WIP different small python scripts to generate corresponding images

The final API will be derived from these scripts into a different
repository, which then only holds the corresponding functions that
provide the corresponding functionalities described in the associated
master thesis.

model_based_correction.py

@@ -0,0 +1,61 @@
+import math
+
+import matplotlib.pyplot as plt
+import numpy as np
+from graph_tool.all import *
+
+from src import centrality
+from src import plot
+from src import fitting
+
+def random_graph(n=5000, seed=None):
+    """
+    Uniformly random point cloud generation.
+    `n` [int] Number of points to generate. Default 5000 seems like a good starting point in point density and corresponding runtime for the subsequent calculations.
+    @return [numpy.ndarray] Array of shape(n, 2) containing the coordinates for each point of the generated point cloud.
+    """
+    if seed is None:
+        import secrets
+        seed = secrets.randbits(128)
+    rng = np.random.default_rng(seed=seed)
+    return rng.random((n, 2)), seed
+
+
+def spatial_graph(adata):
+    g, pos = graph_tool.generation.triangulation(adata, type="delaunay")
+    g.vp["pos"] = pos
+    weight = g.new_edge_property("double")
+    for e in g.edges():
+        weight[e] = math.sqrt(sum(map(abs, pos[e.source()].a - pos[e.target()].a)))**2
+    return g, weight
+
+
+points, seed = random_graph()
+g, weight = spatial_graph(points)
+g = GraphView(g)
+
+# calculate centrality values
+vp = closeness(g, weight=weight)
+vp.a = np.nan_to_num(vp.a) # correct floating point values
+# ep.a = np.nan_to_num(ep.a) # correct floating point values
+
+# calculate convex hull
+convex_hull = centrality.convex_hull(g)
+
+# plot graph with convex_hull
+fig = plt.figure(figsize=(15, 5))
+ax0, ax1 = fig.subplots(1, 2)
+plot.graph_plot(fig, ax0, g, vp, convex_hull, f"Closeness without prediction")
+
+# generate model based on convex hull and associated centrality values
+quantification = plot.quantification_data(g, vp, convex_hull)
+
+# optimize model's piece-wise linear function
+d = quantification[:, 0]
+C = quantification[:, 1]
+m_opt, c0_opt, b_opt, aic_opt = fitting.fit_piece_wise_linear(d, C)
+
+vp = centrality.correct(g, vp, m_opt, c0_opt, b_opt)
+plot.graph_plot(fig, ax1, g, vp, convex_hull, f"Closeness with model prediction")
+
+fig.savefig(f"model_prediction_comparison.svg", format='svg')