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using System;
using System.Collections.Generic;
using System.Text;
using Numpy.Models;
namespace Numpy
{
public static partial class np
{
/// <summary>
/// Calculate the absolute value element-wise.
///
/// np.abs is a shorthand for this function.
/// </summary>
/// <param name="x">
/// Input array.
/// </param>
/// <param name="@out">
/// A location into which the result is stored. If provided, it must have
/// a shape that the inputs broadcast to. If not provided or None,
/// a freshly-allocated array is returned. A tuple (possible only as a
/// keyword argument) must have length equal to the number of outputs.
/// </param>
/// <param name="@where">
/// Values of True indicate to calculate the ufunc at that position, values
/// of False indicate to leave the value in the output alone.
/// </param>
/// <returns>
/// An ndarray containing the absolute value of
/// each element in x. For complex input, a + ib, the
/// absolute value is .
/// This is a scalar if x is a scalar.
/// </returns>
public static NDarray abs(NDarray x, NDarray @out = null, NDarray @where = null)
=> NumPy.Instance.absolute(x, @out, @where);
/// <summary>
/// Return the minimum of an array or minimum along an axis.
///
/// Notes
///
/// NaN values are propagated, that is if at least one item is NaN, the
/// corresponding min value will be NaN as well. To ignore NaN values
/// (MATLAB behavior), please use nanmin.
///
/// Don’t use amin for element-wise comparison of 2 arrays; when
/// a.shape[0] is 2, minimum(a[0], a[1]) is faster than
/// amin(a, axis=0).
/// </summary>
/// <param name="a">
/// Input data.
/// </param>
/// <param name="axis">
/// Axis or axes along which to operate. By default, flattened input is
/// used.
///
/// If this is a tuple of ints, the minimum is selected over multiple axes,
/// instead of a single axis or all the axes as before.
/// </param>
/// <param name="@out">
/// Alternative output array in which to place the result. Must
/// be of the same shape and buffer length as the expected output.
/// See doc.ufuncs (Section “Output arguments”) for more details.
/// </param>
/// <param name="keepdims">
/// If this is set to True, the axes which are reduced are left
/// in the result as dimensions with size one. With this option,
/// the result will broadcast correctly against the input array.
///
/// If the default value is passed, then keepdims will not be
/// passed through to the amin method of sub-classes of
/// ndarray, however any non-default value will be. If the
/// sub-class’ method does not implement keepdims any
/// exceptions will be raised.
/// </param>
/// <param name="initial">
/// The maximum value of an output element. Must be present to allow
/// computation on empty slice. See reduce for details.
/// </param>
/// <returns>
/// Minimum of a. If axis is None, the result is a scalar value.
/// If axis is given, the result is an array of dimension
/// a.ndim - 1.
/// </returns>
public static NDarray min(NDarray a, int[] axis = null, NDarray @out = null, bool? keepdims = null, ValueType initial = null)
=> NumPy.Instance.amin(a, axis: axis, @out: @out, keepdims: keepdims, initial: initial);
/// <summary>
/// Return the maximum of an array or maximum along an axis.
///
/// Notes
///
/// NaN values are propagated, that is if at least one item is NaN, the
/// corresponding max value will be NaN as well. To ignore NaN values
/// (MATLAB behavior), please use nanmax.
///
/// Don’t use amax for element-wise comparison of 2 arrays; when
/// a.shape[0] is 2, maximum(a[0], a[1]) is faster than
/// amax(a, axis=0).
/// </summary>
/// <param name="a">
/// Input data.
/// </param>
/// <param name="axis">
/// Axis or axes along which to operate. By default, flattened input is
/// used.
///
/// If this is a tuple of ints, the maximum is selected over multiple axes,
/// instead of a single axis or all the axes as before.
/// </param>
/// <param name="@out">
/// Alternative output array in which to place the result. Must
/// be of the same shape and buffer length as the expected output.
/// See doc.ufuncs (Section “Output arguments”) for more details.
/// </param>
/// <param name="keepdims">
/// If this is set to True, the axes which are reduced are left
/// in the result as dimensions with size one. With this option,
/// the result will broadcast correctly against the input array.
///
/// If the default value is passed, then keepdims will not be
/// passed through to the amax method of sub-classes of
/// ndarray, however any non-default value will be. If the
/// sub-class’ method does not implement keepdims any
/// exceptions will be raised.
/// </param>
/// <param name="initial">
/// The minimum value of an output element. Must be present to allow
/// computation on empty slice. See reduce for details.
/// </param>
/// <returns>
/// Maximum of a. If axis is None, the result is a scalar value.
/// If axis is given, the result is an array of dimension
/// a.ndim - 1.
/// </returns>
public static NDarray max(NDarray a, int[] axis = null, NDarray @out = null, bool? keepdims = null, ValueType initial = null)
=> NumPy.Instance.amax(a, axis: axis, @out: @out, keepdims: keepdims, initial: initial);
}
}