def compprep(spectrum, sn_name, z, source):
old_wave = spectrum[:, 0] # wavelengths
old_flux = spectrum[:, 1] # fluxes
try:
old_error = spectrum[:, 2] # check if supernovae has error array
except IndexError:
old_error = np.array([0]) # if not, set default
if sn_name == '2011fe':
old_error = np.sqrt(old_error)
old_ivar = df.genivar(old_wave, old_flux,
old_error) # generate inverse variance
snr = prep.getsnr(old_flux, old_ivar)
if source == 'cfa': # choosing source dataset
sne = prep.ReadExtin('extinction.dat')
if source == 'bsnip':
sne = prep.ReadExtin('extinctionbsnip.dat')
if source == 'csp':
sne = prep.ReadExtin('extinctioncsp.dat')
old_wave *= 1 + float(z) # Redshift back
if source == 'uv':
sne = prep.ReadExtin('extinctionuv.dat')
if source == 'other':
sne = prep.ReadExtin('extinctionother.dat')
newdata = []
old_wave = old_wave * u.Angstrom
old_flux = old_flux * u.Unit('W m-2 angstrom-1 sr-1')
spec1d = Spectrum1D.from_array(old_wave, old_flux)
test_flux = test_dered.dered(sne, sn_name, spec1d.wavelength, spec1d.flux)
new_flux = test_flux.value
old_wave = old_wave.value
old_wave = old_wave / (1. + z)
old_flux = np.asarray(old_flux)
new_flux = np.asarray(new_flux)
# s = scale_composites_in_range(old_flux, new_flux)
# old_flux = s*old_flux
new_wave = old_wave / (1. + z)
new_error = old_error
new_ivar = df.genivar(new_wave, new_flux, new_error)
newdata = prep.Interpo(new_wave, new_flux, new_ivar)
return newdata, snr
def compprep(spectrum,
sn_name,
z,
source,
use_old_error=True,
testing=False,
filename=None,
mjd=None,
mjd_max=None):
""" Performs clipping, deredshifting, variance spectrum generation, MW extinction correction,
and interpolation. If testing is True, several plots will be made to assess the quality
of this processing.
"""
old_wave = spectrum[:, 0] # wavelengths
old_flux = spectrum[:, 1] # fluxes
try:
old_error = spectrum[:, 2] # check if supernovae has error array
except IndexError:
old_error = None # if not, set default
if sn_name == '2011fe' and source == 'other':
old_error = np.sqrt(old_error)
if old_error is not None:
old_var = old_error**2.
else:
old_var = None
if old_var is not None:
num_non_zeros = np.count_nonzero(old_var)
if len(old_var) - num_non_zeros > 100:
old_var = None
elif old_var[-1] == 0.:
old_var[-1] = old_var[-2]
elif True in np.isnan(old_var):
nan_inds = np.transpose(np.argwhere(np.isnan(old_var)))[0]
for ind in nan_inds:
if ind != 0:
old_var[ind] = old_var[ind - 1]
else:
old_var[ind] = old_var[ind + 1]
# if testing:
# plt.plot(old_wave, old_flux)
# plt.plot(old_wave/(1.+z), old_flux)
# plt.plot(old_wave*(1.+z), old_flux)
# plt.xlim(5800,6000)
# # plt.show()
# if old_var is not None:
# plt.plot(old_wave, old_var)
# plt.show()
# old_var = None
vexp, SNR = df.find_vexp(old_wave, old_flux, var_y=old_var)
if testing:
print vexp, SNR
if source != 'csp': #already deredshifted
old_wave = old_wave / (1. + z) #deredshift for clipping
old_wave, old_flux, old_var = df.clip(
old_wave, old_flux, old_var, vexp, testing=testing,
filename=filename) #clip emission/absorption lines
