Initial commit based of previous repositories contents

src/centrality.py

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+import math
+
+import numpy as np
+
+from src import plot
+
+def convex_hull(G):
+    # gift wrapping algorithm
+    pos = G.vp["pos"]
+    x = []
+    y = []
+    for v in G.vertices():
+        # print(pos[v])
+        ver = pos[v]
+        x.append(ver[0])
+        y.append(ver[1])
+
+    min_x = min(x)
+    min_y = min(y)
+    max_x = max(x)
+    max_y = max(y)
+    pos = np.stack((np.array(x), np.array(y)), axis=-1)
+
+    convex_hull = []
+    hull_point = np.array([min_x,pos[np.argmin(x)][1]])
+    convex_hull.append(hull_point)
+    endpoint = None
+    i = 0
+    while not plot.Vector.eql(endpoint, convex_hull[0]):
+        endpoint = pos[0]
+        for point in pos:
+            if plot.Vector.eql(endpoint, hull_point) or plot.Vector.cross_z(plot.Vector.vec(convex_hull[i], endpoint), plot.Vector.vec(convex_hull[i], point)) < 0:
+                endpoint = point
+        i += 1
+        hull_point = endpoint
+        convex_hull.append(hull_point)
+    return convex_hull
+
+def correct(G, centrality, m_opt, c0_opt, b_opt):
+    """
+    Correct a given centrality through the calculated model.
+    @param pos [Array-2d] Positions of points (i.e. `g.vp["pos"].get_2d_array()`)
+    @param centrality [VertexPropertyMap] Result of a centrality calculation of `graph-tool` on graph G
+    @param m_opt [Float] Model m value (slope of linear function)
+    @param c0_opt [Float] Model c0 value (offset of linear function)
+    @param b_opt [Float] Model b value (intersection point)
+    @return [VertexPropertyMap] Corrected centrality values based on @param centrality
+    """
+    corrected_metric = {}
+    pos = G.vp["pos"]
+    x = []
+    y = []
+    for v in G.vertices():
+        # print(pos[v])
+        ver = pos[v]
+        x.append(ver[0])
+        y.append(ver[1])
+
+    keys = iter(centrality.a)
+    hull = convex_hull(x, y)
+
+    points = np.stack((np.array(x), np.array(y)), axis=-1)
+    for point in pos:
+        min_distance = math.inf
+        key = next(keys)
+        for edge in hull:
+            vector = plot.Vector.vec(point, edge)
+            distance = plot.Vector.vec_len(vector)
+            if distance < min_distance:
+                min_distance = distance
+        if min_distance <= b_opt:
+            # requires correction
+            value = centrality[key]
+            delta = (m_opt * b_opt) - (m_opt * min_distance)
+            corrected_metric[key] = value + delta
+        else:
+            # unaffected by boundary
+            corrected_metric[key] = centrality[key]
+
+    return corrected_metric