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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>
/// Translates slice objects to concatenation along the first axis.<br></br>
///
/// This is a simple way to build up arrays quickly.<br></br>
/// There are two use cases.<br></br>
///
/// If slice notation is used, the syntax start:stop:step is equivalent
/// to np.arange(start, stop, step) inside of the brackets.<br></br>
/// However, if
/// step is an imaginary number (i.e.<br></br>
/// 100j) then its integer portion is
/// interpreted as a number-of-points desired and the start and stop are
/// inclusive.<br></br>
/// In other words start:stop:stepj is interpreted as
/// np.linspace(start, stop, step, endpoint=1) inside of the brackets.<br></br>
///
/// After expansion of slice notation, all comma separated sequences are
/// concatenated together.<br></br>
///
/// Optional character strings placed as the first element of the index
/// expression can be used to change the output.<br></br>
/// The strings ‘r’ or ‘c’ result
/// in matrix output.<br></br>
/// If the result is 1-D and ‘r’ is specified a 1 x N (row)
/// matrix is produced.<br></br>
/// If the result is 1-D and ‘c’ is specified, then a N x 1
/// (column) matrix is produced.<br></br>
/// If the result is 2-D then both provide the
/// same matrix result.<br></br>
///
/// A string integer specifies which axis to stack multiple comma separated
/// arrays along.<br></br>
/// A string of two comma-separated integers allows indication
/// of the minimum number of dimensions to force each entry into as the
/// second integer (the axis to concatenate along is still the first integer).<br></br>
///
/// A string with three comma-separated integers allows specification of the
/// axis to concatenate along, the minimum number of dimensions to force the
/// entries to, and which axis should contain the start of the arrays which
/// are less than the specified number of dimensions.<br></br>
/// In other words the third
/// integer allows you to specify where the 1’s should be placed in the shape
/// of the arrays that have their shapes upgraded.<br></br>
/// By default, they are placed
/// in the front of the shape tuple.<br></br>
/// The third argument allows you to specify
/// where the start of the array should be instead.<br></br>
/// Thus, a third argument of
/// ‘0’ would place the 1’s at the end of the array shape.<br></br>
/// Negative integers
/// specify where in the new shape tuple the last dimension of upgraded arrays
/// should be placed, so the default is ‘-1’.
/// </summary>
public static void r_()
=> NumPy.Instance.r_();
/// <summary>
/// A nicer way to build up index tuples for arrays.<br></br>
///
/// For any index combination, including slicing and axis insertion,
/// a[indices] is the same as a[np.index_exp[indices]] for any
/// array a.<br></br>
/// However, np.index_exp[indices] can be used anywhere
/// in Python code and returns a tuple of slice objects that can be
/// used in the construction of complex index expressions.<br></br>
///
/// Notes
///
/// You can do all this with slice() plus a few special objects,
/// but there’s a lot to remember and this version is simpler because
/// it uses the standard array indexing syntax.
/// </summary>
/// <param name="maketuple">
/// If True, always returns a tuple.
/// </param>
public static void s_(bool maketuple)
=> NumPy.Instance.s_(maketuple);
/// <summary>
/// Return the indices of the elements that are non-zero.<br></br>
///
/// Returns a tuple of arrays, one for each dimension of a,
/// containing the indices of the non-zero elements in that
/// dimension.<br></br>
/// The values in a are always tested and returned in
/// row-major, C-style order.<br></br>
/// The corresponding non-zero
/// values can be obtained with:
///
/// To group the indices by element, rather than dimension, use:
///
/// The result of this is always a 2-D array, with a row for
/// each non-zero element.
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <returns>
/// Indices of elements that are non-zero.
/// </returns>
public static NDarray[] nonzero(NDarray a)
=> NumPy.Instance.nonzero(a);
/// <summary>
/// Return elements chosen from x or y depending on condition.<br></br>
///
/// Notes
///
/// If all the arrays are 1-D, where is equivalent to:
/// </summary>
/// <param name="condition">
/// Where True, yield x, otherwise yield y.
/// </param>
/// <param name="y">
/// Values from which to choose.<br></br>
/// x, y and condition need to be
/// broadcastable to some shape.
/// </param>
/// <param name="x">
/// Values from which to choose.<br></br>
/// x, y and condition need to be
/// broadcastable to some shape.
