# import packages
import pandas as pd
import pandas_profiling as pd_prof
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
# for downloading file
from google.colab import files
## Use random forest to create and evaluate new model
from sklearn.ensemble import RandomForestClassifier
!pip install astroquery
Requirement already satisfied: astroquery in /usr/local/lib/python3.6/dist-packages (0.4.1) Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from astroquery) (1.15.0) Requirement already satisfied: astropy>=3.1 in /usr/local/lib/python3.6/dist-packages (from astroquery) (4.1) Requirement already satisfied: requests>=2.4.3 in /usr/local/lib/python3.6/dist-packages (from astroquery) (2.23.0) Requirement already satisfied: html5lib>=0.999 in /usr/local/lib/python3.6/dist-packages (from astroquery) (1.0.1) Requirement already satisfied: beautifulsoup4>=4.3.2 in /usr/local/lib/python3.6/dist-packages (from astroquery) (4.6.3) Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from astroquery) (1.18.5) Requirement already satisfied: keyring>=4.0 in /usr/local/lib/python3.6/dist-packages (from astroquery) (21.5.0) Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.4.3->astroquery) (3.0.4) Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.4.3->astroquery) (1.24.3) Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.4.3->astroquery) (2.10) Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.4.3->astroquery) (2020.12.5) Requirement already satisfied: webencodings in /usr/local/lib/python3.6/dist-packages (from html5lib>=0.999->astroquery) (0.5.1) Requirement already satisfied: SecretStorage>=3.2; sys_platform == "linux" in /usr/local/lib/python3.6/dist-packages (from keyring>=4.0->astroquery) (3.3.0) Requirement already satisfied: importlib-metadata>=1; python_version < "3.8" in /usr/local/lib/python3.6/dist-packages (from keyring>=4.0->astroquery) (3.1.1) Requirement already satisfied: jeepney>=0.4.2; sys_platform == "linux" in /usr/local/lib/python3.6/dist-packages (from keyring>=4.0->astroquery) (0.6.0) Requirement already satisfied: cryptography>=2.0 in /usr/local/lib/python3.6/dist-packages (from SecretStorage>=3.2; sys_platform == "linux"->keyring>=4.0->astroquery) (3.3.1) Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=1; python_version < "3.8"->keyring>=4.0->astroquery) (3.4.0) Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.6/dist-packages (from cryptography>=2.0->SecretStorage>=3.2; sys_platform == "linux"->keyring>=4.0->astroquery) (1.14.4) Requirement already satisfied: pycparser in /usr/local/lib/python3.6/dist-packages (from cffi>=1.12->cryptography>=2.0->SecretStorage>=3.2; sys_platform == "linux"->keyring>=4.0->astroquery) (2.20)
# import astroquery
import astropy.units as u
import astropy.coordinates as coord
from astroquery.gaia import Gaia
from astroquery.vizier import Vizier
Created TAP+ (v1.2.1) - Connection: Host: gea.esac.esa.int Use HTTPS: True Port: 443 SSL Port: 443 Created TAP+ (v1.2.1) - Connection: Host: geadata.esac.esa.int Use HTTPS: True Port: 443 SSL Port: 443
## making a GAIA cone_search of 30m radius around NGC3766 center
coordinate = coord.SkyCoord.from_name('NGC3766')
print(coordinate)
radius = u.Quantity(0.8, u.deg)
Gaia.ROW_LIMIT = -1
j = Gaia.cone_search_async(coordinate, radius)
r = j.get_results()
print(type(r))
<SkyCoord (ICRS): (ra, dec) in deg (174.075, -61.615)> INFO: Query finished. [astroquery.utils.tap.core] <class 'astropy.table.table.Table'>
## save the ASCII table as a panadas dataframe
all_stars = r.to_pandas()
all_stars
solution_id | designation | source_id | random_index | ref_epoch | ra | ra_error | dec | dec_error | parallax | parallax_error | parallax_over_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_bad_obs_al | astrometric_gof_al | astrometric_chi2_al | astrometric_excess_noise | astrometric_excess_noise_sig | astrometric_params_solved | astrometric_primary_flag | astrometric_weight_al | astrometric_pseudo_colour | astrometric_pseudo_colour_error | mean_varpi_factor_al | ... | phot_rp_mean_flux | phot_rp_mean_flux_error | phot_rp_mean_flux_over_error | phot_rp_mean_mag | phot_bp_rp_excess_factor | phot_proc_mode | bp_rp | bp_g | g_rp | radial_velocity | radial_velocity_error | rv_nb_transits | rv_template_teff | rv_template_logg | rv_template_fe_h | phot_variable_flag | l | b | ecl_lon | ecl_lat | priam_flags | teff_val | teff_percentile_lower | teff_percentile_upper | a_g_val | a_g_percentile_lower | a_g_percentile_upper | e_bp_min_rp_val | e_bp_min_rp_percentile_lower | e_bp_min_rp_percentile_upper | flame_flags | radius_val | radius_percentile_lower | radius_percentile_upper | lum_val | lum_percentile_lower | lum_percentile_upper | datalink_url | epoch_photometry_url | dist | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1635721458409799680 | Gaia DR2 5334208201785186560 | 5334208201785186560 | 595460342 | 2015.5 | 174.073642 | 0.472828 | -61.614653 | 0.250960 | -0.695449 | 0.411972 | -1.688097 | -11.868175 | 0.996948 | 4.197831 | 0.505422 | -0.138907 | -0.265170 | 0.282489 | -0.419210 | 0.003054 | -0.347333 | 0.090266 | 0.291185 | 0.171235 | -0.225816 | 131 | 0 | 130 | 1 | -0.234029 | 120.683662 | 0.000000 | 0.000000 | 31 | False | 0.190621 | 1.401700 | 0.086980 | 0.073929 | ... | 522.328609 | 23.609277 | 22.123871 | 17.967060 | 2.410011 | 0 | 0.979300 | -0.327131 | 1.306431 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.125669 | -0.034392 | 212.815523 | -55.761049 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.000741 | |
1 | 1635721458409799680 | Gaia DR2 5334208304865630208 | 5334208304865630208 | 648553357 | 2015.5 | 174.074696 | 5.327171 | -61.613932 | 3.187506 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | -0.208444 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 39 | 0 | 38 | 1 | 3.163009 | 64.891205 | 7.387883 | 9.836476 | 3 | False | 0.008789 | NaN | NaN | -0.327540 | ... | NaN | NaN | NaN | NaN | NaN | 1 | NaN | NaN | NaN | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.125943 | -0.033558 | 212.815255 | -55.760184 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.001082 | |
2 | 1635721458409799680 | Gaia DR2 5334208304864405248 | 5334208304864405248 | 111246398 | 2015.5 | 174.077325 | 0.109879 | -61.615129 | 0.069090 | 0.391415 | 0.099926 | 3.917049 | -9.930170 | 0.231860 | 2.452449 | 0.148982 | -0.235604 | -0.222603 | -0.553335 | 0.216787 | 0.084098 | 0.330659 | -0.282677 | 0.064832 | 0.371071 | -0.242430 | 157 | 0 | 156 | 1 | 0.639170 | 161.683411 | 0.198162 | 0.863255 | 31 | False | 2.588396 | 1.540610 | 0.019658 | 0.007752 | ... | 2444.931311 | 36.529465 | 66.930389 | 16.291254 | 1.428980 | 0 | 1.029282 | 0.270058 | 0.759224 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.127483 | -0.034347 | 212.818331 | -55.760157 | 102001 | 5215.174805 | 4805.0 | 5754.333496 | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.001105 | |
3 | 1635721458409799680 | Gaia DR2 5334208201785179648 | 5334208201785179648 | 1506337893 | 2015.5 | 174.074270 | 0.325136 | -61.616094 | 0.276855 | 0.555246 | 0.332560 | 1.669611 | -4.874109 | 0.604785 | 1.538106 | 0.593330 | -0.210834 | -0.272371 | -0.568558 | 0.187147 | -0.140817 | 0.268885 | -0.147709 | 0.160472 | 0.173339 | -0.073803 | 148 | 0 | 147 | 1 | 3.456599 | 207.719818 | 1.110453 | 2.527485 | 31 | False | 0.208933 | 1.492084 | 0.077694 | 0.155023 | ... | 693.228738 | 37.596551 | 18.438625 | 17.659729 | 2.028401 | 0 | 1.270449 | 0.040741 | 1.229708 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.126368 | -0.035688 | 212.817701 | -55.761864 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.001147 | |
4 | 1635721458409799680 | Gaia DR2 5334208201786103680 | 5334208201786103680 | 1035601844 | 2015.5 | 174.078111 | 5.318735 | -61.615813 | 1.105737 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.179653 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 49 | 0 | 49 | 0 | 3.529527 | 85.070488 | 2.849068 | 3.095800 | 3 | False | 0.038146 | NaN | NaN | -0.298005 | ... | NaN | NaN | NaN | NaN | NaN | 0 | NaN | NaN | NaN | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.128037 | -0.034895 | 212.819658 | -55.760380 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.001680 | |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
641173 | 1635721458409799680 | Gaia DR2 5334423839210689024 | 5334423839210689024 | 752531040 | 2015.5 | 172.742583 | 0.344167 | -61.132781 | 0.278580 | 0.179066 | 0.369776 | 0.484256 | -6.736121 | 0.718091 | 0.902574 | 0.492490 | 0.010542 | -0.190570 | -0.311312 | -0.234703 | 0.345033 | -0.206900 | -0.218316 | 0.084081 | 0.072144 | 0.060932 | 269 | 0 | 268 | 1 | 1.635341 | 301.586212 | 0.000000 | 0.000000 | 31 | False | 0.100378 | 1.460464 | 0.076742 | -0.075243 | ... | 266.157451 | 8.306463 | 32.042213 | 18.699074 | 1.617207 | 0 | 1.410778 | 0.380215 | 1.030563 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 293.373821 | 0.236813 | 211.405549 | -55.866909 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.799999 | |
641174 | 1635721458409799680 | Gaia DR2 5335744009086616064 | 5335744009086616064 | 1435141115 | 2015.5 | 173.356180 | 0.601299 | -60.893543 | 0.503211 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | -0.149839 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 229 | 0 | 229 | 0 | 18.701391 | 897.093262 | 4.586459 | 28.798994 | 3 | False | 0.030276 | NaN | NaN | -0.025095 | ... | NaN | NaN | NaN | NaN | NaN | 2 | NaN | NaN | NaN | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 293.585171 | 0.554596 | 211.484883 | -55.487894 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.799999 | |
641175 | 1635721458409799680 | Gaia DR2 5335787340979117824 | 5335787340979117824 | 661120566 | 2015.5 | 175.006222 | 0.672003 | -60.951800 | 0.545426 | -0.415728 | 0.670892 | -0.619665 | -3.043897 | 1.954741 | 1.299077 | 0.978110 | -0.160142 | 0.285788 | 0.501596 | -0.455451 | 0.367743 | -0.381783 | -0.099220 | 0.141691 | -0.180839 | -0.435134 | 180 | 0 | 178 | 2 | -0.152341 | 169.523178 | 0.000000 | 0.000000 | 31 | False | 0.051858 | 1.460640 | 0.146437 | 0.006311 | ... | 368.018461 | 3.938269 | 93.446762 | 18.347246 | 2.986848 | 0 | 2.320631 | 0.406834 | 1.913797 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.370441 | 0.727327 | 212.568298 | -54.973401 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.799999 | |
641176 | 1635721458409799680 | Gaia DR2 5334381203098780928 | 5334381203098780928 | 207855570 | 2015.5 | 172.392028 | 0.619581 | -61.628393 | 0.355711 | -0.242914 | 0.549460 | -0.442095 | -8.560214 | 1.267448 | 2.478058 | 0.776909 | -0.407906 | -0.150815 | -0.420806 | 0.126057 | 0.181458 | 0.152150 | -0.000767 | -0.175537 | 0.493100 | -0.505934 | 156 | 0 | 154 | 2 | 5.168045 | 256.246521 | 1.677437 | 18.981586 | 31 | False | 0.087715 | 1.291614 | 0.121466 | -0.141428 | ... | 833.900548 | 75.413352 | 11.057731 | 17.459135 | 4.203367 | 0 | 1.469765 | -0.616373 | 2.086138 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 293.367081 | -0.286425 | 211.821201 | -56.336042 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.799999 | |
641177 | 1635721458409799680 | Gaia DR2 5335757070095897472 | 5335757070095897472 | 1316316633 | 2015.5 | 174.226576 | 2.007322 | -60.818339 | 1.310055 | -0.227477 | 1.974572 | -0.115203 | -6.548984 | 3.974460 | -2.161827 | 2.212148 | -0.027873 | -0.145755 | -0.211495 | -0.294697 | 0.296509 | -0.307884 | -0.165775 | -0.041825 | 0.259863 | -0.199192 | 140 | 0 | 140 | 0 | 9.115553 | 345.759003 | 8.267921 | 11.062434 | 31 | False | 0.008167 | 1.854075 | 0.401154 | -0.161823 | ... | NaN | NaN | NaN | NaN | NaN | 2 | NaN | NaN | NaN | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 293.968900 | 0.749803 | 211.928706 | -55.139264 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.799999 |
641178 rows × 97 columns
all_stars_filtered = all_stars[all_stars['parallax_over_error'] > 3]
all_stars_filtered = all_stars_filtered[(all_stars_filtered['pmdec_over_error'] > 3) & (all_stars_filtered['pmra_over_error'] > 3)]
all_stars_filtered.shape
(88492, 99)
## plotting the skyplot
sns.set(rc={'figure.figsize':(8.7,6.27)})
skyplot = sns.scatterplot(x='ra', y='dec',
data = all_stars_filtered)
skyplot.invert_xaxis()
plt.title('Skyplot of GAIA data')
plt.show()
#### Finding Cantat catalogue
catalog_list = Vizier.find_catalogs('Cantat')
{k:v.description for k,v in catalog_list.items()}
{'I/349': 'StarHorse, Gaia DR2 photo-astrometric distances (Anders+, 2019)', 'J/A+A/561/A94': 'Velocities and photometry in Trumpler 20 (Donati+, 2014)', 'J/A+A/564/A133': 'Gaia FGK benchmark stars: metallicity (Jofre+, 2014)', 'J/A+A/569/A17': 'Gaia-ESO Survey: NGC6705 (Cantat-Gaudin+, 2014)', 'J/A+A/582/A81': 'Gaia FGK benchmark stars: abundances (Jofre+, 2015)', 'J/A+A/588/A120': 'Equivalent widths in 10 open clusters (Cantat-Gaudin+, 2016)', 'J/A+A/591/A37': 'Gaia-ESO Survey. Parameters for cluster members (Jacobson+, 2016)', 'J/A+A/597/A10': 'South Ecliptic Pole stars radial velocities (Fremat+, 2017)', 'J/A+A/598/A68': 'Gaia-ESO Survey. Trumpler 23 (Overbeek+, 2017)', 'J/A+A/601/A19': 'Gaia DR1 open cluster members (Gaia Collaboration+, 2017)', 'J/A+A/603/A2': 'Gaia-ESO Survey abundances radial distribution (Magrini+, 2017)', 'J/A+A/605/A79': 'TGAS Cepheids and RR Lyrae stars (Gaia Collaboration+, 2017)', 'J/A+A/615/A49': 'TGAS stars membership in 128 open clusters (Cantat-Gaudin+, 2018)', 'J/A+A/616/A10': '46 open clusters GaiaDR2 HR diagrams (Gaia Collaboration, 2018)', 'J/A+A/616/A12': 'Gaia DR2 sources in GC and dSph (Gaia Collaboration+, 2018)', 'J/A+A/618/A59': 'Gaia DR2 confirmed new nearby open clusters (Castro-Ginard+, 2018)', 'J/A+A/618/A93': 'Gaia DR2 open clusters in the Milky Way (Cantat-Gaudin+, 2018)', 'J/A+A/619/A155': 'Open cluster kinematics with Gaia DR2 (Soubiran+, 2018)', 'J/A+A/621/A115': 'Vela OB2 members (Cantat-Gaudin+, 2019)', 'J/A+A/623/A108': 'Age of 269 GDR2 open clusters (Bossini+, 2019)', 'J/A+A/623/A110': 'Gaia DR2. Variable stars in CMD (Gaia Collaboration+, 2019)', 'J/A+A/623/A80': 'Open clusters in APOGEE and GALAH surveys (Carrera+, 2019)', 'J/A+A/624/A126': 'New open clusters in Perseus direction (Cantat-Gaudin+, 2019)', 'J/A+A/626/A17': 'Young population in Vela-Puppis region (Cantat-Gaudin+, 2019)', 'J/A+A/627/A119': 'Extended halo of NGC 2682 (M 67) (Carrera+ 2019)', 'J/A+A/627/A35': 'New open clusters in Galactic anti-centre (Castro-Ginard+, 2019)', 'J/A+A/633/A99': 'Gaia DR2 open clusters in the Milky Way. II (Cantat-Gaudin+, 2020)', 'J/A+A/635/A45': '570 new open clusters in the Galactic disc (Castro-Ginard+, 2020)', 'J/A+A/640/A1': 'Portrait Galactic disc (Cantat-Gaudin+, 2020)', 'J/MNRAS/446/1411': 'Trumpler 5 photometric BV catalog (Donati+, 2015)'}
