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1349 lines (1313 loc) · 56.4 KB
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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 Python.Included;
using Numpy.Models;
namespace Numpy
{
public partial class NumPy
{
/// <summary>
/// Translates slice objects to concatenation along the first axis.
///
/// This is a simple way to build up arrays quickly. There are two use cases.
///
/// If slice notation is used, the syntax start:stop:step is equivalent
/// to np.arange(start, stop, step) inside of the brackets. However, if
/// step is an imaginary number (i.e. 100j) then its integer portion is
/// interpreted as a number-of-points desired and the start and stop are
/// inclusive. In other words start:stop:stepj is interpreted as
/// np.linspace(start, stop, step, endpoint=1) inside of the brackets.
/// After expansion of slice notation, all comma separated sequences are
/// concatenated together.
///
/// Optional character strings placed as the first element of the index
/// expression can be used to change the output. The strings ‘r’ or ‘c’ result
/// in matrix output. If the result is 1-D and ‘r’ is specified a 1 x N (row)
/// matrix is produced. If the result is 1-D and ‘c’ is specified, then a N x 1
/// (column) matrix is produced. If the result is 2-D then both provide the
/// same matrix result.
///
/// A string integer specifies which axis to stack multiple comma separated
/// arrays along. 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).
///
/// 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. 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. By default, they are placed
/// in the front of the shape tuple. The third argument allows you to specify
/// where the start of the array should be instead. Thus, a third argument of
/// ‘0’ would place the 1’s at the end of the array shape. 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 void r_()
{
//auto-generated code, do not change
var __self__=self;
dynamic py = __self__.InvokeMethod("r_");
}
/// <summary>
/// A nicer way to build up index tuples for arrays.
///
/// For any index combination, including slicing and axis insertion,
/// a[indices] is the same as a[np.index_exp[indices]] for any
/// array a. 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.
///
/// 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 void s_(bool maketuple)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
maketuple,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("s_", pyargs, kwargs);
}
/// <summary>
/// Return the indices of the elements that are non-zero.
///
/// Returns a tuple of arrays, one for each dimension of a,
/// containing the indices of the non-zero elements in that
/// dimension. The values in a are always tested and returned in
/// row-major, C-style order. 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 NDarray[] nonzero(NDarray a)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("nonzero", pyargs, kwargs);
return ToCsharp<NDarray[]>(py);
}
/// <summary>
/// Return elements chosen from x or y depending on condition.
///
/// 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. x, y and condition need to be
/// broadcastable to some shape.
/// </param>
/// <param name="x">
/// Values from which to choose. 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 NDarray @where(NDarray condition, NDarray y, NDarray x)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
condition,
y,
x,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("where", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return an array representing the indices of a grid.
///
/// Compute an array where the subarrays contain index values 0,1,…
/// varying only along the corresponding axis.
///
/// Notes
///
/// The output shape is obtained by prepending the number of dimensions
/// in front of the tuple of dimensions, i.e. if dimensions is a tuple
/// (r0, ..., rN-1) of length N, the output shape is
/// (N,r0,...,rN-1).
///
/// The subarrays grid[k] contains the N-D array of indices along the
/// k-th axis. 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 NDarray indices(int[] dimensions, Dtype dtype = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
dimensions,
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
dynamic py = __self__.InvokeMethod("indices", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Construct an open mesh from multiple sequences.
///
/// 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.
///
/// Using ix_ one can quickly construct index arrays that will index
/// the cross product. 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.
/// 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. Together these arrays form an open mesh.
/// </returns>
public NDarray[] ix_(NDarray[] args)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
args,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("ix_", pyargs, kwargs);
return ToCsharp<NDarray[]>(py);
}
/*
/// <summary>
/// Converts a tuple of index arrays into an array of flat
/// indices, applying boundary modes to the multi-index.
///
/// 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. Can specify
/// either one mode or a tuple of modes, one mode per index.
///
/// 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 NDarray ravel_multi_index(tuple of array_like multi_index, tuple of ints dims, string mode = "raise", string order = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
multi_index,
dims,
});
var kwargs=new PyDict();
if (mode!="raise") kwargs["mode"]=ToPython(mode);
if (order!=null) kwargs["order"]=ToPython(order);
dynamic py = __self__.InvokeMethod("ravel_multi_index", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
*/
/// <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. 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 NDarray[] unravel_index(NDarray indices, Shape shape, string order = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
indices,
shape,
});
var kwargs=new PyDict();
if (order!=null) kwargs["order"]=ToPython(order);
dynamic py = __self__.InvokeMethod("unravel_index", pyargs, kwargs);
return ToCsharp<NDarray[]>(py);
}
/// <summary>
/// Return the indices to access the main diagonal of an array.
///
/// 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). 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].
///
/// 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 void diag_indices(int n, int? ndim = 2)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
n,
});
var kwargs=new PyDict();
if (ndim!=2) kwargs["ndim"]=ToPython(ndim);
dynamic py = __self__.InvokeMethod("diag_indices", pyargs, kwargs);
}
/// <summary>
/// Return the indices to access the main diagonal of an n-dimensional array.
