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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 Numpy.Models;
namespace Numpy
{
public partial class NumPy
{
/// <summary>
/// Return a new array of given shape and type, without initializing entries.<br></br>
///
/// Notes
///
/// empty, unlike zeros, does not set the array values to zero,
/// and may therefore be marginally faster.<br></br>
/// On the other hand, it requires
/// the user to manually set all the values in the array, and should be
/// used with caution.
/// </summary>
/// <param name="shape">
/// Shape of the empty array, e.g., (2, 3) or 2.
/// </param>
/// <param name="dtype">
/// Desired output data-type for the array, e.g, numpy.int8. Default is
/// numpy.float64.
/// </param>
/// <param name="order">
/// Whether to store multi-dimensional data in row-major
/// (C-style) or column-major (Fortran-style) order in
/// memory.
/// </param>
/// <returns>
/// Array of uninitialized (arbitrary) data of the given shape, dtype, and
/// order.<br></br>
/// Object arrays will be initialized to None.
/// </returns>
public NDarray empty(Shape shape, Dtype dtype = null, string order = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
shape,
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (order!=null) kwargs["order"]=ToPython(order);
dynamic py = __self__.InvokeMethod("empty", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return a new array with the same shape and type as a given array.<br></br>
///
/// Notes
///
/// This function does not initialize the returned array; to do that use
/// zeros_like or ones_like instead.<br></br>
/// It may be marginally faster than
/// the functions that do set the array values.
/// </summary>
/// <param name="prototype">
/// The shape and data-type of prototype define these same attributes
/// of the returned array.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result.<br></br>
/// ‘C’ means C-order,
/// ‘F’ means F-order, ‘A’ means ‘F’ if prototype is Fortran
/// contiguous, ‘C’ otherwise.<br></br>
/// ‘K’ means match the layout of prototype
/// as closely as possible.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array.<br></br>
/// Defaults
/// to True.
/// </param>
/// <returns>
/// Array of uninitialized (arbitrary) data with the same
/// shape and type as prototype.
/// </returns>
public NDarray empty_like(NDarray prototype, Dtype dtype = null, string order = null, bool? subok = true)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
prototype,
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (order!=null) kwargs["order"]=ToPython(order);
if (subok!=true) kwargs["subok"]=ToPython(subok);
dynamic py = __self__.InvokeMethod("empty_like", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return a new array with the same shape and type as a given array.<br></br>
///
/// Notes
///
/// This function does not initialize the returned array; to do that use
/// zeros_like or ones_like instead.<br></br>
/// It may be marginally faster than
/// the functions that do set the array values.
/// </summary>
/// <param name="prototype">
/// The shape and data-type of prototype define these same attributes
/// of the returned array.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result.<br></br>
/// ‘C’ means C-order,
/// ‘F’ means F-order, ‘A’ means ‘F’ if prototype is Fortran
/// contiguous, ‘C’ otherwise.<br></br>
/// ‘K’ means match the layout of prototype
/// as closely as possible.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array.<br></br>
/// Defaults
/// to True.
/// </param>
/// <returns>
/// Array of uninitialized (arbitrary) data with the same
/// shape and type as prototype.
/// </returns>
public NDarray<T> empty_like<T>(T[] prototype, Dtype dtype = null, string order = null, bool? subok = true)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
SharpToSharp<NDarray>(prototype),
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (order!=null) kwargs["order"]=ToPython(order);
if (subok!=true) kwargs["subok"]=ToPython(subok);
dynamic py = __self__.InvokeMethod("empty_like", pyargs, kwargs);
return ToCsharp<NDarray<T>>(py);
}
/// <summary>
/// Return a new array with the same shape and type as a given array.<br></br>
///
/// Notes
///
/// This function does not initialize the returned array; to do that use
/// zeros_like or ones_like instead.<br></br>
/// It may be marginally faster than
/// the functions that do set the array values.
/// </summary>
/// <param name="prototype">
/// The shape and data-type of prototype define these same attributes
/// of the returned array.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result.<br></br>
/// ‘C’ means C-order,
/// ‘F’ means F-order, ‘A’ means ‘F’ if prototype is Fortran
/// contiguous, ‘C’ otherwise.<br></br>
/// ‘K’ means match the layout of prototype
/// as closely as possible.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array.<br></br>
/// Defaults
/// to True.
