def sed_to_alpha(sed, Q):
# Initialize
cs = 3e10 # cm s-1
alpha = 0.0
spitzer_system = convert_lib.set_SCAO()
band_name_list = [
'J',
'H',
'K',
'IR1',
'IR2',
'IR3',
'IR4',
'MP1',
]
log_l_F = []
e_log_l_F = []
log_l = []
yso_class = ''
# Make lambda and flux*lambda list
for i, band_name in enumerate(band_name_list):
# Choose the band that detected.
if Q[i] != 'A' and Q[i] != 'S':
continue
# Index start at 3 because we skip band J, H, and K.
band_flux = sed[i]
e_band_flux = sed[8 + i]
wavelength = 10 * spitzer_system[band_name][1] # um
# Specific freq. to specific flux
cvt = cs / (wavelength**2) # cm-1 s-1
log_l_F.append(np.log(wavelength * cvt * band_flux))
e_log_l_F.append(
np.log(wavelength * (e_band_flux + band_flux) / band_flux))
log_l.append(np.log(wavelength))
print(log_l_F)
print(log_l)
print(e_log_l_F)
w_log_l_F = np.divide(1, np.array(e_log_l_F))
paras = np.polyfit(x=log_l_F, y=log_l, deg=1, w=w_log_l_F)
alpha = paras[0]
# Plot the result for debugging
'''
p = np.poly1d(paras)
x_samples = np.logspace(
np.log10(spitzer_system['IR1'][1]),
np.log10(spitzer_system['MP1'][1]),
10
)
plt.plot(np.log10(x_samples), p(np.log10(x_samples)))
plt.scatter(log_l, log_l_F, c = 'r')
plt.xlabel(r"log($\lambda$)")
plt.ylabel(r"log($\lambda F_{\lambda}$)")
plt.show()
'''
return alpha
#--------------------------------------------
# Main code
if __name__ == "__main__":
VERBOSE = 0
# Measure time
start_time = time.time()
#-----------------------------------
# Load argv
if len(argv) != 2:
print("The number of arguments is wrong.")
print("tbl_to_sed_An_2011.py [An 2011 table]")
exit()
inp_table_name = argv[1]
#-----------------------------------
# Load data from input table
SCAO_system = convert_lib.set_SCAO()
inp_table = np.loadtxt(inp_table_name, dtype=str)
JUmag = np.array(np.transpose([inp_table[:, 4], inp_table[:, 5]]),
dtype=float)
HUmag = np.array(np.transpose([inp_table[:, 6], inp_table[:, 7]]),
dtype=float)
KUmag = np.array(np.transpose([inp_table[:, 8], inp_table[:, 9]]),
dtype=float)
IR1mag = np.array(inp_table[:, 10], dtype=float)
IR2mag = np.array(inp_table[:, 11], dtype=float)
IR3mag = np.array(inp_table[:, 12], dtype=float)
IR4mag = np.array(inp_table[:, 13], dtype=float)
MP1mag = np.array(inp_table[:, 14], dtype=float)
#-----------------------------------
JUflux = convert_lib.ensemble_mag_to_mjy(JUmag, 'J', SCAO_system)
HUflux = convert_lib.ensemble_mag_to_mjy(HUmag, 'H', SCAO_system)
# Measure time
start_time = time.time()
#-----------------------------------
# Load argv
if len(argv) != 5:
print ("The number of arguments is wrong.")
print ("Usage: plot_corner_exU.py [band index code] [sed table] [cls table] [Q table]")
print ("Example: plot_corner_exU.py 678 sed_table.txt cls_table.txt Q_table.txt")
exit()
band_index_code = argv[1]
sed_table_name = argv[2]
cls_table_name = argv[3]
Q_table_name = argv[4]
#-----------------------------------
# Initialize the band system
SCAO_system = set_SCAO()
bands = [
'J',
'H',
'K',
'IR1',
'IR2',
'IR3',
'IR4',
'MP1'
]
#-----------------------------------
# Load data
print("Load data")
sed_table = np.loadtxt(sed_table_name)
cls_table = np.loadtxt(cls_table_name, dtype = int)
def sed_to_alpha(sed, Q):
# Initialize
cs = 3e10 # cm s-1
alpha = 0.0
spitzer_system = convert_lib.set_SCAO()
band_name_list = [
'K',
'IR1',
'IR2',
'IR3',
'IR4',
'MP1',
]
log_l_F = []
e_log_l_F = []
log_l = []
yso_class = ''
# Make lambda and flux*lambda list
for i, band_name in enumerate(band_name_list):
# Choose the band that detected.
if Q[2 + i] != 'A':
continue
# Index start at 3 because we skip band J, H, and K.
band_flux = sed[2 + i]
e_band_flux = sed[8 + 2 + i]
wavelength = 1e-4 * spitzer_system[band_name][1] # um to cm
# Specific freq. to specific flux
cvt = cs / (wavelength**2) # cm-1 s-1
log_l_F.append(np.log(wavelength * cvt * band_flux))
e_log_l_F.append(
np.log(wavelength * (e_band_flux + band_flux) / band_flux))
log_l.append(np.log(wavelength))
w_log_l_F = np.divide(1, np.array(e_log_l_F))
try:
paras = np.polyfit(log_l_F, log_l, 1, w=w_log_l_F)
except:
exit()
# Calculate alpha and derive yso class
alpha = paras[0]
if alpha >= 0.3:
yso_class = 'I'
elif alpha >= -0.3 and alpha < 0.3:
yso_class = 'Flat'
elif alpha >= -1.6 and alpha < -0.3:
yso_class = 'II'
elif alpha < -1.6:
yso_class = 'III'
#--------------------------------------------
# For debugging
# Plot the result for debugging
'''
p = np.poly1d(paras)
x_samples = np.logspace(
np.log10(spitzer_system['IR1'][1]),
np.log10(spitzer_system['MP1'][1]),
10
)
plt.plot(np.log10(x_samples), p(np.log10(x_samples)))
plt.scatter(
log_l,
log_l_F,
c = 'r',
label = r'$\alpha$=%f, Class %s' % (alpha, yso_class))
plt.xlabel(r"log($\lambda$)")
plt.ylabel(r"log($\lambda F_{\lambda}$)")
plt.legend()
plt.show()
'''
return alpha, yso_class
