Merge pull request #574 from Zephyr-Blessed/fix/update-numpy-doc-urls

All 124 URLs validated. Fixes applied for 18 broken URLs including random function paths, deprecated API redirects, and documentation structure changes.

Author: Nucs

src/NumSharp.Core/APIs/np.cs

@@ -13,10 +13,10 @@ public static partial class np
         /// <summary>
         ///     A convenient alias for None, useful for indexing arrays.
         /// </summary>
-        /// <remarks>https://docs.scipy.org/doc/numpy-1.17.0/reference/arrays.indexing.html<br></br><br></br>https://stackoverflow.com/questions/42190783/what-does-three-dots-in-python-mean-when-indexing-what-looks-like-a-number</remarks>
+        /// <remarks>https://numpy.org/doc/stable/user/basics.indexing.html<br></br><br></br>https://stackoverflow.com/questions/42190783/what-does-three-dots-in-python-mean-when-indexing-what-looks-like-a-number</remarks>
         public static readonly Slice newaxis = new Slice(null, null, 1) {IsNewAxis = true};
 
-        // https://docs.scipy.org/doc/numpy-1.16.0/user/basics.types.html
+        // https://numpy.org/doc/stable/user/basics.types.html
         public static readonly Type bool_ = typeof(bool);
         public static readonly Type bool8 = bool_;
         public static readonly Type @bool = bool_;

src/NumSharp.Core/APIs/np.cumsum.cs

@@ -11,7 +11,7 @@ public static partial class np
         /// <param name="axis">Axis along which the cumulative sum is computed. The default (None) is to compute the cumsum over the flattened array.</param>
         /// <param name="typeCode">Type of the returned array and of the accumulator in which the elements are summed. If dtype is not specified, it defaults to the dtype of a, unless a has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used.</param>
         /// <returns>A new array holding the result is returned unless out is specified, in which case a reference to out is returned. The result has the same size as a, and the same shape as a if axis is not None or a is a 1-d array.</returns>
-        /// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.cumsum.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.cumsum.html</remarks>
         public static NDArray cumsum(NDArray arr, int? axis = null, NPTypeCode? typeCode = null)
         {
             return arr.TensorEngine.ReduceCumAdd(arr, axis, typeCode);

src/NumSharp.Core/APIs/np.fromfile.cs

@@ -14,7 +14,7 @@ public static partial class np
         /// <param name="file">filename.</param>
         /// <param name="dtype">Data type of the returned array. For binary files, it is used to determine the size and byte-order of the items in the file.</param>
         /// <returns></returns>
-        /// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.fromfile.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.fromfile.html</remarks>
         public static NDArray fromfile(string file, NPTypeCode dtype)
         {
             return fromfile(file, dtype.AsType());
@@ -27,7 +27,7 @@ public static NDArray fromfile(string file, NPTypeCode dtype)
         /// <param name="file">filename.</param>
         /// <param name="dtype">Data type of the returned array. For binary files, it is used to determine the size and byte-order of the items in the file.</param>
         /// <returns></returns>
-        /// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.fromfile.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.fromfile.html</remarks>
         public static NDArray fromfile(string file, Type dtype)
         {
             unsafe

src/NumSharp.Core/APIs/np.size.cs

@@ -10,7 +10,7 @@ public partial class np
         /// <param name="a">Input data.</param>
         /// <param name="axis">Axis along which the elements are counted. By default, give the total number of elements.</param>
         /// <returns>Number of elements along the specified axis.</returns>
-        /// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.ma.size.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.ma.size.html</remarks>
         public static int size(NDArray a, int? axis = null)
         {
             if (a == null)

src/NumSharp.Core/APIs/np.tofile.cs

@@ -10,7 +10,7 @@ public partial class NDArray
         ///     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().
         /// </summary>
         /// <param name="fid">An open file object, or a string containing a filename.</param>
-        /// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.tofile.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.ndarray.tofile.html</remarks>
         public void tofile(string fid)
         {
             //TODO! support for sliced data (if sliced, clone and then save)

src/NumSharp.Core/Backends/Default/ArrayManipulation/Default.Broadcasting.cs

@@ -6,7 +6,7 @@ namespace NumSharp.Backends
 {
     public partial class DefaultEngine
     {
-        /// <remarks>Based on https://docs.scipy.org/doc/numpy-1.16.1/user/basics.broadcasting.html </remarks>
+        /// <remarks>Based on https://numpy.org/doc/stable/user/basics.broadcasting.html </remarks>
         public static Shape ResolveReturnShape(Shape leftShape, Shape rightShape)
         {
             if (leftShape == rightShape)
@@ -78,7 +78,7 @@ public static Shape ResolveReturnShape(Shape leftShape, Shape rightShape)
             return mit; //implicit cast
         }
 
