from analysis.paths import fourteenB_HI_data_path, iram_co21_data_path
from analysis.constants import moment0_name
'''
Reproject the CO to match the HI.
Not taking the beam difference into account here since they are quite close.
Once this is merged https://github.com/astrofrog/reproject/pull/115/files,
make a full regrid of the whole cube.
'''
hi_header = fits.getheader(fourteenB_HI_data_path(moment0_name))
co_ico = fits.open(iram_co21_data_path("m33.ico.fits"))[0]
proj = Projection(co_ico.data.squeeze(),
wcs=WCS(co_ico.header).dropaxis(3).dropaxis(2))
rep_array, footprint = reproject_interp(proj.hdu, hi_header)
new_header = hi_header.copy()
# Need to change some of the properties before saving
keys = ["BMAJ", "BMIN", "BPA", "BUNIT"]
for key in keys:
new_header[key] = co_ico.header[key]
new_hdu = fits.PrimaryHDU(rep_array, header=new_header)
new_hdu.writeto(iram_co21_data_path("m33.ico.hireprojection.fits",
import matplotlib.pyplot as p
from pandas import DataFrame
from astropy.modeling import models, fitting
from astropy.io import fits
from analysis.paths import (fourteenB_HI_data_path, iram_co21_data_path,
paper1_figures_path, paper1_tables_path)
from analysis.constants import (rotsub_cube_name, rotsub_mask_name,
co21_mass_conversion, hi_freq)
from analysis.galaxy_params import gal
'''
Create profiles of HI and CO after rotation subtraction.
'''
co_cube = SpectralCube.read(iram_co21_data_path("m33.co21_iram.rotsub.fits"))
# mask = fits.getdata(iram_co21_data_path("m33.co21_new_assign_clfind.fits"))
# co_cube = co_cube.with_mask(mask.astype(bool))
hi_cube = SpectralCube.read(fourteenB_HI_data_path(rotsub_cube_name))
hi_mask = fits.open(fourteenB_HI_data_path(rotsub_mask_name))[0]
hi_cube = hi_cube.with_mask(hi_mask.data > 0)
hi_radius = gal.radius(header=hi_cube.header)
co_radius = gal.radius(header=co_cube.header)
# Perform the same analysis split up into radial bins
dr = 500 * u.pc
max_radius = (6.0 * u.kpc).to(u.pc)
To avoid a whole cube regrid, I use the rotation subtracted cubes and extract
zeroth moments from spectral slabs.
'''
np.random.seed(34678953)
# Plot a bunch
verbose = False
# slicer = (slice(825, 1033), slice(360, 692))
slicer = (slice(None), slice(None))
# Load in the rotation subtracted cubes
hi_cube = SpectralCube.read(fourteenB_HI_data_path(rotsub_cube_name))
co_cube = SpectralCube.read(iram_co21_data_path("m33.co21_iram.rotsub.fits"))
start_vel = - 30 * u.km / u.s
end_vel = 30 * u.km / u.s
del_vel = 3. * u.km / u.s
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 = []
p.savefig(paper1_figures_path("filfinder_radial_profile_hi.png"))
fraction_in_peak = np.sqrt(2 * np.pi) * (fit[0] - fit[2]) * \
(fit[1] / pixscale) * skeleton.sum() / np.nansum(mom0.value)
print("Fraction of emission above the background level: {}"
.format(fraction_in_peak))
# 0.543
# raw_input("Next plot?")
p.clf()
# Now use the regridded integrated CO map with the same skeleton
# Created with co_comparison/co_reproject.py
mom0_co_fits = \
fits.open(iram_co21_data_path("m33.ico.hireprojection.fits"))[0]
# Correction for beam efficiency added in.
mom0_co = \
Projection(mom0_co_fits.data / 0.75, wcs=WCS(mom0_co_fits.header))
mom0_co.meta['beam'] = Beam.from_fits_header(mom0_co_fits.header)
# On the same grid, so pixscales are the same
beam_width_co = \
mom0_co.meta['beam'].major.to(u.deg).value * (np.pi / 180.) * \
gal.distance.to(u.pc).value
fit_co, err_co, model_co, profile_co = \
fit_skeleton_width(skeleton, mom0_co.value, pixscale, beam_width_co)
# p.errorbar(profile_co[0], profile_co[1], yerr=profile_co[2], fmt="D",
# color="b")
