def gauss_kern(size, sizey=None):
""" Returns a normalized 2D gauss kernel array for convolutions """
size = int(size)
if not sizey:
sizey = size
else:
sizey = int(sizey)
#print size, sizey
x, y = mgrid[-size:size+1, -sizey:sizey+1]
g = exp(-(x**2/float(size)+y**2/float(sizey)))
return g / g.sum()
def blur_image(im, n, ny=None) :
""" blurs the image by convolving with a gaussian kernel of typical
size n. The optional keyword argument ny allows for a different
size in the y direction.
"""
g = gauss_kern(n, sizey=ny)
improc = signal.convolve(im,g, mode='valid')
return(improc)
from pylab import figure, show, clf, savefig, cm
from scipy import *
xmin, xmax, ymin, ymax = -70, 70, -70, 70
extent = xmin, xmax, ymin, ymax
X, Y = mgrid[-70:70, -70:70]
Z = cos((X**2+Y**2)/200.)+ random.normal(size=X.shape)
#Z = cos((X**2+Y**2)/200.)
fig1 = figure(1)
fig1.clf()
ax1a = fig1.add_subplot(131)
ax1a.imshow(abs(Z), cmap=cm.jet, alpha=.9, interpolation='bilinear', extent=extent)
ax1a.set_title(r'Noisey')
P = gauss_kern(3)
ax1b = fig1.add_subplot(132)
ax1b.imshow(abs(P), cmap=cm.jet, alpha=.9, interpolation='bilinear', extent=extent)
ax1b.set_title(r'Convolving Gaussian')
U = blur_image(Z, 3)
ax1c = fig1.add_subplot(133)
ax1c.imshow(abs(U), cmap=cm.jet, alpha=.9, interpolation='bilinear', extent=extent)
ax1c.set_title(r'Cleaned')
savefig('convolve-gaussian.png')
