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using System;
using System.Collections.Generic;
using System.Linq;
using System.Runtime.InteropServices;
using System.Text;
using Numpy.Models;
using Python.Runtime;
namespace Numpy
{
public partial class NDarray : PythonObject
{
// these are manual overrides of functions or properties that can not be automatically generated
public NDarray(PyObject pyobj) : base(pyobj)
{
}
public NDarray(NDarray t) : base((PyObject) t.PyObject)
{
}
/// <summary>
/// Returns a copy of the array data
/// </summary>
public T[] GetData<T>()
{
// note: this implementation works only for device CPU
long ptr = PyObject.ctypes.data;
int size = PyObject.size;
object array = null;
if (typeof(T) == typeof(byte)) array = new byte[size];
else if (typeof(T) == typeof(short)) array = new short[size];
else if (typeof(T) == typeof(int)) array = new int[size];
else if (typeof(T) == typeof(long)) array = new long[size];
else if (typeof(T) == typeof(float)) array = new float[size];
else if (typeof(T) == typeof(double)) array = new double[size];
else if (typeof(T) == typeof(bool)) array = new byte[size];
else
throw new InvalidOperationException(
"Can not copy the data with data type due to limitations of Marshal.Copy: " + typeof(T).Name);
switch (array)
{
case byte[] a:
Marshal.Copy(new IntPtr(ptr), a, 0, a.Length);
break;
case short[] a:
Marshal.Copy(new IntPtr(ptr), a, 0, a.Length);
break;
case int[] a:
Marshal.Copy(new IntPtr(ptr), a, 0, a.Length);
break;
case long[] a:
Marshal.Copy(new IntPtr(ptr), a, 0, a.Length);
break;
case float[] a:
Marshal.Copy(new IntPtr(ptr), a, 0, a.Length);
break;
case double[] a:
Marshal.Copy(new IntPtr(ptr), a, 0, a.Length);
break;
}
// special handling for types that are not supported by Marshal.Copy: must be converted i.e. 1 => true, 0 => false
if (typeof(T) == typeof(bool)) return (T[])(object)((byte[])array).Select(x=>x>0).ToArray();
return (T[]) array;
}
/// <summary>
/// Information about the memory layout of the array.
/// </summary>
public Flags flags => new Flags(self.GetAttr("flags")); // TODO: implement Flags
/// <summary>
/// Tuple of array dimensions.
/// </summary>
public Shape shape => new Shape( self.GetAttr("shape").As<int[]>());
/// <summary>
/// Tuple of bytes to step in each dimension when traversing an array.
/// </summary>
public int[] strides => self.GetAttr("strides").As<int[]>();
/// <summary>
/// Number of array dimensions.
/// </summary>
public int ndim => self.GetAttr("ndim").As<int>();
/// <summary>
/// Python buffer object pointing to the start of the array’s data.
/// </summary>
public PyObject data => self.GetAttr("data");
/// <summary>
/// Number of elements in the array.
/// </summary>
public int size => self.GetAttr("size").As<int>();
/// <summary>
/// Length of one array element in bytes.
/// </summary>
public int itemsize => self.GetAttr("itemsize").As<int>();
/// <summary>
/// Total bytes consumed by the elements of the array.
/// </summary>
public int nbytes => self.GetAttr("nbytes").As<int>();
/// <summary>
/// Base object if memory is from some other object.
/// </summary>
public NDarray @base
{
get
{
PyObject base_obj = self.GetAttr("base");
if (base_obj.IsNone())
return null;
return new NDarray(base_obj);
}
}
/// <summary>
/// Data-type of the array’s elements.
/// </summary>
public Dtype dtype => new Dtype(self.GetAttr("dtype"));
/// <summary>
/// Same as self.transpose(), except that self is returned if self.ndim < 2.
/// </summary>
public NDarray T => new NDarray(self.GetAttr("T"));
///// <summary>
///// The real part of the array.
