numpy.lib.shape

module documentation

(source)

Undocumented

Function	`apply_along_axis`	Apply a function to 1-D slices along the given axis.
Function	`apply_over_axes`	Apply a function repeatedly over multiple axes.
Function	`array_split`	Split an array into multiple sub-arrays.
Function	`column_stack`	Stack 1-D arrays as columns into a 2-D array.
Function	`dsplit`	Split array into multiple sub-arrays along the 3rd axis (depth).
Function	`dstack`	Stack arrays in sequence depth wise (along third axis).
Function	`expand_dims`	Expand the shape of an array.
Function	`get_array_prepare`	Find the wrapper for the array with the highest priority.
Function	`get_array_wrap`	Find the wrapper for the array with the highest priority.
Function	`hsplit`	Split an array into multiple sub-arrays horizontally (column-wise).
Function	`kron`	Kronecker product of two arrays.
Function	`put_along_axis`	Put values into the destination array by matching 1d index and data slices.
Function	`split`	Split an array into multiple sub-arrays as views into `ary`.
Function	`take_along_axis`	Take values from the input array by matching 1d index and data slices.
Function	`tile`	Construct an array by repeating A the number of times given by reps.
Function	`vsplit`	Split an array into multiple sub-arrays vertically (row-wise).
Variable	`array_function_dispatch`	Undocumented
Function	`_apply_along_axis_dispatcher`	Undocumented
Function	`_apply_over_axes_dispatcher`	Undocumented
Function	`_array_split_dispatcher`	Undocumented
Function	`_column_stack_dispatcher`	Undocumented
Function	`_dstack_dispatcher`	Undocumented
Function	`_expand_dims_dispatcher`	Undocumented
Function	`_hvdsplit_dispatcher`	Undocumented
Function	`_kron_dispatcher`	Undocumented
Function	`_make_along_axis_idx`	Undocumented
Function	`_put_along_axis_dispatcher`	Undocumented
Function	`_replace_zero_by_x_arrays`	Undocumented
Function	`_split_dispatcher`	Undocumented
Function	`_take_along_axis_dispatcher`	Undocumented
Function	`_tile_dispatcher`	Undocumented

@array_function_dispatch(_apply_along_axis_dispatcher)
def apply_along_axis(func1d, axis, arr, *args, **kwargs): (source) ¶

Apply a function to 1-D slices along the given axis.

Execute func1d(a, *args, **kwargs) where func1d operates on 1-D arrays and a is a 1-D slice of arr along axis.

This is equivalent to (but faster than) the following use of ndindex and s_, which sets each of ii, jj, and kk to a tuple of indices:

Ni, Nk = a.shape[:axis], a.shape[axis+1:]
for ii in ndindex(Ni):
    for kk in ndindex(Nk):
        f = func1d(arr[ii + s_[:,] + kk])
        Nj = f.shape
        for jj in ndindex(Nj):
            out[ii + jj + kk] = f[jj]

Equivalently, eliminating the inner loop, this can be expressed as:

Ni, Nk = a.shape[:axis], a.shape[axis+1:]
for ii in ndindex(Ni):
    for kk in ndindex(Nk):
        out[ii + s_[...,] + kk] = func1d(arr[ii + s_[:,] + kk])

See Also

apply_over_axes: Apply a function repeatedly over multiple axes.

Examples

>>> def my_func(a):
...     """Average first and last element of a 1-D array"""
...     return (a[0] + a[-1]) * 0.5
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> np.apply_along_axis(my_func, 0, b)
array([4., 5., 6.])
>>> np.apply_along_axis(my_func, 1, b)
array([2.,  5.,  8.])

For a function that returns a 1D array, the number of dimensions in outarr is the same as arr.

>>> b = np.array([[8,1,7], [4,3,9], [5,2,6]])
>>> np.apply_along_axis(sorted, 1, b)
array([[1, 7, 8],
       [3, 4, 9],
       [2, 5, 6]])

For a function that returns a higher dimensional array, those dimensions are inserted in place of the axis dimension.

