flopscope.numpy.promote_types
fnp.promote_types(*args, **kwargs)[flopscope source]
Returns the data type with the smallest size and smallest scalar kind to which both ``type1`` and ``type2`` may be safely cast. The returned data type is always considered "canonical", this mainly means that the promoted dtype will always be in native byte order.
Adapted from NumPy docs np.promote_types
Return smallest type to which both types may be safely cast.
Returns the data type with the smallest size and smallest scalar kind to which both type1 and type2 may be safely cast. The returned data type is always considered "canonical", this mainly means that the promoted dtype will always be in native byte order.
This function is symmetric, but rarely associative.
Parameters
- type1:dtype or dtype specifier
First data type.
- type2:dtype or dtype specifier
Second data type.
Returns
- out:dtype
The promoted data type.
See also
Notes
Please see flops.result_type for additional information about promotion.
Starting in NumPy 1.9, promote_types function now returns a valid string length when given an integer or float dtype as one argument and a string dtype as another argument. Previously it always returned the input string dtype, even if it wasn't long enough to store the max integer/float value converted to a string.
NumPy now supports promotion for more structured dtypes. It will now remove unnecessary padding from a structure dtype and promote included fields individually.
Examples
>>> import flopscope.numpy as fnp
>>> flops.promote_types('f4', 'f8')
dtype('float64')>>> flops.promote_types('i8', 'f4')
dtype('float64')>>> flops.promote_types('>i8', '<c8')
dtype('complex128')>>> flops.promote_types('i4', 'S8')
dtype('S11')An example of a non-associative case:
>>> p = flops.promote_types
>>> p('S', p('i1', 'u1'))
dtype('S6')
>>> p(p('S', 'i1'), 'u1')
dtype('S4')