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9977 lines (9697 loc) · 454 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 NDarray
{
/// <summary>
/// Copy an element of an array to a standard Python scalar and return it.
///
/// Notes
///
/// When the data type of a is longdouble or clongdouble, item() returns
/// a scalar array object because there is no available Python scalar that
/// would not lose information. Void arrays return a buffer object for item(),
/// unless fields are defined, in which case a tuple is returned.
///
/// item is very similar to a[args], except, instead of an array scalar,
/// a standard Python scalar is returned. This can be useful for speeding up
/// access to elements of the array and doing arithmetic on elements of the
/// array using Python’s optimized math.
/// </summary>
/// <returns>
/// A copy of the specified element of the array as a suitable
/// Python scalar
/// </returns>
public T item<T>(params int[] args)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
args,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("item", pyargs, kwargs);
return ToCsharp<T>(py);
}
/*
/// <summary>
/// Return the array as a (possibly nested) list.
///
/// Return a copy of the array data as a (nested) Python list.
/// Data items are converted to the nearest compatible Python type.
///
/// Notes
///
/// The array may be recreated, a = np.array(a.tolist()).
/// </summary>
/// <returns>
/// The possibly nested list of array elements.
/// </returns>
public List<T> tolist<T>()
{
//auto-generated code, do not change
var __self__=self;
dynamic py = __self__.InvokeMethod("tolist");
return ToCsharp<List<T>>(py);
}
*/
/// <summary>
/// Write array to a file as text or binary (default).
///
/// Data is always written in ‘C’ order, independent of the order of a.
/// The data produced by this method can be recovered using the function
/// fromfile().
///
/// Notes
///
/// This is a convenience function for quick storage of array data.
/// Information on endianness and precision is lost, so this method is not a
/// good choice for files intended to archive data or transport data between
/// machines with different endianness. Some of these problems can be overcome
/// by outputting the data as text files, at the expense of speed and file
/// size.
///
/// When fid is a file object, array contents are directly written to the
/// file, bypassing the file object’s write method. As a result, tofile
/// cannot be used with files objects supporting compression (e.g., GzipFile)
/// or file-like objects that do not support fileno() (e.g., BytesIO).
/// </summary>
/// <param name="fid">
/// An open file object, or a string containing a filename.
/// </param>
/// <param name="sep">
/// Separator between array items for text output.
/// If “” (empty), a binary file is written, equivalent to
/// file.write(a.tobytes()).
/// </param>
/// <param name="format">
/// Format string for text file output.
/// Each entry in the array is formatted to text by first converting
/// it to the closest Python type, and then using “format” % item.
/// </param>
public void tofile(string fid, string sep, string format)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
fid,
sep,
format,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("tofile", pyargs, kwargs);
}
/// <summary>
/// Dump a pickle of the array to the specified file.
/// The array can be read back with pickle.load or numpy.load.
/// </summary>
/// <param name="file">
/// A string naming the dump file.
/// </param>
public void dump(string file)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
file,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("dump", pyargs, kwargs);
}
/// <summary>
/// Returns the pickle of the array as a string.
/// pickle.loads or numpy.loads will convert the string back to an array.
/// </summary>
public void dumps()
{
//auto-generated code, do not change
var __self__=self;
dynamic py = __self__.InvokeMethod("dumps");
}
/// <summary>
/// Copy of the array, cast to a specified type.
///
/// Notes
///
/// Starting in NumPy 1.9, astype method now returns an error if the string
/// dtype to cast to is not long enough in ‘safe’ casting mode to hold the max
/// value of integer/float array that is being casted. Previously the casting
/// was allowed even if the result was truncated.
/// </summary>
/// <param name="dtype">
/// Typecode or data-type to which the array is cast.
/// </param>
/// <param name="order">
/// Controls the memory layout order of the result.
/// ‘C’ means C order, ‘F’ means Fortran order, ‘A’
/// means ‘F’ order if all the arrays are Fortran contiguous,
/// ‘C’ order otherwise, and ‘K’ means as close to the
/// order the array elements appear in memory as possible.
/// Default is ‘K’.
/// </param>
/// <param name="casting">
/// Controls what kind of data casting may occur. Defaults to ‘unsafe’
/// for backwards compatibility.
/// </param>
/// <param name="subok">
/// If True, then sub-classes will be passed-through (default), otherwise
/// the returned array will be forced to be a base-class array.
/// </param>
/// <param name="copy">
/// By default, astype always returns a newly allocated array. If this
/// is set to false, and the dtype, order, and subok
/// requirements are satisfied, the input array is returned instead
/// of a copy.
/// </param>
/// <returns>
/// Unless copy is False and the other conditions for returning the input
/// array are satisfied (see description for copy input parameter), arr_t
/// is a new array of the same shape as the input array, with dtype, order
/// given by dtype, order.
/// </returns>
public NDarray astype(Dtype dtype, string order = null, string casting = null, bool? subok = null, bool? copy = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
dtype,
});
var kwargs=new PyDict();
if (order!=null) kwargs["order"]=ToPython(order);
if (casting!=null) kwargs["casting"]=ToPython(casting);
if (subok!=null) kwargs["subok"]=ToPython(subok);
if (copy!=null) kwargs["copy"]=ToPython(copy);
dynamic py = __self__.InvokeMethod("astype", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Swap the bytes of the array elements
///
/// Toggle between low-endian and big-endian data representation by
/// returning a byteswapped array, optionally swapped in-place.
/// </summary>
/// <param name="inplace">
/// If True, swap bytes in-place, default is False.
/// </param>
/// <returns>
/// The byteswapped array. If inplace is True, this is
/// a view to self.
/// </returns>
public NDarray byteswap(bool? inplace = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
});
var kwargs=new PyDict();
if (inplace!=null) kwargs["inplace"]=ToPython(inplace);
dynamic py = __self__.InvokeMethod("byteswap", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return a copy of the array.
/// </summary>
/// <param name="order">
/// Controls the memory layout of the copy. ‘C’ means C-order,
/// ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous,
/// ‘C’ otherwise. ‘K’ means match the layout of a as closely
/// as possible. (Note that this function and numpy.copy are very
/// similar, but have different default values for their order=
/// arguments.)
/// </param>
public void copy(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("copy", pyargs, kwargs);
}
/// <summary>
/// Returns a field of the given array as a certain type.
///
/// A field is a view of the array data with a given data-type. The values in
/// the view are determined by the given type and the offset into the current
/// array in bytes. The offset needs to be such that the view dtype fits in the
/// array dtype; for example an array of dtype complex128 has 16-byte elements.
/// If taking a view with a 32-bit integer (4 bytes), the offset needs to be
/// between 0 and 12 bytes.
/// </summary>
/// <param name="dtype">
/// The data type of the view. The dtype size of the view can not be larger
/// than that of the array itself.
/// </param>
/// <param name="offset">
/// Number of bytes to skip before beginning the element view.
/// </param>
public void getfield(Dtype dtype, int offset)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
dtype,
offset,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("getfield", pyargs, kwargs);
}
/// <summary>
/// Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY),
/// respectively.
///
/// These Boolean-valued flags affect how numpy interprets the memory
/// area used by a (see Notes below). The ALIGNED flag can only
/// be set to True if the data is actually aligned according to the type.
/// The WRITEBACKIFCOPY and (deprecated) UPDATEIFCOPY flags can never be set
/// to True. The flag WRITEABLE can only be set to True if the array owns its
/// own memory, or the ultimate owner of the memory exposes a writeable buffer
/// interface, or is a string. (The exception for string is made so that
/// unpickling can be done without copying memory.)
///
/// Notes
///
/// Array flags provide information about how the memory area used
/// for the array is to be interpreted. There are 7 Boolean flags
/// in use, only four of which can be changed by the user:
/// WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED.
///
/// WRITEABLE (W) the data area can be written to;
///
/// ALIGNED (A) the data and strides are aligned appropriately for the hardware
/// (as determined by the compiler);
///
/// UPDATEIFCOPY (U) (deprecated), replaced by WRITEBACKIFCOPY;
///
/// WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced
/// by .base). When the C-API function PyArray_ResolveWritebackIfCopy is
/// called, the base array will be updated with the contents of this array.
///
/// All flags can be accessed using the single (upper case) letter as well
/// as the full name.
/// </summary>
/// <param name="write">
/// Describes whether or not a can be written to.
/// </param>
/// <param name="align">
/// Describes whether or not a is aligned properly for its type.
/// </param>
/// <param name="uic">
/// Describes whether or not a is a copy of another “base” array.
/// </param>
public void setflags(bool? write = null, bool? align = null, bool? uic = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
});
var kwargs=new PyDict();
if (write!=null) kwargs["write"]=ToPython(write);
if (align!=null) kwargs["align"]=ToPython(align);
if (uic!=null) kwargs["uic"]=ToPython(uic);
dynamic py = __self__.InvokeMethod("setflags", pyargs, kwargs);
}
/// <summary>
/// Fill the array with a scalar value.
/// </summary>
/// <param name="@value">
/// All elements of a will be assigned this value.
/// </param>
public void fill(ValueType @value)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
@value,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("fill", pyargs, kwargs);
}
/// <summary>
/// Returns a view of the array with axes transposed.
