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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 the minimum of an array or minimum along an axis.<br></br>
///
/// Notes
///
/// NaN values are propagated, that is if at least one item is NaN, the
/// corresponding min value will be NaN as well.<br></br>
/// To ignore NaN values
/// (MATLAB behavior), please use nanmin.<br></br>
///
/// 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.<br></br>
/// By default, flattened input is
/// used.<br></br>
///
/// 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.<br></br>
/// Must
/// be of the same shape and buffer length as the expected output.<br></br>
///
/// 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.<br></br>
/// With this option,
/// the result will broadcast correctly against the input array.<br></br>
///
/// 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.<br></br>
/// 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.<br></br>
/// Must be present to allow
/// computation on empty slice.<br></br>
/// See reduce for details.
/// </param>
/// <returns>
/// Minimum of a.<br></br>
/// If axis is None, the result is a scalar value.<br></br>
///
/// If axis is given, the result is an array of dimension
/// a.ndim - 1.
/// </returns>
public static NDarray amin(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.<br></br>
///
/// Notes
///
/// NaN values are propagated, that is if at least one item is NaN, the
/// corresponding max value will be NaN as well.<br></br>
/// To ignore NaN values
/// (MATLAB behavior), please use nanmax.<br></br>
///
/// 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.<br></br>
/// By default, flattened input is
/// used.<br></br>
///
/// 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.<br></br>
/// Must
/// be of the same shape and buffer length as the expected output.<br></br>
///
/// 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.<br></br>
/// With this option,
/// the result will broadcast correctly against the input array.<br></br>
///
/// 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.<br></br>
/// 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.<br></br>
/// Must be present to allow
/// computation on empty slice.<br></br>
/// See reduce for details.
/// </param>
/// <returns>
/// Maximum of a.<br></br>
/// If axis is None, the result is a scalar value.<br></br>
///
/// If axis is given, the result is an array of dimension
/// a.ndim - 1.
/// </returns>
public static NDarray amax(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);
/// <summary>
/// Return minimum of an array or minimum along an axis, ignoring any NaNs.<br></br>
///
/// When all-NaN slices are encountered a RuntimeWarning is raised and
/// Nan is returned for that slice.<br></br>
///
/// Notes
///
/// NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
/// (IEEE 754).<br></br>
/// This means that Not a Number is not equivalent to infinity.<br></br>
///
/// Positive infinity is treated as a very large number and negative
/// infinity is treated as a very small (i.e.<br></br>
/// negative) number.<br></br>
///
/// If the input has a integer type the function is equivalent to np.min.
/// </summary>
/// <param name="a">
/// Array containing numbers whose minimum is desired.<br></br>
/// If a is not an
/// array, a conversion is attempted.
/// </param>
/// <param name="axis">
/// Axis or axes along which the minimum is computed.<br></br>
/// The default is to compute
/// the minimum of the flattened array.
/// </param>
/// <param name="out">
/// Alternate output array in which to place the result.<br></br>
/// The default
/// is None; if provided, it must have the same shape as the
/// expected output, but the type will be cast if necessary.<br></br>
/// See
/// doc.ufuncs for 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.<br></br>
/// With this option,
/// the result will broadcast correctly against the original a.<br></br>
///
/// If the value is anything but the default, then
/// keepdims will be passed through to the min method
/// of sub-classes of ndarray.<br></br>
/// If the sub-classes methods
/// does not implement keepdims any exceptions will be raised.
/// </param>
/// <returns>
/// An array with the same shape as a, with the specified axis
/// removed.<br></br>
/// If a is a 0-d array, or if axis is None, an ndarray
/// scalar is returned.<br></br>
/// The same dtype as a is returned.
/// </returns>
public static NDarray nanmin(NDarray a, int[] axis = null, NDarray @out = null, bool? keepdims = null)
=> NumPy.Instance.nanmin(a, axis:axis, @out:@out, keepdims:keepdims);
/// <summary>
/// Return the maximum of an array or maximum along an axis, ignoring any
/// NaNs.<br></br>
/// When all-NaN slices are encountered a RuntimeWarning is
/// raised and NaN is returned for that slice.<br></br>
///
/// Notes
///
/// NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
/// (IEEE 754).<br></br>
/// This means that Not a Number is not equivalent to infinity.<br></br>
///
/// Positive infinity is treated as a very large number and negative
/// infinity is treated as a very small (i.e.<br></br>
/// negative) number.<br></br>
///
/// If the input has a integer type the function is equivalent to np.max.
