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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 Numpy.Models;
using Python.Included;
namespace Numpy
{
public static partial class np
{
/// <summary>
/// Copies values from one array to another, broadcasting as necessary.<br></br>
///
/// Raises a TypeError if the casting rule is violated, and if
/// where is provided, it selects which elements to copy.
/// </summary>
/// <param name="dst">
/// The array into which values are copied.
/// </param>
/// <param name="src">
/// The array from which values are copied.
/// </param>
/// <param name="casting">
/// Controls what kind of data casting may occur when copying.
/// </param>
/// <param name="where">
/// A boolean array which is broadcasted to match the dimensions
/// of dst, and selects elements to copy from src to dst
/// wherever it contains the value True.
/// </param>
public static void copyto(NDarray dst, NDarray src, string casting = "same_kind", NDarray @where = null)
=> NumPy.Instance.copyto(dst, src, casting:casting, @where:@where);
/// <summary>
/// Copies values from one array to another, broadcasting as necessary.<br></br>
///
/// Raises a TypeError if the casting rule is violated, and if
/// where is provided, it selects which elements to copy.
/// </summary>
/// <param name="dst">
/// The array into which values are copied.
/// </param>
/// <param name="src">
/// The array from which values are copied.
/// </param>
/// <param name="casting">
/// Controls what kind of data casting may occur when copying.
/// </param>
/// <param name="where">
/// A boolean array which is broadcasted to match the dimensions
/// of dst, and selects elements to copy from src to dst
/// wherever it contains the value True.
/// </param>
public static void copyto(NDarray dst, NDarray src, string casting = "same_kind", bool[] @where = null)
=> NumPy.Instance.copyto(dst, src, casting:casting, @where:@where);
/// <summary>
/// Gives a new shape to an array without changing its data.<br></br>
///
/// Notes
///
/// It is not always possible to change the shape of an array without
/// copying the data.<br></br>
/// If you want an error to be raised when the data is copied,
/// you should assign the new shape to the shape attribute of the array:
///
/// The order keyword gives the index ordering both for fetching the values
/// from a, and then placing the values into the output array.<br></br>
///
/// For example, let’s say you have an array:
///
/// You can think of reshaping as first raveling the array (using the given
/// index order), then inserting the elements from the raveled array into the
/// new array using the same kind of index ordering as was used for the
/// raveling.
/// </summary>
/// <param name="a">
/// Array to be reshaped.
/// </param>
/// <param name="newshape">
/// The new shape should be compatible with the original shape.<br></br>
/// If
/// an integer, then the result will be a 1-D array of that length.<br></br>
///
/// One shape dimension can be -1. In this case, the value is
/// inferred from the length of the array and remaining dimensions.
/// </param>
/// <param name="order">
/// Read the elements of a using this index order, and place the
/// elements into the reshaped array using this index order.<br></br>
/// ‘C’
/// means to read / write the elements using C-like index order,
/// with the last axis index changing fastest, back to the first
/// axis index changing slowest.<br></br>
/// ‘F’ means to read / write the
/// elements using Fortran-like index order, with the first index
/// changing fastest, and the last index changing slowest.<br></br>
/// Note that
/// the ‘C’ and ‘F’ options take no account of the memory layout of
/// the underlying array, and only refer to the order of indexing.<br></br>
///
/// ‘A’ means to read / write the elements in Fortran-like index
/// order if a is Fortran contiguous in memory, C-like order
/// otherwise.
/// </param>
/// <returns>
/// This will be a new view object if possible; otherwise, it will
/// be a copy.<br></br>
/// Note there is no guarantee of the memory layout (C- or
/// Fortran- contiguous) of the returned array.