>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> np.apply_along_axis(np.diag, -1, b)
array([[[1, 0, 0],
        [0, 2, 0],
        [0, 0, 3]],
       [[4, 0, 0],
        [0, 5, 0],
        [0, 0, 6]],
       [[7, 0, 0],
        [0, 8, 0],
        [0, 0, 9]]])

Parameters
func1d:function(`M`, ) -> (`Nj...)`	This function should accept 1-D arrays. It is applied to 1-D slices of `arr` along the specified axis.
axis:`integer`	Axis along which `arr` is sliced.
arr:ndarray(`Ni...`, `M`, `Nk...)`	Input array.
*args:`any`	Additional arguments to `func1d`.
**kwargs:`any`	Additional named arguments to `func1d`. New in version 1.9.0.
Returns
ndarray(`Ni...`, `Nj...`, `Nk...)`	out - The output array. The shape of `out` is identical to the shape of `arr`, except along the `axis` dimension. This axis is removed, and replaced with new dimensions equal to the shape of the return value of `func1d`. So if `func1d` returns a scalar `out` will have one fewer dimensions than `arr`.

@array_function_dispatch(_apply_over_axes_dispatcher)
def apply_over_axes(func, a, axes): (source) ¶

Apply a function repeatedly over multiple axes.

func is called as res = func(a, axis), where axis is the first element of axes. The result res of the function call must have either the same dimensions as a or one less dimension. If res has one less dimension than a, a dimension is inserted before axis. The call to func is then repeated for each axis in axes, with res as the first argument.

See Also

apply_along_axis: Apply a function to 1-D slices of an array along the given axis.

Notes

This function is equivalent to tuple axis arguments to reorderable ufuncs with keepdims=True. Tuple axis arguments to ufuncs have been available since version 1.7.0.

Examples

>>> a = np.arange(24).reshape(2,3,4)
>>> a
array([[[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]],
       [[12, 13, 14, 15],
        [16, 17, 18, 19],
        [20, 21, 22, 23]]])

Sum over axes 0 and 2. The result has same number of dimensions as the original array:

>>> np.apply_over_axes(np.sum, a, [0,2])
array([[[ 60],
        [ 92],
        [124]]])

Tuple axis arguments to ufuncs are equivalent:

>>> np.sum(a, axis=(0,2), keepdims=True)
array([[[ 60],
        [ 92],
        [124]]])

Parameters
func:`function`	This function must take two arguments, `func(a, axis)`.
a:`array_like`	Input array.
axes:`array_like`	Axes over which `func` is applied; the elements must be integers.
Returns
`ndarray`	apply_over_axis - The output array. The number of dimensions is the same as `a`, but the shape can be different. This depends on whether `func` changes the shape of its output with respect to its input.

@array_function_dispatch(_array_split_dispatcher)
def array_split(ary, indices_or_sections, axis=0): (source) ¶

Split an array into multiple sub-arrays.

Please refer to the split documentation. The only difference between these functions is that array_split allows indices_or_sections to be an integer that does not equally divide the axis. For an array of length l that should be split into n sections, it returns l % n sub-arrays of size l//n + 1 and the rest of size l//n.

See Also

split: Split array into multiple sub-arrays of equal size.

Examples

>>> x = np.arange(8.0)
>>> np.array_split(x, 3)
[array([0.,  1.,  2.]), array([3.,  4.,  5.]), array([6.,  7.])]

>>> x = np.arange(9)
>>> np.array_split(x, 4)
[array([0, 1, 2]), array([3, 4]), array([5, 6]), array([7, 8])]

@array_function_dispatch(_column_stack_dispatcher)
def column_stack(tup): (source) ¶

Stack 1-D arrays as columns into a 2-D array.

Take a sequence of 1-D arrays and stack them as columns to make a single 2-D array. 2-D arrays are stacked as-is, just like with hstack. 1-D arrays are turned into 2-D columns first.

See Also

stack, hstack, vstack, concatenate

Examples

>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.column_stack((a,b))
array([[1, 2],
       [2, 3],
       [3, 4]])

Parameters
tup:`sequence` of 1-D or 2-D arrays.	Arrays to stack. All of them must have the same first dimension.
Returns
2-D array	stacked - The array formed by stacking the given arrays.

