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// Copyright (c) 2019 by the SciSharp Team
// Code generated by CodeMinion: https://github.com/SciSharp/CodeMinion
using System;
using System.Collections;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Runtime.InteropServices;
using System.Text;
using Python.Runtime;
using Python.Included;
using Numpy.Models;
namespace Numpy
{
public static partial class np
{
/// <summary>
/// Return a new array of given shape and type, without initializing entries.
///
/// Notes
///
/// empty, unlike zeros, does not set the array values to zero,
/// and may therefore be marginally faster. On the other hand, it requires
/// the user to manually set all the values in the array, and should be
/// used with caution.
/// </summary>
/// <param name="shape">
/// Shape of the empty array, e.g., (2, 3) or 2.
/// </param>
/// <param name="dtype">
/// Desired output data-type for the array, e.g, numpy.int8. Default is
/// numpy.float64.
/// </param>
/// <param name="order">
/// Whether to store multi-dimensional data in row-major
/// (C-style) or column-major (Fortran-style) order in
/// memory.
/// </param>
/// <returns>
/// Array of uninitialized (arbitrary) data of the given shape, dtype, and
/// order. Object arrays will be initialized to None.
/// </returns>
public static NDarray empty(Shape shape, Dtype dtype = null, string order = null)
=> NumPy.Instance.empty(shape, dtype:dtype, order:order);
/// <summary>
/// Return a new array with the same shape and type as a given array.
///
/// Notes
///
/// This function does not initialize the returned array; to do that use
/// zeros_like or ones_like instead. It may be marginally faster than
/// the functions that do set the array values.
/// </summary>
/// <param name="prototype">
/// The shape and data-type of prototype define these same attributes
/// of the returned array.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result. ‘C’ means C-order,
/// ‘F’ means F-order, ‘A’ means ‘F’ if prototype is Fortran
/// contiguous, ‘C’ otherwise. ‘K’ means match the layout of prototype
/// as closely as possible.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array. Defaults
/// to True.
/// </param>
/// <returns>
/// Array of uninitialized (arbitrary) data with the same
/// shape and type as prototype.
/// </returns>
public static NDarray empty_like(NDarray prototype, Dtype dtype = null, string order = null, bool? subok = true)
=> NumPy.Instance.empty_like(prototype, dtype:dtype, order:order, subok:subok);
/// <summary>
/// Return a new array with the same shape and type as a given array.
///
/// Notes
///
/// This function does not initialize the returned array; to do that use
/// zeros_like or ones_like instead. It may be marginally faster than
/// the functions that do set the array values.
/// </summary>
/// <param name="prototype">
/// The shape and data-type of prototype define these same attributes
/// of the returned array.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result. ‘C’ means C-order,
/// ‘F’ means F-order, ‘A’ means ‘F’ if prototype is Fortran
/// contiguous, ‘C’ otherwise. ‘K’ means match the layout of prototype
/// as closely as possible.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array. Defaults
/// to True.
/// </param>
/// <returns>
/// Array of uninitialized (arbitrary) data with the same
/// shape and type as prototype.
/// </returns>
public static NDarray<T> empty_like<T>(T[] prototype, Dtype dtype = null, string order = null, bool? subok = true)
=> NumPy.Instance.empty_like(prototype, dtype:dtype, order:order, subok:subok);
/// <summary>
/// Return a new array with the same shape and type as a given array.
///
/// Notes
///
/// This function does not initialize the returned array; to do that use
/// zeros_like or ones_like instead. It may be marginally faster than
/// the functions that do set the array values.
/// </summary>
/// <param name="prototype">
/// The shape and data-type of prototype define these same attributes
/// of the returned array.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result. ‘C’ means C-order,
/// ‘F’ means F-order, ‘A’ means ‘F’ if prototype is Fortran
/// contiguous, ‘C’ otherwise. ‘K’ means match the layout of prototype
/// as closely as possible.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array. Defaults
/// to True.
/// </param>
/// <returns>
/// Array of uninitialized (arbitrary) data with the same
/// shape and type as prototype.
/// </returns>
public static NDarray<T> empty_like<T>(T[,] prototype, Dtype dtype = null, string order = null, bool? subok = true)
=> NumPy.Instance.empty_like(prototype, dtype:dtype, order:order, subok:subok);
/// <summary>
/// Return a 2-D array with ones on the diagonal and zeros elsewhere.