old_wave = old_wave * (1. + z) #reredshift for MW extinction correction
temp_ivar, SNR = df.genivar(old_wave,
old_flux,
old_var,
vexp=vexp,
testing=testing,
source=source) # generate inverse variance
#code to save foundation spec for david
# print filename
# plt.plot(old_wave, old_flux)
# plt.show()
# plt.plot(old_wave, temp_ivar)
# plt.show()
# file_path = '../../Foundation/mod_TNS_spec/' + filename.split('.')[0] + '_modified.flm'
# print file_path
# with open(file_path, 'w') as file:
# file.write('# Orginal file name = ' + filename + '\n')
# file.write('# z = ' + str(z) + '\n')
# # file.write('# MJD = ' + str(mjd) + '\n')
# # file.write('# MJD_max = ' + str(mjd_max) + '\n')
# file.write('\n')
# err = np.sqrt(1./np.asarray(temp_ivar))
# data = np.c_[old_wave,old_flux,err]
# table = tabulate(data, headers=['Wavelength', 'Flux', 'Error'],
# tablefmt = 'ascii')
# file.write(table)
if testing:
print SNR
if old_var is not None:
old_ivar = 1. / old_var
else:
old_ivar = temp_ivar
# snr = getsnr(old_flux, old_ivar)
if source == 'cfa': # choosing source dataset
# z = ReadParam()
sne = ReadExtin('extinction.dat')
if source == 'bsnip':
sne = ReadExtin('extinctionbsnip.dat')
if source == 'csp':
sne = ReadExtin('extinctioncsp.dat')
if source == 'uv':
sne = ReadExtin('extinctionuv.dat')
if source == 'other':
sne = ReadExtin('extinctionother.dat')
if source == 'swift_uv':
sne = ReadExtin('extinctionswiftuv.dat')
if source == 'foley_hst':
sne = ReadExtin('extinctionhst.dat')
if source == 'foundation':
sne = ReadExtin('extinctionfoundation.dat')
# host_reddened = ReadExtin('../data/info_files/ryan_av.txt')
newdata = []
old_wave = old_wave * u.Angstrom # wavelengths
old_flux = old_flux * u.Unit('W m-2 angstrom-1 sr-1')
spec1d = Spectrum1D.from_array(old_wave, old_flux)
spec1d_ivar = Spectrum1D.from_array(old_wave, old_ivar)
dered_flux, dered_ivar = test_dered.dered(
sne,
sn_name,
spec1d.wavelength,
spec1d.flux,
spec1d_ivar.flux,
source=source
) # Dereddening (see if sne in extinction files match the SN name)
# new_flux = host_correction(sne, sn_name, old_wave, new_flux)
# new_flux = old_flux
if testing:
new_flux_plot = copy.deepcopy(dered_flux)
new_ivar_plot = copy.deepcopy(dered_ivar)
old_wave_plot = copy.deepcopy(old_wave)
new_flux = dered_flux.value
new_ivar = dered_ivar.value
old_wave = old_wave.value
if testing:
av_specific = 0.2384 #2005lz
av_specific = 0.4089 #2007af
r_v = 2.5
new_flux_host, new_ivar_host = test_dered.host_correction(
av_specific, r_v, sn_name, old_wave_plot, new_flux_plot,
new_ivar_plot)
new_flux_host = new_flux_host.value
old_flux = old_flux.value
s = scale_composites_in_range(new_flux, old_flux)
new_flux_scaled = s * new_flux
s = scale_composites_in_range(new_flux_host, old_flux)
new_flux_host_scaled = s * new_flux_host
valid_data = np.where(old_wave > 4000)
norm = 10. / np.nanmax(new_flux_host_scaled[valid_data])
old_flux_norm = old_flux * norm
new_flux_norm = new_flux_scaled * norm
new_flux_host_norm = new_flux_host_scaled * norm
plt.rc('font', family='serif')
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(10, 8, forward=True)