@array_function_dispatch(_hvdsplit_dispatcher)
def dsplit(ary, indices_or_sections): (source) ¶

Split array into multiple sub-arrays along the 3rd axis (depth).

Please refer to the split documentation. dsplit is equivalent to split with axis=2, the array is always split along the third axis provided the array dimension is greater than or equal to 3.

See Also

split: Split an array into multiple sub-arrays of equal size.

Examples

>>> x = np.arange(16.0).reshape(2, 2, 4)
>>> x
array([[[ 0.,   1.,   2.,   3.],
        [ 4.,   5.,   6.,   7.]],
       [[ 8.,   9.,  10.,  11.],
        [12.,  13.,  14.,  15.]]])
>>> np.dsplit(x, 2)
[array([[[ 0.,  1.],
        [ 4.,  5.]],
       [[ 8.,  9.],
        [12., 13.]]]), array([[[ 2.,  3.],
        [ 6.,  7.]],
       [[10., 11.],
        [14., 15.]]])]
>>> np.dsplit(x, np.array([3, 6]))
[array([[[ 0.,   1.,   2.],
        [ 4.,   5.,   6.]],
       [[ 8.,   9.,  10.],
        [12.,  13.,  14.]]]),
 array([[[ 3.],
        [ 7.]],
       [[11.],
        [15.]]]),
array([], shape=(2, 2, 0), dtype=float64)]

@array_function_dispatch(_dstack_dispatcher)
def dstack(tup): (source) ¶

Stack arrays in sequence depth wise (along third axis).

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 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.

See Also

concatenate: Join a sequence of arrays along an existing axis.
stack: Join a sequence of arrays along a new axis.
block: Assemble an nd-array from nested lists of blocks.
vstack: Stack arrays in sequence vertically (row wise).
hstack: Stack arrays in sequence horizontally (column wise).
column_stack: Stack 1-D arrays as columns into a 2-D array.
dsplit: Split array along third axis.

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]]])

Parameters
tup:`sequence` of `arrays`	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
`ndarray`	stacked - The array formed by stacking the given arrays, will be at least 3-D.

@array_function_dispatch(_expand_dims_dispatcher)
def expand_dims(a, axis): (source) ¶

Expand the shape of an array.

Insert a new axis that will appear at the axis position in the expanded array shape.

See Also

squeeze: The inverse operation, removing singleton dimensions
reshape: Insert, remove, and combine dimensions, and resize existing ones

doc.indexing, atleast_1d, atleast_2d, atleast_3d

Examples

>>> x = np.array([1, 2])
>>> x.shape
(2,)

The following is equivalent to x[np.newaxis, :] or x[np.newaxis]:

>>> y = np.expand_dims(x, axis=0)
>>> y
array([[1, 2]])
>>> y.shape
(1, 2)

The following is equivalent to x[:, np.newaxis]:

>>> y = np.expand_dims(x, axis=1)
>>> y
array([[1],
       [2]])
>>> y.shape
(2, 1)

axis may also be a tuple:

>>> y = np.expand_dims(x, axis=(0, 1))
>>> y
array([[[1, 2]]])

>>> y = np.expand_dims(x, axis=(2, 0))
>>> y
array([[[1],
        [2]]])

Note that some examples may use None instead of np.newaxis. These are the same objects:

>>> np.newaxis is None
True

Parameters
a:`array_like`	Input array.
axis:`int` or `tuple` of `ints`	Position in the expanded axes where the new axis (or axes) is placed. Deprecated since version 1.13.0: Passing an axis where `axis > a.ndim` will be treated as `axis == a.ndim`, and passing `axis < -a.ndim - 1` will be treated as `axis == 0`. This behavior is deprecated. Changed in version 1.18.0: A tuple of axes is now supported. Out of range axes as described above are now forbidden and raise an `AxisError`.
Returns
`ndarray`	result - View of `a` with the number of dimensions increased.

def get_array_prepare(*args): (source) ¶

Find the wrapper for the array with the highest priority.

In case of ties, leftmost wins. If no wrapper is found, return None

def get_array_wrap(*args): (source) ¶

Find the wrapper for the array with the highest priority.

