# We also want to account for the shape of the masked data
# (mostly for M31)
# norm_mask = np.isfinite(proj_coldens).astype(np.float) / \
# np.isfinite(proj_coldens).sum()
# pspec_mask = PowerSpectrum(fits.PrimaryHDU(norm_mask, proj_coldens.header),
# distance=fitinfo_dict[gal]['distance'])
# pspec_mask.run(verbose=False, beam_correct=False, fit_2D=False,
# high_cut=0.1 / u.pix,
# use_pyfftw=True, threads=ncores,
# apodize_kernel=fitinfo_dict[gal]['apod_kern'])
pspec = PowerSpectrum(proj_coldens,
distance=fitinfo_dict[gal]['distance'])
pspec.compute_pspec(use_pyfftw=True,
threads=ncores,
apodize_kernel=fitinfo_dict[gal]['apod_kern'])
# Divide out the normalized mask pspec
# pspec._ps2D /= pspec_mask.ps2D
pspec.compute_radial_pspec()
# pspec.fit_pspec() # high_cut=0.1 / u.pix,)
# pspec.run(verbose=False, beam_correct=False, fit_2D=False,
# high_cut=0.1 / u.pix,
# use_pyfftw=True, threads=ncores,
# apodize_kernel=fitinfo_dict[gal]['apod_kern'])
if make_interactive:
print(pspec.slope)
pspec.plot_fit(show_residual=False, show_2D=True)
plt.draw()
input(gal)
continue
# Check to see if the pspec file already exists:
save_pspec_name = osjoin(
pspec_posn_folder,
f"{fitinfo_dict[gal]['filename'].rstrip('fits')}_y_{y}_x_{x}_pspec.pkl"
)
if not os.path.exists(save_pspec_name):
pspec = PowerSpectrum(mom0_cutout.hdu,
beam=mom0.beam,
distance=fitinfo_dict[gal]['distance'])
pspec.compute_pspec(
use_pyfftw=False,
threads=ncores,
apodize_kernel=fitinfo_dict[gal]['apod_kern'],
alpha=alpha)
pspec.compute_radial_pspec()
pspec.save_results(save_pspec_name)
else:
pspec = PowerSpectrum.load_results(save_pspec_name)
pspec.load_beam()
# pspec.run(high_cut=10**-1.29 / u.pix, low_cut=10**-2. / u.pix,
# fit_2D=True,
# verbose=False,
# # save_name=os.path.join(perposn_figfolder, "{}.png".format(out_name)),