/// </summary>
/// <param name="a">
/// Array containing numbers whose maximum is desired.<br></br>
/// If a is not an
/// array, a conversion is attempted.
/// </param>
/// <param name="axis">
/// Axis or axes along which the maximum is computed.<br></br>
/// The default is to compute
/// the maximum of the flattened array.
/// </param>
/// <param name="out">
/// Alternate output array in which to place the result.<br></br>
/// The default
/// is None; if provided, it must have the same shape as the
/// expected output, but the type will be cast if necessary.<br></br>
/// See
/// doc.ufuncs for 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.<br></br>
/// With this option,
/// the result will broadcast correctly against the original a.<br></br>
///
/// If the value is anything but the default, then
/// keepdims will be passed through to the max method
/// of sub-classes of ndarray.<br></br>
/// If the sub-classes methods
/// does not implement keepdims any exceptions will be raised.
/// </param>
/// <returns>
/// An array with the same shape as a, with the specified axis removed.<br></br>
///
/// If a is a 0-d array, or if axis is None, an ndarray scalar is
/// returned.<br></br>
/// The same dtype as a is returned.
/// </returns>
public static NDarray nanmax(NDarray a, int[] axis = null, NDarray @out = null, bool? keepdims = null)
=> NumPy.Instance.nanmax(a, axis:axis, @out:@out, keepdims:keepdims);
/// <summary>
/// Range of values (maximum - minimum) along an axis.<br></br>
///
/// The name of the function comes from the acronym for ‘peak to peak’.
/// </summary>
/// <param name="a">
/// Input values.
/// </param>
/// <param name="axis">
/// Axis along which to find the peaks.<br></br>
/// By default, flatten the
/// array.<br></br>
/// axis may be negative, in
/// which case it counts from the last to the first axis.<br></br>
///
/// If this is a tuple of ints, a reduction is performed on 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.<br></br>
/// It must
/// have the same shape and buffer length as the expected output,
/// but the type of the output values will be cast if necessary.
/// </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.<br></br>
/// With this option,
/// the result will broadcast correctly against the input array.<br></br>
///
/// If the default value is passed, then keepdims will not be
/// passed through to the ptp method of sub-classes of
/// ndarray, however any non-default value will be.<br></br>
/// If the
/// sub-class’ method does not implement keepdims any
/// exceptions will be raised.
/// </param>
/// <returns>
/// A new array holding the result, unless out was
/// specified, in which case a reference to out is returned.
/// </returns>
public static NDarray ptp(NDarray a, int[] axis = null, NDarray @out = null, bool? keepdims = null)
=> NumPy.Instance.ptp(a, axis:axis, @out:@out, keepdims:keepdims);
/// <summary>
/// Compute the q-th percentile of the data along the specified axis.<br></br>
///
/// Returns the q-th percentile(s) of the array elements.<br></br>
///
/// Notes
///
/// Given a vector V of length N, the q-th percentile of
/// V is the value q/100 of the way from the minimum to the
/// maximum in a sorted copy of V.<br></br>
/// The values and distances of
/// the two nearest neighbors as well as the interpolation parameter
/// will determine the percentile if the normalized ranking does not
/// match the location of q exactly.<br></br>
/// This function is the same as
/// the median if q=50, the same as the minimum if q=0 and the
/// same as the maximum if q=100.
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array.
/// </param>
/// <param name="q">
/// Percentile or sequence of percentiles to compute, which must be between
/// 0 and 100 inclusive.
/// </param>
/// <param name="axis">
/// Axis or axes along which the percentiles are computed.<br></br>
/// The
/// default is to compute the percentile(s) along a flattened
/// version of the array.
/// </param>
/// <param name="out">
/// Alternative output array in which to place the result.<br></br>
/// It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow the input array a to be modified by intermediate
/// calculations, to save memory.<br></br>
/// In this case, the contents of the input
/// a after this function completes is undefined.
/// </param>
/// <param name="interpolation">
/// This optional parameter specifies the interpolation method to
/// use when the desired percentile lies between two data points
/// i < j:
/// </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.<br></br>
/// With this option, the
/// result will broadcast correctly against the original array a.