/// </returns>
public static NDarray reshape(NDarray a, Shape newshape, string order = null)
=> NumPy.Instance.reshape(a, newshape, order:order);
/// <summary>
/// Return a contiguous flattened array.<br></br>
///
/// A 1-D array, containing the elements of the input, is returned.<br></br>
/// A copy is
/// made only if needed.<br></br>
///
/// As of NumPy 1.10, the returned array will have the same type as the input
/// array.<br></br>
/// (for example, a masked array will be returned for a masked array
/// input)
///
/// Notes
///
/// In row-major, C-style order, in two dimensions, the row index
/// varies the slowest, and the column index the quickest.<br></br>
/// This can
/// be generalized to multiple dimensions, where row-major order
/// implies that the index along the first axis varies slowest, and
/// the index along the last quickest.<br></br>
/// The opposite holds for
/// column-major, Fortran-style index ordering.<br></br>
///
/// When a view is desired in as many cases as possible, arr.reshape(-1)
/// may be preferable.
/// </summary>
/// <param name="a">
/// Input array.<br></br>
/// The elements in a are read in the order specified by
/// order, and packed as a 1-D array.
/// </param>
/// <param name="order">
/// The elements of a are read using this index order.<br></br>
/// ‘C’ means
/// to index the elements in row-major, C-style order,
/// with the last axis index changing fastest, back to the first
/// axis index changing slowest.<br></br>
/// ‘F’ means to index the elements
/// in column-major, Fortran-style order, with the
/// first index changing fastest, and the last index changing
/// slowest.<br></br>
/// Note that the ‘C’ and ‘F’ options take no account of
/// the memory layout of the underlying array, and only refer to
/// the order of axis indexing.<br></br>
/// ‘A’ means to read the elements in
/// Fortran-like index order if a is Fortran contiguous in
/// memory, C-like order otherwise.<br></br>
/// ‘K’ means to read the
/// elements in the order they occur in memory, except for
/// reversing the data when strides are negative.<br></br>
/// By default, ‘C’
/// index order is used.
/// </param>
/// <returns>
/// y is an array of the same subtype as a, with shape (a.size,).<br></br>
///
/// Note that matrices are special cased for backward compatibility, if a
/// is a matrix, then y is a 1-D ndarray.
/// </returns>
public static NDarray ravel(NDarray a, string order = null)
=> NumPy.Instance.ravel(a, order:order);
/// <summary>
/// Return a copy of the array collapsed into one dimension.
/// </summary>
/// <param name="order">
/// ‘C’ means to flatten in row-major (C-style) order.<br></br>
///
/// ‘F’ means to flatten in column-major (Fortran-
/// style) order.<br></br>
/// ‘A’ means to flatten in column-major
/// order if a is Fortran contiguous in memory,
/// row-major order otherwise.<br></br>
/// ‘K’ means to flatten
/// a in the order the elements occur in memory.<br></br>
///
/// The default is ‘C’.
/// </param>
/// <returns>
/// A copy of the input array, flattened to one dimension.
/// </returns>
public static NDarray flatten(string order = null)
=> NumPy.Instance.flatten(order:order);
/// <summary>
/// Move axes of an array to new positions.<br></br>
///
/// Other axes remain in their original order.
/// </summary>
/// <param name="a">
/// The array whose axes should be reordered.
/// </param>
/// <param name="source">
/// Original positions of the axes to move.<br></br>
/// These must be unique.
/// </param>
/// <param name="destination">
/// Destination positions for each of the original axes.<br></br>
/// These must also be
/// unique.
/// </param>
/// <returns>
/// Array with moved axes.<br></br>
/// This array is a view of the input array.
/// </returns>
public static NDarray moveaxis(NDarray a, int[] source, int[] destination)
=> NumPy.Instance.moveaxis(a, source, destination);
/// <summary>
/// Roll the specified axis backwards, until it lies in a given position.<br></br>
///
/// This function continues to be supported for backward compatibility, but you
/// should prefer moveaxis.<br></br>
/// The moveaxis function was added in NumPy
/// 1.11.
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="axis">
/// The axis to roll backwards.<br></br>
/// The positions of the other axes do not
/// change relative to one another.
/// </param>
/// <param name="start">
/// The axis is rolled until it lies before this position.<br></br>
/// The default,
/// 0, results in a “complete” roll.
/// </param>
/// <returns>
/// For NumPy >= 1.10.0 a view of a is always returned.<br></br>
/// For earlier
/// NumPy versions a view of a is returned only if the order of the
/// axes is changed, otherwise the input array is returned.
