lgage = table['log-age']
lgsfr = np.log10(table['SFR'])
lgms = np.log10(table['M*_tot'])
lgsfr_interp = np.interp(lgAges, lgage, lgsfr)
lgms_interp = np.interp(lgAges, lgage, lgms)
print 10**lgms_interp
lgMs = np.append(lgMs, lgms_interp)
lgSSFR = np.append(lgSSFR, lgsfr_interp - lgms_interp)
lgsSFR = lgSSFR.reshape((13, 7))[None, :, :, None, None]
else:
lgsSFR = np.log10(models.SFR.value) - 9
#==============================================================================
# Mock Observation
#==============================================================================
eazy_library = LoadEAZYFilters(
'C:\Users\mac\Documents\Python Scripts\SPS\FILTER.RES.CANDELS')
print eazy_library.filternames
filters = FilterSet()
filters.addEAZYFilter(eazy_library, range(len(eazy_library.filternames)))
# FUV, NUV, U, B, V, R, I, J, H, K
synphot = Observe(models, filters, redshift=0.001)
mags = synphot.AB[0]
U_V = mags[2] - mags[4]
V_J = mags[4] - mags[7]
FUV_R = mags[0] - mags[5]
R_K = mags[5] - mags[9]
#==============================================================================
# Renormalization
#==============================================================================
use_bc = False
if use_bc:
BC03_out = asc.read('Lilly/CSP result.txt')
lgMs = BC03_out['lgM*'].reshape((M_seed.size, Ages.size)).T
lgSSFR = BC03_out['lgsSFR'].reshape((M_seed.size, Ages.size)).T
Ms = 10**lgMs
SSFR = 10**lgSSFR
#==============================================================================
# Mock Observation
#==============================================================================
eazy_library = LoadEAZYFilters('FILTER.RES.CANDELS')
print eazy_library.filternames
filters = FilterSet()
filters.addEAZYFilter(eazy_library, range(len(eazy_library.filternames)))
Names = ['FUV', 'NUV', 'U', 'B', 'V', 'R', 'I', 'J', 'H', 'K']
synphot = Observe(models, filters, redshift=0.001)
mags = synphot.AB[0]
FUV_V = mags[0] - mags[4]
NUV_V = mags[1] - mags[4]
U_V = mags[2] - mags[4]
V_J = mags[4] - mags[7]
V_K = mags[4] - mags[9]
NUV_R = mags[1] - mags[5]
with quantity_support():
Fix, axes = plt.subplots(SFH.size, 1, figsize=(8, 8))
for i, tau in enumerate(SFH):
ax = axes[i]
for j, T in enumerate(Ages):
key = '%s-%s' % (str(i), str(j))
ax.semilogy(models[key].wave,
np.squeeze(models[key].SED),
label='T = {0:.1f}'.format(T))
ax.set_ylim([1e3, 1e9])
ax.set_xlim([0, 1e4])
ax.legend(loc='best', ncol=2)
ax.set_title(r'$\tau = ${0:.1f}'.format(tau), fontsize=15)
plt.tight_layout()
eazy_library = LoadEAZYFilters('data/FILTER.RES.v8')
eazy_library.search('J')
filters = FilterSet()
filters.addEAZYFilter(eazy_library, [139, 141, 125]) # U, V, J
filters.addTophatFilter(centre=1500 * u.AA, width=150 * u.AA) # FUV
UV = np.empty((SFH.size, Ages.size))
VJ = np.empty((SFH.size, Ages.size))
for i, tau in enumerate(SFH):
for j, T in enumerate(Ages):
key = '%s-%s' % (str(i), str(j))
synphot = Observe(models[key], filters, redshift=0.)