///
/// For a 1-D array, this has no effect. (To change between column and
/// row vectors, first cast the 1-D array into a matrix object.)
/// For a 2-D array, this is the usual matrix transpose.
/// For an n-D array, if axes are given, their order indicates how the
/// axes are permuted (see Examples). If axes are not provided and
/// a.shape = (i[0], i[1], ... i[n-2], i[n-1]), then
/// a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0]).
/// </summary>
/// <returns>
/// View of a, with axes suitably permuted.
/// </returns>
public NDarray transpose(int[] axes = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
axes,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("transpose", 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>
/// For unpickling.
///
/// The state argument must be a sequence that contains the following
/// elements:
/// </summary>
/// <param name="version">
/// optional pickle version. If omitted defaults to 0.
/// </param>
/// <param name="rawdata">
/// a binary string with the data (or a list if ‘a’ is an object array)
/// </param>
public void __setstate__(int version, Shape shape, Dtype dtype, bool isFortran, string rawdata)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
version,
shape,
dtype,
isFortran,
rawdata,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("__setstate__", 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="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(Shape newshape, string order = null)
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.reshape(@this, newshape, order:order);
}
/// <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="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(string order = null)
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.ravel(@this, order:order);
}
/// <summary>
/// Move axes of an array to new positions.
///
/// Other axes remain in their original order.
/// </summary>
/// <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(int[] source, int[] destination)
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.moveaxis(@this, source, destination);
}
/// <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="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(int axis, int? start = 0)
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.rollaxis(@this, axis, start:start);
}
/// <summary>
/// Interchange two axes of an array.
/// </summary>
/// <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(int axis1, int axis2)
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.swapaxes(@this, axis1, axis2);
}
/// <summary>
/// Produce an object that mimics broadcasting.
/// </summary>
/// <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 in1)
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.broadcast(@this, in1);
}
/// <summary>
/// Broadcast an array to a new shape.
///
/// Notes
/// </summary>
/// <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(Shape shape, bool? subok = false)
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.broadcast_to(@this, shape, subok:subok);
}
/// <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="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(int axis)
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.expand_dims(@this, axis);
}
/// <summary>
/// Remove single-dimensional entries from the shape of an array.
/// </summary>
/// <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(int[] axis = null)
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.squeeze(@this, axis:axis);
}
/// <summary>
/// Return an array converted to a float type.
/// </summary>
/// <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(Dtype dtype = null)
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.asfarray(@this, dtype:dtype);
}
/// <summary>
/// Return an array (ndim >= 1) laid out in Fortran order in memory.
/// </summary>
/// <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(Dtype dtype = null)
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.asfortranarray(@this, dtype:dtype);
}
/// <summary>
/// Convert the input to an array, checking for NaNs or Infs.
/// </summary>
/// <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(Dtype dtype = null, string order = null)
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.asarray_chkfinite(@this, dtype:dtype, order:order);
}
/// <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="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(Dtype dtype, string[] requirements = null)
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.require(@this, dtype, requirements);
}
/// <summary>
/// Split an array into multiple sub-arrays.
/// </summary>
/// <param name="indices_or_sections">
/// If indices_or_sections is an integer, N, the array will be divided
/// into N equal arrays along axis. If such a split is not possible,
/// an error is raised.
///
/// If indices_or_sections is a 1-D array of sorted integers, the entries
/// indicate where along axis the array is split. For example,
/// [2, 3] would, for axis=0, result in
///
/// If an index exceeds the dimension of the array along axis,
/// an empty sub-array is returned correspondingly.
/// </param>
/// <param name="axis">
/// The axis along which to split, default is 0.
/// </param>
/// <returns>
/// A list of sub-arrays.
/// </returns>
public NDarray[] split(int[] indices_or_sections, int? axis = 0)
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.split(@this, indices_or_sections, axis:axis);
}
/// <summary>
/// Construct an array by repeating A the number of times given by reps.
///
/// If reps has length d, the result will have dimension of
/// max(d, A.ndim).
///
/// If A.ndim < d, A is promoted to be d-dimensional by prepending new
/// axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication,
/// or shape (1, 1, 3) for 3-D replication. If this is not the desired
/// behavior, promote A to d-dimensions manually before calling this
/// function.
///
/// If A.ndim > d, reps is promoted to A.ndim by pre-pending 1’s to it.
/// Thus for an A of shape (2, 3, 4, 5), a reps of (2, 2) is treated as
/// (1, 1, 2, 2).
///
/// Note : Although tile may be used for broadcasting, it is strongly
/// recommended to use numpy’s broadcasting operations and functions.
/// </summary>
/// <param name="reps">
/// The number of repetitions of A along each axis.
/// </param>
/// <returns>
/// The tiled output array.
/// </returns>
public NDarray tile(NDarray reps)
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.tile(@this, reps);
}
/// <summary>
/// Repeat elements of an array.
/// </summary>
/// <param name="repeats">
/// The number of repetitions for each element. repeats is broadcasted
/// to fit the shape of the given axis.
/// </param>
/// <param name="axis">
/// The axis along which to repeat values. By default, use the
/// flattened input array, and return a flat output array.
/// </param>
/// <returns>
/// Output array which has the same shape as a, except along
/// the given axis.
/// </returns>
public NDarray repeat(int[] repeats, int? axis = null)
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.repeat(@this, repeats, axis:axis);
}
/// <summary>
/// Return a new array with sub-arrays along an axis deleted. For a one
/// dimensional array, this returns those entries not returned by
/// arr[obj].
///
/// Notes
///
/// Often it is preferable to use a boolean mask. For example:
///
/// Is equivalent to np.delete(arr, [0,2,4], axis=0), but allows further
/// use of mask.
/// </summary>
/// <param name="obj">
/// Indicate which sub-arrays to remove.
/// </param>
/// <param name="axis">
/// The axis along which to delete the subarray defined by obj.
/// If axis is None, obj is applied to the flattened array.
/// </param>
/// <returns>
/// A copy of arr with the elements specified by obj removed. Note
/// that delete does not occur in-place. If axis is None, out is
/// a flattened array.
/// </returns>
public NDarray delete(Slice obj, int? axis = null)
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.delete(@this, obj, axis:axis);
}
/// <summary>
/// Insert values along the given axis before the given indices.
///
/// Notes
///
/// Note that for higher dimensional inserts obj=0 behaves very different
/// from obj=[0] just like arr[:,0,:] = values is different from
/// arr[:,[0],:] = values.
/// </summary>
/// <param name="obj">
/// Object that defines the index or indices before which values is
/// inserted.
///
/// Support for multiple insertions when obj is a single scalar or a
/// sequence with one element (similar to calling insert multiple
/// times).
/// </param>
/// <param name="values">
/// Values to insert into arr. If the type of values is different
/// from that of arr, values is converted to the type of arr.
/// values should be shaped so that arr[...,obj,...] = values
/// is legal.
/// </param>
/// <param name="axis">
/// Axis along which to insert values. If axis is None then arr
/// is flattened first.
/// </param>
/// <returns>
/// A copy of arr with values inserted. Note that insert
/// does not occur in-place: a new array is returned. If
/// axis is None, out is a flattened array.
/// </returns>
public NDarray insert(int obj = 0, NDarray values = null, int? axis = null)
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.insert(@this, obj, values, axis:axis);
}
/// <summary>
/// Append values to the end of an array.
/// </summary>
/// <param name="values">
/// These values are appended to a copy of arr. It must be of the
/// correct shape (the same shape as arr, excluding axis). If
/// axis is not specified, values can be any shape and will be
/// flattened before use.
/// </param>
/// <param name="axis">
/// The axis along which values are appended. If axis is not
/// given, both arr and values are flattened before use.
/// </param>
/// <returns>
/// A copy of arr with values appended to axis. Note that
/// append does not occur in-place: a new array is allocated and
/// filled. If axis is None, out is a flattened array.
/// </returns>
public NDarray append(NDarray values, int? axis = null)
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.append(@this, values, axis:axis);
}
/// <summary>
/// Trim the leading and/or trailing zeros from a 1-D array or sequence.
/// </summary>
/// <param name="trim">
/// A string with ‘f’ representing trim from front and ‘b’ to trim from
/// back. Default is ‘fb’, trim zeros from both front and back of the
/// array.
/// </param>
/// <returns>
/// The result of trimming the input. The input data type is preserved.
/// </returns>
public NDarray trim_zeros(string trim = "fb")
{
//auto-generated code, do not change
var @this=this;
return NumPy.Instance.trim_zeros(@this, trim:trim);
}
/// <summary>
/// Find the unique elements of an array.
///
/// Returns the sorted unique elements of an array. There are three optional
/// outputs in addition to the unique elements:
///
/// Notes
///
/// When an axis is specified the subarrays indexed by the axis are sorted.
/// This is done by making the specified axis the first dimension of the array
/// and then flattening the subarrays in C order. The flattened subarrays are
/// then viewed as a structured type with each element given a label, with the
/// effect that we end up with a 1-D array of structured types that can be
/// treated in the same way as any other 1-D array. The result is that the
/// flattened subarrays are sorted in lexicographic order starting with the
/// first element.
/// </summary>
/// <param name="return_index">
/// If True, also return the indices of ar (along the specified axis,
/// if provided, or in the flattened array) that result in the unique array.
/// </param>
/// <param name="return_inverse">