/// </returns>
public static NDarray rollaxis(NDarray a, int axis, int? start = 0)
=> NumPy.Instance.rollaxis(a, axis, start:start);
/// <summary>
/// Interchange two axes of an array.
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="axis1">
/// First axis.
/// </param>
/// <param name="axis2">
/// Second axis.
/// </param>
/// <returns>
/// For NumPy >= 1.10.0, if a is an ndarray, then a view of a is
/// returned; otherwise a new array is created.<br></br>
/// For earlier NumPy
/// versions a view of a is returned only if the order of the
/// axes is changed, otherwise the input array is returned.
/// </returns>
public static NDarray swapaxes(NDarray a, int axis1, int axis2)
=> NumPy.Instance.swapaxes(a, axis1, axis2);
/// <summary>
/// Permute the dimensions of an array.<br></br>
///
/// Notes
///
/// Use transpose(a, argsort(axes)) to invert the transposition of tensors
/// when using the axes keyword argument.<br></br>
///
/// Transposing a 1-D array returns an unchanged view of the original array.
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="axes">
/// By default, reverse the dimensions, otherwise permute the axes
/// according to the values given.
/// </param>
/// <returns>
/// a with its axes permuted.<br></br>
/// A view is returned whenever
/// possible.
/// </returns>
public static NDarray transpose(NDarray a, int[] axes = null)
=> NumPy.Instance.transpose(a, axes:axes);
/// <summary>
/// Convert inputs to arrays with at least one dimension.<br></br>
///
/// Scalar inputs are converted to 1-dimensional arrays, whilst
/// higher-dimensional inputs are preserved.
/// </summary>
/// <param name="arys">
/// One or more input arrays.
/// </param>
/// <returns>
/// An array, or list of arrays, each with a.ndim >= 1.<br></br>
///
/// Copies are made only if necessary.
/// </returns>
public static NDarray atleast_1d(params NDarray[] arys)
=> NumPy.Instance.atleast_1d(arys);
/// <summary>
/// View inputs as arrays with at least two dimensions.
/// </summary>
/// <param name="arys">
/// One or more array-like sequences.<br></br>
/// Non-array inputs are converted
/// to arrays.<br></br>
/// Arrays that already have two or more dimensions are
/// preserved.
/// </param>
/// <returns>
/// An array, or list of arrays, each with a.ndim >= 2.<br></br>
///
/// Copies are avoided where possible, and views with two or more
/// dimensions are returned.
/// </returns>
public static NDarray atleast_2d(params NDarray[] arys)
=> NumPy.Instance.atleast_2d(arys);
/// <summary>
/// View inputs as arrays with at least three dimensions.
/// </summary>
/// <param name="arys">
/// One or more array-like sequences.<br></br>
/// Non-array inputs are converted to
/// arrays.<br></br>
/// Arrays that already have three or more dimensions are
/// preserved.
/// </param>
/// <returns>
/// An array, or list of arrays, each with a.ndim >= 3.<br></br>
/// Copies are
/// avoided where possible, and views with three or more dimensions are
/// returned.<br></br>
/// For example, a 1-D array of shape (N,) becomes a view
/// of shape (1, N, 1), and a 2-D array of shape (M, N) becomes a
/// view of shape (M, N, 1).
/// </returns>
public static NDarray atleast_3d(params NDarray[] arys)
=> NumPy.Instance.atleast_3d(arys);
/// <summary>
/// Produce an object that mimics broadcasting.
/// </summary>
/// <param name="in2">
/// Input parameters.
/// </param>
/// <param name="in1">
/// Input parameters.
/// </param>
/// <returns>
/// Broadcast the input parameters against one another, and
/// return an object that encapsulates the result.<br></br>
///
/// Amongst others, it has shape and nd properties, and
/// may be used as an iterator.
/// </returns>
public static NDarray broadcast(NDarray in2, NDarray in1)
=> NumPy.Instance.broadcast(in2, in1);
/// <summary>
/// Broadcast an array to a new shape.<br></br>
///
/// Notes
/// </summary>
/// <param name="array">
/// The array to broadcast.
