# 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)
# Need a mask from the HI
# Adjust the sigma in a single channel to the moment0 in the slab
# sigma = 0.00152659 * hi_slab.shape[0] * \
"""
Create the bubble catalogue of M33 with the 14B-088 map
"""
data_path = "/media/eric/MyRAID/M33/14B-088/HI/full_imaging/"
# data_path = "/lustre/home/ekoch/m33/"
# Prefix for save files
name = "M33_14B-088"
cube = SpectralCube.read(os.path.join(data_path, "M33_14B-088_HI.clean.image.pbcov_gt_0.3_masked.fits"))
lwidth = fits.getdata(os.path.join(data_path, "M33_14B-088_HI.clean.image.pbcov_gt_0.3_masked.rotsub.lwidth.fits"))
sigma = sig_clip(cube[-1].value, nsig=10)
# Create a cruddy linewidth map. This will need to be improved, but that can
# be done with just the saved bubble objects
# lwidth = cube.with_mask(cube > 2 * sigma * u.Jy).linewidth_sigma()
lwidth = lwidth * u.m / u.s
galaxy_props = {
"center_coord": SkyCoord(23.461667, 30.660194, unit=(u.deg, u.deg), frame="fk5"),
"inclination": 56.0 * u.deg,
"position_angle": 201.0 * u.deg,
"scale_height": 100.0 * u.pc,
}
scales = 3.0 * np.arange(1, 15, np.sqrt(2))
# data_path = "/Users/eric/Data/"
# data_path = "/media/eric/Data_3/VLA/THINGS/HO_II/"
# cube = SpectralCube.read(os.path.join(data_path, "IC1613_NA_ICL001.fits"))
cube = SpectralCube.read(os.path.join(data_path, "IC10_NA_ICL001.fits"))
# cube = SpectralCube.read(os.path.join(data_path, "M33_14B-088_HI.clean.image.pbcov_gt_0.3_masked.fits"))
# cube = SpectralCube.read(os.path.join(data_path, "HO_II_NA_CUBE_THINGS.FITS"))
# Remove empty channels
# cube = cube[:, 500:1500, 500:1500]
# cube = cube[:, 500:1500, 500:1500]
scales = 3. * np.arange(1, 15, np.sqrt(2))
# Find sigma in an empty channel
# sigma = sig_clip(cube[-1].value, nsig=10)
# bubble_15 = BubbleFinder2D(cube[200], channel=15, sigma=sigma, auto_cut=True,
# scales=scales)
sigma = sig_clip(cube[0].value, nsig=10)
bubble_15 = BubbleFinder2D(cube[53], channel=15, sigma=sigma, auto_cut=True)
bubble_15.multiscale_bubblefind(overlap_frac=0.5,
edge_find=True,
nsig=1.5,
verbose=False,
min_in_mask=0.75)
bubble_15.visualize_regions(show=True,
edges=True,
region_col='b',
edge_col='g')