/// </param>
/// <returns>
/// If q is a single percentile and axis=None, then the result
/// is a scalar.<br></br>
/// If multiple percentiles are given, first axis of
/// the result corresponds to the percentiles.<br></br>
/// The other axes are
/// the axes that remain after the reduction of a.<br></br>
/// If the input
/// contains integers or floats smaller than float64, the output
/// data-type is float64. Otherwise, the output data-type is the
/// same as that of the input.<br></br>
/// If out is specified, that array is
/// returned instead.
/// </returns>
public static NDarray<double> percentile(NDarray a, NDarray<float> q, int[] axis, NDarray @out = null, bool? overwrite_input = false, string interpolation = "linear", bool? keepdims = false)
=> NumPy.Instance.percentile(a, q, axis:axis, @out:@out, overwrite_input:overwrite_input, interpolation:interpolation, keepdims:keepdims);
/// <summary>
/// Compute the q-th percentile of the data along the specified axis.<br></br>
///
/// Returns the q-th percentile(s) of the array elements.<br></br>
///
/// Notes
///
/// Given a vector V of length N, the q-th percentile of
/// V is the value q/100 of the way from the minimum to the
/// maximum in a sorted copy of V.<br></br>
/// The values and distances of
/// the two nearest neighbors as well as the interpolation parameter
/// will determine the percentile if the normalized ranking does not
/// match the location of q exactly.<br></br>
/// This function is the same as
/// the median if q=50, the same as the minimum if q=0 and the
/// same as the maximum if q=100.
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array.
/// </param>
/// <param name="q">
/// Percentile or sequence of percentiles to compute, which must be between
/// 0 and 100 inclusive.
/// </param>
/// <param name="out">
/// Alternative output array in which to place the result.<br></br>
/// It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow the input array a to be modified by intermediate
/// calculations, to save memory.<br></br>
/// In this case, the contents of the input
/// a after this function completes is undefined.
/// </param>
/// <param name="interpolation">
/// This optional parameter specifies the interpolation method to
/// use when the desired percentile lies between two data points
/// i < j:
/// </param>
/// <returns>
/// If q is a single percentile and axis=None, then the result
/// is a scalar.<br></br>
/// If multiple percentiles are given, first axis of
/// the result corresponds to the percentiles.<br></br>
/// The other axes are
/// the axes that remain after the reduction of a.<br></br>
/// If the input
/// contains integers or floats smaller than float64, the output
/// data-type is float64. Otherwise, the output data-type is the
/// same as that of the input.<br></br>
/// If out is specified, that array is
/// returned instead.
/// </returns>
public static double percentile(NDarray a, NDarray<float> q, NDarray @out = null, bool? overwrite_input = false, string interpolation = "linear")
=> NumPy.Instance.percentile(a, q, @out:@out, overwrite_input:overwrite_input, interpolation:interpolation);
/// <summary>
/// Compute the qth percentile of the data along the specified axis,
/// while ignoring nan values.<br></br>
///
/// Returns the qth percentile(s) of the array elements.<br></br>
///
/// Notes
///
/// Given a vector V of length N, the q-th percentile of
/// V is the value q/100 of the way from the minimum to the
/// maximum in a sorted copy of V.<br></br>
/// The values and distances of
/// the two nearest neighbors as well as the interpolation parameter
/// will determine the percentile if the normalized ranking does not
/// match the location of q exactly.<br></br>
/// This function is the same as
/// the median if q=50, the same as the minimum if q=0 and the
/// same as the maximum if q=100.
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array, containing
/// nan values to be ignored.
/// </param>
/// <param name="q">
/// Percentile or sequence of percentiles to compute, which must be between
/// 0 and 100 inclusive.
/// </param>
/// <param name="axis">
/// Axis or axes along which the percentiles are computed.<br></br>
/// The
/// default is to compute the percentile(s) along a flattened
/// version of the array.
/// </param>
/// <param name="out">
/// Alternative output array in which to place the result.<br></br>
/// It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow the input array a to be modified by intermediate
/// calculations, to save memory.<br></br>
/// In this case, the contents of the input
/// a after this function completes is undefined.
