adapted docsting of lookup function

src/scirpy/ir_dist/_util.py

@@ -243,19 +243,19 @@ def lookup(
         To get distance between two objects we need to look up the features of the two objects.
         The distance between those two features is then the distance between the two objects.
 
-        To do so, we first use the `object_ids` together with the forward lookup table to look up 
-        the indices of the objects in the distance matrix. Afterwards we pick the according row for each object
-        out of the distance matrix and construct a `rows` matrix (n_object_ids x n_features).
+        To do so, we first use the `object_ids` together with the `forward_lookup_table` to look up 
+        the indices of the objects in the feature `distance_matrix`. Afterwards we pick the according row for each object
+        out of the `distance_matrix` and construct a `rows` matrix (n_object_ids x n_features).
         
-        "nan"s (index = -1) are not looked up in the feature distance matrix, they return a row of zeros
+        "nan"s (index = -1) are not looked up in the feature `distance_matrix`, they return a row of zeros
         instead.
 
-        Then we use the entries of the reverse lookup table to construct a reverse_lookup_matrix (n_features x n_object_ids).
-        By multiplying the rows matrix with the reverse_lookup_matrix we get the final object distance matrix that shows
+        Then we use the entries of the `reverse_lookup_table` to construct a `reverse_lookup_matrix` (n_features x n_object_ids).
+        By multiplying the `rows` matrix with the `reverse_lookup_matrix` we get the final `object_distance_matrix` that shows
         the distances between the objects with the given `object_ids` regarding a certain feature column.
 
         It might not be obvious at first sight that the matrix multiplication between `rows` and `reverse_lookup_matrix` gives
-        us the desired result. But this trick allows us to use the built-in sparse matrix multiplication of the `scipy.sparse`
+        us the desired result. But this trick allows us to use the built-in sparse matrix multiplication of `scipy.sparse`
         for enhanced performance.
         
         Parameters