/// </param>
/// <returns>
/// An array with elements from x where condition is True, and elements
/// from y elsewhere.
/// </returns>
public static NDarray @where(NDarray condition, NDarray y, NDarray x)
=> NumPy.Instance.@where(condition, y, x);
/// <summary>
/// Return an array representing the indices of a grid.<br></br>
///
/// Compute an array where the subarrays contain index values 0,1,…
/// varying only along the corresponding axis.<br></br>
///
/// Notes
///
/// The output shape is obtained by prepending the number of dimensions
/// in front of the tuple of dimensions, i.e.<br></br>
/// if dimensions is a tuple
/// (r0, ..., rN-1) of length N, the output shape is
/// (N,r0,...,rN-1).<br></br>
///
/// The subarrays grid[k] contains the N-D array of indices along the
/// k-th axis.<br></br>
/// Explicitly:
/// </summary>
/// <param name="dimensions">
/// The shape of the grid.
/// </param>
/// <param name="dtype">
/// Data type of the result.
/// </param>
/// <returns>
/// The array of grid indices,
/// grid.shape = (len(dimensions),) + tuple(dimensions).
/// </returns>
public static NDarray indices(int[] dimensions, Dtype dtype = null)
=> NumPy.Instance.indices(dimensions, dtype:dtype);
/// <summary>
/// Construct an open mesh from multiple sequences.<br></br>
///
/// This function takes N 1-D sequences and returns N outputs with N
/// dimensions each, such that the shape is 1 in all but one dimension
/// and the dimension with the non-unit shape value cycles through all
/// N dimensions.<br></br>
///
/// Using ix_ one can quickly construct index arrays that will index
/// the cross product.<br></br>
/// a[np.ix_([1,3],[2,5])] returns the array
/// [[a[1,2] a[1,5]], [a[3,2] a[3,5]]].
/// </summary>
/// <param name="args">
/// Each sequence should be of integer or boolean type.<br></br>
///
/// Boolean sequences will be interpreted as boolean masks for the
/// corresponding dimension (equivalent to passing in
/// np.nonzero(boolean_sequence)).
/// </param>
/// <returns>
/// N arrays with N dimensions each, with N the number of input
/// sequences.<br></br>
/// Together these arrays form an open mesh.
/// </returns>
public static NDarray[] ix_(params NDarray[] args)
=> NumPy.Instance.ix_(args);
/*
/// <summary>
/// Converts a tuple of index arrays into an array of flat
/// indices, applying boundary modes to the multi-index.<br></br>
///
/// Notes
/// </summary>
/// <param name="multi_index">
/// A tuple of integer arrays, one array for each dimension.
/// </param>
/// <param name="dims">
/// The shape of array into which the indices from multi_index apply.
/// </param>
/// <param name="mode">
/// Specifies how out-of-bounds indices are handled.<br></br>
/// Can specify
/// either one mode or a tuple of modes, one mode per index.<br></br>
///
/// In ‘clip’ mode, a negative index which would normally
/// wrap will clip to 0 instead.
/// </param>
/// <param name="order">
/// Determines whether the multi-index should be viewed as
/// indexing in row-major (C-style) or column-major
/// (Fortran-style) order.
/// </param>
/// <returns>
/// An array of indices into the flattened version of an array
/// of dimensions dims.
/// </returns>
public static NDarray ravel_multi_index(tuple of array_like multi_index, tuple of ints dims, string mode = "raise", string order = null)
=> NumPy.Instance.ravel_multi_index(multi_index, dims, mode:mode, order:order);
*/
/// <summary>
/// Converts a flat index or array of flat indices into a tuple
/// of coordinate arrays.
/// </summary>
/// <param name="indices">
/// An integer array whose elements are indices into the flattened
/// version of an array of dimensions shape.<br></br>
/// Before version 1.6.0,
/// this function accepted just one index value.
/// </param>
/// <param name="shape">
/// The shape of the array to use for unraveling indices.
/// </param>
/// <param name="order">
/// Determines whether the indices should be viewed as indexing in
/// row-major (C-style) or column-major (Fortran-style) order.
/// </param>
/// <returns>
/// Each array in the tuple has the same shape as the indices
/// array.
