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1705 lines (1659 loc) · 66.7 KB
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// Copyright (c) 2020 by Meinrad Recheis (Member of SciSharp)
// 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;
#if PYTHON_INCLUDED
using Python.Included;
#endif
namespace Numpy
{
public static partial class np
{
/// <summary>
/// Copies values from one array to another, broadcasting as necessary.<br></br>
///
/// Raises a TypeError if the casting rule is violated, and if
/// where is provided, it selects which elements to copy.
/// </summary>
/// <param name="dst">
/// The array into which values are copied.
/// </param>
/// <param name="src">
/// The array from which values are copied.
/// </param>
/// <param name="casting">
/// Controls what kind of data casting may occur when copying.
/// </param>
/// <param name="where">
/// A boolean array which is broadcasted to match the dimensions
/// of dst, and selects elements to copy from src to dst
/// wherever it contains the value True.
/// </param>
public static void copyto(NDarray dst, NDarray src, string casting = "same_kind", NDarray @where = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
dst,
src,
});
var kwargs=new PyDict();
if (casting!="same_kind") kwargs["casting"]=ToPython(casting);
if (@where!=null) kwargs["where"]=ToPython(@where);
dynamic py = __self__.InvokeMethod("copyto", pyargs, kwargs);
}
/// <summary>
/// Copies values from one array to another, broadcasting as necessary.<br></br>
///
/// Raises a TypeError if the casting rule is violated, and if
/// where is provided, it selects which elements to copy.
/// </summary>
/// <param name="dst">
/// The array into which values are copied.
/// </param>
/// <param name="src">
/// The array from which values are copied.
/// </param>
/// <param name="casting">
/// Controls what kind of data casting may occur when copying.
/// </param>
/// <param name="where">
/// A boolean array which is broadcasted to match the dimensions
/// of dst, and selects elements to copy from src to dst
/// wherever it contains the value True.
/// </param>
public static void copyto(NDarray dst, NDarray src, string casting = "same_kind", bool[] @where = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
dst,
src,
});
var kwargs=new PyDict();
if (casting!="same_kind") kwargs["casting"]=ToPython(casting);
if (@where!=null) kwargs["where"]=ToPython(@where);
dynamic py = __self__.InvokeMethod("copyto", pyargs, kwargs);
}
/// <summary>
/// Gives a new shape to an array without changing its data.<br></br>
///
/// Notes
///
/// It is not always possible to change the shape of an array without
/// copying the data.<br></br>
/// If you want an error to be raised when the data is copied,
/// you should assign the new shape to the shape attribute of the array:
///
/// The order keyword gives the index ordering both for fetching the values
/// from a, and then placing the values into the output array.<br></br>
///
/// For example, let’s say you have an array:
///
/// You can think of reshaping as first raveling the array (using the given
/// index order), then inserting the elements from the raveled array into the
/// new array using the same kind of index ordering as was used for the
/// raveling.
/// </summary>
/// <param name="a">
/// Array to be reshaped.
/// </param>
/// <param name="newshape">
/// The new shape should be compatible with the original shape.<br></br>
/// If
/// an integer, then the result will be a 1-D array of that length.<br></br>
///
/// One shape dimension can be -1. In this case, the value is
/// inferred from the length of the array and remaining dimensions.
/// </param>
/// <param name="order">
/// Read the elements of a using this index order, and place the
/// elements into the reshaped array using this index order.<br></br>
/// ‘C’
/// means to read / write the elements using C-like index order,
/// with the last axis index changing fastest, back to the first
/// axis index changing slowest.<br></br>
/// ‘F’ means to read / write the
/// elements using Fortran-like index order, with the first index
/// changing fastest, and the last index changing slowest.<br></br>
/// Note that
/// the ‘C’ and ‘F’ options take no account of the memory layout of
/// the underlying array, and only refer to the order of indexing.<br></br>
///
/// ‘A’ means to read / write the elements in Fortran-like index
/// order if a is Fortran contiguous in memory, C-like order
/// otherwise.
