Improved docstrings

Author: lucasimi

src/tdamapper/core.py

@@ -55,7 +55,6 @@
 )
 
 S = TypeVar("S")
-
 T = TypeVar("T")
 
 

src/tdamapper/cover.py

@@ -21,10 +21,13 @@
 from tdamapper.utils.vptree import PivotingStrategy, VPTree, VPTreeKind
 
 T = TypeVar("T")
-T_contra = TypeVar("T_contra", contravariant=True)
 S = TypeVar("S")
+
+T_contra = TypeVar("T_contra", contravariant=True)
 S_contra = TypeVar("S_contra", contravariant=True)
 
+CubicalAlgorithm = Literal["standard", "proximity"]
+
 
 class _Pullback(Generic[S_contra, T_contra]):
     """
@@ -291,21 +294,17 @@ class BaseCubicalCover:
 
     :param n_intervals: The number of intervals to use for each dimension.
         Must be positive and less than or equal to the length of the dataset.
-        Defaults to 1.
     :param overlap_frac: The fraction of overlap between adjacent intervals on
         each dimension, must be in the range (0.0, 0.5]. If not specified, the
         overlap_frac is computed such that the volume of the overlap within
-        each hypercube is half the total volume. Defaults to None.
+        each hypercube is half the total volume.
     :param kind: Specifies whether to use a flat or a hierarchical vantage
-        point tree. Acceptable values are 'flat' or 'hierarchical'. Defaults to
-        'flat'.
+        point tree.
     :param leaf_capacity: The maximum number of points in a leaf node of the
-        vantage point tree. Must be a positive value. Defaults to 1.
+        vantage point tree. Must be a positive value.
     :param leaf_radius: The radius of the leaf nodes. If not specified, it
-        defaults to the value of `radius`. Must be a positive value. Defaults
-        to None.
+        defaults to the value of `radius`. Must be a positive value.
     :param pivoting: The method used for pivoting in the vantage point tree.
-        Acceptable values are None, 'random', or 'furthest'. Defaults to None.
     """
 
     _overlap_frac: float
@@ -553,9 +552,6 @@ def apply(self, X: ArrayRead[NDArray[np.float_]]) -> Iterator[list[int]]:
                 yield neigh_ids
 
 
-CubicalAlgorithm = Literal["standard", "proximity"]
-
-
 class CubicalCover(ParamsMixin):
     """
     Wrapper class for cubical cover algorithms, which cover data with open

src/tdamapper/learn.py

@@ -26,7 +26,6 @@
 from tdamapper.protocols import ArrayRead, Clustering, Cover
 
 S_contra = TypeVar("S_contra", contravariant=True)
-
 T_contra = TypeVar("T_contra", contravariant=True)