/// </param>
/// <param name="interpolation">
/// This optional parameter specifies the interpolation method to
/// use when the desired percentile lies between two data points
/// i < j:
/// </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.<br></br>
/// With this option, the
/// result will broadcast correctly against the original array a.<br></br>
///
/// If this is anything but the default value it will be passed
/// through (in the special case of an empty array) to the
/// mean function of the underlying array.<br></br>
/// If the array is
/// a sub-class and mean does not have the kwarg keepdims this
/// will raise a RuntimeError.
/// </param>
/// <returns>
/// If q is a single percentile and axis=None, then the result
/// is a scalar.<br></br>
/// If multiple percentiles are given, first axis of
/// the result corresponds to the percentiles.<br></br>
/// The other axes are
/// the axes that remain after the reduction of a.<br></br>
/// If the input
/// contains integers or floats smaller than float64, the output
/// data-type is float64. Otherwise, the output data-type is the
/// same as that of the input.<br></br>
/// If out is specified, that array is
/// returned instead.
/// </returns>
public static NDarray<double> nanpercentile(NDarray a, NDarray<float> q, int[] axis, NDarray @out = null, bool? overwrite_input = false, string interpolation = "linear", bool? keepdims = null)
=> NumPy.Instance.nanpercentile(a, q, axis:axis, @out:@out, overwrite_input:overwrite_input, interpolation:interpolation, keepdims:keepdims);
/// <summary>
/// Compute the qth percentile of the data along the specified axis,
/// while ignoring nan values.<br></br>
///
/// Returns the qth percentile(s) of the array elements.<br></br>
///
/// Notes
///
/// Given a vector V of length N, the q-th percentile of
/// V is the value q/100 of the way from the minimum to the
/// maximum in a sorted copy of V.<br></br>
/// The values and distances of
/// the two nearest neighbors as well as the interpolation parameter
/// will determine the percentile if the normalized ranking does not
/// match the location of q exactly.<br></br>
/// This function is the same as
/// the median if q=50, the same as the minimum if q=0 and the
/// same as the maximum if q=100.
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array, containing
/// nan values to be ignored.
/// </param>
/// <param name="q">
/// Percentile or sequence of percentiles to compute, which must be between
/// 0 and 100 inclusive.
/// </param>
/// <param name="out">
/// Alternative output array in which to place the result.<br></br>
/// It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow the input array a to be modified by intermediate
/// calculations, to save memory.<br></br>
/// In this case, the contents of the input
/// a after this function completes is undefined.
/// </param>
/// <param name="interpolation">
/// This optional parameter specifies the interpolation method to
/// use when the desired percentile lies between two data points
/// i < j:
/// </param>
/// <returns>
/// If q is a single percentile and axis=None, then the result
/// is a scalar.<br></br>
/// If multiple percentiles are given, first axis of
/// the result corresponds to the percentiles.<br></br>
/// The other axes are
/// the axes that remain after the reduction of a.<br></br>
/// If the input
/// contains integers or floats smaller than float64, the output
/// data-type is float64. Otherwise, the output data-type is the
/// same as that of the input.<br></br>
/// If out is specified, that array is
/// returned instead.
/// </returns>
public static double nanpercentile(NDarray a, NDarray<float> q, NDarray @out = null, bool? overwrite_input = false, string interpolation = "linear")
=> NumPy.Instance.nanpercentile(a, q, @out:@out, overwrite_input:overwrite_input, interpolation:interpolation);
/// <summary>
/// Compute the q-th quantile of the data along the specified axis.<br></br>
///
/// ..versionadded:: 1.15.0
///
/// Notes
///
/// Given a vector V of length N, the q-th quantile of
/// V is the value q of the way from the minimum to the
/// maximum in a sorted copy of V.<br></br>
/// The values and distances of
/// the two nearest neighbors as well as the interpolation parameter
/// will determine the quantile if the normalized ranking does not
/// match the location of q exactly.<br></br>
/// This function is the same as
/// the median if q=0.5, the same as the minimum if q=0.0 and the
/// same as the maximum if q=1.0.
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array.
/// </param>
/// <param name="q">
/// Quantile or sequence of quantiles to compute, which must be between
/// 0 and 1 inclusive.
/// </param>
/// <param name="axis">
/// Axis or axes along which the quantiles are computed.<br></br>
/// The
/// default is to compute the quantile(s) along a flattened
/// version of the array.