/// </returns>
public static NDarray[] unravel_index(NDarray indices, Shape shape, string order = null)
=> NumPy.Instance.unravel_index(indices, shape, order:order);
/// <summary>
/// Return the indices to access the main diagonal of an array.<br></br>
///
/// This returns a tuple of indices that can be used to access the main
/// diagonal of an array a with a.ndim >= 2 dimensions and shape
/// (n, n, …, n).<br></br>
/// For a.ndim = 2 this is the usual diagonal, for
/// a.ndim > 2 this is the set of indices to access a[i, i, ..., i]
/// for i = [0..n-1].<br></br>
///
/// Notes
/// </summary>
/// <param name="n">
/// The size, along each dimension, of the arrays for which the returned
/// indices can be used.
/// </param>
/// <param name="ndim">
/// The number of dimensions.
/// </param>
public static void diag_indices(int n, int? ndim = 2)
=> NumPy.Instance.diag_indices(n, ndim:ndim);
/// <summary>
/// Return the indices to access the main diagonal of an n-dimensional array.<br></br>
///
/// See diag_indices for full details.<br></br>
///
/// Notes
/// </summary>
public static void diag_indices_from(NDarray arr)
=> NumPy.Instance.diag_indices_from(arr);
/// <summary>
/// Return the indices to access (n, n) arrays, given a masking function.<br></br>
///
/// Assume mask_func is a function that, for a square array a of size
/// (n, n) with a possible offset argument k, when called as
/// mask_func(a, k) returns a new array with zeros in certain locations
/// (functions like triu or tril do precisely this).<br></br>
/// Then this function
/// returns the indices where the non-zero values would be located.<br></br>
///
/// Notes
/// </summary>
/// <param name="n">
/// The returned indices will be valid to access arrays of shape (n, n).
/// </param>
/// <param name="mask_func">
/// A function whose call signature is similar to that of triu, tril.<br></br>
///
/// That is, mask_func(x, k) returns a boolean array, shaped like x.<br></br>
///
/// k is an optional argument to the function.
/// </param>
/// <param name="k">
/// An optional argument which is passed through to mask_func.<br></br>
/// Functions
/// like triu, tril take a second argument that is interpreted as an
/// offset.
/// </param>
/// <returns>
/// The n arrays of indices corresponding to the locations where
/// mask_func(np.ones((n, n)), k) is True.
/// </returns>
public static NDarray[] mask_indices(int n, Delegate mask_func, int k = 0)
=> NumPy.Instance.mask_indices(n, mask_func, k:k);
/// <summary>
/// Return the indices for the lower-triangle of an (n, m) array.<br></br>
///
/// Notes
/// </summary>
/// <param name="n">
/// The row dimension of the arrays for which the returned
/// indices will be valid.
/// </param>
/// <param name="k">
/// Diagonal offset (see tril for details).
/// </param>
/// <param name="m">
/// The column dimension of the arrays for which the returned
/// arrays will be valid.<br></br>
///
/// By default m is taken equal to n.
/// </param>
/// <returns>
/// The indices for the triangle.<br></br>
/// The returned tuple contains two arrays,
/// each with the indices along one dimension of the array.
/// </returns>
public static NDarray[] tril_indices(int n, int? k = 0, int? m = null)
=> NumPy.Instance.tril_indices(n, k:k, m:m);
/// <summary>
/// Return the indices for the lower-triangle of arr.<br></br>
///
/// See tril_indices for full details.<br></br>
///
/// Notes
/// </summary>
/// <param name="arr">
/// The indices will be valid for square arrays whose dimensions are
/// the same as arr.
/// </param>
/// <param name="k">
/// Diagonal offset (see tril for details).
/// </param>
public static void tril_indices_from(NDarray arr, int? k = 0)
=> NumPy.Instance.tril_indices_from(arr, k:k);
/// <summary>
/// Return the indices for the upper-triangle of an (n, m) array.<br></br>
///
/// Notes
/// </summary>
/// <param name="n">
/// The size of the arrays for which the returned indices will
/// be valid.
/// </param>
/// <param name="k">
/// Diagonal offset (see triu for details).
/// </param>
/// <param name="m">
/// The column dimension of the arrays for which the returned
/// arrays will be valid.<br></br>
///
/// By default m is taken equal to n.
/// </param>
/// <returns>
/// The indices for the triangle.<br></br>
/// The returned tuple contains two arrays,
/// each with the indices along one dimension of the array.<br></br>
/// Can be used
/// to slice a ndarray of shape(n, n).
