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%")