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sf.physics.fock.SQRT

mrmustard.physics.fock.SQRT = array([  0.        ,   1.        ,   1.41421356, ..., 999.9985    ,        999.999     , 999.9995    ])
ndarray(shape, dtype=float, buffer=None, offset=0,

strides=None, order=None)

An array object represents a multidimensional, homogeneous array of fixed-size items. An associated data-type object describes the format of each element in the array (its byte-order, how many bytes it occupies in memory, whether it is an integer, a floating point number, or something else, etc.)

Arrays should be constructed using array, zeros or empty (refer to the See Also section below). The parameters given here refer to a low-level method (ndarray(…)) for instantiating an array.

For more information, refer to the numpy module and examine the methods and attributes of an array.

Parameters:

  • below) ((for the __new__ method; see Notes) –

  • shape (tuple of ints) – Shape of created array.

  • dtype (data-type, optional) – Any object that can be interpreted as a numpy data type.

  • buffer (object exposing buffer interface, optional) – Used to fill the array with data.

  • offset (int, optional) – Offset of array data in buffer.

  • strides (tuple of ints, optional) – Strides of data in memory.

  • order ({'C', 'F'}, optional) – Row-major (C-style) or column-major (Fortran-style) order.

mrmustard.physics.fock.T

Transpose of the array.

Type:

ndarray

mrmustard.physics.fock.data

The array’s elements, in memory.

Type:

buffer

mrmustard.physics.fock.dtype

Describes the format of the elements in the array.

Type:

dtype object

mrmustard.physics.fock.flags

Dictionary containing information related to memory use, e.g., ‘C_CONTIGUOUS’, ‘OWNDATA’, ‘WRITEABLE’, etc.

Type:

dict

mrmustard.physics.fock.flat

Flattened version of the array as an iterator. The iterator allows assignments, e.g., x.flat = 3 (See ndarray.flat for assignment examples; TODO).

Type:

numpy.flatiter object

mrmustard.physics.fock.imag

Imaginary part of the array.

Type:

ndarray

mrmustard.physics.fock.real

Real part of the array.

Type:

ndarray

mrmustard.physics.fock.size

Number of elements in the array.

Type:

int

mrmustard.physics.fock.itemsize

The memory use of each array element in bytes.

Type:

int

mrmustard.physics.fock.nbytes

The total number of bytes required to store the array data, i.e., itemsize * size.

Type:

int

mrmustard.physics.fock.ndim

The array’s number of dimensions.

Type:

int

mrmustard.physics.fock.shape

Shape of the array.

Type:

tuple of ints

mrmustard.physics.fock.strides

The step-size required to move from one element to the next in memory. For example, a contiguous (3, 4) array of type int16 in C-order has strides (8, 2). This implies that to move from element to element in memory requires jumps of 2 bytes. To move from row-to-row, one needs to jump 8 bytes at a time (2 * 4).

Type:

tuple of ints

mrmustard.physics.fock.ctypes

Class containing properties of the array needed for interaction with ctypes.

Type:

ctypes object

mrmustard.physics.fock.base

If the array is a view into another array, that array is its base (unless that array is also a view). The base array is where the array data is actually stored.

Type:

ndarray

See also

array

Construct an array.

zeros

Create an array, each element of which is zero.

empty

Create an array, but leave its allocated memory unchanged (i.e., it contains “garbage”).

dtype

Create a data-type.

numpy.typing.NDArray

An ndarray alias generic w.r.t. its dtype.type <numpy.dtype.type>.

Notes

There are two modes of creating an array using __new__:

  1. If buffer is None, then only shape, dtype, and order are used.

  2. If buffer is an object exposing the buffer interface, then all keywords are interpreted.

No __init__ method is needed because the array is fully initialized after the __new__ method.

Examples

These examples illustrate the low-level ndarray constructor. Refer to the See Also section above for easier ways of constructing an ndarray.

First mode, buffer is None:

>>> np.ndarray(shape=(2,2), dtype=float, order='F')
array([[0.0e+000, 0.0e+000], # random
       [     nan, 2.5e-323]])

Second mode:

>>> np.ndarray((2,), buffer=np.array([1,2,3]),
...            offset=np.int_().itemsize,
...            dtype=int) # offset = 1*itemsize, i.e. skip first element
array([2, 3])
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