def mag_in_filter(filter, spectrum, redshift):
"""
Same as before just does not use em line.
"""
f_lam = 0
weight = 0
if redshift == 0: redshift = 1e-8
# calculate distances
dp = 1e-3*2.99792458e8*cc.proper_distance(H_0, omega_m0, omega_r0, omega_lam0, 1/(1 + redshift))[0] * 1e6 * 3.09e18 # in cm
dl = (1 + redshift) * dp
# redshift spectrum
# This part is for BC03 spectra
lum_lam = spectrum['flux'] * solar_lum # bc03 spectra are in solar lum per angstrom
flux_redshifted = lum_lam / (4 * np.pi * dl**2 * (1+redshift))
lam_redshifted = spectrum['lambda'] * (1+redshift)
# calculate magnitude
# filter['lambda'] is in nm # convert all to angstroms
for i in range(len(filter)):
lam_index = np.argmin(abs(lam_redshifted - (filter['lambda'][i]*10)))
# the spectrum's index for the wavelength of the throughput curve
f_lam += flux_redshifted[lam_index] * lam_redshifted[lam_index]**2 * filter['throughput'][i] *\
np.exp(-1 * igm_trans(lam_redshifted[lam_index], redshift)) / (3631 * c * 1e-23)
# in the above expression: wavelength in angstroms, flux density in ergs/s/cm^2/angstrom, filter throughput in fractions
weight += filter['throughput'][i]
#print f_lam, weight
mag = -2.5 * np.log10(f_lam/weight)
return mag
def mag_in_filter_line(filter, spectrum, redshift, ew):
f_lam = 0
weight = 0
if redshift == 0: redshift = 1e-8
# calculate distances
dp = 1e-3*2.99792458e8*cc.proper_distance(H_0, omega_m0, omega_r0, omega_lam0, 1/(1 + redshift))[0] * 1e6 * 3.09e18 # in cm
dl = (1 + redshift) * dp
# redshift spectrum
# This part is for BC03 spectra
lum_lam = spectrum['flux'] * solar_lum # bc03 spectra are in solar lum per angstrom
flux_redshifted = lum_lam / (4 * np.pi * dl**2 * (1+redshift))
lam_redshifted = spectrum['lambda'] * (1+redshift)
# add lines and check if it indeed becomes bluer in the corresponding filter
lya_redshifted = 1215.67 * (1 + redshift)
redshifted_lines = np.array([lya_redshifted], dtype='float64') # the len of this arr is the num of lines you want to add
ew_rest = np.array([ew]) # rest frame eq width # this is assumed
ew_obs = np.array([ew_rest * (1 + redshift)]) # this should have the same len as the redshifted_lines array
# observed eq width
# convert lam and flux arr to numpy arrays with dtype-'float64'
# they were 'float32' before
lam_redshifted = np.array([lam_redshifted], dtype='float64')
flux_redshifted = np.array([flux_redshifted], dtype='float64')
ind_arr = np.array([np.argmin(abs(lam_redshifted - redshifted_lines))])
lam_redshifted = np.insert(lam_redshifted, ind_arr, redshifted_lines)
flux_to_add = np.array([flux_redshifted[0][ind_arr[i]] * 2 * ew_obs[i] / \
(lam_redshifted[ind_arr[i] + 1] - lam_redshifted[ind_arr[i] - 1]) for i in range(len(ew_obs))])
flux_redshifted = np.insert(flux_redshifted, ind_arr, flux_to_add)
print ew_obs
print flux_to_add, flux_redshifted[ind_arr] * 2 * ew_obs / (lam_redshifted[ind_arr + 1] - lam_redshifted[ind_arr - 1])
for i in range(len(filter)):
lam_index = np.argmin(abs(lam_redshifted - (filter['lambda'][i]*10))) # the spectrum's index for the wavelength of the throughput curve
f_lam += flux_redshifted[lam_index] * lam_redshifted[lam_index]**2 * filter['throughput'][i] *\
np.exp(-1 * igm_trans(lam_redshifted[lam_index], redshift)) / (3631 * c * 1e-23)
# in the above expression: wavelength in angstroms, flux density in ergs/s/cm^2/angstrom, filter throughput in fractions
weight += filter['throughput'][i]
mag = -2.5 * np.log10(f_lam/weight)
return mag
def mag_in_filter(filter, spectrum, redshift):
"""
Same as before just does not use em line.
