agedi.api.dataset
=================

.. py:module:: agedi.api.dataset

.. autoapi-nested-parse::

   Dataset creation.



Functions
---------

.. autoapisummary::

   agedi.api.dataset.create_dataset


Module Contents
---------------

.. py:function:: create_dataset(data: Sequence[ase.Atoms], cutoff: float = 6.0, batch_size: int = 64, train_split: Union[float, int] = 0.9, val_split: Union[float, int] = 0.1, mask: str = 'none', confinement: Optional[Tuple[float, float]] = None, conditioning: str = 'none', conditioning_type: str = 'scalar', repeat: Optional[int] = None, canonical_cell: bool = False, regressor_data: Optional[Sequence[ase.Atoms]] = None, properties: Optional[List[Dict]] = None) -> agedi.data.Dataset

   Create and setup an AGeDi Dataset from ASE Atoms objects.

   :param data: ASE Atoms objects to add to the dataset.
   :type data: Sequence[Atoms]
   :param cutoff: Neighbour-list cutoff radius in Ångström.
   :type cutoff: float, optional
   :param batch_size: Mini-batch size used during training/validation.
   :type batch_size: int, optional
   :param train_split: Fraction or absolute number of samples for the training split.
   :type train_split: Union[float, int], optional
   :param val_split: Fraction or absolute number of samples for the validation split.
   :type val_split: Union[float, int], optional
   :param mask: Atom-mask method (e.g. ``"MaskFixed"`` or ``"none"``).
   :type mask: str, optional
   :param confinement: Z-axis confinement bounds ``(z_min, z_max)``.
   :type confinement: Tuple[float, float], optional
   :param conditioning: Name of the per-structure property to use as a conditioning signal.
                        The value is read from ``atoms.info[conditioning]`` or the
                        corresponding ``atoms.get_<conditioning>()`` method.  Ignored when
                        set to ``"none"`` (default).
   :type conditioning: str, optional
   :param conditioning_type: ``"scalar"`` (default) or ``"node"``; controls how the conditioning
                             property is broadcast onto the graph.
   :type conditioning_type: str, optional
   :param repeat: When given, augment the dataset by repeating each structure up to
                  ``repeat`` times along the first two cell vectors.
   :type repeat: int, optional
   :param canonical_cell: Store cells in canonical lower-triangular form.
   :type canonical_cell: bool, optional
   :param regressor_data: Additional ASE Atoms objects used to train a regressor head.
   :type regressor_data: Sequence[Atoms], optional
   :param properties: Per-structure property dictionaries; **must** contain exactly one
                      entry per element in *data*.  Each dictionary is merged into the
                      corresponding graph object via ``setattr``, matching the layout
                      accepted by :meth:`~agedi.data.Dataset.add_atoms_data`.  Keys
                      already produced by the *conditioning* logic are overwritten by
                      values in *properties* when both are present.
   :type properties: List[Dict], optional

   :returns: A fully set-up :class:`~agedi.data.Dataset` ready for training.
   :rtype: Dataset