In case of ties, leftmost wins. If no wrapper is found, return None

@array_function_dispatch(_hvdsplit_dispatcher)
def hsplit(ary, indices_or_sections): (source) ¶

Split an array into multiple sub-arrays horizontally (column-wise).

Please refer to the split documentation. hsplit is equivalent to split with axis=1, the array is always split along the second axis except for 1-D arrays, where it is split at axis=0.

See Also

split: Split an array into multiple sub-arrays of equal size.

Examples

>>> x = np.arange(16.0).reshape(4, 4)
>>> x
array([[ 0.,   1.,   2.,   3.],
       [ 4.,   5.,   6.,   7.],
       [ 8.,   9.,  10.,  11.],
       [12.,  13.,  14.,  15.]])
>>> np.hsplit(x, 2)
[array([[  0.,   1.],
       [  4.,   5.],
       [  8.,   9.],
       [12.,  13.]]),
 array([[  2.,   3.],
       [  6.,   7.],
       [10.,  11.],
       [14.,  15.]])]
>>> np.hsplit(x, np.array([3, 6]))
[array([[ 0.,   1.,   2.],
       [ 4.,   5.,   6.],
       [ 8.,   9.,  10.],
       [12.,  13.,  14.]]),
 array([[ 3.],
       [ 7.],
       [11.],
       [15.]]),
 array([], shape=(4, 0), dtype=float64)]

With a higher dimensional array the split is still along the second axis.

>>> x = np.arange(8.0).reshape(2, 2, 2)
>>> x
array([[[0.,  1.],
        [2.,  3.]],
       [[4.,  5.],
        [6.,  7.]]])
>>> np.hsplit(x, 2)
[array([[[0.,  1.]],
       [[4.,  5.]]]),
 array([[[2.,  3.]],
       [[6.,  7.]]])]

With a 1-D array, the split is along axis 0.

>>> x = np.array([0, 1, 2, 3, 4, 5])
>>> np.hsplit(x, 2)
[array([0, 1, 2]), array([3, 4, 5])]

@array_function_dispatch(_kron_dispatcher)
def kron(a, b): (source) ¶

Kronecker product of two arrays.

Computes the Kronecker product, a composite array made of blocks of the second array scaled by the first.

See Also

outer: The outer product

Notes

The function assumes that the number of dimensions of a and b are the same, if necessary prepending the smallest with ones. If a.shape = (r0,r1,..,rN) and b.shape = (s0,s1,...,sN), the Kronecker product has shape (r0*s0, r1*s1, ..., rN*SN). The elements are products of elements from a and b, organized explicitly by:

kron(a,b)[k0,k1,...,kN] = a[i0,i1,...,iN] * b[j0,j1,...,jN]

where:

kt = it * st + jt,  t = 0,...,N

In the common 2-D case (N=1), the block structure can be visualized:

[[ a[0,0]*b,   a[0,1]*b,  ... , a[0,-1]*b  ],
 [  ...                              ...   ],
 [ a[-1,0]*b,  a[-1,1]*b, ... , a[-1,-1]*b ]]

Examples

>>> np.kron([1,10,100], [5,6,7])
array([  5,   6,   7, ..., 500, 600, 700])
>>> np.kron([5,6,7], [1,10,100])
array([  5,  50, 500, ...,   7,  70, 700])

>>> np.kron(np.eye(2), np.ones((2,2)))
array([[1.,  1.,  0.,  0.],
       [1.,  1.,  0.,  0.],
       [0.,  0.,  1.,  1.],
       [0.,  0.,  1.,  1.]])

>>> a = np.arange(100).reshape((2,5,2,5))
>>> b = np.arange(24).reshape((2,3,4))
>>> c = np.kron(a,b)
>>> c.shape
(2, 10, 6, 20)
>>> I = (1,3,0,2)
>>> J = (0,2,1)
>>> J1 = (0,) + J             # extend to ndim=4
>>> S1 = (1,) + b.shape
>>> K = tuple(np.array(I) * np.array(S1) + np.array(J1))
>>> c[K] == a[I]*b[J]
True

Parameters
a:`array_like`
b:`array_like`
Returns
`ndarray`	out

@array_function_dispatch(_put_along_axis_dispatcher)
def put_along_axis(arr, indices, values, axis): (source) ¶

Put values into the destination array by matching 1d index and data slices.

