Remove actually unused depth parameter and make Clippy happy.

rstar/src/algorithm/bulk_load/bulk_load_sequential.rs

@@ -11,7 +11,7 @@ use num_traits::Float;
 
 use super::cluster_group_iterator::{calculate_number_of_clusters_on_axis, ClusterGroupIterator};
 
-fn bulk_load_recursive<T, Params>(elements: Vec<T>, depth: usize) -> ParentNode<T>
+fn bulk_load_recursive<T, Params>(elements: Vec<T>) -> ParentNode<T>
 where
     T: RTreeObject,
     <T::Envelope as Envelope>::Point: Point,
@@ -28,7 +28,6 @@ where
 
     let iterator = PartitioningTask::<_, Params> {
         number_of_clusters_on_axis,
-        depth,
         work_queue: vec![PartitioningState {
             current_axis: <T::Envelope as Envelope>::Point::DIMENSIONS,
             elements,
@@ -50,7 +49,6 @@ struct PartitioningState<T: RTreeObject> {
 /// Successively partitions the given elements into  cluster groups and finally into clusters.
 struct PartitioningTask<T: RTreeObject, Params: RTreeParams> {
     work_queue: Vec<PartitioningState<T>>,
-    depth: usize,
     number_of_clusters_on_axis: usize,
     _params: core::marker::PhantomData<Params>,
 }
@@ -66,7 +64,7 @@ impl<T: RTreeObject, Params: RTreeParams> Iterator for PartitioningTask<T, Param
             } = next;
             if current_axis == 0 {
                 // Partitioning finished successfully on all axis. The remaining cluster forms a new node
-                let data = bulk_load_recursive::<_, Params>(elements, self.depth - 1);
+                let data = bulk_load_recursive::<_, Params>(elements);
                 return RTreeNode::Parent(data).into();
             } else {
                 // The cluster group needs to be partitioned further along the next axis
@@ -95,9 +93,7 @@ where
     <T::Envelope as Envelope>::Point: Point,
     Params: RTreeParams,
 {
-    let m = Params::MAX_SIZE;
-    let depth = (elements.len() as f32).log(m as f32).ceil() as usize;
-    bulk_load_recursive::<_, Params>(elements, depth)
+    bulk_load_recursive::<_, Params>(elements)
 }
 
 #[cfg(test)]

rstar/src/algorithm/nearest_neighbor.rs

@@ -28,8 +28,7 @@ where
     T: PointDistance,
 {
     fn partial_cmp(&self, other: &Self) -> Option<::core::cmp::Ordering> {
-        // Inverse comparison creates a min heap
-        other.distance.partial_cmp(&self.distance)
+        Some(self.cmp(other))
     }
 }
 
@@ -40,7 +39,8 @@ where
     T: PointDistance,
 {
     fn cmp(&self, other: &Self) -> ::core::cmp::Ordering {
-        self.partial_cmp(other).unwrap()
+        // Inverse comparison creates a min heap
+        other.distance.partial_cmp(&self.distance).unwrap()
     }
 }
 

rstar/src/algorithm/rstar.rs

@@ -150,7 +150,7 @@ where
     }
 }
 
-fn choose_subtree<T>(node: &mut ParentNode<T>, to_insert: &RTreeNode<T>) -> usize
+fn choose_subtree<T>(node: &ParentNode<T>, to_insert: &RTreeNode<T>) -> usize
 where
     T: RTreeObject,
 {