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
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