Render svg graphs in initial response rather then requesting each graph individually. Initial load file size of dashboard will be larger, unsure if I will rollback this change

utils.py

@@ -1,4 +1,5 @@
 from datetime import datetime, date, timedelta
+import numpy as np
 import json
 
 
@@ -44,10 +45,20 @@ def get_topsets_for_person(person_topsets):
         # Sort topsets by StartDate in descending order
         sorted_topsets = sorted(exercise_topsets, key=lambda x: x['StartDate'], reverse=True)
 
+        # Extracting values and calculating value ranges for SVG dimensions
+        estimated_1rm = [t['Estimated1RM'] for t in exercise_topsets]
+        repetitions = [t['Repetitions'] for t in exercise_topsets]
+        weight = [t['Weight'] for t in exercise_topsets]
+        start_dates = [t['StartDate'] for t in exercise_topsets]
+        messages = [f'{t["Repetitions"]} x {t["Weight"]}kg ({t["Estimated1RM"]}kg E1RM) on {t["StartDate"].strftime("%d %b %y")}' for t in exercise_topsets]
+
+        exercise_progress = get_exercise_graph_model(exercise_topsets[0]['ExerciseName'], estimated_1rm, repetitions, weight, start_dates, messages)
+
         exercises_topsets.append({
             'ExerciseId': e['ExerciseId'],
             'ExerciseName': e['ExerciseName'],
-            'Topsets': sorted_topsets
+            'Topsets': sorted_topsets,
+            'ExerciseProgressGraph': exercise_progress
         })
 
     return exercises_topsets
@@ -223,3 +234,75 @@ def get_date_info(input_date, selected_view):
             'start_date': first_day_of_year,
             'end_date': last_day_of_year,
         }
+
+def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_dates, messages):
+    min_date, max_date = min(start_dates), max(start_dates)
+    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)
+    
+    # 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]
+
+    best_fit_points = []
+    # trry 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 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 = {
+        'label': 'Reps',
+        'color': '#388fed',
+        'points': list(repetitions_points)
+    }
+    weight = {
+        'label': 'Weight',
+        'color': '#bd3178',
+        'points': list(weight_points)
+    }
+    estimated_1rm = {
+        'label': 'E1RM',
+        'color': '#2ca02c',
+        'points': list(estimated_1rm_points)
+    }
+
+    plot_labels = zip(relative_positions, 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],
+        'best_fit_points': best_fit_points,
+        'plot_labels': plot_labels
+    }