fix: correct 18 broken NumPy documentation URLs

Updates URLs that were pointing to incorrect paths:

1. Random functions (12 URLs): Added /random/ directory
   - reference/generated/numpy.random.* → reference/random/generated/numpy.random.*

2. arrays.indexing (1 URL): Corrected section path
   - reference/arrays.indexing.html → user/basics.indexing.html

3. c-api.dtype (1 URL): Fixed path format
   - reference/c-api.dtype.html → reference/c-api/dtype.html

4. routines.random (1 URL): Updated to new location
   - reference/routines.random.html → reference/random/index.html

5. asscalar (6 URLs): Function removed in NumPy 1.23, now links to replacement
   - numpy.asscalar.html → numpy.ndarray.item.html

6. find_common_type (6 URLs): Function deprecated, now links to replacement
   - numpy.find_common_type.html → numpy.result_type.html

7. itemset (4 URLs): Function removed in NumPy 2.4, now links to alternative
   - numpy.ndarray.itemset.html → numpy.put.html

All 126 URLs now return HTTP 200.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: Nucs

src/NumSharp.Core/APIs/np.cs

@@ -13,7 +13,7 @@ public static partial class np
         /// <summary>
         ///     A convenient alias for None, useful for indexing arrays.
         /// </summary>
-        /// <remarks>https://numpy.org/doc/stable/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://numpy.org/doc/stable/user/basics.types.html

src/NumSharp.Core/Creation/np.dtype.cs

@@ -377,7 +377,7 @@ internal enum NPY_SCALARKIND
     };
 
     /// <summary>
-    ///     https://numpy.org/doc/stable/reference/c-api.dtype.html#enumerated-types
+    ///     https://numpy.org/doc/stable/reference/c-api/dtype.html#enumerated-types
     /// </summary>
     internal enum NPY_TYPECHAR
     {