"""
f_lam = 0
weight = 0
if redshift == 0: redshift = 1e-8
# calculate distances
dp = 1e-3 * 2.99792458e8 * cc.proper_distance(
H_0, omega_m0, omega_r0, omega_lam0, 1 /
(1 + redshift))[0] * 1e6 * 3.09e18 # in cm
dl = (1 + redshift) * dp
# redshift spectrum
# This part is for BC03 spectra
lum_lam = spectrum[
'flux'] * solar_lum # bc03 spectra are in solar lum per angstrom
flux_redshifted = lum_lam / (4 * np.pi * dl**2 * (1 + redshift))
lam_redshifted = spectrum['lambda'] * (1 + redshift)
# calculate magnitude
# filter['lambda'] is in nm # convert all to angstroms
for i in range(len(filter)):
lam_index = np.argmin(abs(lam_redshifted - (filter['lambda'][i] * 10)))
# the spectrum's index for the wavelength of the throughput curve
#print lam_redshifted[lam_index], filter['lambda'][i]*10, flux_redshifted[lam_index]
f_lam += flux_redshifted[lam_index] * lam_redshifted[lam_index]**2 * filter['throughput'][i] *\
np.exp(-1 * igm_trans(lam_redshifted[lam_index], redshift)) / (3631 * c * 1e-23)
# in the above expression: wavelength in angstroms, flux density in ergs/s/cm^2/angstrom, filter throughput in fractions
weight += filter['throughput'][i]
#print sum(flux_redshifted), '\n'
#print f_lam, weight, '\n'
mag = -2.5 * np.log10(f_lam / weight)
return mag
solar_lum = 3.86e33
#spec_l_nu = spec['Flux'] * solar_lum * (spec['Lambda'])**2 / 2.99792458e18
spec_l_lam = spec['Flux'] * solar_lum
# Convert to Flux
# Redshift template spectra
# Redshift range
test_z = np.array((0.5, 1.0, 2.0, 3.0, 5.0))
fig1 = plt.figure()
ax1 = fig1.add_subplot(111, title = '$Redshifted\ Template\ Spectra$')
ax1.set_xlabel('$\lambda\ [\mu m]$', fontsize=14)
ax1.set_ylabel('$F_{\lambda}\ [erg\ s^{-1}\ cm^{-2}\ \AA^{-1}]$', fontsize=14)
ax1.set_yscale('log')
plot_labels = ['0.5', '1.0', '2.0', '3.0', '5.0']
for count in range(len(test_z)):
f_nu = 0
redshift = test_z[count]
dp = 1e-3*3e8*cc.proper_distance(H_0, omega_m0, omega_r0, omega_lam0, 1/(1 + redshift))[0] * 1e6 * 3.09e18 # in cm
dl = (1 + redshift) * dp
f_lam = spec_l_lam / (4 * np.pi * dl**2 * (1 + redshift))
ax1.plot(spec['Lambda'] * (1 + redshift)/1e4, f_lam, label=plot_labels[count])
ax1.set_xlim(0.07, 3)
ax1.minorticks_on()
ax1.tick_params('both', width=1, length=3, which='minor')
ax1.tick_params('both', width=1, length=4.7, which='major')
ax1.legend(loc=0, numpoints=1, prop={'size':14})
plt.show()
def mag_in_filter_line(filter, spectrum, redshift, ew):
"""
Takes in f_lambda in the rest frame and the throughput
curve of the filter and spits out the AB magnitude of the
given spectrum in the filter.
Also needs redshift.
It can also use SDSS spectra which won't need to be redshifted
and calculate magnitudes in chosen filters with or without lines
added.
