HostStar

class compass.model.HostStar(target)

Bases: object

Host star of the candidates. Properties of the host star have the units given in gaiadr3.gaia_source.

ra

Properties of the host star.

Type:

float

ra_error

Properties of the host star.

Type:

float

dec

Properties of the host star.

Type:

float

dec_error

Properties of the host star.

Type:

float

ref_epoch

Properties of the host star.

Type:

float

parallax

Properties of the host star.

Type:

float

parallax_error

Properties of the host star.

Type:

float

pmra

Properties of the host star.

Type:

float

pmdec

Properties of the host star.

Type:

float

pmra_error

Properties of the host star.

Type:

float

pmdec_error

Properties of the host star.

Type:

float

pmra_pmdec_corr

Properties of the host star.

Type:

float

parallax_pmra_corr

Properties of the host star.

Type:

float

parallax_pmdec_corr

Properties of the host star.

Type:

float

phot_g_mean_mag

Properties of the host star.

Type:

float

phot_bp_mean_mag

Properties of the host star.

Type:

float

phot_rp_mean_mag

Properties of the host star.

Type:

float

object_found

Boolean whether the object was found.

Type:

Boolean

cone_tmass_cross

Containing the cone cross matched objects.

Type:

pandas.DataFrame

cone_tmass_cross

containing the cone Gaia objects.

Type:

pandas.DataFrame

candidates

Containing id, p_ratio and p_ratio_catalogue.

Type:

pandas.DataFrame

Searches for the given target id in the Simbad database for the Gaia source id and returns the data on the star.

Parameters:

target (str) – Name of the target.

Attributes Summary

logger

Methods Summary

binning_parameters(df, x_col_name, ...)

Gaussian 2D fits to each bin.

calc_background_model_parameters(...[, std_fit])

Fit the binning parameters. For each catalogue and variable there are coeff and cov attributes.

concat_binning_parameters(df_catalogue, ...)

Concat the binning parameters of the combinations of pmra, pmdec, parallax.

cone_gaia_objects(cone_radius)

Cone around the target star and colour transform G-Band to K_S-Band.

cone_tmasscross_objects(cone_radius)

Cone around the target star and cross match with 2MASS.

evaluate_candidates_table(candidates_df, ...)

Returns all the candidates data of this host star for both catalogues.

Attributes Documentation

logger = <AstropyLogger astroquery (ERROR)>

Methods Documentation

binning_parameters(df, x_col_name, y_col_name, binsize, band)

Gaussian 2D fits to each bin.

Parameters:

  • df (pandas.DataFrame) – Cone catalouge data.

  • x_col_name (str) – pmra, pmdec or parallax.

  • y_col_name (str) – pmra, pmdec or parallax.

  • binsize (float) – Number of objects in each single bin.

  • band (str) –

    Bandwidth from the catalogue column e.g. ‘ks_m_calc’ for Gaia or ‘ks_m’ for 2MASS.

    ’h_m_calc’ for Gaia or ‘h_m’ for 2MASS. ‘j_m_calc’ for Gaia or ‘j_m’ for 2MASS.

Returns:

pandas.DataFrame – Parameters for each bin and each correlation.

calc_background_model_parameters(list_of_df_bp, band, candidates_df, include_candidates, std_fit='exp')
Fit the binning parameters. For each catalogue and variable there are coeff and cov attributes.
The syntax:

variable _ mean or stddev _ coeff or cov _ catalogue name

The coeff attributes contain the fitting coefficients:

len(coeff)=3: Fitted with helperfunctions.func_exp. len(coeff)=2: Fitted with helperfunctions.func_lin. len(coeff)=1: Fitted with helperfunctions.const.

Parameters:

  • list_of_df_bp (list of pandas.DataFrame s)

  • Parameters. (Binned 2D Gaussian)

  • band (str) – Bandwidth from the catalogue column e.g. ‘ks_m_calc’ for Gaia or ‘ks_m’ for 2MASS.

  • candidates_df (pandas.DataFrame) – Data on all candidates

  • star. (of this host)

  • include_candidates (Boolean) – Including the data of the caniddates in the fitting.

concat_binning_parameters(df_catalogue, band, binsize)

Concat the binning parameters of the combinations of pmra, pmdec, parallax.

Parameters:

  • df_catalogue (pandas.DataFrame) – Cone catalouge data.

  • band (str) – Bandwidth from the catalogue column e.g. ‘ks_m_calc’ for Gaia or ‘ks_m’ for 2MASS.

Returns:

pandas.DataFrame – Binning parameters of different catalogues and variables parameters in a single dataframe.

cone_gaia_objects(cone_radius)

Cone around the target star and colour transform G-Band to K_S-Band.

Parameters:

cone_radius (float) – Search cone radius in degree.

cone_tmasscross_objects(cone_radius)

Cone around the target star and cross match with 2MASS.

Parameters:

cone_radius (float) – Search cone radius in degree.

evaluate_candidates_table(candidates_df, sigma_model_min, sigma_cc_min)

Returns all the candidates data of this host star for both catalogues.

Parameters:

  • candidates_df (pandas.DataFrame) – Data on all candidates of this host star.

  • sigma_model_min (float or int) – The inflating factor for the model likelihood.

  • sigma_cc_min (float or int) – The inflating factor for its likelihood.