dist_scale = (np.pi / 180.) * gal.distance.to(u.kpc).value
if make_plot:
phys_radius = data["r"] * scale * dist_scale
plot_pars = pars.copy()
plot_pars[2] *= scale * dist_scale
p.errorbar(phys_radius, data["Vt"], yerr=data['eVt'],
fmt='-', color='b', label="This work",
drawstyle='steps-mid')
p.plot(phys_radius, vcirc(phys_radius, *plot_pars), 'r-')
p.ylabel(r"V$_{\mathrm{circ}}$ (km / s)")
p.xlabel(r"Radius (kpc)")
p.grid()
p.draw()
p.savefig(paper1_figures_path("M33_vrot_{}_wfit.pdf".format(params)))
p.savefig(paper1_figures_path("M33_vrot_{}_wfit.png".format(params)))
# raw_input("Next plot?")
p.clf()
# load in the Corbelli curve for comparison
corbelli = Table.read(c_hi_analysispath("rotation_curves/corbelli_rotation_curve.csv"))
p.errorbar(phys_radius, data["Vt"], yerr=data['eVt'],
fmt='-', color='b', label="This work", drawstyle='steps-mid')
p.errorbar(corbelli["R"][corbelli["R"] <= 8.0],
corbelli["Vr"][corbelli["R"] <= 8.0],
yerr=corbelli["dVr"][corbelli["R"] <= 8.0],
fmt='--', color='r', label="Corbelli et al. (2014)",
drawstyle='steps-mid')
color="r", label="CO(2-1)")
# p.plot(bin_centers, hi_vals / np.nanmax(hi_vals), 'bD-',
# label="HI")
# p.plot(bin_centers, co_vals / np.nanmax(co_vals), 'ro-',
# label="CO(2-1)")
# p.xlim([0.0, 200])
p.ylim([0.0, 1.1])
p.xlabel("Distance from mask edge (pc)")
p.ylabel("Normalized Intensity")
p.vlines(0.0, 0.0, 1.1, 'k')
p.legend(loc='upper left')
p.grid()
p.draw()
p.savefig(paper1_figures_path("mask_edge_radial_profiles.pdf"))
p.savefig(paper1_figures_path("mask_edge_radial_profiles.png"))
# raw_input("Next plot?")
p.clf()
# Show the total number of elements in each distance bin
p.semilogy(bin_centers, binned_elements, 'bD-')
p.xlabel("Distance from mask edge (pc)")
p.ylabel("Number of pixels")
p.grid()
p.savefig(paper1_figures_path("mask_edge_radial_profiles_numbin.pdf"))
p.savefig(paper1_figures_path("mask_edge_radial_profiles_numbin.png"))
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
p.tight_layout()
def updater(i):
pb.update()
layer = layers[i]
im = ax.imshow(layer.value, origin='lower',
norm=ImageNormalize(vmin=-0.001,
vmax=mx,
stretch=AsinhStretch()),
cmap=p.cm.gray_r)
# ax.annotate("${0:.0f} km/s$".format(center_vels[i]),
# (0.53, 0.9),
# xycoords='axes fraction', color='k',
# fontsize=15.5)
ani = anim.FuncAnimation(fig, updater, range(len(center_vels)))
# p.show()
p.ioff()
writer = anim.writers['ffmpeg'](fps=3)
ani.save(paper1_figures_path("m33_rotsub_movie.mp4"), writer=writer, dpi=300)
p.ion()
ax = p.subplot(121, projection=skew.wcs)
ax.imshow(skew.value,
origin='lower', vmin=-2, vmax=2,
interpolation='nearest', cmap='seismic')
ax.set_title("Skewness")
ax.set_ylabel("DEC (J2000)")
ax.set_xlabel("RA (J2000)")
ax2 = p.subplot(122, projection=kurt.wcs)
ax2.imshow(kurt.value, vmin=1, vmax=4,
origin='lower', interpolation='nearest', cmap='binary')
ax2.set_title("Kurtosis")
ax2.set_xlabel("RA (J2000)")
lat = ax2.coords[1]
lat.set_ticklabel_visible(False)
p.savefig(paper1_figures_path("skew_kurt_maps.pdf"))
p.savefig(paper1_figures_path("skew_kurt_maps.png"))