/// </param>
/// <param name="shape">
/// The shape of the desired array.
/// </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>
/// <returns>
/// A readonly view on the original array with the given shape.<br></br>
/// It is
/// typically not contiguous.<br></br>
/// Furthermore, more than one element of a
/// broadcasted array may refer to a single memory location.
/// </returns>
public static NDarray broadcast_to(NDarray array, Shape shape, bool? subok = false)
=> NumPy.Instance.broadcast_to(array, shape, subok:subok);
/// <summary>
/// Broadcast any number of arrays against each other.
/// </summary>
/// <param name="args">
/// The arrays to broadcast.
/// </param>
/// <param name="subok">
/// If True, then sub-classes will be passed-through, otherwise
/// the returned arrays will be forced to be a base-class array (default).
/// </param>
/// <returns>
/// These arrays are views on the original arrays.<br></br>
/// They are typically
/// not contiguous.<br></br>
/// Furthermore, more than one element of a
/// broadcasted array may refer to a single memory location.<br></br>
/// If you
/// need to write to the arrays, make copies first.
/// </returns>
public static NDarray[] broadcast_arrays(NDarray[] args, bool? subok = null)
=> NumPy.Instance.broadcast_arrays(args, subok:subok);
/// <summary>
/// Expand the shape of an array.<br></br>
///
/// Insert a new axis that will appear at the axis position in the expanded
/// array shape.
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="axis">
/// Position in the expanded axes where the new axis is placed.
/// </param>
/// <returns>
/// Output array.<br></br>
/// The number of dimensions is one greater than that of
/// the input array.
/// </returns>
public static NDarray expand_dims(NDarray a, int axis)
=> NumPy.Instance.expand_dims(a, axis);
/// <summary>
/// Remove single-dimensional entries from the shape of an array.
/// </summary>
/// <param name="a">
/// Input data.
/// </param>
/// <param name="axis">
/// Selects a subset of the single-dimensional entries in the
/// shape.<br></br>
/// If an axis is selected with shape entry greater than
/// one, an error is raised.
/// </param>
/// <returns>
/// The input array, but with all or a subset of the
/// dimensions of length 1 removed.<br></br>
/// This is always a itself
/// or a view into a.
/// </returns>
public static NDarray squeeze(NDarray a, int[] axis = null)
=> NumPy.Instance.squeeze(a, axis:axis);
/// <summary>
/// Return an array converted to a float type.
/// </summary>
/// <param name="a">
/// The input array.
/// </param>
/// <param name="dtype">
/// Float type code to coerce input array a.<br></br>
/// If dtype is one of the
/// ‘int’ dtypes, it is replaced with float64.
/// </param>
/// <returns>
/// The input a as a float ndarray.
/// </returns>
public static NDarray asfarray(NDarray a, Dtype dtype = null)
=> NumPy.Instance.asfarray(a, dtype:dtype);
/// <summary>
/// Return an array (ndim >= 1) laid out in Fortran order in memory.
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="dtype">
/// By default, the data-type is inferred from the input data.
/// </param>
/// <returns>
/// The input a in Fortran, or column-major, order.
/// </returns>
public static NDarray asfortranarray(NDarray a, Dtype dtype = null)
=> NumPy.Instance.asfortranarray(a, dtype:dtype);
/// <summary>
/// Convert the input to an array, checking for NaNs or Infs.
/// </summary>
/// <param name="a">
/// Input data, in any form that can be converted to an array.<br></br>
/// This
/// includes lists, lists of tuples, tuples, tuples of tuples, tuples
/// of lists and ndarrays.<br></br>
/// Success requires no NaNs or Infs.
/// </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.<br></br>
///
/// Defaults to ‘C’.
/// </param>
/// <returns>
/// Array interpretation of a.<br></br>
/// No copy is performed if the input
/// is already an ndarray.<br></br>
/// If a is a subclass of ndarray, a base
/// class ndarray is returned.