"""
f_lam = 0
weight = 0
if redshift == 0: redshift = 1e-8
# calculate distances
dp = 1e-3 * 2.99792458e8 * cc.proper_distance(
H_0, omega_m0, omega_r0, omega_lam0, 1 /
(1 + redshift))[0] * 1e6 * 3.08567758e18 # in cm
dl = (1 + redshift) * dp
# redshift spectrum
# This part is for BC03 spectra
lum_lam = spectrum[
'flux'] * solar_lum # bc03 spectra are in solar lum per angstrom
flux_redshifted = lum_lam / (4 * np.pi * dl**2 * (1 + redshift))
lam_redshifted = spectrum['lambda'] * (1 + redshift)
# add lines and check if it indeed becomes bluer in the corresponding filter
lya_redshifted = 1215.67 * (1 + redshift)
redshifted_lines = np.array(
[lya_redshifted], dtype='float64'
) # the len of this arr is the num of lines you want to add
ew_rest = np.array([ew]) # rest frame eq width # this is assumed
ew_obs = np.array([
ew_rest * (1 + redshift)
]) # this should have the same len as the redshifted_lines array
# observed eq width
# convert lam and flux arr to numpy arrays with dtype-'float64'
# they were 'float32' before
lam_redshifted = np.array([lam_redshifted], dtype='float64')
flux_redshifted = np.array([flux_redshifted], dtype='float64')
flux_redshifted_noline = flux_redshifted
ind_arr = np.array([np.argmin(abs(lam_redshifted - redshifted_lines))])
lam_redshifted = np.insert(lam_redshifted, ind_arr[0], redshifted_lines)
print lam_redshifted[150:155]
#print lam_redshifted[ind_arr[0] + 1] - lam_redshifted[ind_arr[0] - 1]
flux_to_add = np.array([flux_redshifted[0][ind_arr[i]] * 2 * ew_obs[i] / \
(lam_redshifted[ind_arr[i] + 1] - lam_redshifted[ind_arr[i] - 1]) for i in range(len(ew_obs))])
flux_redshifted = np.insert(flux_redshifted, ind_arr[0], flux_to_add)
#print ind_arr
print flux_to_add
#print flux_redshifted[150:155]
if flux_to_add < 0:
print flux_to_add, "Error! Negative flux for emission line"
# calculate magnitude
# filter['lambda'] is in nm # convert all to angstroms
count_for_noline = 0
f_lam_noline = 0
for i in range(len(filter)):
lam_index = np.argmin(abs(lam_redshifted - (filter['lambda'][i] * 10)))
# the spectrum's index for the wavelength of the throughput curve
f_lam += flux_redshifted[lam_index] * lam_redshifted[lam_index]**2 * filter['throughput'][i] *\
np.exp(-1 * igm_trans(lam_redshifted[lam_index], redshift)) / (3631 * c * 1e-23)
f_lam_noline += flux_redshifted_noline[0][lam_index] * lam_redshifted[lam_index]**2 * filter['throughput'][i] *\
np.exp(-1 * igm_trans(lam_redshifted[lam_index], redshift)) / (3631 * c * 1e-23)
# in the above expression: wavelength in angstroms, flux density in ergs/s/cm^2/angstrom, filter throughput in fractions
if f_lam != f_lam_noline:
#print i
print lam_redshifted[lam_index], filter['lambda'][i] * 10
print filter['throughput'][i]
print flux_redshifted[lam_index - 2:lam_index +
3], flux_redshifted_noline[0][lam_index -
2:lam_index +
3]
print f_lam, f_lam_noline
print '\n'
break
weight += filter['throughput'][i]
count_for_noline += 1
# for debugging try print flam, ON EACH STEP, using flux_redshifted with line and
# flux_redshifted without the line (along with the corresponding lambdas of course)
# and check the step where it starts deviating
# also add line to flat spectrum which has all values zero
# This part is for SDSS spectra
"""
sdss_spectrum = spectrum
flux_redshifted = sdss_spectrum['flux'] * 1e-17
lam_redshifted = np.power(10, sdss_spectrum['loglam'])
# add lines and check if it indeed becomes bluer in the corresponding filter
index1 = np.argmin(abs(lam_redshifted - 7000))
index2 = np.argmin(abs(lam_redshifted - 6200))
lam_redshifted = np.array([lam_redshifted], dtype='float64')
flux_redshifted = np.array([flux_redshifted], dtype='float64')
lam_redshifted = np.insert(lam_redshifted, [index1, index2], [7000.0, 6200])
flux_redshifted = np.insert(flux_redshifted, [index1, index2],\
[flux_redshifted[0][index1] * 236, flux_redshifted[0][index2] * 472])
for i in range(len(filter)):
lam_index = np.argmin(abs(lam_redshifted - (filter['lambda'][i]*10)))
f_lam += flux_redshifted[lam_index] * lam_redshifted[lam_index]**2 * filter['throughput'][i] / (3631 * c * 1e-23)
weight += filter['throughput'][i]
"""
#print sum(flux_redshifted)
#print flux_to_add
#print f_lam, weight
mag = -2.5 * np.log10(f_lam / weight)
return mag
# Convert to Flux
# Redshift template spectra
# Redshift range
test_z = np.array((0.5, 1.0, 2.0, 3.0, 5.0))
fig1 = plt.figure()
ax1 = fig1.add_subplot(111, title='$Redshifted\ Template\ Spectra$')
ax1.set_xlabel('$\lambda\ [\mu m]$', fontsize=14)
ax1.set_ylabel('$F_{\lambda}\ [erg\ s^{-1}\ cm^{-2}\ \AA^{-1}]$', fontsize=14)
ax1.set_yscale('log')
plot_labels = ['0.5', '1.0', '2.0', '3.0', '5.0']
for count in range(len(test_z)):
f_nu = 0
redshift = test_z[count]
dp = 1e-3 * 3e8 * cc.proper_distance(
H_0, omega_m0, omega_r0, omega_lam0, 1 /
(1 + redshift))[0] * 1e6 * 3.09e18 # in cm
dl = (1 + redshift) * dp
f_lam = spec_l_lam / (4 * np.pi * dl**2 * (1 + redshift))
ax1.plot(spec['Lambda'] * (1 + redshift) / 1e4,
f_lam,
label=plot_labels[count])
ax1.set_xlim(0.07, 3)
ax1.minorticks_on()
ax1.tick_params('both', width=1, length=3, which='minor')
ax1.tick_params('both', width=1, length=4.7, which='major')
ax1.legend(loc=0, numpoints=1, prop={'size': 14})
plt.show()
def mag_in_filter_line(filter, spectrum, redshift, ew):
"""
Takes in f_lambda in the rest frame and the throughput
curve of the filter and spits out the AB magnitude of the
given spectrum in the filter.
