Calculate spatial correlation using TurbuStat for optical sattelite data

[mohseniaref]

I would like to estimate spatial correlation for my data which you can assume is numpy array and pixel size is around 30 meter or 0.000277778 degree on ground(earth) . I attempted the following example and it worked for me! I was wondering if I can work with any data using the following command and create a header which can not influence my result

rnoise_img = make_extended(256, powerlaw=3.)
plt.imshow(rnoise_img)
rnoise_hdu = fits.PrimaryHDU(rnoise_img)
pspec.run(verbose=True,fit_2D=False)

Second, After creating example of numpy array which has pixel size around 30 meter, I estimated 1Dslope or 2D slope. I was wondering if there is technique to remove spatial correlated noise using PSD?

from turbustat.simulator import make_extended

from turbustat.statistics import PowerSpectrum

from astropy.io import fits

import warnings

import numpy as np

import matplotlib.pyplot as plt

import astropy.units as u

import matplotlib

moment0 = fits.open("/home/mohammad/Project/SpatialCorrelation/TurbuStat v1.0 tutorial data/Design4_flatrho_0021_00_radmc_moment0.fits")[0]

pspec = PowerSpectrum(moment0, distance=250 * u.pc)

pspec.run(verbose=True, xunit=u.pix**-1)

params=pspec.fit.params
print(pspec.slope2D, pspec.slope2D_err)