fields.mixins.dense_mathops.DenseTensorFieldDMOMixin.determine_op_dependence

DenseTensorFieldDMOMixin.determine_op_dependence(opname: str, *args, **kwargs)

Infer the symbolic coordinate dependence resulting from a differential operation.

This utility method performs symbolic dependence tracking by delegating to the internal dependence object (self.dependence) and extracting the resulting coordinate axis dependencies after applying the specified operation.

It is commonly used by high-level field and component operations to automatically annotate new objects (e.g., gradients, divergences, Laplacians) with their correct symbolic dependence.

Parameters:

• opname (str) – The name of the symbolic operation to perform (e.g., "gradient", "laplacian", "divergence", "raise_index", etc.). This must be a method of self.dependence.

• *args (tuple) – Positional arguments passed directly to the method being invoked.

• **kwargs (dict) – Keyword arguments passed directly to the method being invoked.

Returns:

A list of coordinate axis names (e.g., ["r", "theta"]) on which the resulting field depends after the symbolic operation.

Return type:

List[str]

Raises:

ValueError – If the given operation name is not a valid method of self.dependence.

Examples

>>> field.determine_op_dependence("gradient", basis="covariant")
['r', 'theta']

>>> field.determine_op_dependence("laplacian")
['r', 'theta']

Notes

This method is purely symbolic and does not perform any numerical evaluation. It is used for metadata propagation and symbolic analysis of coordinate dependence.