/// </param>
/// <returns>
/// This will be a new view object if possible; otherwise, it will
/// be a copy.<br></br>
/// Note there is no guarantee of the memory layout (C- or
/// Fortran- contiguous) of the returned array.
/// </returns>
public static NDarray reshape(this NDarray a, Shape newshape, string order = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
newshape,
});
var kwargs=new PyDict();
if (order!=null) kwargs["order"]=ToPython(order);
dynamic py = __self__.InvokeMethod("reshape", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return a contiguous flattened array.<br></br>
///
/// A 1-D array, containing the elements of the input, is returned.<br></br>
/// A copy is
/// made only if needed.<br></br>
///
/// As of NumPy 1.10, the returned array will have the same type as the input
/// array.<br></br>
/// (for example, a masked array will be returned for a masked array
/// input)
///
/// Notes
///
/// In row-major, C-style order, in two dimensions, the row index
/// varies the slowest, and the column index the quickest.<br></br>
/// This can
/// be generalized to multiple dimensions, where row-major order
/// implies that the index along the first axis varies slowest, and
/// the index along the last quickest.<br></br>
/// The opposite holds for
/// column-major, Fortran-style index ordering.<br></br>
///
/// When a view is desired in as many cases as possible, arr.reshape(-1)
/// may be preferable.
/// </summary>
/// <param name="a">
/// Input array.<br></br>
/// The elements in a are read in the order specified by
/// order, and packed as a 1-D array.
/// </param>
/// <param name="order">
/// The elements of a are read using this index order.<br></br>
/// ‘C’ means
/// to index the elements in row-major, C-style order,
/// with the last axis index changing fastest, back to the first
/// axis index changing slowest.<br></br>
/// ‘F’ means to index the elements
/// in column-major, Fortran-style order, with the
/// first index changing fastest, and the last index changing
/// slowest.<br></br>
/// Note that the ‘C’ and ‘F’ options take no account of
/// the memory layout of the underlying array, and only refer to
/// the order of axis indexing.<br></br>
/// ‘A’ means to read the elements in
/// Fortran-like index order if a is Fortran contiguous in
/// memory, C-like order otherwise.<br></br>
/// ‘K’ means to read the
/// elements in the order they occur in memory, except for
/// reversing the data when strides are negative.<br></br>
/// By default, ‘C’
/// index order is used.
/// </param>
/// <returns>
/// y is an array of the same subtype as a, with shape (a.size,).<br></br>
///
/// Note that matrices are special cased for backward compatibility, if a
/// is a matrix, then y is a 1-D ndarray.
/// </returns>
public static NDarray ravel(this NDarray a, string order = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
});
var kwargs=new PyDict();
if (order!=null) kwargs["order"]=ToPython(order);
dynamic py = __self__.InvokeMethod("ravel", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return a copy of the array collapsed into one dimension.
/// </summary>
/// <param name="order">
/// ‘C’ means to flatten in row-major (C-style) order.<br></br>
///
/// ‘F’ means to flatten in column-major (Fortran-
/// style) order.<br></br>
/// ‘A’ means to flatten in column-major
/// order if a is Fortran contiguous in memory,
/// row-major order otherwise.<br></br>
/// ‘K’ means to flatten
/// a in the order the elements occur in memory.<br></br>
///
/// The default is ‘C’.
/// </param>
/// <returns>
/// A copy of the input array, flattened to one dimension.
/// </returns>
public static NDarray flatten(string order = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
});
var kwargs=new PyDict();
if (order!=null) kwargs["order"]=ToPython(order);
dynamic py = __self__.InvokeMethod("flatten", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Move axes of an array to new positions.<br></br>
///
/// Other axes remain in their original order.
/// </summary>
/// <param name="a">
/// The array whose axes should be reordered.
/// </param>
/// <param name="source">
/// Original positions of the axes to move.<br></br>
/// These must be unique.
/// </param>
/// <param name="destination">
/// Destination positions for each of the original axes.<br></br>
/// These must also be
/// unique.
