from turbustat.statistics.psds import make_radial_freq_arrays
import astropy.io.fits as fits
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import astropy.units as u
plt.rcParams['axes.unicode_minus'] = False
size = 512
slope = 3
test_img = fits.PrimaryHDU(
make_extended(size,
powerlaw=slope,
ellip=0.4,
theta=(60 * u.deg).to(u.rad),
randomseed=345987))
# The power-law behaviour continues up to ~1/4 of the size
pspec = PowerSpectrum(test_img)
pspec.run(fit_2D=True,
radial_pspec_kwargs={'binsize': 2.0},
fit_kwargs={'weighted_fit': False},
low_cut=1 / (15 * u.pix),
verbose=False)
print("{0}+/-{1}".format(pspec.slope, pspec.slope_err))
print("{0}+/-{1}".format(pspec.slope2D, pspec.slope2D_err))
print("{0}+/-{1}".format(pspec.ellip2D, pspec.ellip2D_err))
print("{0}+/-{1}".format(pspec.theta2D, pspec.theta2D_err))
col_pal = sb.color_palette()
plt.rcParams['axes.unicode_minus'] = False
size = 256
markers = ['D', 'o']
# Make a single figure example to save space in the paper.
fig = plt.figure(figsize=figsize)
slope = 3.0
test_img = fits.PrimaryHDU(make_extended(size, powerlaw=slope))
# The power-law behaviour continues up to ~1/4 of the size
delvar = DeltaVariance(test_img).run(xlow=3 * u.pix,
xhigh=0.25 * size * u.pix,
boundary='wrap')
plt.xscale("log")
plt.yscale("log")
plt.errorbar(delvar.lags.value,
delvar.delta_var,
yerr=delvar.delta_var_error,
fmt=markers[0],
label='TurbuStat')
# Now plot the IDL output
tab = Table.read("deltavar_{}.txt".format(slope), format='ascii')
image_sizes = [32, 64, 128, 256, 512, 1024, 2048]
samp_time = [1e-4, 1e-3, 1e-2, 1e-2, 1e-2, 1e-1, 1e-1]
twod_results = {}
for Stat in twod_stats:
twod_results[Stat.__name__ + "_memory"] = []
twod_results[Stat.__name__ + "_time"] = []
threed_results = {}
for Stat in threed_stats:
threed_results[Stat.__name__ + "_memory"] = []
threed_results[Stat.__name__ + "_time"] = []
for size, del_t in zip(image_sizes, samp_time):
img = np.abs(make_extended(size))
img_hdr = create_image_header(pixel_scale, beamfwhm, (size, size),
restfreq, bunit)
img_hdu = fits.PrimaryHDU(img, img_hdr)
# Run 2D stats
for Stat in twod_stats:
kwargs = {}
run_kwargs = {}
# MVC has different inputs than the rest
if Stat == MVC:
inputs = [img_hdu] * 3
elif Stat == Wavelet:
from turbustat.statistics import PowerSpectrum
from turbustat.simulator import make_extended
from turbustat.statistics.psds import make_radial_freq_arrays
import astropy.io.fits as fits
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import astropy.units as u
plt.rcParams['axes.unicode_minus'] = False
size = 512
slope = 3
test_img = fits.PrimaryHDU(make_extended(size, powerlaw=slope,
ellip=0.4,
theta=(60 * u.deg).to(u.rad),
randomseed=345987))
# The power-law behaviour continues up to ~1/4 of the size
pspec = PowerSpectrum(test_img)
pspec.run(fit_2D=True, radial_pspec_kwargs={'binsize': 2.0},
fit_kwargs={'weighted_fit': False},
low_cut=1 / (15 * u.pix),
verbose=False)
print("{0}+/-{1}".format(pspec.slope, pspec.slope_err))
print("{0}+/-{1}".format(pspec.slope2D, pspec.slope2D_err))
print("{0}+/-{1}".format(pspec.ellip2D, pspec.ellip2D_err))
print("{0}+/-{1}".format(pspec.theta2D, pspec.theta2D_err))
# pspec.plot_fit(show_2D=True)
np.abs(np.fft.rfft(data4[shape[0] // 2]))**2,
label='Tukey')
plt.legend(frameon=True)
plt.xlabel("Freq. (1 / pix)")
plt.ylabel("Power")
plt.savefig(osjoin(fig_path, '1d_apods_pspec.png'))
plt.close()
# Example of 2D Tukey power-spectrum
plt.imshow(np.log10(np.fft.fftshift(np.abs(np.fft.fft2(data4))**2)))
plt.savefig(osjoin(fig_path, '2d_tukey_pspec.png'))
plt.close()
# This is easier to show with a red noise image due to the limited
# inertial range in the sim data.
rnoise_img = make_extended(256, powerlaw=3.)
pixel_scale = 3 * u.arcsec
beamfwhm = 3 * u.arcsec
imshape = rnoise_img.shape
restfreq = 1.4 * u.GHz
bunit = u.K
plaw_hdu = create_fits_hdu(rnoise_img, pixel_scale, beamfwhm, imshape,
restfreq, bunit)
plt.imshow(plaw_hdu.data, cmap='viridis')
plt.savefig(osjoin(fig_path, "rednoise_slope3_img.png"))
plt.close()
pspec = PowerSpectrum(plaw_hdu)
plt.loglog(freqs, np.abs(np.fft.rfft(data3[shape[0] // 2]))**2, label='Split Cosine')
plt.loglog(freqs, np.abs(np.fft.rfft(data4[shape[0] // 2]))**2, label='Tukey')
plt.legend(frameon=True)
plt.xlabel("Freq. (1 / pix)")
plt.ylabel("Power")
plt.savefig(osjoin(fig_path, '1d_apods_pspec.png'))
plt.close()
# Example of 2D Tukey power-spectrum
plt.imshow(np.log10(np.fft.fftshift(np.abs(np.fft.fft2(data4))**2)))
plt.savefig(osjoin(fig_path, '2d_tukey_pspec.png'))
plt.close()
# This is easier to show with a red noise image due to the limited
# inertial range in the sim data.
rnoise_img = make_extended(256, powerlaw=3.)
pixel_scale = 3 * u.arcsec
beamfwhm = 3 * u.arcsec
imshape = rnoise_img.shape
restfreq = 1.4 * u.GHz
bunit = u.K
plaw_hdu = create_fits_hdu(rnoise_img, pixel_scale, beamfwhm, imshape,
restfreq, bunit)
plt.imshow(plaw_hdu.data, cmap='viridis')
plt.savefig(osjoin(fig_path, "rednoise_slope3_img.png"))
plt.close()
pspec = PowerSpectrum(plaw_hdu)
col_pal = sb.color_palette()
plt.rcParams['axes.unicode_minus'] = False
size = 256
markers = ['D', 'o']
# Make a single figure example to save space in the paper.
fig = plt.figure(figsize=figsize)
slope = 3.0
test_img = fits.PrimaryHDU(make_extended(size, powerlaw=slope))
# The power-law behaviour continues up to ~1/4 of the size
delvar = DeltaVariance(test_img).run(xlow=3 * u.pix,
xhigh=0.25 * size * u.pix,
boundary='wrap')
plt.xscale("log")
plt.yscale("log")
plt.errorbar(delvar.lags.value, delvar.delta_var,
yerr=delvar.delta_var_error,
fmt=markers[0], label='TurbuStat')
# Now plot the IDL output
tab = Table.read("deltavar_{}.txt".format(slope), format='ascii')
# First is pixel scale, second is delvar, then delvar error, and finally
# the fit values