In [1]:
import splat
import wisps
from astropy.io import fits, ascii
import matplotlib.pyplot as plt
import glob
import pandas as pd
import numpy as np
%matplotlib inline
In [2]:
data='/Users/caganze/research/wisps/data/manjavacas/final_data_journal/*.csv'
In [3]:
spectra=[]
for f in glob.glob(data):
d=ascii.read(f).to_pandas()
s=wisps.Spectrum(wave=d.col1, flux=d.col2, noise=d.col3)
s._filename=f.split('/')[-1].split('.csv')[0]
spectra.append(s)
In [4]:
indices=pd.DataFrame([s.indices for s in spectra])
snr1=[s.snr['snr1'] for s in spectra]
snr2=[s.snr['snr2'] for s in spectra]
ftests=pd.DataFrame([s.f_test() for s in spectra])
In [5]:
df=pd.DataFrame()
for k in indices.columns: df[k]=indices[k]
for k in ftests.columns: df[k]=ftests[k]
df['snr1']=snr1
df['snr2']=snr2
df['name']=[s.filename for s in spectra]
In [6]:
#wisps.datasets['spex']
In [7]:
df.to_pickle(wisps.LIBRARIES+'/manjavacas.pkl')
In [8]:
for s in spectra:
s.splat_spectrum.plot(ylabel=s.filename)
In [9]:
#ghjkla
#spex=wisps.spex_sample_ids(stype='spex_sample', from_file=False)
#spexids=pd.DataFrame([x for x in spex['Indices']])
#for k in spexids.columns: spex[k]=spexids[k]
#ref=wisps.Annotator.reformat_table(spexids)
df['spectra']=spectra
df=wisps.Annotator.reformat_table(df)
In [10]:
f=df[(df['CH_4/H-Cont'] <0.5) & (df['H-cont/J-Cont'] >0.6)].spectra.apply(lambda x: x.splat_spectrum.plot(ylabel=x.filename))
In [11]:
ydwarfs=df[df['spt'].apply(lambda x: splat.typeToNum(x))>37]
#df[(df['CH_4/H-Cont'] <0.5) & (df['H-cont/J-Cont'] >0.6)]
In [25]:
schn='/Users/caganze/research/wisps/data/schneider/*.txt'
In [ ]:
spectra_schn=[]
for f in glob.glob(data):
d=ascii.read(f).to_pandas()
s=wisps.Spectrum(wave=d.col1, flux=d.col2, noise=d.col3)
s._filename=f.split('/')[-1].split('.csv')[0]
spectra_schn.append(s)
In [31]:
ascii.read(glob.glob(schn)[0]).to_pandas()
Out[31]:
In [ ]: