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Copy pathNumPy.array_manipulation.gen.cs
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1539 lines (1495 loc) · 61.5 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>
/// Copies values from one array to another, broadcasting as necessary.
///
/// 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 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.
///
/// 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 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.
///
/// Notes
///
/// It is not always possible to change the shape of an array without
/// copying the data. 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.
/// 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. If
/// an integer, then the result will be a 1-D array of that length.
/// 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. ‘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. ‘F’ means to read / write the
/// elements using Fortran-like index order, with the first index
/// changing fastest, and the last index changing slowest. 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.
/// ‘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. Note there is no guarantee of the memory layout (C- or
/// Fortran- contiguous) of the returned array.
/// </returns>
public NDarray reshape(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.
///
/// A 1-D array, containing the elements of the input, is returned. A copy is
/// made only if needed.
///
/// As of NumPy 1.10, the returned array will have the same type as the input
/// array. (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. 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. The opposite holds for
/// column-major, Fortran-style index ordering.
///
/// When a view is desired in as many cases as possible, arr.reshape(-1)
/// may be preferable.
/// </summary>
/// <param name="a">
/// Input array. 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. ‘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. ‘F’ means to index the elements
/// in column-major, Fortran-style order, with the
/// first index changing fastest, and the last index changing
/// slowest. 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. ‘A’ means to read the elements in
/// Fortran-like index order if a is Fortran contiguous in
/// memory, C-like order otherwise. ‘K’ means to read the
/// elements in the order they occur in memory, except for
/// reversing the data when strides are negative. By default, ‘C’
/// index order is used.
/// </param>
/// <returns>
/// y is an array of the same subtype as a, with shape (a.size,).
/// Note that matrices are special cased for backward compatibility, if a
/// is a matrix, then y is a 1-D ndarray.
/// </returns>
public NDarray ravel(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.
/// ‘F’ means to flatten in column-major (Fortran-
/// style) order. ‘A’ means to flatten in column-major
/// order if a is Fortran contiguous in memory,
/// row-major order otherwise. ‘K’ means to flatten
/// a in the order the elements occur in memory.
/// The default is ‘C’.
/// </param>
/// <returns>
/// A copy of the input array, flattened to one dimension.
/// </returns>
public 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.
///
/// 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. These must be unique.
/// </param>
/// <param name="destination">
/// Destination positions for each of the original axes. These must also be
/// unique.
/// </param>
/// <returns>
/// Array with moved axes. This array is a view of the input array.
/// </returns>
public NDarray moveaxis(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.
///
/// This function continues to be supported for backward compatibility, but you
/// should prefer moveaxis. The moveaxis function was added in NumPy
/// 1.11.
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="axis">
/// The axis to roll backwards. 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. The default,
/// 0, results in a “complete” roll.
/// </param>
/// <returns>
/// For NumPy >= 1.10.0 a view of a is always returned. 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 NDarray rollaxis(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. 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 NDarray swapaxes(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.
///
/// Notes
///
/// Use transpose(a, argsort(axes)) to invert the transposition of tensors
/// when using the axes keyword argument.
///
/// 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. A view is returned whenever
/// possible.
/// </returns>
public NDarray transpose(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.
///
/// 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.
/// Copies are made only if necessary.
/// </returns>
public 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. Non-array inputs are converted
/// to arrays. Arrays that already have two or more dimensions are
/// preserved.
/// </param>
/// <returns>
/// An array, or list of arrays, each with a.ndim >= 2.
/// Copies are avoided where possible, and views with two or more
/// dimensions are returned.
/// </returns>
public 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. Non-array inputs are converted to
/// arrays. Arrays that already have three or more dimensions are
/// preserved.
/// </param>
/// <returns>
/// An array, or list of arrays, each with a.ndim >= 3. Copies are
/// avoided where possible, and views with three or more dimensions are
/// returned. 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 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.
/// Amongst others, it has shape and nd properties, and
/// may be used as an iterator.
/// </returns>
public NDarray broadcast(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.
///
/// 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. It is
/// typically not contiguous. Furthermore, more than one element of a
/// broadcasted array may refer to a single memory location.
/// </returns>
public NDarray broadcast_to(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. They are typically
/// not contiguous. Furthermore, more than one element of a
/// broadcasted array may refer to a single memory location. If you
/// need to write to the arrays, make copies first.
/// </returns>
public 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.
///
/// 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. The number of dimensions is one greater than that of
/// the input array.
/// </returns>
public NDarray expand_dims(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. 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. This is always a itself
/// or a view into a.
/// </returns>
public NDarray squeeze(NDarray a, int[] 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. If dtype is one of the
/// ‘int’ dtypes, it is replaced with float64.
/// </param>
/// <returns>
/// The input a as a float ndarray.
