Spatial dimension check in `MultiIndexSet.from_degree()` is now more forgiving.

Spatial dimension check in MultiIndexSet.from_degree() is now more forgiving.

The type and value check for spatial dimension in the constructor MultiIndexSet.from_degree() is now more forgiving as long as the provided value:

• is strictly positive
• is a whole number
• can be compared (<= 0) in an if statement without ambiguity
• can be converted to an int without ambiguity

This means values like 4. (a float but a whole number) or np.array([4])[0] (i.e., type numpy.int64 is not int) are now a valid spatial_dimension value. They all will, however, be converted to int as the attribute value of the resulting MultiIndexSet instance.

This commit should resolve Issue #77 (closed).

Additional changes:

• A new verification function verify_spatial_dimension() is added to the module minterpy.core.verification. The function checks for correctness of spatial dimension and forces the verified value to be of int type.
• lp_degree in MultiIndexSet.from_degree() may now throw a custom error message when a ValueError is raised.
• A new test suite for the functions in minterpy.core.verification is added.
• The test suite on MultiIndexSet.from_degree is extended to include testing the behavior of passing different types and values (both valid and invalid) for spatial dimension and lp-degree.

PS: NumPy throws a DeprecationWarning for converting an array with ndim > 0 to a scalar via float or int. This means that, in the future, the NumPy development team deems converting a NumPy array with dimension larger than 0 is ambiguous. We will follow this decision. Currently, passing np.array([0]) is still acceptable as a spatial dimension because NumPy still allows it; we, however, don't include such a spatial dimension in the test.