/// </summary>
/// <param name="N">
/// Number of rows in the output.
/// </param>
/// <param name="M">
/// Number of columns in the output. If None, defaults to N.
/// </param>
/// <param name="k">
/// Index of the diagonal: 0 (the default) refers to the main diagonal,
/// a positive value refers to an upper diagonal, and a negative value
/// to a lower diagonal.
/// </param>
/// <param name="dtype">
/// Data-type of the returned array.
/// </param>
/// <param name="order">
/// Whether the output should be stored in row-major (C-style) or
/// column-major (Fortran-style) order in memory.
/// </param>
/// <returns>
/// An array where all elements are equal to zero, except for the k-th
/// diagonal, whose values are equal to one.
/// </returns>
public static NDarray eye(int N, int? M = null, int? k = 0, Dtype dtype = null, string order = null)
=> NumPy.Instance.eye(N, M:M, k:k, dtype:dtype, order:order);
/// <summary>
/// Return the identity array.
///
/// The identity array is a square array with ones on
/// the main diagonal.
/// </summary>
/// <param name="n">
/// Number of rows (and columns) in n x n output.
/// </param>
/// <param name="dtype">
/// Data-type of the output. Defaults to float.
/// </param>
/// <returns>
/// n x n array with its main diagonal set to one,
/// and all other elements 0.
/// </returns>
public static NDarray identity(int n, Dtype dtype = null)
=> NumPy.Instance.identity(n, dtype:dtype);
/// <summary>
/// Return a new array of given shape and type, filled with ones.
/// </summary>
/// <param name="shape">
/// Shape of the new array, e.g., (2, 3) or 2.
/// </param>
/// <param name="dtype">
/// The desired data-type for the array, e.g., numpy.int8. Default is
/// numpy.float64.
/// </param>
/// <param name="order">
/// Whether to store multi-dimensional data in row-major
/// (C-style) or column-major (Fortran-style) order in
/// memory.
/// </param>
/// <returns>
/// Array of ones with the given shape, dtype, and order.
/// </returns>
public static NDarray ones(Shape shape, Dtype dtype = null, string order = null)
=> NumPy.Instance.ones(shape, dtype:dtype, order:order);
/// <summary>
/// Return an array of ones with the same shape and type as a given array.
/// </summary>
/// <param name="a">
/// The shape and data-type of a define these same attributes of
/// the returned array.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result. ‘C’ means C-order,
/// ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous,
/// ‘C’ otherwise. ‘K’ means match the layout of a as closely
/// as possible.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array. Defaults
/// to True.
/// </param>
/// <returns>
/// Array of ones with the same shape and type as a.
/// </returns>
public static NDarray ones_like(NDarray a, Dtype dtype = null, string order = null, bool? subok = true)
=> NumPy.Instance.ones_like(a, dtype:dtype, order:order, subok:subok);
/// <summary>
/// Return an array of ones with the same shape and type as a given array.
/// </summary>
/// <param name="a">
/// The shape and data-type of a define these same attributes of
/// the returned array.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result. ‘C’ means C-order,
/// ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous,
/// ‘C’ otherwise. ‘K’ means match the layout of a as closely
/// as possible.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array. Defaults
/// to True.
/// </param>
/// <returns>
/// Array of ones with the same shape and type as a.
/// </returns>
public static NDarray<T> ones_like<T>(T[] a, Dtype dtype = null, string order = null, bool? subok = true)
=> NumPy.Instance.ones_like(a, dtype:dtype, order:order, subok:subok);
/// <summary>
/// Return an array of ones with the same shape and type as a given array.
/// </summary>
/// <param name="a">
/// The shape and data-type of a define these same attributes of
/// the returned array.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result. ‘C’ means C-order,
/// ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous,
/// ‘C’ otherwise. ‘K’ means match the layout of a as closely
/// as possible.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array. Defaults
/// to True.
/// </param>
/// <returns>
/// Array of ones with the same shape and type as a.
