array(object, dtype=None, *, copy=True, order='K', subok=False, ndmin=0, like=None)
openfermion.utils.channel_state.array()
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.
If object is a scalar, a 0-dimensional array containing object is
returned.
dtype : data-type, optional
The desired data-type for the array. If not given, NumPy will try to use
a default dtype that can represent the values (by applying promotion
rules when necessary.)
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 prepended to the shape as
needed to meet this requirement.
like : array_like, optional
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.
.. versionadded:: 1.20.0
Returns
out : ndarray An array object satisfying the specified requirements.
See Also
empty_like : Return an empty array with shape and type of input. ones_like : Return an array of ones with shape and type of input. zeros_like : Return an array of zeros with shape and type of input. full_like : Return a new array with shape of input filled with value. empty : Return a new uninitialized array. ones : Return a new array setting values to one. zeros : Return a new array setting values to zero. full : Return a new array of given shape filled with value.
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]])