///// </summary>
//public NDarray real => new NDarray(self.GetAttr("real"));
///// <summary>
///// The imaginary part of the array.
///// </summary>
//public NDarray imag => new NDarray(self.GetAttr("imag"));
/// <summary>
/// A 1-D iterator over the array.
/// </summary>
public PyObject flat => self.GetAttr("flat"); // todo: wrap and support usecases
/// <summary>
/// An object to simplify the interaction of the array with the ctypes module.
/// </summary>
public PyObject ctypes => self.GetAttr("ctypes"); // TODO: wrap ctypes
/// <summary>
/// Length of the array (same as size)
/// </summary>
public int len => self.InvokeMethod("__len__").As<int>();
/// <summary>
/// Insert scalar into an array (scalar is cast to array’s dtype, if possible)
///
/// There must be at least 1 argument, and define the last argument
/// as item. Then, a.itemset(*args) is equivalent to but faster
/// than a[args] = item. The item should be a scalar value and args
/// must select a single item in the array a.
///
/// Notes
///
/// Compared to indexing syntax, itemset provides some speed increase
/// for placing a scalar into a particular location in an ndarray,
/// if you must do this. However, generally this is discouraged:
/// among other problems, it complicates the appearance of the code.
/// Also, when using itemset (and item) inside a loop, be sure
/// to assign the methods to a local variable to avoid the attribute
/// look-up at each loop iteration.
/// </summary>
/// <param name="args">
/// If one argument: a scalar, only used in case a is of size 1.
/// If two arguments: the last argument is the value to be set
/// and must be a scalar, the first argument specifies a single array
/// element location. It is either an int or a tuple.
/// </param>
public void itemset(params object[] args)
{
var pyargs = ToTuple(args);
var kwargs = new PyDict();
dynamic py = self.InvokeMethod("itemset", pyargs, kwargs);
}
/// <summary>
/// Construct Python bytes containing the raw data bytes in the array.
///
/// Constructs Python bytes showing a copy of the raw contents of
/// data memory. The bytes object can be produced in either ‘C’ or ‘Fortran’,
/// or ‘Any’ order (the default is ‘C’-order). ‘Any’ order means C-order
/// unless the F_CONTIGUOUS flag in the array is set, in which case it
/// means ‘Fortran’ order.
///
/// This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings.
/// </summary>
/// <param name="order">
/// Order of the data for multidimensional arrays:
/// C, Fortran, or the same as for the original array.
/// </param>
/// <returns>
/// Python bytes exhibiting a copy of a’s raw data.
/// </returns>
public byte[] tostring(string order = null)
{
return tobytes();
}
/// <summary>
/// Construct Python bytes containing the raw data bytes in the array.
///
/// Constructs Python bytes showing a copy of the raw contents of
/// data memory. The bytes object can be produced in either ‘C’ or ‘Fortran’,
/// or ‘Any’ order (the default is ‘C’-order). ‘Any’ order means C-order
/// unless the F_CONTIGUOUS flag in the array is set, in which case it
/// means ‘Fortran’ order.
/// </summary>
/// <param name="order">
/// Order of the data for multidimensional arrays:
/// C, Fortran, or the same as for the original array.
/// </param>
/// <returns>
/// Python bytes exhibiting a copy of a’s raw data.
/// </returns>
public byte[] tobytes(string order = null)
{
throw new NotImplementedException("TODO: this needs to be implemented with Marshal.Copy");
var pyargs = ToTuple(new object[]
{
});
var kwargs = new PyDict();
if (order != null) kwargs["order"] = ToPython(order);
dynamic py = self.InvokeMethod("tobytes", pyargs, kwargs);
return ToCsharp<byte[]>(py);
}
/// <summary>
/// New view of array with the same data.
///
/// Notes
///
/// a.view() is used two different ways:
///
/// a.view(some_dtype) or a.view(dtype=some_dtype) constructs a view
/// of the array’s memory with a different data-type. This can cause a
/// reinterpretation of the bytes of memory.