src/NumSharp.Core/Logic/np.find_common_type.cs

@@ -644,7 +644,7 @@ private static readonly (NPTypeCode Type, int Priority)[] powerPriorities =
         /// <param name="array_types">A list of dtypes or dtype convertible objects representing arrays. Can be null.</param>
         /// <param name="scalar_types">A list of dtypes or dtype convertible objects representing scalars.Can be null.</param>
         /// <returns>The common data type, which is the maximum of array_types ignoring scalar_types, unless the maximum of scalar_types is of a different kind (dtype.kind). If the kind is not understood, then None is returned.</returns>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.find_common_type.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.result_type.html</remarks>
         public static NPTypeCode find_common_type(NPTypeCode[] array_types, NPTypeCode[] scalar_types)
         {
             return _FindCommonType(array_types ?? Array.Empty<NPTypeCode>(), scalar_types ?? Array.Empty<NPTypeCode>());
@@ -658,7 +658,7 @@ public static NPTypeCode find_common_type(NPTypeCode[] array_types, NPTypeCode[]
         /// <param name="array_types">A list of dtypes or dtype convertible objects representing arrays. Can be null.</param>
         /// <param name="scalar_types">A list of dtypes or dtype convertible objects representing scalars.Can be null.</param>
         /// <returns>The common data type, which is the maximum of array_types ignoring scalar_types, unless the maximum of scalar_types is of a different kind (dtype.kind). If the kind is not understood, then None is returned.</returns>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.find_common_type.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.result_type.html</remarks>
         public static NPTypeCode find_common_type(Type[] array_types)
         {
             return _FindCommonType(array_types?.Select(v => v.GetTypeCode()).ToArray() ?? Array.Empty<NPTypeCode>(), Array.Empty<NPTypeCode>());
@@ -670,7 +670,7 @@ public static NPTypeCode find_common_type(Type[] array_types)
         /// <param name="array_types">A list of dtypes or dtype convertible objects representing arrays. Can be null.</param>
         /// <param name="scalar_types">A list of dtypes or dtype convertible objects representing scalars.Can be null.</param>
         /// <returns>The common data type, which is the maximum of array_types ignoring scalar_types, unless the maximum of scalar_types is of a different kind (dtype.kind). If the kind is not understood, then None is returned.</returns>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.find_common_type.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.result_type.html</remarks>
         public static NPTypeCode find_common_type(Type[] array_types, Type[] scalar_types)
         {
             return _FindCommonType(array_types?.Select(v => v.GetTypeCode()).ToArray() ?? Array.Empty<NPTypeCode>(), scalar_types?.Select(v => v.GetTypeCode()).ToArray() ?? Array.Empty<NPTypeCode>());
@@ -682,7 +682,7 @@ public static NPTypeCode find_common_type(Type[] array_types, Type[] scalar_type
         /// <param name="array_types">A list of dtypes or dtype convertible objects representing arrays. Can be null.</param>
         /// <param name="scalar_types">A list of dtypes or dtype convertible objects representing scalars.Can be null.</param>
         /// <returns>The common data type, which is the maximum of array_types ignoring scalar_types, unless the maximum of scalar_types is of a different kind (dtype.kind). If the kind is not understood, then None is returned.</returns>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.find_common_type.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.result_type.html</remarks>
         public static NPTypeCode find_common_type(string[] array_types, string[] scalar_types)
         {
             return _FindCommonType(array_types?.Select(v => np.dtype(v).typecode).ToArray() ?? Array.Empty<NPTypeCode>(), scalar_types?.Select(v => np.dtype(v).typecode).ToArray() ?? Array.Empty<NPTypeCode>());
@@ -694,7 +694,7 @@ public static NPTypeCode find_common_type(string[] array_types, string[] scalar_
         /// <param name="array_types">A list of dtypes or dtype convertible objects representing arrays. Can be null.</param>
         /// <param name="scalar_types">A list of dtypes or dtype convertible objects representing scalars.Can be null.</param>
         /// <returns>The common data type, which is the maximum of array_types ignoring scalar_types, unless the maximum of scalar_types is of a different kind (dtype.kind). If the kind is not understood, then None is returned.</returns>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.find_common_type.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.result_type.html</remarks>
         public static NPTypeCode find_common_type(Type[] array_types, NPTypeCode[] scalar_types)
         {
             return _FindCommonType(array_types?.Select(v => v.GetTypeCode()).ToArray() ?? Array.Empty<NPTypeCode>(), scalar_types ?? Array.Empty<NPTypeCode>());
@@ -706,7 +706,7 @@ public static NPTypeCode find_common_type(Type[] array_types, NPTypeCode[] scala
         /// <param name="array_types">A list of dtypes or dtype convertible objects representing arrays. Can be null.</param>
         /// <param name="scalar_types">A list of dtypes or dtype convertible objects representing scalars.Can be null.</param>
         /// <returns>The common data type, which is the maximum of array_types ignoring scalar_types, unless the maximum of scalar_types is of a different kind (dtype.kind). If the kind is not understood, then None is returned.</returns>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.find_common_type.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.result_type.html</remarks>
         public static NPTypeCode find_common_type(NPTypeCode[] array_types, Type[] scalar_types)
         {
             return _FindCommonType(array_types ?? Array.Empty<NPTypeCode>(), scalar_types?.Select(v => v.GetTypeCode()).ToArray() ?? Array.Empty<NPTypeCode>());