///
/// See diag_indices for full details.
///
/// Notes
/// </summary>
public void diag_indices_from(NDarray arr)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
arr,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("diag_indices_from", pyargs, kwargs);
}
/// <summary>
/// Return the indices to access (n, n) arrays, given a masking function.
///
/// 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). Then this function
/// returns the indices where the non-zero values would be located.
///
/// 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.
/// That is, mask_func(x, k) returns a boolean array, shaped like x.
/// k is an optional argument to the function.
/// </param>
/// <param name="k">
/// An optional argument which is passed through to mask_func. 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 NDarray[] mask_indices(int n, Delegate mask_func, int k = 0)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
n,
mask_func,
k,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("mask_indices", pyargs, kwargs);
return ToCsharp<NDarray[]>(py);
}
/// <summary>
/// Return the indices for the lower-triangle of an (n, m) array.
///
/// 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.
/// By default m is taken equal to n.
/// </param>
/// <returns>
/// The indices for the triangle. The returned tuple contains two arrays,
/// each with the indices along one dimension of the array.
/// </returns>
public NDarray[] tril_indices(int n, int? k = 0, int? m = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
n,
});
var kwargs=new PyDict();
if (k!=0) kwargs["k"]=ToPython(k);
if (m!=null) kwargs["m"]=ToPython(m);
dynamic py = __self__.InvokeMethod("tril_indices", pyargs, kwargs);
return ToCsharp<NDarray[]>(py);
}
/// <summary>
/// Return the indices for the lower-triangle of arr.
///
/// See tril_indices for full details.
///
/// 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 void tril_indices_from(NDarray arr, int? k = 0)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
arr,
});
var kwargs=new PyDict();
if (k!=0) kwargs["k"]=ToPython(k);
dynamic py = __self__.InvokeMethod("tril_indices_from", pyargs, kwargs);
}
/// <summary>
/// Return the indices for the upper-triangle of an (n, m) array.
///
/// 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.
/// By default m is taken equal to n.
/// </param>
/// <returns>
/// The indices for the triangle. The returned tuple contains two arrays,
/// each with the indices along one dimension of the array. Can be used
/// to slice a ndarray of shape(n, n).
/// </returns>
public NDarray[] triu_indices(int n, int? k = 0, int? m = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
n,
});
var kwargs=new PyDict();
if (k!=0) kwargs["k"]=ToPython(k);
if (m!=null) kwargs["m"]=ToPython(m);
dynamic py = __self__.InvokeMethod("triu_indices", pyargs, kwargs);
return ToCsharp<NDarray[]>(py);
}
/// <summary>
/// Return the indices for the upper-triangle of arr.
///
/// See triu_indices for full details.
///
/// 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 NDarray[] triu_indices_from(NDarray arr, int? k = 0)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
arr,
});
var kwargs=new PyDict();
if (k!=0) kwargs["k"]=ToPython(k);
dynamic py = __self__.InvokeMethod("triu_indices_from", pyargs, kwargs);
return ToCsharp<NDarray[]>(py);
}
/// <summary>
/// Take elements from an array along an axis.
///
/// 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. A call such as
/// np.take(arr, indices, axis=3) is equivalent to
/// arr[:,:,:,indices,...].
///
/// 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.
///
/// Also allow scalars for indices.
/// </param>
/// <param name="axis">
/// The axis over which to select values. By default, the flattened
/// input array is used.
/// </param>
/// <param name="@out">
/// If provided, the result will be placed in this array. It should
/// be of the appropriate shape and dtype.
/// </param>
/// <param name="mode">
/// Specifies how out-of-bounds indices will behave.
///
/// ‘clip’ mode means that all indices that are too large are replaced
/// by the index that addresses the last element along that axis. Note
/// that this disables indexing with negative numbers.
/// </param>
/// <returns>
/// The returned array has the same type as a.
/// </returns>
public NDarray take(NDarray[] a, NDarray[] indices, int? axis = null, NDarray @out = null, string mode = "raise")
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
indices,
});
var kwargs=new PyDict();
if (axis!=null) kwargs["axis"]=ToPython(axis);
if (@out!=null) kwargs["out"]=ToPython(@out);
if (mode!="raise") kwargs["mode"]=ToPython(mode);
dynamic py = __self__.InvokeMethod("take", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Take values from the input array by matching 1d index and data slices.
///
/// 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. These slices can be different lengths.
///
/// Functions returning an index along an axis, like argsort and
/// argpartition, produce suitable indices for this function.
///
/// 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. 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. 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 void take_along_axis(NDarray arr, NDarray indices, int axis)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
arr,
indices,
axis,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("take_along_axis", pyargs, kwargs);
}
/// <summary>
/// Construct an array from an index array and a set of arrays to choose from.
///
/// 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)]).
