pymatgen.alchemy.transmuters module
This module implements various transmuter classes. Transmuters are essentially classes that generate TransformedStructures from various data sources. They enable the high-throughput generation of new structures and input files.
It also includes the helper function, batch_write_vasp_input to generate an entire directory of vasp input files for running.
- class CifTransmuter(cif_string, transformations=None, primitive=True, extend_collection=False)[source]
Bases:
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)[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]
Bases:
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)[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.
- Returns:
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.
- Returns:
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.
- 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.