plt.minorticks_on()
plt.xticks(fontsize=20)
# ax.xaxis.set_ticks(np.arange(np.round(wave[0],-3),np.round(wave[-1],-3),1000))
plt.yticks(fontsize=20)
plt.tick_params(which='major',
bottom='on',
top='on',
left='on',
right='on',
length=10)
plt.tick_params(which='minor',
bottom='on',
top='on',
left='on',
right='on',
length=5)
plt.plot(old_wave,
old_flux_norm,
linewidth=2,
color='#000080',
label='Before Dereddening')
plt.plot(old_wave,
new_flux_norm,
linewidth=2,
color='gold',
label='Milky Way Corrected')
# plt.plot(old_wave, new_flux_host_norm, linewidth = 2, color = '#d95f02', label='Host Corrected')
plt.ylabel('Relative Flux', fontsize=30)
plt.xlabel('Rest Wavelength ' + "($\mathrm{\AA}$)", fontsize=30)
plt.xlim([old_wave[0] - 200, old_wave[-1] + 200])
plt.legend(loc=1, fontsize=20)
# plt.savefig('../../../Paper_Drafts/reprocessing_updated/red_corr.pdf', dpi = 300, bbox_inches = 'tight')
plt.show()
# plt.plot(old_wave, old_ivar)
# plt.plot(old_wave, new_ivar)
# plt.show()
new_wave = old_wave / (1. + z) # Deredshifting
if not use_old_error:
new_var = None
else:
new_var = old_var # Placeholder if it needs to be changed
#var = new_flux*0+1
# newdata = Interpo(new_wave, new_flux, new_ivar) # Do the interpolation
newdata, scale, var_final = Interpo_flux_conserving(new_wave,
new_flux,
new_ivar,
testing=testing)
if testing:
# newdata_test = Interpo(new_wave, new_flux_host_norm, new_ivar)
newdata_test, scale, var_final = Interpo_flux_conserving(
new_wave, new_flux_host_norm, new_ivar)
interp_wave = newdata_test[0, :]
interp_flux = newdata_test[1, :]
plt.rc('font', family='serif')
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(10, 8, forward=True)
plt.minorticks_on()
plt.xticks(fontsize=20)
# ax.xaxis.set_ticks(np.arange(np.round(wave[0],-3),np.round(wave[-1],-3),1000))
plt.yticks(fontsize=20)
plt.tick_params(which='major',
bottom='on',
top='on',
left='on',
right='on',
length=10)
plt.tick_params(which='minor',
bottom='on',
top='on',
left='on',
right='on',
length=5)
plt.plot(new_wave,
2. * new_flux_host_norm,
linewidth=2,
color='#d95f02',
label='Before Interpolation')
plt.plot(interp_wave,
interp_flux,
linewidth=2,
color='darkgreen',
label='After Interpolation')
plt.ylabel('Relative Flux', fontsize=30)
plt.xlabel('Rest Wavelength ' + "($\mathrm{\AA}$)", fontsize=30)
plt.xlim([new_wave[0] - 200, new_wave[-1] + 200])
plt.legend(loc=1, fontsize=20)
# plt.savefig('../../../Paper_Drafts/reprocessing_updated/interp_deredshift.pdf', dpi = 300, bbox_inches = 'tight')
plt.show()
return newdata, SNR
def compprep(spectrum, sn_name, z, source):
old_wave = spectrum[:, 0] # wavelengths
old_flux = spectrum[:, 1] # fluxes
try:
old_error = spectrum[:, 2] # check if supernovae has error array
except IndexError:
old_error = np.array([0]) # if not, set default
old_ivar = df.genivar(old_wave, old_flux,
old_error) # generate inverse variance
snr = prep.getsnr(old_flux, old_ivar)
if source == 'cfa': # choosing source dataset
# z = ReadParam()
sne = prep.ReadExtin('extinction.dat')
if source == 'bsnip':
sne = prep.ReadExtin('extinctionbsnip.dat')
