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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;
namespace Numpy
{
public static partial class np
{
/// <summary>
/// Return a sorted copy of an array.
///
/// Notes
///
/// The various sorting algorithms are characterized by their average speed,
/// worst case performance, work space size, and whether they are stable. A
/// stable sort keeps items with the same key in the same relative
/// order. The three available algorithms have the following
/// properties:
///
/// All the sort algorithms make temporary copies of the data when
/// sorting along any but the last axis. Consequently, sorting along
/// the last axis is faster and uses less space than sorting along
/// any other axis.
///
/// The sort order for complex numbers is lexicographic. If both the real
/// and imaginary parts are non-nan then the order is determined by the
/// real parts except when they are equal, in which case the order is
/// determined by the imaginary parts.
///
/// Previous to numpy 1.4.0 sorting real and complex arrays containing nan
/// values led to undefined behaviour. In numpy versions >= 1.4.0 nan
/// values are sorted to the end. The extended sort order is:
///
/// where R is a non-nan real value. Complex values with the same nan
/// placements are sorted according to the non-nan part if it exists.
/// Non-nan values are sorted as before.
///
/// quicksort has been changed to an introsort which will switch
/// heapsort when it does not make enough progress. This makes its
/// worst case O(n*log(n)).
///
/// ‘stable’ automatically choses the best stable sorting algorithm
/// for the data type being sorted. It is currently mapped to
/// merge sort.
/// </summary>
/// <param name="a">
/// Array to be sorted.
/// </param>
/// <param name="axis">
/// Axis along which to sort. If None, the array is flattened before
/// sorting. The default is -1, which sorts along the last axis.
/// </param>
/// <param name="kind">
/// Sorting algorithm. Default is ‘quicksort’.
/// </param>
/// <param name="order">
/// When a is an array with fields defined, this argument specifies
/// which fields to compare first, second, etc. A single field can
/// be specified as a string, and not all fields need be specified,
/// but unspecified fields will still be used, in the order in which
/// they come up in the dtype, to break ties.
/// </param>
/// <returns>
/// Array of the same type and shape as a.
/// </returns>
public static NDarray sort(NDarray a, int? axis = -1, string kind = "quicksort", string order = null)
=> NumPy.Instance.sort(a, axis:axis, kind:kind, order:order);
/// <summary>
/// Perform an indirect stable sort using a sequence of keys.
///
/// Given multiple sorting keys, which can be interpreted as columns in a
/// spreadsheet, lexsort returns an array of integer indices that describes
/// the sort order by multiple columns. The last key in the sequence is used
/// for the primary sort order, the second-to-last key for the secondary sort
/// order, and so on. The keys argument must be a sequence of objects that
/// can be converted to arrays of the same shape. If a 2D array is provided
/// for the keys argument, it’s rows are interpreted as the sorting keys and
/// sorting is according to the last row, second last row etc.
/// </summary>
/// <param name="keys">
/// The k different “columns” to be sorted. The last column (or row if
/// keys is a 2D array) is the primary sort key.
/// </param>
/// <param name="axis">
/// Axis to be indirectly sorted. By default, sort over the last axis.
/// </param>
/// <returns>
/// Array of indices that sort the keys along the specified axis.
/// </returns>
public static NDarray lexsort(NDarray keys, int? axis = -1)
=> NumPy.Instance.lexsort(keys, axis:axis);
/// <summary>
/// Returns the indices that would sort an array.
///
/// Perform an indirect sort along the given axis using the algorithm specified
/// by the kind keyword. It returns an array of indices of the same shape as
/// a that index data along the given axis in sorted order.
///
/// Notes
///
/// See sort for notes on the different sorting algorithms.
///
/// As of NumPy 1.4.0 argsort works with real/complex arrays containing
/// nan values. The enhanced sort order is documented in sort.
/// </summary>
/// <param name="a">
/// Array to sort.
/// </param>
/// <param name="axis">
/// Axis along which to sort. The default is -1 (the last axis). If None,
/// the flattened array is used.
/// </param>
/// <param name="kind">
/// Sorting algorithm.
/// </param>
/// <param name="order">
/// When a is an array with fields defined, this argument specifies
/// which fields to compare first, second, etc. A single field can
/// be specified as a string, and not all fields need be specified,
/// but unspecified fields will still be used, in the order in which
/// they come up in the dtype, to break ties.
/// </param>
/// <returns>
/// Array of indices that sort a along the specified axis.
/// If a is one-dimensional, a[index_array] yields a sorted a.
