plt.axvline(1 / beam_size,
linestyle=':',
linewidth=4,
alpha=0.8,
color='gray')
# plt.axvline(1 / beam_gauss_width)
plt.grid()
plt.subplot(122)
# plt.title(filename)
plt.imshow(np.log10(pspec.ps2D), origin='lower', cmap='plasma')
cbar = plt.colorbar()
# Add contour showing region fit
yy_freq, xx_freq = make_radial_freq_arrays(pspec.ps2D.shape)
freqs_dist = np.sqrt(yy_freq**2 + xx_freq**2)
mask = freqs_dist <= high_cut
plt.contour(mask, colors=['k'], linestyles=['--'])
plt.tight_layout()
plt.draw()
plot_savename = osjoin(
plot_folder,
"{0}.pspec_wbeam.png".format(filename.rstrip(".fits")))
ax1 = plt.subplot(121)
im1 = ax1.imshow(test_img.data.T, cmap='viridis', origin='lower')
divider = make_axes_locatable(ax1)
cax1 = divider.append_axes("left", "5%", pad="3%")
cb = plt.colorbar(im1, cax=cax1)
cb.set_label(r"Image Value")
cax1.yaxis.set_ticks_position('left')
cax1.yaxis.set_label_position('left')
ax1.axes.xaxis.set_ticklabels([])
ax1.axes.yaxis.set_ticklabels([])
ax = plt.subplot(122)
yy_freq, xx_freq = make_radial_freq_arrays(pspec.ps2D.shape)
freqs_dist = np.sqrt(yy_freq**2 + xx_freq**2)
mask = np.logical_and(freqs_dist >= pspec.low_cut.value,
freqs_dist <= pspec.high_cut.value)
# Scale the colour map to be values within the mask
vmax = np.log10(pspec.ps2D[mask]).max()
vmin = np.log10(pspec.ps2D[mask]).min()
im2 = plt.imshow(np.log10(pspec.ps2D), interpolation="nearest",
origin="lower", vmax=vmax, vmin=vmin)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")