-        /// <remarks>Based on https://docs.scipy.org/doc/numpy-1.16.1/user/basics.broadcasting.html </remarks>
+        /// <remarks>Based on https://numpy.org/doc/stable/user/basics.broadcasting.html </remarks>
         public static Shape ResolveReturnShape(params Shape[] shapes)
         {
             if (shapes.Length == 0)
@@ -134,7 +134,7 @@ public static Shape ResolveReturnShape(params Shape[] shapes)
             return mit.Clean();
         }
 
-        /// <remarks>Based on https://docs.scipy.org/doc/numpy-1.16.1/user/basics.broadcasting.html </remarks>
+        /// <remarks>Based on https://numpy.org/doc/stable/user/basics.broadcasting.html </remarks>
         public static Shape ResolveReturnShape(params NDArray[] shapes)
         {
             if (shapes.Length == 0)
@@ -191,7 +191,7 @@ public static Shape ResolveReturnShape(params NDArray[] shapes)
             return mit.Clean();
         }
 
-        /// <remarks>Based on https://docs.scipy.org/doc/numpy-1.16.1/user/basics.broadcasting.html </remarks>
+        /// <remarks>Based on https://numpy.org/doc/stable/user/basics.broadcasting.html </remarks>
         public static Shape[] Broadcast(params Shape[] shapes)
         {
             if (shapes.Length == 0)
@@ -268,7 +268,7 @@ public static Shape[] Broadcast(params Shape[] shapes)
 
         //private static readonly int[][] _zeros = new int[][] {new int[0], new int[] {0}, new int[] {0, 0}, new int[] {0, 0, 0}, new int[] {0, 0, 0, 0}, new int[] {0, 0, 0, 0, 0}, new int[] {0, 0, 0, 0, 0, 0}, new int[] {0, 0, 0, 0, 0, 0, 0}, new int[] {0, 0, 0, 0, 0, 0, 0, 0}, new int[] {0, 0, 0, 0, 0, 0, 0, 0, 0}, new int[] {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, new int[] {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, new int[] {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, new int[] {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, new int[] {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, new int[] {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, new int[] {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, new int[] {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},};
 
-        /// <remarks>Based on https://docs.scipy.org/doc/numpy-1.16.1/user/basics.broadcasting.html </remarks>
+        /// <remarks>Based on https://numpy.org/doc/stable/user/basics.broadcasting.html </remarks>
         public static (Shape LeftShape, Shape RightShape) Broadcast(Shape leftShape, Shape rightShape)
         {
             if (leftShape._hashCode != 0 && leftShape._hashCode == rightShape._hashCode)
@@ -373,7 +373,7 @@ public static (Shape LeftShape, Shape RightShape) Broadcast(Shape leftShape, Sha
             return (leftResult, rightResult);
         }
 
-        /// <remarks>Based on https://docs.scipy.org/doc/numpy-1.16.1/user/basics.broadcasting.html </remarks>
+        /// <remarks>Based on https://numpy.org/doc/stable/user/basics.broadcasting.html </remarks>
         public static NDArray[] Broadcast(params NDArray[] arrays)
         {
             if (arrays.Length == 0)
@@ -390,7 +390,7 @@ public static NDArray[] Broadcast(params NDArray[] arrays)
             return arrays;
         }
 
-        /// <remarks>Based on https://docs.scipy.org/doc/numpy-1.16.1/user/basics.broadcasting.html </remarks>
+        /// <remarks>Based on https://numpy.org/doc/stable/user/basics.broadcasting.html </remarks>
         public unsafe static bool AreBroadcastable(params Shape[] shapes)
         {
             if (shapes.Length <= 1)
@@ -442,7 +442,7 @@ public unsafe static bool AreBroadcastable(params Shape[] shapes)
             return true;
         }
 
-        /// <remarks>Based on https://docs.scipy.org/doc/numpy-1.16.1/user/basics.broadcasting.html </remarks>
+        /// <remarks>Based on https://numpy.org/doc/stable/user/basics.broadcasting.html </remarks>
         public unsafe static bool AreBroadcastable(params int[][] shapes)
         {
             if (shapes.Length <= 1)
@@ -494,7 +494,7 @@ public unsafe static bool AreBroadcastable(params int[][] shapes)
             return true;
         }
 
