[SciPy-dev] A proposal for dtype/dtypedescr in numpy objects
faltet at carabos.com
Thu Jan 5 15:40:57 CST 2006
In my struggle for getting consistent behaviours with data types, I've
ended with a new proposal for treating them. The basic thing is that I
suggest to deprecate .dtype as being a first-class attribute and
replace it instead by the descriptor type container, which I find
quite more useful for end users. The current .dtype type will be still
accessible (mainly for developers) but buried in .dtype.type.
.dtypedescr --> moved into .dtype
.dtype --> moved into .dtype.type
.dtype.dtypestr --> moved into .dtype.str
What is achieved with that? Well, not much, except easy of use and
type comparison correctness. For example, with the next setup:
>>> import numpy
we have currently:
>>> a.dtype == b.dtype
With the new proposal, we would have:
>>> a.dtype == b.dtype
The advantages of the new proposal are:
- No more .dtype and .dtypedescr lying together, just current
.dtypedescr renamed to .dtype. I think that current .dtype does not
provide more useful information than current .dtypedesc, and giving
it a shorter name than .dtypedescr seems to indicate that it is more
useful to users (and in my opinion, it isn't).
- Current .dtype is still accessible, but specifying and extra name in
path: .dtype.type (can be changed into .dtype.type_ or
whatever). This should be useful mainly for developers.
- Added a useful dtype(descr).name so that one can quickly access to
the type name.
- Comparison between data types works as it should now (without having
to create a metaclass for PyType_Type).
- Backward incompatible change. However, provided the advantages are
desirable, I think it is better changing now than later.
- I don't specially like the string representation for the new .dtype
class. For example, I'd find dtype('Int32') much better than
dtype('<i4'). However, this would represent more changes in the
code, but they can be made later on (much less disruptive than the
- Some other issues that I'm not aware of.
I'm attaching the patch for latest SVN. Once applied (please, pay
attention to the "XXX" signs in patched code), it passes all tests.
However, it may remain some gotchas (specially those cases that are
not checked in current tests). In case you are considering this change
to check in, please, tell me and I will revise much more carefully the
patch. If don't, never mind, it has been a good learning experience
Uh, sorry for proposing this sort of things in the hours previous to a
public release of numpy.
>0,0< Francesc Altet http://www.carabos.com/
V V Cárabos Coop. V. Enjoy Data
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