numpy.polynomial.hermite.hermx#
- polynomial.hermite.hermx = array([0. , 0.5])#
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
orempty
(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
- (for the __new__ method; see Notes below)
- shapetuple of ints
Shape of created array.
- dtypedata-type, optional
Any object that can be interpreted as a numpy data type.
- bufferobject exposing buffer interface, optional
Used to fill the array with data.
- offsetint, optional
Offset of array data in buffer.
- stridestuple of ints, optional
Strides of data in memory.
- order{‘C’, ‘F’}, optional
Row-major (C-style) or column-major (Fortran-style) order.
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])
- Attributes
- Tndarray
Transpose of the array.
- databuffer
The array’s elements, in memory.
- dtypedtype object
Describes the format of the elements in the array.
- flagsdict
Dictionary containing information related to memory use, e.g., ‘C_CONTIGUOUS’, ‘OWNDATA’, ‘WRITEABLE’, etc.
- flatnumpy.flatiter object
Flattened version of the array as an iterator. The iterator allows assignments, e.g.,
x.flat = 3
(Seendarray.flat
for assignment examples; TODO).- imagndarray
Imaginary part of the array.
- realndarray
Real part of the array.
- sizeint
Number of elements in the array.
- itemsizeint
The memory use of each array element in bytes.
- nbytesint
The total number of bytes required to store the array data, i.e.,
itemsize * size
.- ndimint
The array’s number of dimensions.
- shapetuple of ints
Shape of the array.
- stridestuple of ints
The step-size required to move from one element to the next in memory. For example, a contiguous
(3, 4)
array of typeint16
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
).- ctypesctypes object
Class containing properties of the array needed for interaction with ctypes.
- basendarray
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.