8000 DOC: Add unravel_index examples to np.arg[min|max|sort] by ElieGouzien · Pull Request #9333 · numpy/numpy · GitHub
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DOC: Add unravel_index examples to np.arg[min|max|sort] #9333

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DOC: Add unravel_index examples to np.arg[min|max|sort] #9333

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08d8225
Exemples for np.arg[min|max|sort] on how to retrieve indices for d-di…
ElieGouzien Jun 29, 2017
364a575
Exemples for np.arg[min|max|sort] on how to retrieve indices for d-di…
ElieGouzien Jun 29, 2017
2b514e3
Exemples for np.arg[min|max|sort] : use of "coordinates" instead of "…
ElieGouzien Jun 29, 2017
51d0710
Revert "coordinates" to "indices".
ElieGouzien Aug 4, 2017
cf43db7
Doc : show that the result of the exemple is directly usable as indices.
ElieGouzien Aug 7, 2017
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18 changes: 18 additions & 0 deletions numpy/core/fromnumeric.py
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Original file line number Diff line number Diff line change
Expand Up @@ -890,6 +890,14 @@ def argsort(a, axis=-1, kind='quicksort', order=None):
array([[0, 1],
[0, 1]])

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Can you add a short description here to explain the example? That would also be appreciated in the other examples.

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What about "Retrieving global sorting indices:" ?
I'm not native English speaker so don't hesitate to propose a reformulated version.

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Maybe: "Indices along each axis for sorting the flattened array"

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@ElieGouzien ElieGouzien Jun 29, 2017 •
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Numpy superpowers come from the fact that the user can forget that he lives in a flat space, so I prefer avoiding a description of how it works to privilege what we get. See my new commit for my next intent (which is far from perfect).

Indices of the sorted elements of a N-dimensional array:
>>> np.unravel_index(np.argsort(x, axis=None), x.shape)
(array([0, 1, 1, 0]), array([0, 0, 1, 1]))
>>> from np.testing import assert_equal
>>> assert_equal(x[(array([0, 1, 1, 0]), array([0, 0, 1, 1]))], np.sort(x, axis=None))
>>> list(zip(*np.unravel_index(np.argsort(x, axis=None), x.shape)))
[(0, 0), (1, 0), (1, 1), (0, 1)]
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@eric-wieser eric-wieser Sep 22, 2017 •
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This might be clearer as:

>>> ind = np.unravel_index(np.argsort(x, axis=None), x.shape)
>>> ind
(array([0, 1, 1, 0]), array([0, 0, 1, 1]))
>>> x[ind]  # same as np.sort(x, axis=None)
<whatever it is>

Repeating the call to unravel_index is obscuring the point, and assert_equal isn't that useful in docs. (we don't run doctests)


Sorting with keys:

>>> x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])
Expand Down Expand Up @@ -952,6 +960,11 @@ def argmax(a, axis=None, out=None):
>>> np.argmax(a, axis=1)
array([2, 2])

Indices of the maximal elements of a N-dimensional array:
>>> np.unravel_index(np.argmax(a, axis=None), a.shape)
(1, 2)
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Would be nice to show that the result of this can be used to index directly into a, and the same for the argsort example

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For sure, it's committed. Let me know if you like the way I've written it.

>>> np.testing.assert_equal(a[(1, 2)], np.max(a))

>>> b = np.arange(6)
>>> b[1] = 5
>>> b
Expand Down Expand Up @@ -1008,6 +1021,11 @@ def argmin(a, axis=None, out=None):
>>> np.argmin(a, axis=1)
array([0, 0])

Indices of the minimum elements of a N-dimensional array:
>>> np.unravel_index(np.argmin(a, axis=None), a.shape)
(0, 0)
>>> np.testing.assert_equal(a[(0, 0)], np.min(a))

>>> b = np.arange(6)
>>> b[4] = 0
>>> b
Expand Down
0