///
/// a.view(ndarray_subclass) or a.view(type=ndarray_subclass) just
/// returns an instance of ndarray_subclass that looks at the same array
/// (same shape, dtype, etc.) This does not cause a reinterpretation of the
/// memory.
///
/// For a.view(some_dtype), if some_dtype has a different number of
/// bytes per entry than the previous dtype (for example, converting a
/// regular array to a structured array), then the behavior of the view
/// cannot be predicted just from the superficial appearance of a (shown
/// by print(a)). It also depends on exactly how a is stored in
/// memory. Therefore if a is C-ordered versus fortran-ordered, versus
/// defined as a slice or transpose, etc., the view may give different
/// results.
/// </summary>
/// <param name="dtype">
/// Data-type descriptor of the returned view, e.g., float32 or int16. The
/// default, None, results in the view having the same data-type as a.
/// This argument can also be specified as an ndarray sub-class, which
/// then specifies the type of the returned object (this is equivalent to
/// setting the type parameter).
/// </param>
/// <param name="type">
/// Type of the returned view, e.g., ndarray or matrix. Again, the
/// default None results in type preservation.
/// </param>
public void view(Dtype dtype = null, Type type = null)
{
throw new NotImplementedException("Get python type 'ndarray' and 'matrix' and substitute them for the given .NET type");
var pyargs = ToTuple(new object[]
{
});
var kwargs = new PyDict();
if (dtype != null) kwargs["dtype"] = ToPython(dtype);
if (type != null) kwargs["type"] = ToPython(type);
dynamic py = self.InvokeMethod("view", pyargs, kwargs);
}
/// <summary>
/// Change shape and size of array in-place.
///
/// Notes
///
/// This reallocates space for the data area if necessary.
///
/// Only contiguous arrays (data elements consecutive in memory) can be
/// resized.
///
/// The purpose of the reference count check is to make sure you
/// do not use this array as a buffer for another Python object and then
/// reallocate the memory. However, reference counts can increase in
/// other ways so if you are sure that you have not shared the memory
/// for this array with another Python object, then you may safely set
/// refcheck to False.
/// </summary>
/// <param name="new_shape">
/// Shape of resized array.
/// </param>
/// <param name="refcheck">
/// If False, reference count will not be checked. Default is True.
/// </param>
public void resize(Shape new_shape, bool? refcheck = null)
{
var pyargs = ToTuple(new object[]
{
new_shape,
});
var kwargs = new PyDict();
if (refcheck != null) kwargs["refcheck"] = ToPython(refcheck);
dynamic py = self.InvokeMethod("resize", 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>
/// <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(params int[] newshape)
{
//auto-generated code, do not change
var @this = this;
return NumPy.Instance.reshape(@this, new Shape(newshape));
}
/// <summary>
/// returns the 'array([ .... ])'-representation known from the console
/// </summary>
public string repr => self.InvokeMethod("__repr__").As<string>();
/// <summary>
/// returns the '[ .... ]'-representation
/// </summary>
public string str => self.InvokeMethod("__str__").As<string>();
public NDarray this[string slicing_notation]
{
get
{
var tuple=new PyTuple(Slice.ParseSlices(slicing_notation).Select(s =>
{
if (s.IsIndex)
return new PyInt(s.Start.Value);
else
return s.ToPython();
}).ToArray());
return new NDarray(this.PyObject[tuple]);
}
set
{
var tuple = new PyTuple(Slice.ParseSlices(slicing_notation).Select(s =>
{