if source == 'csp':
sne = prep.ReadExtin('extinctioncsp.dat')
old_wave *= 1 + float(z) # Redshift back
if source == 'uv':
sne = prep.ReadExtin('extinctionuv.dat')
if source == 'other':
sne = prep.ReadExtin('extinctionother.dat')
# host_reddened = ReadExtin('../data/info_files/ryan_av.txt')
newdata = []
dered_wave = old_wave / (1. + z)
new_error = old_error # Placeholder if it needs to be changed
norm = 1. / np.amax(old_flux)
new_flux = old_flux * norm
new_ivar = df.genivar(dered_wave, new_flux,
new_error) # generate new inverse variance
#var = new_flux*0+1
dered_wave, new_flux, new_ivar = clip(dered_wave, new_flux, new_ivar)
newdata = Interpo(dered_wave, new_flux, new_ivar, sn_name,
plot=True) # Do the interpolation
interp_wave = newdata[0] * u.Angstrom # wavelengths
interp_flux = newdata[1] * u.Unit('W m-2 angstrom-1 sr-1')
spec1d = Spectrum1D.from_array(interp_wave, interp_flux)
test_flux = test_dered.dered(
sne, sn_name, spec1d.wavelength, spec1d.flux
) # Deredenning (see if sne in extinction files match the SN name)
# new_flux = host_correction(sne, sn_name, old_wave, new_flux)
# new_flux = old_flux
mw_flux = test_flux.value
interp_wave = interp_wave.value
interp_flux = interp_flux.value
interp_flux = np.nan_to_num(interp_flux)
mw_flux = np.nan_to_num(mw_flux)
s = scale_composites_in_range(mw_flux, interp_flux)
mw_flux = s * mw_flux
if sn_name == '2006sr':
av = .1294 #2006sr
name = '2006sr'
else:
av = 2.9711 #2005a
name = '2005a'
# name = '2005a'
host_wave = interp_wave * u.Angstrom # wavelengths
host_flux = mw_flux * u.Unit('W m-2 angstrom-1 sr-1')
spec1d = Spectrum1D.from_array(host_wave, host_flux)
new_flux_host, new_ivar_host = test_dered.host_correction(
av, 2.5, name, spec1d.wavelength, spec1d.flux, [0])
new_flux_host = np.asarray(new_flux_host.value)
s = scale_composites_in_range(new_flux_host, interp_flux)
new_flux_host = s * new_flux_host
scale = 10.
valid_data = np.where(interp_wave > 3600)
norm = 1. / np.nanmax(new_flux_host[valid_data])
new_flux_host = new_flux_host * norm * scale
mw_flux = mw_flux * norm * scale
interp_flux = interp_flux * norm * scale
interp_flux[interp_flux == 0] = np.nan
mw_flux[mw_flux == 0] = np.nan
new_flux_host[new_flux_host == 0] = np.nan
plt.rc('font', family='serif')
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(10, 8, forward=True)
plt.minorticks_on()
plt.xticks(fontsize=20)
ax.xaxis.set_ticks(
np.arange(np.round(interp_wave[0], -3), np.round(interp_wave[-1], -3),
1000))
plt.yticks(fontsize=20)
plt.tick_params(which='major',
bottom='on',
top='on',
left='on',
right='on',
length=10)
plt.tick_params(which='minor',
bottom='on',
top='on',
left='on',
right='on',
length=5)
plt.plot(interp_wave,
interp_flux,
linewidth=2,
color='#d95f02',
label='Before Dereddening')
plt.plot(interp_wave,
mw_flux,
linewidth=2,
color='#1b9e77',
label='MW Corrected')
plt.plot(interp_wave,
new_flux_host,
linewidth=2,
color='#7570b3',
label='Host Corrected')
plt.ylabel('Relative Flux', fontsize=30)
plt.xlabel('Rest Wavelength ' + "($\mathrm{\AA}$)", fontsize=30)
