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max.pipelines.context.TokenSlice
TokenSlice
max.pipelines.context.TokenSlice
- 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.
T
max.pipelines.context.T
Transpose of the array.
-
Type:
-
ndarray
data
max.pipelines.context.data
The array’s elements, in memory.
-
Type:
dtype
max.pipelines.context.dtype
Describes the format of the elements in the array.
-
Type:
-
dtype object
flags
max.pipelines.context.flags
Dictionary containing information related to memory use, e.g., ‘C_CONTIGUOUS’, ‘OWNDATA’, ‘WRITEABLE’, etc.
-
Type:
flat
max.pipelines.context.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
imag
max.pipelines.context.imag
Imaginary part of the array.
-
Type:
-
ndarray
real
max.pipelines.context.real
Real part of the array.
-
Type:
-
ndarray
size
max.pipelines.context.size
Number of elements in the array.
-
Type:
itemsize
max.pipelines.context.itemsize
The memory use of each array element in bytes.
-
Type:
nbytes
max.pipelines.context.nbytes
The total number of bytes required to store the array data,
i.e., itemsize * size.
-
Type:
ndim
max.pipelines.context.ndim
The array’s number of dimensions.
-
Type:
shape
max.pipelines.context.shape
Shape of the array.
-
Type:
-
tuple of ints
strides
max.pipelines.context.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
ctypes
max.pipelines.context.ctypes
Class containing properties of the array needed for interaction with ctypes.
-
Type:
-
ctypes object
base
max.pipelines.context.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
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__:
- 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:
>>> import numpy as np
>>> 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
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