/// </returns>
public static NDarray<T> ones_like<T>(T[,] a, Dtype dtype = null, string order = null, bool? subok = true)
=> NumPy.Instance.ones_like(a, dtype:dtype, order:order, subok:subok);
/// <summary>
/// Return a new array of given shape and type, filled with zeros.
/// </summary>
/// <param name="shape">
/// Shape of the new array, e.g., (2, 3) or 2.
/// </param>
/// <param name="dtype">
/// The desired data-type for the array, e.g., numpy.int8. Default is
/// numpy.float64.
/// </param>
/// <param name="order">
/// Whether to store multi-dimensional data in row-major
/// (C-style) or column-major (Fortran-style) order in
/// memory.
/// </param>
/// <returns>
/// Array of zeros with the given shape, dtype, and order.
/// </returns>
public static NDarray zeros(Shape shape, Dtype dtype = null, string order = null)
=> NumPy.Instance.zeros(shape, dtype:dtype, order:order);
/// <summary>
/// Return an array of zeros with the same shape and type as a given array.
/// </summary>
/// <param name="a">
/// The shape and data-type of a define these same attributes of
/// the returned array.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result. ‘C’ means C-order,
/// ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous,
/// ‘C’ otherwise. ‘K’ means match the layout of a as closely
/// as possible.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array. Defaults
/// to True.
/// </param>
/// <returns>
/// Array of zeros with the same shape and type as a.
/// </returns>
public static NDarray zeros_like(NDarray a, Dtype dtype = null, string order = null, bool? subok = true)
=> NumPy.Instance.zeros_like(a, dtype:dtype, order:order, subok:subok);
/// <summary>
/// Return an array of zeros with the same shape and type as a given array.
/// </summary>
/// <param name="a">
/// The shape and data-type of a define these same attributes of
/// the returned array.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result. ‘C’ means C-order,
/// ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous,
/// ‘C’ otherwise. ‘K’ means match the layout of a as closely
/// as possible.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array. Defaults
/// to True.
/// </param>
/// <returns>
/// Array of zeros with the same shape and type as a.
/// </returns>
public static NDarray<T> zeros_like<T>(T[] a, Dtype dtype = null, string order = null, bool? subok = true)
=> NumPy.Instance.zeros_like(a, dtype:dtype, order:order, subok:subok);
/// <summary>
/// Return an array of zeros with the same shape and type as a given array.
/// </summary>
/// <param name="a">
/// The shape and data-type of a define these same attributes of
/// the returned array.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result. ‘C’ means C-order,
/// ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous,
/// ‘C’ otherwise. ‘K’ means match the layout of a as closely
/// as possible.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array. Defaults
/// to True.
/// </param>
/// <returns>
/// Array of zeros with the same shape and type as a.
/// </returns>
public static NDarray<T> zeros_like<T>(T[,] a, Dtype dtype = null, string order = null, bool? subok = true)
=> NumPy.Instance.zeros_like(a, dtype:dtype, order:order, subok:subok);
/// <summary>
/// Return a new array of given shape and type, filled with fill_value.
/// </summary>
/// <param name="shape">
/// Shape of the new array, e.g., (2, 3) or 2.
/// </param>
/// <param name="fill_value">
/// Fill value.
/// </param>
/// <param name="order">
/// Whether to store multidimensional data in C- or Fortran-contiguous
/// (row- or column-wise) order in memory.
/// </param>
/// <returns>
/// Array of fill_value with the given shape, dtype, and order.
/// </returns>
public static NDarray full(Shape shape, ValueType fill_value, Dtype dtype = null, string order = null)
=> NumPy.Instance.full(shape, fill_value, dtype:dtype, order:order);
/// <summary>
/// Return a full array with the same shape and type as a given array.
/// </summary>
/// <param name="a">
/// The shape and data-type of a define these same attributes of
/// the returned array.
/// </param>
/// <param name="fill_value">
/// Fill value.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result. ‘C’ means C-order,
/// ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous,
/// ‘C’ otherwise. ‘K’ means match the layout of a as closely
/// as possible.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array. Defaults
/// to True.
/// </param>
/// <returns>
/// Array of fill_value with the same shape and type as a.