///
/// But this omits some subtleties. 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. 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. a and all of the choices must be broadcastable to the
/// same shape. 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. 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 NDarray choose(NDarray<int> a, NDarray[] choices, NDarray @out = null, string mode = "raise")
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
choices,
});
var kwargs=new PyDict();
if (@out!=null) kwargs["out"]=ToPython(@out);
if (mode!="raise") kwargs["mode"]=ToPython(mode);
dynamic py = __self__.InvokeMethod("choose", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return selected slices of an array along given axis.
///
/// When working along a given axis, a slice along that axis is returned in
/// output for each index where condition evaluates to True. When
/// working on a 1-D array, compress is equivalent to extract.
/// </summary>
/// <param name="condition">
/// Array that selects which entries to return. 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. If None (default), work on the
/// flattened array.
/// </param>
/// <param name="@out">
/// Output array. 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 NDarray compress(NDarray<bool> condition, NDarray a, int? axis = null, NDarray @out = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
condition,
a,
});
var kwargs=new PyDict();
if (axis!=null) kwargs["axis"]=ToPython(axis);
if (@out!=null) kwargs["out"]=ToPython(@out);
dynamic py = __self__.InvokeMethod("compress", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return specified diagonals.
///
/// 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]. 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. 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.
///
/// 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.
///
/// In NumPy 1.7 and 1.8, it continues to return a copy of the diagonal,
/// but depending on this fact is deprecated. Writing to the resulting
/// array continues to work as it used to, but a FutureWarning is issued.
///
/// Starting in NumPy 1.9 it returns a read-only view on the original array.
/// Attempting to write to the resulting array will produce an error.
///
/// In some future release, it will return a read/write view and writing to
/// the returned array will alter your original array. The returned array
/// will have the same type as the input array.
///
/// If you don’t write to the array returned by this function, then you can
/// just ignore all of the above.
///
/// 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). 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. Can be positive or
/// negative. 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. 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. 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.
///
/// 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 NDarray diagonal(NDarray a, int? offset = 0, int? axis1 = 0, int? axis2 = 1)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
});
var kwargs=new PyDict();
if (offset!=0) kwargs["offset"]=ToPython(offset);
if (axis1!=0) kwargs["axis1"]=ToPython(axis1);
if (axis2!=1) kwargs["axis2"]=ToPython(axis2);
dynamic py = __self__.InvokeMethod("diagonal", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <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. 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. 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 NDarray @select(NDarray<bool>[] condlist, NDarray[] choicelist, object @default = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
condlist,
choicelist,
});
var kwargs=new PyDict();
if (@default!=null) kwargs["default"]=ToPython(@default);
dynamic py = __self__.InvokeMethod("select", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Create a view into the array with the given shape and strides.
///
/// Notes
///
/// as_strided creates a view into the array given the exact strides
/// and shape. 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.
/// It is advisable to always use the original x.strides when
/// calculating new strides to avoid reliance on a contiguous memory
/// layout.
///
/// Furthermore, arrays created with this function often contain self
/// overlapping memory, so that two elements are identical.
/// Vectorized write operations on such arrays will typically be
/// unpredictable. They may even give different results for small, large,
/// or transposed arrays.
/// 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.
///
/// 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. Defaults to x.shape.
/// </param>
/// <param name="strides">
/// The strides of the new array. 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.
/// Otherwise it will be writable if the original array was. It
/// is advisable to set this to False if possible (see Notes).
/// </param>
public NDarray lib_stride_tricks_as_strided(NDarray x, Shape shape = null, int[] strides = null, bool? subok = false, bool? writeable = true)
{
//auto-generated code, do not change
var lib = self.GetAttr("lib");
var stride_tricks = lib.GetAttr("stride_tricks");
var __self__=stride_tricks;
var pyargs=ToTuple(new object[]
{
x,
});
var kwargs=new PyDict();
if (shape!=null) kwargs["shape"]=ToPython(shape);
if (strides!=null) kwargs["strides"]=ToPython(strides);
if (subok!=false) kwargs["subok"]=ToPython(subok);
if (writeable!=true) kwargs["writeable"]=ToPython(writeable);
dynamic py = __self__.InvokeMethod("as_strided", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Change elements of an array based on conditional and input values.
///
/// 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.
///
/// 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. Must have the same size as a.
/// </param>
/// <param name="vals">
/// Values to put into a. Only the first N elements are used, where
/// N is the number of True values in mask. 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 void place(NDarray arr, NDarray mask, NDarray vals)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
arr,
mask,
vals,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("place", pyargs, kwargs);
}
/// <summary>
/// Replaces specified elements of an array with given values.
///
/// The indexing works on the flattened target array. 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. If v is shorter than
/// ind it will be repeated as necessary.
/// </param>
/// <param name="mode">
/// Specifies how out-of-bounds indices will behave.
///
/// ‘clip’ mode means that all indices that are too large are replaced
/// by the index that addresses the last element along that axis. Note
/// that this disables indexing with negative numbers.
/// </param>
public void put(NDarray a, NDarray ind, NDarray v, string mode = "raise")
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
ind,
v,
});
var kwargs=new PyDict();
if (mode!="raise") kwargs["mode"]=ToPython(mode);
dynamic py = __self__.InvokeMethod("put", pyargs, kwargs);