def get_kcorrections(row):
maggies = np.array(pow(10, -0.4 * row[1:6]))
invar = pow(0.4 * math.log(10) * maggies * np.array(row[6:12]), -2)
k_tuple = np.concatenate(([row[0]], maggies, invar))
kcorrect_tuple = kcorrect.fit_coeffs(k_tuple)
k_coeff = kcorrect.reconstruct_maggies(kcorrect_tuple)
final_input = maggies / np.array(k_coeff[1:])
return [-2.5 * math.log(item, 10) for item in final_input], k_coeff[0]
def looking_at_redshift(z_low,z_high,name):
# setting things up
cosmo = FlatLambdaCDM(H0=70 * u.km / u.s / u.Mpc, Om0=0.3)
kcorrect.load_templates()
kcorrect.load_filters()
# form of input for fit_coeff
# redshift umaggies gmaggies rmaggies imaggies zmaggies uinvvar ginvvar rinvvar iinvvar zinvvar
# output: redshift u_rec g_rec r_rec i_rec z_rec
x = []
y = []
array = np.load('/home/calum/Documents/MPhysProj/mgs_sample/mgs_kcorrect_array.npy')
print('Number of results:',len(array))
print('Starting for loop...')
# need to sort out the coefficents so they're in units of maggies!
#Note that the conversion to the inverse variances from the maggies and the magnitude errors is (0.4 ln(10) × maggies × magerr)-2
# maggies are simply related to magnitudes by 10−0.4m
for row in array:
# kcorrect
maggies = np.array(pow(10,-0.4*row[2:7]))
invar = pow(0.4*math.log(10)*maggies*np.array(row[7:]),-2)
k_tuple = np.concatenate(([row[1]],maggies,invar))
kcorrect_tuple = kcorrect.fit_coeffs(k_tuple)
k_coeff = kcorrect.reconstruct_maggies(kcorrect_tuple)
final_input = maggies/np.array(k_coeff[1:])
kcorrection_array = [-2.5*math.log(item,10) for item in final_input]
# put above in a function probs
y_val = float(row[2]) - kcorrection_array[1] - float(row[4]) + kcorrection_array[3]
z = k_coeff[0]
if y_val > 0 and y_val < 6.5:
if z > z_low and z < z_high:
x_val = float(row[0])-5*(math.log(cosmo.luminosity_distance(z).to(u.pc).value/10 ,10))
if x_val < -15.5 and x_val > -23.5:
x.append(x_val)
y.append(y_val)
contour_plot_array = np.array([x,y])
print('Length of output:',len(x))
np.save('/home/calum/Documents/MPhysProj/data/'+name+'.npy',contour_plot_array)
print('Finished:',name)
maggie_i = 10**((22.5 - data['mag_i'][ind]) / 2.5)
maggie_z = 10**((22.5 - data['mag_z'][ind]) / 2.5)
import kcorrect, numpy
kcorrect.load_templates()
kcorrect.load_filters()
z_arr = data['z'][ind]
for i in range(n_source):
print(i)
a = [
data['z'][ind[i]], maggie_u[i], maggie_g[i], maggie_r[i], maggie_i[i],
maggie_z[i], 6.216309e+16, 3.454767e+17, 1.827409e+17, 1.080889e+16,
3163927000000000.0
]
c = kcorrect.fit_coeffs(a)
m = kcorrect.reconstruct_maggies(c)
maggie_u[i] = m[1]
maggie_g[i] = m[2]
maggie_r[i] = m[3]
maggie_i[i] = m[4]
maggie_z[i] = m[5]
type_arr = np.zeros(len(ind))
type_arr = type_arr - 999
O2_index = np.log10((data['o21'][ind] + data['o22'][ind]) / data['hb'][ind])
O3_index = np.log10(data['o3'][ind] / data['hb'][ind])
sigma_o3 = np.log10(np.sqrt(data['sigma_o3'][ind]**2)) #-55**2))
sigma_star = np.log10(data['VDISP'][ind])
indt = np.where(sigma_star == 0)
array = np.load(
'/home/calum/Documents/Mphys_data/mgs_multiwavelength/final_mgs_array.npy')
print('Number of results:', len(array))
print('Starting for loop...')
