8000 DOC: v/h/dstack docstr shouldn't imply deprecation by ahaldane · Pull Request #10057 · numpy/numpy · GitHub
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DOC: v/h/dstack docstr shouldn't imply deprecation #10057

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Nov 21, 2017
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DOC: v/h/dstack docstr shouldn't imply deprecation #10057

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8000
43 changes: 19 additions & 24 deletions numpy/core/shape_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -183,23 +183,25 @@ def vstack(tup):
"""
Stack arrays in sequence vertically (row wise).

Take a sequence of arrays and stack them vertically to make a single
array. Rebuild arrays divided by `vsplit`.
This is equivalent to concatenation along the first axis after 1-D arrays
of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds arrays divided by
`vsplit`.

This function continues to be supported for backward compatibility, but
you should prefer ``np.concatenate`` or ``np.stack``. The ``np.stack``
function was added in NumPy 1.10.
This function makes most sense for arrays with up to 3 dimensions. For
instance, for pixel-data with a height (first axis), width (second axis),
and r/g/b channels (third axis). The functions `concatenate`, `stack` and
`block` provide more general stacking and concatenation operations.

Parameters
----------
tup : sequence of ndarrays
Tuple containing arrays to be stacked. The arrays must have the same
shape along all but the first axis.
The arrays must have the same shape along all but the first axis.
1-D arrays must have the same length.

Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
The array formed by stacking the given arrays, will be at least 2-D.

See Also
--------
Expand All @@ -210,11 +212,6 @@ def vstack(tup):
vsplit : Split array into a list of multiple sub-arrays vertically.
block : Assemble arrays from blocks.

Notes
-----
Equivalent to ``np.concatenate(tup, axis=0)`` if `tup` contains arrays that
are at least 2-dimensional.

Examples
--------
>>> a = np.array([1, 2, 3])
Expand All @@ -240,17 +237,20 @@ def hstack(tup):
"""
Stack arrays in sequence horizontally (column wise).

Take a sequence of arrays and stack them horizontally to make
a single array. Rebuild arrays divided by `hsplit`.
This is equivalent to concatenation along the second axis, except for 1-D
arrays where it concatenates along the first axis. Rebuilds arrays divided
by `hsplit`.

This function continues to be supported for backward compatibility, but
you should prefer ``np.concatenate`` or ``np.stack``. The ``np.stack``
function was added in NumPy 1.10.
This function makes most sense for arrays with up to 3 dimensions. For
instance, for pixel-data with a height (first axis), width (second axis),
and r/g/b channels (third axis). The functions `concatenate`, `stack` and
`block` provide more general stacking and concatenation operations.

Parameters
----------
tup : sequence of ndarrays
All arrays must have the same shape along all but the second axis.
The arrays must have the same shape along all but the second axis,
except 1-D arrays which can be any length.

Returns
-------
Expand All @@ -266,11 +266,6 @@ def hstack(tup):
hsplit : Split array along second axis.
block : Assemble arrays from blocks.

Notes
-----
Equivalent to ``np.concatenate(tup, axis=1)`` if `tup` contains arrays that
are at least 2-dimensional.

Examples
--------
>>> a = np.array((1,2,3))
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26 changes: 11 additions & 15 deletions numpy/lib/shape_base.py
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Expand Up @@ -354,25 +354,26 @@ 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.
This is equivalent to concatenation along the third axis after 2-D arrays
of shape `(M,N)` have been reshaped to `(M,N,1)` and 1-D arrays of shape
`(N,)` have been reshaped to `(1,N,1)`. Rebuilds arrays divided by
`dsplit`.

This function continues to be supported for backward compatibility, but
you should prefer ``np.concatenate`` or ``np.stack``. The ``np.stack``
function was added in NumPy 1.10.
This function makes most sense for arrays with up to 3 dimensions. For
instance, for pixel-data with a height (first axis), width (second axis),
and r/g/b channels (third axis). The functions `concatenate`, `stack` and
`block` provide more 8DA2 general stacking and concatenation operations.

Parameters
----------
tup : sequence of arrays
Arrays to stack. All of them must have the same shape along all
but the third axis.
The arrays must have the same shape along all but the third axis.
1-D or 2-D arrays must have the same shape.

Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
The array formed by stacking the given arrays, will be at least 3-D.

See Also
--------
Expand All @@ -382,11 +383,6 @@ def dstack(tup):
concatenate : Join a sequence of arrays along an existing axis.
dsplit : Split array along third axis.

Notes
-----
Equivalent to ``np.concatenate(tup, axis=2)`` if `tup` contains arrays that
are at least 3-dimensional.

Examples
--------
>>> a = np.array((1,2,3))
Expand Down
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