/// </param>
/// <param name="out">
/// Alternative output array in which to place the result.<br></br>
/// It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow the input array a to be modified by intermediate
/// calculations, to save memory.<br></br>
/// In this case, the contents of the input
/// a after this function completes is undefined.
/// </param>
/// <param name="interpolation">
/// This optional parameter specifies the interpolation method to
/// use when the desired quantile lies between two data points
/// i < j:
/// </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.<br></br>
/// With this option, the
/// result will broadcast correctly against the original array a.
/// </param>
/// <returns>
/// If q is a single quantile and axis=None, then the result
/// is a scalar.<br></br>
/// If multiple quantiles are given, first axis of
/// the result corresponds to the quantiles.<br></br>
/// The other axes are
/// the axes that remain after the reduction of a.<br></br>
/// If the input
/// contains integers or floats smaller than float64, the output
/// data-type is float64. Otherwise, the output data-type is the
/// same as that of the input.<br></br>
/// If out is specified, that array is
/// returned instead.
/// </returns>
public static NDarray<double> quantile(NDarray a, NDarray<float> q, int[] axis, NDarray @out = null, bool? overwrite_input = false, string interpolation = "linear", bool? keepdims = false)
=> NumPy.Instance.quantile(a, q, axis:axis, @out:@out, overwrite_input:overwrite_input, interpolation:interpolation, keepdims:keepdims);
/// <summary>
/// Compute the q-th quantile of the data along the specified axis.<br></br>
///
/// ..versionadded:: 1.15.0
///
/// Notes
///
/// Given a vector V of length N, the q-th quantile of
/// V is the value q of the way from the minimum to the
/// maximum in a sorted copy of V.<br></br>
/// The values and distances of
/// the two nearest neighbors as well as the interpolation parameter
/// will determine the quantile if the normalized ranking does not
/// match the location of q exactly.<br></br>
/// This function is the same as
/// the median if q=0.5, the same as the minimum if q=0.0 and the
/// same as the maximum if q=1.0.
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array.
/// </param>
/// <param name="q">
/// Quantile or sequence of quantiles to compute, which must be between
/// 0 and 1 inclusive.
/// </param>
/// <param name="out">
/// Alternative output array in which to place the result.<br></br>
/// It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow the input array a to be modified by intermediate
/// calculations, to save memory.<br></br>
/// In this case, the contents of the input
/// a after this function completes is undefined.
/// </param>
/// <param name="interpolation">
/// This optional parameter specifies the interpolation method to
/// use when the desired quantile lies between two data points
/// i < j:
/// </param>
/// <returns>
/// If q is a single quantile and axis=None, then the result
/// is a scalar.<br></br>
/// If multiple quantiles are given, first axis of
/// the result corresponds to the quantiles.<br></br>
/// The other axes are
/// the axes that remain after the reduction of a.<br></br>
/// If the input
/// contains integers or floats smaller than float64, the output
/// data-type is float64. Otherwise, the output data-type is the
/// same as that of the input.<br></br>
/// If out is specified, that array is
/// returned instead.
/// </returns>
public static double quantile(NDarray a, NDarray<float> q, NDarray @out = null, bool? overwrite_input = false, string interpolation = "linear")
=> NumPy.Instance.quantile(a, q, @out:@out, overwrite_input:overwrite_input, interpolation:interpolation);
/// <summary>
/// Compute the qth quantile of the data along the specified axis,
/// while ignoring nan values.<br></br>
///
/// Returns the qth quantile(s) of the array elements.<br></br>
///
/// .. versionadded:: 1.15.0
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array, containing
/// nan values to be ignored
/// </param>
/// <param name="q">
/// Quantile or sequence of quantiles to compute, which must be between
/// 0 and 1 inclusive.
/// </param>
/// <param name="axis">
/// Axis or axes along which the quantiles are computed.<br></br>
/// The
/// default is to compute the quantile(s) along a flattened
/// version of the array.
/// </param>
/// <param name="out">
/// Alternative output array in which to place the result.<br></br>
/// It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow the input array a to be modified by intermediate
/// calculations, to save memory.<br></br>
/// In this case, the contents of the input
/// a after this function completes is undefined.