# plt.savefig('../../Paper_Drafts/red_corr.pdf', dpi = 300, bbox_inches = 'tight')
plt.xlim([dered_wave[0] - 200, dered_wave[-1] + 200])
plt.legend(loc=1, fontsize=20)
if sn_name == '2005a':
plt.ylim([-.05 * scale, 1.05 * scale])
# plt.savefig('../../../Paper_Drafts/reprocessing/red_corr_large_av.pdf', dpi = 300, bbox_inches = 'tight')
else:
plt.ylim([-.05 * scale, 1.05 * scale])
# plt.savefig('../../../Paper_Drafts/reprocessing/red_corr_small_av.pdf', dpi = 300, bbox_inches = 'tight')
plt.show()
# new_wave = old_wave/(1.+z) # Deredshifting
# print 'new spectra',newdata
return newdata, snr
def compprep(spectrum, sn_name, z, source):
old_wave = spectrum[:, 0] # wavelengths
old_flux = spectrum[:, 1] # fluxes
try:
old_error = spectrum[:, 2] # check if supernovae has error array
except IndexError:
old_error = np.array([0]) # if not, set default
old_ivar = df.genivar(old_wave, old_flux,
old_error) # generate inverse variance
snr = prep.getsnr(old_flux, old_ivar)
if source == 'cfa': # choosing source dataset
# z = ReadParam()
sne = prep.ReadExtin('extinction.dat')
if source == 'bsnip':
sne = prep.ReadExtin('extinctionbsnip.dat')
if source == 'csp':
sne = prep.ReadExtin('extinctioncsp.dat')
old_wave *= 1 + float(z) # Redshift back
if source == 'uv':
sne = prep.ReadExtin('extinctionuv.dat')
if source == 'other':
sne = prep.ReadExtin('extinctionother.dat')
# host_reddened = ReadExtin('../data/info_files/ryan_av.txt')
newdata = []
old_wave = old_wave * u.Angstrom # wavelengths
old_flux = old_flux * u.Unit('W m-2 angstrom-1 sr-1')
spec1d = Spectrum1D.from_array(old_wave, old_flux)
test_flux = test_dered.dered(
sne, sn_name, spec1d.wavelength, spec1d.flux
) # Deredenning (see if sne in extinction files match the SN name)
# new_flux = host_correction(sne, sn_name, old_wave, new_flux)
# new_flux = old_flux
new_flux = test_flux.value
old_wave = old_wave.value
old_wave = old_wave / (1. + z)
old_flux = np.asarray(old_flux)
new_flux = np.asarray(new_flux)
s = scale_composites_in_range(old_flux, new_flux)
old_flux = s * old_flux
# plt.rc('font', family='serif')
# fig, ax = plt.subplots(1,1)
# fig.set_size_inches(10, 8, forward = True)
# ax.get_yaxis().set_ticks([])
# plt.plot(old_wave, old_flux, linewidth = 2, color = 'r')
# plt.plot(old_wave, new_flux, linewidth = 2, color = '#3F5D7D')
# plt.ylabel('Relative Flux')
# plt.xlabel('Wavelength ' + "($\mathrm{\AA}$)")
# # plt.savefig('../../Paper_Drafts/MW_corr.png', dpi = 300, bbox_inches = 'tight')
# plt.show()
av = .1294 #2006sr
# av = 2.9711 #2005a
name = '2006sr'
# name = '2005a'
host_wave = old_wave * u.Angstrom # wavelengths
host_flux = new_flux * u.Unit('W m-2 angstrom-1 sr-1')
spec1d = Spectrum1D.from_array(host_wave, host_flux)
new_flux_host, new_ivar_host = test_dered.host_correction(
av, 2.5, name, spec1d.wavelength, spec1d.flux, [0])
new_flux = np.asarray(new_flux)
new_flux_host = np.asarray(new_flux_host.value)
s = scale_composites_in_range(new_flux_host, new_flux)
new_flux_host = s * new_flux_host
norm = 1. / np.amax(new_flux_host)
new_flux_host = new_flux_host * norm
new_flux = new_flux * norm
old_flux = old_flux * norm
