%pylab inline
import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage as ndimage
import scipy.stats as stats
from scipy.stats import multivariate_normal

# a blank image

isize = 100
image_0 = np.zeros((isize,isize))
plt.imshow(image_0, cmap='gray', vmin=-0.1, vmax=0.1)

# a gaussian galaxy

def gal2d(grid, m=None, c=None):
    var = multivariate_normal(mean=m, cov=cov)
    return var.pdf(grid)

x = np.arange(0, isize, 1)
y0, x0 = np.meshgrid(x, x)
grid = np.dstack((y0, x0))

mean = [30,30]
cov = [[20, 20], [20, 100]]
image_gal = gal2d(grid,m=mean,c=cov)
image_gal *= 100

plt.imshow(image_gal, cmap='gray',vmin=-0.1, vmax=0.1)

# useful functions for filtering 
ndimage.gaussian_filter(image, sigma=(sig, sig), order=0)