On Mon, Dec 13, 2010 at 12:20 PM, Bruce Southey <bsouthey@gmail.com> wrote:
From the np.median doc string: "If the input contains integers, or floats of smaller precision than 64, then the output data-type is float64."
arr = np.array([[0,1,2,3,4,5]], dtype='float32') np.median(arr, axis=0).dtype dtype('float32') np.median(arr, axis=1).dtype dtype('float32') np.median(arr, axis=None).dtype dtype('float64')
So the output doesn't agree with the doc string.
What is the desired dtype of the accumulator and the output for when the input dtype is less than float64? Should it depend on axis?
I'm trying to duplicate the behavior of np.median (and other numpy/scipy functions) in the Bottleneck package and am running into a few corner cases while unit testing.
Here's another one:
np.sum([np.nan]).dtype dtype('float64') np.nansum([1,np.nan]).dtype dtype('float64') np.nansum([np.nan]).dtype <snip> AttributeError: 'float' object has no attribute 'dtype'
I just duplicated the numpy behavior for that one since it was easy to do. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion Unless something has changed since the docstring was written, this is
On 12/13/2010 11:59 AM, Keith Goodman wrote: probably an inherited 'bug' from np.mean() as the author expected that the docstring of mean was correct. For my 'old' 2.0 dev version:
>>> np.mean( np.array([[0,1,2,3,4,5]], dtype='float32'), axis=1).dtype dtype('float32') >>> np.mean( np.array([[0,1,2,3,4,5]], dtype='float32')).dtype dtype('float64')
Same issue with np.std and np.var.