This iterates over matching 1d slices oriented along the specified axis in the index and data arrays, and uses the former to place values into the latter. These slices can be different lengths.

Functions returning an index along an axis, like argsort and argpartition, produce suitable indices for this function.

New in version 1.15.0.

Notes

This is equivalent to (but faster than) the following use of ndindex and s_, which sets each of ii and kk to a tuple of indices:

Ni, M, Nk = a.shape[:axis], a.shape[axis], a.shape[axis+1:]
J = indices.shape[axis]  # Need not equal M

for ii in ndindex(Ni):
    for kk in ndindex(Nk):
        a_1d       = a      [ii + s_[:,] + kk]
        indices_1d = indices[ii + s_[:,] + kk]
        values_1d  = values [ii + s_[:,] + kk]
        for j in range(J):
            a_1d[indices_1d[j]] = values_1d[j]

Equivalently, eliminating the inner loop, the last two lines would be:

a_1d[indices_1d] = values_1d

See Also

take_along_axis: Take values from the input array by matching 1d index and data slices

Examples

For this sample array

>>> a = np.array([[10, 30, 20], [60, 40, 50]])

We can replace the maximum values with:

>>> ai = np.expand_dims(np.argmax(a, axis=1), axis=1)
>>> ai
array([[1],
       [0]])
>>> np.put_along_axis(a, ai, 99, axis=1)
>>> a
array([[10, 99, 20],
       [99, 40, 50]])

Parameters
arr:ndarray(`Ni...`, `M`, `Nk...)`	Destination array.
indices:ndarray(`Ni...`, J, `Nk...)`	Indices to change along each 1d slice of `arr`. This must match the dimension of arr, but dimensions in Ni and Nj may be 1 to broadcast against `arr`.
values:array_like(`Ni...`, J, `Nk...)`	values to insert at those indices. Its shape and dimension are broadcast to match that of `indices`.
axis:`int`	The axis to take 1d slices along. If axis is None, the destination array is treated as if a flattened 1d view had been created of it.

@array_function_dispatch(_split_dispatcher)
def split(ary, indices_or_sections, axis=0): (source) ¶

Split an array into multiple sub-arrays as views into ary.

See Also

array_split: Split an array into multiple sub-arrays of equal or near-equal size. Does not raise an exception if an equal division cannot be made.
hsplit: Split array into multiple sub-arrays horizontally (column-wise).
vsplit: Split array into multiple sub-arrays vertically (row wise).
dsplit: Split array into multiple sub-arrays along the 3rd axis (depth).
concatenate: Join a sequence of arrays along an existing axis.
stack: Join a sequence of arrays along a new axis.
hstack: Stack arrays in sequence horizontally (column wise).
vstack: Stack arrays in sequence vertically (row wise).
dstack: Stack arrays in sequence depth wise (along third dimension).

Examples

>>> x = np.arange(9.0)
>>> np.split(x, 3)
[array([0.,  1.,  2.]), array([3.,  4.,  5.]), array([6.,  7.,  8.])]

>>> x = np.arange(8.0)
>>> np.split(x, [3, 5, 6, 10])
[array([0.,  1.,  2.]),
 array([3.,  4.]),
 array([5.]),
 array([6.,  7.]),
 array([], dtype=float64)]

Parameters
ary:`ndarray`	Array to be divided into sub-arrays.
indices_or_sections:`int` or 1-D array	If `indices_or_sections` is an integer, N, the array will be divided into N equal arrays along `axis`. If such a split is not possible, an error is raised. If `indices_or_sections` is a 1-D array of sorted integers, the entries indicate where along `axis` the array is split. For example, `[2, 3]` would, for `axis=0`, result in ary[:2] ary[2:3] ary[3:] If an index exceeds the dimension of the array along `axis`, an empty sub-array is returned correspondingly.
axis:`int`, optional	The axis along which to split, default is 0.
Returns
`list` of `ndarrays`	sub-arrays - A list of sub-arrays as views into `ary`.
Raises
`ValueError`	If `indices_or_sections` is given as an integer, but a split does not result in equal division.

