Add line of best fit (adding dependency on numpy)

db.py

@@ -1,5 +1,6 @@
 import os
 import psycopg2
+import numpy as np
 from psycopg2.extras import RealDictCursor
 from datetime import datetime
 from urllib.parse import urlparse
@@ -505,6 +506,17 @@ class DataBase():
         total_span = date_range.days or 1
         relative_positions = [(date - min_date).days / total_span for date in start_dates]
 
+        # Convert relative positions and scaled estimated 1RM values to numpy arrays
+        x = np.array(relative_positions)
+        y = np.array(estimated_1rm_scaled)
+
+        # Calculate the slope (m) and y-intercept (b) of the line of best fit
+        m, b = np.polyfit(x, y, 1)
+
+        # Generate points along the line of best fit
+        y_best_fit = [m * xi + b for xi in x]
+        best_fit_points = zip(y_best_fit, relative_positions)
+
         # Create messages and zip data for SVG plotting
         messages = [f'{t["repetitions"]} x {t["weight"]}kg ({t["estimated_1rm"]}kg E1RM) on {t["start_date"].strftime("%d %b %y")}' for t in topsets]
         data_points = zip(estimated_1rm_scaled, relative_positions, messages)
@@ -514,5 +526,6 @@ class DataBase():
             'exercise_name': topsets[0]['exercise_name'],
             'vb_width': vb_width,
             'vb_height': vb_height,
-            'data_points': list(data_points)
+            'data_points': list(data_points),
+            'best_fit_points': list(best_fit_points),
         }