if (s.IsIndex)
return new PyInt(s.Start.Value);
else
return s.ToPython();
}).ToArray());
self.SetItem(tuple, ToPython(value));
}
}
public NDarray this[params int[] coords]
{
get
{
var tuple = ToTuple(coords);
return new NDarray(this.PyObject[tuple]);
}
set
{
var tuple = ToTuple(coords);
self.SetItem(tuple, ToPython(value));
}
}
public NDarray this[params NDarray[] indices]
{
get
{
var tuple = new PyTuple(indices.Select(a => (PyObject)a.PyObject).ToArray());
return new NDarray(this.PyObject[tuple]);
}
set
{
var tuple = new PyTuple(indices.Select(a => (PyObject)a.PyObject).ToArray());
self.SetItem(tuple, ToPython(value));
}
}
public NDarray this[params object[] arrays_slices_or_indices]
{
get
{
var pyobjs = arrays_slices_or_indices.Select<object ,PyObject>(x =>
{
switch (x)
{
case int i: return new PyInt(i);
case NDarray a: return a.PyObject;
case string s: return new Slice(s).ToPython();
default: return ToPython(x);
}
}).ToArray();
var tuple = new PyTuple(pyobjs);
return new NDarray(this.PyObject[tuple]);
}
set
{
var pyobjs = arrays_slices_or_indices.Select<object, PyObject>(x =>
{
switch (x)
{
case int i: return new PyInt(i);
case NDarray a: return a.PyObject;
case string s: return new Slice(s).ToPython();
default: return ToPython(x);
}
}).ToArray();
var tuple = new PyTuple(pyobjs);
self.SetItem(tuple, ToPython(value));
}
}
/// <summary>
/// Convert an array of size 1 to its scalar equivalent.
/// </summary>
/// <returns>
/// Scalar representation of a. The output data type is the same type
/// returned by the input’s item method.
/// </returns>
public T asscalar<T>()
{
return NumPy.Instance.asscalar<T>(this);
}
}
public class NDarray<T> : NDarray
{
public NDarray(NDarray t) : base(t)
{
}
public NDarray(PyObject pyobject) : base(pyobject)
{
}
/// <summary>
/// Returns a copy of the array data
/// </summary>
public T[] GetData()
{
return base.GetData<T>();
}
public new NDarray<T> this[string slicing_notation]
{
get
{
var tuple = new PyTuple(Slice.ParseSlices(slicing_notation).Select(s =>
{
if (s.IsIndex)
return new PyInt(s.Start.Value);
else
return s.ToPython();
}).ToArray());
return new NDarray<T>(this.PyObject[tuple]);
}
set
{
var tuple = new PyTuple(Slice.ParseSlices(slicing_notation).Select(s =>
{
if (s.IsIndex)
return new PyInt(s.Start.Value);
else
return s.ToPython();
}).ToArray());
self.SetItem(tuple, ToPython(value));
}
}
public new NDarray this[params int[] coords]
{
get
{
var tuple = ToTuple(coords);
return new NDarray<T>(this.PyObject[tuple]);
}
set
{
var tuple = ToTuple(coords);
self.SetItem(tuple, ToPython(value));
}
}
public new NDarray this[params NDarray[] indices]
{
get
{
var tuple = new PyTuple(indices.Select(a => (PyObject)a.PyObject).ToArray());
return new NDarray<T>(this.PyObject[tuple]);
}
set
{
var tuple = new PyTuple(indices.Select(a => (PyObject)a.PyObject).ToArray());
self.SetItem(tuple, ToPython(value));
}
}
public new NDarray this[params object[] arrays_slices_or_indices]
{
get
{
var pyobjs = arrays_slices_or_indices.Select<object, PyObject>(x =>
{
switch (x)
{
case int i: return new PyInt(i);
case NDarray a: return a.PyObject;
case string s: return new Slice(s).ToPython();
default: return ToPython(x);
}
}).ToArray();
var tuple = new PyTuple(pyobjs);
return new NDarray(this.PyObject[tuple]);
}
set
{
var pyobjs = arrays_slices_or_indices.Select<object, PyObject>(x =>
{
switch (x)
{
case int i: return new PyInt(i);
case NDarray a: return a.PyObject;
case string s: return new Slice(s).ToPython();
default: return ToPython(x);
}
}).ToArray();
var tuple = new PyTuple(pyobjs);
self.SetItem(tuple, ToPython(value));
}
}
}
}