## cheking the tables in the GAIA DR2 paper
Vizier.ROW_LIMIT = -1
#catalogs = Vizier.get_catalogs(catalog_list['J/A+A/633/A99'])
#catalogs
## saving only NGC 3766 data from Cantat GAIA DR2 paper
cantat_3766 = Vizier(catalog = 'J/A+A/633/A99/members', row_limit = -1).query_constraints(Cluster="NGC_3766")
cantat_3766 = cantat_3766[0].to_pandas()
cantat_3766
RA_ICRS | DE_ICRS | Source | Plx | pmRA | pmDE | RV | Gmag | BP-RP | Proba | Cluster | _RA.icrs | _DE.icrs | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 174.195211 | -61.479252 | 5335665295192871936 | 0.4705 | -7.012 | 1.041 | NaN | 17.737400 | 1.1943 | 0.5 | NGC_3766 | 174.195274 | -61.479256 |
1 | 174.521895 | -61.604049 | 5335659140506554752 | 0.4452 | -6.705 | 0.834 | NaN | 17.822300 | 1.4727 | 0.2 | NGC_3766 | 174.521956 | -61.604052 |
2 | 174.447990 | -61.635711 | 5335658831266493056 | 0.3478 | -6.265 | 0.635 | NaN | 16.277500 | 0.9769 | 0.1 | NGC_3766 | 174.448046 | -61.635713 |
3 | 174.516866 | -61.556410 | 5335660961569919232 | 0.4432 | -6.704 | 0.777 | NaN | 12.143400 | 0.1415 | 0.3 | NGC_3766 | 174.516927 | -61.556414 |
4 | 174.439503 | -61.377736 | 5335671205118524288 | 0.4133 | -6.378 | 0.567 | NaN | 14.920500 | 0.9065 | 0.1 | NGC_3766 | 174.439560 | -61.377738 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
1390 | 173.832089 | -61.545743 | 5334212496766217088 | 0.4893 | -6.506 | 1.006 | NaN | 15.071000 | 0.7857 | 0.9 | NGC_3766 | 173.832147 | -61.545747 |
1391 | 173.740258 | -61.484316 | 5334213007832344320 | 0.3333 | -6.699 | 0.714 | NaN | 17.230101 | 1.2828 | 0.1 | NGC_3766 | 173.740318 | -61.484319 |
1392 | 173.628115 | -61.453283 | 5334214077314246912 | 0.5075 | -6.451 | 1.631 | NaN | 15.896900 | 0.9724 | 0.1 | NGC_3766 | 173.628173 | -61.453290 |
1393 | 173.808609 | -61.487716 | 5334213115241584896 | 0.5697 | -6.801 | 1.315 | NaN | 17.310301 | 1.2831 | 0.3 | NGC_3766 | 173.808671 | -61.487721 |
1394 | 173.536850 | -61.654957 | 5334216894813725440 | 0.3723 | -7.289 | 1.747 | NaN | 17.828699 | 1.2321 | 0.1 | NGC_3766 | 173.536916 | -61.654964 |
1395 rows × 13 columns
# renaming the cantat table to match it with gaia_data
cantat_3766 = cantat_3766.rename(columns={'Source':'source_id',
'Proba':'PMemb'})
# taking the subset of only source_id and PMemb
cantat_3766 = cantat_3766.loc[:,['source_id', 'PMemb']]
# join the two table on source_id
cantat_3766 = all_stars_filtered.join(cantat_3766.set_index('source_id'), on='source_id')
# dropping the rows, where we don't have PMemb
# (i.e. the source id was not in the cantat table)
cantat_3766 = cantat_3766.dropna(subset=['PMemb'])
cantat_3766
solution_id | designation | source_id | random_index | ref_epoch | ra | ra_error | dec | dec_error | parallax | parallax_error | parallax_over_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_bad_obs_al | astrometric_gof_al | astrometric_chi2_al | astrometric_excess_noise | astrometric_excess_noise_sig | astrometric_params_solved | astrometric_primary_flag | astrometric_weight_al | astrometric_pseudo_colour | astrometric_pseudo_colour_error | mean_varpi_factor_al | ... | phot_rp_mean_mag | phot_bp_rp_excess_factor | phot_proc_mode | bp_rp | bp_g | g_rp | radial_velocity | radial_velocity_error | rv_nb_transits | rv_template_teff | rv_template_logg | rv_template_fe_h | phot_variable_flag | l | b | ecl_lon | ecl_lat | priam_flags | teff_val | teff_percentile_lower | teff_percentile_upper | a_g_val | a_g_percentile_lower | a_g_percentile_upper | e_bp_min_rp_val | e_bp_min_rp_percentile_lower | e_bp_min_rp_percentile_upper | flame_flags | radius_val | radius_percentile_lower | radius_percentile_upper | lum_val | lum_percentile_lower | lum_percentile_upper | datalink_url | epoch_photometry_url | dist | pmra_over_error | pmdec_over_error | PMemb | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7 | 1635721458409799680 | Gaia DR2 5334208304878429184 | 5334208304878429184 | 1131689298 | 2015.5 | 174.079200 | 0.038525 | -61.612725 | 0.027951 | 0.431941 | 0.040436 | 10.682202 | -6.976923 | 0.076055 | 1.098638 | 0.058655 | 0.068737 | -0.338576 | -0.230299 | -0.088723 | 0.129381 | 0.010359 | -0.232264 | 0.108452 | 0.233223 | 0.050619 | 184 | 184 | 184 | 0 | 8.822382 | 402.049103 | 0.000000 | 0.000000 | 31 | False | 322.265320 | 1.759862 | 0.008077 | 0.002711 | ... | 12.046483 | 1.191182 | 0 | 0.145277 | 0.026115 | 0.119162 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.127648 | -0.031788 | 212.816452 | -55.757820 | 102001 | 9674.000000 | 9365.000000 | 9824.000000 | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.003024 | 91.735585 | 18.730578 | 1.0 | |
10 | 1635721458409799680 | Gaia DR2 5334208201799207680 | 5334208201799207680 | 111178695 | 2015.5 | 174.078507 | 0.034568 | -61.617738 | 0.029462 | 0.461972 | 0.037725 | 12.245805 | -6.705430 | 0.063807 | 1.195871 | 0.056996 | -0.086070 | -0.187057 | -0.504295 | -0.013300 | 0.205240 | 0.081850 | -0.236470 | 0.116135 | 0.244518 | 0.098781 | 203 | 203 | 198 | 5 | 8.925547 | 425.700531 | 0.000000 | 0.000000 | 31 | False | 335.689453 | 1.756988 | 0.008680 | 0.084656 | ... | 11.022589 | 1.178857 | 1 | 0.105139 | 0.021565 | 0.083573 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.128769 | -0.036686 | 212.822301 | -55.761616 | 100002 | 9582.500000 | 9471.000000 | 9694.000000 | 0.4688 | 0.1140 | 0.8060 | 0.2370 | 0.0580 | 0.4041 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.003199 | 105.088982 | 20.981628 | 1.0 | |
16 | 1635721458409799680 | Gaia DR2 5334208197475777152 | 5334208197475777152 | 887841497 | 2015.5 | 174.073881 | 0.040114 | -61.619086 | 0.034597 | 0.427450 | 0.044728 | 9.556643 | -6.723508 | 0.113323 | 0.691220 | 0.080728 | -0.003161 | -0.001094 | -0.104134 | 0.006200 | 0.124020 | 0.015251 | -0.129372 | 0.030571 | 0.093071 | -0.231579 | 151 | 151 | 141 | 10 | 10.196207 | 381.662659 | 0.064293 | 1.117645 | 31 | False | 144.112106 | 1.754939 | 0.010054 | -0.166603 | ... | 11.379885 | 1.190707 | 0 | 0.148150 | 0.027697 | 0.120454 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.127048 | -0.038607 | 212.821207 | -55.764121 | 102001 | 9697.666992 | 9245.250000 | 9824.000000 | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.004118 | 59.330467 | 8.562283 | 0.6 | |
22 | 1635721458409799680 | Gaia DR2 5334208201799203072 | 5334208201799203072 | 578588760 | 2015.5 | 174.078057 | 0.072739 | -61.619506 | 0.049836 | 0.501199 | 0.051079 | 9.812325 | -6.493202 | 0.123608 | 0.741959 | 0.091304 | -0.163163 | 0.021164 | -0.860898 | 0.157684 | 0.163004 | 0.182868 | -0.582306 | 0.017386 | 0.232234 | -0.091188 | 134 | 134 | 134 | 0 | 5.982381 | 249.887863 | 0.000000 | 0.000000 | 31 | False | 342.676086 | 1.749100 | 0.010798 | -0.114155 | ... | 11.988286 | 1.194956 | 1 | 0.191898 | 0.041530 | 0.150369 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.129071 | -0.038441 | 212.824241 | -55.763024 | 100002 | 9218.750000 | 7879.333496 | 9579.000000 | 0.6920 | 0.3582 | 0.9000 | 0.3490 | 0.2287 | 0.4421 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.004729 | 52.530591 | 8.126227 | 0.5 | |
26 | 1635721458409799680 | Gaia DR2 5334208407957797120 | 5334208407957797120 | 1462919753 | 2015.5 | 174.067849 | 0.040567 | -61.618733 | 0.044320 | 0.401176 | 0.047923 | 8.371324 | -6.811881 | 0.070323 | 0.806354 | 0.078850 | 0.023981 | -0.312429 | -0.564358 | -0.167868 | 0.120978 | -0.059390 | -0.604405 | 0.275903 | 0.279363 | 0.171062 | 151 | 151 | 151 | 0 | 4.612320 | 238.921371 | 0.000000 | 0.000000 | 31 | False | 316.529022 | 1.750545 | 0.009926 | 0.140734 | ... | 12.191602 | 1.191845 | 0 | 0.140621 | 0.023656 | 0.116965 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.124200 | -0.039091 | 212.817143 | -55.765887 | 102001 | 9631.000000 | 9254.000000 | 9773.750000 | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.005052 | 96.864992 | 10.226375 | 0.4 | |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
104667 | 1635721458409799680 | Gaia DR2 5334226240663383808 | 5334226240663383808 | 1196845616 | 2015.5 | 173.582901 | 0.013413 | -61.406415 | 0.016843 | 0.340990 | 0.017817 | 19.138027 | -6.466442 | 0.025638 | 0.977490 | 0.033322 | 0.022268 | -0.206760 | -0.393277 | 0.085195 | 0.082193 | 0.110880 | -0.373026 | 0.171602 | 0.082048 | 0.115018 | 285 | 0 | 284 | 1 | -4.186047 | 190.821075 | 0.000000 | 0.000000 | 31 | False | 93.787468 | 1.482849 | 0.003299 | -0.031248 | ... | 12.718704 | 1.274832 | 0 | 1.466496 | 0.676726 | 0.789770 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 293.841230 | 0.096949 | 212.259342 | -55.777523 | 100001 | 4388.834961 | 4262.069824 | 4574.500000 | 0.4650 | 0.3423 | 0.5383 | 0.2247 | 0.1784 | 0.2575 | 200111 | 10.091752 | 9.289189 | 10.700990 | 34.042767 | 29.024714 | 39.060822 | https://gea.esac.esa.int/data-server/datalink/... | 0.314021 | 252.224453 | 29.334440 | 0.2 | |
104808 | 1635721458409799680 | Gaia DR2 5335719411837020032 | 5335719411837020032 | 383822759 | 2015.5 | 174.179099 | 0.033514 | -61.304683 | 0.032270 | 0.512893 | 0.040556 | 12.646508 | -6.729186 | 0.066267 | 1.024816 | 0.060197 | -0.014499 | -0.178555 | -0.430664 | 0.002231 | 0.194078 | 0.074682 | -0.242885 | 0.168868 | 0.211480 | 0.039074 | 283 | 0 | 283 | 0 | -0.601388 | 263.415558 | 0.000000 | 0.000000 | 31 | False | 7.968105 | 1.557056 | 0.008646 | -0.034972 | ... | 15.413404 | 1.256584 | 0 | 1.067652 | 0.432825 | 0.634827 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.085353 | 0.277046 | 212.494267 | -55.504664 | 100001 | 5055.000000 | 4993.000000 | 5276.500000 | NaN | NaN | NaN | NaN | NaN | NaN | 200111 | 1.414196 | 1.297956 | 1.449535 | 1.176517 | 0.964884 | 1.388151 | https://gea.esac.esa.int/data-server/datalink/... | 0.314279 | 101.547152 | 17.024436 | 0.7 | |
104811 | 1635721458409799680 | Gaia DR2 5334215623503357056 | 5334215623503357056 | 965484878 | 2015.5 | 173.417127 | 0.034537 | -61.647611 | 0.030310 | 0.380045 | 0.041666 | 9.121274 | -6.635323 | 0.065853 | 0.923174 | 0.051138 | 0.008160 | -0.209039 | -0.349436 | -0.093429 | 0.248419 | -0.053761 | -0.065945 | 0.114520 | 0.152179 | 0.020440 | 208 | 0 | 208 | 0 | -0.148449 | 199.364105 | 0.000000 | 0.000000 | 31 | False | 12.271348 | 1.580482 | 0.008665 | -0.087356 | ... | NaN | NaN | 2 | NaN | NaN | NaN | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 293.836874 | -0.156828 | 212.462501 | -56.004332 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.314287 | 100.760156 | 18.052498 | 0.4 | |
104889 | 1635721458409799680 | Gaia DR2 5335716869181854336 | 5335716869181854336 | 1275494979 | 2015.5 | 173.866277 | 0.104264 | -61.316810 | 0.091426 | 0.379821 | 0.119557 | 3.176890 | -6.697952 | 0.185801 | 1.797836 | 0.159545 | -0.230804 | 0.048993 | -0.609351 | 0.277233 | -0.013797 | 0.274353 | -0.385961 | 0.124128 | 0.349111 | -0.200922 | 258 | 0 | 258 | 0 | 0.067101 | 253.843674 | 0.000000 | 0.000000 | 31 | False | 1.187623 | 1.454929 | 0.021854 | -0.075688 | ... | 16.831707 | 1.426107 | 0 | 1.452913 | 0.545601 | 0.907312 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 293.944998 | 0.222278 | 212.319514 | -55.618383 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.314422 | 36.049153 | 11.268500 | 0.1 | |
105689 | 1635721458409799680 | Gaia DR2 5334223869841146496 | 5334223869841146496 | 48337135 | 2015.5 | 173.450732 | 0.040652 | -61.508640 | 0.043105 | 0.439741 | 0.056694 | 7.756429 | -6.566127 | 0.084108 | 1.182332 | 0.075012 | 0.015570 | -0.130144 | -0.098090 | 0.004861 | 0.204548 | 0.083418 | -0.054951 | 0.264451 | 0.152831 | -0.000695 | 257 | 0 | 255 | 2 | -0.307930 | 242.523605 | 0.000000 | 0.000000 | 31 | False | 4.790699 | 1.541352 | 0.011694 | 0.001998 | ... | 15.857260 | 1.253688 | 0 | 1.161785 | 0.493565 | 0.668221 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 293.811018 | -0.019350 | 212.307573 | -55.894589 | 100001 | 4922.709961 | 4848.290039 | 5020.192383 | 0.8905 | 0.5552 | 1.1450 | 0.4490 | 0.2630 | 0.5721 | 200111 | 1.416517 | 1.362039 | 1.460337 | 1.061586 | 0.765921 | 1.357250 | https://gea.esac.esa.int/data-server/datalink/... | 0.315743 | 78.067934 | 15.761898 | 0.5 |
1345 rows × 100 columns
# saving both cantat and Gaia files as csv
# if you want to save, comment out the next two lines
cantat_3766.to_csv('NGC_3766_cantat.csv')
# all_stars.to_csv('NGC_3766_Gaia_30m.csv')
cantat_3766.describe()
solution_id | source_id | random_index | ref_epoch | ra | ra_error | dec | dec_error | parallax | parallax_error | parallax_over_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_bad_obs_al | astrometric_gof_al | astrometric_chi2_al | astrometric_excess_noise | astrometric_excess_noise_sig | astrometric_params_solved | astrometric_weight_al | astrometric_pseudo_colour | astrometric_pseudo_colour_error | mean_varpi_factor_al | astrometric_matched_observations | visibility_periods_used | ... | phot_rp_mean_flux | phot_rp_mean_flux_error | phot_rp_mean_flux_over_error | phot_rp_mean_mag | phot_bp_rp_excess_factor | phot_proc_mode | bp_rp | bp_g | g_rp | radial_velocity | radial_velocity_error | rv_nb_transits | rv_template_teff | rv_template_logg | rv_template_fe_h | l | b | ecl_lon | ecl_lat | priam_flags | teff_val | teff_percentile_lower | teff_percentile_upper | a_g_val | a_g_percentile_lower | a_g_percentile_upper | e_bp_min_rp_val | e_bp_min_rp_percentile_lower | e_bp_min_rp_percentile_upper | flame_flags | radius_val | radius_percentile_lower | radius_percentile_upper | lum_val | lum_percentile_lower | lum_percentile_upper | dist | pmra_over_error | pmdec_over_error | PMemb | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 1.358000e+03 | 1.358000e+03 | 1.358000e+03 | 1358.0 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.0 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | ... | 1.345000e+03 | 1345.000000 | 1345.000000 | 1345.000000 | 1344.000000 | 1358.000000 | 1344.000000 | 1344.000000 | 1345.000000 | 7.000000 | 7.000000 | 1358.000000 | 7.000000 | 7.000000 | 7.0 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1126.000000 | 1126.000000 | 1126.000000 | 1126.000000 | 705.000000 | 705.000000 | 705.000000 | 705.000000 | 705.000000 | 705.000000 | 645.0 | 645.000000 | 645.000000 | 645.000000 | 645.000000 | 645.000000 | 645.000000 | 1358.000000 | 1358.000000 | 1358.000000 | 1358.000000 |
mean | 1.635721e+18 | 5.334617e+18 | 8.405839e+08 | 2015.5 | 174.071575 | 0.043936 | -61.604683 | 0.041459 | 0.451233 | 0.051576 | 11.326612 | -6.728480 | 0.086189 | 0.984931 | 0.077625 | 0.004731 | -0.116806 | -0.427590 | 0.005155 | 0.173457 | 0.056225 | -0.328121 | 0.162477 | 0.191084 | 0.024606 | 238.798969 | 31.706186 | 237.056701 | 1.742268 | 2.200283 | 312.288391 | 0.049650 | 1.380207 | 31.0 | 47.418640 | 1.607744 | 0.010553 | -0.013360 | 27.382180 | 16.857879 | ... | 1.994604e+05 | 1003.554474 | 413.775513 | 14.601149 | 1.268321 | 0.086156 | 0.834401 | 0.315271 | 0.520156 | -4.501915 | 0.673221 | 0.047128 | 4328.571289 | 3.428571 | 0.0 | 294.121919 | -0.025329 | 212.802077 | -55.754636 | 100201.804618 | 6623.263672 | 6306.333496 | 6924.939453 | 0.759955 | 0.492425 | 1.004542 | 0.378452 | 0.242196 | 0.500033 | 200111.0 | 3.075979 | 2.817126 | 3.384270 | 77.577660 | 67.863472 | 87.291855 | 0.139595 | 101.802594 | 16.334399 | 0.509423 |
std | 0.000000e+00 | 6.714607e+14 | 4.859962e+08 | 0.0 | 0.240842 | 0.023957 | 0.115445 | 0.023520 | 0.059931 | 0.027627 | 5.656367 | 0.271696 | 0.046871 | 0.253630 | 0.046816 | 0.092069 | 0.111910 | 0.112749 | 0.104203 | 0.108305 | 0.097435 | 0.138586 | 0.107183 | 0.110670 | 0.115644 | 37.217856 | 77.424086 | 37.170668 | 2.669155 | 6.144193 | 275.357025 | 0.113645 | 7.564309 | 0.0 | 82.129440 | 0.098413 | 0.005803 | 0.061461 | 4.129504 | 1.544613 | ... | 2.672622e+06 | 15842.529115 | 320.043213 | 1.802729 | 0.138848 | 0.300968 | 0.468429 | 0.222054 | 0.272651 | 14.147385 | 0.490243 | 0.734949 | 576.524902 | 0.731925 | 0.0 | 0.115537 | 0.114442 | 0.207292 | 0.113302 | 601.279426 | 1747.816895 | 1637.593262 | 1764.303101 | 0.324999 | 0.299344 | 0.367292 | 0.161720 | 0.147130 | 0.185630 | 0.0 | 19.736931 | 18.306589 | 22.103130 | 1102.031494 | 963.601929 | 1240.878418 | 0.083963 | 50.647096 | 8.465254 | 0.296726 |
min | 1.635721e+18 | 5.334149e+18 | 4.126710e+05 | 2015.5 | 173.417127 | 0.013413 | -61.910596 | 0.014418 | 0.332896 | 0.016530 | 3.019621 | -7.712736 | 0.025638 | 0.101040 | 0.025679 | -0.344106 | -0.516385 | -0.860898 | -0.339244 | -0.316974 | -0.379649 | -0.854649 | -0.354136 | -0.375429 | -0.573144 | 98.000000 | 0.000000 | 98.000000 | 0.000000 | -6.530201 | 76.550354 | 0.000000 | 0.000000 | 31.0 | 0.705465 | 1.157450 | 0.003265 | -0.261051 | 12.000000 | 9.000000 | ... | 1.027628e+03 | 6.788959 | 5.183451 | 5.264738 | 1.154781 | 0.000000 | -0.110109 | -1.098209 | -0.075066 | -19.099449 | 0.202318 | 0.000000 | 3600.000000 | 3.000000 | 0.0 | 293.811018 | -0.337450 | 212.259095 | -56.067374 | 100001.000000 | 3290.750000 | 3282.750000 | 3306.333252 | 0.077700 | 0.004400 | 0.200300 | 0.044700 | 0.001900 | 0.105900 | 200111.0 | 0.857373 | 0.673135 | 0.896289 | 0.395953 | 0.295856 | 0.488960 | 0.003024 | 17.195768 | 1.530515 | 0.100000 |
25% | 1.635721e+18 | 5.334201e+18 | 4.280635e+08 | 2015.5 | 173.926336 | 0.025765 | -61.670881 | 0.024447 | 0.405606 | 0.030197 | 6.614598 | -6.862408 | 0.050251 | 0.834218 | 0.044438 | -0.054851 | -0.182556 | -0.500718 | -0.074898 | 0.103306 | -0.016373 | -0.417284 | 0.096918 | 0.122306 | -0.040833 | 217.000000 | 0.000000 | 215.000000 | 0.000000 | -1.174071 | 202.067711 | 0.000000 | 0.000000 | 31.0 | 3.817597 | 1.537823 | 0.006094 | -0.055702 | 25.000000 | 16.000000 | ... | 3.213921e+03 | 17.423210 | 137.199142 | 13.473837 | 1.194906 | 0.000000 | 0.423513 | 0.126806 | 0.278272 | -16.530542 | 0.285499 | 0.000000 | 3850.000000 | 3.000000 | 0.0 | 294.055259 | -0.093408 | 212.677349 | -55.817413 | 100001.000000 | 5113.420410 | 4949.473633 | 5318.376465 | 0.549800 | 0.296900 | 0.769300 | 0.277700 | 0.145000 | 0.383600 | 200111.0 | 1.373179 | 1.217217 | 1.470122 | 1.202450 | 0.952389 | 1.464472 | 0.069097 | 59.300748 | 9.621796 | 0.200000 |
50% | 1.635721e+18 | 5.334209e+18 | 8.370466e+08 | 2015.5 | 174.066962 | 0.036701 | -61.609000 | 0.034138 | 0.452220 | 0.042800 | 10.365438 | -6.731009 | 0.071460 | 0.970831 | 0.064180 | 0.009920 | -0.120122 | -0.425622 | -0.003468 | 0.180204 | 0.052188 | -0.329302 | 0.171626 | 0.215061 | 0.029908 | 241.000000 | 0.000000 | 240.000000 | 1.000000 | 0.282457 | 245.882706 | 0.000000 | 0.000000 | 31.0 | 12.442091 | 1.588659 | 0.008712 | -0.015228 | 28.000000 | 17.000000 | ... | 8.400808e+03 | 26.952790 | 312.309570 | 14.951118 | 1.242643 | 0.000000 | 0.916677 | 0.336640 | 0.572518 | -1.744673 | 0.407522 | 0.000000 | 4500.000000 | 3.000000 | 0.0 | 294.120603 | -0.027841 | 212.806533 | -55.755977 | 100001.000000 | 5832.000000 | 5586.333008 | 6330.148438 | 0.704000 | 0.441700 | 0.919100 | 0.350500 | 0.215600 | 0.459000 | 200111.0 | 1.577313 | 1.421356 | 1.704762 | 2.588136 | 2.089829 | 3.072648 | 0.124303 | 93.912660 | 14.757517 | 0.500000 |
75% | 1.635721e+18 | 5.335661e+18 | 1.241264e+09 | 2015.5 | 174.213895 | 0.056943 | -61.537746 | 0.052895 | 0.494158 | 0.066531 | 15.333289 | -6.603608 | 0.113302 | 1.097845 | 0.099223 | 0.059491 | -0.053632 | -0.362937 | 0.078155 | 0.248950 | 0.125169 | -0.241701 | 0.234742 | 0.268459 | 0.090697 | 264.000000 | 0.000000 | 263.000000 | 2.000000 | 2.532486 | 311.697929 | 0.050997 | 0.191975 | 31.0 | 50.627675 | 1.694443 | 0.013695 | 0.031141 | 30.000000 | 18.000000 | ... | 3.275167e+04 | 59.697425 | 652.262146 | 15.994332 | 1.297263 | 0.000000 | 1.170160 | 0.448370 | 0.713730 | 0.976271 | 1.059858 | 0.000000 | 4750.000000 | 3.750000 | 0.0 | 294.190186 | 0.043993 | 212.931285 | -55.687459 | 100001.000000 | 8362.875000 | 7775.000000 | 8930.750000 | 0.885300 | 0.614400 | 1.147000 | 0.442000 | 0.302900 | 0.575000 | 200111.0 | 1.824228 | 1.659638 | 1.998176 | 5.346476 | 4.568976 | 6.213562 | 0.208008 | 133.922495 | 21.780832 | 0.800000 |
max | 1.635721e+18 | 5.335720e+18 | 1.692908e+09 | 2015.5 | 174.698741 | 0.154707 | -61.304683 | 0.276046 | 0.585019 | 0.167542 | 34.029800 | -5.829765 | 0.378838 | 1.901250 | 0.692472 | 0.568737 | 0.362534 | 0.336245 | 0.446711 | 0.509618 | 0.526214 | 0.580278 | 0.531098 | 0.576435 | 0.788249 | 329.000000 | 318.000000 | 326.000000 | 25.000000 | 56.953041 | 4911.870117 | 1.051814 | 153.240205 | 31.0 | 432.876923 | 1.794202 | 0.048654 | 0.239823 | 37.000000 | 19.000000 | ... | 6.293220e+07 | 391072.672071 | 1756.054077 | 17.232330 | 4.631139 | 2.000000 | 4.108443 | 2.716626 | 1.970177 | 20.439260 | 1.411992 | 15.000000 | 5000.000000 | 4.500000 | 0.0 | 294.423883 | 0.277046 | 213.332423 | -55.452479 | 102011.000000 | 9697.666992 | 9545.666992 | 9860.666992 | 2.540500 | 2.170500 | 3.008000 | 1.261500 | 1.049900 | 1.427800 | 200111.0 | 342.377197 | 314.299286 | 375.893005 | 18573.439453 | 16210.509766 | 21303.781250 | 0.315743 | 257.704865 | 50.842703 | 1.000000 |
8 rows × 94 columns
## plotting the skyplot
skyplot = sns.scatterplot(x = cantat_3766['ra'], y = cantat_3766['dec'],
hue = cantat_3766['PMemb'])
skyplot.invert_xaxis()
plt.title('Sky Plot of the Cantat data')
plt.show()
# import member dataset
member = cantat_3766.copy()
### adding their distance from the center of the clusters
## NGC 3766
center = coord.SkyCoord.from_name('NGC3766')
center_ra, center_dec = center.ra.degree, center.dec.degree
distance = np.sqrt( ((member['ra'] - center_ra)*np.cos(np.radians(member['dec'])))**2 + (member['dec'] - center_dec)**2 )
member['dist_3766_center'] = distance
# maximum distance of stars in Cantat Data
max(member.dist_3766_center)
0.31621627751902387
member['member'] = np.full(len(member), 1)
member.head()
solution_id | designation | source_id | random_index | ref_epoch | ra | ra_error | dec | dec_error | parallax | parallax_error | parallax_over_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_bad_obs_al | astrometric_gof_al | astrometric_chi2_al | astrometric_excess_noise | astrometric_excess_noise_sig | astrometric_params_solved | astrometric_primary_flag | astrometric_weight_al | astrometric_pseudo_colour | astrometric_pseudo_colour_error | mean_varpi_factor_al | ... | phot_proc_mode | bp_rp | bp_g | g_rp | radial_velocity | radial_velocity_error | rv_nb_transits | rv_template_teff | rv_template_logg | rv_template_fe_h | phot_variable_flag | l | b | ecl_lon | ecl_lat | priam_flags | teff_val | teff_percentile_lower | teff_percentile_upper | a_g_val | a_g_percentile_lower | a_g_percentile_upper | e_bp_min_rp_val | e_bp_min_rp_percentile_lower | e_bp_min_rp_percentile_upper | flame_flags | radius_val | radius_percentile_lower | radius_percentile_upper | lum_val | lum_percentile_lower | lum_percentile_upper | datalink_url | epoch_photometry_url | dist | pmra_over_error | pmdec_over_error | PMemb | dist_3766_center | member | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7 | 1635721458409799680 | Gaia DR2 5334208304878429184 | 5334208304878429184 | 1131689298 | 2015.5 | 174.079200 | 0.038525 | -61.612725 | 0.027951 | 0.431941 | 0.040436 | 10.682202 | -6.976923 | 0.076055 | 1.098638 | 0.058655 | 0.068737 | -0.338576 | -0.230299 | -0.088723 | 0.129381 | 0.010359 | -0.232264 | 0.108452 | 0.233223 | 0.050619 | 184 | 184 | 184 | 0 | 8.822382 | 402.049103 | 0.000000 | 0.000000 | 31 | False | 322.265320 | 1.759862 | 0.008077 | 0.002711 | ... | 0 | 0.145277 | 0.026115 | 0.119162 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.127648 | -0.031788 | 212.816452 | -55.757820 | 102001 | 9674.000000 | 9365.000000 | 9824.00 | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.003024 | 91.735585 | 18.730578 | 1 | 0.003027 | 1 | |
10 | 1635721458409799680 | Gaia DR2 5334208201799207680 | 5334208201799207680 | 111178695 | 2015.5 | 174.078507 | 0.034568 | -61.617738 | 0.029462 | 0.461972 | 0.037725 | 12.245805 | -6.705430 | 0.063807 | 1.195871 | 0.056996 | -0.086070 | -0.187057 | -0.504295 | -0.013300 | 0.205240 | 0.081850 | -0.236470 | 0.116135 | 0.244518 | 0.098781 | 203 | 203 | 198 | 5 | 8.925547 | 425.700531 | 0.000000 | 0.000000 | 31 | False | 335.689453 | 1.756988 | 0.008680 | 0.084656 | ... | 1 | 0.105139 | 0.021565 | 0.083573 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.128769 | -0.036686 | 212.822301 | -55.761616 | 100002 | 9582.500000 | 9471.000000 | 9694.00 | 0.4688 | 0.1140 | 0.806 | 0.237 | 0.0580 | 0.4041 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.003199 | 105.088982 | 20.981628 | 1 | 0.003206 | 1 | |
16 | 1635721458409799680 | Gaia DR2 5334208197475777152 | 5334208197475777152 | 887841497 | 2015.5 | 174.073881 | 0.040114 | -61.619086 | 0.034597 | 0.427450 | 0.044728 | 9.556643 | -6.723508 | 0.113323 | 0.691220 | 0.080728 | -0.003161 | -0.001094 | -0.104134 | 0.006200 | 0.124020 | 0.015251 | -0.129372 | 0.030571 | 0.093071 | -0.231579 | 151 | 151 | 141 | 10 | 10.196207 | 381.662659 | 0.064293 | 1.117645 | 31 | False | 144.112106 | 1.754939 | 0.010054 | -0.166603 | ... | 0 | 0.148150 | 0.027697 | 0.120454 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.127048 | -0.038607 | 212.821207 | -55.764121 | 102001 | 9697.666992 | 9245.250000 | 9824.00 | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.004118 | 59.330467 | 8.562283 | 1 | 0.004120 | 1 | |
22 | 1635721458409799680 | Gaia DR2 5334208201799203072 | 5334208201799203072 | 578588760 | 2015.5 | 174.078057 | 0.072739 | -61.619506 | 0.049836 | 0.501199 | 0.051079 | 9.812325 | -6.493202 | 0.123608 | 0.741959 | 0.091304 | -0.163163 | 0.021164 | -0.860898 | 0.157684 | 0.163004 | 0.182868 | -0.582306 | 0.017386 | 0.232234 | -0.091188 | 134 | 134 | 134 | 0 | 5.982381 | 249.887863 | 0.000000 | 0.000000 | 31 | False | 342.676086 | 1.749100 | 0.010798 | -0.114155 | ... | 1 | 0.191898 | 0.041530 | 0.150369 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.129071 | -0.038441 | 212.824241 | -55.763024 | 100002 | 9218.750000 | 7879.333496 | 9579.00 | 0.6920 | 0.3582 | 0.900 | 0.349 | 0.2287 | 0.4421 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.004729 | 52.530591 | 8.126227 | 1 | 0.004735 | 1 | |
26 | 1635721458409799680 | Gaia DR2 5334208407957797120 | 5334208407957797120 | 1462919753 | 2015.5 | 174.067849 | 0.040567 | -61.618733 | 0.044320 | 0.401176 | 0.047923 | 8.371324 | -6.811881 | 0.070323 | 0.806354 | 0.078850 | 0.023981 | -0.312429 | -0.564358 | -0.167868 | 0.120978 | -0.059390 | -0.604405 | 0.275903 | 0.279363 | 0.171062 | 151 | 151 | 151 | 0 | 4.612320 | 238.921371 | 0.000000 | 0.000000 | 31 | False | 316.529022 | 1.750545 | 0.009926 | 0.140734 | ... | 0 | 0.140621 | 0.023656 | 0.116965 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.124200 | -0.039091 | 212.817143 | -55.765887 | 102001 | 9631.000000 | 9254.000000 | 9773.75 | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.005052 | 96.864992 | 10.226375 | 1 | 0.005049 | 1 |
5 rows × 102 columns
### adding their distance from the center of the clusters
## NGC 3766
center = coord.SkyCoord.from_name('NGC3766')
center_ra, center_dec = center.ra.degree, center.dec.degree
distance = np.sqrt( ((all_stars_filtered['ra'] - center_ra)*np.cos(np.radians(all_stars_filtered['dec'])))**2 + (all_stars_filtered['dec'] - center_dec)**2 )
all_stars_filtered['dist_3766_center'] = distance
non_member = all_stars_filtered[all_stars_filtered['dist_3766_center'] >= 0.7].sample(len(member), random_state = 42)
non_member['member'] = np.full(len(non_member), 0)
non_member.head()