/// </param>
/// <returns>
/// Array of uninitialized (arbitrary) data with the same
/// shape and type as prototype.
/// </returns>
public NDarray<T> empty_like<T>(T[,] prototype, Dtype dtype = null, string order = null, bool? subok = true)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
SharpToSharp<NDarray>(prototype),
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (order!=null) kwargs["order"]=ToPython(order);
if (subok!=true) kwargs["subok"]=ToPython(subok);
dynamic py = __self__.InvokeMethod("empty_like", pyargs, kwargs);
return ToCsharp<NDarray<T>>(py);
}
/// <summary>
/// Return a 2-D array with ones on the diagonal and zeros elsewhere.
/// </summary>
/// <param name="N">
/// Number of rows in the output.
/// </param>
/// <param name="M">
/// Number of columns in the output.<br></br>
/// If None, defaults to N.
/// </param>
/// <param name="k">
/// Index of the diagonal: 0 (the default) refers to the main diagonal,
/// a positive value refers to an upper diagonal, and a negative value
/// to a lower diagonal.
/// </param>
/// <param name="dtype">
/// Data-type of the returned array.
/// </param>
/// <param name="order">
/// Whether the output should be stored in row-major (C-style) or
/// column-major (Fortran-style) order in memory.
/// </param>
/// <returns>
/// An array where all elements are equal to zero, except for the k-th
/// diagonal, whose values are equal to one.
/// </returns>
public NDarray eye(int N, int? M = null, int? k = 0, Dtype dtype = null, string order = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
N,
});
var kwargs=new PyDict();
if (M!=null) kwargs["M"]=ToPython(M);
if (k!=0) kwargs["k"]=ToPython(k);
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (order!=null) kwargs["order"]=ToPython(order);
dynamic py = __self__.InvokeMethod("eye", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return the identity array.<br></br>
///
/// The identity array is a square array with ones on
/// the main diagonal.
/// </summary>
/// <param name="n">
/// Number of rows (and columns) in n x n output.
/// </param>
/// <param name="dtype">
/// Data-type of the output.<br></br>
/// Defaults to float.
/// </param>
/// <returns>
/// n x n array with its main diagonal set to one,
/// and all other elements 0.
/// </returns>
public NDarray identity(int n, Dtype dtype = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
n,
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
dynamic py = __self__.InvokeMethod("identity", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return a new array of given shape and type, filled with ones.
/// </summary>
/// <param name="shape">
/// Shape of the new array, e.g., (2, 3) or 2.
/// </param>
/// <param name="dtype">
/// The desired data-type for the array, e.g., numpy.int8. Default is
/// numpy.float64.
/// </param>
/// <param name="order">
/// Whether to store multi-dimensional data in row-major
/// (C-style) or column-major (Fortran-style) order in
/// memory.
/// </param>
/// <returns>
/// Array of ones with the given shape, dtype, and order.
/// </returns>
public NDarray ones(Shape shape, Dtype dtype = null, string order = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
shape,
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (order!=null) kwargs["order"]=ToPython(order);
dynamic py = __self__.InvokeMethod("ones", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return an array of ones with the same shape and type as a given array.
/// </summary>
/// <param name="a">
/// The shape and data-type of a define these same attributes of
/// the returned array.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result.<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.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array.<br></br>
/// Defaults
/// to True.
/// </param>
/// <returns>
/// Array of ones with the same shape and type as a.
/// </returns>
public NDarray ones_like(NDarray a, Dtype dtype = null, string order = null, bool? subok = true)
{
//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);
if (subok!=true) kwargs["subok"]=ToPython(subok);
dynamic py = __self__.InvokeMethod("ones_like", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return an array of ones with the same shape and type as a given array.
/// </summary>
/// <param name="a">
/// The shape and data-type of a define these same attributes of
/// the returned array.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result.<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.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array.<br></br>
/// Defaults
/// to True.
/// </param>
/// <returns>
/// Array of ones with the same shape and type as a.
/// </returns>
public NDarray<T> ones_like<T>(T[] a, Dtype dtype = null, string order = null, bool? subok = true)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
SharpToSharp<NDarray>(a),
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (order!=null) kwargs["order"]=ToPython(order);
if (subok!=true) kwargs["subok"]=ToPython(subok);
dynamic py = __self__.InvokeMethod("ones_like", pyargs, kwargs);
return ToCsharp<NDarray<T>>(py);
}
/// <summary>
/// Return an array of ones with the same shape and type as a given array.