@array_function_dispatch(_take_along_axis_dispatcher)
def take_along_axis(arr, indices, axis): (source) ¶

Take values from the input array by matching 1d index and data slices.

This iterates over matching 1d slices oriented along the specified axis in the index and data arrays, and uses the former to look up values in the latter. These slices can be different lengths.

Functions returning an index along an axis, like argsort and argpartition, produce suitable indices for this function.

New in version 1.15.0.

Notes

This is equivalent to (but faster than) the following use of ndindex and s_, which sets each of ii and kk to a tuple of indices:

Ni, M, Nk = a.shape[:axis], a.shape[axis], a.shape[axis+1:]
J = indices.shape[axis]  # Need not equal M
out = np.empty(Ni + (J,) + Nk)

for ii in ndindex(Ni):
    for kk in ndindex(Nk):
        a_1d       = a      [ii + s_[:,] + kk]
        indices_1d = indices[ii + s_[:,] + kk]
        out_1d     = out    [ii + s_[:,] + kk]
        for j in range(J):
            out_1d[j] = a_1d[indices_1d[j]]

Equivalently, eliminating the inner loop, the last two lines would be:

out_1d[:] = a_1d[indices_1d]

See Also

take: Take along an axis, using the same indices for every 1d slice
put_along_axis: Put values into the destination array by matching 1d index and data slices

Examples

For this sample array

>>> a = np.array([[10, 30, 20], [60, 40, 50]])

We can sort either by using sort directly, or argsort and this function

>>> np.sort(a, axis=1)
array([[10, 20, 30],
       [40, 50, 60]])
>>> ai = np.argsort(a, axis=1); ai
array([[0, 2, 1],
       [1, 2, 0]])
>>> np.take_along_axis(a, ai, axis=1)
array([[10, 20, 30],
       [40, 50, 60]])

The same works for max and min, if you expand the dimensions:

>>> np.expand_dims(np.max(a, axis=1), axis=1)
array([[30],
       [60]])
>>> ai = np.expand_dims(np.argmax(a, axis=1), axis=1)
>>> ai
array([[1],
       [0]])
>>> np.take_along_axis(a, ai, axis=1)
array([[30],
       [60]])

If we want to get the max and min at the same time, we can stack the indices first

>>> ai_min = np.expand_dims(np.argmin(a, axis=1), axis=1)
>>> ai_max = np.expand_dims(np.argmax(a, axis=1), axis=1)
>>> ai = np.concatenate([ai_min, ai_max], axis=1)
>>> ai
array([[0, 1],
       [1, 0]])
>>> np.take_along_axis(a, ai, axis=1)
array([[10, 30],
       [40, 60]])

Parameters
arr:ndarray(`Ni...`, `M`, `Nk...)`	Source array
indices:ndarray(`Ni...`, J, `Nk...)`	Indices to take along each 1d slice of `arr`. This must match the dimension of arr, but dimensions Ni and Nj only need to broadcast against `arr`.
axis:`int`	The axis to take 1d slices along. If axis is None, the input array is treated as if it had first been flattened to 1d, for consistency with `sort` and `argsort`.
Returns
ndarray(`Ni...`, J, `Nk...)`	out - The indexed result.

@array_function_dispatch(_tile_dispatcher)
def tile(A, reps): (source) ¶

Construct an array by repeating A the number of times given by reps.

If reps has length d, the result will have dimension of max(d, A.ndim).

If A.ndim < d, A is promoted to be d-dimensional by prepending new axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication, or shape (1, 1, 3) for 3-D replication. If this is not the desired behavior, promote A to d-dimensions manually before calling this function.

If A.ndim > d, reps is promoted to A.ndim by pre-pending 1's to it. Thus for an A of shape (2, 3, 4, 5), a reps of (2, 2) is treated as (1, 1, 2, 2).

Note : Although tile may be used for broadcasting, it is strongly recommended to use numpy's broadcasting operations and functions.