/// More generally, np.take_along_axis(a, index_array, axis=a) always
/// yields the sorted a, irrespective of dimensionality.
/// </returns>
public static NDarray argsort(NDarray a, int? axis = -1, string kind = "quicksort", string order = null)
=> NumPy.Instance.argsort(a, axis:axis, kind:kind, order:order);
/// <summary>
/// Sort an array, in-place.
///
/// Notes
///
/// See sort for notes on the different sorting algorithms.
/// </summary>
/// <param name="axis">
/// Axis along which to sort. Default is -1, which means sort along the
/// last axis.
/// </param>
/// <param name="kind">
/// Sorting algorithm. Default is ‘quicksort’.
/// </param>
/// <param name="order">
/// When a is an array with fields defined, this argument specifies
/// which fields to compare first, second, etc. A single field can
/// be specified as a string, and not all fields need be specified,
/// but unspecified fields will still be used, in the order in which
/// they come up in the dtype, to break ties.
/// </param>
public static void sort(int? axis = -1, string kind = null, string order = null)
=> NumPy.Instance.sort(axis:axis, kind:kind, order:order);
/// <summary>
/// Return a copy of an array sorted along the first axis.
///
/// Notes
///
/// np.msort(a) is equivalent to np.sort(a, axis=0).
/// </summary>
/// <param name="a">
/// Array to be sorted.
/// </param>
/// <returns>
/// Array of the same type and shape as a.
/// </returns>
public static NDarray msort(NDarray a)
=> NumPy.Instance.msort(a);
/// <summary>
/// Sort a complex array using the real part first, then the imaginary part.
/// </summary>
/// <param name="a">
/// Input array
/// </param>
/// <returns>
/// Always returns a sorted complex array.
/// </returns>
public static NDarray sort_complex(NDarray a)
=> NumPy.Instance.sort_complex(a);
/// <summary>
/// Return a partitioned copy of an array.
///
/// Creates a copy of the array with its elements rearranged in such a
/// way that the value of the element in k-th position is in the
/// position it would be in a sorted array. All elements smaller than
/// the k-th element are moved before this element and all equal or
/// greater are moved behind it. The ordering of the elements in the two
/// partitions is undefined.
///
/// Notes
///
/// The various selection algorithms are characterized by their average
/// speed, worst case performance, work space size, and whether they are
/// stable. A stable sort keeps items with the same key in the same
/// relative order. The available algorithms have the following
/// properties:
///
/// All the partition algorithms make temporary copies of the data when
/// partitioning along any but the last axis. Consequently,
/// partitioning along the last axis is faster and uses less space than
/// partitioning along any other axis.
///
/// The sort order for complex numbers is lexicographic. If both the
/// real and imaginary parts are non-nan then the order is determined by
/// the real parts except when they are equal, in which case the order
/// is determined by the imaginary parts.
/// </summary>
/// <param name="a">
/// Array to be sorted.
/// </param>
/// <param name="kth">
/// Element index to partition by. The k-th value of the element
/// will be in its final sorted position and all smaller elements
/// will be moved before it and all equal or greater elements behind
/// it. The order of all elements in the partitions is undefined. If
/// provided with a sequence of k-th it will partition all elements
/// indexed by k-th of them into their sorted position at once.
/// </param>
/// <param name="axis">
/// Axis along which to sort. If None, the array is flattened before
/// sorting. The default is -1, which sorts along the last axis.
/// </param>
/// <param name="kind">
/// Selection algorithm. Default is ‘introselect’.
/// </param>
/// <param name="order">
/// When a is an array with fields defined, this argument
/// specifies which fields to compare first, second, etc. A single
/// field can be specified as a string. Not all fields need be
/// specified, but unspecified fields will still be used, in the
/// order in which they come up in the dtype, to break ties.
/// </param>
/// <returns>
/// Array of the same type and shape as a.
/// </returns>
public static NDarray partition(NDarray a, int[] kth, int? axis = -1, string kind = "introselect", string order = null)
=> NumPy.Instance.partition(a, kth, axis:axis, kind:kind, order:order);
/// <summary>
/// Perform an indirect partition along the given axis using the
/// algorithm specified by the kind keyword. It returns an array of
/// indices of the same shape as a that index data along the given
/// axis in partitioned order.
///
/// Notes
///
/// See partition for notes on the different selection algorithms.
/// </summary>
/// <param name="a">
/// Array to sort.