/// </returns>
public static NDarray asarray_chkfinite(NDarray a, Dtype dtype = null, string order = null)
=> NumPy.Instance.asarray_chkfinite(a, dtype:dtype, order:order);
/// <summary>
/// Return an ndarray of the provided type that satisfies requirements.<br></br>
///
/// This function is useful to be sure that an array with the correct flags
/// is returned for passing to compiled code (perhaps through ctypes).<br></br>
///
/// Notes
///
/// The returned array will be guaranteed to have the listed requirements
/// by making a copy if needed.
/// </summary>
/// <param name="a">
/// The object to be converted to a type-and-requirement-satisfying array.
/// </param>
/// <param name="dtype">
/// The required data-type.<br></br>
/// If None preserve the current dtype.<br></br>
/// If your
/// application requires the data to be in native byteorder, include
/// a byteorder specification as a part of the dtype specification.
/// </param>
/// <param name="requirements">
/// The requirements list can be any of the following
/// </param>
public static NDarray require(NDarray a, Dtype dtype, string[] requirements = null)
=> NumPy.Instance.require(a, dtype, requirements:requirements);
/// <summary>
/// Join a sequence of arrays along an existing axis.<br></br>
///
/// Notes
///
/// When one or more of the arrays to be concatenated is a MaskedArray,
/// this function will return a MaskedArray object instead of an ndarray,
/// but the input masks are not preserved.<br></br>
/// In cases where a MaskedArray
/// is expected as input, use the ma.concatenate function from the masked
/// array module instead.
/// </summary>
/// <param name="arys">
/// The arrays must have the same shape, except in the dimension
/// corresponding to axis (the first, by default).
/// </param>
/// <param name="axis">
/// The axis along which the arrays will be joined.<br></br>
/// If axis is None,
/// arrays are flattened before use.<br></br>
/// Default is 0.
/// </param>
/// <param name="out">
/// If provided, the destination to place the result.<br></br>
/// The shape must be
/// correct, matching that of what concatenate would have returned if no
/// out argument were specified.
/// </param>
/// <returns>
/// The concatenated array.
/// </returns>
public static NDarray concatenate(NDarray[] arys, int? axis = 0, NDarray @out = null)
=> NumPy.Instance.concatenate(arys, axis:axis, @out:@out);
/// <summary>
/// Join a sequence of arrays along a new axis.<br></br>
///
/// The axis parameter specifies the index of the new axis in the dimensions
/// of the result.<br></br>
/// For example, if axis=0 it will be the first dimension
/// and if axis=-1 it will be the last dimension.
/// </summary>
/// <param name="arrays">
/// Each array must have the same shape.
/// </param>
/// <param name="axis">
/// The axis in the result array along which the input arrays are stacked.
/// </param>
/// <param name="out">
/// If provided, the destination to place the result.<br></br>
/// The shape must be
/// correct, matching that of what stack would have returned if no
/// out argument were specified.
/// </param>
/// <returns>
/// The stacked array has one more dimension than the input arrays.
/// </returns>
public static NDarray stack(NDarray[] arrays, int? axis = 0, NDarray @out = null)
=> NumPy.Instance.stack(arrays, axis:axis, @out:@out);
/// <summary>
/// Stack 1-D arrays as columns into a 2-D array.<br></br>
///
/// Take a sequence of 1-D arrays and stack them as columns
/// to make a single 2-D array.<br></br>
/// 2-D arrays are stacked as-is,
/// just like with hstack.<br></br>
/// 1-D arrays are turned into 2-D columns
/// first.
/// </summary>
/// <param name="tup">
/// Arrays to stack.<br></br>
/// All of them must have the same first dimension.
/// </param>
/// <returns>
/// The array formed by stacking the given arrays.
