def observed_to_comoving_grid(obins, nurest, cosmo):
"""
Convert an observed (ang x ang x freq) grid to a comoving grid
APPROXIMATION: Assume a single (mean) redshift for the entire cube, for converting angle to transverse comoving distance.
Parameters
----------
obins : tuple or list of arrays, length (M+1, N+1, P+1)
bin edges of angular and frequency directions
nurest : float
observed (redshifted) frequency of mapped line at z=0
cosmo :
"""
logging.info("Converting (ang x ang x frq) grid to comoving grid...")
xa, ya, zf = obins
redshift = nurest/zf - 1.
redshift_avg = np.mean(redshift)
depth_coMpc = fn.z_to_cmpc(redshift, cosmo)
xc = fn.arcmin_to_cmpc(xa, redshift_avg, cosmo)
yc = fn.arcmin_to_cmpc(ya, redshift_avg, cosmo)
zc = np.linspace(np.min(depth_coMpc), np.max(depth_coMpc), depth_coMpc.size)
logging.info(" x : (%8.2f to %8.2f arcmin) --> (%8.2f to %8.2f Mpc)" % (np.min(xa), np.max(xa), np.min(xc), np.max(xc)))
logging.info(" y : (%8.2f to %8.2f arcmin) --> (%8.2f to %8.2f Mpc)" % (np.min(ya), np.max(ya), np.min(yc), np.max(yc)))
logging.info(" z : (%8.2f to %8.2f GHz ) --> (%8.2f to %8.2f Mpc)" % (np.min(zf), np.max(zf), np.min(zc), np.max(zc)))
logging.info("")
return xc, yc, zc
def observed_to_covolume_grid(obins, nurest, cosmo):
"""Convert angxangxfreq bins to grid of comoving cell volumes.
Parameters
----------
obins : 3-tuple of 1d arrays
arrays of bin edges in (arcmin, arcmin, and [freq]) respectively
nurest : float
rest frame frequency of spectral line of interest
cosmo :
Returns
-------
vol : 3d array
3d array of volume elements, in Mpc^3
"""
xo, yo, zo = obins
zred_bins = nurest / zo - 1. # 1d array of redshift bin edges, in depth direction
zred_mid = _bin_midpoints(
zred_bins) # 1d array of redshift bin midpoints, in depth direction
zcobins = fn.z_to_cmpc(zred_bins,
cosmo) # Bin edges in z-direction [comoving Mpc]
dzco = _bin_lengths(zcobins) # Bin lengths in z-direction [comoving Mpc]
# Check that binning is equal in x and y directions
assert np.allclose((xo[1] - xo[0]), _bin_lengths(xo))
assert np.allclose((yo[1] - yo[0]), _bin_lengths(yo))
dxa = xo[1] - xo[0] # single bin length, in x[arcmin]
dya = yo[1] - yo[0] # single bin length, in y[arcmin]
dxco = fn.arcmin_to_cmpc(
dxa, zred_mid, cosmo
) # Bin lengths in x-direction, sampled along the z-direction [co-moving Mpc]
dyco = fn.arcmin_to_cmpc(
dya, zred_mid, cosmo
) # Bin lengths in y-direction, sampled along the z-direction [co-moving Mpc]
dvco_1d = np.abs(dxco * dyco * dzco)
vol = np.tile(dvco_1d,
(xo.size - 1, yo.size - 1,
1)) # Create 3D array of comoving element volumes [Mpc^3]
return vol
def observed_to_covolume_grid(obins, nurest, cosmo):
"""Convert angxangxfreq bins to grid of comoving cell volumes.
Parameters
----------
obins : 3-tuple of 1d arrays
arrays of bin edges in (arcmin, arcmin, and [freq]) respectively
nurest : float
rest frame frequency of spectral line of interest
cosmo :
Returns
-------
vol : 3d array
3d array of volume elements, in Mpc^3
"""
xo, yo, zo = obins
zred_bins = nurest/zo - 1. # 1d array of redshift bin edges, in depth direction
zred_mid = _bin_midpoints(zred_bins) # 1d array of redshift bin midpoints, in depth direction
zcobins = fn.z_to_cmpc(zred_bins, cosmo) # Bin edges in z-direction [comoving Mpc]
dzco = _bin_lengths(zcobins) # Bin lengths in z-direction [comoving Mpc]
# Check that binning is equal in x and y directions
assert np.allclose( (xo[1]-xo[0]), _bin_lengths(xo) )
assert np.allclose( (yo[1]-yo[0]), _bin_lengths(yo) )
dxa = xo[1]-xo[0] # single bin length, in x[arcmin]
dya = yo[1]-yo[0] # single bin length, in y[arcmin]
dxco = fn.arcmin_to_cmpc(dxa, zred_mid, cosmo) # Bin lengths in x-direction, sampled along the z-direction [co-moving Mpc]
dyco = fn.arcmin_to_cmpc(dya, zred_mid, cosmo) # Bin lengths in y-direction, sampled along the z-direction [co-moving Mpc]
dvco_1d = np.abs(dxco*dyco*dzco)
vol = np.tile(dvco_1d, (xo.size-1, yo.size-1, 1)) # Create 3D array of comoving element volumes [Mpc^3]
return vol
def observed_to_comoving_grid(obins, nurest, cosmo):
"""
Convert an observed (ang x ang x freq) grid to a comoving grid
APPROXIMATION: Assume a single (mean) redshift for the entire cube, for converting angle to transverse comoving distance.
Parameters
----------
obins : tuple or list of arrays, length (M+1, N+1, P+1)
bin edges of angular and frequency directions
nurest : float
observed (redshifted) frequency of mapped line at z=0
cosmo :
"""
logging.info("Converting (ang x ang x frq) grid to comoving grid...")
xa, ya, zf = obins
redshift = nurest / zf - 1.
redshift_avg = np.mean(redshift)
depth_coMpc = fn.z_to_cmpc(redshift, cosmo)
xc = fn.arcmin_to_cmpc(xa, redshift_avg, cosmo)
yc = fn.arcmin_to_cmpc(ya, redshift_avg, cosmo)
zc = np.linspace(np.min(depth_coMpc), np.max(depth_coMpc),
depth_coMpc.size)
logging.info(" x : (%8.2f to %8.2f arcmin) --> (%8.2f to %8.2f Mpc)" %
(np.min(xa), np.max(xa), np.min(xc), np.max(xc)))
logging.info(" y : (%8.2f to %8.2f arcmin) --> (%8.2f to %8.2f Mpc)" %
(np.min(ya), np.max(ya), np.min(yc), np.max(yc)))
logging.info(" z : (%8.2f to %8.2f GHz ) --> (%8.2f to %8.2f Mpc)" %
(np.min(zf), np.max(zf), np.min(zc), np.max(zc)))
logging.info("")
return xc, yc, zc
