Update docstring links

phiml/math/__init__.py

@@ -10,7 +10,7 @@
 Instead, they delegate the actual computation to either NumPy, TensorFlow or PyTorch, depending on the configuration.
 This allows the user to write simulation code once and have it run with various computation backends.
 
-See the documentation at https://tum-pbs.github.io/PhiML/Math.html
+See the documentation at https://tum-pbs.github.io/PhiML/
 """
 
 from ..backend._dtype import DType

phiml/math/_ops.py

@@ -1952,7 +1952,7 @@ def to_float(x: TensorOrTree) -> TensorOrTree:
     
     The precision can be set globally using `math.set_global_precision()` and locally using `with math.precision()`.
     
-    See the `phiml.math` module documentation at https://tum-pbs.github.io/PhiML/Math.html
+    See the documentation at https://tum-pbs.github.io/PhiML/Data_Types.html
 
     See Also:
         `cast()`.
@@ -1982,7 +1982,7 @@ def to_complex(x: TensorOrTree) -> TensorOrTree:
 
     The precision can be set globally using `math.set_global_precision()` and locally using `with math.precision()`.
 
-    See the `phiml.math` module documentation at https://tum-pbs.github.io/PhiML/Math.html
+    See the documentation at https://tum-pbs.github.io/PhiML/Data_Types.html
 
     See Also:
         `cast()`.

phiml/math/_shape.py

@@ -46,7 +46,7 @@ def __init__(self, sizes: tuple, names: tuple, types: tuple, item_names: tuple):
         self.sizes: tuple = sizes
         """
         Ordered dimension sizes as `tuple`.
-        The size of a dimension can be an `int` or a `Tensor` for [non-uniform shapes](https://tum-pbs.github.io/PhiML/Math.html#non-uniform-tensors).
+        The size of a dimension can be an `int` or a `Tensor` for [non-uniform shapes](https://tum-pbs.github.io/PhiML/Non_Uniform.html).
         
         See Also:
             `Shape.get_size()`, `Shape.size`, `Shape.shape`.
@@ -539,7 +539,7 @@ def unstack(self, dim='dims') -> Tuple['Shape']:
         The dimension listing the sizes of the shape is referred to as `'dims'`.
 
         Non-uniform tensor shapes may be unstacked along other dimensions as well, see
-        https://tum-pbs.github.io/PhiML/Math.html#non-uniform-tensors
+        https://tum-pbs.github.io/PhiML/Non_Uniform.html
 
         Args:
             dim: dimension to unstack
@@ -833,7 +833,7 @@ def shape(self) -> 'Shape':
         Higher-order `Shape`.
         The returned shape will always contain the channel dimension `dims` with a size equal to the `Shape.rank` of this shape.
 
-        For uniform shapes, `Shape.shape` will only contain the dimension `dims` but the shapes of [non-uniform shapes](https://tum-pbs.github.io/PhiML/Math.html#non-uniform-tensors)
+        For uniform shapes, `Shape.shape` will only contain the dimension `dims` but the shapes of [non-uniform shapes](https://tum-pbs.github.io/PhiML/Non_Uniform.html)
         may contain additional dimensions.
 
         See Also:

phiml/math/_tensors.py

@@ -34,7 +34,7 @@ class Tensor:
     To check whether a value is a tensor, use `isinstance(value, Tensor)`.
 
     To construct a Tensor, use `phiml.math.tensor()`, `phiml.math.wrap()` or one of the basic tensor creation functions,
-    see https://tum-pbs.github.io/PhiML/Math.html#tensor-creation .
+    see https://tum-pbs.github.io/PhiML/Tensors.html .
 
     Tensors are not editable.
     When backed by an editable native tensor, e.g. a `numpy.ndarray`, do not edit the underlying data structure.
@@ -825,7 +825,7 @@ class TensorDim(BoundDim):
 
     Indexing a `TensorDim` as `tdim[start:stop:step]` returns a sliced `Tensor`.
 
-    See the documentation at https://tum-pbs.github.io/PhiML/Math.html#indexing-slicing-unstacking .
+    See the documentation at https://tum-pbs.github.io/PhiML/Introduction.html#Slicing .
     """
 
     def __init__(self, tensor: Tensor, name: str):

phiml/math/magic.py

@@ -766,12 +766,12 @@ def valid_key(key):
             return {name: selection for name, selection in zip(channel(obj).names, item[1:])}
         elif len(item) == shape(obj).channel_rank:
             if len(item) > 1:
-                warnings.warn("NumPy-style slicing for more than one channel dimension is highly discouraged. Use a dict or the special slicing syntax value.dim[slice] instead. See https://tum-pbs.github.io/PhiML/Math.html", SyntaxWarning, stacklevel=3)
+                warnings.warn("NumPy-style slicing for more than one channel dimension is highly discouraged. Use a dict or the special slicing syntax value.dim[slice] instead. See https://tum-pbs.github.io/PhiML/Introduction.html#Slicing", SyntaxWarning, stacklevel=3)
             return {name: selection for name, selection in zip(channel(obj).names, item)}
         elif shape(obj).channel_rank == 1 and all(isinstance(e, str) for e in item):
             return {channel(obj).name: item}
         else:
-            raise AssertionError(f"Cannot slice {obj}[{item}]. Use a dict or the special slicing syntax value.dim[slice] instead. See https://tum-pbs.github.io/PhiML/Math.html")
+            raise AssertionError(f"Cannot slice {obj}[{item}]. Use a dict or the special slicing syntax value.dim[slice] instead. See https://tum-pbs.github.io/PhiML/Introduction.html#Slicing")
     else:
         if shape(obj).channel_rank == 1:
             return {channel(obj).name: item}