/// </returns>
public static NDarray[] triu_indices(int n, int? k = 0, int? m = null)
=> NumPy.Instance.triu_indices(n, k:k, m:m);
/// <summary>
/// Return the indices for the upper-triangle of arr.<br></br>
///
/// See triu_indices for full details.<br></br>
///
/// Notes
/// </summary>
/// <param name="arr">
/// The indices will be valid for square arrays.
/// </param>
/// <param name="k">
/// Diagonal offset (see triu for details).
/// </param>
/// <returns>
/// Indices for the upper-triangle of arr.
/// </returns>
public static NDarray[] triu_indices_from(NDarray arr, int? k = 0)
=> NumPy.Instance.triu_indices_from(arr, k:k);
/// <summary>
/// Take elements from an array along an axis.<br></br>
///
/// When axis is not None, this function does the same thing as “fancy”
/// indexing (indexing arrays using arrays); however, it can be easier to use
/// if you need elements along a given axis.<br></br>
/// A call such as
/// np.take(arr, indices, axis=3) is equivalent to
/// arr[:,:,:,indices,...].<br></br>
///
/// Explained without fancy indexing, this is equivalent to the following use
/// of ndindex, which sets each of ii, jj, and kk to a tuple of
/// indices:
///
/// Notes
///
/// By eliminating the inner loop in the description above, and using s_ to
/// build simple slice objects, take can be expressed in terms of applying
/// fancy indexing to each 1-d slice:
///
/// For this reason, it is equivalent to (but faster than) the following use
/// of apply_along_axis:
/// </summary>
/// <param name="a">
/// The source array.
/// </param>
/// <param name="indices">
/// The indices of the values to extract.<br></br>
///
/// Also allow scalars for indices.
/// </param>
/// <param name="axis">
/// The axis over which to select values.<br></br>
/// By default, the flattened
/// input array is used.
/// </param>
/// <param name="out">
/// If provided, the result will be placed in this array.<br></br>
/// It should
/// be of the appropriate shape and dtype.
/// </param>
/// <param name="mode">
/// Specifies how out-of-bounds indices will behave.<br></br>
///
/// ‘clip’ mode means that all indices that are too large are replaced
/// by the index that addresses the last element along that axis.<br></br>
/// Note
/// that this disables indexing with negative numbers.
/// </param>
/// <returns>
/// The returned array has the same type as a.
/// </returns>
public static NDarray take(NDarray[] a, NDarray[] indices, int? axis = null, NDarray @out = null, string mode = "raise")
=> NumPy.Instance.take(a, indices, axis:axis, @out:@out, mode:mode);
/// <summary>
/// Take values from the input array by matching 1d index and data slices.<br></br>
///
/// This iterates over matching 1d slices oriented along the specified axis in
/// the index and data arrays, and uses the former to look up values in the
/// latter.<br></br>
/// These slices can be different lengths.<br></br>
///
/// Functions returning an index along an axis, like argsort and
/// argpartition, produce suitable indices for this function.<br></br>
///
/// Notes
///
/// This is equivalent to (but faster than) the following use of ndindex and
/// s_, which sets each of ii and kk to a tuple of indices:
///
/// Equivalently, eliminating the inner loop, the last two lines would be:
/// </summary>
/// <param name="arr">
/// Source array
/// </param>
/// <param name="indices">
/// Indices to take along each 1d slice of arr.<br></br>
/// This must match the
/// dimension of arr, but dimensions Ni and Nj only need to broadcast
/// against arr.
/// </param>
/// <param name="axis">
/// The axis to take 1d slices along.<br></br>
/// If axis is None, the input array is
/// treated as if it had first been flattened to 1d, for consistency with
/// sort and argsort.
