Utils#

apax.utils.convert.atoms_to_inputs(atoms_list: list[Atoms], pos_unit: str = 'Ang') → dict[str, dict[str, list]][source]#

Converts an list of ASE atoms to a dict where all inputs are sorted by their shape (ragged/fixed). Units are adjusted if ASE compatible and provided in the inputpipeline.

Parameters:

atoms_list – List of all structures. Enties are ASE atoms objects.

Returns:

inputs – Inputs are untrainable system-determining properties.
labels – Labels are trainable system properties.

apax.utils.convert.atoms_to_labels(atoms_list: list[Atoms], pos_unit: str = 'Ang', energy_unit: str = 'eV') → dict[str, dict[str, list]][source]#

Converts an list of ASE atoms to a dict of labels Units are adjusted if ASE compatible and provided in the inputpipeline.

Parameters:: atoms_list – List of all structures. Enties are ASE atoms objects.
Returns:: Labels are trainable system properties.
Return type:: labels

apax.utils.convert.tf_to_jax_dict(data_dict: dict[str, list]) → dict[source]#

Converts a dict of tf.Tensors to a dict of jax.numpy.arrays. tf.Tensors must be padded.

Parameters:: data_dict – Dict padded of tf.Tensors
Returns:: Dict of jax.numpy.arrays
Return type:: data_dict

apax.utils.data.load_data(data_path)[source]#

Non ASE compatible parameters have to be saved in an exta file that has the same name as the datapath but with the extension _labels.npz.

Example

example for the npz-file:

dipole = np.random.rand(3, 1)
charge = np.random.rand(3, 2)
mat = np.random.rand(3, 1)
shape = ['ragged', 'ragged', 'fixed']

np.savez(
    "data_path_labels.npz",
    dipole=dipole,
    charge=charge,
    mat=mat,
    shape=shape,
)

shape has to be in the same order than the parameters

Parameters:: data_path – Path to the ASE readable file that includes all structures.
Returns:: List of all structures where entries are ASE atoms objects.
Return type:: list

apax.utils.data.split_atoms(atoms_list, train_idxs, val_idxs=None)[source]#

Split the list of atoms into training and validation sets (validation is optional).

Parameters:

atoms_list (list[ase.Atoms]) – List of atoms.
train_idxs (list[int]) – List of indices for the training set.
val_idxs (list[int], optional) – List of indices for the validation set.

Returns:

Tuple containing lists of atoms for training and validation sets.

Return type:

Tuple[list, list]