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September 28, 2015 10:35
An initial commit of unumpy. I use the numpy.vectorize function to vectorize each of the umath functions so that they may be applied to numpy arrays. Note that, at the moment, the abs function is not included, as this conflicts with a python keyword. I’m sure there is a better way to handle this?
Added some extensions to operators to handle numpy arrays of UncertainFunction objects. If it is an instance of a numpy array, I vectorize a lambda function representing the operation with numpy.vectorize, and return the array with the operation applied. I use the to_uncertain_func to ensure they are UncertainFunction objects throughout the array. If you think this approach is sound, then the CONSTANT_TYPES should be expanded to include other numpy data types (ie. np.float32, np.float16, np.uint64, np.uint32, np.uint16, np.uint8, np.int64, np.int32, np.int16, np.int8)
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Added a ‘unumpy’ component that simply vectorizes each of the functions in the existing umath file.
Added some extensions to operators to handle numpy arrays of
UncertainFunction objects. If it is an instance of a numpy array, I
vectorize a lambda function representing the operation with
numpy.vectorize, and return the array with the operation applied. I use
the to_uncertain_func to ensure they are UncertainFunction objects
throughout the array.
If you think this approach is sound, then the CONSTANT_TYPES should be
expanded to include other numpy data types (ie. np.float32, np.float16,
np.uint64, np.uint32, np.uint16, np.uint8, np.int64, np.int32,
np.int16, np.int8)