/// </param>
public static void take_along_axis(NDarray arr, NDarray indices, int axis)
=> NumPy.Instance.take_along_axis(arr, indices, axis);
/// <summary>
/// Construct an array from an index array and a set of arrays to choose from.<br></br>
///
/// First of all, if confused or uncertain, definitely look at the Examples -
/// in its full generality, this function is less simple than it might
/// seem from the following code description (below ndi =
/// numpy.lib.index_tricks):
///
/// np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)]).<br></br>
///
/// But this omits some subtleties.<br></br>
/// Here is a fully general summary:
///
/// Given an “index” array (a) of integers and a sequence of n arrays
/// (choices), a and each choice array are first broadcast, as necessary,
/// to arrays of a common shape; calling these Ba and Bchoices[i], i =
/// 0,…,n-1 we have that, necessarily, Ba.shape == Bchoices[i].shape
/// for each i.<br></br>
/// Then, a new array with shape Ba.shape is created as
/// follows:
///
/// Notes
///
/// To reduce the chance of misinterpretation, even though the following
/// “abuse” is nominally supported, choices should neither be, nor be
/// thought of as, a single array, i.e., the outermost sequence-like container
/// should be either a list or a tuple.
/// </summary>
/// <param name="a">
/// This array must contain integers in [0, n-1], where n is the number
/// of choices, unless mode=wrap or mode=clip, in which cases any
/// integers are permissible.
/// </param>
/// <param name="choices">
/// Choice arrays.<br></br>
/// a and all of the choices must be broadcastable to the
/// same shape.<br></br>
/// If choices is itself an array (not recommended), then
/// its outermost dimension (i.e., the one corresponding to
/// choices.shape[0]) is taken as defining the “sequence”.
/// </param>
/// <param name="out">
/// If provided, the result will be inserted into this array.<br></br>
/// It should
/// be of the appropriate shape and dtype.
/// </param>
/// <param name="mode">
/// Specifies how indices outside [0, n-1] will be treated:
/// </param>
/// <returns>
/// The merged result.
/// </returns>
public static NDarray choose(NDarray<int> a, NDarray[] choices, NDarray @out = null, string mode = "raise")
=> NumPy.Instance.choose(a, choices, @out:@out, mode:mode);
/// <summary>
/// Return selected slices of an array along given axis.<br></br>
///
/// When working along a given axis, a slice along that axis is returned in
/// output for each index where condition evaluates to True.<br></br>
/// When
/// working on a 1-D array, compress is equivalent to extract.
/// </summary>
/// <param name="condition">
/// Array that selects which entries to return.<br></br>
/// If len(condition)
/// is less than the size of a along the given axis, then output is
/// truncated to the length of the condition array.
/// </param>
/// <param name="a">
/// Array from which to extract a part.
/// </param>
/// <param name="axis">
/// Axis along which to take slices.<br></br>
/// If None (default), work on the
/// flattened array.
/// </param>
/// <param name="out">
/// Output array.<br></br>
/// Its type is preserved and it must be of the right
/// shape to hold the output.
/// </param>
/// <returns>
/// A copy of a without the slices along axis for which condition
/// is false.
/// </returns>
public static NDarray compress(NDarray<bool> condition, NDarray a, int? axis = null, NDarray @out = null)
=> NumPy.Instance.compress(condition, a, axis:axis, @out:@out);
/// <summary>
/// Return specified diagonals.<br></br>
///
/// If a is 2-D, returns the diagonal of a with the given offset,
/// i.e., the collection of elements of the form a[i, i+offset].<br></br>
/// If
/// a has more than two dimensions, then the axes specified by axis1
/// and axis2 are used to determine the 2-D sub-array whose diagonal is
/// returned.<br></br>
/// The shape of the resulting array can be determined by
/// removing axis1 and axis2 and appending an index to the right equal
/// to the size of the resulting diagonals.<br></br>
///
/// In versions of NumPy prior to 1.7, this function always returned a new,
/// independent array containing a copy of the values in the diagonal.<br></br>
///
/// In NumPy 1.7 and 1.8, it continues to return a copy of the diagonal,
/// but depending on this fact is deprecated.<br></br>
/// Writing to the resulting
/// array continues to work as it used to, but a FutureWarning is issued.<br></br>
///
/// Starting in NumPy 1.9 it returns a read-only view on the original array.<br></br>
///
/// Attempting to write to the resulting array will produce an error.<br></br>
///
/// In some future release, it will return a read/write view and writing to
/// the returned array will alter your original array.<br></br>
/// The returned array
/// will have the same type as the input array.<br></br>
///
/// If you don’t write to the array returned by this function, then you can
/// just ignore all of the above.<br></br>
///
/// If you depend on the current behavior, then we suggest copying the
/// returned array explicitly, i.e., use np.diagonal(a).copy() instead
/// of just np.diagonal(a).<br></br>
/// This will work with both past and future
/// versions of NumPy.
