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.

effected_visualization.py

@@ -0,0 +1,91 @@
+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):
+    """
+    Generate the spatial graph using delaunay for the given `adata`.
+    `adata` will contain the calculated spatial graph contents in the keys
+    adata.obsm['spatial']` in case the `adata` is created from a dataset of *squidpy*.
+    @return [Graph] generated networkx graph from adata.obsp['spatial_distances']
+    """
+    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, 12))
+ax0 = fig.subplots(1, 1)
+
+# 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)
+
+# TODO
+# should this be part of the plotting function itself, it should not be necessary for me to do this
+d_curve = np.linspace(min(d), max(d), 500)
+C_curve = np.piecewise(
+    d_curve,
+    [d_curve <= b_opt, d_curve > b_opt],
+    [lambda x: m_opt * x + c0_opt, lambda x: m_opt * b_opt + c0_opt]
+)
+# plot graphs effected / uneffected nodes
+plot.graph_plot_effected(fig, ax0, g, vp, convex_hull, b_opt, f"Random Graph (seed: {seed})")
+
+# # linear regression model
+# m_reg, c0_reg, b_reg, aic_reg = fitting.fit_cut(d, C)
+# print(f"m_reg = {m_reg}")
+
+# # TODO
+# # should this be part of the plotting function itself, it should not be necessary for me to do this
+# d_curve = np.linspace(min(d), max(d), 500)
+# C_curve = np.piecewise(
+#     d_curve,
+#     [d_curve <= b_reg, d_curve > b_reg],
+#     [lambda x: m_reg * x + c0_reg, lambda x: m_reg * b_reg + c0_reg]
+# )
+# ax1.plot(d_curve, C_curve, color='k', linewidth=1, label=f"Top Cut | AIC: {aic_reg}")
+# ax1.legend()
+
+fig.savefig(f"model_closeness_5000_effected_vs_uneffected.svg", format='svg')
+