// Copyright (c) 2019 by the SciSharp Team

// Code generated by CodeMinion: https://github.com/SciSharp/CodeMinion

using System;

using System.Collections;

using System.Collections.Generic;

using System.IO;

using System.Linq;

using System.Runtime.InteropServices;

using System.Text;

using Python.Runtime;

using Numpy.Models;

namespace Numpy

{

public partial class NDarray

{

/// <summary>

/// Copy an element of an array to a standard Python scalar and return it.<br></br>

///

/// 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.<br></br>

/// Void arrays return a buffer object for item(),

/// unless fields are defined, in which case a tuple is returned.<br></br>

///

/// item is very similar to a[args], except, instead of an array scalar,

/// a standard Python scalar is returned.<br></br>

/// 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.<br></br>

///

/// Return a copy of the array data as a (nested) Python list.<br></br>

///

/// Data items are converted to the nearest compatible Python type.<br></br>

///

/// 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).<br></br>

///

/// Data is always written in ‘C’ order, independent of the order of a.<br></br>

///

/// The data produced by this method can be recovered using the function

/// fromfile().<br></br>

///

/// Notes

///

/// This is a convenience function for quick storage of array data.<br></br>

///

/// 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.<br></br>

/// Some of these problems can be overcome

/// by outputting the data as text files, at the expense of speed and file

/// size.<br></br>

///

/// When fid is a file object, array contents are directly written to the

/// file, bypassing the file object’s write method.<br></br>

/// 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.<br></br>

///

/// If “” (empty), a binary file is written, equivalent to

/// file.write(a.tobytes()).

/// </param>

/// <param name="format">

/// Format string for text file output.<br></br>

///

/// 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.<br></br>

///

/// 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.<br></br>

///

/// 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.<br></br>

///

/// 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.<br></br>

/// 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.<br></br>

///

/// ‘C’ means C order, ‘F’ means Fortran order, ‘A’

/// means ‘F’ order if all the arrays are Fortran contiguous,

/// ‘C’ order otherwise, and ‘K’ means as close to the

/// order the array elements appear in memory as possible.<br></br>

///

/// Default is ‘K’.

/// </param>

/// <param name="casting">

/// Controls what kind of data casting may occur.<br></br>

/// 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.<br></br>

/// 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.<br></br>

/// 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.<br></br>

/// ‘C’ means C-order,

/// ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous,

/// ‘C’ otherwise.<br></br>

/// ‘K’ means match the layout of a as closely

/// as possible.<br></br>

/// (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.<br></br>

///

/// A field is a view of the array data with a given data-type.<br></br>

/// The values in

/// the view are determined by the given type and the offset into the current

/// array in bytes.<br></br>

/// 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.<br></br>

///

/// 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.<br></br>

/// 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.<br></br>

///

/// These Boolean-valued flags affect how numpy interprets the memory

/// area used by a (see Notes below).<br></br>

/// The ALIGNED flag can only

/// be set to True if the data is actually aligned according to the type.<br></br>

///

/// The WRITEBACKIFCOPY and (deprecated) UPDATEIFCOPY flags can never be set

/// to True.<br></br>

/// 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.<br></br>

/// (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.<br></br>

/// There are 7 Boolean flags

/// in use, only four of which can be changed by the user:

/// WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED.<br></br>

///

/// 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).<br></br>

/// When the C-API function PyArray_ResolveWritebackIfCopy is

/// called, the base array will be updated with the contents of this array.<br></br>

///

/// 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.<br></br>

///

/// For a 1-D array, this has no effect.<br></br>

/// (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.<br></br>

///

/// For an n-D array, if axes are given, their order indicates how the

/// axes are permuted (see Examples).<br></br>

/// 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(params int[] axes)

{

//auto-generated code, do not change

var __self__=self;

var pyargs=ToTuple(new object[]

{

});

var kwargs=new PyDict();

if (axes!=null) kwargs["axes"]=ToPython(axes);

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.<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 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.<br></br>

///

/// The state argument must be a sequence that contains the following

/// elements:

/// </summary>

/// <param name="version">

/// optional pickle version.<br></br>

/// 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.<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="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 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.<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="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 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.<br></br>

///

/// Other axes remain in their original order.

/// </summary>

/// <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 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.<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="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 &gt;= 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 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 &gt;= 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 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.<br></br>

///

/// 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.<br></br>

///

/// 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.<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 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.<br></br>

///

/// 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.<br></br>

/// 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.<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 NDarray squeeze(params int[] axis)

{

//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.<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 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 &gt;= 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.<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 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.<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="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 NDarray require(Dtype dtype, string[] requirements = null)

{

//auto-generated code, do not change

var @this=this;

return NumPy.Instance.require(@this, dtype, requirements: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.<br></br>

/// If such a split is not possible,

/// an error is raised.<br></br>

///

/// If indices_or_sections is a 1-D array of sorted integers, the entries

/// indicate where along axis the array is split.<br></br>

/// 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.<br></br>

///

/// If reps has length d, the result will have dimension of

/// max(d, A.ndim).<br></br>

///

/// If A.ndim &lt; d, A is promoted to be d-dimensional by prepending new

/// axes.<br></br>

/// So a shape (3,) array is promoted to (1, 3) for 2-D replication,

/// or shape (1, 1, 3) for 3-D replication.<br></br>

/// If this is not the desired

/// behavior, promote A to d-dimensions manually before calling this

/// function.<br></br>

///

/// If A.ndim &gt; d, reps is promoted to A.ndim by pre-pending 1’s to it.<br></br>

///

/// Thus for an A of shape (2, 3, 4, 5), a reps of (2, 2) is treated as

/// (1, 1, 2, 2).<br></br>

///

/// 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.<br></br>

/// repeats is broadcasted

/// to fit the shape of the given axis.

/// </param>

/// <param name="axis">

/// The axis along which to repeat values.<br></br>

/// 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.<br></br>

/// For a one

/// dimensional array, this returns those entries not returned by

/// arr[obj].<br></br>

///

/// Notes

///

/// Often it is preferable to use a boolean mask.<br></br>

/// 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.<br></br>

///

/// If axis is None, obj is applied to the flattened array.

/// </param>

/// <returns>

/// A copy of arr with the elements specified by obj removed.<br></br>

/// Note

/// that delete does not occur in-place.<br></br>

/// 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.<br></br>

///

/// 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.<br></br>

///

/// Support for multiple insertions when obj is a single scalar or a

/// sequence with one element (similar to calling insert multiple