/// </param>
/// <returns>
/// Array with moved axes.<br></br>
/// This array is a view of the input array.
/// </returns>
public static NDarray moveaxis(this NDarray a, int[] source, int[] destination)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
source,
destination,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("moveaxis", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Roll the specified axis backwards, until it lies in a given position.<br></br>
///
/// This function continues to be supported for backward compatibility, but you
/// should prefer moveaxis.<br></br>
/// The moveaxis function was added in NumPy
/// 1.11.
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="axis">
/// The axis to roll backwards.<br></br>
/// The positions of the other axes do not
/// change relative to one another.
/// </param>
/// <param name="start">
/// The axis is rolled until it lies before this position.<br></br>
/// The default,
/// 0, results in a “complete” roll.
/// </param>
/// <returns>
/// For NumPy >= 1.10.0 a view of a is always returned.<br></br>
/// For earlier
/// NumPy versions a view of a is returned only if the order of the
/// axes is changed, otherwise the input array is returned.
/// </returns>
public static NDarray rollaxis(this NDarray a, int axis, int? start = 0)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
axis,
});
var kwargs=new PyDict();
if (start!=0) kwargs["start"]=ToPython(start);
dynamic py = __self__.InvokeMethod("rollaxis", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Interchange two axes of an array.
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="axis1">
/// First axis.
/// </param>
/// <param name="axis2">
/// Second axis.
/// </param>
/// <returns>
/// For NumPy >= 1.10.0, if a is an ndarray, then a view of a is
/// returned; otherwise a new array is created.<br></br>
/// For earlier NumPy
/// versions a view of a is returned only if the order of the
/// axes is changed, otherwise the input array is returned.
/// </returns>
public static NDarray swapaxes(this NDarray a, int axis1, int axis2)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
axis1,
axis2,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("swapaxes", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Permute the dimensions of an array.<br></br>
///
/// Notes
///
/// Use transpose(a, argsort(axes)) to invert the transposition of tensors
/// when using the axes keyword argument.<br></br>
///
/// Transposing a 1-D array returns an unchanged view of the original array.
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="axes">
/// By default, reverse the dimensions, otherwise permute the axes
/// according to the values given.
/// </param>
/// <returns>
/// a with its axes permuted.<br></br>
/// A view is returned whenever
/// possible.
/// </returns>
public static NDarray transpose(this NDarray a, int[] axes = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
});
var kwargs=new PyDict();
if (axes!=null) kwargs["axes"]=ToPython(axes);
dynamic py = __self__.InvokeMethod("transpose", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Permute the dimensions of an array.<br></br>
///
/// Notes
///
/// Use transpose(a, argsort(axes)) to invert the transposition of tensors
/// when using the axes keyword argument.<br></br>
///
/// Transposing a 1-D array returns an unchanged view of the original array.
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="axes">
/// By default, reverse the dimensions, otherwise permute the axes
/// according to the values given.
/// </param>
/// <returns>
/// a with its axes permuted.<br></br>
/// A view is returned whenever
/// possible.
/// </returns>
public static NDarray transpose(this NDarray[] a, int[] axes = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
});
var kwargs=new PyDict();
if (axes!=null) kwargs["axes"]=ToPython(axes);
dynamic py = __self__.InvokeMethod("transpose", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Convert inputs to arrays with at least one dimension.<br></br>
///
/// Scalar inputs are converted to 1-dimensional arrays, whilst
/// higher-dimensional inputs are preserved.
/// </summary>
/// <param name="arys">
/// One or more input arrays.
/// </param>
/// <returns>
/// An array, or list of arrays, each with a.ndim >= 1.<br></br>
///
/// Copies are made only if necessary.
/// </returns>
public static NDarray atleast_1d(params NDarray[] arys)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
arys,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("atleast_1d", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// View inputs as arrays with at least two dimensions.
/// </summary>
/// <param name="arys">
/// One or more array-like sequences.<br></br>
/// Non-array inputs are converted
/// to arrays.<br></br>
/// Arrays that already have two or more dimensions are
/// preserved.