/// </param>
/// <param name="interpolation">
/// This optional parameter specifies the interpolation method to
/// use when the desired quantile lies between two data points
/// i < j:
/// </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.<br></br>
/// With this option, the
/// result will broadcast correctly against the original array a.<br></br>
///
/// If this is anything but the default value it will be passed
/// through (in the special case of an empty array) to the
/// mean function of the underlying array.<br></br>
/// If the array is
/// a sub-class and mean does not have the kwarg keepdims this
/// will raise a RuntimeError.
/// </param>
/// <returns>
/// If q is a single percentile and axis=None, then the result
/// is a scalar.<br></br>
/// If multiple quantiles are given, first axis of
/// the result corresponds to the quantiles.<br></br>
/// The other axes are
/// the axes that remain after the reduction of a.<br></br>
/// If the input
/// contains integers or floats smaller than float64, the output
/// data-type is float64. Otherwise, the output data-type is the
/// same as that of the input.<br></br>
/// If out is specified, that array is
/// returned instead.
/// </returns>
public static NDarray<double> nanquantile(NDarray a, NDarray<float> q, int[] axis, NDarray @out = null, bool? overwrite_input = false, string interpolation = "linear", bool? keepdims = null)
=> NumPy.Instance.nanquantile(a, q, axis:axis, @out:@out, overwrite_input:overwrite_input, interpolation:interpolation, keepdims:keepdims);
/// <summary>
/// Compute the qth quantile of the data along the specified axis,
/// while ignoring nan values.<br></br>
///
/// Returns the qth quantile(s) of the array elements.<br></br>
///
/// .. versionadded:: 1.15.0
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array, containing
/// nan values to be ignored
/// </param>
/// <param name="q">
/// Quantile or sequence of quantiles to compute, which must be between
/// 0 and 1 inclusive.
/// </param>
/// <param name="out">
/// Alternative output array in which to place the result.<br></br>
/// It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow the input array a to be modified by intermediate
/// calculations, to save memory.<br></br>
/// In this case, the contents of the input
/// a after this function completes is undefined.
/// </param>
/// <param name="interpolation">
/// This optional parameter specifies the interpolation method to
/// use when the desired quantile lies between two data points
/// i < j:
/// </param>
/// <returns>
/// If q is a single percentile and axis=None, then the result
/// is a scalar.<br></br>
/// If multiple quantiles are given, first axis of
/// the result corresponds to the quantiles.<br></br>
/// The other axes are
/// the axes that remain after the reduction of a.<br></br>
/// If the input
/// contains integers or floats smaller than float64, the output
/// data-type is float64. Otherwise, the output data-type is the
/// same as that of the input.<br></br>
/// If out is specified, that array is
/// returned instead.
/// </returns>
public static double nanquantile(NDarray a, NDarray<float> q, NDarray @out = null, bool? overwrite_input = false, string interpolation = "linear")
=> NumPy.Instance.nanquantile(a, q, @out:@out, overwrite_input:overwrite_input, interpolation:interpolation);
/// <summary>
/// Compute the median along the specified axis.<br></br>
///
/// Returns the median of the array elements.<br></br>
///
/// Notes
///
/// Given a vector V of length N, the median of V is the
/// middle value of a sorted copy of V, V_sorted - i
/// e., V_sorted[(N-1)/2], when N is odd, and the average of the
/// two middle values of V_sorted when N is even.
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array.
/// </param>
/// <param name="axis">
/// Axis or axes along which the medians are computed.<br></br>
/// The default
/// is to compute the median along a flattened version of the array.<br></br>
///
/// A sequence of axes is supported since version 1.9.0.
/// </param>
/// <param name="out">
/// Alternative output array in which to place the result.<br></br>
/// It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow use of memory of input array a for
/// calculations.<br></br>
/// The input array will be modified by the call to
/// median.<br></br>
/// This will save memory when you do not need to preserve
/// the contents of the input array.<br></br>
/// Treat the input as undefined,
/// but it will probably be fully or partially sorted.<br></br>
/// Default is
/// False.<br></br>
/// If overwrite_input is True and a is not already an
/// ndarray, an error will be raised.
/// </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.<br></br>
/// With this option,
/// the result will broadcast correctly against the original arr.
/// </param>
/// <returns>
/// A new array holding the result.<br></br>
/// If the input contains integers
/// or floats smaller than float64, then the output data-type is
/// np.float64. Otherwise, the data-type of the output is the
/// same as that of the input.<br></br>
/// If out is specified, that array is
/// returned instead.
