Profile Interpolation

We demonstrate the profile interpolator which delivers predictions on internal stellar structure at the end of MESA evolution.

To use the profile interpolator we first import the ProfileInterpolator and CompileData objects from the POSYDON library.

from posydon.interpolation.profile_interpolation import ProfileInterpolator, CompileData

Extracting Profile Data from h5 Files for Training Interpolators

To extract profile data from existing grids, we pass arguments train_path and test_path pointing to h5 files containing instances of the PSyGrid class from which the training and testing grids can be loaded. These can be constructed by running one’s own simulations or by loading a precomputed simulation stored in the data directory of the POSYDON repository. The profile_names argument denotes which profiles will be extracted from the grid and saved to filename using the function save. The Boolean argument``hms_s2`` should be set to True only to extract star 2 profiles from the HMS-HMS grid.

compiler = CompileData(train_path = '/path/to/training/grid.h5',
                       test_path = '/path/to/testing/grid.h5',
                       hms_s2 = False,
                       profile_names=['radius','logRho',
                       'x_mass_fraction_H','y_mass_fraction_He',
                       'z_mass_fraction_metals','omega','energy'])
compiler.save(filename = '/path/for/profiles/datafile.pkl')

Loading a Pretrained Interpolator

To load a pretrained interpolator we instantiate a ProfileInterpolator object instance and then use the load function which specifies the path to a pkl file from which the pretrained interpolator can be loaded.

model = ProfileInterpolator()
model.load(filename = "/path/to/profile/interpolator.pkl")

Training the Interpolator

The interpolator is trained on profiles stored in a pkl file (passed with argument filename) generated with the CompileData class. The function load_profiles also randomly splits the training data into a training set and validation set based on the argument valid_split, which specifies the percentage of the training data that should be used for validation. The profile interpolation is anchored to POSYDON’s IF interpolation, and so we pass the name of the interpolator file to the train function using the IF_interpolator argument. The four following arguments specify the numbers of epochs and level of patience for callback that will be used for training the neural networks in the density and composition models. The loss_history argument provides an option to return the loss histories for the composition and density model training. Once again, the Boolean argument hms_s2 should be set to True to build an interpolator for star 2 profiles from the HMS-HMS grid.

model = ProfileInterpolator()
model.load_profiles(filename = "/path/to/profiles/datafile.pkl",
                    valid_split=0.2)
model.train(IF_interpolator = "/path/to/IFinterpolator.pkl",
            density_epochs=3000,
            density_patience=200,
            comp_bounds_epochs=500,
            comp_bounds_patience=50,
            loss_history=False,
            hms_s2=False)

Using the Interpolator

Once the interpolator has been trained or loaded from a pkl file it can be used to predict profiles for sets of initial conditions passed through argument inputs. These initial conditions must be in log space and in shape (N,3) for N binaries. The order of the coordinates is star 1 mass, star 2 mass, period. The prediction function returns four arrays containing the profiles’ coordinates along mass enclosed, log density, Hydrogen mass fraction, and Helium mass fraction. All profiles share the same coordinates.

mass_coords, density_profiles, h_profiles, he_profiles = model.predict(inputs)

Finally a trained interpolator can be easily saved by specifying a path to a pkl file where the interpolator will be saved to.

model.save(filename = "path/for/profile/interpolator.pkl")

Evaluating on Testing Data

To evaluate the interpolator on the testing grid, we can pull the testing data out of the ProfileInterpolator class as follows:

test_initial = model.test_initial
test_mass_coords = np.transpose(np.array(model.test_scalars["total_mass"])*np.linspace(0,1,200)[:,np.newaxis])
test_density_profiles = model.test_profiles[:,model.names.index("logRho")]
test_H_profiles = model.test_profiles[:,model.names.index("x_mass_fraction_H")]
test_He_profiles = model.test_profiles[:,model.names.index("y_mass_fraction_He")]