add fig3 plots

plot_fig3.py

@@ -0,0 +1,122 @@
+import cmasher as cmr
+import matplotlib.pyplot as plt
+import networkx as nx
+import numpy as np
+import seaborn as sns
+
+from lcs import *
+
+with open(f"Data/zkc_tmax_comparison.json") as file:
+    data = json.load(file)
+    tmax = np.array(data["tmax"], dtype=float)
+    A = np.array(data["A"], dtype=float)[0, 0, 0]
+    Q = np.array(data["Q"], dtype=float)
+
+n_c, n_t, n_r, n, _ = Q.shape
+
+G = nx.Graph(A.astype(int))
+
+cc = clustering_coefficient(A)
+deg = degrees(A)
+
+kc = nx.core_number(G)
+coreness = np.zeros(n)
+coreness[list(kc)] = list(kc.values())
+
+# plotting settings
+
+colormap = cmr.redshift
+clist = [colormap(0.15), colormap(0.3), colormap(0.7), colormap(0.85)]
+
+## Coreness difference
+
+ms = 4
+alpha = 1
+
+x = tmax
+
+y1 = np.zeros([n_r, n_t, n])
+y2 = np.zeros([n_r, n_t, n])
+for i in range(n_r):
+    y1[i] = [nodal_performance(Q[0, j, i], A) for j in range(n_t)]
+    y2[i] = [nodal_performance(Q[1, j, i], A) for j in range(n_t)]
+
+# plt.figure(figsize=(4, 3))
+plt.figure(figsize=(4, 6))
+plt.subplot(211)
+
+core_values = np.unique(coreness)
+
+for idx, k in enumerate(core_values):
+    n_k = sum(coreness == k)
+    y = np.zeros([n_r, n_t, n_k])
+    for i in range(n_r):
+        y[i] = (y1[i, :, coreness == k] - y2[i, :, coreness == k]).T
+    ymean = y.mean(axis=2).mean(axis=0)
+    ystd = y.mean(axis=2).std(axis=0)
+    plt.semilogx(
+        x,
+        ymean,
+        "o-",
+        markersize=ms,
+        color=clist[idx],
+        alpha=alpha,
+        label=f"{int(k)}-core nodes",
+    )
+plt.semilogx(tmax, np.zeros_like(tmax), "k--")
+
+plt.xlim([tmax.min() - 0.5, tmax.max() + 1000])
+plt.yticks([-0.2, -0.1, 0, 0.1])
+plt.legend()
+plt.ylabel(r"$\varepsilon_{SC} - \varepsilon_{CC}$")
+plt.xlabel(r"$t_{max}$")
+sns.despine()
+# plt.tight_layout()
+# plt.savefig("Figures/Fig3/figure3a.png", dpi=1000)
+# plt.savefig("Figures/Fig3/figure3a.pdf", dpi=1000)
+
+plt.subplot(212)
+# Degree difference
+ms = 4
+alpha = 1
+
+x = tmax
+y1 = np.zeros([n_r, n_t, n])
+y2 = np.zeros([n_r, n_t, n])
+for i in range(n_r):
+    y1[i] = [nodal_performance(Q[0, j, i], A) for j in range(n_t)]
+    y2[i] = [nodal_performance(Q[1, j, i], A) for j in range(n_t)]
+
+# plt.figure(figsize=(4, 3))
+
+deg_bounds = [[1, 5], [6, 10], [11, 15], [16, 20]]
+for idx, d in enumerate(deg_bounds):
+    n_d = sum((d[0] <= deg) & (deg <= d[1]))
+    y = np.zeros([n_r, n_t, n_d])
+    for i in range(n_r):
+        y[i] = (
+            y1[i, :, (d[0] <= deg) & (deg <= d[1])]
+            - y2[i, :, (d[0] <= deg) & (deg <= d[1])]
+        ).T
+    ymean = y.mean(axis=2).mean(axis=0)
+    ystd = y.mean(axis=2).std(axis=0)
+    plt.semilogx(
+        x,
+        ymean,
+        "o-",
+        markersize=ms,
+        color=clist[idx],
+        alpha=alpha,
+        label=rf"{int(d[0])}$\leq k\leq${int(d[1])}",
+    )
+plt.semilogx(tmax, np.zeros_like(tmax), "k--")
+
+plt.xlim([tmax.min() - 0.5, tmax.max() + 1000])
+plt.yticks([-0.1, 0, 0.1])
+plt.ylabel(r"$\varepsilon_{SC} - \varepsilon_{CC}$")
+plt.xlabel(r"$t_{max}$")
+plt.legend()
+plt.tight_layout()
+sns.despine()
+plt.savefig("Figures/Fig3/figure3b.png", dpi=1000)
+plt.savefig("Figures/Fig3/figure3b.pdf", dpi=1000)