solution_id | designation | source_id | random_index | ref_epoch | ra | ra_error | dec | dec_error | parallax | parallax_error | parallax_over_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_bad_obs_al | astrometric_gof_al | astrometric_chi2_al | astrometric_excess_noise | astrometric_excess_noise_sig | astrometric_params_solved | astrometric_primary_flag | astrometric_weight_al | astrometric_pseudo_colour | astrometric_pseudo_colour_error | mean_varpi_factor_al | ... | phot_bp_rp_excess_factor | phot_proc_mode | bp_rp | bp_g | g_rp | radial_velocity | radial_velocity_error | rv_nb_transits | rv_template_teff | rv_template_logg | rv_template_fe_h | phot_variable_flag | l | b | ecl_lon | ecl_lat | priam_flags | teff_val | teff_percentile_lower | teff_percentile_upper | a_g_val | a_g_percentile_lower | a_g_percentile_upper | e_bp_min_rp_val | e_bp_min_rp_percentile_lower | e_bp_min_rp_percentile_upper | flame_flags | radius_val | radius_percentile_lower | radius_percentile_upper | lum_val | lum_percentile_lower | lum_percentile_upper | datalink_url | epoch_photometry_url | dist | pmra_over_error | pmdec_over_error | dist_3766_center | member | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
617823 | 1635721458409799680 | Gaia DR2 5334067361219911552 | 5334067361219911552 | 1485257795 | 2015.5 | 173.963069 | 0.024403 | -62.397810 | 0.026290 | 0.543053 | 0.030432 | 17.845013 | -7.199559 | 0.045806 | -0.365707 | 0.049048 | -0.006626 | -0.135588 | -0.363607 | 0.034692 | 0.025151 | 0.064834 | -0.339126 | 0.229853 | 0.178700 | 0.052562 | 300 | 0 | 297 | 3 | -3.666178 | 211.523102 | 0.000000 | 0.000000 | 31 | False | 12.124643 | 1.548822 | 0.006892 | -0.049745 | ... | 1.236786 | 0 | 1.141031 | 0.495297 | 0.645734 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.301066 | -0.799419 | 213.743720 | -56.350475 | 100001 | 4977.213379 | 4951.020020 | 5079.000000 | NaN | NaN | NaN | NaN | NaN | NaN | 200111 | 1.675584 | 1.609097 | 1.693360 | 1.552287 | 1.341949 | 1.762625 | https://gea.esac.esa.int/data-server/datalink/... | 0.784568 | 157.174318 | 7.456097 | 0.784526 | 0 | |
530949 | 1635721458409799680 | Gaia DR2 5335763907702909568 | 5335763907702909568 | 685584120 | 2015.5 | 173.544370 | 0.048282 | -60.935932 | 0.047295 | 0.401559 | 0.063844 | 6.289723 | -8.142450 | 0.091663 | 0.898640 | 0.083363 | 0.057911 | -0.166037 | -0.391190 | 0.069381 | 0.200272 | 0.148745 | -0.070929 | 0.287029 | 0.198812 | 0.090721 | 291 | 0 | 289 | 2 | 0.373232 | 292.308197 | 0.079807 | 0.238277 | 31 | False | 3.568815 | 1.512340 | 0.013093 | 0.081437 | ... | 1.267171 | 0 | 1.237896 | 0.530428 | 0.707468 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 293.685101 | 0.541161 | 211.652722 | -55.454503 | 100001 | 4867.000000 | 4679.490234 | 5021.680176 | 0.717 | 0.259 | 1.1181 | 0.353 | 0.126 | 0.5461 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.725374 | 88.830475 | 10.779859 | 0.726347 | 0 | |
624419 | 1635721458409799680 | Gaia DR2 5334887768703069056 | 5334887768703069056 | 514673397 | 2015.5 | 175.544689 | 0.067587 | -61.989530 | 0.078370 | 0.630082 | 0.101007 | 6.237980 | -5.725657 | 0.137690 | 1.645013 | 0.131754 | 0.013374 | 0.010386 | -0.321125 | 0.114564 | 0.336550 | 0.182636 | -0.055799 | 0.182267 | 0.345230 | 0.010739 | 237 | 0 | 236 | 1 | 1.948992 | 274.726379 | 0.249715 | 1.368120 | 31 | False | 1.924166 | 1.550022 | 0.018283 | 0.029515 | ... | 1.332509 | 0 | 1.234053 | 0.473362 | 0.760691 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.897171 | -0.203033 | 214.159219 | -55.540007 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.788975 | 41.583635 | 12.485483 | 0.785282 | 0 | |
547882 | 1635721458409799680 | Gaia DR2 5335742188017827456 | 5335742188017827456 | 1616161577 | 2015.5 | 173.174670 | 0.087488 | -61.017738 | 0.073819 | 0.315781 | 0.099393 | 3.177104 | -6.366508 | 0.179986 | 1.431496 | 0.134705 | -0.139535 | -0.035547 | -0.453815 | 0.138151 | 0.192497 | 0.156941 | -0.138739 | 0.065031 | 0.277307 | -0.165912 | 269 | 0 | 265 | 4 | 1.065108 | 284.344086 | 0.119837 | 0.220189 | 31 | False | 1.389606 | 1.474433 | 0.021660 | 0.086228 | ... | 1.366468 | 0 | 1.445168 | 0.586653 | 0.858515 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 293.538134 | 0.409761 | 211.526797 | -55.638319 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.737191 | 35.372176 | 10.626864 | 0.739616 | 0 | |
594412 | 1635721458409799680 | Gaia DR2 5334020906851033472 | 5334020906851033472 | 753989146 | 2015.5 | 174.048999 | 0.025578 | -62.383768 | 0.031578 | 0.512822 | 0.032749 | 15.659157 | -4.112660 | 0.048281 | 0.988130 | 0.059762 | 0.000398 | -0.125370 | -0.357549 | 0.002108 | -0.044556 | 0.005542 | -0.500710 | 0.254377 | 0.171051 | 0.068217 | 278 | 0 | 278 | 0 | -0.530557 | 260.143280 | 0.000000 | 0.000000 | 31 | False | 11.281908 | 1.563492 | 0.006922 | -0.071460 | ... | 1.230774 | 0 | 0.992830 | 0.410459 | 0.582372 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.335170 | -0.774517 | 213.775970 | -56.312221 | 100001 | 5150.000000 | 4960.000000 | 5357.000000 | NaN | NaN | NaN | NaN | NaN | NaN | 200111 | 1.586112 | 1.465902 | 1.709956 | 1.594378 | 1.352355 | 1.836401 | https://gea.esac.esa.int/data-server/datalink/... | 0.768862 | 85.181354 | 16.534542 | 0.768863 | 0 |
5 rows × 101 columns
sns.distplot(member['dist_3766_center'])
sns.distplot(non_member['dist_3766_center'])
plt.show()
/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) /usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
training_data = pd.concat([member, non_member])
# Examining the descriptive statistics of each column
training_data.describe()
solution_id | source_id | random_index | ref_epoch | ra | ra_error | dec | dec_error | parallax | parallax_error | parallax_over_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_bad_obs_al | astrometric_gof_al | astrometric_chi2_al | astrometric_excess_noise | astrometric_excess_noise_sig | astrometric_params_solved | astrometric_weight_al | astrometric_pseudo_colour | astrometric_pseudo_colour_error | mean_varpi_factor_al | astrometric_matched_observations | visibility_periods_used | ... | phot_rp_mean_flux_over_error | phot_rp_mean_mag | phot_bp_rp_excess_factor | phot_proc_mode | bp_rp | bp_g | g_rp | radial_velocity | radial_velocity_error | rv_nb_transits | rv_template_teff | rv_template_logg | rv_template_fe_h | l | b | ecl_lon | ecl_lat | priam_flags | teff_val | teff_percentile_lower | teff_percentile_upper | a_g_val | a_g_percentile_lower | a_g_percentile_upper | e_bp_min_rp_val | e_bp_min_rp_percentile_lower | e_bp_min_rp_percentile_upper | flame_flags | radius_val | radius_percentile_lower | radius_percentile_upper | lum_val | lum_percentile_lower | lum_percentile_upper | dist | pmra_over_error | pmdec_over_error | PMemb | dist_3766_center | member | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 2.690000e+03 | 2.690000e+03 | 2.690000e+03 | 2690.0 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.0 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | ... | 2640.000000 | 2640.000000 | 2640.000000 | 2690.000000 | 2640.000000 | 2640.000000 | 2640.000000 | 24.000000 | 24.000000 | 2690.000000 | 24.000000 | 24.000000 | 24.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 1847.000000 | 1847.000000 | 1847.000000 | 1847.000000 | 1034.000000 | 1034.000000 | 1034.000000 | 1034.000000 | 1034.000000 | 1034.000000 | 1056.0 | 1056.000000 | 1056.000000 | 1056.000000 | 1056.000000 | 1056.000000 | 1056.000000 | 2690.000000 | 2690.000000 | 2690.000000 | 1345.0 | 2690.000000 | 2690.000000 |
mean | 1.635721e+18 | 5.334780e+18 | 8.371501e+08 | 2015.5 | 174.084459 | 0.066992 | -61.538229 | 0.062444 | 0.558447 | 0.080791 | 10.295998 | -7.096944 | 0.136166 | 1.300539 | 0.114932 | -0.005584 | -0.118669 | -0.381542 | 0.007030 | 0.173548 | 0.055493 | -0.257661 | 0.175203 | 0.167158 | 0.003516 | 254.173234 | 18.671375 | 252.585502 | 1.587732 | 2.673294 | 412.997589 | 0.163077 | 4.906309 | 31.0 | 31.199852 | 1.553646 | 0.016498 | -0.012059 | 29.058364 | 17.035316 | ... | 354.910004 | 15.237627 | 1.325449 | 0.081784 | 1.101479 | 0.433776 | 0.667703 | -6.255727 | 1.654193 | 0.058736 | 4804.166504 | 3.270833 | -0.062500 | 294.109193 | 0.037947 | 212.729208 | -55.702968 | 100162.431511 | 6000.077637 | 5729.759766 | 6298.539062 | 0.856197 | 0.595110 | 1.116244 | 0.425542 | 0.293164 | 0.556680 | 200111.0 | 3.108058 | 2.811337 | 3.403092 | 52.026173 | 45.148445 | 58.903931 | 0.444899 | 94.476621 | 21.639689 | 1.0 | 0.445167 | 0.500000 |
std | 0.000000e+00 | 7.120528e+14 | 4.846834e+08 | 0.0 | 0.809550 | 0.092100 | 0.375449 | 0.073639 | 0.499492 | 0.100092 | 8.415555 | 4.232745 | 0.188985 | 2.496381 | 0.138382 | 0.093992 | 0.104941 | 0.131508 | 0.100720 | 0.099614 | 0.097879 | 0.151290 | 0.112455 | 0.108858 | 0.128865 | 35.888641 | 62.044513 | 35.944126 | 2.382516 | 9.323015 | 1497.734619 | 0.496292 | 53.944471 | 0.0 | 68.154724 | 0.110422 | 0.019889 | 0.061136 | 3.990591 | 1.480460 | ... | 324.312714 | 1.769814 | 0.193937 | 0.315702 | 0.530906 | 0.277126 | 0.298738 | 24.357167 | 2.903980 | 0.711359 | 633.471680 | 0.510310 | 0.306186 | 0.389617 | 0.371503 | 0.690759 | 0.371935 | 544.868348 | 1653.693848 | 1552.989746 | 1670.659302 | 0.443962 | 0.431111 | 0.497621 | 0.222305 | 0.213860 | 0.253555 | 0.0 | 15.667116 | 14.504870 | 17.539892 | 862.054138 | 753.700684 | 970.745117 | 0.312023 | 78.675109 | 29.346795 | 0.0 | 0.312285 | 0.500093 |
min | 1.635721e+18 | 5.333378e+18 | 4.126710e+05 | 2015.5 | 172.406209 | 0.012523 | -62.414053 | 0.012379 | 0.096016 | 0.015392 | 3.010611 | -113.169157 | 0.023667 | -69.735741 | 0.022937 | -0.954692 | -0.613589 | -0.860898 | -0.784206 | -0.238388 | -0.728506 | -0.773552 | -0.354136 | -0.563904 | -0.953900 | 98.000000 | 0.000000 | 98.000000 | 0.000000 | -9.866885 | 61.426208 | 0.000000 | 0.000000 | 31.0 | 0.012269 | 1.109872 | 0.003074 | -0.335110 | 12.000000 | 8.000000 | ... | 2.460022 | 5.264738 | 1.154781 | 0.000000 | -0.110109 | -0.670374 | -0.075066 | -65.135911 | 0.202318 | 0.000000 | 3600.000000 | 3.000000 | -1.500000 | 293.328366 | -0.830776 | 211.399258 | -56.557982 | 100001.000000 | 3290.750000 | 3197.500000 | 3306.333252 | 0.016000 | 0.002000 | 0.144000 | 0.008500 | 0.000000 | 0.056100 | 200111.0 | 0.609846 | 0.499326 | 0.665201 | 0.106122 | 0.096722 | 0.115522 | 0.003024 | 3.113406 | 3.002077 | 1.0 | 0.003027 | 0.000000 |
25% | 1.635721e+18 | 5.334205e+18 | 4.218254e+08 | 2015.5 | 173.719674 | 0.027821 | -61.707681 | 0.026647 | 0.379664 | 0.033367 | 4.800555 | -7.120129 | 0.054535 | 0.846504 | 0.048364 | -0.060207 | -0.179700 | -0.465591 | -0.063094 | 0.108881 | -0.009968 | -0.361584 | 0.107365 | 0.092923 | -0.066633 | 234.000000 | 0.000000 | 232.000000 | 0.000000 | -0.980714 | 220.326660 | 0.000000 | 0.000000 | 31.0 | 1.838432 | 1.490859 | 0.006791 | -0.053357 | 27.000000 | 16.000000 | ... | 105.664001 | 14.196423 | 1.225822 | 0.000000 | 0.821448 | 0.278444 | 0.512876 | -19.447737 | 0.382266 | 0.000000 | 4500.000000 | 3.000000 | 0.000000 | 293.922444 | -0.127884 | 212.380328 | -55.879180 | 100001.000000 | 4928.829102 | 4808.467773 | 5076.705078 | 0.571625 | 0.314225 | 0.794450 | 0.285725 | 0.153600 | 0.392400 | 200111.0 | 1.329153 | 1.199500 | 1.426784 | 1.085015 | 0.842584 | 1.336733 | 0.123690 | 45.573678 | 9.320613 | 1.0 | 0.123652 | 0.000000 |
50% | 1.635721e+18 | 5.334381e+18 | 8.405232e+08 | 2015.5 | 174.064404 | 0.043841 | -61.581177 | 0.042374 | 0.456162 | 0.053353 | 7.994664 | -6.734682 | 0.088405 | 1.068744 | 0.076964 | -0.002423 | -0.123961 | -0.390061 | 0.001597 | 0.180352 | 0.053931 | -0.256770 | 0.185580 | 0.176257 | 0.016116 | 260.000000 | 0.000000 | 258.000000 | 1.000000 | 0.597607 | 265.430573 | 0.000000 | 0.000000 | 31.0 | 5.486614 | 1.548086 | 0.010970 | -0.011375 | 30.000000 | 17.000000 | ... | 222.471130 | 15.600630 | 1.274376 | 0.000000 | 1.128533 | 0.426147 | 0.681430 | -6.827385 | 0.667475 | 0.000000 | 5000.000000 | 3.000000 | 0.000000 | 294.115536 | -0.002571 | 212.784256 | -55.736742 | 100001.000000 | 5279.250000 | 5070.000000 | 5522.157227 | 0.751650 | 0.487650 | 0.975100 | 0.371000 | 0.241950 | 0.485100 | 200111.0 | 1.611250 | 1.453910 | 1.749896 | 2.499070 | 2.006824 | 2.960566 | 0.506473 | 78.229195 | 15.537792 | 1.0 | 0.508114 | 0.500000 |
75% | 1.635721e+18 | 5.335672e+18 | 1.245755e+09 | 2015.5 | 174.428081 | 0.073253 | -61.340490 | 0.069704 | 0.535136 | 0.088967 | 13.501408 | -6.378384 | 0.144357 | 1.648697 | 0.128665 | 0.051327 | -0.056762 | -0.313129 | 0.071632 | 0.240808 | 0.119414 | -0.159608 | 0.251491 | 0.245264 | 0.080395 | 280.000000 | 0.000000 | 279.000000 | 2.000000 | 2.589785 | 322.549988 | 0.128600 | 0.726651 | 31.0 | 24.599032 | 1.611632 | 0.018342 | 0.029491 | 32.000000 | 18.000000 | ... | 542.517212 | 16.473679 | 1.349093 | 0.000000 | 1.388607 | 0.550070 | 0.834280 | 9.619295 | 1.462687 | 0.000000 | 5000.000000 | 3.500000 | 0.000000 | 294.285170 | 0.245939 | 213.054404 | -55.511504 | 100001.000000 | 6940.500000 | 6654.000000 | 7182.250000 | 1.009000 | 0.712925 | 1.287650 | 0.500575 | 0.354850 | 0.647775 | 200111.0 | 2.083066 | 1.868889 | 2.249926 | 5.851069 | 4.906713 | 6.750319 | 0.750809 | 125.490733 | 24.702755 | 1.0 | 0.750425 | 1.000000 |
max | 1.635721e+18 | 5.335930e+18 | 1.692908e+09 | 2015.5 | 175.751617 | 1.901509 | -60.815980 | 1.308046 | 9.502661 | 1.670181 | 140.642731 | 19.480213 | 2.981010 | 31.991151 | 2.355574 | 0.672897 | 0.432696 | 0.647326 | 0.446711 | 0.573540 | 0.526214 | 0.824760 | 0.602479 | 0.576435 | 0.800347 | 337.000000 | 318.000000 | 336.000000 | 31.000000 | 159.678391 | 46954.214844 | 8.149992 | 1931.784492 | 31.0 | 472.890381 | 1.915086 | 0.281806 | 0.239823 | 38.000000 | 19.000000 | ... | 2344.896973 | 19.645258 | 4.323397 | 2.000000 | 4.108443 | 2.716626 | 2.303423 | 43.480961 | 12.422131 | 15.000000 | 6000.000000 | 4.500000 | 0.000000 | 294.924320 | 0.761542 | 214.234553 | -54.965249 | 102011.000000 | 9697.666992 | 9545.666992 | 9860.666992 | 2.913000 | 2.838500 | 3.008000 | 1.467700 | 1.422500 | 1.515400 | 200111.0 | 342.377197 | 314.299286 | 375.893005 | 18573.439453 | 16210.509766 | 21303.781250 | 0.799935 | 1414.566757 | 697.630795 | 1.0 | 0.803653 | 1.000000 |
8 rows × 96 columns
