Minor updates

.gitignore

@@ -1,4 +1,5 @@
 __pycache__
+.env
 .idea
 .pytest_cache
 resources/data

er/graph/generator.py

@@ -67,7 +67,6 @@ def hexagonal_lattice(num_nodes, periodic=False):
             if "contraction" in data:
                 del data["contraction"]
 
-
     # Split the position in two distinct keys
     for node, data in lattice.nodes(data=True):
         data["x"], data["y"] = data["pos"]
@@ -76,6 +75,80 @@ def hexagonal_lattice(num_nodes, periodic=False):
     return _relabel_nodes_by_distance(lattice, spatial_center=True)
 
 
+def hex_tri_lattice():
+    m = 60
+    n = 120
+
+    graph = nx.generators.triangular_lattice_graph(m, n)
+
+    graph.graph["label"] = "Hexagonal + triangular lattice"
+
+    # Split the position in two distinct keys
+    for node, data in graph.nodes(data=True):
+        data["x"], data["y"] = data["pos"]
+        del data["pos"]
+
+    # Periodicity
+    for i in range(n // 2 + 1):
+        node = (i, 0)
+        graph.add_edges_from([(node, n) for n in graph.adj[(i, m)]])
+
+    for i in range(n // 2 + 1):
+        graph.remove_node((i, m))
+
+    for i in range(n // 4 + 2, n // 2, 3):
+        for j in range(1, m, 2):
+            try:
+                graph.remove_node((i, j))
+            except nx.NetworkXError:
+                pass
+    for i in range(n // 4 + 1, n // 2, 3):
+        for j in range(0, m + 1, 2):
+            try:
+                graph.remove_node((i, j))
+            except nx.NetworkXError:
+                pass
+
+    return _relabel_nodes_by_distance(graph, spatial_center=True)
+
+
+def hex_connectivity_lattice():
+    m = 60
+    n = 120
+
+    graph = nx.generators.triangular_lattice_graph(m, n)
+
+    graph.graph["label"] = "Hexagonal + triangular lattice"
+
+    # Split the position in two distinct keys
+    for node, data in graph.nodes(data=True):
+        data["x"], data["y"] = data["pos"]
+        del data["pos"]
+
+    # Periodicity
+    for i in range(n // 2 + 1):
+        node = (i, 0)
+        graph.add_edges_from([(node, n) for n in graph.adj[(i, m)]])
+
+    for i in range(n // 2 + 1):
+        graph.remove_node((i, m))
+
+    for i in range(n // 4 + 2, n // 2, 3):
+        for j in range(1, m, 2):
+            try:
+                graph.remove_node((i, j))
+            except nx.NetworkXError:
+                pass
+    for i in range(n // 4 + 1, n // 2, 3):
+        for j in range(0, m + 1, 2):
+            try:
+                graph.remove_node((i, j))
+            except nx.NetworkXError:
+                pass
+
+    return _relabel_nodes_by_distance(graph, spatial_center=True)
+
+
 def from_matfile(filename, adj_key="C", nodes_key="nodes"):
     """Generates a graph based on a Matlab file (.mat).
 

er/plot/graph.py

@@ -21,7 +21,7 @@ def _length(edge):
 
         return np.hypot(*np.subtract(x_u, x_v))
 
-    return [edge for edge in graph.edges if _length(edge) > 1.5]
+    return [edge for edge in graph.edges if _length(edge) > 2]
 
 
 def heatmap(graph: nx.Graph, node_values=None, edge_values=None, cmap=None,

er/simulation/simulation.py

@@ -129,7 +129,7 @@ def _run(self, id=None):
         fpt = np.full(network.size(), np.inf)
         fpt[self.walker.node] = self.walker.time
 
-        times = np.array([w.time for w in walkers], dtype=np.float)
+        times = np.array([w.time for w in walkers], dtype=float)
 
         steps = 0
         while times.min() < fpt.max():

er/simulation/trajectory.py

@@ -56,7 +56,7 @@ def trajectories(self, num_walkers, max_time, start_nodes=None,
                 walkers[i].start = node
                 walkers[i].node = node
 
-        times = np.array([w.time for w in walkers], dtype=np.float)
+        times = np.array([w.time for w in walkers], dtype=float)
 
         trajs = [Trajectory([w.node], [w.time], id=k)
                  for k, w in enumerate(walkers)]
@@ -115,7 +115,7 @@ def trajectories_to_target(self, num_walkers, target, keep=None,
         walkers = np.array([copy(self.walker) for _ in range(num_walkers)])
 
         active_idx = set(range(num_walkers))
-        times = np.array([w.time for w in walkers], dtype=np.float)
+        times = np.array([w.time for w in walkers], dtype=float)
 
         paths = np.array([Trajectory([w.node], times=[w.time], id=i)
                           for i, w in enumerate(walkers)])