mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 22:21:06 +08:00
Johannes Schönberger
73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
import numpy as np
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def regular_grid(ar_shape, n_points):
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"""Find `n_points` regularly spaced along `ar_shape`.
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The returned points (as slices) should be as close to cubically-spaced as
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possible. Essentially, the points are spaced by the Nth root of the input
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array size, where N is the number of dimensions. However, if an array
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dimension cannot fit a full step size, it is "discarded", and the
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computation is done for only the remaining dimensions.
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Parameters
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----------
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ar_shape : array-like of ints
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The shape of the space embedding the grid. ``len(ar_shape)`` is the
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number of dimensions.
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n_points : int
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The (approximate) number of points to embed in the space.
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Returns
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-------
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slices : list of slice objects
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A slice along each dimension of `ar_shape`, such that the intersection
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of all the slices give the coordinates of regularly spaced points.
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Examples
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--------
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>>> ar = np.zeros((20, 40))
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>>> g = regular_grid(ar.shape, 8)
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>>> g
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[slice(5.0, None, 10.0), slice(5.0, None, 10.0)]
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>>> ar[g] = 1
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>>> ar.sum()
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8.0
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>>> ar = np.zeros((20, 40))
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>>> g = regular_grid(ar.shape, 32)
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>>> g
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[slice(2.0, None, 5.0), slice(2.0, None, 5.0)]
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>>> ar[g] = 1
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>>> ar.sum()
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32.0
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>>> ar = np.zeros((3, 20, 40))
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>>> g = regular_grid(ar.shape, 8)
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>>> g
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[slice(1.0, None, 3.0), slice(5.0, None, 10.0), slice(5.0, None, 10.0)]
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>>> ar[g] = 1
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>>> ar.sum()
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8.0
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"""
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ar_shape = np.asanyarray(ar_shape)
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ndim = len(ar_shape)
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unsort_dim_idxs = np.argsort(np.argsort(ar_shape))
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sorted_dims = np.sort(ar_shape)
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space_size = float(np.prod(ar_shape))
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if space_size <= n_points:
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return [slice(None)] * ndim
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stepsizes = (space_size / n_points) ** (1.0 / ndim) * np.ones(ndim)
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if (sorted_dims < stepsizes).any():
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for dim in range(ndim):
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stepsizes[dim] = sorted_dims[dim]
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space_size = float(np.prod(sorted_dims[dim+1:]))
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stepsizes[dim+1:] = ((space_size / n_points) **
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(1.0 / (ndim - dim - 1)))
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if (sorted_dims >= stepsizes).all():
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break
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starts = stepsizes // 2
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stepsizes = np.round(stepsizes)
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slices = [slice(start, None, step) for
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start, step in zip(starts, stepsizes)]
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slices = [slices[i] for i in unsort_dim_idxs]
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return slices
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