[Numpy-discussion] Indexing with callables (was: Yorick-like functionality)
Pauli Virtanen
pav@iki...
Thu May 14 05:54:50 CDT 2009
Wed, 13 May 2009 13:18:45 -0700, David J Strozzi kirjoitti:
[clip]
> Many of you probably know of the interpreter yorick by Dave Munro. As a
> Livermoron, I use it all the time. There are some built-in functions
> there, analogous to but above and beyond numpy's sum() and diff(), which
> are quite useful for common operations on gridded data. Of course one
> can write their own, but maybe they should be cleanly canonized?
+0 from me for zcen and other, having small functions probably won't hurt
much
[clip]
> Besides zcen, yorick has builtins for "point centering", "un-zone
> centering," etc. Also, due to its slick syntax you can give these
> things as array "indexes":
>
> x(zcen), y(dif), z(:,sum,:)
I think you can easily subclass numpy.ndarray to offer the same feature,
see below. I don't know if we want to add this feature (indexing with
callables) to the Numpy's fancy indexing itself. Thoughts?
-----
import numpy as np
import inspect
class YNdarray(np.ndarray):
"""
A subclass of ndarray that implements Yorick-like indexing with functions.
Beware: not adequately tested...
"""
def __getitem__(self, key_):
if not isinstance(key_, tuple):
key = (key_,)
scalar_key = True
else:
key = key_
scalar_key = False
key = list(key)
# expand ellipsis manually
while Ellipsis in key:
j = key.index(Ellipsis)
key[j:j+1] = [slice(None)] * (self.ndim - len(key))
# handle reducing or mutating callables
arr = self
new_key = []
real_axis = 0
for j, v in enumerate(key):
if callable(v):
arr2 = self._reduce_axis(arr, v, real_axis)
new_key.extend([slice(None)] * (arr2.ndim - arr.ndim + 1))
arr = arr2
elif v is not None:
real_axis += 1
new_key.append(v)
else:
new_key.append(v)
# final get
if scalar_key:
return np.ndarray.__getitem__(arr, new_key[0])
else:
return np.ndarray.__getitem__(arr, tuple(new_key))
def _reduce_axis(self, arr, func, axis):
return func(arr, axis=axis)
x = np.arange(2*3*4).reshape(2,3,4).view(YNdarray)
# Now,
assert np.allclose(x[np.sum,...], np.sum(x, axis=0))
assert np.allclose(x[:,np.sum,:], np.sum(x, axis=1))
assert np.allclose(x[:,:,np.sum], np.sum(x, axis=2))
assert np.allclose(x[:,np.sum,None,np.sum], x.sum(axis=1).sum(axis=1)[:,None])
def get(v, s, axis=0):
"""Index `v` with slice `s` along given axis"""
ix = [slice(None)] * v.ndim
ix[axis] = s
return v[ix]
def drop_last(v, axis=0):
"""Remove one element from given array in given dimension"""
return get(v, slice(None, -1), axis)
assert np.allclose(x[:,drop_last,:], x[:,:-1,:])
def zcen(v, axis=0):
return .5*(get(v, slice(None,-1), axis) + get(v, slice(1,None), axis))
assert np.allclose(x[0,1,zcen], .5*(x[0,1,1:] + x[0,1,:-1]))
def append_one(v, axis=0):
"""Append one element to the given array in given dimension,
fill with ones"""
new_shape = list(v.shape)
new_shape[axis] += 1
v2 = np.empty(new_shape, dtype=v.dtype)
get(v2, slice(None, -1), axis)[:] = v
get(v2, -1, axis)[:] = 1
return v2
assert np.allclose(x[:,np.diff,0], np.diff(x.view(np.ndarray)[:,:,0], axis=1))
assert np.allclose(x[0,append_one,:], [[0,1,2,3],
[4,5,6,7],
[8,9,10,11],
[1,1,1,1]])
assert np.allclose(x[:,append_one,0], [[0,4,8,1],
[12,16,20,1]])
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