/// </summary>
/// <param name="a">
/// Array from which the diagonals are taken.
/// </param>
/// <param name="offset">
/// Offset of the diagonal from the main diagonal.<br></br>
/// Can be positive or
/// negative.<br></br>
/// Defaults to main diagonal (0).
/// </param>
/// <param name="axis1">
/// Axis to be used as the first axis of the 2-D sub-arrays from which
/// the diagonals should be taken.<br></br>
/// Defaults to first axis (0).
/// </param>
/// <param name="axis2">
/// Axis to be used as the second axis of the 2-D sub-arrays from
/// which the diagonals should be taken.<br></br>
/// Defaults to second axis (1).
/// </param>
/// <returns>
/// If a is 2-D, then a 1-D array containing the diagonal and of the
/// same type as a is returned unless a is a matrix, in which case
/// a 1-D array rather than a (2-D) matrix is returned in order to
/// maintain backward compatibility.<br></br>
///
/// If a.ndim > 2, then the dimensions specified by axis1 and axis2
/// are removed, and a new axis inserted at the end corresponding to the
/// diagonal.
/// </returns>
public static NDarray diagonal(NDarray a, int? offset = 0, int? axis1 = 0, int? axis2 = 1)
=> NumPy.Instance.diagonal(a, offset:offset, axis1:axis1, axis2:axis2);
/// <summary>
/// Return an array drawn from elements in choicelist, depending on conditions.
/// </summary>
/// <param name="condlist">
/// The list of conditions which determine from which array in choicelist
/// the output elements are taken.<br></br>
/// When multiple conditions are satisfied,
/// the first one encountered in condlist is used.
/// </param>
/// <param name="choicelist">
/// The list of arrays from which the output elements are taken.<br></br>
/// It has
/// to be of the same length as condlist.
/// </param>
/// <param name="default">
/// The element inserted in output when all conditions evaluate to False.
/// </param>
/// <returns>
/// The output at position m is the m-th element of the array in
/// choicelist where the m-th element of the corresponding array in
/// condlist is True.
/// </returns>
public static NDarray @select(NDarray<bool>[] condlist, NDarray[] choicelist, object @default = null)
=> NumPy.Instance.@select(condlist, choicelist, @default:@default);
public static partial class lib {
public static partial class stride_tricks {
/// <summary>
/// Create a view into the array with the given shape and strides.<br></br>
///
/// Notes
///
/// as_strided creates a view into the array given the exact strides
/// and shape.<br></br>
/// This means it manipulates the internal data structure of
/// ndarray and, if done incorrectly, the array elements can point to
/// invalid memory and can corrupt results or crash your program.<br></br>
///
/// It is advisable to always use the original x.strides when
/// calculating new strides to avoid reliance on a contiguous memory
/// layout.<br></br>
///
/// Furthermore, arrays created with this function often contain self
/// overlapping memory, so that two elements are identical.<br></br>
///
/// Vectorized write operations on such arrays will typically be
/// unpredictable.<br></br>
/// They may even give different results for small, large,
/// or transposed arrays.<br></br>
///
/// Since writing to these arrays has to be tested and done with great
/// care, you may want to use writeable=False to avoid accidental write
/// operations.<br></br>
///
/// For these reasons it is advisable to avoid as_strided when
/// possible.
/// </summary>
/// <param name="x">
/// Array to create a new.
/// </param>
/// <param name="shape">
/// The shape of the new array.<br></br>
/// Defaults to x.shape.
/// </param>
/// <param name="strides">
/// The strides of the new array.<br></br>
/// Defaults to x.strides.
/// </param>
/// <param name="subok">
/// If True, subclasses are preserved.
/// </param>
/// <param name="writeable">
/// If set to False, the returned array will always be readonly.<br></br>
///
/// Otherwise it will be writable if the original array was.<br></br>
/// It
/// is advisable to set this to False if possible (see Notes).
/// </param>
public static NDarray as_strided(NDarray x, Shape shape = null, int[] strides = null, bool? subok = false, bool? writeable = true)
=> NumPy.Instance.lib_stride_tricks_as_strided(x, shape:shape, strides:strides, subok:subok, writeable:writeable);
}
}
/// <summary>
/// Change elements of an array based on conditional and input values.<br></br>
///
/// Similar to np.copyto(arr, vals, where=mask), the difference is that
/// place uses the first N elements of vals, where N is the number of
/// True values in mask, while copyto uses the elements where mask
/// is True.<br></br>
///
/// Note that extract does the exact opposite of place.
