unified Z-Score

test.py

@@ -1,13 +1,74 @@
 from pyagushka import lyagushka
 from randonautentropy import rndo
 import json
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.interpolate import interp1d
 
-random_data = []
+def generate_random_data(size=1024, max_value=100):
 
-with open('random_values.txt', 'r') as file:
-    for line in file:
-        random_data.append(int(line.strip()))
+    random_data = []
+    max_value_bytes = (max_value.bit_length() + 7) // 8
+    max_int_for_bytes = 2**(max_value_bytes * 8) - 1
+    min_bytes_needed =  max_value_bytes * size
+    mod_cutoff = max_int_for_bytes - (max_int_for_bytes % max_value) - 1
 
-anomalies = json.loads(lyagushka(random_data, 1.0, 5))
+    # Populate the 'random_data' array
+    while len(random_data) < size:
+        hex_data = rndo.get(length=min_bytes_needed)
+        hex_chunks = list((hex_data[0+i:2 * max_value_bytes+i] for i in range(0, len(hex_data), 2 * max_value_bytes)))
+        for i in hex_chunks:
+            num = int(i, 16)
+            if num <= mod_cutoff and len(random_data) < size:
+                random_data.append( num % (max_value + 1) )
 
-print(anomalies)
+    return random_data
+
+# load the random test data
+dataset = []
+# with open('random_values.txt', 'r') as file:
+#     for line in file:
+#         random_data.append(int(line.strip()))
+
+dataset = generate_random_data(1024, 1024)
+dataset.sort()
+
+# calculate the anomalies in the data
+analysis_results = json.loads(lyagushka(dataset, 3.0, 7))
+print(analysis_results)
+print(len(analysis_results))
+
+# Initialize plot
+plt.figure(figsize=(10, 6))
+
+# Plot dataset points
+for point in dataset:
+    plt.plot(point, 0, 'ko')  # Plot dataset as black dots at y=0
+
+# Process each cluster/gap for plotting
+for result in analysis_results:
+    if result['num_elements'] > 0:  # It's a cluster
+        color = 'blue'  # Color for clusters
+    else:  # It's a gap
+        color = 'green'  # Color for gaps
+
+    # Generate start and end points for the segment
+    start = result['centroid'] - result['span_length'] / 2
+    end = result['centroid'] + result['span_length'] / 2
+    z_score = result['z_score'] if result['z_score'] is not None else 0
+
+    # Plot a line segment for the cluster/gap
+    plt.plot([start, end], [z_score, z_score], color=color, linewidth=2)
+
+# Enhancements for visualization
+plt.xlabel('Integer Value')
+plt.ylabel('Z-Score')
+plt.title('Cluster and Gap Analysis with Distinct Z-Score Curves')
+plt.grid(True)
+
+# Custom legend
+plt.plot([], [], color='blue', label='Clusters')
+plt.plot([], [], color='green', label='Gaps')
+plt.legend()
+
+plt.show()