/// </param>
/// <param name="kth">
/// Element index to partition by. The k-th element will be in its
/// final sorted position and all smaller elements will be moved
/// before it and all larger elements behind it. The order all
/// elements in the partitions is undefined. If provided with a
/// sequence of k-th it will partition all of them into their sorted
/// position at once.
/// </param>
/// <param name="axis">
/// Axis along which to sort. The default is -1 (the last axis). If
/// None, the flattened array is used.
/// </param>
/// <param name="kind">
/// Selection algorithm. Default is ‘introselect’
/// </param>
/// <param name="order">
/// When a is an array with fields defined, this argument
/// specifies which fields to compare first, second, etc. A single
/// field can be specified as a string, and not all fields need be
/// specified, but unspecified fields will still be used, in the
/// order in which they come up in the dtype, to break ties.
/// </param>
/// <returns>
/// Array of indices that partition a along the specified axis.
/// If a is one-dimensional, a[index_array] yields a partitioned a.
/// More generally, np.take_along_axis(a, index_array, axis=a) always
/// yields the partitioned a, irrespective of dimensionality.
/// </returns>
public static NDarray argpartition(NDarray a, int[] kth, int? axis = -1, string kind = "introselect", string order = null)
=> NumPy.Instance.argpartition(a, kth, axis:axis, kind:kind, order:order);
/// <summary>
/// Returns the indices of the maximum values along an axis.
///
/// Notes
///
/// In case of multiple occurrences of the maximum values, the indices
/// corresponding to the first occurrence are returned.
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="axis">
/// By default, the index is into the flattened array, otherwise
/// along the specified axis.
/// </param>
/// <param name="@out">
/// If provided, the result will be inserted into this array. It should
/// be of the appropriate shape and dtype.
/// </param>
/// <returns>
/// Array of indices into the array. It has the same shape as a.shape
/// with the dimension along axis removed.
/// </returns>
public static NDarray argmax(NDarray a, int? axis = null, NDarray @out = null)
=> NumPy.Instance.argmax(a, axis:axis, @out:@out);
/// <summary>
/// Return the indices of the maximum values in the specified axis ignoring
/// NaNs. For all-NaN slices ValueError is raised. Warning: the
/// results cannot be trusted if a slice contains only NaNs and -Infs.
/// </summary>
/// <param name="a">
/// Input data.
/// </param>
/// <param name="axis">
/// Axis along which to operate. By default flattened input is used.
/// </param>
/// <returns>
/// An array of indices or a single index value.
/// </returns>
public static NDarray nanargmax(NDarray a, int? axis = null)
=> NumPy.Instance.nanargmax(a, axis:axis);
/// <summary>
/// Returns the indices of the minimum values along an axis.
///
/// Notes
///
/// In case of multiple occurrences of the minimum values, the indices
/// corresponding to the first occurrence are returned.
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="axis">
/// By default, the index is into the flattened array, otherwise
/// along the specified axis.
/// </param>
/// <param name="@out">
/// If provided, the result will be inserted into this array. It should
/// be of the appropriate shape and dtype.
/// </param>
/// <returns>
/// Array of indices into the array. It has the same shape as a.shape
/// with the dimension along axis removed.
/// </returns>
public static NDarray argmin(NDarray a, int? axis = null, NDarray @out = null)
=> NumPy.Instance.argmin(a, axis:axis, @out:@out);
/// <summary>
/// Return the indices of the minimum values in the specified axis ignoring
/// NaNs. For all-NaN slices ValueError is raised. Warning: the results
/// cannot be trusted if a slice contains only NaNs and Infs.
/// </summary>
/// <param name="a">
/// Input data.
/// </param>
/// <param name="axis">
/// Axis along which to operate. By default flattened input is used.
/// </param>
/// <returns>
/// An array of indices or a single index value.
/// </returns>
public static NDarray nanargmin(NDarray a, int? axis = null)
=> NumPy.Instance.nanargmin(a, axis:axis);
/// <summary>
/// Find the indices of array elements that are non-zero, grouped by element.
///
/// Notes
///
/// np.argwhere(a) is the same as np.transpose(np.nonzero(a)).
///
/// The output of argwhere is not suitable for indexing arrays.
/// For this purpose use nonzero(a) instead.
/// </summary>
/// <param name="a">
/// Input data.
/// </param>
/// <returns>
/// Indices of elements that are non-zero. Indices are grouped by element.
/// </returns>
public static NDarray argwhere(NDarray a)
=> NumPy.Instance.argwhere(a);
/// <summary>
/// Return indices that are non-zero in the flattened version of a.
///
/// This is equivalent to np.nonzero(np.ravel(a))[0].