/// </summary>
/// <param name="a">
/// The shape and data-type of a define these same attributes of
/// the returned array.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result.<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.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array.<br></br>
/// Defaults
/// to True.
/// </param>
/// <returns>
/// Array of ones with the same shape and type as a.
/// </returns>
public NDarray<T> ones_like<T>(T[,] a, Dtype dtype = null, string order = null, bool? subok = true)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
SharpToSharp<NDarray>(a),
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (order!=null) kwargs["order"]=ToPython(order);
if (subok!=true) kwargs["subok"]=ToPython(subok);
dynamic py = __self__.InvokeMethod("ones_like", pyargs, kwargs);
return ToCsharp<NDarray<T>>(py);
}
/// <summary>
/// Return a new array of given shape and type, filled with zeros.
/// </summary>
/// <param name="shape">
/// Shape of the new array, e.g., (2, 3) or 2.
/// </param>
/// <param name="dtype">
/// The desired data-type for the array, e.g., numpy.int8. Default is
/// numpy.float64.
/// </param>
/// <param name="order">
/// Whether to store multi-dimensional data in row-major
/// (C-style) or column-major (Fortran-style) order in
/// memory.
/// </param>
/// <returns>
/// Array of zeros with the given shape, dtype, and order.
/// </returns>
public NDarray zeros(Shape shape, Dtype dtype = null, string order = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
shape,
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (order!=null) kwargs["order"]=ToPython(order);
dynamic py = __self__.InvokeMethod("zeros", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return an array of zeros with the same shape and type as a given array.
/// </summary>
/// <param name="a">
/// The shape and data-type of a define these same attributes of
/// the returned array.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result.<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.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array.<br></br>
/// Defaults
/// to True.
/// </param>
/// <returns>
/// Array of zeros with the same shape and type as a.
/// </returns>
public NDarray zeros_like(NDarray a, Dtype dtype = null, string order = null, bool? subok = true)
{
//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);
if (subok!=true) kwargs["subok"]=ToPython(subok);
dynamic py = __self__.InvokeMethod("zeros_like", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return an array of zeros with the same shape and type as a given array.
/// </summary>
/// <param name="a">
/// The shape and data-type of a define these same attributes of
/// the returned array.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result.<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.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array.<br></br>
/// Defaults
/// to True.
/// </param>
/// <returns>
/// Array of zeros with the same shape and type as a.
/// </returns>
public NDarray<T> zeros_like<T>(T[] a, Dtype dtype = null, string order = null, bool? subok = true)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
SharpToSharp<NDarray>(a),
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (order!=null) kwargs["order"]=ToPython(order);
if (subok!=true) kwargs["subok"]=ToPython(subok);
dynamic py = __self__.InvokeMethod("zeros_like", pyargs, kwargs);
return ToCsharp<NDarray<T>>(py);
}
/// <summary>
/// Return an array of zeros with the same shape and type as a given array.
/// </summary>
/// <param name="a">
/// The shape and data-type of a define these same attributes of
/// the returned array.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result.<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.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array.<br></br>
/// Defaults
/// to True.
/// </param>
/// <returns>
/// Array of zeros with the same shape and type as a.
/// </returns>
public NDarray<T> zeros_like<T>(T[,] a, Dtype dtype = null, string order = null, bool? subok = true)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
SharpToSharp<NDarray>(a),
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (order!=null) kwargs["order"]=ToPython(order);
if (subok!=true) kwargs["subok"]=ToPython(subok);
dynamic py = __self__.InvokeMethod("zeros_like", pyargs, kwargs);
return ToCsharp<NDarray<T>>(py);
}
/// <summary>
/// Return a new array of given shape and type, filled with fill_value.
/// </summary>
/// <param name="shape">
/// Shape of the new array, e.g., (2, 3) or 2.
/// </param>
/// <param name="fill_value">
/// Fill value.
/// </param>
/// <param name="order">
/// Whether to store multidimensional data in C- or Fortran-contiguous
/// (row- or column-wise) order in memory.
/// </param>
/// <returns>
/// Array of fill_value with the given shape, dtype, and order.
