concatenate¶

Documentation¶

treetensor.numpy.concatenate(arrays, *args, **kwargs)[source]¶

Numpy Version Related

This documentation is based on numpy.concatenate in numpy v1.21.6. Its arguments’ arrangements depend on the version of numpy you installed.

If some arguments listed here are not working properly, please check your numpy’s version with the following command and find its documentation.

1	python -c 'import numpy as np;print(np.__version__)'

The arguments and keyword arguments supported in numpy v1.21.6 is listed below.

Description From Numpy v1.21¶

numpy.concatenate((a1, a2, ...), axis=0, out=None, dtype=None, casting="same_kind")¶: Join a sequence of arrays along an existing axis.

Parameters¶

a1, a2, … : sequence of array_like
The arrays must have the same shape, except in the dimension corresponding to axis (the first, by default).

axis : int, optional
The axis along which the arrays will be joined. If axis is None, arrays are flattened before use. Default is 0.

out : ndarray, optional
If provided, the destination to place the result. The shape must be correct, matching that of what concatenate would have returned if no out argument were specified.

dtype : str or dtype
If provided, the destination array will have this dtype. Cannot be provided together with out.

New in version 1.20.0.

casting : {‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional
Controls what kind of data casting may occur. Defaults to ‘same_kind’.

New in version 1.20.0.

Returns¶

res : ndarray
The concatenated array.

See Also¶

ma.concatenate : Concatenate function that preserves input masks. array_split : Split an array into multiple sub-arrays of equal or

near-equal size.

split : Split array into a list of multiple sub-arrays of equal size. 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). stack : Stack a sequence of arrays along a new axis. block : Assemble arrays from blocks. 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). column_stack : Stack 1-D arrays as columns into a 2-D array.

Notes¶

When one or more of the arrays to be concatenated is a MaskedArray, this function will return a MaskedArray object instead of an ndarray, but the input masks are not preserved. In cases where a MaskedArray is expected as input, use the ma.concatenate function from the masked array module instead.

Examples¶

>>> a = np.array([[1, 2], [3, 4]])
>>> b = np.array([[5, 6]])
>>> np.concatenate((a, b), axis=0)
array([[1, 2],
       [3, 4],
       [5, 6]])
>>> np.concatenate((a, b.T), axis=1)
array([[1, 2, 5],
       [3, 4, 6]])
>>> np.concatenate((a, b), axis=None)
array([1, 2, 3, 4, 5, 6])

This function will not preserve masking of MaskedArray inputs.

>>> a = np.ma.arange(3)
>>> a[1] = np.ma.masked
>>> b = np.arange(2, 5)
>>> a
masked_array(data=[0, --, 2],
             mask=[False,  True, False],
       fill_value=999999)
>>> b
array([2, 3, 4])
>>> np.concatenate([a, b])
masked_array(data=[0, 1, 2, 2, 3, 4],
             mask=False,
       fill_value=999999)
>>> np.ma.concatenate([a, b])
masked_array(data=[0, --, 2, 2, 3, 4],
             mask=[False,  True, False, False, False, False],
       fill_value=999999)