/// </summary>
/// <param name="a">
/// Input data.
/// </param>
/// <returns>
/// Output array, containing the indices of the elements of a.ravel()
/// that are non-zero.
/// </returns>
public static NDarray flatnonzero(NDarray a)
=> NumPy.Instance.flatnonzero(a);
/// <summary>
/// Find indices where elements should be inserted to maintain order.
///
/// Find the indices into a sorted array a such that, if the
/// corresponding elements in v were inserted before the indices, the
/// order of a would be preserved.
///
/// Assuming that a is sorted:
///
/// Notes
///
/// Binary search is used to find the required insertion points.
///
/// As of NumPy 1.4.0 searchsorted works with real/complex arrays containing
/// nan values. The enhanced sort order is documented in sort.
///
/// This function is a faster version of the builtin python bisect.bisect_left
/// (side='left') and bisect.bisect_right (side='right') functions,
/// which is also vectorized in the v argument.
/// </summary>
/// <param name="a">
/// Input array. If sorter is None, then it must be sorted in
/// ascending order, otherwise sorter must be an array of indices
/// that sort it.
/// </param>
/// <param name="v">
/// Values to insert into a.
/// </param>
/// <param name="side">
/// If ‘left’, the index of the first suitable location found is given.
/// If ‘right’, return the last such index. If there is no suitable
/// index, return either 0 or N (where N is the length of a).
/// </param>
/// <param name="sorter">
/// Optional array of integer indices that sort array a into ascending
/// order. They are typically the result of argsort.
/// </param>
/// <returns>
/// Array of insertion points with the same shape as v.
/// </returns>
public static NDarray<int> searchsorted(NDarray a, NDarray v, string side = "left", NDarray sorter = null)
=> NumPy.Instance.searchsorted(a, v, side:side, sorter:sorter);
/// <summary>
/// Return the elements of an array that satisfy some condition.
///
/// This is equivalent to np.compress(ravel(condition), ravel(arr)). If
/// condition is boolean np.extract is equivalent to arr[condition].
///
/// Note that place does the exact opposite of extract.
/// </summary>
/// <param name="condition">
/// An array whose nonzero or True entries indicate the elements of arr
/// to extract.
/// </param>
/// <param name="arr">
/// Input array of the same size as condition.
/// </param>
/// <returns>
/// Rank 1 array of values from arr where condition is True.
/// </returns>
public static NDarray extract(NDarray condition, NDarray arr)
=> NumPy.Instance.extract(condition, arr);
/// <summary>
/// Counts the number of non-zero values in the array a.
///
/// The word “non-zero” is in reference to the Python 2.x
/// built-in method __nonzero__() (renamed __bool__()
/// in Python 3.x) of Python objects that tests an object’s
/// “truthfulness”. For example, any number is considered
/// truthful if it is nonzero, whereas any string is considered
/// truthful if it is not the empty string. Thus, this function
/// (recursively) counts how many elements in a (and in
/// sub-arrays thereof) have their __nonzero__() or __bool__()
/// method evaluated to True.
/// </summary>
/// <param name="a">
/// The array for which to count non-zeros.
/// </param>
/// <param name="axis">
/// Axis or tuple of axes along which to count non-zeros.
/// Default is None, meaning that non-zeros will be counted
/// along a flattened version of a.
/// </param>
/// <returns>
/// Number of non-zero values in the array along a given axis.
/// Otherwise, the total number of non-zero values in the array
/// is returned.
/// </returns>
public static NDarray<int> count_nonzero(NDarray a, int[] axis)
=> NumPy.Instance.count_nonzero(a, axis:axis);
/// <summary>
/// Counts the number of non-zero values in the array a.
///
/// The word “non-zero” is in reference to the Python 2.x
/// built-in method __nonzero__() (renamed __bool__()
/// in Python 3.x) of Python objects that tests an object’s
/// “truthfulness”. For example, any number is considered
/// truthful if it is nonzero, whereas any string is considered
/// truthful if it is not the empty string. Thus, this function
/// (recursively) counts how many elements in a (and in
/// sub-arrays thereof) have their __nonzero__() or __bool__()
/// method evaluated to True.
/// </summary>
/// <param name="a">
/// The array for which to count non-zeros.
/// </param>
/// <returns>
/// Number of non-zero values in the array along a given axis.
/// Otherwise, the total number of non-zero values in the array
/// is returned.
/// </returns>
public static int count_nonzero(NDarray a)
=> NumPy.Instance.count_nonzero(a);
}
}