/// </returns>
public NDarray full(Shape shape, ValueType fill_value, Dtype dtype = null, string order = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
shape,
fill_value,
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (order!=null) kwargs["order"]=ToPython(order);
dynamic py = __self__.InvokeMethod("full", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return a full array with the same shape and type as a given array.
/// </summary>
/// <param name="a">
/// The shape and data-type of a define these same attributes of
/// the returned array.
/// </param>
/// <param name="fill_value">
/// Fill value.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result.<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.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array.<br></br>
/// Defaults
/// to True.
/// </param>
/// <returns>
/// Array of fill_value with the same shape and type as a.
/// </returns>
public NDarray full_like(NDarray a, ValueType fill_value, Dtype dtype = null, string order = null, bool? subok = true)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
fill_value,
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (order!=null) kwargs["order"]=ToPython(order);
if (subok!=true) kwargs["subok"]=ToPython(subok);
dynamic py = __self__.InvokeMethod("full_like", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return a full array with the same shape and type as a given array.
/// </summary>
/// <param name="a">
/// The shape and data-type of a define these same attributes of
/// the returned array.
/// </param>
/// <param name="fill_value">
/// Fill value.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result.<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.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array.<br></br>
/// Defaults
/// to True.
/// </param>
/// <returns>
/// Array of fill_value with the same shape and type as a.
/// </returns>
public NDarray<T> full_like<T>(T[] a, ValueType fill_value, Dtype dtype = null, string order = null, bool? subok = true)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
SharpToSharp<NDarray>(a),
fill_value,
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (order!=null) kwargs["order"]=ToPython(order);
if (subok!=true) kwargs["subok"]=ToPython(subok);
dynamic py = __self__.InvokeMethod("full_like", pyargs, kwargs);
return ToCsharp<NDarray<T>>(py);
}
/// <summary>
/// Return a full array with the same shape and type as a given array.
/// </summary>
/// <param name="a">
/// The shape and data-type of a define these same attributes of
/// the returned array.
/// </param>
/// <param name="fill_value">
/// Fill value.
/// </param>
/// <param name="dtype">
/// Overrides the data type of the result.
/// </param>
/// <param name="order">
/// Overrides the memory layout of the result.<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.
/// </param>
/// <param name="subok">
/// If True, then the newly created array will use the sub-class
/// type of ‘a’, otherwise it will be a base-class array.<br></br>
/// Defaults
/// to True.
/// </param>
/// <returns>
/// Array of fill_value with the same shape and type as a.
/// </returns>
public NDarray<T> full_like<T>(T[,] a, ValueType fill_value, Dtype dtype = null, string order = null, bool? subok = true)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
SharpToSharp<NDarray>(a),
fill_value,
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (order!=null) kwargs["order"]=ToPython(order);
if (subok!=true) kwargs["subok"]=ToPython(subok);
dynamic py = __self__.InvokeMethod("full_like", pyargs, kwargs);
return ToCsharp<NDarray<T>>(py);
}
/// <summary>
/// Convert the input to an array.
/// </summary>
/// <param name="a">
/// Input data, in any form that can be converted to an array.<br></br>
/// This
/// includes lists, lists of tuples, tuples, tuples of tuples, tuples
/// of lists and ndarrays.
/// </param>
/// <param name="dtype">
/// By default, the data-type is inferred from the input data.
/// </param>
/// <param name="order">
/// Whether to use row-major (C-style) or
/// column-major (Fortran-style) memory representation.<br></br>
///
/// Defaults to ‘C’.
/// </param>
/// <returns>
/// Array interpretation of a.<br></br>
/// No copy is performed if the input
/// is already an ndarray with matching dtype and order.<br></br>
/// If a is a
/// subclass of ndarray, a base class ndarray is returned.
/// </returns>
public NDarray asarray(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", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Convert the input to an array.
/// </summary>
/// <param name="a">
/// Input data, in any form that can be converted to an array.<br></br>
/// This
/// includes lists, lists of tuples, tuples, tuples of tuples, tuples
/// of lists and ndarrays.
/// </param>
/// <param name="dtype">
/// By default, the data-type is inferred from the input data.
/// </param>
/// <param name="order">
/// Whether to use row-major (C-style) or
/// column-major (Fortran-style) memory representation.<br></br>
///
/// Defaults to ‘C’.
