Welcome to compass’s documentation!

The code described by this documentation is available on https://github.com/herzphi/compass. This package presents a method to evaluate the likelihood of a candidate being a true companion to a host star using a proper motion model based on stochastic models.

Usage

Installation

You can install compass by installing this repo:

pip install git+https://github.com/herzphi/compass.git

Code

To get the odds ratios of all candidates use the Survey class:

import pandas as pd
from compass import model
from compass import helperfunctions

observation = pd.read_csv('observation.csv')
survey_object = model.Survey(observation, magnitudes_column_name)
survey_object.set_fieldstar_models(
    # Color transformed column name from Gaias G-Band.
    magnitudes_column_name_CALC,
    # Column name of the corresponding magnitude in 2MASS.
    magnitudes_column_name_2MASS,
    cone_radius=0.3,  # in degree
    binsize=200  # Number of objects in a single magnitude bin
)
# Inflating parameters to adjust the sharp drop-off of the Gaussians.

survey_object.set_evaluated_fieldstar_models(
    sigma_cc_min=0,
    sigma_model_min=0
)

Indices and tables

compass.model Module

Classes

`BackgroundModel`(candidate_mag, ...)
`Candidate`(df_survey, index_candidate, ...)	Model, true data and likelihoods, p_ratios of one candidate.
`CovarianceMatrix`()
`HostStar`(target)	Host star of the candidates.
`Survey`(survey, survey_bandfilter_colname)	Creates odds ratio table based on the observational data of candidates and the field star models.

compass.helperfunctions Module

Functions

`add_ellp_patch`(pdf, major, minor, angle, ...)	Adds a patch to a figure.
`add_ellp_patch2`(x_mean, y_mean, major, ...)	Adds a patch to a figure.
`calc_prime_1`(prime_0, pm, plx, time, plx_proj)
`color_trafo_2MASS_H`(g_band_mag, bp_rp_mag)
`color_trafo_2MASS_J`(g_band_mag, bp_rp_mag)
`color_trafo_2MASS_K_S`(g_band_mag, bp_rp_mag)
`convert_df_to_array`(df, x_col_name, y_col_name)	Convert dataframe to two 1D arrays.
`convolution2d`(a, b, A, B)	Returns a Gaussian2D functions and its covariance matrix of the convolution from the two Gaussian2D.
`ellipse`(x_mean, y_mean, cov, color, axis[, ...])
`func_const`(x, a)
`func_exp`(x, a, b, c)
`func_exp_inc`(x, a, b, c)
`func_lin`(x, a, b)
`gaussian1D`(g2d_astropy_func, x_or_y)
`get_ellipse_props`(cov, confidence)	Calculates the eigenvalues/eigenvectors of the covariance matrix and returns the major and minor axis length of the ellipse. Args: cov (numpy.array (2x2)): Covariance matrix. confidence (float): Probability between 0 and 1. Returns: major_axis (float): Semi-major axis. minor_axis (float): Semi-minor axis. angle (float): Angle of rotation of the ellipse.
`get_g2d_func`(x_mean, y_mean, x_std, y_std, rho)
`get_g2d_parameters`(x_data, y_data)	Calculate 2D Gaussian parameters based on two arrays.
`n_dim_gauss_evaluated`(obs, mean, cov)
`parallax_projection`(time, host_star)

compass.preset_plots Module

Functions

`odds_ratio_sep_mag_plot`(candidates_table, ...)	Creates a odds ratio vs.
`p_ratio_plot`(candidate_object, target, band)
`p_ratio_relative_position`(candidate_df, ...)
`plot_pm_plx_binning_parameters`(...)
`pm_mag_plot`(catalogue, catalogue_name, ...)
`propagation_plot`(candidate_object, ...)