-        /// <remarks>Based on https://docs.scipy.org/doc/numpy-1.16.1/user/basics.broadcasting.html </remarks>
+        /// <remarks>Based on https://numpy.org/doc/stable/user/basics.broadcasting.html </remarks>
         public static bool AreBroadcastable(params NDArray[] arrays)
         {
             if (arrays.Length <= 1)

src/NumSharp.Core/Backends/Default/Math/BLAS/Default.Dot.cs

@@ -11,7 +11,7 @@ namespace NumSharp.Backends
     public partial class DefaultEngine
     {
 
-        /// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.dot.html</remarks>
         public override NDArray Dot(in NDArray left, in NDArray right)
         {
             //Dot product of two arrays.Specifically,

src/NumSharp.Core/Backends/Default/Math/BLAS/Default.MatMul.2D2D.cs

@@ -17,7 +17,7 @@ protected static NDArray MultiplyMatrix(NDArray left, NDArray right, NDArray @ou
             return null;
         }
 #else
-        /// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.multiply.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.multiply.html</remarks>
         [SuppressMessage("ReSharper", "JoinDeclarationAndInitializer")]
         [MethodImpl(OptimizeAndInline)]
         protected static NDArray MultiplyMatrix(NDArray left, NDArray right, NDArray @out = null)

src/NumSharp.Core/Backends/Default/Math/BLAS/Default.MatMul.cs

@@ -10,7 +10,7 @@ namespace NumSharp.Backends
 {
     public partial class DefaultEngine
     {
-        /// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.matmul.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.matmul.html</remarks>
         public override NDArray Matmul(NDArray lhs, NDArray rhs)
         {
             if (lhs.Shape.IsScalar || rhs.Shape.IsScalar)

src/NumSharp.Core/Backends/NDArray.cs

@@ -35,7 +35,7 @@ namespace NumSharp
     ///     An associated data-type object describes the format of each element in the array (its byte-order,<br></br>
     ///     how many bytes it occupies in memory, whether it is an integer, a floating point number, or something else, etc.)
     /// </summary>
-    /// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html</remarks>
+    /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html</remarks>
     [DebuggerTypeProxy(nameof(NDArrayDebuggerProxy))]
     [SuppressMessage("ReSharper", "ParameterHidesMember")]
     public partial class NDArray : IIndex, ICloneable, IEnumerable
@@ -342,7 +342,7 @@ public int[] shape
         /// <summary>
         ///     A 1-D iterator over the array.
         /// </summary>
-        /// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.flat.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flat.html</remarks>
         public NDArray flat
         {
             get
@@ -357,7 +357,7 @@ public NDArray flat
         ///     The transposed array. <br></br>
         ///     Same as self.transpose().
         /// </summary>
-        /// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.T.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.ndarray.T.html</remarks>
         public NDArray T
         {
             get
@@ -422,7 +422,7 @@ protected internal IArraySlice Array
         /// <param name="dtype">The dtype to cast this array.</param>
         /// <param name="copy">By default, astype always returns a newly allocated array. If this is set to false, the input internal array is replaced instead of returning a new NDArray with the casted data.</param>
         /// <returns>An <see cref="NDArray"/> of given <paramref name="dtype"/>.</returns>
-        /// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.astype.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.ndarray.astype.html</remarks>
         [SuppressMessage("ReSharper", "ParameterHidesMember")]
         public NDArray astype(Type dtype, bool copy = true) => TensorEngine.Cast(this, dtype, copy);
 
@@ -432,7 +432,7 @@ protected internal IArraySlice Array
         /// <param name="dtype">The dtype to cast this array.</param>
         /// <param name="copy">By default, astype always returns a newly allocated array. If this is set to false, the input internal array is replaced instead of returning a new NDArray with the casted data.</param>
         /// <returns>An <see cref="NDArray"/> of given <paramref name="dtype"/>.</returns>
-        /// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.astype.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.ndarray.astype.html</remarks>
         public NDArray astype(NPTypeCode typeCode, bool copy = true) => TensorEngine.Cast(this, typeCode, copy);
 
         /// <summary>
@@ -505,7 +505,7 @@ IEnumerable _empty()
         ///     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>
         /// <returns></returns>
-        /// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.view.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.ndarray.view.html</remarks>
         public NDArray view(Type dtype = null)
         {
             //TODO! this shouldnt be a cast in case dtype != null, it should be an unsafe reinterpret (see remarks).
@@ -525,7 +525,7 @@ public NDArray view(Type dtype = null)
         ///     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>
         /// <returns></returns>
-        /// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.view.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.ndarray.view.html</remarks>
         public NDArray<T> view<T>() where T : unmanaged 
             => view(typeof(T)).AsGeneric<T>();