/// </param>
/// <returns>
/// Array interpretation of a.<br></br>
/// No copy is performed if the input
/// is already an ndarray with matching dtype and order.<br></br>
/// If a is a
/// subclass of ndarray, a base class ndarray is returned.
/// </returns>
public NDarray<T> asarray<T>(T[] a, Dtype dtype = null, string order = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
SharpToSharp<NDarray>(a),
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (order!=null) kwargs["order"]=ToPython(order);
dynamic py = __self__.InvokeMethod("asarray", pyargs, kwargs);
return ToCsharp<NDarray<T>>(py);
}
/// <summary>
/// Convert the input to an array.
/// </summary>
/// <param name="a">
/// Input data, in any form that can be converted to an array.<br></br>
/// This
/// includes lists, lists of tuples, tuples, tuples of tuples, tuples
/// of lists and ndarrays.
/// </param>
/// <param name="dtype">
/// By default, the data-type is inferred from the input data.
/// </param>
/// <param name="order">
/// Whether to use row-major (C-style) or
/// column-major (Fortran-style) memory representation.<br></br>
///
/// Defaults to ‘C’.
/// </param>
/// <returns>
/// Array interpretation of a.<br></br>
/// No copy is performed if the input
/// is already an ndarray with matching dtype and order.<br></br>
/// If a is a
/// subclass of ndarray, a base class ndarray is returned.
/// </returns>
public NDarray<T> asarray<T>(T[,] a, Dtype dtype = null, string order = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
SharpToSharp<NDarray>(a),
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (order!=null) kwargs["order"]=ToPython(order);
dynamic py = __self__.InvokeMethod("asarray", pyargs, kwargs);
return ToCsharp<NDarray<T>>(py);
}
/// <summary>
/// Convert the input to an ndarray, but pass ndarray subclasses through.
/// </summary>
/// <param name="a">
/// Input data, in any form that can be converted to an array.<br></br>
/// This
/// includes scalars, lists, lists of tuples, tuples, tuples of tuples,
/// tuples of lists, and ndarrays.
/// </param>
/// <param name="dtype">
/// By default, the data-type is inferred from the input data.
/// </param>
/// <param name="order">
/// Whether to use row-major (C-style) or column-major
/// (Fortran-style) memory representation.<br></br>
/// Defaults to ‘C’.
/// </param>
/// <returns>
/// Array interpretation of a.<br></br>
/// If a is an ndarray or a subclass
/// of ndarray, it is returned as-is and no copy is performed.
/// </returns>
public NDarray asanyarray(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("asanyarray", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Convert the input to an ndarray, but pass ndarray subclasses through.
/// </summary>
/// <param name="a">
/// Input data, in any form that can be converted to an array.<br></br>
/// This
/// includes scalars, lists, lists of tuples, tuples, tuples of tuples,
/// tuples of lists, and ndarrays.
/// </param>
/// <param name="dtype">
/// By default, the data-type is inferred from the input data.
/// </param>
/// <param name="order">
/// Whether to use row-major (C-style) or column-major
/// (Fortran-style) memory representation.<br></br>
/// Defaults to ‘C’.
/// </param>
/// <returns>
/// Array interpretation of a.<br></br>
/// If a is an ndarray or a subclass
/// of ndarray, it is returned as-is and no copy is performed.
/// </returns>
public NDarray<T> asanyarray<T>(T[] a, Dtype dtype = null, string order = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
SharpToSharp<NDarray>(a),
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (order!=null) kwargs["order"]=ToPython(order);
dynamic py = __self__.InvokeMethod("asanyarray", pyargs, kwargs);
return ToCsharp<NDarray<T>>(py);
}
/// <summary>
/// Convert the input to an ndarray, but pass ndarray subclasses through.
/// </summary>
/// <param name="a">
/// Input data, in any form that can be converted to an array.<br></br>
/// This
/// includes scalars, lists, lists of tuples, tuples, tuples of tuples,
/// tuples of lists, and ndarrays.
/// </param>
/// <param name="dtype">
/// By default, the data-type is inferred from the input data.
/// </param>
/// <param name="order">
/// Whether to use row-major (C-style) or column-major
/// (Fortran-style) memory representation.<br></br>
/// Defaults to ‘C’.
/// </param>
/// <returns>
/// Array interpretation of a.<br></br>
/// If a is an ndarray or a subclass
/// of ndarray, it is returned as-is and no copy is performed.