/// </returns>
public static NDarray column_stack(params NDarray[] tup)
=> NumPy.Instance.column_stack(tup);
/// <summary>
/// Stack arrays in sequence depth wise (along third axis).<br></br>
///
/// This is equivalent to concatenation along the third axis after 2-D arrays
/// of shape (M,N) have been reshaped to (M,N,1) and 1-D arrays of shape
/// (N,) have been reshaped to (1,N,1).<br></br>
/// Rebuilds arrays divided by
/// dsplit.<br></br>
///
/// This function makes most sense for arrays with up to 3 dimensions.<br></br>
/// For
/// instance, for pixel-data with a height (first axis), width (second axis),
/// and r/g/b channels (third axis).<br></br>
/// The functions concatenate, stack and
/// block provide more general stacking and concatenation operations.
/// </summary>
/// <param name="tup">
/// The arrays must have the same shape along all but the third axis.<br></br>
///
/// 1-D or 2-D arrays must have the same shape.
/// </param>
/// <returns>
/// The array formed by stacking the given arrays, will be at least 3-D.
/// </returns>
public static NDarray dstack(params NDarray[] tup)
=> NumPy.Instance.dstack(tup);
/// <summary>
/// Stack arrays in sequence horizontally (column wise).<br></br>
///
/// This is equivalent to concatenation along the second axis, except for 1-D
/// arrays where it concatenates along the first axis.<br></br>
/// Rebuilds arrays divided
/// by hsplit.<br></br>
///
/// This function makes most sense for arrays with up to 3 dimensions.<br></br>
/// For
/// instance, for pixel-data with a height (first axis), width (second axis),
/// and r/g/b channels (third axis).<br></br>
/// The functions concatenate, stack and
/// block provide more general stacking and concatenation operations.
/// </summary>
/// <param name="tup">
/// The arrays must have the same shape along all but the second axis,
/// except 1-D arrays which can be any length.
/// </param>
/// <returns>
/// The array formed by stacking the given arrays.
/// </returns>
public static NDarray hstack(params NDarray[] tup)
=> NumPy.Instance.hstack(tup);
/// <summary>
/// Stack arrays in sequence vertically (row wise).<br></br>
///
/// This is equivalent to concatenation along the first axis after 1-D arrays
/// of shape (N,) have been reshaped to (1,N).<br></br>
/// Rebuilds arrays divided by
/// vsplit.<br></br>
///
/// This function makes most sense for arrays with up to 3 dimensions.<br></br>
/// For
/// instance, for pixel-data with a height (first axis), width (second axis),
/// and r/g/b channels (third axis).<br></br>
/// The functions concatenate, stack and
/// block provide more general stacking and concatenation operations.
/// </summary>
/// <param name="tup">
/// The arrays must have the same shape along all but the first axis.<br></br>
///
/// 1-D arrays must have the same length.
/// </param>
/// <returns>
/// The array formed by stacking the given arrays, will be at least 2-D.
/// </returns>
public static NDarray vstack(params NDarray[] tup)
=> NumPy.Instance.vstack(tup);
/*
/// <summary>
/// Assemble an nd-array from nested lists of blocks.<br></br>
///
/// Blocks in the innermost lists are concatenated (see concatenate) along
/// the last dimension (-1), then these are concatenated along the
/// second-last dimension (-2), and so on until the outermost list is reached.<br></br>
///
/// Blocks can be of any dimension, but will not be broadcasted using the normal
/// rules.<br></br>
/// Instead, leading axes of size 1 are inserted, to make block.ndim
/// the same for all blocks.<br></br>
/// This is primarily useful for working with scalars,
/// and means that code like np.block([v, 1]) is valid, where
/// v.ndim == 1.<br></br>
///
/// When the nested list is two levels deep, this allows block matrices to be
/// constructed from their components.<br></br>
///
/// Notes
///
/// When called with only scalars, np.block is equivalent to an ndarray
/// call.<br></br>
/// So np.block([[1, 2], [3, 4]]) is equivalent to
/// np.array([[1, 2], [3, 4]]).<br></br>
///
/// This function does not enforce that the blocks lie on a fixed grid.<br></br>
///
/// np.block([[a, b], [c, d]]) is not restricted to arrays of the form:
///
/// But is also allowed to produce, for some a, b, c, d:
///
/// Since concatenation happens along the last axis first, block is _not_
/// capable of producing the following directly:
///
/// Matlab’s “square bracket stacking”, [A, B, ...; p, q, ...], is
/// equivalent to np.block([[A, B, ...], [p, q, ...]]).
