full upload so not to lose anything important

tools/anomaly_scatter_plot.py

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+import matplotlib.pyplot as plt
+import numpy as np
+
+# Fix the random seed for reproducibility
+np.random.seed(0)
+
+# 1. Generate NORMAL DATA (e.g. points roughly around the origin)
+#    We'll keep them relatively close together so that a circle can enclose them easily.
+normal_data = np.random.randn(50, 2) * 0.75
+
+# 2. Generate ANOMALOUS DATA
+#    - Cluster 1: 3 points close together
+anomaly_cluster_1 = np.array([[3.0, 3.0], [3.2, 3.1], [2.8, 2.9], [0.4, 4.0]])
+
+#    - Cluster 2: A single point
+
+# 3. Compute the center and radius for a boundary circle around normal data
+center = normal_data.mean(axis=0)
+distances = np.linalg.norm(normal_data - center, axis=1)
+radius = (
+    np.max(distances) + 0.2
+)  # Add a small margin to ensure all normal points are inside
+
+# Create coordinates for plotting the circular boundary
+theta = np.linspace(0, 2 * np.pi, 200)
+circle_x = center[0] + radius * np.cos(theta)
+circle_y = center[1] + radius * np.sin(theta)
+
+# 4. Plot the data
+plt.figure(figsize=(7, 7))
+
+# Scatter normal points with 'o'
+plt.scatter(
+    normal_data[:, 0], normal_data[:, 1], marker="o", color="blue", label="Normal Data"
+)
+
+# Scatter anomalous points with 'x', but separate them by cluster for clarity
+plt.scatter(
+    anomaly_cluster_1[:, 0],
+    anomaly_cluster_1[:, 1],
+    marker="x",
+    color="red",
+    label="Anomalies",
+)
+
+# Plot the boundary (circle) around the normal data
+plt.plot(circle_x, circle_y, linestyle="--", color="black", label="Boundary")
+
+# 5. Annotate/label the clusters
+# Label the normal cluster near its center
+# plt.text(center[0], center[1],
+#          'Normal Cluster',
+#          horizontalalignment='center',
+#          verticalalignment='center',
+#          fontsize=9,
+#          bbox=dict(facecolor='white', alpha=0.7))
+#
+# # Label anomaly cluster 1 near its centroid
+# ac1_center = anomaly_cluster_1.mean(axis=0)
+# plt.text(ac1_center[0], ac1_center[1],
+#          'Anomaly Cluster 1',
+#          horizontalalignment='center',
+#          verticalalignment='center',
+#          fontsize=9,
+#          bbox=dict(facecolor='white', alpha=0.7))
+#
+# # Label anomaly cluster 2
+# ac2_point = anomaly_cluster_2[0]
+# plt.text(ac2_point[0]+0.2, ac2_point[1],
+#          'Anomaly Cluster 2',
+#          horizontalalignment='left',
+#          verticalalignment='center',
+#          fontsize=9,
+#          bbox=dict(facecolor='white', alpha=0.7))
+
+# Add legend and make plot look nice
+plt.legend(loc="upper left")
+# plt.title('2D Scatter Plot Showing Normal and Anomalous Clusters')
+plt.xlabel("x")
+plt.ylabel("y")
+plt.tick_params(
+    axis="both",  # changes apply to the x-axis
+    which="both",  # both major and minor ticks are affected
+    bottom=False,  # ticks along the bottom edge are off
+    top=False,  # ticks along the top edge are off
+    left=False,  # ticks along the top edge are off
+    right=False,  # ticks along the top edge are off
+    labelbottom=False,
+    labelleft=False,
+)  #
+# plt.grid(True)
+plt.axis("equal")  # Makes circles look circular rather than elliptical
+plt.savefig("scatter_plot.png")