aiida_vasp.utils.workchains#
Utils for the workchains.
Auxiliary routines that are not part of any of the workchain classes, but needed to make code more compact in the workchains.
Module Contents#
Functions#
Prepare the inputs dictionary for a calculation. |
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Compare two StructureData objects A, B and return a delta (A - B) of the relevant properties. |
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Suggest a sensible k-point sampling based on a supplied spacing. |
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Compose an ExitCode instance based on a status and message. |
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Convert site magnetization to MAGMOM used for restart NOTE: Only tested for colinear cases |
API#
- aiida_vasp.utils.workchains.prepare_process_inputs(inputs: Any, namespaces: Optional[List[str]] = None, exclude_parameters: Optional[List[str]] = None) aiida.common.extendeddicts.AttributeDict[source]#
Prepare the inputs dictionary for a calculation.
Any remaining bare dictionaries in the inputs dictionary will be wrapped in a Dict data node except for the ‘options’, ‘metadata’, ‘potential’ and any key specified in the parameters namespaces. They all should remain a standard dictionary. Another exception are dictionaries whose keys are not strings but for example tuples.
- aiida_vasp.utils.workchains.compare_structures(structure_a: Any, structure_b: Any) aiida.common.extendeddicts.AttributeDict[source]#
Compare two StructureData objects A, B and return a delta (A - B) of the relevant properties.
- aiida_vasp.utils.workchains.fetch_k_grid(rec_cell: Any, k_spacing: Any) List[int][source]#
Suggest a sensible k-point sampling based on a supplied spacing.
- Parameters:
rec_cell – A two dimensional ndarray of floats defining the reciprocal lattice with each vector as row elements.
k_spacing – The k-point spacing.
- Return kgrid:
The k-point grid given the supplied rec_cell and kstep
This is usable for instance when performing plane wave cutoff convergence tests without a base k-point grid.