/// </returns>
public static NDarray full_like(NDarray a, ValueType fill_value, Dtype dtype = null, string order = null, bool? subok = true)
=> NumPy.Instance.full_like(a, fill_value, dtype:dtype, order:order, subok:subok);
/// <summary>
/// Return a full array with the same shape and type as a given array.
/// </summary>
/// <param name="a">
/// The shape and data-type of a define these same attributes of
/// the returned array.
/// </param>
/// <param name="fill_value">
/// Fill value.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result. ‘C’ means C-order,
/// ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous,
/// ‘C’ otherwise. ‘K’ means match the layout of a as closely
/// as possible.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array. Defaults
/// to True.
/// </param>
/// <returns>
/// Array of fill_value with the same shape and type as a.
/// </returns>
public static NDarray<T> full_like<T>(T[] a, ValueType fill_value, Dtype dtype = null, string order = null, bool? subok = true)
=> NumPy.Instance.full_like(a, fill_value, dtype:dtype, order:order, subok:subok);
/// <summary>
/// Return a full array with the same shape and type as a given array.
/// </summary>
/// <param name="a">
/// The shape and data-type of a define these same attributes of
/// the returned array.
/// </param>
/// <param name="fill_value">
/// Fill value.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result. ‘C’ means C-order,
/// ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous,
/// ‘C’ otherwise. ‘K’ means match the layout of a as closely
/// as possible.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array. Defaults
/// to True.
/// </param>
/// <returns>
/// Array of fill_value with the same shape and type as a.
/// </returns>
public static NDarray<T> full_like<T>(T[,] a, ValueType fill_value, Dtype dtype = null, string order = null, bool? subok = true)
=> NumPy.Instance.full_like(a, fill_value, dtype:dtype, order:order, subok:subok);
/// <summary>
/// Create an array.
///
/// 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.
/// </summary>
/// <param name="@object">
/// An array, any object exposing the array interface, an object whose
/// __array__ method returns an array, or any (nested) sequence.
/// </param>
/// <param name="dtype">
/// The desired data-type for the array. If not given, then the type will
/// be determined as the minimum type required to hold the objects in the
/// sequence. This argument can only be used to ‘upcast’ the array. For
/// downcasting, use the .astype(t) method.
/// </param>
/// <param name="copy">
/// 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.).
/// </param>
/// <param name="order">
/// 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.
///
/// 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’.
/// </param>
/// <param name="subok">
/// If True, then sub-classes will be passed-through, otherwise
/// the returned array will be forced to be a base-class array (default).
/// </param>
/// <param name="ndmin">
/// Specifies the minimum number of dimensions that the resulting
/// array should have. Ones will be pre-pended to the shape as
/// needed to meet this requirement.
/// </param>
/// <returns>
/// An array object satisfying the specified requirements.
/// </returns>
public static NDarray array(NDarray @object, Dtype dtype = null, bool? copy = true, string order = null, bool? subok = false, int? ndmin = 0)
=> NumPy.Instance.array(@object, dtype:dtype, copy:copy, order:order, subok:subok, ndmin:ndmin);
/// <summary>
/// Create an array.
///
/// 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.
/// </summary>
/// <param name="@object">
/// An array, any object exposing the array interface, an object whose
/// __array__ method returns an array, or any (nested) sequence.
/// </param>
/// <param name="dtype">
/// The desired data-type for the array. If not given, then the type will
/// be determined as the minimum type required to hold the objects in the
/// sequence. This argument can only be used to ‘upcast’ the array. For
/// downcasting, use the .astype(t) method.
/// </param>
/// <param name="copy">
/// 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.).
/// </param>
/// <param name="order">
/// 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.
///
/// 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’.
/// </param>
/// <param name="subok">
/// If True, then sub-classes will be passed-through, otherwise
/// the returned array will be forced to be a base-class array (default).
/// </param>
/// <param name="ndmin">
/// Specifies the minimum number of dimensions that the resulting
/// array should have. Ones will be pre-pended to the shape as
/// needed to meet this requirement.
/// </param>
/// <returns>
/// An array object satisfying the specified requirements.
/// </returns>
public static NDarray<T> array<T>(T[] @object, Dtype dtype = null, bool? copy = true, string order = null, bool? subok = false, int? ndmin = 0)
=> NumPy.Instance.array(@object, dtype:dtype, copy:copy, order:order, subok:subok, ndmin:ndmin);
/// <summary>
/// Create an array.
