On Thu, 2013-04-25 at 14:04 -0600, Charles R Harris wrote:
On Thu, Apr 25, 2013 at 1:51 PM, <josef.pktd@gmail.com> wrote: On Thu, Apr 25, 2013 at 3:40 PM, Robert Kern <robert.kern@gmail.com> wrote: > On Thu, Apr 25, 2013 at 8:21 PM, Andrew Giessel > <andrew_giessel@hms.harvard.edu> wrote: >> I respect this opinion. However (and maybe this is legacy), while reading >> through the numeric.py source file, I was surprised at how short many of the >> functions are, generally. Functions like ones() and zeros() are pretty >> simple wrappers which call empty() and then copy over values. > > Many of these are short, but they do tend to do at least two things > that someone would otherwise have to do. This really isn't the case > for iteraxis() and rollaxis(). One can use rollaxis() pretty much > everywhere you would use iteraxis(), but not vice-versa. > >> FWIW, I had used numpy for over two years before realizing that the default >> behavior of iterating on a numpy array was to return slices over the first >> axis (although, this makes sense because it makes a 1d array like a list), >> and I think it is generally left out of any tutorials or guides.
That definitely sounds like a documentation problem. I'm using often that it's a python iterator in the first dimension, and can be used with *args and tuple unpacking. (I didn't need it with anything else than axis=0 or axis=-1 for matplotlib IIRC)
I never used rollaxis, but I have seen it a lot when I was still reading the nipy source.
In general, I think that there are already too many aliases in numpy, or function whether it's not really clear if they are aliases or something slightly different.
It took me more than a year to remember what `expand_dims` is called, (I always tried, add_axis) until I bookmarked it for a while.
After thinking about it, I'm in favor of this small function. Rollaxis takes a bit of thought and document reading to figure out how to use it, whereas this function covers a common use with an easy to understand API. I'm not completely satisfied with the name, it isn't as memorable as I'd like, but that is a small quibble.
What I am not quite happy with is, that it feels that if we want to keep it open to understanding multiple axes (and maybe also be a method of that same name) at some point, defaulting to flat iteration may be better. So from a future point of view, maybe it should have axes=None as default? I.e. (oh, evil code!) the long term goal could be something like this (obviously it would be preferable and much faster in C...): def iteraxes(arr, axis=None, order='C'): view_shape = [] view_strides = [] op_axes = [] count_axes = 0 if axis is None: op_axes = range(arr.ndim) else: if not isinstance(axis, tuple): axis = {axis} else: axis = set(axis) # ignores duplicates... for ax in range(arr.ndim): if ax in axis: axis.remove(ax) op_axes.append(ax) else: view_shape.append(arr.shape[ax]) view_strides.append(arr.strides[ax]) if len(axis) != 0: raise ValueError i = np.nditer(arr, op_axes=[op_axes], order=order) for s in i: view = np.lib.stride_tricks.as_strided(s, view_shape, view_strides) yield view
Chuck
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