run_pipeline

MESA low_res_grids were run. This includes random grids and reruns.

This is an EXAMPLE of the pipeline for a grid_type=HMS-HMS, metallicity=1e-01_Zsun, compression=LITE/ORIGINAL

STEP 1: grid slices creation [‘grid_low_res_0’,’grid_low_res_1’,’grid_low_res_2’,’grid_low_res_3’, ‘grid_low_res_4’,’grid_low_res_5’,’grid_random_1’, ‘grid_low_res_rerun_opacity_max’] –> output *.h5

STEP 2: grid slices concatenation [[‘grid_low_res_0’,’grid_low_res_1’,’grid_low_res_2’,’grid_low_res_3’, ‘grid_low_res_4’,’grid_low_res_5’],[‘grid_low_res_0’,’grid_low_res_1’, ‘grid_low_res_2’,’grid_low_res_3’,’grid_low_res_4’,’grid_low_res_5’, ‘grid_low_res_rerun_opacity_max’]] –> grid_low_res_combined.h5, grid_low_res_combined_rerun1.h5

STEP 2.1: plot grid slices [‘grid_low_res_combined’,’grid_random_1’,’grid_low_res_combined_rerun1’] –> loop over all plot types

STEP 2.2: check failure rate [‘grid_low_res_combined’,’grid_random_1’,’grid_low_res_combined_rerun1’] –> check failure rate

STEP 3: calculate extra values [‘grid_low_res_combined’,’grid_random_1’,’grid_low_res_combined_rerun1’] –> do post processing on the ORIGINAL grid and append back on the LITE gird the post processed quantities

STEP 3.1: plot extra values [‘grid_low_res_combined’,’grid_random_1’,’grid_low_res_combined_rerun1’] –> loop over all plot types

STEP 3.2: check rates of compact object types [‘grid_low_res_combined’,’grid_random_1’,’grid_low_res_combined_rerun1’] –> check rates of compact object types

STEP 4: train interpolators [‘grid_low_res_combined_rerun1’] –> train the interpolators

STEP 4.1: plot interpolator accuracy and confusion matricies [‘grid_random_1’] –> loop over all plot types

STEP 9: export dataset

STEP RERUN: rerun grid with a fix grid_low_res_combined.rerun(index=logic) –> grid_low_res_rerun_1/grid.csv

–> grid_random_1_rerun_1/grid.csv

– run gird fix and do next post processing

run_pipeline.calculate_extra_values(i, path_to_csv_file, verbose=False)

Calculating extra values, e.g. values derived from the final profile.

run_pipeline.combine_grid_slices(i, path_to_csv_file, verbose=False)

Combining grid slices to one grid.

run_pipeline.copy_ini_file(grid_path, rerun_type, destination, cluster)

Copies the ini file and make replacements according to the rerun.

run_pipeline.create_grid_slice(i, path_to_csv_file, verbose=False, overwrite_psygrid=True)

Creates a new PSyGrid slice.

run_pipeline.do_check(i, path_to_csv_file, verbose=False)

Perform a check on a grid.

run_pipeline.export_dataset(i, path_to_csv_file, verbose=False)

Moving the data set to a place containing all, what is needed to run a population synthesis with POSYDON.

run_pipeline.logic_rerun(grid, rerun_type)

Get the runs, which need a rerun.

run_pipeline.plot_grid(i, path_to_csv_file, verbose=False)

Creates plots of a grid.

run_pipeline.rerun(i, path_to_csv_file, verbose=False)

Generate files to start a rerun.

run_pipeline.train_interpolators(i, path_to_csv_file, verbose=False)

Train an interpolator on a grid.

run_pipeline.zams_file_name(dirname)

Gives the name of the ZAMS file depending on the metallicity.