# Choosing the features
feature_columns = ['parallax',
'pmra', 'pmdec']
features = training_data.loc[:,feature_columns]
targets = training_data['member']
# Dropping the NULL values from the using training set
# adding features and targets in a training set
training_set = pd.concat((features, targets), axis=1)
# dropping NA
training_set = training_set.dropna()
# finding where dtype is float64
float64_data = np.where(training_set.dtypes == 'float64')[0]
# change the data type to float32 from float64
training_set.iloc[:, float64_data] = training_set.iloc[:, float64_data].astype('float32')
# set features, targets again
features, targets = training_set.iloc[:,:-1], training_set.iloc[:,-1]
features.describe()
parallax | pmra | pmdec | |
---|---|---|---|
count | 2690.000000 | 2690.000000 | 2690.000000 |
mean | 0.558447 | -7.096939 | 1.300540 |
std | 0.499491 | 4.232746 | 2.496381 |
min | 0.096016 | -113.169159 | -69.735741 |
25% | 0.379664 | -7.120129 | 0.846504 |
50% | 0.456162 | -6.734682 | 1.068744 |
75% | 0.535136 | -6.378384 | 1.648697 |
max | 9.502661 | 19.480213 | 31.991152 |
targets.value_counts()
1 1345 0 1345 Name: member, dtype: int64
# histogram of PMemb in the training data
sns.distplot(training_set['member'])
plt.show()
/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
sns.set(rc={'figure.figsize':(8.7,6.27)})
skyplot = sns.scatterplot(x = 'ra', y='dec', palette='YlOrRd', hue = 'member', data = training_data)
skyplot.invert_xaxis()
plt.title('Sky Plot of Training data for NGC 3766')
plt.show()
# CMD marked with the membership probabilities of the stars
# (PMemb >= 0.5 stars are the probable stars)
#cmd = sns.scatterplot(x = 'bp_rp', y='phot_g_mean_mag', palette='YlOrRd', hue = 'PMemb', data = training_data)
#cmd.invert_yaxis()
#plt.title('')
#plt.show()
# proper motion plot marked with the membership probabilities of the stars
fig, axes = plt.subplots(1, 2, figsize=(18,7))
fig.suptitle('CMD of Training data for NGC 3766 ')
sns.scatterplot(x = 'bp_rp', y='phot_g_mean_mag', palette='YlOrRd', color = 'red',
data = member, ax = axes[0])
axes[0].set_title('Member Stars')
axes[0].invert_yaxis()
#plt.show()
sns.scatterplot(x = 'bp_rp', y='phot_g_mean_mag', palette='YlOrRd', color = 'orange',
data = non_member, ax = axes[1])
axes[1].set_title('Non_member Stars')
axes[1].invert_yaxis()
plt.show()
# proper motion plot marked with the membership probabilities of the stars
fig, axes = plt.subplots(1, 2, figsize=(16,7))
fig.suptitle('Proper Motion Plot of Training data for NGC 3766 ')
sns.scatterplot(x = 'pmra', y='pmdec', palette='YlOrRd', color = 'red',
data = member, ax = axes[0])
axes[0].set_title('Member Stars')
#plt.show()
sns.scatterplot(x = 'pmra', y='pmdec', palette='YlOrRd', color = 'orange',
data = non_member, ax = axes[1])
axes[1].set_title('Non_member Stars')
plt.show()
sns.distplot(member.parallax, label='member')
sns.distplot(non_member.parallax, label = 'non_member')
plt.xlim(-0.5,6)
plt.title('Parallax of Training data for NGC 3766 ')
plt.legend()
plt.show()
/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) /usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
from mpl_toolkits.mplot3d import Axes3D
ax = plt.figure().gca(projection='3d')
ax.scatter(member.pmra, member.pmdec, member.parallax)
ax.set_xlabel('pmra')
ax.set_ylabel('pmdec')
ax.set_zlabel('parallax')
plt.title('member stars in the 3D space')
plt.show()
ax = plt.figure().gca(projection='3d')
ax.scatter(non_member.pmra, non_member.pmdec, non_member.parallax)
ax.set_xlabel('pmra')
ax.set_ylabel('pmdec')
ax.set_zlabel('parallax')
plt.title('non-member stars in the 3D space')
plt.show()
# Use Random Forest on whole dataset using 100 different trees
rfc = RandomForestClassifier(n_estimators = 100, oob_score = True)
rfc.fit(features, targets)
RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None, criterion='gini', max_depth=None, max_features='auto', max_leaf_nodes=None, max_samples=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None, oob_score=True, random_state=None, verbose=0, warm_start=False)
# checking the feature importance,
# ( this is higher for a variable if the model efficiency become lower as we remove that variable)
feature_imp = pd.Series(rfc.feature_importances_, index=features.columns).sort_values(ascending = False)
feature_imp
pmra 0.373074 parallax 0.314468 pmdec 0.312458 dtype: float64
# plotting as a barplot
# Creating a bar plot
sns.barplot(x=feature_imp, y=feature_imp.index)
# Add labels to the graph
plt.xlabel('Feature Importance Score')
plt.ylabel('Features')
plt.title("Visualizing Important Features")
plt.show()
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.metrics import precision_score, recall_score, classification_report
from sklearn.model_selection import train_test_split
# splitting our dataset using 0.3 test ratio (30% test data, 70% train data)
train_features, test_features, train_targets, test_targets = train_test_split(features,
targets,
test_size = 0.3,
random_state=258)
def evaluate_model(model):
test_predict = model.predict(test_features)
train_predict = model.predict(train_features)
print('Model Accuracy:')
print("Precision on training data: %.3f" % precision_score(train_targets, train_predict))
print("Precision on testing data: %.3f" % precision_score(test_targets, test_predict))
print('Accuracy on test data: %.3f' % accuracy_score(test_targets, test_predict))
sns.heatmap(confusion_matrix(test_targets, test_predict), cmap= 'Greens', annot = True)
plt.ylabel('Actual')
plt.xlabel('Predicted')
plt.title('Confusion Matrix')
plt.show()
print("Classification Report: \n", classification_report(test_targets, test_predict))
from sklearn.svm import SVC
# SVC model
svc_clf = SVC(kernel='rbf', gamma = 'scale', random_state=42)
svc_clf.fit(train_features, train_targets)
SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf', max_iter=-1, probability=False, random_state=42, shrinking=True, tol=0.001, verbose=False)
evaluate_model(svc_clf)
Model Accuracy: Precision on training data: 0.751 Precision on testing data: 0.762 Accuracy on test data: 0.838
Classification Report: precision recall f1-score support 0 1.00 0.66 0.80 387 1 0.76 1.00 0.87 420 accuracy 0.84 807 macro avg 0.88 0.83 0.83 807 weighted avg 0.88 0.84 0.83 807
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
gnb.fit(train_features, train_targets)
evaluate_model(gnb)
Model Accuracy: Precision on training data: 0.879 Precision on testing data: 0.896 Accuracy on test data: 0.942
Classification Report: precision recall f1-score support 0 1.00 0.88 0.94 403 1 0.90 1.00 0.95 404 accuracy 0.94 807 macro avg 0.95 0.94 0.94 807 weighted avg 0.95 0.94 0.94 807
from sklearn import neighbors
from sklearn.model_selection import cross_val_score, GridSearchCV
knn_cv = neighbors.KNeighborsClassifier()
parameter_grid = {'n_neighbors': [1,2,3,4,5,6,7,8]}
#use gridsearch to test all values for n_neighbors
knn_gscv = GridSearchCV(knn_cv, parameter_grid, cv=5, scoring='precision')
#fit model to data
knn_gscv.fit(train_features, train_targets)
GridSearchCV(cv=5, error_score=nan, estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights='uniform'), iid='deprecated', n_jobs=None, param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8]}, pre_dispatch='2*n_jobs', refit=True, return_train_score=False, scoring='precision', verbose=0)
# top performance
print("Top Performance: ", knn_gscv.best_params_)
# score for top_performance
print("Top CV score: ", knn_gscv.best_score_)
Top Performance: {'n_neighbors': 2} Top CV score: 0.9211235624022134
n_neighbors = 2
knn = neighbors.KNeighborsClassifier(n_neighbors,)
knn.fit(train_features, train_targets)
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=2, p=2, weights='uniform')
evaluate_model(knn)
Model Accuracy: Precision on training data: 1.000 Precision on testing data: 0.930 Accuracy on test data: 0.913
Classification Report: precision recall f1-score support 0 0.90 0.93 0.91 403 1 0.93 0.89 0.91 404 accuracy 0.91 807 macro avg 0.91 0.91 0.91 807 weighted avg 0.91 0.91 0.91 807
from sklearn import tree
dtc = tree.DecisionTreeClassifier()
dtc.fit(train_features, train_targets)
test_predict = dtc.predict(test_features)
dtc.get_params()
{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': None, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'presort': 'deprecated', 'random_state': None, 'splitter': 'best'}
from sklearn.model_selection import RandomizedSearchCV
max_features = ['auto', 'sqrt']
# Maximum number of levels
max_depth = [int(x) for x in np.linspace(10, 100, num = 10)]
max_depth.append(None)
min_samples_split = [2, 5, 10]
min_samples_leaf = [1, 2, 4]
np.random.seed(25)
random_states = np.random.choice(range(1,50), size = 10, replace=False)
ccp_alpha = [2**i for i in range(-10,0)]
random_grid = {'max_features': max_features,
'max_depth': max_depth,
'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf,
'random_state' : random_states,
'ccp_alpha': ccp_alpha}
random_grid
{'ccp_alpha': [0.0009765625, 0.001953125, 0.00390625, 0.0078125, 0.015625, 0.03125, 0.0625, 0.125, 0.25, 0.5], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, None], 'max_features': ['auto', 'sqrt'], 'min_samples_leaf': [1, 2, 4], 'min_samples_split': [2, 5, 10], 'random_state': array([44, 15, 41, 34, 48, 8, 33, 20, 39, 36])}
# base model
dtc = tree.DecisionTreeClassifier()
dtc_random = RandomizedSearchCV(estimator = dtc, param_distributions = random_grid,
n_iter = 100, cv = 5, verbose=2, random_state=42, n_jobs = -1,
scoring = 'precision')
dtc_random.fit(train_features, train_targets)
Fitting 5 folds for each of 100 candidates, totalling 500 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers. [Parallel(n_jobs=-1)]: Done 186 tasks | elapsed: 2.7s [Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed: 4.5s finished
RandomizedSearchCV(cv=5, error_score=nan, estimator=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini', max_depth=None, max_features=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, presort='deprecated', random_state=None, splitter='best'), i... 0.00390625, 0.0078125, 0.015625, 0.03125, 0.0625, 0.125, 0.25, 0.5], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, None], 'max_features': ['auto', 'sqrt'], 'min_samples_leaf': [1, 2, 4], 'min_samples_split': [2, 5, 10], 'random_state': array([44, 15, 41, 34, 48, 8, 33, 20, 39, 36])}, pre_dispatch='2*n_jobs', random_state=42, refit=True, return_train_score=False, scoring='precision', verbose=2)
dtc_random.best_params_
{'ccp_alpha': 0.0078125, 'max_depth': 20, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 2, 'random_state': 48}
dtc_random.best_score_
0.9369494618862596
base_model_dtc = tree.DecisionTreeClassifier()
base_model_dtc.fit(train_features, train_targets)
evaluate_model(base_model_dtc)
Model Accuracy: Precision on training data: 1.000 Precision on testing data: 0.938 Accuracy on test data: 0.936
Classification Report: precision recall f1-score support 0 0.93 0.94 0.94 403 1 0.94 0.93 0.94 404 accuracy 0.94 807 macro avg 0.94 0.94 0.94 807 weighted avg 0.94 0.94 0.94 807
best_random_dtc = dtc_random.best_estimator_
evaluate_model(best_random_dtc)
Model Accuracy: Precision on training data: 0.937 Precision on testing data: 0.942 Accuracy on test data: 0.949
Classification Report: precision recall f1-score support 0 0.96 0.94 0.95 403 1 0.94 0.96 0.95 404 accuracy 0.95 807 macro avg 0.95 0.95 0.95 807 weighted avg 0.95 0.95 0.95 807
from sklearn.model_selection import RandomizedSearchCV
n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1000, num = 10)]
max_features = ['auto', 'sqrt']
# Maximum number of levels
max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]
max_depth.append(None)
min_samples_split = [2, 5, 10]
min_samples_leaf = [1, 2, 4]
bootstrap = [True, False]
ccp_alpha = [2**i for i in range(-10,0)]
random_grid = {'n_estimators': n_estimators,
'max_features': max_features,
'max_depth': max_depth,
'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf,
'bootstrap': bootstrap,
'ccp_alpha': ccp_alpha}
random_grid
{'bootstrap': [True, False], 'ccp_alpha': [0.0009765625, 0.001953125, 0.00390625, 0.0078125, 0.015625, 0.03125, 0.0625, 0.125, 0.25, 0.5], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None], 'max_features': ['auto', 'sqrt'], 'min_samples_leaf': [1, 2, 4], 'min_samples_split': [2, 5, 10], 'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]}
rfc = RandomForestClassifier()
rfc_random = RandomizedSearchCV(estimator = rfc, param_distributions = random_grid,
n_iter = 100, cv = 5, verbose=2, random_state=42, n_jobs = -1,
scoring = 'precision')
rfc_random.fit(train_features, train_targets)
Fitting 5 folds for each of 100 candidates, totalling 500 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers. [Parallel(n_jobs=-1)]: Done 37 tasks | elapsed: 37.9s [Parallel(n_jobs=-1)]: Done 158 tasks | elapsed: 3.3min [Parallel(n_jobs=-1)]: Done 361 tasks | elapsed: 6.9min [Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed: 9.8min finished
RandomizedSearchCV(cv=5, error_score=nan, estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None, criterion='gini', max_depth=None, max_features='auto', max_leaf_nodes=None, max_samples=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs... 0.00390625, 0.0078125, 0.015625, 0.03125, 0.0625, 0.125, 0.25, 0.5], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None], 'max_features': ['auto', 'sqrt'], 'min_samples_leaf': [1, 2, 4], 'min_samples_split': [2, 5, 10], 'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]}, pre_dispatch='2*n_jobs', random_state=42, refit=True, return_train_score=False, scoring='precision', verbose=2)
rfc_random.best_params_
{'bootstrap': True, 'ccp_alpha': 0.0078125, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 200}
base_model = RandomForestClassifier(n_estimators = 100, random_state = 42,
oob_score = True)
base_model.fit(train_features, train_targets)
evaluate_model(base_model)
Model Accuracy: Precision on training data: 1.000 Precision on testing data: 0.961 Accuracy on test data: 0.952