plt.rc('font', family='serif')
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(10, 8, forward=True)
plt.minorticks_on()
plt.xticks(fontsize=20)
ax.xaxis.set_ticks(
np.arange(np.round(old_wave[0], -3), np.round(old_wave[-1], -3), 1000))
plt.yticks(fontsize=20)
plt.tick_params(which='major',
bottom='on',
top='on',
left='on',
right='on',
length=10)
plt.tick_params(which='minor',
bottom='on',
top='on',
left='on',
right='on',
length=5)
plt.plot(old_wave, old_flux, linewidth=2, color='#d95f02')
plt.plot(old_wave, new_flux, linewidth=2, color='#1b9e77')
plt.plot(host_wave.value, new_flux_host, linewidth=2, color='#7570b3')
plt.ylabel('Relative Flux', fontsize=30)
plt.xlabel('Rest Wavelength ' + "($\mathrm{\AA}$)", fontsize=30)
plt.savefig('../../Paper_Drafts/red_corr.pdf',
dpi=300,
bbox_inches='tight')
# plt.ylim([-.2,1.01])
# plt.savefig('../../Paper_Drafts/red_corr_large.pdf', dpi = 300, bbox_inches = 'tight')
plt.show()
# new_wave = old_wave/(1.+z) # Deredshifting
new_wave = old_wave
new_error = old_error # Placeholder if it needs to be changed
norm = 1. / np.amax(new_flux)
new_flux = new_flux * norm
new_ivar = df.genivar(new_wave, new_flux,
new_error) # generate new inverse variance
#var = new_flux*0+1
newdata = prep.Interpo(new_wave, new_flux,
new_ivar) # Do the interpolation
plt.rc('font', family='serif')
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(10, 8, forward=True)
plt.minorticks_on()
plt.xticks(fontsize=20)
ax.xaxis.set_ticks(
np.arange(np.round(old_wave[0], -3), np.round(old_wave[-1], -3), 1000))
plt.yticks(fontsize=20)
plt.tick_params(which='major',
bottom='on',
top='on',
left='on',
right='on',
length=10)
plt.tick_params(which='minor',
bottom='on',
top='on',
left='on',
right='on',
length=5)
plt.plot(old_wave, new_flux, linewidth=2, color='r')
plt.plot(newdata[0], newdata[1], linewidth=2, color='#3F5D7D')
plt.ylabel('Relative Flux', fontsize=30)
plt.xlabel('Rest Wavelength ' + "($\mathrm{\AA}$)", fontsize=30)
plt.savefig('../../Paper_Drafts/interp.pdf', dpi=300, bbox_inches='tight')
# plt.ylim([-.3,1.])
# plt.savefig('../../Paper_Drafts/interp_large.pdf', dpi = 300, bbox_inches = 'tight')
plt.show()
# print 'new spectra',newdata
return newdata, snr
data = bsnip_vals[sn_name.lower() + '-' + longdate]
redshift = data[1] / c
newdata, snr = compprep(spectrum, sn_name, redshift, source)
raise TypeError
### ENDS HERE
fname = '../data/spectra/cfa/sn2003kg/sn2003kg-20031129.12-fast.flm'
spectrum = np.loadtxt(fname)
old_wave = spectrum[:, 0]
old_flux = spectrum[:, 1]
real_error = spectrum[:, 2]
if real_error[0] != 0.:
old_error = np.zeros(len(old_wave), float)
new_ivar = df.genivar(old_wave, old_flux, old_error)
new_var = 1. / new_ivar
real_var = real_error * real_error
scale = scale_composites_in_range(new_var, real_var)
# new_var = new_var*scale
new_var = new_var * 2.02
print scale
norm = 1. / np.amax(real_var)
real_var = real_var * norm
new_var = new_var * norm
plt.rc('font', family='serif')
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(10, 8, forward=True)
plt.minorticks_on()
plt.xticks(fontsize=20)