/// </summary>
/// <param name="arrays">
/// If passed a single ndarray or scalar (a nested list of depth 0), this
/// is returned unmodified (and not copied).<br></br>
///
/// Elements shapes must match along the appropriate axes (without
/// broadcasting), but leading 1s will be prepended to the shape as
/// necessary to make the dimensions match.
/// </param>
/// <returns>
/// The array assembled from the given blocks.<br></br>
///
/// The dimensionality of the output is equal to the greatest of:
/// * the dimensionality of all the inputs
/// * the depth to which the input list is nested
/// </returns>
public static NDarray block(nested list of array_like or scalars (but not tuples) arrays)
=> NumPy.Instance.block(arrays);
*/
/// <summary>
/// Split an array into multiple sub-arrays.
/// </summary>
/// <param name="ary">
/// Array to be divided into sub-arrays.
/// </param>
/// <param name="indices_or_sections">
/// If indices_or_sections is an integer, N, the array will be divided
/// into N equal arrays along axis.<br></br>
/// If such a split is not possible,
/// an error is raised.<br></br>
///
/// If indices_or_sections is a 1-D array of sorted integers, the entries
/// indicate where along axis the array is split.<br></br>
/// For example,
/// [2, 3] would, for axis=0, result in
///
/// If an index exceeds the dimension of the array along axis,
/// an empty sub-array is returned correspondingly.
/// </param>
/// <param name="axis">
/// The axis along which to split, default is 0.
/// </param>
/// <returns>
/// A list of sub-arrays.
/// </returns>
public static NDarray[] split(NDarray ary, int[] indices_or_sections, int? axis = 0)
=> NumPy.Instance.split(ary, indices_or_sections, axis:axis);
/// <summary>
/// Construct an array by repeating A the number of times given by reps.<br></br>
///
/// If reps has length d, the result will have dimension of
/// max(d, A.ndim).<br></br>
///
/// If A.ndim < d, A is promoted to be d-dimensional by prepending new
/// axes.<br></br>
/// So a shape (3,) array is promoted to (1, 3) for 2-D replication,
/// or shape (1, 1, 3) for 3-D replication.<br></br>
/// If this is not the desired
/// behavior, promote A to d-dimensions manually before calling this
/// function.<br></br>
///
/// If A.ndim > d, reps is promoted to A.ndim by pre-pending 1’s to it.<br></br>
///
/// Thus for an A of shape (2, 3, 4, 5), a reps of (2, 2) is treated as
/// (1, 1, 2, 2).<br></br>
///
/// Note : Although tile may be used for broadcasting, it is strongly
/// recommended to use numpy’s broadcasting operations and functions.
/// </summary>
/// <param name="A">
/// The input array.
/// </param>
/// <param name="reps">
/// The number of repetitions of A along each axis.
/// </param>
/// <returns>
/// The tiled output array.
/// </returns>
public static NDarray tile(NDarray A, NDarray reps)
=> NumPy.Instance.tile(A, reps);
/// <summary>
/// Repeat elements of an array.
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="repeats">
/// The number of repetitions for each element.<br></br>
/// repeats is broadcasted
/// to fit the shape of the given axis.
/// </param>
/// <param name="axis">
/// The axis along which to repeat values.<br></br>
/// By default, use the
/// flattened input array, and return a flat output array.
/// </param>
/// <returns>
/// Output array which has the same shape as a, except along
/// the given axis.
/// </returns>
public static NDarray repeat(NDarray a, int[] repeats, int? axis = null)
=> NumPy.Instance.repeat(a, repeats, axis:axis);
/// <summary>
/// Return a new array with sub-arrays along an axis deleted.<br></br>
/// For a one
/// dimensional array, this returns those entries not returned by
/// arr[obj].<br></br>
///
/// Notes
///
/// Often it is preferable to use a boolean mask.<br></br>
/// For example:
///
/// Is equivalent to np.delete(arr, [0,2,4], axis=0), but allows further
/// use of mask.