Classification Report: precision recall f1-score support 0 0.94 0.96 0.95 387 1 0.96 0.95 0.95 420 accuracy 0.95 807 macro avg 0.95 0.95 0.95 807 weighted avg 0.95 0.95 0.95 807
best_random = rfc_random.best_estimator_
evaluate_model(best_random)
Model Accuracy: Precision on training data: 0.946 Precision on testing data: 0.968 Accuracy on test data: 0.943
Classification Report: precision recall f1-score support 0 0.92 0.97 0.94 387 1 0.97 0.92 0.94 420 accuracy 0.94 807 macro avg 0.94 0.94 0.94 807 weighted avg 0.94 0.94 0.94 807
# descriptive stats
all_stars_filtered.describe()
solution_id | source_id | random_index | ref_epoch | ra | ra_error | dec | dec_error | parallax | parallax_error | parallax_over_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_bad_obs_al | astrometric_gof_al | astrometric_chi2_al | astrometric_excess_noise | astrometric_excess_noise_sig | astrometric_params_solved | astrometric_weight_al | astrometric_pseudo_colour | astrometric_pseudo_colour_error | mean_varpi_factor_al | astrometric_matched_observations | visibility_periods_used | ... | phot_rp_mean_flux | phot_rp_mean_flux_error | phot_rp_mean_flux_over_error | phot_rp_mean_mag | phot_bp_rp_excess_factor | phot_proc_mode | bp_rp | bp_g | g_rp | radial_velocity | radial_velocity_error | rv_nb_transits | rv_template_teff | rv_template_logg | rv_template_fe_h | l | b | ecl_lon | ecl_lat | priam_flags | teff_val | teff_percentile_lower | teff_percentile_upper | a_g_val | a_g_percentile_lower | a_g_percentile_upper | e_bp_min_rp_val | e_bp_min_rp_percentile_lower | e_bp_min_rp_percentile_upper | flame_flags | radius_val | radius_percentile_lower | radius_percentile_upper | lum_val | lum_percentile_lower | lum_percentile_upper | dist | pmra_over_error | pmdec_over_error | dist_3766_center | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 8.849200e+04 | 8.849200e+04 | 8.849200e+04 | 88492.0 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.0 | 88492.000000 | 88485.000000 | 88485.000000 | 88492.000000 | 88492.000000 | 88492.000000 | ... | 8.557700e+04 | 8.557700e+04 | 85577.000000 | 85577.000000 | 85559.000000 | 88492.000000 | 85559.000000 | 85575.000000 | 85577.000000 | 1269.000000 | 1269.000000 | 88492.000000 | 1269.000000 | 1269.000000 | 1269.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 47884.000000 | 47884.000000 | 47884.000000 | 47884.000000 | 23601.000000 | 23601.000000 | 23601.000000 | 23601.000000 | 23601.000000 | 23601.000000 | 26577.0 | 26577.000000 | 26577.000000 | 26577.000000 | 26577.000000 | 26577.000000 | 26577.000000 | 88492.000000 | 88492.000000 | 88492.000000 | 88492.000000 |
mean | 1.635721e+18 | 5.334944e+18 | 8.462535e+08 | 2015.5 | 174.087411 | 0.096373 | -61.525161 | 0.091111 | 0.696391 | 0.117785 | 9.220138 | -7.630639 | 0.199143 | 1.713202 | 0.167360 | -0.015591 | -0.132323 | -0.352467 | 0.007184 | 0.173277 | 0.055937 | -0.207725 | 0.189009 | 0.139879 | -0.011168 | 261.742530 | 6.324459 | 260.322639 | 1.419891 | 3.171744 | 536.167236 | 0.311542 | 9.140319 | 31.0 | 14.669279 | 1.496958 | 0.024316 | -0.009564 | 29.917371 | 16.996700 | ... | 2.982186e+04 | 1.223609e+02 | 289.889771 | 15.878297 | 1.403201 | 0.092799 | 1.379788 | 0.548903 | 0.830849 | -0.621799 | 1.773304 | 0.076549 | 4979.354004 | 3.258471 | -0.042553 | 294.106924 | 0.050690 | 212.714851 | -55.692693 | 100108.470053 | 5050.312012 | 4845.702637 | 5345.553711 | 1.049514 | 0.805981 | 1.338835 | 0.520819 | 0.397866 | 0.668173 | 200111.0 | 3.116315 | 2.788340 | 3.368828 | 16.138004 | 13.290249 | 18.985895 | 0.526834 | 85.209951 | 26.082106 | 0.527082 |
std | 0.000000e+00 | 7.299506e+14 | 4.880187e+08 | 0.0 | 0.844151 | 0.126478 | 0.380858 | 0.117002 | 0.815288 | 0.149910 | 12.404838 | 5.599557 | 0.276544 | 3.472840 | 0.220609 | 0.091203 | 0.099980 | 0.129382 | 0.103150 | 0.098597 | 0.102539 | 0.154101 | 0.114297 | 0.107678 | 0.129417 | 29.914756 | 39.003592 | 29.953723 | 2.126294 | 11.853771 | 4595.695312 | 0.769559 | 133.426016 | 0.0 | 43.946407 | 0.102226 | 0.030079 | 0.063537 | 3.339600 | 1.429264 | ... | 1.542183e+06 | 1.127505e+04 | 304.695343 | 1.512971 | 0.284002 | 0.366802 | 0.457142 | 0.288054 | 0.267850 | 25.024655 | 2.609303 | 0.697986 | 596.625854 | 0.471356 | 0.249134 | 0.403301 | 0.379758 | 0.705252 | 0.385964 | 450.892567 | 927.264343 | 896.097839 | 956.622009 | 0.632642 | 0.621306 | 0.680121 | 0.317833 | 0.309767 | 0.345479 | 0.0 | 6.503063 | 5.872905 | 7.142126 | 369.527130 | 297.474701 | 442.826660 | 0.192229 | 95.989096 | 41.975123 | 0.192407 |
min | 1.635721e+18 | 5.333378e+18 | 1.780800e+04 | 2015.5 | 172.392181 | 0.011657 | -62.414053 | 0.011804 | 0.066402 | 0.014436 | 3.000006 | -401.640683 | 0.022462 | -120.727923 | 0.021073 | -0.957623 | -0.779774 | -0.936759 | -0.954607 | -0.819697 | -0.910001 | -0.935669 | -0.714867 | -0.677316 | -0.968425 | 51.000000 | 0.000000 | 46.000000 | 0.000000 | -11.402146 | 51.827137 | 0.000000 | 0.000000 | 31.0 | 0.005042 | 0.172492 | 0.002789 | -0.497789 | 7.000000 | 6.000000 | ... | 8.683234e+01 | 6.057287e-01 | 0.100542 | 3.324805 | 1.090912 | 0.000000 | -0.371742 | -1.617760 | -0.133965 | -75.542949 | 0.110645 | 0.000000 | 3600.000000 | 3.000000 | -1.500000 | 293.326670 | -0.833314 | 211.395362 | -56.560219 | 100001.000000 | 3267.500000 | 3124.000000 | 3299.000000 | 0.008000 | 0.000000 | 0.025100 | 0.004000 | 0.000000 | 0.016100 | 200111.0 | 0.500413 | 0.331566 | 0.517189 | 0.035314 | 0.034275 | 0.036353 | 0.001105 | 3.002781 | 3.000049 | 0.001113 |
25% | 1.635721e+18 | 5.334198e+18 | 4.249714e+08 | 2015.5 | 173.404229 | 0.032830 | -61.808529 | 0.032106 | 0.328766 | 0.040469 | 3.933262 | -8.779066 | 0.064646 | 0.898101 | 0.058208 | -0.067336 | -0.195464 | -0.437144 | -0.062216 | 0.110445 | -0.010975 | -0.312729 | 0.123619 | 0.066718 | -0.080915 | 246.000000 | 0.000000 | 244.000000 | 0.000000 | -0.631755 | 236.950962 | 0.000000 | 0.000000 | 31.0 | 0.904197 | 1.446114 | 0.008468 | -0.053068 | 28.000000 | 16.000000 | ... | 1.377852e+03 | 1.322655e+01 | 78.835060 | 15.035714 | 1.258114 | 0.000000 | 1.100633 | 0.403022 | 0.659146 | -16.312925 | 0.486965 | 0.000000 | 4500.000000 | 3.000000 | 0.000000 | 293.775459 | -0.234005 | 212.133250 | -55.996211 | 100001.000000 | 4593.804688 | 4384.500000 | 4919.000000 | 0.595000 | 0.360000 | 0.824600 | 0.293000 | 0.175300 | 0.405700 | 200111.0 | 1.188224 | 1.083032 | 1.267759 | 0.843361 | 0.684887 | 1.000031 | 0.392685 | 31.069858 | 8.575895 | 0.392651 |
50% | 1.635721e+18 | 5.334899e+18 | 8.466096e+08 | 2015.5 | 174.106396 | 0.057589 | -61.499481 | 0.054751 | 0.465950 | 0.070169 | 5.620413 | -6.907018 | 0.115279 | 1.603305 | 0.099490 | -0.010353 | -0.132708 | -0.363370 | 0.001719 | 0.178972 | 0.056790 | -0.214674 | 0.197359 | 0.141889 | 0.000965 | 264.000000 | 0.000000 | 263.000000 | 1.000000 | 0.881353 | 276.600998 | 0.000000 | 0.000000 | 31.0 | 2.918702 | 1.514001 | 0.014718 | -0.009433 | 30.000000 | 17.000000 | ... | 3.007497e+03 | 1.695335e+01 | 171.509674 | 16.066406 | 1.313173 | 0.000000 | 1.305906 | 0.501957 | 0.783722 | -2.895651 | 0.920231 | 0.000000 | 5000.000000 | 3.000000 | 0.000000 | 294.112025 | 0.071444 | 212.697317 | -55.673259 | 100001.000000 | 4963.500000 | 4833.616699 | 5132.910156 | 0.884000 | 0.614400 | 1.169100 | 0.438500 | 0.303400 | 0.580000 | 200111.0 | 1.680683 | 1.526282 | 1.818841 | 2.090767 | 1.704363 | 2.460725 | 0.561069 | 61.053154 | 16.163669 | 0.561294 |
75% | 1.635721e+18 | 5.335699e+18 | 1.266427e+09 | 2015.5 | 174.759877 | 0.103718 | -61.220431 | 0.098336 | 0.734621 | 0.126915 | 9.842104 | -5.656070 | 0.206655 | 2.563652 | 0.179060 | 0.041125 | -0.071079 | -0.280912 | 0.071873 | 0.238572 | 0.123238 | -0.110223 | 0.264227 | 0.214728 | 0.069817 | 282.000000 | 0.000000 | 280.000000 | 2.000000 | 2.777849 | 328.130730 | 0.276050 | 1.418052 | 31.0 | 9.015933 | 1.558275 | 0.026626 | 0.034302 | 32.000000 | 18.000000 | ... | 7.771098e+03 | 2.599259e+01 | 395.612213 | 16.913914 | 1.426045 | 0.000000 | 1.602916 | 0.655095 | 0.973984 | 13.032301 | 1.859839 | 0.000000 | 5500.000000 | 3.500000 | 0.000000 | 294.424039 | 0.359304 | 213.266833 | -55.376603 | 100001.000000 | 5278.866699 | 5069.259766 | 5521.790039 | 1.413300 | 1.138400 | 1.810000 | 0.700500 | 0.559000 | 0.904300 | 200111.0 | 2.560982 | 2.321067 | 2.779947 | 5.918767 | 4.900854 | 6.964908 | 0.687395 | 111.150162 | 29.967447 | 0.687794 |
max | 1.635721e+18 | 5.335930e+18 | 1.692908e+09 | 2015.5 | 175.757679 | 2.570982 | -60.815437 | 2.649437 | 42.994458 | 2.851582 | 635.248901 | 190.853117 | 4.486900 | 153.342791 | 4.381951 | 0.900647 | 0.753407 | 0.951427 | 0.666746 | 0.799786 | 0.795375 | 0.926248 | 0.758049 | 0.822862 | 0.954156 | 371.000000 | 371.000000 | 369.000000 | 118.000000 | 459.058655 | 739893.500000 | 13.119400 | 19219.231143 | 31.0 | 523.571167 | 2.732113 | 0.501559 | 0.369040 | 43.000000 | 19.000000 | ... | 3.757041e+08 | 3.125155e+06 | 5791.619141 | 19.915216 | 4.980681 | 2.000000 | 5.293848 | 3.707506 | 2.491007 | 201.444045 | 19.767395 | 20.000000 | 6500.000000 | 4.500000 | 0.000000 | 294.926222 | 0.765192 | 214.237986 | -54.961212 | 102211.000000 | 9740.500000 | 9546.666992 | 9860.666992 | 3.213700 | 2.906500 | 3.468500 | 1.632000 | 1.472900 | 1.749000 | 200111.0 | 342.377197 | 314.299286 | 375.893005 | 43368.144531 | 32627.091797 | 54109.195312 | 0.799986 | 8046.901311 | 3562.870471 | 0.803857 |
8 rows × 94 columns
# chosing only GAIA stars close to 0.40 degree radius of the center
all_stars_filtered = all_stars_filtered.dropna(subset = feature_columns)
GAIA_target_stars = all_stars_filtered[all_stars_filtered['dist_3766_center'] <= 0.40]
# removing the member stars from GAIA data
GAIA_target_stars = pd.concat([GAIA_target_stars, training_data.drop(columns=['PMemb', 'member']),
training_data.drop(columns=['PMemb', 'member'])]).drop_duplicates(keep=False)
# select the set of predictor variables from the new dataset
new_features = GAIA_target_stars.loc[:, feature_columns]
new_features = new_features.astype('float32')
# train the model again using all the features and targets of the previous dataset
# rfc.fit(features, targets)
# estimate the membership classification of the stars
GAIA_target_stars['member'] = best_random.predict(new_features)
GAIA_target_stars['member'].value_counts()
0 20746 1 828 Name: member, dtype: int64
# estimate the membership probability of the stars
GAIA_target_stars['PMemb'] = best_random.predict_proba(new_features)[:,1]
sum(GAIA_target_stars['PMemb'] >= 0.5)
828
potentialMember = GAIA_target_stars[GAIA_target_stars['member'] == 1]
len(potentialMember)
828
potentialMember.describe()
solution_id | source_id | random_index | ref_epoch | ra | ra_error | dec | dec_error | parallax | parallax_error | parallax_over_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_bad_obs_al | astrometric_gof_al | astrometric_chi2_al | astrometric_excess_noise | astrometric_excess_noise_sig | astrometric_params_solved | astrometric_weight_al | astrometric_pseudo_colour | astrometric_pseudo_colour_error | mean_varpi_factor_al | astrometric_matched_observations | visibility_periods_used | ... | phot_rp_mean_flux_over_error | phot_rp_mean_mag | phot_bp_rp_excess_factor | phot_proc_mode | bp_rp | bp_g | g_rp | radial_velocity | radial_velocity_error | rv_nb_transits | rv_template_teff | rv_template_logg | rv_template_fe_h | l | b | ecl_lon | ecl_lat | priam_flags | teff_val | teff_percentile_lower | teff_percentile_upper | a_g_val | a_g_percentile_lower | a_g_percentile_upper | e_bp_min_rp_val | e_bp_min_rp_percentile_lower | e_bp_min_rp_percentile_upper | flame_flags | radius_val | radius_percentile_lower | radius_percentile_upper | lum_val | lum_percentile_lower | lum_percentile_upper | dist | pmra_over_error | pmdec_over_error | dist_3766_center | member | PMemb | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 8.280000e+02 | 8.280000e+02 | 8.280000e+02 | 828.0 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.0 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | ... | 808.000000 | 808.000000 | 807.000000 | 828.000000 | 807.000000 | 807.000000 | 808.000000 | 2.000000 | 2.000000 | 828.000000 | 2.000000 | 2.00000 | 2.0 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 497.000000 | 497.000000 | 497.000000 | 497.000000 | 280.000000 | 280.000000 | 280.000000 | 280.000000 | 280.000000 | 280.000000 | 355.0 | 355.000000 | 355.000000 | 355.000000 | 355.000000 | 355.000000 | 355.000000 | 828.000000 | 828.000000 | 828.000000 | 828.000000 | 828.0 | 828.000000 |
mean | 1.635721e+18 | 5.334879e+18 | 8.664464e+08 | 2015.5 | 174.104005 | 0.057966 | -61.599802 | 0.056686 | 0.443210 | 0.070520 | 9.081001 | -6.710555 | 0.115078 | 0.955930 | 0.104416 | -0.007597 | -0.128404 | -0.381686 | -0.001068 | 0.170038 | 0.048664 | -0.300879 | 0.183738 | 0.135562 | 0.015427 | 266.144928 | 10.258454 | 264.743961 | 1.400966 | 1.257283 | 308.211792 | 0.082262 | 1.224947 | 31.0 | 19.802937 | 1.547394 | 0.014730 | -0.001016 | 30.346618 | 16.868357 | ... | 307.843201 | 15.632066 | 1.308911 | 0.064010 | 1.129240 | 0.448860 | 0.680755 | 5.435351 | 1.386662 | 0.016908 | 5250.000000 | 3.75000 | 0.0 | 294.135433 | -0.016775 | 212.816199 | -55.740265 | 100137.891348 | 5784.331543 | 5528.875488 | 6095.122559 | 0.906406 | 0.630301 | 1.167686 | 0.448891 | 0.310875 | 0.581358 | 200111.0 | 2.239105 | 2.028564 | 2.428880 | 63.319939 | 53.105579 | 73.534271 | 0.302455 | 86.732895 | 12.683328 | 0.302492 | 1.0 | 0.777271 |
std | 0.000000e+00 | 7.489140e+14 | 4.806607e+08 | 0.0 | 0.458849 | 0.035351 | 0.225348 | 0.033033 | 0.070409 | 0.041902 | 6.000494 | 0.436813 | 0.071003 | 0.308332 | 0.062095 | 0.089198 | 0.099824 | 0.109240 | 0.105887 | 0.106673 | 0.103039 | 0.130102 | 0.100655 | 0.103330 | 0.117759 | 28.247580 | 49.171746 | 28.204078 | 1.816517 | 4.847138 | 266.560944 | 0.155981 | 7.271492 | 0.0 | 51.023819 | 0.086382 | 0.008897 | 0.065258 | 3.103129 | 1.214907 | ... | 294.206177 | 1.451073 | 0.122368 | 0.294257 | 0.414360 | 0.204301 | 0.233448 | 2.376970 | 1.514416 | 0.357614 | 353.553406 | 1.06066 | 0.0 | 0.220312 | 0.223324 | 0.398815 | 0.219147 | 505.483484 | 1302.706055 | 1223.522583 | 1354.723877 | 0.453899 | 0.426418 | 0.495528 | 0.227425 | 0.211576 | 0.250895 | 0.0 | 4.410867 | 4.279086 | 4.641055 | 1092.439697 | 915.799988 | 1269.079224 | 0.084965 | 56.257639 | 8.732675 | 0.084983 | 0.0 | 0.137153 |