src/NumSharp.Core/Manipulation/NDArray.itemset.cs

@@ -7,7 +7,7 @@ public partial class NDArray
         /// <summary>
         ///     Insert scalar into an array (scalar is cast to array’s dtype, if possible)
         /// </summary>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.ndarray.itemset.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.put.html</remarks>
         public void itemset(ref Shape shape, ValueType val) 
         {
             SetValue(val, shape.dimensions); //TODO! if T != dtype, we need to cast!
@@ -16,7 +16,7 @@ public void itemset(ref Shape shape, ValueType val)
         /// <summary>
         ///     Insert scalar into an array (scalar is cast to array’s dtype, if possible)
         /// </summary>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.ndarray.itemset.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.put.html</remarks>
         public void itemset(Shape shape, ValueType val) 
         {
             SetValue(val, shape.dimensions); //TODO! if T != dtype, we need to cast!
@@ -25,7 +25,7 @@ public void itemset(Shape shape, ValueType val)
         /// <summary>
         ///     Insert scalar into an array (scalar is cast to array’s dtype, if possible)
         /// </summary>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.ndarray.itemset.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.put.html</remarks>
         public void itemset(int[] shape, ValueType val)
         {
             SetValue(val, shape); //TODO! if T != dtype, we need to cast!
@@ -34,7 +34,7 @@ public void itemset(int[] shape, ValueType val)
         /// <summary>
         ///     Insert scalar into an array (scalar is cast to array’s dtype, if possible)
         /// </summary>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.ndarray.itemset.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.put.html</remarks>
         public void itemset<T>(int[] shape, T val) where T : unmanaged
         {
             SetValue<T>(val, shape); //TODO! if T != dtype, we need to cast!

src/NumSharp.Core/Manipulation/np.asscalar.cs

@@ -11,7 +11,7 @@ public static partial class np
         /// </summary>
         /// <param name="nd">Input NDArray of size 1.</param>
         /// <returns></returns>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.asscalar.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.ndarray.item.html</remarks>
         public static T asscalar<T>(NDArray nd) where T : unmanaged
         {
             if (nd.size != 1)
@@ -27,7 +27,7 @@ public static T asscalar<T>(NDArray nd) where T : unmanaged
         /// </summary>
         /// <param name="arr">Input array of size 1.</param>
         /// <returns></returns>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.asscalar.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.ndarray.item.html</remarks>
         public static T asscalar<T>(Array arr)
         {
             if (arr.Length != 1)
@@ -43,7 +43,7 @@ public static T asscalar<T>(Array arr)
         /// </summary>
         /// <param name="nd">Input NDArray of size 1.</param>
         /// <returns></returns>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.asscalar.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.ndarray.item.html</remarks>
         public static ValueType asscalar(NDArray nd)
         {
             if (nd.size != 1)
@@ -56,7 +56,7 @@ public static ValueType asscalar(NDArray nd)
         /// </summary>
         /// <param name="arr">Input array of size 1.</param>
         /// <returns></returns>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.asscalar.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.ndarray.item.html</remarks>
         public static ValueType asscalar(Array arr)
         {
             if (arr.Length != 1)
@@ -69,7 +69,7 @@ public static ValueType asscalar(Array arr)
         /// </summary>
         /// <param name="arr">Input array of size 1.</param>
         /// <returns></returns>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.asscalar.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.ndarray.item.html</remarks>
         public static T asscalar<T>(ArraySlice<T> arr) where T : unmanaged
         {
             if (arr.Count != 1)
@@ -82,7 +82,7 @@ public static T asscalar<T>(ArraySlice<T> arr) where T : unmanaged
         /// </summary>
         /// <param name="arr">Input array of size 1.</param>
         /// <returns></returns>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.asscalar.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.ndarray.item.html</remarks>
         public static T asscalar<T>(IArraySlice arr) where T : unmanaged
         {
             if (arr.Count != 1)

src/NumSharp.Core/RandomSampling/np.random.binomial.cs

@@ -17,7 +17,7 @@ public partial class NumPyRandom
         /// <param name="p">Parameter of the distribution, >= 0 and &lt;=1.</param>
         /// <param name="shape">Output Shape</param>
         /// <returns>Drawn samples from the parameterized binomial distribution, where each sample is equal to the number of successes over the n trials.</returns>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.random.binomial.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/random/generated/numpy.random.binomial.html</remarks>
         public NDArray binomial(int n, double p, Shape shape) => binomial(n, p, shape.dimensions);
 
