array¶

Documentation¶

treetensor.numpy.array(p_object, *args, **kwargs)[source]¶

In treetensor, you can create a tree of np.ndarray with array().

Examples:

>>> import numpy as np
>>> import treetensor.numpy as tnp
>>> tnp.array({
>>>     'a': [1, 2, 3],
>>>     'b': [[4, 5], [5, 6]],
>>>     'c': True,
>>> })
tnp.ndarray({
    'a': np.array([1, 2, 3]),
    'b': np.array([[4, 5], [5, 6]]),
    'c': np.array(True),
})

Numpy Version Related

This documentation is based on numpy.array 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¶

array(object, dtype=None, *, copy=True, order='K', subok=False, ndmin=0,: like=None)

Create an array.

Parameters¶

object : array_like
An array, any object exposing the array interface, an object whose __array__ method returns an array, or any (nested) sequence.

dtype : data-type, optional
The desired data-type for the array. If not given, then the type will be determined as the minimum type required to hold the objects in the sequence.

copy : bool, optional
If true (default), then the object is copied. Otherwise, a copy will only be made if __array__ returns a copy, if obj is a nested sequence, or if a copy is needed to satisfy any of the other requirements (dtype, order, etc.).

order : {‘K’, ‘A’, ‘C’, ‘F’}, optional
Specify the memory layout of the array. If object is not an array, the newly created array will be in C order (row major) unless ‘F’ is specified, in which case it will be in Fortran order (column major). If object is an array the following holds.

order

no copy

copy=True

‘K’

unchanged

F & C order preserved, otherwise most similar order

‘A’

unchanged

F order if input is F and not C, otherwise C order

‘C’

C order

C order

‘F’

F order

F order

When copy=False and a copy is made for other reasons, the result is the same as if copy=True, with some exceptions for ‘A’, see the Notes section. The default order is ‘K’.

subok : bool, optional
If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default).

ndmin : int, optional
Specifies the minimum number of dimensions that the resulting array should have. Ones will be pre-pended to the shape as needed to meet this requirement.

like : array_like
Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as like supports the __array_function__ protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument.

New in version 1.20.0.

Returns¶

out : ndarray
An array object satisfying the specified requirements.

Notes¶

When order is ‘A’ and object is an array in neither ‘C’ nor ‘F’ order, and a copy is forced by a change in dtype, then the order of the result is not necessarily ‘C’ as expected. This is likely a bug.

Examples¶

>>> np.array([1, 2, 3])
array([1, 2, 3])

Upcasting:

>>> np.array([1, 2, 3.0])
array([ 1.,  2.,  3.])

More than one dimension:

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

Minimum dimensions 2:

>>> np.array([1, 2, 3], ndmin=2)
array([[1, 2, 3]])

Type provided:

>>> np.array([1, 2, 3], dtype=complex)
array([ 1.+0.j,  2.+0.j,  3.+0.j])

Data-type consisting of more than one element:

>>> x = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i4')])
>>> x['a']
array([1, 3])

Creating an array from sub-classes:

>>> np.array(np.mat('1 2; 3 4'))
array([[1, 2],
       [3, 4]])

>>> np.array(np.mat('1 2; 3 4'), subok=True)
matrix([[1, 2],
        [3, 4]])