///
/// 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.
/// </summary>
/// <param name="@object">
/// An array, any object exposing the array interface, an object whose
/// __array__ method returns an array, or any (nested) sequence.
/// </param>
/// <param name="dtype">
/// The desired data-type for the array. If not given, then the type will
/// be determined as the minimum type required to hold the objects in the
/// sequence. This argument can only be used to ‘upcast’ the array. For
/// downcasting, use the .astype(t) method.
/// </param>
/// <param name="copy">
/// 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.).
/// </param>
/// <param name="order">
/// 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.
///
/// 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’.
/// </param>
/// <param name="subok">
/// If True, then sub-classes will be passed-through, otherwise
/// the returned array will be forced to be a base-class array (default).
/// </param>
/// <param name="ndmin">
/// Specifies the minimum number of dimensions that the resulting
/// array should have. Ones will be pre-pended to the shape as
/// needed to meet this requirement.
/// </param>
/// <returns>
/// An array object satisfying the specified requirements.
/// </returns>
public static NDarray<T> array<T>(T[,] @object, Dtype dtype = null, bool? copy = true, string order = null, bool? subok = false, int? ndmin = 0)
=> NumPy.Instance.array(@object, dtype:dtype, copy:copy, order:order, subok:subok, ndmin:ndmin);
/// <summary>
/// Convert the input to an array.
/// </summary>
/// <param name="a">
/// Input data, in any form that can be converted to an array. This
/// includes lists, lists of tuples, tuples, tuples of tuples, tuples
/// of lists and ndarrays.
/// </param>
/// <param name="dtype">
/// By default, the data-type is inferred from the input data.
/// </param>
/// <param name="order">
/// Whether to use row-major (C-style) or
/// column-major (Fortran-style) memory representation.
/// Defaults to ‘C’.
/// </param>
/// <returns>
/// Array interpretation of a. No copy is performed if the input
/// is already an ndarray with matching dtype and order. If a is a
/// subclass of ndarray, a base class ndarray is returned.
/// </returns>
public static NDarray asarray(NDarray a, Dtype dtype = null, string order = null)
=> NumPy.Instance.asarray(a, dtype:dtype, order:order);
/// <summary>
/// Convert the input to an array.
/// </summary>
/// <param name="a">
/// Input data, in any form that can be converted to an array. This
/// includes lists, lists of tuples, tuples, tuples of tuples, tuples
/// of lists and ndarrays.
/// </param>
/// <param name="dtype">
/// By default, the data-type is inferred from the input data.
/// </param>
/// <param name="order">
/// Whether to use row-major (C-style) or
/// column-major (Fortran-style) memory representation.
/// Defaults to ‘C’.
/// </param>
/// <returns>
/// Array interpretation of a. No copy is performed if the input
/// is already an ndarray with matching dtype and order. If a is a
/// subclass of ndarray, a base class ndarray is returned.
/// </returns>
public static NDarray<T> asarray<T>(T[] a, Dtype dtype = null, string order = null)
=> NumPy.Instance.asarray(a, dtype:dtype, order:order);
/// <summary>
/// Convert the input to an array.
/// </summary>
/// <param name="a">
/// Input data, in any form that can be converted to an array. This
/// includes lists, lists of tuples, tuples, tuples of tuples, tuples
/// of lists and ndarrays.
/// </param>
/// <param name="dtype">
/// By default, the data-type is inferred from the input data.
/// </param>
/// <param name="order">
/// Whether to use row-major (C-style) or
/// column-major (Fortran-style) memory representation.
/// Defaults to ‘C’.
/// </param>
/// <returns>
/// Array interpretation of a. No copy is performed if the input
/// is already an ndarray with matching dtype and order. If a is a
/// subclass of ndarray, a base class ndarray is returned.
/// </returns>
public static NDarray<T> asarray<T>(T[,] a, Dtype dtype = null, string order = null)
=> NumPy.Instance.asarray(a, dtype:dtype, order:order);
/// <summary>
/// Convert the input to an ndarray, but pass ndarray subclasses through.