min | 1.635721e+18 | 5.334126e+18 | 9.754970e+05 | 2015.5 | 173.243674 | 0.012594 | -62.009758 | 0.013827 | 0.333849 | 0.016164 | 3.008756 | -7.498698 | 0.023865 | 0.138207 | 0.025005 | -0.778783 | -0.742463 | -0.915828 | -0.457533 | -0.229285 | -0.325204 | -0.679248 | -0.280185 | -0.346029 | -0.775599 | 128.000000 | 0.000000 | 127.000000 | 0.000000 | -7.739281 | 108.809547 | 0.000000 | 0.000000 | 31.0 | 0.340738 | 1.272550 | 0.003078 | -0.188372 | 15.000000 | 9.000000 | ... | 4.196263 | 5.982515 | 1.156466 | 0.000000 | 0.007877 | -0.149338 | 0.003888 | 3.754580 | 0.315808 | 0.000000 | 5000.000000 | 3.00000 | 0.0 | 293.732432 | -0.429550 | 212.125085 | -56.151659 | 100001.000000 | 3434.000000 | 3389.000000 | 3965.459961 | 0.145500 | 0.008000 | 0.352000 | 0.073700 | 0.009200 | 0.161600 | 200111.0 | 0.721940 | 0.663391 | 0.788102 | 0.382739 | 0.269785 | 0.495692 | 0.018223 | 16.853522 | 3.034241 | 0.018217 | 1.0 | 0.502270 |
25% | 1.635721e+18 | 5.334178e+18 | 4.408911e+08 | 2015.5 | 173.707846 | 0.028147 | -61.799240 | 0.028460 | 0.383862 | 0.034786 | 4.280530 | -7.080515 | 0.055084 | 0.738547 | 0.050925 | -0.067168 | -0.185554 | -0.450420 | -0.074948 | 0.095606 | -0.023939 | -0.385716 | 0.119554 | 0.060047 | -0.056711 | 251.000000 | 0.000000 | 249.000000 | 0.000000 | -0.942759 | 233.586803 | 0.000000 | 0.000000 | 31.0 | 1.421712 | 1.493478 | 0.007210 | -0.047971 | 29.000000 | 16.000000 | ... | 95.084335 | 14.867832 | 1.232614 | 0.000000 | 0.920974 | 0.342463 | 0.556746 | 4.594966 | 0.851235 | 0.000000 | 5125.000000 | 3.37500 | 0.0 | 293.950511 | -0.214450 | 212.450278 | -55.926364 | 100001.000000 | 4990.234863 | 4835.000000 | 5179.745117 | 0.604500 | 0.355975 | 0.842050 | 0.296325 | 0.175525 | 0.418775 | 200111.0 | 1.405971 | 1.249301 | 1.503659 | 1.333122 | 1.014716 | 1.643861 | 0.256802 | 41.932702 | 6.263991 | 0.257090 | 1.0 | 0.633693 |
50% | 1.635721e+18 | 5.334225e+18 | 9.002400e+08 | 2015.5 | 174.126219 | 0.049256 | -61.596263 | 0.049248 | 0.436762 | 0.060864 | 7.211335 | -6.713204 | 0.098733 | 0.965712 | 0.089831 | 0.000152 | -0.130596 | -0.384706 | -0.007433 | 0.173012 | 0.045485 | -0.314194 | 0.188604 | 0.138169 | 0.027680 | 269.000000 | 0.000000 | 268.000000 | 1.000000 | 0.526954 | 270.430237 | 0.000000 | 0.000000 | 31.0 | 3.921420 | 1.545469 | 0.012572 | -0.000882 | 31.000000 | 17.000000 | ... | 183.392960 | 15.911783 | 1.271755 | 0.000000 | 1.135337 | 0.438643 | 0.683579 | 5.435351 | 1.386662 | 0.000000 | 5250.000000 | 3.75000 | 0.0 | 294.161068 | -0.000780 | 212.827638 | -55.726682 | 100001.000000 | 5265.392578 | 5049.342773 | 5452.029785 | 0.833500 | 0.535450 | 1.053750 | 0.413000 | 0.264650 | 0.525400 | 200111.0 | 1.677088 | 1.516749 | 1.825119 | 2.366287 | 1.874290 | 2.888693 | 0.331141 | 67.468525 | 9.898707 | 0.331617 | 1.0 | 0.796437 |
75% | 1.635721e+18 | 5.335670e+18 | 1.271271e+09 | 2015.5 | 174.513730 | 0.080902 | -61.397629 | 0.079796 | 0.493862 | 0.098886 | 12.224483 | -6.327729 | 0.160721 | 1.201044 | 0.145225 | 0.052210 | -0.068188 | -0.323250 | 0.065165 | 0.244117 | 0.121553 | -0.229104 | 0.251272 | 0.212968 | 0.094258 | 285.000000 | 0.000000 | 283.000000 | 2.000000 | 1.727017 | 309.177605 | 0.112306 | 0.353815 | 31.0 | 13.513373 | 1.590089 | 0.020513 | 0.045840 | 32.000000 | 18.000000 | ... | 442.508850 | 16.733938 | 1.356502 | 0.000000 | 1.380724 | 0.551573 | 0.835304 | 6.275737 | 1.922088 | 0.000000 | 5375.000000 | 4.12500 | 0.0 | 294.320302 | 0.179045 | 213.179444 | -55.547770 | 100001.000000 | 5992.109863 | 5769.750000 | 6675.270020 | 1.056175 | 0.755775 | 1.350100 | 0.525375 | 0.374300 | 0.670375 | 200111.0 | 2.050789 | 1.874341 | 2.238647 | 4.819128 | 4.042440 | 5.846224 | 0.368573 | 122.028991 | 16.855997 | 0.368629 | 1.0 | 0.907752 |
max | 1.635721e+18 | 5.335728e+18 | 1.681522e+09 | 2015.5 | 174.898037 | 0.219098 | -61.220907 | 0.161054 | 0.590783 | 0.193342 | 32.681595 | -5.964021 | 0.375835 | 1.477897 | 0.311953 | 0.303474 | 0.235416 | 0.233257 | 0.624010 | 0.739432 | 0.668929 | 0.197986 | 0.622897 | 0.429969 | 0.344986 | 333.000000 | 333.000000 | 329.000000 | 18.000000 | 65.939606 | 6526.287598 | 1.007324 | 175.647977 | 31.0 | 409.532379 | 1.803916 | 0.042392 | 0.191723 | 38.000000 | 19.000000 | ... | 1811.243042 | 17.973690 | 2.624807 | 2.000000 | 2.562157 | 1.358486 | 1.597757 | 7.116123 | 2.457515 | 9.000000 | 5500.000000 | 4.50000 | 0.0 | 294.526748 | 0.361597 | 213.519396 | -55.366605 | 102011.000000 | 9674.000000 | 9530.500000 | 9822.000000 | 2.687500 | 2.510700 | 3.012600 | 1.347000 | 1.245600 | 1.522000 | 200111.0 | 78.651558 | 77.168541 | 82.700775 | 20587.173828 | 17258.289062 | 23916.058594 | 0.400603 | 287.883550 | 53.655195 | 0.399918 | 1.0 | 0.941000 |
8 rows × 96 columns
member.describe()
solution_id | source_id | random_index | ref_epoch | ra | ra_error | dec | dec_error | parallax | parallax_error | parallax_over_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_bad_obs_al | astrometric_gof_al | astrometric_chi2_al | astrometric_excess_noise | astrometric_excess_noise_sig | astrometric_params_solved | astrometric_weight_al | astrometric_pseudo_colour | astrometric_pseudo_colour_error | mean_varpi_factor_al | astrometric_matched_observations | visibility_periods_used | ... | phot_rp_mean_flux_over_error | phot_rp_mean_mag | phot_bp_rp_excess_factor | phot_proc_mode | bp_rp | bp_g | g_rp | radial_velocity | radial_velocity_error | rv_nb_transits | rv_template_teff | rv_template_logg | rv_template_fe_h | l | b | ecl_lon | ecl_lat | priam_flags | teff_val | teff_percentile_lower | teff_percentile_upper | a_g_val | a_g_percentile_lower | a_g_percentile_upper | e_bp_min_rp_val | e_bp_min_rp_percentile_lower | e_bp_min_rp_percentile_upper | flame_flags | radius_val | radius_percentile_lower | radius_percentile_upper | lum_val | lum_percentile_lower | lum_percentile_upper | dist | pmra_over_error | pmdec_over_error | PMemb | dist_3766_center | member | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 1.345000e+03 | 1.345000e+03 | 1.345000e+03 | 1345.0 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.0 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | ... | 1332.000000 | 1332.000000 | 1332.000000 | 1345.000000 | 1332.000000 | 1332.000000 | 1332.000000 | 7.000000 | 7.000000 | 1345.000000 | 7.000000 | 7.000000 | 7.0 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1124.000000 | 1124.000000 | 1124.000000 | 1124.000000 | 704.000000 | 704.000000 | 704.000000 | 704.000000 | 704.000000 | 704.000000 | 645.0 | 645.000000 | 645.000000 | 645.000000 | 645.000000 | 645.000000 | 645.000000 | 1345.000000 | 1345.000000 | 1345.000000 | 1345.0 | 1345.000000 | 1345.0 |
mean | 1.635721e+18 | 5.334609e+18 | 8.424531e+08 | 2015.5 | 174.069570 | 0.043488 | -61.605326 | 0.040766 | 0.451516 | 0.051044 | 11.389517 | -6.729265 | 0.085252 | 0.989706 | 0.076118 | 0.004484 | -0.117640 | -0.427358 | 0.005188 | 0.174022 | 0.056509 | -0.326855 | 0.162926 | 0.191589 | 0.023870 | 238.846840 | 32.005948 | 237.107063 | 1.739777 | 2.201549 | 312.656036 | 0.048518 | 1.378081 | 31.0 | 47.825153 | 1.608523 | 0.010429 | -0.013727 | 27.389591 | 16.869145 | ... | 416.553314 | 14.584754 | 1.264339 | 0.086989 | 0.831568 | 0.315800 | 0.515768 | -4.501915 | 0.673221 | 0.047584 | 4328.571289 | 3.428571 | 0.0 | 294.121187 | -0.026219 | 212.801672 | -55.755764 | 100202.161922 | 6623.167480 | 6306.059082 | 6925.103027 | 0.760011 | 0.492358 | 1.004757 | 0.378493 | 0.242183 | 0.500143 | 200111.0 | 3.075979 | 2.817126 | 3.384270 | 77.577660 | 67.863472 | 87.291855 | 0.139323 | 102.326219 | 16.470604 | 1.0 | 0.139332 | 1.0 |
std | 0.000000e+00 | 6.676445e+14 | 4.857395e+08 | 0.0 | 0.240581 | 0.023430 | 0.115405 | 0.021349 | 0.059907 | 0.027003 | 5.637852 | 0.271107 | 0.045445 | 0.247746 | 0.040575 | 0.090899 | 0.111030 | 0.112825 | 0.103957 | 0.107163 | 0.096844 | 0.137262 | 0.106514 | 0.109707 | 0.113991 | 37.037952 | 77.736879 | 36.979911 | 2.673019 | 6.167279 | 276.595795 | 0.111442 | 7.595787 | 0.0 | 82.410362 | 0.098359 | 0.005612 | 0.061237 | 4.107544 | 1.523372 | ... | 319.959290 | 1.801677 | 0.098499 | 0.302301 | 0.468737 | 0.218305 | 0.266216 | 14.147385 | 0.490243 | 0.738480 | 576.524902 | 0.731925 | 0.0 | 0.115599 | 0.114211 | 0.207730 | 0.112881 | 601.754838 | 1747.951538 | 1637.770142 | 1764.354858 | 0.325226 | 0.299551 | 0.367508 | 0.161831 | 0.147235 | 0.185739 | 0.0 | 19.736931 | 18.306589 | 22.103130 | 1102.031494 | 963.601929 | 1240.878418 | 0.084134 | 50.487480 | 8.391206 | 0.0 | 0.084147 | 0.0 |
min | 1.635721e+18 | 5.334149e+18 | 4.126710e+05 | 2015.5 | 173.417127 | 0.013413 | -61.910596 | 0.014418 | 0.332896 | 0.016530 | 3.019621 | -7.712736 | 0.025638 | 0.141068 | 0.025679 | -0.344106 | -0.516385 | -0.860898 | -0.339244 | -0.238388 | -0.379649 | -0.773552 | -0.354136 | -0.162664 | -0.573144 | 98.000000 | 0.000000 | 98.000000 | 0.000000 | -6.530201 | 76.550354 | 0.000000 | 0.000000 | 31.0 | 0.705465 | 1.157450 | 0.003265 | -0.261051 | 12.000000 | 9.000000 | ... | 15.671761 | 5.264738 | 1.154781 | 0.000000 | -0.110109 | -0.313632 | -0.075066 | -19.099449 | 0.202318 | 0.000000 | 3600.000000 | 3.000000 | 0.0 | 293.811018 | -0.337450 | 212.259095 | -56.067374 | 100001.000000 | 3290.750000 | 3282.750000 | 3306.333252 | 0.077700 | 0.004400 | 0.200300 | 0.044700 | 0.001900 | 0.105900 | 200111.0 | 0.857373 | 0.673135 | 0.896289 | 0.395953 | 0.295856 | 0.488960 | 0.003024 | 22.618452 | 3.044901 | 1.0 | 0.003027 | 1.0 |
25% | 1.635721e+18 | 5.334201e+18 | 4.361924e+08 | 2015.5 | 173.923115 | 0.025723 | -61.671202 | 0.024419 | 0.405809 | 0.030119 | 6.676829 | -6.862526 | 0.050117 | 0.838106 | 0.044354 | -0.054873 | -0.182762 | -0.500197 | -0.074832 | 0.103585 | -0.016283 | -0.415739 | 0.097576 | 0.123156 | -0.041602 | 217.000000 | 0.000000 | 215.000000 | 0.000000 | -1.176565 | 201.939651 | 0.000000 | 0.000000 | 31.0 | 3.873206 | 1.538251 | 0.006059 | -0.056026 | 25.000000 | 16.000000 | ... | 139.019272 | 13.459515 | 1.194687 | 0.000000 | 0.420239 | 0.126806 | 0.277201 | -16.530542 | 0.285499 | 0.000000 | 3850.000000 | 3.000000 | 0.0 | 294.054819 | -0.094208 | 212.677334 | -55.817697 | 100001.000000 | 5114.622070 | 4949.875000 | 5320.053711 | 0.549775 | 0.296675 | 0.768850 | 0.277650 | 0.144975 | 0.383450 | 200111.0 | 1.373179 | 1.217217 | 1.470122 | 1.202450 | 0.952389 | 1.464472 | 0.068702 | 60.219528 | 9.703554 | 1.0 | 0.068670 | 1.0 |
50% | 1.635721e+18 | 5.334209e+18 | 8.458704e+08 | 2015.5 | 174.065546 | 0.036468 | -61.609265 | 0.033929 | 0.452331 | 0.042692 | 10.458754 | -6.731312 | 0.070778 | 0.971946 | 0.063715 | 0.009946 | -0.120852 | -0.425340 | -0.003822 | 0.180204 | 0.052146 | -0.328883 | 0.171602 | 0.215119 | 0.030227 | 241.000000 | 0.000000 | 240.000000 | 1.000000 | 0.269433 | 245.356064 | 0.000000 | 0.000000 | 31.0 | 12.641415 | 1.589826 | 0.008666 | -0.015479 | 28.000000 | 17.000000 | ... | 318.587158 | 14.935242 | 1.242013 | 0.000000 | 0.912283 | 0.336052 | 0.568142 | -1.744673 | 0.407522 | 0.000000 | 4500.000000 | 3.000000 | 0.0 | 294.120099 | -0.028699 | 212.806062 | -55.756861 | 100001.000000 | 5832.000000 | 5586.333008 | 6330.148438 | 0.704000 | 0.441450 | 0.919100 | 0.350500 | 0.215450 | 0.459000 | 200111.0 | 1.577313 | 1.421356 | 1.704762 | 2.588136 | 2.089829 | 3.072648 | 0.123689 | 94.723077 | 14.920059 | 1.0 | 0.123610 | 1.0 |
75% | 1.635721e+18 | 5.335660e+18 | 1.244792e+09 | 2015.5 | 174.208830 | 0.056126 | -61.539197 | 0.052457 | 0.494176 | 0.065644 | 15.397300 | -6.604067 | 0.112538 | 1.098638 | 0.098271 | 0.059390 | -0.054688 | -0.362898 | 0.078027 | 0.249361 | 0.125008 | -0.241238 | 0.235030 | 0.268205 | 0.090699 | 264.000000 | 0.000000 | 263.000000 | 2.000000 | 2.522232 | 311.681702 | 0.049732 | 0.180974 | 31.0 | 51.038136 | 1.694710 | 0.013562 | 0.030366 | 30.000000 | 18.000000 | ... | 656.966125 | 15.988084 | 1.295670 | 0.000000 | 1.166913 | 0.448165 | 0.711086 | 0.976271 | 1.059858 | 0.000000 | 4750.000000 | 3.750000 | 0.0 | 294.189791 | 0.042120 | 212.930528 | -55.690403 | 100001.000000 | 8367.812500 | 7775.000000 | 8931.750000 | 0.885800 | 0.614975 | 1.147375 | 0.442250 | 0.302900 | 0.575000 | 200111.0 | 1.824228 | 1.659638 | 1.998176 | 5.346476 | 4.568976 | 6.213562 | 0.208084 | 134.090450 | 21.796754 | 1.0 | 0.208140 | 1.0 |
max | 1.635721e+18 | 5.335720e+18 | 1.692908e+09 | 2015.5 | 174.698741 | 0.139428 | -61.304683 | 0.120454 | 0.585019 | 0.167542 | 34.029800 | -5.829765 | 0.277472 | 1.901250 | 0.236357 | 0.482920 | 0.357274 | 0.336245 | 0.446711 | 0.509618 | 0.526214 | 0.580278 | 0.531098 | 0.576435 | 0.695688 | 329.000000 | 318.000000 | 326.000000 | 25.000000 | 56.953041 | 4911.870117 | 1.051814 | 153.240205 | 31.0 | 432.876923 | 1.794202 | 0.028900 | 0.239823 | 37.000000 | 19.000000 | ... | 1756.054077 | 17.232330 | 2.767901 | 2.000000 | 4.108443 | 2.716626 | 1.710671 | 20.439260 | 1.411992 | 15.000000 | 5000.000000 | 4.500000 | 0.0 | 294.423883 | 0.277046 | 213.332423 | -55.452479 | 102011.000000 | 9697.666992 | 9545.666992 | 9860.666992 | 2.540500 | 2.170500 | 3.008000 | 1.261500 | 1.049900 | 1.427800 | 200111.0 | 342.377197 | 314.299286 | 375.893005 | 18573.439453 | 16210.509766 | 21303.781250 | 0.315743 | 257.704865 | 50.842703 | 1.0 | 0.316216 | 1.0 |
8 rows × 96 columns
# CMD of predicted members
cmd = sns.scatterplot(x = 'bp_rp', y = 'phot_g_mean_mag', hue= 'PMemb',
palette='YlOrRd', data = GAIA_target_stars[GAIA_target_stars['PMemb'] >= 0.5] )
cmd.invert_yaxis()
skyplot = sns.scatterplot(x = 'ra', y = 'dec', hue= 'PMemb',
palette='YlOrRd', data = GAIA_target_stars[GAIA_target_stars['PMemb'] >= 0.5] )
skyplot.invert_xaxis()
# pm plot
sns.scatterplot(x = 'pmra', y = 'pmdec', hue= 'PMemb',
palette='YlOrRd', data = GAIA_target_stars[GAIA_target_stars['PMemb'] >= 0.5] )
<matplotlib.axes._subplots.AxesSubplot at 0x7f4e646c8b38>
# parallax plot
sns.scatterplot(x = 'parallax', y = 'PMemb',
