def z_angular_mpc(zmean):
'''returns the minimum angular cut to be applied
in order to discard nonlinearities
'''
cosmo = LambdaCDM(H0=67, Om0=0.319, Ode0=0.681, Ob0=0.049)
angle = cosmo.arcsec_per_kpc_comoving(zmean).value * 12000. / 60
return angle
'fits_filename' : 'hlsp_candels_hst_wfc3_gs-tot_f160w_v1.0_drz'
}
}
}
for field in list(FIELDS.keys()):
for filter in FIELDS[field]:
FIELDS[field][filter]['data'] = fits.getdata(f'/home/ppxlf2/CANDELS/{field}/{FIELDS[field][filter]["fits_filename"]}.fits', memmap=True)
with open('resources/wcs_memory.pickle', 'rb') as handle:
WCS_MEMORY = pickle.load(handle)
_h = 0.6774
SKIRT_Z0 = 0.002265
SKIRT_OUTSIZE = 190
FOV = 60
COSMO = LambdaCDM(H0=100*_h, Om0=0.3089, Ob0=0.0486, Ode0=0.6911, Tcmb0=2.73)
ps = FOV * u.kpc * COSMO.arcsec_per_kpc_comoving(SKIRT_Z0)/SKIRT_OUTSIZE
""" UNITS """
SIM_UNITS = u.W / u.m**2 / u.arcsecond**2 * ps**2 # units of the datacube produced by SKIRT
OUT_UNITS = u.Jy * u.Hz / u.micron
SNAP_MAP = np.load('resources/snapTNG.npy')
REFERENCE_Z = 0.5
from astropy.convolution import convolve
from astropy import constants as const
from scipy import ndimage as ndi
from scipy.stats import median_abs_deviation
from pyphot import Sun, unit
lib = pyphot.get_library()
import sys
import glob
skirt_z0 = 0.002265
cosmo = LambdaCDM(H0=100 * h, Om0=0.3089, Ob0=0.0486, Ode0=0.6911, Tcmb0=2.73)
ps = 60 * u.kpc * cosmo.arcsec_per_kpc_comoving(0.002265) / 319 / u.pix
def to_Jansky(l, cube):
return (cube.T * (ps.value**2 * u.W / u.m**2 /
(const.c / (l * u.micron).to(u.m))).to(u.Jy)).T
def to_ergs(l, cube):
return (cube.T * (ps.value**2 * u.W / u.m**2 /
(u.AA)).to(u.erg / u.cm**2 / u.AA / u.s)).T
def to_eps(band, header):
band = band.to(u.erg / u.cm**2 / u.AA / u.s,
equivalencies=u.spectral_density(u.AA, header['PHOTPLAM']))
