norm_sync2 = 1.0
alpha_sync2 = -1.2
norm_bb1 = 1.0
temp1 = 40
norm_bb2 = 1.0
temp2 = 60
norm_agn = -9.
alpha_agn = -2.
nu = 10**np.linspace(6, 18, 10000) # frequency range
redshift = [2.0, 2.0, 2.0, 2.0, 2.0]
# generate with the provided model
fnu = md.sync_law(nu, [norm_sync1, alpha_sync1], redshift[0]) + \
md.sync_law(nu, [norm_sync2, alpha_sync2], redshift[1]) + \
md.BB_law(nu, [norm_bb1, temp1], redshift[2]) + \
md.BB_law(nu, [norm_bb2, temp2], redshift[3]) + \
md.AGN_law(nu, [norm_agn, alpha_agn], redshift[4])
# list of the filters
filter_name = np.array([
'VLA_L', 'VLA_C', 'VLA_C', 'VLA_X', 'VLA_X', 'ATCA_47', 'ALMA_3', 'ALMA_6',
'ALMA_6_nr1', 'laboca_870', 'spire_500', 'spire_350', 'spire_250',
'pacs_160', 'pacs_70', 'mips_24', 'irs_16', 'irac_4'
])
data_nature = np.array([
'd', 'd', 'd', 'd', 'd', 'd', 'd', 'd', 'd', 'u', 'u', 'u', 'd', 'd', 'd',
'd', 'd', 'd'
]) # "d" for detections, "u" for upper limit
arrangement = np.array([
'4', '6', '5', '6', '5', '4', '7', '3', '2', '1', '1', '1', '1', '1', '1',
import mm_utilities as mm
import read_files as rd
#def fake_sync_source():
# define the parameters of the model and create
# the normalisation will effectively be values-23 due to the Jansky transformation
alpha = -1.0
norm_sync = 1.0
norm_bb = 1.0
temp = 40
nu = 10**np.linspace(6, 16, 10000) # frequency range
redshift = [0., 0.]
# generate with the provided model
fnu = md.sync_law(nu, [norm_sync, alpha], redshift[0]) + md.BB_law(
nu, [norm_bb, temp], redshift[1])
#print md.sync_law(nu, [norm_sync, alpha], redshift)
#print
#print md.BB_law(nu, [norm_bb, temp], redshift)
# list of the filters
filter_name = np.array([
'74MHz(VLA)', '408MHz', '1.4GHz', '4.85GHz', '8.4GHz', 'ALMA_3', 'ALMA_6',
'laboca_870', 'spire_500', 'spire_350', 'spire_250', 'pacs_160',
'pacs_100', 'pacs_70', 'mips_24'
])
data_nature = np.array([
'd', 'd', 'd', 'd', 'd', 'd', 'd', 'd', 'd', 'd', 'd', 'd', 'd', 'd', 'd'
]) # "d" for detections, "ul" for upper limit
arrangement = np.array([
'1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1'
import mm_utilities as mm
import read_files as rd
norm_sync = 1.0
alpha_sync = -1.0
norm_bb = 1.0
temp = 40
norm_agn = -9.
alpha_agn = -2.
nu = 10**np.linspace(6, 18, 10000) # frequency range
redshift = [0., 0., 0.]
# generate with the provided model
fnu = md.sync_law(nu, [norm_sync, alpha_sync], redshift[0]) + \
md.BB_law(nu, [norm_bb, temp], redshift[1]) + \
md.AGN_law(nu, [norm_agn, alpha_agn], redshift[2])
# list of the filters
filter_name = np.array([
'74MHz(VLA)', '408MHz', 'ALMA_3', 'ALMA_6', 'laboca_870', 'spire_500',
'spire_350', 'spire_250', 'pacs_160', 'pacs_100', 'pacs_70', 'mips_24',
'irac_4', 'irac_3', 'irac_2', 'irac_1'
])
data_nature = np.array([
'd', 'd', 'u', 'd', 'd', 'd', 'd', 'd', 'd', 'd', 'd', 'd', 'd', 'd', 'd',
'd'
]) # "d" for detections, "ul" for upper limit
arrangement = np.array([
'1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1',
'1'