Also needs redshift.
It can also use SDSS spectra which won't need to be redshifted
and calculate magnitudes in chosen filters with or without lines
added.
"""
f_lam = 0
weight = 0
if redshift == 0: redshift = 1e-8
# calculate distances
dp = 1e-3*2.99792458e8*cc.proper_distance(H_0, omega_m0, omega_r0, omega_lam0, 1/(1 + redshift))[0] * 1e6 * 3.09e18 # in cm
dl = (1 + redshift) * dp
# redshift spectrum
# This part is for BC03 spectra
lum_lam = spectrum['flux'] * solar_lum # bc03 spectra are in solar lum per angstrom
flux_redshifted = lum_lam / (4 * np.pi * dl**2 * (1+redshift))
lam_redshifted = spectrum['lambda'] * (1+redshift)
# add lines and check if it indeed becomes bluer in the corresponding filter
lya_redshifted = 1215.67 * (1 + redshift)
redshifted_lines = np.array([lya_redshifted], dtype='float64') # the len of this arr is the num of lines you want to add
ew_rest = np.array([ew]) # rest frame eq width # this is assumed
ew_obs = np.array([ew_rest * (1 + redshift)]) # this should have the same len as the redshifted_lines array
# observed eq width
# convert lam and flux arr to numpy arrays with dtype-'float64'
# they were 'float32' before
lam_redshifted = np.array([lam_redshifted], dtype='float64')
flux_redshifted = np.array([flux_redshifted], dtype='float64')
ind_arr = np.array([np.argmin(abs(lam_redshifted - redshifted_lines))])
lam_redshifted = np.insert(lam_redshifted, ind_arr[0] + 1, redshifted_lines)
flux_to_add = np.array([flux_redshifted[0][ind_arr[i]] * ew_obs[i] / \
(lam_redshifted[ind_arr[i] + 1] - lam_redshifted[ind_arr[i]]) for i in range(len(ew_obs))])
flux_redshifted = np.insert(flux_redshifted, ind_arr[0] + 1, flux_to_add)
if flux_to_add < 0 :
print flux_to_add, "Error"
# calculate magnitude
# filter['lambda'] is in nm # convert all to angstroms
for i in range(len(filter)):
lam_index = np.argmin(abs(lam_redshifted - (filter['lambda'][i]*10))) # the spectrum's index for the wavelength of the throughput curve
f_lam += flux_redshifted[lam_index] * lam_redshifted[lam_index]**2 * filter['throughput'][i] *\
np.exp(-1 * igm_trans(lam_redshifted[lam_index], redshift)) / (3631 * c * 1e-23)
# in the above expression: wavelength in angstroms, flux density in ergs/s/cm^2/angstrom, filter throughput in fractions
weight += filter['throughput'][i]
# This part is for SDSS spectra
"""
sdss_spectrum = spectrum
flux_redshifted = sdss_spectrum['flux'] * 1e-17
lam_redshifted = np.power(10, sdss_spectrum['loglam'])
# add lines and check if it indeed becomes bluer in the corresponding filter
index1 = np.argmin(abs(lam_redshifted - 7000))
index2 = np.argmin(abs(lam_redshifted - 6200))
lam_redshifted = np.array([lam_redshifted], dtype='float64')
flux_redshifted = np.array([flux_redshifted], dtype='float64')
lam_redshifted = np.insert(lam_redshifted, [index1, index2], [7000.0, 6200])
flux_redshifted = np.insert(flux_redshifted, [index1, index2],\
[flux_redshifted[0][index1] * 236, flux_redshifted[0][index2] * 472])
for i in range(len(filter)):
lam_index = np.argmin(abs(lam_redshifted - (filter['lambda'][i]*10)))
f_lam += flux_redshifted[lam_index] * lam_redshifted[lam_index]**2 * filter['throughput'][i] / (3631 * c * 1e-23)
weight += filter['throughput'][i]
"""
#print f_lam, weight
mag = -2.5 * np.log10(f_lam/weight)
return mag