Gaia

Real data!

gully
Sept 14, 2016

Outline:

More exploring

Import these first-- I auto import them every time!:



In [1]:

    
#! cat /Users/gully/.ipython/profile_default/startup/start.ipy



In [2]:

    
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%config InlineBackend.figure_format = 'retina'
%matplotlib inline



In [3]:

    
import pandas as pd



In [4]:

    
from astropy import units as u
from astropy.coordinates import SkyCoord

1. Retrieve existing catalogs

Look at Grankin list



In [5]:

    
d1 = pd.read_csv('../../ApJdataFrames/data/Grankin08/table1_plus.csv') #local version



In [8]:

    
d1.head(2)









    Out[8]:






  
    
      
      Name
      HBC
      SpT
      JD_min_max
      N_seasons
      V_range
      N_obs
      avgB_V
      avgV_R
      mult
      ...
      filename_guess
      filename
      dat_fn
      HBC_name
      pref_name
      RA
      DEC
      SpT_simbad
      Otype_simbad
      alt_name
    
  
  
    
      0
      Anon 1
      366
      M0
      48953--50760
      6
      13.58--13.36
      96
      1.84
      1.84
      
      ...
      hbc366.dat.gz
      hbc366.dat.gz
      hbc366.dat
      HBC 366
      HBC 366
      04 13 27.227
      +28 16 24.78
      M0
      BYDra
      HBC 366
    
    
      1
      HD 283572
      380
      G6 IV
      48854--53300
      13
      9.16--8.89
      425
      0.77
      0.70
      
      ...
      hd283572.dat.gz
      hd283572.dat.gz
      hd283572.dat
      HBC 380
      HD 283572
      04 21 58.8471
      +28 18 06.505
      G5IVe
      TTau*
      HBC 380
    
  

2 rows × 21 columns



In [9]:

    
d1.columns









    Out[9]:





Index(['Name', 'HBC', 'SpT', 'JD_min_max', 'N_seasons', 'V_range', 'N_obs',
       'avgB_V', 'avgV_R', 'mult', 'ref', 'filename_guess', 'filename',
       'dat_fn', 'HBC_name', 'pref_name', 'RA', 'DEC', 'SpT_simbad',
       'Otype_simbad', 'alt_name'],
      dtype='object')



In [10]:

    
c1 = SkyCoord(d1.RA.values, d1.DEC.values, unit=(u.hourangle, u.deg), frame='icrs')

2. Read in the Gaia data



In [11]:

    
df_list = []

This takes a finite amount of RAM, but should be fine for modern laptops.



In [12]:

    
for i in range(16):
    df_list.append(pd.read_csv('../data/TgasSource_000-000-{:03d}.csv'.format(i)))



In [13]:

    
tt = pd.concat(df_list, ignore_index=True)



In [15]:

    
plt.figure(figsize=(10,4))
ax = sns.jointplot(tt.ra, tt.dec, kind='hex', size=8)
ax.ax_joint.plot(c1.ra.deg, c1.dec.deg, '.', alpha=1.0)









    Out[15]:





[<matplotlib.lines.Line2D at 0x1209fac18>]






    





<matplotlib.figure.Figure at 0x1209c3550>



In [16]:

    
cg = SkyCoord(tt.ra.values, tt.dec.values, unit=(u.deg, u.deg), frame='icrs')

Match



In [17]:

    
idx, d2d, blah = c1.match_to_catalog_sky(cg)



In [18]:

    
vec_units = d2d.to(u.arcsecond)
vec = vec_units.value



In [20]:

    
bins = np.arange(0, 4, 0.2)
sns.distplot(vec, bins=bins, kde=False),
plt.xlim(0,4)
plt.xlabel('match separation (arcsec)')









    Out[20]:





<matplotlib.text.Text at 0x132f349e8>

Forced to match to nearest neighbor



In [21]:

    
len(set(idx)), idx.shape[0]









    Out[21]:





(39, 48)

... yielding some redundancies in cross matching



In [22]:

    
tt_sub = tt.iloc[idx]
tt_sub = tt_sub.reset_index()
tt_sub = tt_sub.drop('index', axis=1)



In [23]:

    
d1 = d1.reset_index()
d1 = d1.drop('index', axis=1)



In [24]:

    
x1 = pd.concat([d1, tt_sub], axis=1)



In [25]:

    
x1.shape









    Out[25]:





(48, 80)



In [26]:

    
col_order = d1.columns.values.tolist() + tt_sub.columns.values.tolist()
x1 = x1[col_order]
x0 = x1.copy()



In [27]:

    
x0['xmatch_sep_as'] = vec



In [28]:

    
x0['Gaia_match'] = vec < 2.0 #Fairly liberal, 1.0 might be better.