# need to sort out the coefficents so they're in units of maggies!
#Note that the conversion to the inverse variances from the maggies and the magnitude errors is (0.4 ln(10) × maggies × magerr)-2
# maggies are simply related to magnitudes by 10−0.4m
for row in array:
# kcorrect
maggies = np.array(pow(10, -0.4 * row[2:7]))
invar = pow(0.4 * math.log(10) * maggies * np.array(row[7:12]), -2)
k_tuple = np.concatenate(([row[1]], maggies, invar))
kcorrect_tuple = kcorrect.fit_coeffs(k_tuple)
k_coeff = kcorrect.reconstruct_maggies(kcorrect_tuple)
final_input = maggies / np.array(k_coeff[1:])
kcorrection_array = [-2.5 * math.log(item, 10) for item in final_input]
# put above in a function probs
y_val = float(row[2]) - kcorrection_array[1] - float(
row[4]) + kcorrection_array[3]
z = k_coeff[0]
if z > 0:
if np.isnan(y_val):
# why wouldn't y_val be a number?
print('do not worry, everything is fine now')
else:
x_val = float(row[0]) - 5 * (math.log(
cosmo.luminosity_distance(z).to(u.pc).value / 10, 10))
obj.append(row[13])
def add_stellar_mass(base, cosmology=WMAP9):
"""
Calculate stellar mass based only on gi colors and redshift and add to base catalog.
Based on GAMA data using Taylor et al (2011).
`base` is modified in-place.
Parameters
----------
base : astropy.table.Table
Returns
-------
base : astropy.table.Table
"""
base['log_sm'] = np.nan
global _HAS_KCORRECT
if not _HAS_KCORRECT:
logging.warn('No kcorrect module. Stellar mass not calculated!')
return base
if 'OBJID_sdss' in base.colnames: # version 2 base catalog with SDSS
postfix = '_sdss'
to_calc_query = Query(C.has_spec, 'OBJID_sdss != -1')
elif 'EXTINCTION_U' in base.colnames: # version 1 base catalog
postfix = ''
to_calc_query = C.has_spec
for b in get_sdss_bands():
base['{}_mag'.format(b)] = base[b] - base['EXTINCTION_{}'.format(
b.upper())]
else: # version 2 base catalog without SDSS
logging.warn('No SDSS bands! Stellar mass not calculated!')
return base
to_calc_mask = to_calc_query.mask(base)
if not to_calc_mask.any():
logging.warn('Stellar mass not calculated because no valid entry!')
return base
if _HAS_KCORRECT != 'LOADED':
kcorrect.load_templates()
kcorrect.load_filters()
_HAS_KCORRECT = 'LOADED'
mag = view_table_as_2d_array(base, ('{}_mag{}'.format(b, postfix)
for b in get_sdss_bands()),
to_calc_mask, np.float32)
mag_err = view_table_as_2d_array(base, ('{}_err{}'.format(b, postfix)
for b in get_sdss_bands()),
to_calc_mask, np.float32)
redshift = view_table_as_2d_array(base, ['SPEC_Z'], to_calc_mask,
np.float32)
# CONVERT SDSS MAGNITUDES INTO MAGGIES
mgy = 10.0**(-0.4 * mag)
mgy_ivar = (0.4 * np.log(10.0) * mgy * mag_err)**-2.0
kcorrect_input = np.hstack((redshift, mgy, mgy_ivar))
# USE ASTROPY TO CALCULATE LUMINOSITY DISTANCE
lf_distmod = cosmology.distmod(redshift.ravel()).value
# RUN KCORRECT FIT, AND CALCULATE STELLAR MASS
tmremain = np.array([0.601525, 0.941511, 0.607033, 0.523732, 0.763937])
sm_not_normed = np.fromiter((np.dot(kcorrect.fit_coeffs(x)[1:], tmremain)
for x in kcorrect_input), np.float64,
len(kcorrect_input))
base['log_sm'][to_calc_mask] = np.log10(sm_not_normed) + (0.4 * lf_distmod)
return base