pymatgen.alchemy.transmuters module

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

Bases: pymatgen.alchemy.transmuters.StandardTransmuter

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)

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)

Bases: pymatgen.alchemy.transmuters.StandardTransmuter

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)

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)

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)

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)

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.

Returns:

List of booleans corresponding to initial transformed structures each boolean describes whether the transformation altered the structure

append_transformed_structures(tstructs_or_transmuter)

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)

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

Parameters:structure_filter – StructureFilter to apply.

extend_transformations(transformations)

Extends a sequence of transformations to the TransformedStructure.

Parameters:transformations – Sequence of Transformations

static from_structures(structures, transformations=None, extend_collection=0)

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.

Returns:

StandardTransmuter

redo_next_change()

Redo the last undone transformation in the TransformedStructure.

Raises:IndexError if already at the latest change.

set_parameter(key, value)

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()

Undo the last transformation in the TransformedStructure.

Raises:IndexError if already at the oldest change.

write_vasp_input(**kwargs)

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)

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.