/// </returns>
public NDarray asfarray(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 NDarray asfortranarray(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. This
/// includes lists, lists of tuples, tuples, tuples of tuples, tuples
/// of lists and ndarrays. 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.
/// Defaults to ‘C’.
/// </param>
/// <returns>
/// Array interpretation of a. No copy is performed if the input
/// is already an ndarray. If a is a subclass of ndarray, a base
/// class ndarray is returned.
/// </returns>
public NDarray asarray_chkfinite(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.
///
/// This function is useful to be sure that an array with the correct flags
/// is returned for passing to compiled code (perhaps through ctypes).
///
/// 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. If None preserve the current dtype. 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 NDarray require(NDarray a, Dtype dtype, string[] requirements = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
dtype,
requirements,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("require", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Join a sequence of arrays along an existing axis.
///
/// 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. 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. If axis is None,
/// arrays are flattened before use. Default is 0.
/// </param>
/// <param name="@out">
/// If provided, the destination to place the result. 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 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.
///
/// The axis parameter specifies the index of the new axis in the dimensions
/// of the result. 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. 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 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 1-D arrays as columns into a 2-D array.
///
/// Take a sequence of 1-D arrays and stack them as columns
/// to make a single 2-D array. 2-D arrays are stacked as-is,
/// just like with hstack. 1-D arrays are turned into 2-D columns
/// first.
/// </summary>
/// <param name="tup">
/// Arrays to stack. All of them must have the same first dimension.
/// </param>
/// <returns>
/// The array formed by stacking the given arrays.
/// </returns>
public NDarray column_stack(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("column_stack", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Stack arrays in sequence depth wise (along third axis).
///
/// 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). Rebuilds arrays divided by
/// dsplit.
///
/// This function makes most sense for arrays with up to 3 dimensions. For
/// instance, for pixel-data with a height (first axis), width (second axis),
/// and r/g/b channels (third axis). 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.
/// 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 NDarray dstack(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).
///
/// This is equivalent to concatenation along the second axis, except for 1-D
/// arrays where it concatenates along the first axis. Rebuilds arrays divided
/// by hsplit.
///
/// This function makes most sense for arrays with up to 3 dimensions. For
/// instance, for pixel-data with a height (first axis), width (second axis),
/// and r/g/b channels (third axis). 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 NDarray hstack(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).
///
/// This is equivalent to concatenation along the first axis after 1-D arrays
/// of shape (N,) have been reshaped to (1,N). Rebuilds arrays divided by
/// vsplit.
///
/// This function makes most sense for arrays with up to 3 dimensions. For
/// instance, for pixel-data with a height (first axis), width (second axis),
/// and r/g/b channels (third axis). 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 first axis.
/// 1-D arrays must have the same length.
/// </param>
/// <returns>
/// The array formed by stacking the given arrays, will be at least 2-D.
/// </returns>
public NDarray vstack(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("vstack", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/*
/// <summary>
/// Assemble an nd-array from nested lists of blocks.
///
/// Blocks in the innermost lists are concatenated (see concatenate) along
/// the last dimension (-1), then these are concatenated along the
/// second-last dimension (-2), and so on until the outermost list is reached.
///
/// Blocks can be of any dimension, but will not be broadcasted using the normal
/// rules. Instead, leading axes of size 1 are inserted, to make block.ndim
/// the same for all blocks. This is primarily useful for working with scalars,
/// and means that code like np.block([v, 1]) is valid, where
/// v.ndim == 1.
///
/// When the nested list is two levels deep, this allows block matrices to be
/// constructed from their components.
///
/// Notes
///
/// When called with only scalars, np.block is equivalent to an ndarray
/// call. So np.block([[1, 2], [3, 4]]) is equivalent to
/// np.array([[1, 2], [3, 4]]).
///
/// This function does not enforce that the blocks lie on a fixed grid.
/// np.block([[a, b], [c, d]]) is not restricted to arrays of the form:
///
/// But is also allowed to produce, for some a, b, c, d:
///
/// Since concatenation happens along the last axis first, block is _not_
/// capable of producing the following directly:
///
/// Matlab’s “square bracket stacking”, [A, B, ...; p, q, ...], is
/// equivalent to np.block([[A, B, ...], [p, q, ...]]).
/// </summary>
/// <param name="arrays">
/// If passed a single ndarray or scalar (a nested list of depth 0), this
/// is returned unmodified (and not copied).
///
/// Elements shapes must match along the appropriate axes (without
/// broadcasting), but leading 1s will be prepended to the shape as
/// necessary to make the dimensions match.
/// </param>
/// <returns>
/// The array assembled from the given blocks.
///
/// The dimensionality of the output is equal to the greatest of:
/// * the dimensionality of all the inputs
/// * the depth to which the input list is nested
/// </returns>
public NDarray block(nested list of array_like or scalars (but not tuples) arrays)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
arrays,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("block", pyargs, kwargs);
return ToCsharp<NDarray>(py);