/// </param>
/// <returns>
/// An array, or list of arrays, each with a.ndim >= 2.<br></br>
///
/// Copies are avoided where possible, and views with two or more
/// dimensions are returned.
/// </returns>
public static NDarray atleast_2d(params NDarray[] arys)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
arys,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("atleast_2d", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// View inputs as arrays with at least three dimensions.
/// </summary>
/// <param name="arys">
/// One or more array-like sequences.<br></br>
/// Non-array inputs are converted to
/// arrays.<br></br>
/// Arrays that already have three or more dimensions are
/// preserved.
/// </param>
/// <returns>
/// An array, or list of arrays, each with a.ndim >= 3.<br></br>
/// Copies are
/// avoided where possible, and views with three or more dimensions are
/// returned.<br></br>
/// For example, a 1-D array of shape (N,) becomes a view
/// of shape (1, N, 1), and a 2-D array of shape (M, N) becomes a
/// view of shape (M, N, 1).
/// </returns>
public static NDarray atleast_3d(params NDarray[] arys)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
arys,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("atleast_3d", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Produce an object that mimics broadcasting.
/// </summary>
/// <param name="in2">
/// Input parameters.
/// </param>
/// <param name="in1">
/// Input parameters.
/// </param>
/// <returns>
/// Broadcast the input parameters against one another, and
/// return an object that encapsulates the result.<br></br>
///
/// Amongst others, it has shape and nd properties, and
/// may be used as an iterator.
/// </returns>
public static NDarray broadcast(this NDarray in2, NDarray in1)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
in2,
in1,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("broadcast", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Broadcast an array to a new shape.<br></br>
///
/// Notes
/// </summary>
/// <param name="array">
/// The array to broadcast.
/// </param>
/// <param name="shape">
/// The shape of the desired array.
/// </param>
/// <param name="subok">
/// If True, then sub-classes will be passed-through, otherwise
/// the returned array will be forced to be a base-class array (default).
/// </param>
/// <returns>
/// A readonly view on the original array with the given shape.<br></br>
/// It is
/// typically not contiguous.<br></br>
/// Furthermore, more than one element of a
/// broadcasted array may refer to a single memory location.
/// </returns>
public static NDarray broadcast_to(this NDarray array, Shape shape, bool? subok = false)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
array,
shape,
});
var kwargs=new PyDict();
if (subok!=false) kwargs["subok"]=ToPython(subok);
dynamic py = __self__.InvokeMethod("broadcast_to", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Broadcast any number of arrays against each other.
/// </summary>
/// <param name="args">
/// The arrays to broadcast.
/// </param>
/// <param name="subok">
/// If True, then sub-classes will be passed-through, otherwise
/// the returned arrays will be forced to be a base-class array (default).
/// </param>
/// <returns>
/// These arrays are views on the original arrays.<br></br>
/// They are typically
/// not contiguous.<br></br>
/// Furthermore, more than one element of a
/// broadcasted array may refer to a single memory location.<br></br>
/// If you
/// need to write to the arrays, make copies first.
/// </returns>
public static NDarray[] broadcast_arrays(NDarray[] args, bool? subok = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
args,
});
var kwargs=new PyDict();
if (subok!=null) kwargs["subok"]=ToPython(subok);
dynamic py = __self__.InvokeMethod("broadcast_arrays", pyargs, kwargs);
return ToCsharp<NDarray[]>(py);
}
/// <summary>
/// Expand the shape of an array.<br></br>
///
/// Insert a new axis that will appear at the axis position in the expanded
/// array shape.
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="axis">
/// Position in the expanded axes where the new axis is placed.
/// </param>
/// <returns>
/// Output array.<br></br>
/// The number of dimensions is one greater than that of
/// the input array.
/// </returns>
public static NDarray expand_dims(this NDarray a, int axis)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
axis,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("expand_dims", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Remove single-dimensional entries from the shape of an array.
/// </summary>
/// <param name="a">
/// Input data.