         /// <summary>
@@ -28,7 +28,7 @@ public partial class NumPyRandom
         /// <param name="p">Parameter of the distribution, >= 0 and &lt;=1.</param>
         /// <param name="dims">Output Shape</param>
         /// <returns>Drawn samples from the parameterized binomial distribution, where each sample is equal to the number of successes over the n trials.</returns>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.random.binomial.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/random/generated/numpy.random.binomial.html</remarks>
         public NDArray binomial(int n, double p, params int[] dims)
         {
             var x = np.zeros(dims);

src/NumSharp.Core/RandomSampling/np.random.chisquare.cs

@@ -15,7 +15,7 @@ public partial class NumPyRandom
         /// <param name="df">Number of degrees of freedom, should be > 0.</param>
         /// <param name="shape">Output Shape</param>
         /// <returns>Drawn samples from the parameterized chi-square distribution.</returns>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.random.chisquare.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/random/generated/numpy.random.chisquare.html</remarks>
         public NDArray chisquare(double df, Shape shape) => chisquare(df, shape.dimensions);
 
         /// <summary>
@@ -26,7 +26,7 @@ public partial class NumPyRandom
         /// <param name="df">Number of degrees of freedom, should be > 0.</param>
         /// <param name="dims">Output Shape</param>
         /// <returns>Drawn samples from the parameterized chi-square distribution.</returns>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.random.chisquare.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/random/generated/numpy.random.chisquare.html</remarks>
         public NDArray chisquare(double df, params int[] dims)
         {
             if (df <= 0)

src/NumSharp.Core/RandomSampling/np.random.choice.cs

@@ -9,7 +9,7 @@ public partial class NumPyRandom
         /// <param name="shape">Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned.</param>
         /// <param name="replace">Whether the sample is with or without replacement</param>
         /// <param name="probabilities">The probabilities associated with each entry in a. If not given the sample assumes a uniform distribution over all entries in a.</param>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.random.choice.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/random/generated/numpy.random.choice.html</remarks>
         public NDArray choice(NDArray arr, Shape shape = default, bool replace = true, double[] probabilities = null)
         {
             int arrSize = arr.size;
@@ -24,7 +24,7 @@ public NDArray choice(NDArray arr, Shape shape = default, bool replace = true, d
         /// <param name="shape">Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned.</param>
         /// <param name="replace">Whether the sample is with or without replacement</param>
         /// <param name="probabilities">The probabilities associated with each entry in a. If not given the sample assumes a uniform distribution over all entries in a.</param>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.random.choice.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/random/generated/numpy.random.choice.html</remarks>
         public NDArray choice(int a, Shape shape = default, bool replace = true, double[] probabilities = null)
         {
             if (shape.IsEmpty)

src/NumSharp.Core/RandomSampling/np.random.cs

@@ -3,7 +3,7 @@
     /// <summary>
     ///     A class that serves as numpy.random.RandomState in python.
     /// </summary>
-    /// <remarks>https://numpy.org/doc/stable/reference/routines.random.html</remarks>
+    /// <remarks>https://numpy.org/doc/stable/reference/random/index.html</remarks>
     public partial class NumPyRandom
     {
         protected internal Randomizer randomizer;

src/NumSharp.Core/RandomSampling/np.random.exponential.cs

@@ -14,7 +14,7 @@ public partial class NumPyRandom
         /// <param name="scale">The scale parameter, \beta = 1/\lambda.</param>
         /// <param name="shape">Output Shape</param>
         /// <returns>Drawn samples from the parameterized exponential distribution.</returns>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.random.exponential.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/random/generated/numpy.random.exponential.html</remarks>
         public NDArray exponential(double scale, Shape shape) => exponential(scale, shape.dimensions);
 
         /// <summary>
@@ -24,7 +24,7 @@ public partial class NumPyRandom
         /// <param name="scale">The scale parameter, \beta = 1/\lambda.</param>
         /// <param name="dims">Output Shape</param>
         /// <returns>Drawn samples from the parameterized exponential distribution.</returns>
-        /// <remarks>https://numpy.org/doc/stable/reference/generated/numpy.random.exponential.html</remarks>
+        /// <remarks>https://numpy.org/doc/stable/reference/random/generated/numpy.random.exponential.html</remarks>
         public NDArray exponential(double scale, params int[] dims)
         {
             var x = np.log(1 - np.random.uniform(0, 1, dims));