On Wed, Oct 14, 2015 at 3:04 PM, Laura Creighton <[email protected]> wrote:
> That code writers in the scientific python world mostly > never think of Decimal users, doesn't mean they don't end up writing > perfectly wonderful tools and libraries we use. :) thankfully :) > sure -- but those are almost guaranteed to convert to float internally, as there is no decimal dtype for numpy. in fact, much numpy code is actually semi-statically typed. The common idiom is to write your functions to take "something that can be turned into a float array", which, in practice, means that calling: np.asarray(the_input_object, dtype-np.float64) Doesn't raise an exception.[1] And, often ends up returning an array with the right shape, also, so maybe: np.asarray(the_input_object, dtype-np.float64).reshape(-1, 2) I guess it would be nice if there were a way to describe that in type annotations. -CHB [1] for those not in the know, "asarray" is more or less: if the input is already an array as specified, return that array unchanged. elif the input is a buffer or memoryview that fits the specification, wrap an ndarray around that buffer. else call np.array() on it -- which will attempt make an appropriate numpy array, and copy the values from the input object into it. I was looking for a way for the Python type hinting to be expressive > enough to handle this common (at least in my world) case. So then, > even if the bokeh developers (just to pick some friends) forget about > me in their type annotations, as you see above, it's generally more complicated than a single scalar dtype... -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception [email protected]
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