Fix issues remaining after rebase onto develop

opt/scopeserver/dataserver/modules/gserver/GServer.py

@@ -274,9 +274,7 @@ def getNextCluster(self, request, context):
 
         search_results: List[CategorisedMatches] = [
             result
-            for result in get_search_results(
-                cluster_metadata["description"], "all", loom, self.config["dataHashSecret"]
-            )
+            for result in get_search_results(cluster_metadata["description"], "all", loom)
             if result.category == f"Clustering: {clustering_meta['name']}"
         ]
 
@@ -329,7 +327,7 @@ def getCellMetaData(self, request, context):
 
     def getFeatures(self, request, context):
         loom = self.lfh.get_loom(loom_file_path=Path(request.loomFilePath))
-        features = get_search_results(request.query, request.filter, loom, self.config["dataHashSecret"])
+        features = get_search_results(request.query, request.filter, loom)
         return s_pb2.FeatureReply(
             features=[
                 s_pb2.FeatureReply.Feature(

opt/scopeserver/dataserver/utils/data.py

@@ -10,7 +10,7 @@
 T = TypeVar("T")
 
 
-def get_99_and_100_percentiles(values: np.ndarray) -> Tuple[np.float, np.float]:
+def get_99_and_100_percentiles(values: np.ndarray) -> Tuple[np.ndarray, np.number]:
     """
     Compute the 99th and 100th (maximum value) of an array of values
     and clamp the 99th percentil value between [0.01, max(values)]

opt/scopeserver/dataserver/utils/labels.py

@@ -50,7 +50,7 @@ def labels() -> Generator[FeatureLabel, None, None]:
             yield FeatureLabel(
                 label=annotation,
                 colour=colours[i],
-                coordinate=Coordinate(x=np.mean(coords["x"]), y=np.mean(coords["y"])),
+                coordinate=Coordinate(x=float(np.mean(coords["x"])), y=float(np.mean(coords["y"]))),
             )
 
     return [label for label in labels()]
@@ -85,7 +85,7 @@ def labels() -> Generator[FeatureLabel, None, None]:
             yield FeatureLabel(
                 label=cluster,
                 colour=colours[i],
-                coordinate=Coordinate(x=np.mean(coords["x"]), y=np.mean(coords["y"])),
+                coordinate=Coordinate(x=float(np.mean(coords["x"])), y=float(np.mean(coords["y"]))),
             )
 
     return [label for label in labels()]

opt/scopeserver/dataserver/utils/loom.py

@@ -55,7 +55,6 @@ def __init__(
 
         logger.info(f"New Loom object created for {file_path}")
         # Metrics
-        self.nUMI = None
         self.species, self.gene_mappings = self.infer_species()
         self.ss_pickle_name = self.abs_file_path.with_suffix(".ss_pkl")
         self.ss = ss.load_ss(self)
@@ -758,8 +757,6 @@ def get_gene_names(self) -> Dict[str, str]:
     ##############
 
     def get_nUMI(self) -> np.ndarray:
-        if self.nUMI is not None:
-            return self.nUMI
         if self.has_ca_attr(name="nUMI"):
             return self.loom_connection.ca.nUMI
         if self.has_ca_attr(name="n_counts"):
@@ -768,10 +765,7 @@ def get_nUMI(self) -> np.ndarray:
         # TODO: Add case here for large files. Sum across 1mio rows takes a very long time to compute
         # Possibly faster fix totals = ds.map([np.sum], axis=1)[0]
 
-        calc_nUMI_start_time = time.time()
-        self.nUMI = self.loom_connection.map([np.sum], axis=1)[0]
-        logger.debug("{0:.5f} seconds elapsed (calculating nUMI) ---".format(time.time() - calc_nUMI_start_time))
-        return self.nUMI
+        return self.loom_connection.map([np.sum], axis=1)[0]
 
     def get_gene_expression_by_gene_symbol(self, gene_symbol: str) -> np.ndarray:
         return self.loom_connection[self.get_genes() == gene_symbol, :][0]
@@ -834,7 +828,7 @@ def get_regulon_genes(self, regulon: str) -> np.ndarray:
                 return self.get_genes()[self.loom_connection.ra.Regulons[regulon] == 1]
         except Exception as err:
             logger.error(err)
-            return []
+            return np.array([])
 
     def has_regulons_AUC(self) -> bool:
         return self.has_legacy_regulons() or self.has_motif_regulons() or self.has_track_regulons()