from astropy.convolution import convolve as ap_convolve

from astropy.convolution import Box2DKernel

box_2D_kernel = Box2DKernel(9)

nan=float('nan')

data=np.where(data==0,nan,data)

data[ :, 0]=nan

data[ :,-1]=nan

data[ 0, :]=nan

data[-1, :]=nan

data2=ap_convolve(data, box_2D_kernel, normalize_kernel=True)

plt.clf()

dd=data-data2

dd=np.ma.masked_invalid(dd)

ddmean=np.mean(dd)

ddstd=np.std(dd)

dd=dd-ddmean

ddcvlo=-ddstd*2.

ddcvhi=+ddstd*2.

ddcvin=(ddcvhi-ddcvlo)/100.

plt.pcolor(dd[:,0:2500].T,np.arange(ddcvlo,ddcvhi,ddcvin),cmap=plt.cm.get_cmap('Blues'))