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()