/// </summary>
/// <param name="arr">
/// Array to put data into.
/// </param>
/// <param name="mask">
/// Boolean mask array.<br></br>
/// Must have the same size as a.
/// </param>
/// <param name="vals">
/// Values to put into a.<br></br>
/// Only the first N elements are used, where
/// N is the number of True values in mask.<br></br>
/// If vals is smaller
/// than N, it will be repeated, and if elements of a are to be masked,
/// this sequence must be non-empty.
/// </param>
public static void place(NDarray arr, NDarray mask, NDarray vals)
=> NumPy.Instance.place(arr, mask, vals);
/// <summary>
/// Replaces specified elements of an array with given values.<br></br>
///
/// The indexing works on the flattened target array.<br></br>
/// put is roughly
/// equivalent to:
/// </summary>
/// <param name="a">
/// Target array.
/// </param>
/// <param name="ind">
/// Target indices, interpreted as integers.
/// </param>
/// <param name="v">
/// Values to place in a at target indices.<br></br>
/// If v is shorter than
/// ind it will be repeated as necessary.
/// </param>
/// <param name="mode">
/// Specifies how out-of-bounds indices will behave.<br></br>
///
/// ‘clip’ mode means that all indices that are too large are replaced
/// by the index that addresses the last element along that axis.<br></br>
/// Note
/// that this disables indexing with negative numbers.
/// </param>
public static void put(NDarray a, NDarray ind, NDarray v, string mode = "raise")
=> NumPy.Instance.put(a, ind, v, mode:mode);
/// <summary>
/// Put values into the destination array by matching 1d index and data slices.<br></br>
///
/// This iterates over matching 1d slices oriented along the specified axis in
/// the index and data arrays, and uses the former to place values into the
/// latter.<br></br>
/// These slices can be different lengths.<br></br>
///
/// Functions returning an index along an axis, like argsort and
/// argpartition, produce suitable indices for this function.<br></br>
///
/// Notes
///
/// This is equivalent to (but faster than) the following use of ndindex and
/// s_, which sets each of ii and kk to a tuple of indices:
///
/// Equivalently, eliminating the inner loop, the last two lines would be:
/// </summary>
/// <param name="arr">
/// Destination array.
/// </param>
/// <param name="indices">
/// Indices to change along each 1d slice of arr.<br></br>
/// This must match the
/// dimension of arr, but dimensions in Ni and Nj may be 1 to broadcast
/// against arr.
/// </param>
/// <param name="values">
/// values to insert at those indices.<br></br>
/// Its shape and dimension are
/// broadcast to match that of indices.
/// </param>
/// <param name="axis">
/// The axis to take 1d slices along.<br></br>
/// If axis is None, the destination
/// array is treated as if a flattened 1d view had been created of it.
/// </param>
public static void put_along_axis(NDarray arr, NDarray indices, NDarray[] values, int axis)
=> NumPy.Instance.put_along_axis(arr, indices, values, axis);
/// <summary>
/// Changes elements of an array based on conditional and input values.<br></br>
///
/// Sets a.flat[n] = values[n] for each n where mask.flat[n]==True.<br></br>
///
/// If values is not the same size as a and mask then it will repeat.<br></br>
///
/// This gives behavior different from a[mask] = values.
/// </summary>
/// <param name="a">
/// Target array.
/// </param>
/// <param name="mask">
/// Boolean mask array.<br></br>
/// It has to be the same shape as a.
/// </param>
/// <param name="values">
/// Values to put into a where mask is True.<br></br>
/// If values is smaller
/// than a it will be repeated.
/// </param>
public static void putmask(NDarray a, NDarray mask, NDarray values)
=> NumPy.Instance.putmask(a, mask, values);
/// <summary>
/// Fill the main diagonal of the given array of any dimensionality.<br></br>
///
/// For an array a with a.ndim >= 2, the diagonal is the list of
/// locations with indices a[i, ..., i] all identical.<br></br>
/// This function
/// modifies the input array in-place, it does not return a value.<br></br>
///
/// Notes
///
/// This functionality can be obtained via diag_indices, but internally
/// this version uses a much faster implementation that never constructs the
/// indices and uses simple slicing.
