分配给numpy数组的包装切片 [英] assigning to a wrapped slice of a numpy array

问题描述

我有一张大图 A 和一张较小的图片 B ，两者都表示为2-D numpy 数组。我想使用 A 作为画布，并在其上写下 B 的翻译副本，以六边形排列。我无法理解的部分是如何处理它，使图像垂直和水平包裹 - 基本上我想要的是将（填充的，必要的）子图像定期镶嵌到圆环上。

I have a large image A and a smaller image B, both expressed as 2-D numpy arrays. I want to use A as the canvas, and write translated copies of B all over it, packed in a hexagonal arrangement. The part I can't get my head around is how to handle it such that the image wraps both vertically and horizontally—essentially what I want is regular tessellation of a (padded, as necessary) sub-image onto a torus.

我见过讨论 numpy.take 和 numpy.roll at
包装Python / numpy中的切片，这告诉我如何访问并返回 复制 包含的数组切片，但是我想分配给它 - 即，对于任意整数 rowOffset 和 columnOffset 我想做相当于：

I've seen the discussion of numpy.take and numpy.roll at wrapping around slices in Python / numpy and that shows me how to access and return a copy of a wrapped slice of an array, but I want to assign to that—i.e., for arbitrary integers rowOffset and columnOffset I want to do the equivalent of:

  A = numpy.zeros((5,11), int)
  B = numpy.array([[1,2,3,4,5,6,7]]) * numpy.array([[10,100,1000]]).T
  # OK, we wouldn't be able to fit more than one or two copies of B into A, but they demonstrate the wrapped placement problem

  wrappedRowIndices = ( numpy.arange(B.shape[0]) + rowOffset ) % A.shape[0]
  wrappedColumnIndices = ( numpy.arange(B.shape[1]) + columnOffset ) % A.shape[1]
  A[ wrappedRowIndices,  : ][ :, wrappedColumnIndices ] = B 

我从评论中看到关于这个问题，
以及从时刻的反思 numpy 数组的代表，根据需要，包装切片无法以视图的形式返回。

I see from a comment on the question, and from a moment's reflection on the way numpy arrays are represented, that there's no way a wrapped slice can be returned as a view in the way this demands.

是否存在（Y）以这种方式分配给数组的包装切片的方法，或者（X）用于执行我想要实现的曲面细分的现有实用程序？

Is there (Y) a way of assigning to wrapped slices of an array in this way, or (X) an existing utility for performing the kind of tessellation I'm trying to achieve?

推荐答案

您当前的代码分解为 __ getitem __ 和 __ setitem __ 。正如您所指出的， __ getitem __ 不会返回视图，因此 __ setitem __ 最终会修改副本。

Your current code breaks down into a __getitem__ and a __setitem__. The __getitem__ does not return a view, as you noted, so the __setitem__ just ends up modifying the copy.

你需要在一个 __ setitem __ （即一组括号）中完成整个事情：

You need to do the whole thing in one __setitem__ (i.e. one set of brackets):

A[wrappedRowIndices[:,np.newaxis], wrappedColumnIndices] = B

由于广播，这相当于：

A[wrappedRowIndices[:,np.newaxis], wrappedColumnIndices[np.newaxis,:]] = B

使用更多索引编制时比一个数组，规则是：

When indexing with more than one array, the rules is that:

# ... here is NOT the python Ellipsis!
y = x[a, b, c, ...]
y[i, j, ..] = x[a[i,j,...], b[i,j,...], ...]

---

实际上内置的， np.ix_（ ） ：

A[np.ix_(wrappedRowIndices, wrappedColumnIndices)] = B

推广到ND，你得到：

def place_wrapped(canvas, brush, position):
    assert canvas.ndim == brush.ndim == len(position)
    ind = np.ix_(*(
        (np.arange(b_dim) + shift) % c_dim
        for b_dim, c_dim, shift in zip(brush.shape, canvas.shape, position)
    ))
    canvas[ind] = brush

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