/// </summary>
/// <param name="a">
/// Input data, in any form that can be converted to an array. This
/// includes scalars, lists, lists of tuples, tuples, tuples of tuples,
/// tuples of lists, and ndarrays.
/// </param>
/// <param name="dtype">
/// By default, the data-type is inferred from the input data.
/// </param>
/// <param name="order">
/// Whether to use row-major (C-style) or column-major
/// (Fortran-style) memory representation. Defaults to ‘C’.
/// </param>
/// <returns>
/// Array interpretation of a. If a is an ndarray or a subclass
/// of ndarray, it is returned as-is and no copy is performed.
/// </returns>
public static NDarray asanyarray(NDarray a, Dtype dtype = null, string order = null)
=> NumPy.Instance.asanyarray(a, dtype:dtype, order:order);
/// <summary>
/// Convert the input to an ndarray, but pass ndarray subclasses through.
/// </summary>
/// <param name="a">
/// Input data, in any form that can be converted to an array. This
/// includes scalars, lists, lists of tuples, tuples, tuples of tuples,
/// tuples of lists, and ndarrays.
/// </param>
/// <param name="dtype">
/// By default, the data-type is inferred from the input data.
/// </param>
/// <param name="order">
/// Whether to use row-major (C-style) or column-major
/// (Fortran-style) memory representation. Defaults to ‘C’.
/// </param>
/// <returns>
/// Array interpretation of a. If a is an ndarray or a subclass
/// of ndarray, it is returned as-is and no copy is performed.
/// </returns>
public static NDarray<T> asanyarray<T>(T[] a, Dtype dtype = null, string order = null)
=> NumPy.Instance.asanyarray(a, dtype:dtype, order:order);
/// <summary>
/// Convert the input to an ndarray, but pass ndarray subclasses through.
/// </summary>
/// <param name="a">
/// Input data, in any form that can be converted to an array. This
/// includes scalars, lists, lists of tuples, tuples, tuples of tuples,
/// tuples of lists, and ndarrays.
/// </param>
/// <param name="dtype">
/// By default, the data-type is inferred from the input data.
/// </param>
/// <param name="order">
/// Whether to use row-major (C-style) or column-major
/// (Fortran-style) memory representation. Defaults to ‘C’.
/// </param>
/// <returns>
/// Array interpretation of a. If a is an ndarray or a subclass
/// of ndarray, it is returned as-is and no copy is performed.
/// </returns>
public static NDarray<T> asanyarray<T>(T[,] a, Dtype dtype = null, string order = null)
=> NumPy.Instance.asanyarray(a, dtype:dtype, order:order);
/// <summary>
/// Return a contiguous array (ndim >= 1) in memory (C order).
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="dtype">
/// Data-type of returned array.
/// </param>
/// <returns>
/// Contiguous array of same shape and content as a, with type dtype
/// if specified.
/// </returns>
public static NDarray ascontiguousarray(NDarray a, Dtype dtype = null)
=> NumPy.Instance.ascontiguousarray(a, dtype:dtype);
/// <summary>
/// Return a contiguous array (ndim >= 1) in memory (C order).
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="dtype">
/// Data-type of returned array.
/// </param>
/// <returns>
/// Contiguous array of same shape and content as a, with type dtype
/// if specified.
/// </returns>
public static NDarray<T> ascontiguousarray<T>(T[] a, Dtype dtype = null)
=> NumPy.Instance.ascontiguousarray(a, dtype:dtype);
/// <summary>
/// Return a contiguous array (ndim >= 1) in memory (C order).
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="dtype">
/// Data-type of returned array.
/// </param>
/// <returns>
/// Contiguous array of same shape and content as a, with type dtype
/// if specified.
/// </returns>
public static NDarray<T> ascontiguousarray<T>(T[,] a, Dtype dtype = null)
=> NumPy.Instance.ascontiguousarray(a, dtype:dtype);
/// <summary>
/// Interpret the input as a matrix.
///
/// Unlike matrix, asmatrix does not make a copy if the input is already
/// a matrix or an ndarray. Equivalent to matrix(data, copy=False).
/// </summary>
/// <param name="data">
/// Input data.
/// </param>
/// <param name="dtype">
/// Data-type of the output matrix.
/// </param>
/// <returns>
/// data interpreted as a matrix.