/// </summary>
/// <param name="a">
/// Array whose diagonal is to be filled, it gets modified in-place.
/// </param>
/// <param name="val">
/// Value to be written on the diagonal, its type must be compatible with
/// that of the array a.
/// </param>
/// <param name="wrap">
/// For tall matrices in NumPy version up to 1.6.2, the
/// diagonal “wrapped” after N columns.<br></br>
/// You can have this behavior
/// with this option.<br></br>
/// This affects only tall matrices.
/// </param>
public static void fill_diagonal(NDarray a, ValueType val, bool wrap = false)
=> NumPy.Instance.fill_diagonal(a, val, wrap:wrap);
/*
/// <summary>
/// Efficient multi-dimensional iterator object to iterate over arrays.<br></br>
///
/// To get started using this object, see the
/// introductory guide to array iteration.<br></br>
///
/// Notes
///
/// nditer supersedes flatiter.<br></br>
/// The iterator implementation behind
/// nditer is also exposed by the NumPy C API.<br></br>
///
/// The Python exposure supplies two iteration interfaces, one which follows
/// the Python iterator protocol, and another which mirrors the C-style
/// do-while pattern.<br></br>
/// The native Python approach is better in most cases, but
/// if you need the iterator’s coordinates or index, use the C-style pattern.
/// </summary>
/// <param name="op">
/// The array(s) to iterate over.
/// </param>
/// <param name="flags">
/// Flags to control the behavior of the iterator.
/// </param>
/// <param name="op_flags">
/// This is a list of flags for each operand.<br></br>
/// At minimum, one of
/// “readonly”, “readwrite”, or “writeonly” must be specified.
/// </param>
/// <param name="op_dtypes">
/// The required data type(s) of the operands.<br></br>
/// If copying or buffering
/// is enabled, the data will be converted to/from their original types.
/// </param>
/// <param name="order">
/// Controls the iteration order.<br></br>
/// ‘C’ means C order, ‘F’ means
/// Fortran order, ‘A’ means ‘F’ order if all the arrays are Fortran
/// contiguous, ‘C’ order otherwise, and ‘K’ means as close to the
/// order the array elements appear in memory as possible.<br></br>
/// This also
/// affects the element memory order of “allocate” operands, as they
/// are allocated to be compatible with iteration order.<br></br>
///
/// Default is ‘K’.
/// </param>
/// <param name="casting">
/// Controls what kind of data casting may occur when making a copy
/// or buffering.<br></br>
/// Setting this to ‘unsafe’ is not recommended,
/// as it can adversely affect accumulations.
/// </param>
/// <param name="op_axes">
/// If provided, is a list of ints or None for each operands.<br></br>
///
/// The list of axes for an operand is a mapping from the dimensions
/// of the iterator to the dimensions of the operand.<br></br>
/// A value of
/// -1 can be placed for entries, causing that dimension to be
/// treated as “newaxis”.
/// </param>
/// <param name="itershape">
/// The desired shape of the iterator.<br></br>
/// This allows “allocate” operands
/// with a dimension mapped by op_axes not corresponding to a dimension
/// of a different operand to get a value not equal to 1 for that
/// dimension.
/// </param>
/// <param name="buffersize">
/// When buffering is enabled, controls the size of the temporary
/// buffers.<br></br>
/// Set to 0 for the default value.
/// </param>
public static void nditer(NDarray op, string[] flags = null, list of list of str op_flags = null, dtype or tuple of dtype(s) op_dtypes = null, string order = null, string casting = null, list of list of ints op_axes = null, tuple of ints itershape = null, int? buffersize = null)
=> NumPy.Instance.nditer(op, flags:flags, op_flags:op_flags, op_dtypes:op_dtypes, order:order, casting:casting, op_axes:op_axes, itershape:itershape, buffersize:buffersize);
*/
/// <summary>
/// Multidimensional index iterator.<br></br>
///
/// Return an iterator yielding pairs of array coordinates and values.
/// </summary>
/// <param name="arr">
/// Input array.
/// </param>
public static void ndenumerate(NDarray arr)
=> NumPy.Instance.ndenumerate(arr);
/// <summary>
/// An N-dimensional iterator object to index arrays.<br></br>