dynamic properties via JS in dash-cytoscape

src/lang_main/analysis/graphs.py

@@ -130,7 +130,7 @@ def convert_graph_to_cytoscape(
     cyto_data: list[CytoscapeData] = []
     # iterate over nodes
     nodes = cast(Iterable[NodeTitle], graph.nodes)
-    for i, node in enumerate(nodes):
+    for node in nodes:
         node_data: CytoscapeData = {
             'data': {
                 'id': node,
@@ -151,7 +151,7 @@ def convert_graph_to_cytoscape(
         ],
         graph.edges.data('weight', default=1),  # type: ignore
     )
-    for i, (source, target, weight) in enumerate(edges):
+    for source, target, weight in edges:
         weights.add(weight)
         edge_data: CytoscapeData = {
             'data': {
@@ -288,27 +288,35 @@ class TokenGraph(DiGraph):
 
     def filter_by_edge_weight(
         self,
-        threshold: int,
+        bound_lower: int | None,
+        bound_upper: int | None,
     ) -> Self:
-        """filters all edges which are below the given threshold
+        """filters all edges which are within the provided bounds
 
         Parameters
         ----------
-        threshold : int
-            edges with weights smaller than this value will be removed
+        bound_lower : int | None
+            lower bound for edge weights, edges with weight equal to this value are retained
+        bound_upper : int | None
+            upper bound for edge weights, edges with weight equal to this value are retained
 
         Returns
         -------
         Self
             a copy of the graph with filtered edges
         """
-        # filter edges by weight
         original_graph_edges = copy.deepcopy(self.edges)
         filtered_graph = self.copy()
 
+        if not any([bound_lower, bound_upper]):
+            logger.warning('No bounds provided, returning original graph.')
+            return filtered_graph
+
         for edge in original_graph_edges:
             weight = typing.cast(int, filtered_graph[edge[0]][edge[1]]['weight'])
-            if weight < threshold:
+            if bound_lower is not None and weight < bound_lower:
+                filtered_graph.remove_edge(edge[0], edge[1])
+            if bound_upper is not None and weight > bound_upper:
                 filtered_graph.remove_edge(edge[0], edge[1])
 
         if filtered_graph._undirected is not None:
@@ -320,14 +328,17 @@ class TokenGraph(DiGraph):
 
     def filter_by_node_degree(
         self,
-        threshold: int,
+        bound_lower: int | None,
+        bound_upper: int | None,
     ) -> Self:
-        """filters all nodes which have a degree below the given threshold
+        """filters all nodes which are within the provided bounds by their degree
 
         Parameters
         ----------
-        threshold : int
-            nodes with a degree smaller than this value will be removed
+        bound_lower : int | None
+            lower bound for node degree, nodes with degree equal to this value are retained
+        bound_upper : int | None
+            upper bound for node degree, nodes with degree equal to this value are retained
 
         Returns
         -------
@@ -338,9 +349,15 @@ class TokenGraph(DiGraph):
         original_graph_nodes = copy.deepcopy(self.nodes)
         filtered_graph = self.copy()
 
+        if not any([bound_lower, bound_upper]):
+            logger.warning('No bounds provided, returning original graph.')
+            return filtered_graph
+
         for node in original_graph_nodes:
             degree = filtered_graph.degree[node]  # type: ignore
-            if degree < threshold:
+            if bound_lower is not None and degree < bound_lower:
+                filtered_graph.remove_node(node)
+            if bound_upper is not None and degree > bound_upper:
                 filtered_graph.remove_node(node)
 
         if filtered_graph._undirected is not None:

src/lang_main/types.py

@@ -21,7 +21,7 @@ class STFRDeviceTypes(enum.StrEnum):
     GPU = 'cuda'
 
 
-# ** datatsets
+# ** datasets
 PandasIndex: TypeAlias = int | np.int64
 ObjectID: TypeAlias = int
 Embedding: TypeAlias = SpacyDoc | Tensor