On 01/26/2018 03:38 PM, Chris Barker wrote: > I was hoping it would dig down to the inner structures looking for a > match to the dtype, rather than looking at the type of the top level. Oh > well. > > So yeah, not sure where you would go from tuple to list -- probably at > the bottom level, but that may not always be unambiguous.
As I remember, numpy has some fairly convoluted code for array creation which tries to make sense of various nested lists/tuples/ndarray combinations. It makes a difference for structured arrays and object arrays. I don't remember the details right now, but I know in some cases the rule is "If it's a Python list, recurse, otherwise assume it is an object array". While numpy does try to be lenient, I think we should guide the user to assume that if they want to specify a structured element, they should only use a tuple or a structured scalar, and if they want to specify a new dimension of elements, they should use a list. I expect less headaches that way. > These points make me think that instead of a `.totuple` method, this > might be more suitable as a new function in np.lib.recfunctions. > > I don't seem to have that module -- and I'm running 1.14.0 -- is this a > new idea? Sorry, I didn't specify it correctly. It is "numpy.lib.recfunctions". It is actually quite old, but has never been officially documented. I think that is because it has been considered "provisional" for a long time. See https://github.com/numpy/numpy/issues/5008 https://github.com/numpy/numpy/issues/2805 I still hesitate to make it more official now, since I'm not sure that structured arrays are yet bug-free enough to encourage more complex uses. Also, the functions in that module encourage "pandas-like" use of structured arrays, but I'm not sure they should be used that way. I've been thinking they should be primarily used for binary interfaces with/to numpy, eg to talk to C programs or to read complicated binary files. > However, I didn't actually need tuples, I needed something I could pack > into a stuctarray, and this does work, without the tolist: > > full = np.array(zip(time, pack_last_axis(uv)), dtype=dt) > > > So maybe that is the way to go. Right, that was my feeling: That we didn't really need `.totuple`, what we actually wanted is a special function for packing a nonstructured-array as a structured-array. > I'm not sure I'd have thought to look for this function, but what can > you do? > > Thanks for your attention to this, > > -CHB > > -- > > 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 > > chris.bar...@noaa.gov <mailto:chris.bar...@noaa.gov> > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
