On Fri, May 15, 2009 at 4:09 PM, David Huard <david.hu...@gmail.com> wrote: > Pauli and David, > > Can this indexing syntax do things that are otherwise awkward with the > current syntax ? Otherwise, I'm not warm to the idea of making indexing more > complex than it is. > > getv : this is useful but it feels a bit redundant with numpy.take. Is there > a reason why take could not support slices ? > > Drop_last: I don't think it is worth cluttering the namespace with a one > liner. > > append_one: A generalized stack method with broadcasting capability would be > more useful in my opinion, eg. ``np.stack(x, 1., axis=1)`` > > zcen: This is indeed useful, particulary in its nd form, that is, when it > can be applied to multiples axes to find the center of a 2D or 3D cell in > one call. I'm appending the version I use below. > > Cheers, > > David > > > # This code is released in the public domain. > import numpy as np > def __midpoints_1d(a): > """Return `a` linearly interpolated at the mid-points.""" > return (a[:-1] + a[1:])/2. > > def midpoints(a, axis=None): > """Return `a` linearly interpolated at the mid-points. > > Parameters > ---------- > a : array-like > Input array. > axis : int or None > Axis along which the interpolation takes place. None stands for all > axes. > > Returns > ------- > out : ndarray > Input array interpolated at the midpoints along the given axis. > > Examples > -------- > >>> a = [1,2,3,4] > >>> midpoints(a) > array([1.5, 2.5, 3.5]) > """ > x = np.asarray(a) > if axis is not None: > return np.apply_along_axis(__midpoints_1d, axis, x) > else: > for i in range(x.ndim): > x = midpoints(x, i) > return x >
zcen is just a moving average, isn't it? For time series (1d), correlate works well, for 2d (nd?), there is >>> a= np.arange(5) >>> b = 1.0*a[:,np.newaxis]*np.arange(4) >>> ndimage.filters.correlate(b,0.5*np.ones((2,1)))[1:,1:] >>> ndimage.filters.correlate(b,0.5*np.ones((2,1)))[1:,1:] Josef _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
