)*l/N)
if abs(z) > 2:
M[k, l] = float(i)/ITER # quanto sto in mandel?
#M[k, l] = 0 # Modo Manicheo
break
#savetxt("Man_mat.dat", M, delimiter = ' ', newline = "\n")
imshow(zip(*M), extent=[xmin, xmax, ymin, ymax]) # density map di M
xlabel("Re(c)")
ylabel("Im(c)")
pink()
colorbar()
show()
def display_ppm(ppm):
pylab.pink()
for i in range(ntissues):
display(ppm[:,:,:,i])
H[2,1,:] = Hyz[:]
return H
#datadir = 'D:\home\Alexis\data\delphine\zozo'
datadir = 'D:\Alexis\data\patient_03S0908'
# Read input image
im = load_image(join(datadir, 'BiasCorIm.img'))
data = im.get_data()
data = data.astype('float')
sigma = 3
lda = 1
# Compute hessian
print('Computing Hessian...')
H = hessian(data, sigma=sigma)
# Singular value decomposition
print('Computing singular values...')
S = svd(H)
# Anisotropy measure
print('Computing FA...')
#I = fa(S)
I = aniso(S, lda=lda)
display(I)
pylab.pink()
pylab.show()
