# pymatgen.alchemy.transmuters module¶

class CifTransmuter(cif_string, transformations=None, primitive=True, extend_collection=False)[source]

Generates a Transmuter from a cif string, possibly containing multiple structures.

Generates a Transmuter from a cif string, possibly containing multiple structures.

Parameters: cif_string – A string containing a cif or a series of cifs transformations – New transformations to be applied to all structures primitive – Whether to generate the primitive cell from the cif. extend_collection – Whether to use more than one output structure from one-to-many transformations. extend_collection can be a number, which determines the maximum branching for each transformation.
static from_filenames(filenames, transformations=None, primitive=True, extend_collection=False)[source]

Generates a TransformedStructureCollection from a cif, possibly containing multiple structures.

Parameters: filenames – List of strings of the cif files transformations – New transformations to be applied to all structures primitive – Same meaning as in __init__. extend_collection – Same meaning as in __init__.
class PoscarTransmuter(poscar_string, transformations=None, extend_collection=False)[source]

Generates a transmuter from a sequence of POSCARs.

Parameters: poscar_string – List of POSCAR strings transformations – New transformations to be applied to all structures. extend_collection – Whether to use more than one output structure from one-to-many transformations.
static from_filenames(poscar_filenames, transformations=None, extend_collection=False)[source]

Convenient constructor to generates a POSCAR transmuter from a list of POSCAR filenames.

Parameters: poscar_filenames – List of POSCAR filenames transformations – New transformations to be applied to all structures. extend_collection – Same meaning as in __init__.
class StandardTransmuter(transformed_structures, transformations=None, extend_collection=0, ncores=None)[source]

Bases: object

An example of a Transmuter object, which performs a sequence of transformations on many structures to generate TransformedStructures.

Initializes a transmuter from an initial list of pymatgen.alchemy.materials.TransformedStructure.

Parameters: transformed_structures ([TransformedStructure]) – Input transformed structures transformations ([Transformations]) – New transformations to be applied to all structures. extend_collection (int) – Whether to use more than one output structure from one-to-many transformations. extend_collection can be an int, which determines the maximum branching for each transformation. ncores (int) – Number of cores to use for applying transformations. Uses multiprocessing.Pool. Default is None, which implies serial.
add_tags(tags)[source]

Add tags for the structures generated by the transmuter.

Parameters: tags – A sequence of tags. Note that this should be a sequence of strings, e.g., [“My awesome structures”, “Project X”].
append_transformation(transformation, extend_collection=False, clear_redo=True)[source]

Appends a transformation to all TransformedStructures.

Parameters: transformation – Transformation to append extend_collection – Whether to use more than one output structure from one-to-many transformations. extend_collection can be a number, which determines the maximum branching for each transformation. clear_redo (bool) – Whether to clear the redo list. By default, this is True, meaning any appends clears the history of undoing. However, when using append_transformation to do a redo, the redo list should not be cleared to allow multiple redos. List of booleans corresponding to initial transformed structures each boolean describes whether the transformation altered the structure
append_transformed_structures(tstructs_or_transmuter)[source]

Method is overloaded to accept either a list of transformed structures or transmuter, it which case it appends the second transmuter”s structures.

Parameters: tstructs_or_transmuter – A list of transformed structures or a transmuter.
apply_filter(structure_filter)[source]

Applies a structure_filter to the list of TransformedStructures in the transmuter.

Parameters: structure_filter – StructureFilter to apply.
extend_transformations(transformations)[source]

Extends a sequence of transformations to the TransformedStructure.

Parameters: transformations – Sequence of Transformations
static from_structures(structures, transformations=None, extend_collection=0)[source]

Alternative constructor from structures rather than TransformedStructures.

Parameters: structures – Sequence of structures transformations – New transformations to be applied to all structures extend_collection – Whether to use more than one output structure from one-to-many transformations. extend_collection can be a number, which determines the maximum branching for each transformation. StandardTransmuter
redo_next_change()[source]

Redo the last undone transformation in the TransformedStructure.

Raises: IndexError if already at the latest change.
set_parameter(key, value)[source]

Add parameters to the transmuter. Additional parameters are stored in the as_dict() output.

Parameters: key – The key for the parameter. value – The value for the parameter.
undo_last_change()[source]

Undo the last transformation in the TransformedStructure.

Raises: IndexError if already at the oldest change.
write_vasp_input(**kwargs)[source]

Batch write vasp input for a sequence of transformed structures to output_dir, following the format output_dir/{formula}_{number}.

Parameters: vasp_input_set – pymatgen.io.vaspio_set.VaspInputSet to create vasp input files from structures output_dir – Directory to output files create_directory (bool) – Create the directory if not present. Defaults to True. subfolder – Callable to create subdirectory name from transformed_structure. e.g., lambda x: x.other_parameters[“tags”][0] to use the first tag. include_cif (bool) – Whether to output a CIF as well. CIF files are generally better supported in visualization programs.
batch_write_vasp_input(transformed_structures, vasp_input_set=<class 'pymatgen.io.vasp.sets.MPRelaxSet'>, output_dir='.', create_directory=True, subfolder=None, include_cif=False, **kwargs)[source]

Batch write vasp input for a sequence of transformed structures to output_dir, following the format output_dir/{group}/{formula}_{number}.

Parameters: transformed_structures – Sequence of TransformedStructures. vasp_input_set – pymatgen.io.vaspio_set.VaspInputSet to creates vasp input files from structures. output_dir – Directory to output files create_directory (bool) – Create the directory if not present. Defaults to True. subfolder – Function to create subdirectory name from transformed_structure. e.g., lambda x: x.other_parameters[“tags”][0] to use the first tag. include_cif (bool) – Boolean indication whether to output a CIF as well. CIF files are generally better supported in visualization programs.