In [33]:

    
plt.figure(figsize=(8,4))
bins = np.arange(2, 14, 0.8)
sns.distplot(x0.parallax[x0.Gaia_match], bins=bins, rug=True)
#sns.distplot(1.0/(x0.parallax[x0.Gaia_match]/1000.0))
plt.xlabel('Parallax (mas)')
plt.savefig('../results/luhman_mamajek2012.png', dpi=300)









    



//anaconda/lib/python3.4/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j



In [34]:

    
x0.Gaia_match.sum(), len(d1)









    Out[34]:





(11, 48)



In [37]:

    
x0.columns









    Out[37]:





Index(['Name', 'HBC', 'SpT', 'JD_min_max', 'N_seasons', 'V_range', 'N_obs',
       'avgB_V', 'avgV_R', 'mult', 'ref', 'filename_guess', 'filename',
       'dat_fn', 'HBC_name', 'pref_name', 'RA', 'DEC', 'SpT_simbad',
       'Otype_simbad', 'alt_name', 'hip', 'tycho2_id', 'solution_id',
       'source_id', 'random_index', 'ref_epoch', 'ra', 'ra_error', 'dec',
       'dec_error', 'parallax', 'parallax_error', 'pmra', 'pmra_error',
       'pmdec', 'pmdec_error', 'ra_dec_corr', 'ra_parallax_corr',
       'ra_pmra_corr', 'ra_pmdec_corr', 'dec_parallax_corr', 'dec_pmra_corr',
       'dec_pmdec_corr', 'parallax_pmra_corr', 'parallax_pmdec_corr',
       'pmra_pmdec_corr', 'astrometric_n_obs_al', 'astrometric_n_obs_ac',
       'astrometric_n_good_obs_al', 'astrometric_n_good_obs_ac',
       'astrometric_n_bad_obs_al', 'astrometric_n_bad_obs_ac',
       'astrometric_delta_q', 'astrometric_excess_noise',
       'astrometric_excess_noise_sig', 'astrometric_primary_flag',
       'astrometric_relegation_factor', 'astrometric_weight_al',
       'astrometric_weight_ac', 'astrometric_priors_used',
       'matched_observations', 'duplicated_source',
       'scan_direction_strength_k1', 'scan_direction_strength_k2',
       'scan_direction_strength_k3', 'scan_direction_strength_k4',
       'scan_direction_mean_k1', 'scan_direction_mean_k2',
       'scan_direction_mean_k3', 'scan_direction_mean_k4', 'phot_g_n_obs',
       'phot_g_mean_flux', 'phot_g_mean_flux_error', 'phot_g_mean_mag',
       'phot_variable_flag', 'l', 'b', 'ecl_lon', 'ecl_lat', 'xmatch_sep_as',
       'Gaia_match'],
      dtype='object')



In [42]:

    
x0['dist_pc'] = 1.0/x0['parallax']*1000



In [49]:

    
x0.shape









    Out[49]:





(48, 83)



In [45]:

    
x0[x0.Gaia_match][['Name', 'SpT', 'parallax', 'dist_pc','phot_g_mean_mag', 'N_seasons']]









    Out[45]:






  
    
      
      Name
      SpT
      parallax
      dist_pc
      phot_g_mean_mag
      N_seasons
    
  
  
    
      1
      HD 283572
      G6 IV
      7.987188
      125.200515
      8.740572
      13
    
    
      16
      TAP 4
      K1
      6.891965
      145.096493
      11.809371
      2
    
    
      22
      TAP 35
      K1
      8.422279
      118.732704
      9.851185
      8
    
    
      27
      TAP 50
      K0 IV
      12.526775
      79.829008
      9.890836
      12
    
    
      29
      V501 Aur
      K2
      1.257640
      795.139856
      9.768918
      10
    
    
      30
      V410 Tau
      K3
      7.784363
      128.462667
      10.194841
      19
    
    
      36
      V1197 Tau
      G8
      9.289501
      107.648413
      10.338743
      10
    
    
      37
      V1199 Tau
      K0
      8.455515
      118.266003
      10.321054
      10
    
    
      38
      V1200 Tau
      G5
      9.897702
      101.033553
      11.016761
      10
    
    
      39
      V1202 Tau
      G0
      8.037722
      124.413353
      10.462657
      10
    
    
      46
      Wa Oph/3
      K0 IV
      7.623325
      131.176361
      10.287677
      9

11 out of 48 have Gaia parallaxes.



In [50]:

    
plt.figure(figsize=(10,4))
ax = sns.jointplot(tt.ra, tt.dec, kind='hex', size=8, xlim=(230,255), ylim=(-40,-10))
ax.ax_joint.plot(c1.ra.deg, c1.dec.deg, '.', alpha=0.5)
ax.ax_joint.scatter(x0.ra[x0.Gaia_match], x0.dec[x0.Gaia_match], 
                    s=x0.parallax[x0.Gaia_match]**3*0.2, c='r',alpha=0.5)









    Out[50]:





<matplotlib.collections.PathCollection at 0x14336ceb8>






    





<matplotlib.figure.Figure at 0x13a2cf320>

Not many Oph Targets from the North.

	Name	HBC	SpT	JD_min_max	N_seasons	V_range	N_obs	avgB_V	avgV_R	mult	...	filename_guess	filename	dat_fn	HBC_name	pref_name	RA	DEC	SpT_simbad	Otype_simbad	alt_name
0	Anon 1	366	M0	48953--50760	6	13.58--13.36	96	1.84	1.84		...	hbc366.dat.gz	hbc366.dat.gz	hbc366.dat	HBC 366	HBC 366	04 13 27.227	+28 16 24.78	M0	BYDra	HBC 366
1	HD 283572	380	G6 IV	48854--53300	13	9.16--8.89	425	0.77	0.70		...	hd283572.dat.gz	hd283572.dat.gz	hd283572.dat	HBC 380	HD 283572	04 21 58.8471	+28 18 06.505	G5IVe	TTau*	HBC 380