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#add spex templates to selection function
#select the highest SNR spectra of each spectral type i.e
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import splat
import wisps
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import copy
import numba
%matplotlib inline
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splat.initializeStandards()
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spts=np.array([splat.typeToNum(x) for x in np.arange(16, 42)])
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#remove object from forbidden list
splat_db0=splat.searchLibrary(vlm=True, giant=False, young=False)
splat_db0['SHORTNAME']=splat_db0.DESIGNATION.apply(splat.designationToShortName)
splat_db=splat_db0[~splat_db0.SHORTNAME.isin(wisps.FORBIDDEN_LIST)].reset_index(drop=True)
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df=splat_db[splat_db.SPEX_TYPE.isin(spts)]
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splat_within_snr=df[df.MEDIAN_SNR.between(5, 200)]
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import splat.database as splatdb
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#binary vetting
folder='/users/caganze/research/wisps/data/'
bindf=pd.read_csv(folder+'/binaries.txt', names=['name', ' '], sep=',')
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groups=splat_within_snr.sort_values('MEDIAN_SNR',ascending=False).groupby('SPEX_TYPE')
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picks=[]
for g in groups:
picks.append(g[1].nlargest(20,'MEDIAN_SNR' ))
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df_picks=pd.concat(picks)
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def create_splat_source(filename):
try:
#create a wisps spectrum
#retrieve splat spectrum
splat_s=splat.getSpectrum(filename=filename)[0]
#spt=splat.typeToNum(splat_s.spex_type)
splat_s.toInstrument('WFC3-G141')
#create a source object
s=wisps.Spectrum(
wave=splat_s.wave.value,
flux=splat_s.flux.value,
noise=splat_s.noise.value)
#put it on wisps resolution
s._splat_spectrum=splat_s
s.normalize()
return s
except :
return
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import wisps
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df_picks['spt']=df_picks.SPEX_TYPE.apply(wisps.make_spt_number)
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df_picks=df_picks.sort_values(by='spt')
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#df_picks
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%%capture
spectra=df_picks.DATA_FILE.apply(create_splat_source)
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def plot_sp_sp(s, a, shortname):
std=splat.STDS_DWARF_SPEX[splat.typeToNum(s.spectral_type[0])]
std.normalize(waverange=[1.1, 1.7])
mask2=np.logical_and(std.wave.value>0.8, std.wave.value<2.5)
mask=np.logical_and(s.wave>0.8, s.wave<2.5)
a.plot(s.wave[mask], s.flux[mask], label=shortname,linewidth=3)
a.plot(std.wave.value[mask2], std.flux.value[mask2], linestyle='--', label='std', alpha=0.5)
a.set_title("{} ".format(s.spectral_type ))
a.legend()
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(spectra.iloc[0])
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#for idx, s in enumerate(spectra.dropna().values):
# s.normalize()
# fig, ax=plt.subplots()
# plot_sp_sp(s, ax, idx)
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def make_wisps(splat_s):
s=wisps.Spectrum(wave=splat_s.wave.value,
flux=splat_s.flux.value,
noise=splat_s.noise.value)
return s
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ydwarfs_stds=pd.read_pickle('/users/caganze/y_standards.pkl')
ydwarfs_data=pd.read_pickle(wisps.OUTPUT_FILES+'/schneider_spectra.pkl')
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t9s=[make_wisps(x) for x in splat.getSpectrum(spt='T9.0')]
t8s=[make_wisps(x) for x in splat.getSpectrum(spt='T8.0')]
y0=[make_wisps(x ) for x in ydwarfs_stds.spec]
y1=[make_wisps(x) for x in ydwarfs_stds.spec]
#l3s=[make_wisps(x) for x in splat.getSpectrum(spt='L3.0', binary=False, vlm=True, young=False)]
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extras=[x for x in np.concatenate([t9s, t8s, y0, y1])]
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retain=np.append(spectra.dropna().values, extras)
retain=np.append(retain, ydwarfs_data)
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dfsp=pd.DataFrame()
dfsp['spectra']=retain
dfsp['spt']= [x.spectral_type for x in retain]
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import pickle
with open(wisps.OUTPUT_FILES+'/l_t_dwarfs_spex.pkl', 'wb') as file:
pickle.dump(retain,file)
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rdpck=pd.read_pickle(wisps.OUTPUT_FILES+'/l_t_dwarfs_spex.pkl')
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bindf['shortname']=bindf.name.apply(str).apply(splat.designationToShortName)
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splat_within_snr['shortname']=splat_within_snr.DESIGNATION.apply(splat.designationToShortName)
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bsample=splat_within_snr[ ~ (splat_within_snr.shortname.isin(bindf.shortname) |
np.isnan(splat_db['PARALLAX']) | (splat_within_snr['SBINARY'] =='Y'))]
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bsample=bsample.reset_index(drop=True)
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%%capture
bsample_spectra=bsample.DATA_FILE.apply(create_splat_source)
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bsample['spectra']=bsample_spectra
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from matplotlib.backends.backend_pdf import PdfPages
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bsample=bsample.drop_duplicates(subset='shortname').reset_index(drop=True)
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dfextr=pd.DataFrame()
#dfextr['NAME']=['J045853.90+643451.9', 'WISE1738+2732', 'WISE1738+2732']
dfextr['SPEX_TYPE']=[x.spectral_type for x in extras]
#dfextr['shortname']=['J045853.90+643451.9', 'WISE1738+2732', 'WISE1738+2732']
dfextr['spectra']=extras
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#add T9 and y dwarfs
#dfextr
bspl=bsample.append(dfextr, ignore_index=True, verify_integrity=True, sort=True)
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bspl=bspl[~bspl.spectra.isna()]
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%%capture
with PdfPages(wisps.OUTPUT_FIGURES+'/multipage_binaries.pdf') as pdf:
for g in np.array_split(bspl, int(len(bspl)/4)):
fig, ax=plt.subplots(ncols=2, nrows=2)
plot_sp_sp(g.spectra.iloc[0], ax[0][0], g.shortname.iloc[0])
plot_sp_sp(g.spectra.iloc[1], ax[0][1], g.shortname.iloc[1])
plot_sp_sp(g.spectra.iloc[2], ax[1][0], g.shortname.iloc[2])
plot_sp_sp(g.spectra.iloc[3], ax[1][1], g.shortname.iloc[3])
pdf.savefig()
plt.close()
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bspl.reset_index(drop=True).to_pickle(wisps.OUTPUT_FILES+'/binary_spex.pkl')
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(bspl[['SOURCE_KEY', 'NAME', 'DESIGNATION', 'RA', 'DEC', 'DISCOVERY_REFERENCE',
'OPT_TYPE', 'NIR_TYPE', 'LIT_TYPE',
'J_2MASS',
'H_2MASS_E', 'GRAVITY_CLASS_NIR','BINARY', 'SBINARY','SIMBAD_NAME', 'SIMBAD_SPT',
'SIMBAD_SPT_REF', 'PARALLAX', 'PARALLAX_E', 'DISTANCE',
'SPEX_GRAVITY_CLASSIFICATION',
'shortname']]).to_excel(wisps.OUTPUT_FILES+"/nonbinaries.xlsx")
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