Minor refactor in attempt to speed up site

utils.py

@@ -217,83 +217,71 @@ def flatten(lst):
     return result
 
 def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_dates, messages, epoch, person_id, exercise_id, min_date=None, max_date=None):
+    # Precompute ranges
     min_date, max_date = min(start_dates), max(start_dates)
+    total_span = (max_date - min_date).days or 1
+
     min_e1rm, max_e1rm = min(estimated_1rm), max(estimated_1rm)
     min_reps, max_reps = min(repetitions), max(repetitions)
     min_weight, max_weight = min(weight), max(weight)
-    
+
+    e1rm_range = max_e1rm - min_e1rm or 1
+    reps_range = max_reps - min_reps or 1
+    weight_range = max_weight - min_weight or 1
+
     # Calculate viewBox dimensions
-    date_range = max_date - min_date
-    total_span = date_range.days or 1
-    e1rm_range = (max_e1rm - min_e1rm) or 1
-    reps_range = (max_reps - min_reps) or 1
-    weight_range = (max_weight - min_weight) or 1
     vb_width, vb_height = total_span, e1rm_range
     vb_width *= 200 / vb_width  # Scale to 200px width
     vb_height *= 75 / vb_height  # Scale to 75px height
 
-    # Scale estimated_1rm values for SVG plotting
-    estimated_1rm_scaled = [((value - min_e1rm) / e1rm_range) * vb_height for value in estimated_1rm]
-    repetitions_scaled = [((value - min_reps) / reps_range) * vb_height for value in repetitions]
-    weight_scaled = [((value - min_weight) / weight_range) * vb_height for value in weight]
-    
-    relative_positions = [(date - min_date).days / total_span for date in start_dates]
-
-    # Convert the slope from scaled units per day to kg per day
-    slope_kg_per_day = (max_e1rm - min_e1rm) / total_span
+    # Use NumPy arrays for efficient scaling
+    relative_positions = np.array([(date - min_date).days / total_span for date in start_dates])
+    estimated_1rm_scaled = ((np.array(estimated_1rm) - min_e1rm) / e1rm_range) * vb_height
+    repetitions_scaled = ((np.array(repetitions) - min_reps) / reps_range) * vb_height
+    weight_scaled = ((np.array(weight) - min_weight) / weight_range) * vb_height
 
+    # Calculate slope and line of best fit
+    slope_kg_per_day = e1rm_range / total_span
     best_fit_formula = {
-        'kg_per_week': round(slope_kg_per_day * 7, 1),  # Convert to kg/week
-        'kg_per_month': round(slope_kg_per_day * 30, 1)  # Convert to kg/month
+        'kg_per_week': round(slope_kg_per_day * 7, 1),
+        'kg_per_month': round(slope_kg_per_day * 30, 1)
     }
 
     best_fit_points = []
-
-    # Catch LinAlgError
     try:
-        # 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 = list(zip(y_best_fit, relative_positions))
-
-    except:
+        # Calculate line of best fit using NumPy
+        m, b = np.polyfit(relative_positions, estimated_1rm_scaled, 1)
+        y_best_fit = m * relative_positions + b
+        best_fit_points = list(zip(y_best_fit.tolist(), relative_positions.tolist()))
+    except np.linalg.LinAlgError:
         pass
 
-    # Create messages and zip data for SVG plotting
-    estimated_1rm_points = zip(estimated_1rm_scaled, relative_positions)
-    repetitions_points = zip(repetitions_scaled, relative_positions)
-    weight_points = zip(weight_scaled, relative_positions)
-
-    repetitions = {
+    # Prepare data for plots
+    repetitions_data = {
         'label': 'Reps',
         'color': '#388fed',
-        'points': list(repetitions_points)
+        'points': list(zip(repetitions_scaled.tolist(), relative_positions.tolist()))
     }
-    weight = {
+    weight_data = {
         'label': 'Weight',
         'color': '#bd3178',
-        'points': list(weight_points)
+        'points': list(zip(weight_scaled.tolist(), relative_positions.tolist()))
     }
-    estimated_1rm = {
+    estimated_1rm_data = {
         'label': 'E1RM',
         'color': '#2ca02c',
-        'points': list(estimated_1rm_points)
+        'points': list(zip(estimated_1rm_scaled.tolist(), relative_positions.tolist()))
     }
 
-    plot_labels = zip(relative_positions, messages)
+    # Prepare plot labels
+    plot_labels = list(zip(relative_positions.tolist(), messages))
 
     # Return exercise data with SVG dimensions and data points
     return {
         'title': title,
         'vb_width': vb_width,
         'vb_height': vb_height,
-        'plots': [repetitions, weight, estimated_1rm],
+        'plots': [repetitions_data, weight_data, estimated_1rm_data],
         'best_fit_points': best_fit_points,
         'best_fit_formula': best_fit_formula,
         'plot_labels': plot_labels,
@@ -305,6 +293,7 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
         'max_date': max_date
     }
 
+
 def get_workout_counts(workouts, period='week'):
     df = pd.DataFrame(workouts)