def test_pspec(plotname="pspec_rnoise_beamsmooth_apodizetukey.pdf",
size=256,
powerlaw=3.,
run_kwargs={
'verbose': False,
'apodize_kernel': 'tukey'
},
plot_kwargs={'fit_color': 'black'},
beam_smooth=True,
pixel_scale=2 * u.arcsec,
bmin=8.09 * u.arcsec,
bmaj=10.01 * u.arcsec,
bpa=-12.9 * u.deg,
restfreq=1.4 * u.GHz,
bunit=u.K):
from spectral_cube import Projection
from radio_beam import Beam
rnoise_img = make_extended(size, powerlaw)
# Create a FITS HDU
rnoise_hdu = create_fits_hdu(rnoise_img, 2 * u.arcsec, 2 * u.arcsec,
rnoise_img.shape, 1.4 * u.GHz, u.K)
pspec = PowerSpectrum(rnoise_hdu)
if beam_smooth:
pencil_beam = Beam(0 * u.deg)
rnoise_proj = Projection.from_hdu(rnoise_hdu).with_beam(pencil_beam)
new_beam = Beam(bmaj, bmin, bpa)
rnoise_conv = rnoise_proj.convolve_to(new_beam)
# hdr = fits.Header(header)
# rnoise_hdu = fits.PrimaryHDU(rnoise_img, header=hdr)
pspec = PowerSpectrum(rnoise_conv)
pspec.run(**run_kwargs)
pspec.plot_fit(save_name=plotname, **plot_kwargs)
return pspec
apodize_kernel='splitcosinebell',
alpha=0.3,
beta=0.8)
pspec5 = PowerSpectrum(plaw_hdu)
pspec5.run(verbose=False,
radial_pspec_kwargs={'binsize': 1.0},
fit_kwargs={'weighted_fit': False},
fit_2D=False,
low_cut=1. / (60 * u.pix),
apodize_kernel='tukey',
alpha=0.3)
# Overplot all of the to demonstrate affect on large-scales.
pspec.plot_fit(color=col_pal[0], label='Original')
pspec2.plot_fit(color=col_pal[1], label='Hanning')
pspec3.plot_fit(color=col_pal[2], label='CosineBell')
pspec4.plot_fit(color=col_pal[3], label='SplitCosineBell')
pspec5.plot_fit(color=col_pal[4], label='Tukey')
plt.legend(frameon=True, loc='lower left')
plt.ylim([-2, 7.5])
plt.tight_layout()
plt.savefig(osjoin(fig_path, "rednoise_pspec_slope3_apod_comparisons.png"))
plt.close()
print("Original: {0:.2f} \nHanning: {1:.2f} \nCosineBell: {2:.2f} \n"
"SplitCosineBell: {3:.2f} \nTukey: {4:.2f}".format(
pspec.slope, pspec2.slope, pspec3.slope, pspec4.slope,
pspec5.slope))
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)
plt.close()
full_save_name = osjoin(data_path, gal, save_name)
pspec.save_results(full_save_name, keep_data=False)
if do_fitpspec:
nsamp = 6000
row_names = []
fit_results = {
'logA': [],
pspec4 = PowerSpectrum(plaw_hdu)
pspec4.run(verbose=False, radial_pspec_kwargs={'binsize': 1.0},
fit_kwargs={'weighted_fit': False}, fit_2D=False,
low_cut=1. / (60 * u.pix),
apodize_kernel='splitcosinebell', alpha=0.3, beta=0.8)
pspec5 = PowerSpectrum(plaw_hdu)
pspec5.run(verbose=False, radial_pspec_kwargs={'binsize': 1.0},
fit_kwargs={'weighted_fit': False}, fit_2D=False,
low_cut=1. / (60 * u.pix),
apodize_kernel='tukey', alpha=0.3)
# Overplot all of the to demonstrate affect on large-scales.
pspec.plot_fit(color=col_pal[0], label='Original')
pspec2.plot_fit(color=col_pal[1], label='Hanning')
pspec3.plot_fit(color=col_pal[2], label='CosineBell')
pspec4.plot_fit(color=col_pal[3], label='SplitCosineBell')
pspec5.plot_fit(color=col_pal[4], label='Tukey')
plt.legend(frameon=True, loc='lower left')
plt.ylim([-2, 7.5])
plt.tight_layout()
plt.savefig(osjoin(fig_path, "rednoise_pspec_slope3_apod_comparisons.png"))
plt.close()
print("Original: {0:.2f} \nHanning: {1:.2f} \nCosineBell: {2:.2f} \n"
"SplitCosineBell: {3:.2f} \nTukey: {4:.2f}"
.format(pspec.slope, pspec2.slope, pspec3.slope, pspec4.slope,
pspec5.slope))