# raw_input("Next plot?")
p.clf()
# Interesting regions:
# Northern HI infall: [1373, 630], [1373, 640], [1373, 650]
nplume_positions = \
np.array([[1375, 603], [1375, 613], [1375, 623], [1375, 633], [1375, 643]])
# SArm positions
sarm_positions = np.array([[680, 730], [670, 730], [660, 730], [650, 730],
[640, 730],
[630, 730]])
total_spectrum_hi = total_spectrum_hi * u.K
# Significant CO emission is limited to within about 6 kpc
total_spectrum_co = total_spectrum_co_radial.sum(0)
p.plot(hi_cube.spectral_axis.to(u.km / u.s).value,
total_spectrum_hi.value,
'b-', drawstyle='steps-mid', label="HI")
p.xlabel("Velocity (km/s)")
p.ylabel("Total Intensity (Jy)")
p.xlim([-100, 100])
p.grid()
p.draw()
p.savefig(paper1_figures_path("total_profile_corrected_velocity_rotsub_hi.pdf"))
p.savefig(paper1_figures_path("total_profile_corrected_velocity_rotsub_hi.png"))
# raw_input("Next plot?")
p.clf()
p.plot(hi_cube.spectral_axis.to(u.km / u.s).value,
(total_spectrum_hi / total_spectrum_hi.max()).value,
'b-', drawstyle='steps-mid', label="HI")
# There's a 1 channel offset from my rotation subtraction in the cube
p.plot(co_cube.spectral_axis.to(u.km / u.s).value,
(total_spectrum_co / total_spectrum_co.max()).value,
# np.roll((total_spectrum_co / total_spectrum_co.max()).value, -1),
'g--', drawstyle='steps-mid', label="CO(2-1)")
p.xlabel("Velocity (km/s)")
p.ylabel("Normalized Total Intensity")
# # and save the plot and the parameters shown in verbose mode.
# p.ioff()
# fils.find_widths(verbose=True, max_distance=500, auto_cut=False, try_nonparam=False)
# Fit Parameters: [ 541.31726502 129.85351117 180.0710914 304.01262168
# Fit Errors: [ 0.89151974 0.48394493 0.27313627 1.1462345 ]
skeleton = medial_axis(~bub.mask)
# Overplot the skeleton on the moment0
ax = p.subplot(111, projection=mom0.wcs)
ax.imshow(mom0.value, origin='lower')
ax.contour(skeleton, colors='r')
p.draw()
p.savefig(paper1_figures_path("moment0_w_skeletons.pdf"), rasterize=True)
p.savefig(paper1_figures_path("moment0_w_skeletons.png"))
# raw_input("Next plot?")
p.clf()
pixscale = \
mom0.header['CDELT2'] * (np.pi / 180.) * gal.distance.to(u.pc).value
beam_width = \
mom0.meta['beam'].major.to(u.deg).value * (np.pi / 180.) * \
gal.distance.to(u.pc).value
# HI profile and fit
fit, err, model, profile = \
fit_skeleton_width(skeleton, mom0.value, pixscale, beam_width)
elif i == Nrows - 1:
ax[i, j].xaxis.set_ticks_position('bottom')
p.setp(ax[i, j].get_xticklabels(), visible=True)
else:
ax[i, j].xaxis.set_ticks_position('none')
p.setp(ax[i, j].get_xticklabels(), visible=False)
ax[i, j].xaxis.set_ticklabels([])
if j == 0:
ax[i, j].yaxis.set_ticks_position('left')
elif j == Ncols - 1:
ax[i, j].yaxis.set_ticks_position('right')
p.setp(ax[i, j].get_yticklabels(), visible=False)
ax[i, j].yaxis.set_ticklabels([])
else:
ax[i, j].yaxis.set_ticks_position('none')
p.setp(ax[i, j].get_yticklabels(), visible=False)
ax[i, j].yaxis.set_ticklabels([])
p.subplots_adjust(hspace=0,
wspace=0)
# p.draw()
# raw_input("Next plot?")
p.savefig(paper1_figures_path("M33_rotsub_channels_{}.pdf".format(n)),
bbox_inches="tight", dpi=300)
p.savefig(paper1_figures_path("M33_rotsub_channels_{}.png".format(n)),
bbox_inches="tight", dpi=300)
p.clf()