/// </param>
/// <param name="axis">
/// Selects a subset of the single-dimensional entries in the
/// shape.<br></br>
/// If an axis is selected with shape entry greater than
/// one, an error is raised.
/// </param>
/// <returns>
/// The input array, but with all or a subset of the
/// dimensions of length 1 removed.<br></br>
/// This is always a itself
/// or a view into a.
/// </returns>
public static NDarray squeeze(this NDarray a, Axis axis = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
});
var kwargs=new PyDict();
if (axis!=null) kwargs["axis"]=ToPython(axis);
dynamic py = __self__.InvokeMethod("squeeze", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return an array converted to a float type.
/// </summary>
/// <param name="a">
/// The input array.
/// </param>
/// <param name="dtype">
/// Float type code to coerce input array a.<br></br>
/// If dtype is one of the
/// ‘int’ dtypes, it is replaced with float64.
/// </param>
/// <returns>
/// The input a as a float ndarray.
/// </returns>
public static NDarray asfarray(this NDarray a, Dtype dtype = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
dynamic py = __self__.InvokeMethod("asfarray", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return an array (ndim >= 1) laid out in Fortran order in memory.
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="dtype">
/// By default, the data-type is inferred from the input data.
/// </param>
/// <returns>
/// The input a in Fortran, or column-major, order.
/// </returns>
public static NDarray asfortranarray(this NDarray a, Dtype dtype = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
dynamic py = __self__.InvokeMethod("asfortranarray", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Convert the input to an array, checking for NaNs or Infs.
/// </summary>
/// <param name="a">
/// Input data, in any form that can be converted to an array.<br></br>
/// This
/// includes lists, lists of tuples, tuples, tuples of tuples, tuples
/// of lists and ndarrays.<br></br>
/// Success requires no NaNs or Infs.
/// </param>
/// <param name="dtype">
/// By default, the data-type is inferred from the input data.
/// </param>
/// <param name="order">
/// Whether to use row-major (C-style) or
/// column-major (Fortran-style) memory representation.<br></br>
///
/// Defaults to ‘C’.
/// </param>
/// <returns>
/// Array interpretation of a.<br></br>
/// No copy is performed if the input
/// is already an ndarray.<br></br>
/// If a is a subclass of ndarray, a base
/// class ndarray is returned.
/// </returns>
public static NDarray asarray_chkfinite(this NDarray a, Dtype dtype = null, string order = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (order!=null) kwargs["order"]=ToPython(order);
dynamic py = __self__.InvokeMethod("asarray_chkfinite", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return an ndarray of the provided type that satisfies requirements.<br></br>
///
/// This function is useful to be sure that an array with the correct flags
/// is returned for passing to compiled code (perhaps through ctypes).<br></br>
///
/// Notes
///
/// The returned array will be guaranteed to have the listed requirements
/// by making a copy if needed.
/// </summary>
/// <param name="a">
/// The object to be converted to a type-and-requirement-satisfying array.
/// </param>
/// <param name="dtype">
/// The required data-type.<br></br>
/// If None preserve the current dtype.<br></br>
/// If your
/// application requires the data to be in native byteorder, include
/// a byteorder specification as a part of the dtype specification.
/// </param>
/// <param name="requirements">
/// The requirements list can be any of the following
/// </param>
public static NDarray require(this NDarray a, Dtype dtype, string[] requirements = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
dtype,
});
var kwargs=new PyDict();
if (requirements!=null) kwargs["requirements"]=ToPython(requirements);
dynamic py = __self__.InvokeMethod("require", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Join a sequence of arrays along an existing axis.<br></br>
///
/// Notes
///
/// When one or more of the arrays to be concatenated is a MaskedArray,
/// this function will return a MaskedArray object instead of an ndarray,
/// but the input masks are not preserved.<br></br>
/// In cases where a MaskedArray
/// is expected as input, use the ma.concatenate function from the masked
/// array module instead.
/// </summary>
/// <param name="arys">
/// The arrays must have the same shape, except in the dimension
/// corresponding to axis (the first, by default).