See Also

repeat: Repeat elements of an array.
broadcast_to: Broadcast an array to a new shape

Examples

>>> a = np.array([0, 1, 2])
>>> np.tile(a, 2)
array([0, 1, 2, 0, 1, 2])
>>> np.tile(a, (2, 2))
array([[0, 1, 2, 0, 1, 2],
       [0, 1, 2, 0, 1, 2]])
>>> np.tile(a, (2, 1, 2))
array([[[0, 1, 2, 0, 1, 2]],
       [[0, 1, 2, 0, 1, 2]]])

>>> b = np.array([[1, 2], [3, 4]])
>>> np.tile(b, 2)
array([[1, 2, 1, 2],
       [3, 4, 3, 4]])
>>> np.tile(b, (2, 1))
array([[1, 2],
       [3, 4],
       [1, 2],
       [3, 4]])

>>> c = np.array([1,2,3,4])
>>> np.tile(c,(4,1))
array([[1, 2, 3, 4],
       [1, 2, 3, 4],
       [1, 2, 3, 4],
       [1, 2, 3, 4]])

Parameters
A:`array_like`	The input array.
reps:`array_like`	The number of repetitions of `A` along each axis.
Returns
`ndarray`	c - The tiled output array.

@array_function_dispatch(_hvdsplit_dispatcher)
def vsplit(ary, indices_or_sections): (source) ¶

Split an array into multiple sub-arrays vertically (row-wise).

Please refer to the split documentation. vsplit is equivalent to split with axis=0 (default), the array is always split along the first axis regardless of the array dimension.

See Also

split: Split an array into multiple sub-arrays of equal size.

Examples

>>> x = np.arange(16.0).reshape(4, 4)
>>> x
array([[ 0.,   1.,   2.,   3.],
       [ 4.,   5.,   6.,   7.],
       [ 8.,   9.,  10.,  11.],
       [12.,  13.,  14.,  15.]])
>>> np.vsplit(x, 2)
[array([[0., 1., 2., 3.],
       [4., 5., 6., 7.]]), array([[ 8.,  9., 10., 11.],
       [12., 13., 14., 15.]])]
>>> np.vsplit(x, np.array([3, 6]))
[array([[ 0.,  1.,  2.,  3.],
       [ 4.,  5.,  6.,  7.],
       [ 8.,  9., 10., 11.]]), array([[12., 13., 14., 15.]]), array([], shape=(0, 4), dtype=float64)]

With a higher dimensional array the split is still along the first axis.

>>> x = np.arange(8.0).reshape(2, 2, 2)
>>> x
array([[[0.,  1.],
        [2.,  3.]],
       [[4.,  5.],
        [6.,  7.]]])
>>> np.vsplit(x, 2)
[array([[[0., 1.],
        [2., 3.]]]), array([[[4., 5.],
        [6., 7.]]])]

array_function_dispatch = (source) ¶

Undocumented

def _apply_along_axis_dispatcher(func1d, axis, arr, *args, **kwargs): (source) ¶

Undocumented

def _apply_over_axes_dispatcher(func, a, axes): (source) ¶

Undocumented

def _array_split_dispatcher(ary, indices_or_sections, axis=None): (source) ¶

Undocumented

def _column_stack_dispatcher(tup): (source) ¶

Undocumented

def _dstack_dispatcher(tup): (source) ¶

Undocumented

def _expand_dims_dispatcher(a, axis): (source) ¶

Undocumented

def _hvdsplit_dispatcher(ary, indices_or_sections): (source) ¶

Undocumented

def _kron_dispatcher(a, b): (source) ¶

Undocumented

def _make_along_axis_idx(arr_shape, indices, axis): (source) ¶

Undocumented

def _put_along_axis_dispatcher(arr, indices, values, axis): (source) ¶

Undocumented

def _replace_zero_by_x_arrays(sub_arys): (source) ¶

Undocumented

def _split_dispatcher(ary, indices_or_sections, axis=None): (source) ¶

Undocumented

def _take_along_axis_dispatcher(arr, indices, axis): (source) ¶

Undocumented

def _tile_dispatcher(A, reps): (source) ¶

Undocumented

numpy.lib.shape_base

`numpy.lib.shape_base`