vels = np.arange(start_vel.value, end_vel.value + del_vel.value,
del_vel.value) * u.km / u.s
# Get the radius array so we can cut to where the CO data is valid
radii = gal.radius(header=hi_cube[0].header)
max_radius = 6.0 * u.kpc
all_dists = []
all_radii = []
all_vals_hi = []
all_vals_co = []
edge_masks = []
skeletons = []
hi_beam = average_beams(hi_cube.beams)
# Estimate the noise level in an equivalent slab
hi_mom0 = hi_cube.spectral_slab(-180 * u.km / u.s, -183 * u.km / u.s).moment0()
sigma = sig_clip(hi_mom0.value, nsig=10) * \
hi_beam.jtok(hi_freq).value
for i, (end, start) in enumerate(ProgressBar(zip(vels[1:], vels[:-1]))):
hi_slab = hi_cube.spectral_slab(start, end)
hi_mom0 = hi_slab.moment0() * hi_beam.jtok(hi_freq) / u.Jy
# Make the CO slab, then reproject onto the HI grid
co_mom0 = co_cube.spectral_slab(start, end).moment0()
co_mom0_reproj = reproject_interp(co_mom0.hdu, hi_mom0.header)[0]
co_mom0 = Projection(co_mom0_reproj, wcs=hi_mom0.wcs)
'''
cube_file = fourteenB_HI_data_path(rotsub_cube_name)
cube = SpectralCube.read(cube_file)
# Begin channel map code here
# integrate over velocities to make channel maps of a set width
vstart = 308 # channels
vend = 788
vstep = 10
all_slabs = np.arange(vstart, vend + vstep, vstep, dtype=int)
# Define the average beam
beam = average_beams(cube.beams)
layers = \
[cube[start:end].moment0().value *
beam.jtok(hi_freq) / 1000. * u.km / u.s
for start, end in zip(all_slabs[:-1], all_slabs[1:])]
# Scale all to the maximum
mx = np.max([np.nanmax(x).value for x in layers])
spec_axis = cube.spectral_axis.to(u.km / u.s).value
center_vels = [(spec_axis[start] + spec_axis[end]) / 2. for start, end in
zip(all_slabs[:-1], all_slabs[1:])]
pb = ProgressBar(len(center_vels))
# p.plot(rs_n.value, sd_n.value, "bD-", label="North")
# p.plot(rs_s.value, sd_s.value, "go-", label="South")
p.ylabel(r"log $\Sigma$ (M$_{\odot}$ pc$^{-2}$)")
p.xlabel(r"R (kpc)")
p.legend(loc='best')
p.grid("on")
p.savefig(paper1_figures_path("M33_Sigma_profile_co21_N_S_dr_{}pc.pdf".format(dr.value)))
p.savefig(paper1_figures_path("M33_Sigma_profile_co21_N_S_dr_{}pc.png".format(dr.value)))
p.close()
# p.show()
# Now get the HI profile on the same scales
proj = Projection(mom0_hi.data * u.Jy * u.m / u.s, meta={'beam': average_beams(hi_cube.beams)},
wcs=WCS(cube[0].header))
rs_hi, sd_hi, sd_sigma_hi = surfdens_radial_profile(gal, cube=hi_cube,
mom0=proj,
max_rad=6 * u.kpc, dr=dr,
beam=average_beams(hi_cube.beams))
# Apply scaling factor
# sd_hi /= 1.45
# sd_sigma_hi /= 1.45
# Overplot these two.
p.errorbar(rs.value, np.log10(sd.value),
yerr=0.434 * sd_sigma.value / sd.value, fmt="-", color="b",
label=r"H$_2$", drawstyle='steps-mid')
p.errorbar(rs_hi.value, np.log10(sd_hi.value),
yerr=0.434 * sd_sigma_hi.value / sd_hi.value, fmt="--", color="g",
# raw_input("Next plot?")
p.clf()
# Zoom in on the interesting portion of the spectrum
spectral_cuts = np.array([[-72, -180], [-200, -280], [-220, -315]]) * \
u.km / u.s
# Now save spectra going through the interesting regions:
for posns, cuts, name in zip(spec_posns, spectral_cuts, names):
num_posns = len(posns)
fig, axes = p.subplots(num_posns, 1, sharey=True, sharex=False,
figsize=(6, 10))
for i, (posn, ax) in enumerate(zip(posns, axes)):
spec = cube.spectral_slab(cuts[0], cuts[1])[:, posn[0], posn[1]]
spec = spec.to(u.K, equivalencies=average_beams(cube.beams).jtok_equiv(hi_freq))
velocities = cube.spectral_slab(cuts[0], cuts[1]).spectral_axis.to(u.km / u.s).value
ax.plot(velocities, spec.value, 'b-', drawstyle='steps-mid')
if i < num_posns - 1:
ax.set_xticklabels([])
else:
ax.set_xlabel("Velocity (km/s)")
for label in ax.get_xticklabels()[1::2]:
label.set_visible(False)
for i, ax in enumerate(axes):
if i == 0:
for label in ax.get_yticklabels()[1::2]:
label.set_visible(False)
else:
for label in ax.get_yticklabels():
bad_posns = \
list(set(np.arange(cube.shape[0])) - set(list(chain(*sequences))))
mask[:, i, j][bad_posns] = False
if verbose:
p.plot(spectrum)
p.plot(smoothed)
p.vlines(np.where(mask[:, i, j])[0][-1], 0, np.nanmax(spectrum))
p.vlines(np.where(mask[:, i, j])[0][0], 0, np.nanmax(spectrum))
p.plot(smoothed * mask[:, i, j], 'bD')
p.draw()
raw_input("Next spectrum?")
kernel = average_beams(cube.beams).as_tophat_kernel(pixscale)
kernel_pix = (kernel.array > 0).sum()
for i in ProgressBar(mask.shape[0]):
mask[i] = nd.binary_opening(mask[i], kernel)
mask[i] = nd.binary_closing(mask[i], kernel)
mask[i] = mo.remove_small_objects(mask[i], min_size=kernel_pix,
connectivity=2)
new_header = cube.header.copy()
new_header["BUNIT"] = ""
new_header["BITPIX"] = 8
mask_hdu = fits.PrimaryHDU(mask.astype('>i2'), header=new_header)
mask_hdu.writeto(fourteenB_HI_data_path(mask_name, no_check=True),