/// </summary>
/// <param name="arr">
/// Input array.
/// </param>
/// <param name="obj">
/// Indicate which sub-arrays to remove.
/// </param>
/// <param name="axis">
/// The axis along which to delete the subarray defined by obj.<br></br>
///
/// If axis is None, obj is applied to the flattened array.
/// </param>
/// <returns>
/// A copy of arr with the elements specified by obj removed.<br></br>
/// Note
/// that delete does not occur in-place.<br></br>
/// If axis is None, out is
/// a flattened array.
/// </returns>
public static NDarray delete(NDarray arr, Slice obj, int? axis = null)
=> NumPy.Instance.delete(arr, obj, axis:axis);
/// <summary>
/// Insert values along the given axis before the given indices.<br></br>
///
/// Notes
///
/// Note that for higher dimensional inserts obj=0 behaves very different
/// from obj=[0] just like arr[:,0,:] = values is different from
/// arr[:,[0],:] = values.
/// </summary>
/// <param name="arr">
/// Input array.
/// </param>
/// <param name="obj">
/// Object that defines the index or indices before which values is
/// inserted.<br></br>
///
/// Support for multiple insertions when obj is a single scalar or a
/// sequence with one element (similar to calling insert multiple
/// times).
/// </param>
/// <param name="values">
/// Values to insert into arr.<br></br>
/// If the type of values is different
/// from that of arr, values is converted to the type of arr.<br></br>
///
/// values should be shaped so that arr[...,obj,...] = values
/// is legal.
/// </param>
/// <param name="axis">
/// Axis along which to insert values.<br></br>
/// If axis is None then arr
/// is flattened first.
/// </param>
/// <returns>
/// A copy of arr with values inserted.<br></br>
/// Note that insert
/// does not occur in-place: a new array is returned.<br></br>
/// If
/// axis is None, out is a flattened array.
/// </returns>
public static NDarray insert(NDarray arr, int obj = 0, NDarray values = null, int? axis = null)
=> NumPy.Instance.insert(arr, obj:obj, values:values, axis:axis);
/// <summary>
/// Append values to the end of an array.
/// </summary>
/// <param name="arr">
/// Values are appended to a copy of this array.
/// </param>
/// <param name="values">
/// These values are appended to a copy of arr.<br></br>
/// It must be of the
/// correct shape (the same shape as arr, excluding axis).<br></br>
/// If
/// axis is not specified, values can be any shape and will be
/// flattened before use.
/// </param>
/// <param name="axis">
/// The axis along which values are appended.<br></br>
/// If axis is not
/// given, both arr and values are flattened before use.
/// </param>
/// <returns>
/// A copy of arr with values appended to axis.<br></br>
/// Note that
/// append does not occur in-place: a new array is allocated and
/// filled.<br></br>
/// If axis is None, out is a flattened array.
/// </returns>
public static NDarray append(NDarray arr, NDarray values, int? axis = null)
=> NumPy.Instance.append(arr, values, axis:axis);
/// <summary>
/// Return a new array with the specified shape.<br></br>
///
/// If the new array is larger than the original array, then the new
/// array is filled with repeated copies of a.<br></br>
/// Note that this behavior
/// is different from a.resize(new_shape) which fills with zeros instead
/// of repeated copies of a.<br></br>
///
/// Notes
///
/// Warning: This functionality does not consider axes separately,
/// i.e.<br></br>
/// it does not apply interpolation/extrapolation.<br></br>
///
/// It fills the return array with the required number of elements, taken
/// from a as they are laid out in memory, disregarding strides and axes.<br></br>
///
/// (This is in case the new shape is smaller.<br></br>
/// For larger, see above.)
/// This functionality is therefore not suitable to resize images,
/// or data where each axis represents a separate and distinct entity.
/// </summary>
/// <param name="a">
/// Array to be resized.
/// </param>
/// <param name="new_shape">
/// Shape of resized array.
/// </param>
/// <returns>
/// The new array is formed from the data in the old array, repeated