Extraction

star_cluster class

Input

Cluster identifier, photometry and parallax observations

A star_cluster instance is created from the input data. For this object, various methods are available for performing the various pre-processing and computation steps necessary for the full pipeline of the isochrone extraction. In short, the workflow is as follows:

1. Compute a Color-Magnitude diagram and transform it using Principal Component Analysis star_cluster.create_CMD (quick ‘n’ dirty version available as well).

2. Create a weight-map from the observation uncertainties star_cluster.create_weights.

3. Tune hyperparameters for Support Vector regression and save the best results star_cluster.SVR_Hyperparameter_tuning and gridsearch_and_ranking.

4. In case the hyperparameters have already been determined: star_cluster.SVR_read_from_file.

5. Extract a single empirical curve star_cluster.curve_extraction.

6. Resample a large number of curves from bootstrapped cluster data star_cluster.resample_curve.

7. Calculate the median and uncertainty bounds star_cluster.interval_stats.

The last three steps can be called simuntaneously using the star_cluster.isochrone_and_intervals method.

Further star_cluster functions

Subsidiary functions called by the star_cluster class object.

Empirical isochrone reader

Function for reading in the empirical isochrones saved for each cluster and for creating a master-table containing all results for the empirical Archive.

Pre-processing

Function that converts the raw .csv input files into pd.DataFrames and only retains the necessary columns. It is automatically called to do this transformation for 8 different catalogs in the main script, so that they may just be imported into the various python scripts by their variable name or the variable name of the list.