DescriptorDatasetBase

class hdnnpy.dataset.descriptor.descriptor_dataset_base.DescriptorDatasetBase(order, structures)

Bases: abc.ABC

Base class of atomic structure based descriptor dataset.

Common instance variables for descriptor datasets are initialized.

Parameters:

• order (int) – Derivative order of descriptor to calculate.
• structures (list [AtomicStructure]) – Descriptors are calculated for these atomic structures.

__getitem__(item)

Return descriptor data this instance has.

If item is string, it returns corresponding descriptor. Available keys can be obtained by descriptors attribute. Otherwise, it returns a list of descriptor sliced by item.

__len__()

Number of atomic structures given at initialization.

calculate_descriptors(structure)

Calculate required descriptors for a structure data.

This is abstract method. Subclass of this base class have to override.

Parameters:structure (AtomicStructure) – A structure data to calculate descriptors. Returns:Calculated descriptors. The length is the same as order given at initialization. Return type:list [ndarray]

clear()

Clear up instance variables to initial state.

generate_feature_keys(*args, **kwargs)

Generate feature keys of current state.

This is abstract method. Subclass of this base class have to override.

Returns:Unique keys of feature dimension. Return type:list [str]

load(file_path, verbose=True, remake=False)

Load dataset from .npz format file.

Only root MPI process load dataset.

It validates following compatibility between loaded dataset and atomic structures given at initialization.

• length of data
• elemental composition
• elements
• tag

It also validates that loaded dataset satisfies requirements.

• feature keys
• order

Parameters:

• file_path (Path) – File path to load dataset.
• verbose (bool, optional) – Print log to stdout.
• remake (bool, optional) – If loaded dataset is lacking in any feature key or any descriptor, recalculate dataset from scratch and overwrite it to file_path. Otherwise, it raises ValueError.

Raises:

• AssertionError – If loaded dataset is incompatible with atomic structures given at initialization.
• ValueError – If loaded dataset is lacking in any feature key or any descriptor and remake=False.

make(verbose=True)

Calculate & retain descriptor dataset

It calculates descriptor dataset by data-parallel using MPI communication.

The calculated dataset is retained in only root MPI process.

Parameters:verbose (bool, optional) – Print log to stdout.

save(file_path, verbose=True)

Save dataset to .npz format file.

Only root MPI process save dataset.

Parameters:

• file_path (Path) – File path to save dataset.
• verbose (bool, optional) – Print log to stdout.

Raises:

RuntimeError – If this instance do not have any data.

DESCRIPTORS = []

Names of descriptors for each derivative order.

Type:list [str]

descriptors

Names of descriptors this instance have.

Type:list [str]

elemental_composition

Elemental composition of atomic structures given at initialization.

Type:list [str]

elements

Elements of atomic structures given at initialization.

Type:list [str]

feature_keys

Unique keys of feature dimension.

Type:list [str]

has_data

True if success to load or make dataset, False otherwise.

Type:bool

n_feature

Length of feature dimension.

Type:int

name = None

Name of this descriptor class.

Type:str

order

Derivative order of descriptor to calculate.

Type:int

tag

Unique tag of atomic structures given at initialization.

Usually, it is a form like <any prefix> <chemical formula>. (ex. CrystalGa2N2)

Type:str