Re: [Numpy-discussion] difference between dtypes
On Wed, Jul 22, 2015 at 7:45 PM, josef.p...@gmail.com wrote: Is there an explanation somewhere of what different basic dtypes mean, across platforms and python versions? np.bool8 type 'numpy.bool_' np.bool_ type 'numpy.bool_' bool type 'bool' Are there any rules and recommendations or is it all folks lore? This may help a little: http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html#arrays-dtypes-constructing Basically, we accept the builtin Python type objects as a dtype argument and do something sensible with them. float - np.float64 because Python floats are C doubles. int - np.int32 or np.int64 depending on whatever a C long is (i.e. depending on the 64bitness of your CPU and how your OS chooses to deal with that). We encode those precision choices as aliases to the corresponding specific numpy scalar types (underscored as necessary to avoid shadowing builtins of the same name): np.float_ is np.float64, for example. See here for why the aliases to Python builtin types, np.int, np.float, etc. still exist: https://github.com/numpy/numpy/pull/6103#issuecomment-123652497 If you just need to pass a dtype= argument and want the precision that matches the native integer and float for your platform, then I prefer to use the Python builtin types instead of the underscored aliases; they just look cleaner. If you need a true numpy scalar type (e.g. to construct a numpy scalar object), of course, you must use one of the numpy scalar types, and the underscored aliases are convenient for that. Never use the aliases to the Python builtin types. -- Robert Kern ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] difference between dtypes
On Fri, Jul 24, 2015 at 3:46 AM, Robert Kern robert.k...@gmail.com wrote: On Wed, Jul 22, 2015 at 7:45 PM, josef.p...@gmail.com wrote: Is there an explanation somewhere of what different basic dtypes mean, across platforms and python versions? np.bool8 type 'numpy.bool_' np.bool_ type 'numpy.bool_' bool type 'bool' Are there any rules and recommendations or is it all folks lore? This may help a little: http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html#arrays-dtypes-constructing Basically, we accept the builtin Python type objects as a dtype argument and do something sensible with them. float - np.float64 because Python floats are C doubles. int - np.int32 or np.int64 depending on whatever a C long is (i.e. depending on the 64bitness of your CPU and how your OS chooses to deal with that). We encode those precision choices as aliases to the corresponding specific numpy scalar types (underscored as necessary to avoid shadowing builtins of the same name): np.float_ is np.float64, for example. See here for why the aliases to Python builtin types, np.int, np.float, etc. still exist: https://github.com/numpy/numpy/pull/6103#issuecomment-123652497 If you just need to pass a dtype= argument and want the precision that matches the native integer and float for your platform, then I prefer to use the Python builtin types instead of the underscored aliases; they just look cleaner. If you need a true numpy scalar type (e.g. to construct a numpy scalar object), of course, you must use one of the numpy scalar types, and the underscored aliases are convenient for that. Never use the aliases to the Python builtin types. (I don't have time to follow up on this for at least two weeks) my thinking was that, if there is no actual difference between bool, np.bool and np.bool_, the np.bool could become an alias and a replacement for np.bool_, so we can get rid of a ugly trailing underscore. If np.float is always float64 it could be mapped to that directly. As the previous discussion on python int versus numpy int on python 3.x, int is at least confusing. Also I'm thinking that maybe adjusting the code to the (mis)interpretation, instead of adjusting killing np.float completely might be nicer, (but changing np.int would be riskier?) Josef -- Robert Kern ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] difference between dtypes
On Fri, Jul 24, 2015 at 10:05 AM, josef.p...@gmail.com wrote: On Fri, Jul 24, 2015 at 3:46 AM, Robert Kern robert.k...@gmail.com wrote: On Wed, Jul 22, 2015 at 7:45 PM, josef.p...@gmail.com wrote: Is there an explanation somewhere of what different basic dtypes mean, across platforms and python versions? np.bool8 type 'numpy.bool_' np.bool_ type 'numpy.bool_' bool type 'bool' Are there any rules and recommendations or is it all folks lore? This may help a little: http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html#arrays-dtypes-constructing Basically, we accept the builtin Python type objects as a dtype argument and do something sensible with them. float - np.float64 because Python floats are C doubles. int - np.int32 or np.int64 depending on whatever a C long is (i.e. depending on the 64bitness of your CPU and how your OS chooses to deal with that). We encode those precision choices as aliases to the corresponding specific numpy scalar types (underscored as necessary to avoid shadowing builtins of the same name): np.float_ is np.float64, for example. See here for why the aliases to Python builtin types, np.int, np.float, etc. still exist: https://github.com/numpy/numpy/pull/6103#issuecomment-123652497 If you just need to pass a dtype= argument and want the precision that matches the native integer and float for your platform, then I prefer to use the Python builtin types instead of the underscored aliases; they just look cleaner. If you need a true numpy scalar type (e.g. to construct a numpy scalar object), of course, you must use one of the numpy scalar types, and the underscored aliases are convenient for that. Never use the aliases to the Python builtin types. (I don't have time to follow up on this for at least two weeks) my thinking was that, if there is no actual difference between bool, np.bool and np.bool_, the np.bool could become an alias and a replacement for np.bool_, so we can get rid of a ugly trailing underscore. If np.float is always float64 it could be mapped to that directly. Well, I'll tell you why that's a bad idea in when you get back in two weeks. -- Robert Kern ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] difference between dtypes
Is there an explanation somewhere of what different basic dtypes mean, across platforms and python versions? np.bool8 type 'numpy.bool_' np.bool_ type 'numpy.bool_' bool type 'bool' Are there any rules and recommendations or is it all folks lore? I'm asking because my intuition picked up by osmosis might be off, and I thought https://github.com/scipy/scipy/pull/5076 is weird (i.e. counter intuitive). Deprecation warnings are always a lot of fun, especially if This log is too long to be displayed. Please reduce the verbosity of your build or download the raw log. Josef ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
