Gaia

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]:

    

df_list = []


    

In [5]:

    

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


    

In [6]:

    

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


    

In [7]:

    

plt.plot(tt.parallax, tt.parallax_error, '.', alpha=0.005)
plt.xscale('log')


    

In [9]:

    

tt.columns


    

    
Out[9]:





Index(['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'],
      dtype='object')

In [10]:

    

plt.plot(tt.phot_g_mean_mag, tt.parallax_error, '.', alpha=0.005)


    

    
Out[10]:





[<matplotlib.lines.Line2D at 0x121667ba8>]






    

In [13]:

    

sns.set_style('ticks')


    

In [16]:

    

sns.jointplot(tt.phot_g_mean_mag, tt.parallax_error, kind='hex', stat_func=None)
plt.savefig('../results/photG_vs_parErr.png', dpi=300)