Author: Brian Kearns <[email protected]>
Branch:
Changeset: r61085:5fb4c2227097
Date: 2013-02-11 06:31 -0500
http://bitbucket.org/pypy/pypy/changeset/5fb4c2227097/
Log: might as well add dstack too
diff --git a/lib_pypy/numpypy/core/shape_base.py
b/lib_pypy/numpypy/core/shape_base.py
--- a/lib_pypy/numpypy/core/shape_base.py
+++ b/lib_pypy/numpypy/core/shape_base.py
@@ -272,3 +272,52 @@
else:
return _numpypy.concatenate(arrs, 1)
+def dstack(tup):
+ """
+ Stack arrays in sequence depth wise (along third axis).
+
+ Takes a sequence of arrays and stack them along the third axis
+ to make a single array. Rebuilds arrays divided by `dsplit`.
+ This is a simple way to stack 2D arrays (images) into a single
+ 3D array for processing.
+
+ Parameters
+ ----------
+ tup : sequence of arrays
+ Arrays to stack. All of them must have the same shape along all
+ but the third axis.
+
+ Returns
+ -------
+ stacked : ndarray
+ The array formed by stacking the given arrays.
+
+ See Also
+ --------
+ vstack : Stack along first axis.
+ hstack : Stack along second axis.
+ concatenate : Join arrays.
+ dsplit : Split array along third axis.
+
+ Notes
+ -----
+ Equivalent to ``np.concatenate(tup, axis=2)``.
+
+ Examples
+ --------
+ >>> a = np.array((1,2,3))
+ >>> b = np.array((2,3,4))
+ >>> np.dstack((a,b))
+ array([[[1, 2],
+ [2, 3],
+ [3, 4]]])
+
+ >>> a = np.array([[1],[2],[3]])
+ >>> b = np.array([[2],[3],[4]])
+ >>> np.dstack((a,b))
+ array([[[1, 2]],
+ [[2, 3]],
+ [[3, 4]]])
+
+ """
+ return _numpypy.concatenate(map(atleast_3d,tup),2)
diff --git a/pypy/module/test_lib_pypy/numpypy/core/test_shape_base.py
b/pypy/module/test_lib_pypy/numpypy/core/test_shape_base.py
--- a/pypy/module/test_lib_pypy/numpypy/core/test_shape_base.py
+++ b/pypy/module/test_lib_pypy/numpypy/core/test_shape_base.py
@@ -94,3 +94,14 @@
[2, 3],
[3, 4]])
+ def test_dstack(self):
+ import numpypy as np
+ a = np.array((1,2,3))
+ b = np.array((2,3,4))
+ c = np.dstack((a,b))
+ assert np.array_equal(c, [[[1, 2], [2, 3], [3, 4]]])
+
+ a = np.array([[1],[2],[3]])
+ b = np.array([[2],[3],[4]])
+ c = np.dstack((a,b))
+ assert np.array_equal(c, [[[1, 2]], [[2, 3]], [[3, 4]]])
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