macromol_voxelize.Image

macromol_voxelize.Image = numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]]

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.

macromol_voxelize.T

Transpose of the array.

Type:: ndarray

macromol_voxelize.data

The array’s elements, in memory.

Type:: buffer

macromol_voxelize.dtype

Describes the format of the elements in the array.

Type:: dtype object

macromol_voxelize.flags

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

Type:: dict

macromol_voxelize.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

macromol_voxelize.imag

Imaginary part of the array.

Type:: ndarray

macromol_voxelize.real

Real part of the array.

Type:: ndarray

macromol_voxelize.size

Number of elements in the array.

Type:: int

macromol_voxelize.itemsize

The memory use of each array element in bytes.

Type:: int

macromol_voxelize.nbytes

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

Type:: int

macromol_voxelize.ndim

The array’s number of dimensions.

Type:: int

macromol_voxelize.shape

Shape of the array.

Type:: tuple of ints

macromol_voxelize.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

macromol_voxelize.ctypes

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

Type:: ctypes object

macromol_voxelize.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.

Notes

There are two modes of creating an array using __new__:

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