/// </returns>
public static Matrix asmatrix(NDarray data, Dtype dtype)
=> NumPy.Instance.asmatrix(data, dtype);
/// <summary>
/// Interpret the input as a matrix.
///
/// Unlike matrix, asmatrix does not make a copy if the input is already
/// a matrix or an ndarray. Equivalent to matrix(data, copy=False).
/// </summary>
/// <param name="data">
/// Input data.
/// </param>
/// <param name="dtype">
/// Data-type of the output matrix.
/// </param>
/// <returns>
/// data interpreted as a matrix.
/// </returns>
public static Matrix asmatrix<T>(T[] data, Dtype dtype)
=> NumPy.Instance.asmatrix(data, dtype);
/// <summary>
/// Interpret the input as a matrix.
///
/// Unlike matrix, asmatrix does not make a copy if the input is already
/// a matrix or an ndarray. Equivalent to matrix(data, copy=False).
/// </summary>
/// <param name="data">
/// Input data.
/// </param>
/// <param name="dtype">
/// Data-type of the output matrix.
/// </param>
/// <returns>
/// data interpreted as a matrix.
/// </returns>
public static Matrix asmatrix<T>(T[,] data, Dtype dtype)
=> NumPy.Instance.asmatrix(data, dtype);
/// <summary>
/// Return an array copy of the given object.
///
/// Notes
///
/// This is equivalent to:
/// </summary>
/// <param name="a">
/// Input data.
/// </param>
/// <param name="order">
/// Controls the memory layout of the copy. ‘C’ means C-order,
/// ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous,
/// ‘C’ otherwise. ‘K’ means match the layout of a as closely
/// as possible. (Note that this function and ndarray.copy are very
/// similar, but have different default values for their order=
/// arguments.)
/// </param>
/// <returns>
/// Array interpretation of a.
/// </returns>
public static NDarray copy(NDarray a, string order = null)
=> NumPy.Instance.copy(a, order:order);
/// <summary>
/// Return an array copy of the given object.
///
/// Notes
///
/// This is equivalent to:
/// </summary>
/// <param name="a">
/// Input data.
/// </param>
/// <param name="order">
/// Controls the memory layout of the copy. ‘C’ means C-order,
/// ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous,
/// ‘C’ otherwise. ‘K’ means match the layout of a as closely
/// as possible. (Note that this function and ndarray.copy are very
/// similar, but have different default values for their order=
/// arguments.)
/// </param>
/// <returns>
/// Array interpretation of a.
/// </returns>
public static NDarray<T> copy<T>(T[] a, string order = null)
=> NumPy.Instance.copy(a, order:order);
/// <summary>
/// Return an array copy of the given object.
///
/// Notes
///
/// This is equivalent to:
/// </summary>
/// <param name="a">
/// Input data.
/// </param>
/// <param name="order">
/// Controls the memory layout of the copy. ‘C’ means C-order,
/// ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous,
/// ‘C’ otherwise. ‘K’ means match the layout of a as closely
/// as possible. (Note that this function and ndarray.copy are very
/// similar, but have different default values for their order=
/// arguments.)
/// </param>
/// <returns>
/// Array interpretation of a.
/// </returns>
public static NDarray<T> copy<T>(T[,] a, string order = null)
=> NumPy.Instance.copy(a, order:order);
/*
/// <summary>
/// Interpret a buffer as a 1-dimensional array.
///
/// Notes
///
/// If the buffer has data that is not in machine byte-order, this should
/// be specified as part of the data-type, e.g.:
///
/// The data of the resulting array will not be byteswapped, but will be
/// interpreted correctly.
/// </summary>
/// <param name="buffer">
/// An object that exposes the buffer interface.
/// </param>
/// <param name="dtype">
/// Data-type of the returned array; default: float.
/// </param>
/// <param name="count">
/// Number of items to read. -1 means all data in the buffer.
/// </param>
/// <param name="offset">
/// Start reading the buffer from this offset (in bytes); default: 0.
/// </param>
public static void frombuffer(buffer_like buffer, Dtype dtype = null, int? count = -1, int? offset = 0)
=> NumPy.Instance.frombuffer(buffer, dtype:dtype, count:count, offset:offset);
*/