palette='YlOrRd', data = GAIA_target_stars[GAIA_target_stars['PMemb'] >= 0.5] )
<matplotlib.axes._subplots.AxesSubplot at 0x7f4e64659b00>
# saving the files as csv
# all_stars.to_csv('gaia_3766_membership_prob.csv')
potentialMember.to_csv('NGC_3766_membership_prob.csv')
# creating subset for the potential member in previous dataset
concatenated = pd.concat([potentialMember.assign(dataset='New_member'), member.assign(dataset='Old_member')])
concatenated
solution_id | designation | source_id | random_index | ref_epoch | ra | ra_error | dec | dec_error | parallax | parallax_error | parallax_over_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_bad_obs_al | astrometric_gof_al | astrometric_chi2_al | astrometric_excess_noise | astrometric_excess_noise_sig | astrometric_params_solved | astrometric_primary_flag | astrometric_weight_al | astrometric_pseudo_colour | astrometric_pseudo_colour_error | mean_varpi_factor_al | ... | bp_rp | bp_g | g_rp | radial_velocity | radial_velocity_error | rv_nb_transits | rv_template_teff | rv_template_logg | rv_template_fe_h | phot_variable_flag | l | b | ecl_lon | ecl_lat | priam_flags | teff_val | teff_percentile_lower | teff_percentile_upper | a_g_val | a_g_percentile_lower | a_g_percentile_upper | e_bp_min_rp_val | e_bp_min_rp_percentile_lower | e_bp_min_rp_percentile_upper | flame_flags | radius_val | radius_percentile_lower | radius_percentile_upper | lum_val | lum_percentile_lower | lum_percentile_upper | datalink_url | epoch_photometry_url | dist | pmra_over_error | pmdec_over_error | dist_3766_center | member | PMemb | dataset | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
338 | 1635721458409799680 | Gaia DR2 5334208407957638272 | 5334208407957638272 | 802944951 | 2015.5 | 174.039949 | 0.058612 | -61.607646 | 0.056858 | 0.586519 | 0.072502 | 8.089740 | -6.489173 | 0.123566 | 0.848730 | 0.109688 | 0.033271 | -0.278224 | -0.187147 | -0.180648 | 0.076126 | -0.083357 | -0.403754 | 0.061030 | 0.274418 | -0.083522 | 173 | 0 | 172 | 1 | -1.719373 | 136.922104 | 0.000000 | 0.000000 | 31 | False | 4.546117 | 1.541758 | 0.014532 | -0.090617 | ... | NaN | NaN | 0.983673 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.108313 | -0.032276 | 212.786521 | -55.767333 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.018223 | 52.515698 | 7.737672 | 0.018217 | 1 | 0.563154 | New_member | |
766 | 1635721458409799680 | Gaia DR2 5334206689970688768 | 5334206689970688768 | 562810260 | 2015.5 | 174.078630 | 0.149676 | -61.641407 | 0.132298 | 0.568788 | 0.172745 | 3.292653 | -6.579990 | 0.301228 | 1.120797 | 0.287315 | 0.085105 | -0.196737 | -0.298064 | 0.004164 | 0.005124 | 0.040176 | -0.057365 | 0.301056 | 0.177107 | 0.167448 | 176 | 0 | 174 | 2 | 2.561639 | 219.809967 | 0.391231 | 1.030532 | 31 | False | 0.628552 | 1.513033 | 0.039376 | 0.129987 | ... | 1.563856 | 0.569481 | 0.994375 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.135607 | -0.059345 | 212.851970 | -55.778400 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.026460 | 21.843851 | 3.900933 | 0.026463 | 1 | 0.908125 | New_member | |
1322 | 1635721458409799680 | Gaia DR2 5334208133079719040 | 5334208133079719040 | 434257841 | 2015.5 | 174.130123 | 0.021635 | -61.637112 | 0.019780 | 0.394277 | 0.025434 | 15.501984 | -7.128697 | 0.045157 | 0.437362 | 0.037572 | 0.072761 | -0.107911 | -0.392961 | -0.075566 | 0.148517 | -0.022163 | -0.316381 | 0.268447 | 0.168135 | 0.035284 | 216 | 0 | 216 | 0 | 3.271853 | 284.786804 | 0.103227 | 2.181580 | 31 | False | 41.375767 | 1.692942 | 0.005291 | 0.047827 | ... | 0.451794 | 0.152245 | 0.299549 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.157815 | -0.048230 | 212.877508 | -55.758142 | 100001 | 7703.000000 | 7423.333496 | 7965.000000 | 1.0020 | 0.7266 | 1.1695 | 0.5185 | 0.3920 | 0.6208 | 200111 | 2.223898 | 2.079999 | 2.394621 | 15.687874 | 13.125473 | 18.250275 | https://gea.esac.esa.int/data-server/datalink/... | 0.034273 | 157.863227 | 11.640774 | 0.034273 | 1 | 0.739011 | New_member | |
1694 | 1635721458409799680 | Gaia DR2 5334206930488832000 | 5334206930488832000 | 902789955 | 2015.5 | 174.029025 | 0.021929 | -61.646821 | 0.022224 | 0.486095 | 0.029137 | 16.682817 | -6.697493 | 0.061479 | 1.437520 | 0.048750 | -0.011897 | 0.189742 | -0.077748 | -0.011686 | 0.154882 | 0.081936 | -0.313348 | 0.360471 | -0.088097 | -0.273432 | 166 | 0 | 166 | 0 | 0.856073 | 176.146637 | 0.000000 | 0.000000 | 31 | False | 73.486725 | 1.713391 | 0.004948 | -0.088797 | ... | 0.341888 | 0.109204 | 0.232684 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.114591 | -0.071291 | 212.828973 | -55.798825 | 100001 | 8369.500000 | 8036.333496 | 8802.000000 | 0.7670 | 0.6140 | 0.8850 | 0.3688 | 0.3029 | 0.4281 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.038599 | 108.938870 | 29.487590 | 0.038591 | 1 | 0.773206 | New_member | |
1950 | 1635721458409799680 | Gaia DR2 5334209198231611136 | 5334209198231611136 | 101907736 | 2015.5 | 173.992506 | 0.073890 | -61.602397 | 0.063561 | 0.451278 | 0.081029 | 5.569371 | -6.138586 | 0.147530 | 0.734541 | 0.117306 | -0.093158 | -0.172155 | -0.511443 | 0.100626 | 0.039040 | 0.160505 | -0.342920 | 0.110770 | 0.314868 | -0.160498 | 196 | 0 | 195 | 1 | 5.347818 | 313.544739 | 0.342622 | 4.270558 | 31 | False | 2.877218 | 1.512530 | 0.018909 | -0.121590 | ... | 1.162205 | 0.401609 | 0.760595 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.085195 | -0.033733 | 212.751444 | -55.779470 | 100001 | 4892.666504 | 4777.000000 | 5013.273438 | 0.9190 | 0.5987 | 1.3567 | 0.4305 | 0.2530 | 0.6590 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.041208 | 41.608934 | 6.261756 | 0.041208 | 1 | 0.632840 | New_member | |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
104667 | 1635721458409799680 | Gaia DR2 5334226240663383808 | 5334226240663383808 | 1196845616 | 2015.5 | 173.582901 | 0.013413 | -61.406415 | 0.016843 | 0.340990 | 0.017817 | 19.138027 | -6.466442 | 0.025638 | 0.977490 | 0.033322 | 0.022268 | -0.206760 | -0.393277 | 0.085195 | 0.082193 | 0.110880 | -0.373026 | 0.171602 | 0.082048 | 0.115018 | 285 | 0 | 284 | 1 | -4.186047 | 190.821075 | 0.000000 | 0.000000 | 31 | False | 93.787468 | 1.482849 | 0.003299 | -0.031248 | ... | 1.466496 | 0.676726 | 0.789770 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 293.841230 | 0.096949 | 212.259342 | -55.777523 | 100001 | 4388.834961 | 4262.069824 | 4574.500000 | 0.4650 | 0.3423 | 0.5383 | 0.2247 | 0.1784 | 0.2575 | 200111 | 10.091752 | 9.289189 | 10.700990 | 34.042767 | 29.024714 | 39.060822 | https://gea.esac.esa.int/data-server/datalink/... | 0.314021 | 252.224453 | 29.334440 | 0.314603 | 1 | 1.000000 | Old_member | |
104808 | 1635721458409799680 | Gaia DR2 5335719411837020032 | 5335719411837020032 | 383822759 | 2015.5 | 174.179099 | 0.033514 | -61.304683 | 0.032270 | 0.512893 | 0.040556 | 12.646508 | -6.729186 | 0.066267 | 1.024816 | 0.060197 | -0.014499 | -0.178555 | -0.430664 | 0.002231 | 0.194078 | 0.074682 | -0.242885 | 0.168868 | 0.211480 | 0.039074 | 283 | 0 | 283 | 0 | -0.601388 | 263.415558 | 0.000000 | 0.000000 | 31 | False | 7.968105 | 1.557056 | 0.008646 | -0.034972 | ... | 1.067652 | 0.432825 | 0.634827 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 294.085353 | 0.277046 | 212.494267 | -55.504664 | 100001 | 5055.000000 | 4993.000000 | 5276.500000 | NaN | NaN | NaN | NaN | NaN | NaN | 200111 | 1.414196 | 1.297956 | 1.449535 | 1.176517 | 0.964884 | 1.388151 | https://gea.esac.esa.int/data-server/datalink/... | 0.314279 | 101.547152 | 17.024436 | 0.314316 | 1 | 1.000000 | Old_member | |
104811 | 1635721458409799680 | Gaia DR2 5334215623503357056 | 5334215623503357056 | 965484878 | 2015.5 | 173.417127 | 0.034537 | -61.647611 | 0.030310 | 0.380045 | 0.041666 | 9.121274 | -6.635323 | 0.065853 | 0.923174 | 0.051138 | 0.008160 | -0.209039 | -0.349436 | -0.093429 | 0.248419 | -0.053761 | -0.065945 | 0.114520 | 0.152179 | 0.020440 | 208 | 0 | 208 | 0 | -0.148449 | 199.364105 | 0.000000 | 0.000000 | 31 | False | 12.271348 | 1.580482 | 0.008665 | -0.087356 | ... | NaN | NaN | NaN | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 293.836874 | -0.156828 | 212.462501 | -56.004332 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.314287 | 100.760156 | 18.052498 | 0.314117 | 1 | 1.000000 | Old_member | |
104889 | 1635721458409799680 | Gaia DR2 5335716869181854336 | 5335716869181854336 | 1275494979 | 2015.5 | 173.866277 | 0.104264 | -61.316810 | 0.091426 | 0.379821 | 0.119557 | 3.176890 | -6.697952 | 0.185801 | 1.797836 | 0.159545 | -0.230804 | 0.048993 | -0.609351 | 0.277233 | -0.013797 | 0.274353 | -0.385961 | 0.124128 | 0.349111 | -0.200922 | 258 | 0 | 258 | 0 | 0.067101 | 253.843674 | 0.000000 | 0.000000 | 31 | False | 1.187623 | 1.454929 | 0.021854 | -0.075688 | ... | 1.452913 | 0.545601 | 0.907312 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 293.944998 | 0.222278 | 212.319514 | -55.618383 | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | <NA> | NaN | NaN | NaN | NaN | NaN | NaN | https://gea.esac.esa.int/data-server/datalink/... | 0.314422 | 36.049153 | 11.268500 | 0.314568 | 1 | 1.000000 | Old_member | |
105689 | 1635721458409799680 | Gaia DR2 5334223869841146496 | 5334223869841146496 | 48337135 | 2015.5 | 173.450732 | 0.040652 | -61.508640 | 0.043105 | 0.439741 | 0.056694 | 7.756429 | -6.566127 | 0.084108 | 1.182332 | 0.075012 | 0.015570 | -0.130144 | -0.098090 | 0.004861 | 0.204548 | 0.083418 | -0.054951 | 0.264451 | 0.152831 | -0.000695 | 257 | 0 | 255 | 2 | -0.307930 | 242.523605 | 0.000000 | 0.000000 | 31 | False | 4.790699 | 1.541352 | 0.011694 | 0.001998 | ... | 1.161785 | 0.493565 | 0.668221 | NaN | NaN | 0 | NaN | NaN | NaN | NOT_AVAILABLE | 293.811018 | -0.019350 | 212.307573 | -55.894589 | 100001 | 4922.709961 | 4848.290039 | 5020.192383 | 0.8905 | 0.5552 | 1.1450 | 0.4490 | 0.2630 | 0.5721 | 200111 | 1.416517 | 1.362039 | 1.460337 | 1.061586 | 0.765921 | 1.357250 | https://gea.esac.esa.int/data-server/datalink/... | 0.315743 | 78.067934 | 15.761898 | 0.316216 | 1 | 1.000000 | Old_member |
2173 rows × 103 columns
concatenated.dataset.value_counts()
Old_member 1345 New_member 828 Name: dataset, dtype: int64
fig, axes = plt.subplots(1, 3, figsize=(20,6))
fig.suptitle('Distribution of the Old and New Members')
sns.distplot(member['parallax'], color = 'b', label = 'cantat',
kde=True, ax=axes[0])
sns.distplot(potentialMember['parallax'], color = 'g', label = 'new_member',
kde=True, ax=axes[0])
sns.distplot(concatenated['parallax'], color = 'r', ax=axes[0], kde=True,
label = 'new_member+cantat')
axes[0].set_title('Parallax Distribution')
axes[0].legend()
sns.distplot(member['pmra'], color = 'b', label = 'cantat',
kde=True, ax=axes[1])
sns.distplot(potentialMember['pmra'], color = 'g', label = 'new_member',
kde=True, ax=axes[1])
sns.distplot(concatenated['pmra'], color = 'r', ax=axes[1], kde=True,
label = 'new_member+cantat')
axes[1].set_title('pmra Distribution')
axes[1].legend()
sns.distplot(member['pmdec'], color = 'b', label = 'cantat',
kde=True, ax=axes[2])
sns.distplot(potentialMember['pmdec'], color = 'g', label = 'new_member',
kde=True, ax=axes[2])
sns.distplot(concatenated['pmdec'], color = 'r', ax=axes[2], kde=True,
label = 'new_member+cantat')
axes[2].set_title('pmdec Distribution')
axes[2].legend()
plt.show()
/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) /usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) /usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) /usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) /usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) /usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) /usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) /usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) /usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
cmd = sns.scatterplot(x='bp_rp', y='phot_g_mean_mag', data=concatenated,
hue='dataset')
cmd.invert_yaxis()
fig, axes = plt.subplots(1, 2, figsize=(14,6))
fig.suptitle('Distribution of the Old and New Members')
skyplot = sns.scatterplot(x='ra', y='dec', data=concatenated,
hue='dataset', ax=axes[0])
skyplot.invert_xaxis()
axes[0].set_title('Sky plot of new and old member')
# proper motion plot
sns.scatterplot(x='pmra', y='pmdec', data=concatenated,
hue='dataset', ax=axes[1])
axes[1].set_title('Proper motion plot of new and old member')
plt.show()
# parallax vs PMemb plot
sns.scatterplot(x = 'parallax', y = 'PMemb', hue = 'dataset',
palette='YlOrRd', data = concatenated )
<matplotlib.axes._subplots.AxesSubplot at 0x7f4e6e63c048>
# pd_prof.ProfileReport(potentialMember)
#files.download('NGC_3766_cantat.csv')
#files.download('NGC_3766_membership_prob.csv')
sns.distplot(concatenated['dist_3766_center'])
/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
<matplotlib.axes._subplots.AxesSubplot at 0x7f4e65d0dd30>
fig, axes = plt.subplots(1, 3, figsize=(20,6))
fig.suptitle('Sky Plot of Old and New (Predicted) Members of NGC 3766')
sns.kdeplot(x='ra', y='dec', data = member, shade=True, color = 'orange',
bw_method = 0.20, cbar=True, ax=axes[0])
axes[0].set_title('Old_member')
sns.kdeplot(x='ra', y='dec', data = potentialMember, shade=True, color = 'blue',
bw_method = 0.20, cbar=True, ax=axes[1])
axes[1].set_title('New_member')
sns.kdeplot(x='ra', y='dec', data = concatenated, shade=True, color = 'g',
bw_method = 0.20, cbar=True, ax=axes[2])
axes[2].set_title('Old_member + New_member')
plt.show()
fig, axes = plt.subplots(1, 3, figsize=(20,6))
fig.suptitle('Proper Motion Plot of Old and New (Predicted) Members of NGC 3766')
sns.kdeplot(x='pmra', y='pmdec', data = member, shade=True, color = 'orange',
bw_method = 0.20, cbar=True, ax=axes[0])
axes[0].set_title('Old_member')
sns.kdeplot(x='pmra', y='pmdec', data = potentialMember, shade=True, color = 'blue',
bw_method = 0.20, cbar=True, ax=axes[1])
axes[1].set_title('New_member')
sns.kdeplot(x='pmra', y='pmdec', data = concatenated, shade=True, color = 'g',
bw_method = 0.20, cbar=True, ax=axes[2])
axes[2].set_title('Old_member + New_member')
plt.show()