/// </param>
/// <param name="axis">
/// The axis along which the arrays will be joined.<br></br>
/// If axis is None,
/// arrays are flattened before use.<br></br>
/// Default is 0.
/// </param>
/// <param name="out">
/// If provided, the destination to place the result.<br></br>
/// The shape must be
/// correct, matching that of what concatenate would have returned if no
/// out argument were specified.
/// </param>
/// <returns>
/// The concatenated array.
/// </returns>
public static NDarray concatenate(NDarray[] arys, int? axis = 0, NDarray @out = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
arys,
});
var kwargs=new PyDict();
if (axis!=0) kwargs["axis"]=ToPython(axis);
if (@out!=null) kwargs["out"]=ToPython(@out);
dynamic py = __self__.InvokeMethod("concatenate", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Join a sequence of arrays along a new axis.<br></br>
///
/// The axis parameter specifies the index of the new axis in the dimensions
/// of the result.<br></br>
/// For example, if axis=0 it will be the first dimension
/// and if axis=-1 it will be the last dimension.
/// </summary>
/// <param name="arrays">
/// Each array must have the same shape.
/// </param>
/// <param name="axis">
/// The axis in the result array along which the input arrays are stacked.
/// </param>
/// <param name="out">
/// If provided, the destination to place the result.<br></br>
/// The shape must be
/// correct, matching that of what stack would have returned if no
/// out argument were specified.
/// </param>
/// <returns>
/// The stacked array has one more dimension than the input arrays.
/// </returns>
public static NDarray stack(NDarray[] arrays, int? axis = 0, NDarray @out = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
arrays,
});
var kwargs=new PyDict();
if (axis!=0) kwargs["axis"]=ToPython(axis);
if (@out!=null) kwargs["out"]=ToPython(@out);
dynamic py = __self__.InvokeMethod("stack", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Stack arrays in sequence depth wise (along third axis).<br></br>
///
/// This is equivalent to concatenation along the third axis after 2-D arrays
/// of shape (M,N) have been reshaped to (M,N,1) and 1-D arrays of shape
/// (N,) have been reshaped to (1,N,1).<br></br>
/// Rebuilds arrays divided by
/// dsplit.<br></br>
///
/// This function makes most sense for arrays with up to 3 dimensions.<br></br>
/// For
/// instance, for pixel-data with a height (first axis), width (second axis),
/// and r/g/b channels (third axis).<br></br>
/// The functions concatenate, stack and
/// block provide more general stacking and concatenation operations.
/// </summary>
/// <param name="tup">
/// The arrays must have the same shape along all but the third axis.<br></br>
///
/// 1-D or 2-D arrays must have the same shape.
/// </param>
/// <returns>
/// The array formed by stacking the given arrays, will be at least 3-D.
/// </returns>
public static NDarray dstack(params NDarray[] tup)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
tup,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("dstack", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Stack arrays in sequence horizontally (column wise).<br></br>
///
/// This is equivalent to concatenation along the second axis, except for 1-D
/// arrays where it concatenates along the first axis.<br></br>
/// Rebuilds arrays divided
/// by hsplit.<br></br>
///
/// This function makes most sense for arrays with up to 3 dimensions.<br></br>
/// For
/// instance, for pixel-data with a height (first axis), width (second axis),
/// and r/g/b channels (third axis).<br></br>
/// The functions concatenate, stack and
/// block provide more general stacking and concatenation operations.
/// </summary>
/// <param name="tup">
/// The arrays must have the same shape along all but the second axis,
/// except 1-D arrays which can be any length.
/// </param>
/// <returns>
/// The array formed by stacking the given arrays.
/// </returns>
public static NDarray hstack(params NDarray[] tup)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
tup,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("hstack", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Stack arrays in sequence vertically (row wise).<br></br>
///
/// This is equivalent to concatenation along the first axis after 1-D arrays
/// of shape (N,) have been reshaped to (1,N).<br></br>
/// Rebuilds arrays divided by
/// vsplit.<br></br>
///
/// This function makes most sense for arrays with up to 3 dimensions.<br></br>