/// </returns>
public static NDarray<double> median(NDarray a, int[] axis, NDarray @out = null, bool? overwrite_input = false, bool? keepdims = false)
=> NumPy.Instance.median(a, axis:axis, @out:@out, overwrite_input:overwrite_input, keepdims:keepdims);
/// <summary>
/// Compute the median along the specified axis.<br></br>
///
/// Returns the median of the array elements.<br></br>
///
/// Notes
///
/// Given a vector V of length N, the median of V is the
/// middle value of a sorted copy of V, V_sorted - i
/// e., V_sorted[(N-1)/2], when N is odd, and the average of the
/// two middle values of V_sorted when N is even.
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array.
/// </param>
/// <param name="out">
/// Alternative output array in which to place the result.<br></br>
/// It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow use of memory of input array a for
/// calculations.<br></br>
/// The input array will be modified by the call to
/// median.<br></br>
/// This will save memory when you do not need to preserve
/// the contents of the input array.<br></br>
/// Treat the input as undefined,
/// but it will probably be fully or partially sorted.<br></br>
/// Default is
/// False.<br></br>
/// If overwrite_input is True and a is not already an
/// ndarray, an error will be raised.
/// </param>
/// <returns>
/// A new array holding the result.<br></br>
/// If the input contains integers
/// or floats smaller than float64, then the output data-type is
/// np.float64. Otherwise, the data-type of the output is the
/// same as that of the input.<br></br>
/// If out is specified, that array is
/// returned instead.
/// </returns>
public static double median(NDarray a, NDarray @out = null, bool? overwrite_input = false)
=> NumPy.Instance.median(a, @out:@out, overwrite_input:overwrite_input);
/// <summary>
/// Compute the weighted average along the specified axis.
/// </summary>
/// <param name="a">
/// Array containing data to be averaged.<br></br>
/// If a is not an array, a
/// conversion is attempted.
/// </param>
/// <param name="axis">
/// Axis or axes along which to average a.<br></br>
/// The default,
/// axis=None, will average over all of the elements of the input array.<br></br>
///
/// If axis is negative it counts from the last to the first axis.<br></br>
///
/// If axis is a tuple of ints, averaging is performed on all of the axes
/// specified in the tuple instead of a single axis or all the axes as
/// before.
/// </param>
/// <param name="weights">
/// An array of weights associated with the values in a.<br></br>
/// Each value in
/// a contributes to the average according to its associated weight.<br></br>
///
/// The weights array can either be 1-D (in which case its length must be
/// the size of a along the given axis) or of the same shape as a.<br></br>
///
/// If weights=None, then all data in a are assumed to have a
/// weight equal to one.
/// </param>
/// <param name="returned">
/// Default is False.<br></br>
/// If True, the tuple (average, sum_of_weights)
/// is returned, otherwise only the average is returned.<br></br>
///
/// If weights=None, sum_of_weights is equivalent to the number of
/// elements over which the average is taken.
/// </param>
/// <returns>
/// Return the average along the specified axis.<br></br>
/// When returned is True,
/// return a tuple with the average as the first element and the sum
/// of the weights as the second element.<br></br>
/// sum_of_weights is of the
/// same type as retval.<br></br>
/// The result dtype follows a genereal pattern.<br></br>
///
/// If weights is None, the result dtype will be that of a , or float64
/// if a is integral.<br></br>
/// Otherwise, if weights is not None and a is non-
/// integral, the result type will be the type of lowest precision capable of
/// representing values of both a and weights.<br></br>
/// If a happens to be
/// integral, the previous rules still applies but the result dtype will
/// at least be float64.
/// </returns>
public static NDarray<double> average(NDarray a, int[] axis, NDarray weights = null, bool? returned = false)
=> NumPy.Instance.average(a, axis:axis, weights:weights, returned:returned);
/// <summary>
/// Compute the weighted average along the specified axis.
/// </summary>
/// <param name="a">
/// Array containing data to be averaged.<br></br>
/// If a is not an array, a
/// conversion is attempted.
/// </param>
/// <param name="weights">
/// An array of weights associated with the values in a.<br></br>
/// Each value in
/// a contributes to the average according to its associated weight.<br></br>
///
/// The weights array can either be 1-D (in which case its length must be