Add exercise progress graphs to new person overview page

features/person_overview.py

@@ -1,4 +1,7 @@
 
+from utils import calculate_estimated_1rm, get_exercise_graph_model
+
+
 class PersonOverview:
     def __init__(self, db_connection_method):
         self.execute = db_connection_method
@@ -111,7 +114,7 @@ class PersonOverview:
         result = self.execute(sql_query, params)
 
         if not result:
-            return {"person_id": person_id, "person_name": None, "workouts": [], "selected_exercises": []}
+            return {"person_id": person_id, "person_name": None, "workouts": [], "selected_exercises": [], "exercise_progress_graphs": []}
 
         # Extract person info from the first row
         person_info = {"person_id": result[0]["person_id"], "person_name": result[0]["person_name"]}
@@ -131,6 +134,9 @@ class PersonOverview:
         workouts = []
         workout_map = {}  # Map to track workouts
 
+        # Initialize the exercise sets dictionary
+        exercise_sets = {exercise["id"]: {"exercise_id": exercise["id"], "name": exercise["name"], "sets": []} for exercise in exercises}
+
         for row in result:
             workout_id = row["workout_id"]
 
@@ -145,16 +151,77 @@ class PersonOverview:
 
             # Add topset to the corresponding exercise
             if row["exercise_id"] and row["topset_id"]:
+                # Add to workout exercises
                 workout_map[workout_id]["exercises"][row["exercise_id"]].append({
                     "repetitions": row["repetitions"],
                     "weight": row["weight"]
                 })
 
+                # Add to the exercise sets dictionary with workout start date
+                exercise_sets[row["exercise_id"]]["sets"].append({
+                    "repetitions": row["repetitions"],
+                    "weight": row["weight"],
+                    "estimated_1rm": calculate_estimated_1rm(row["weight"], row["repetitions"]),
+                    "workout_start_date": row["start_date"],
+                    "exercise_name": row["exercise_name"]
+                })
+
         # Transform into a list of rows
         for workout_id, workout in workout_map.items():
             workouts.append(workout)
+        
+        exercise_progress_graphs = self.generate_exercise_progress_graphs(person_info["person_id"], exercise_sets)
+
+        return {
+            **person_info,
+            "workouts": workouts,
+            "selected_exercises": exercises,
+            "exercise_progress_graphs": exercise_progress_graphs
+        }
+    
+    def generate_exercise_progress_graphs(self, person_id, exercise_sets):
+        exercise_progress_graphs = []
+
+        for exercise_id, exercise_data in exercise_sets.items():
+            # Sort the sets by start date in descending order
+            sorted_exercise_sets = sorted(exercise_data["sets"], key=lambda t: t["workout_start_date"], reverse=True)
+
+            # Extract the required data
+            estimated_1rm = [t["estimated_1rm"] for t in sorted_exercise_sets]
+            repetitions = [t["repetitions"] for t in sorted_exercise_sets]
+            weight = [t["weight"] for t in sorted_exercise_sets]
+            start_dates = [t["workout_start_date"] for t in sorted_exercise_sets]
+            messages = [
+                f'{t["repetitions"]} x {t["weight"]}kg ({t["estimated_1rm"]}kg E1RM) on {t["workout_start_date"].strftime("%d %b %y")}'
+                for t in sorted_exercise_sets
+            ]
+            epoch = "All"
+            exercise_name = sorted_exercise_sets[0]["exercise_name"]
+
+            # Check for valid data before generating the graph
+            if exercise_name and estimated_1rm and repetitions and weight and start_dates and messages:
+                exercise_progress = get_exercise_graph_model(
+                    title=exercise_name,
+                    estimated_1rm=estimated_1rm,
+                    repetitions=repetitions,
+                    weight=weight,
+                    start_dates=start_dates,
+                    messages=messages,
+                    epoch=epoch,
+                    person_id=person_id,
+                    exercise_id=exercise_id,
+                )
+
+                # Append the generated graph model to the list
+                exercise_progress_graphs.append({
+                    "exercise_id": exercise_id,
+                    "exercise_name": exercise_name,
+                    "progress_graph": exercise_progress
+                })
+
+        return exercise_progress_graphs
+
 
-        return {**person_info, "workouts": workouts, "selected_exercises": exercises}
 
 
 

templates/person_overview.html

@@ -94,6 +94,12 @@
 
         {{ render_partial('partials/tags.html',person_id=person_id, tags=tags) }}
 
+        <div class="mt-4 mb-4 w-full grid grid-cols-1 2xl:grid-cols-2 gap-4">
+            {% for graph in exercise_progress_graphs %}
+            {{ render_partial('partials/sparkline.html', **graph.progress_graph) }}
+            {% endfor %}
+        </div>
+
         <div class="flex flex-col mt-3 w-screen sm:w-full overflow-auto">
             <div class="overflow-x-auto rounded-lg">
                 <div class="flex justify-center">

utils.py

@@ -469,3 +469,10 @@ def generate_plot(df, title):
     # Convert Plotly figure to HTML div
     plot_div = pio.to_html(fig, full_html=False)
     return plot_div
+
+def calculate_estimated_1rm(weight, repetitions):
+    # Ensure the inputs are numeric
+    if repetitions == 0:  # Avoid division by zero
+        return 0
+    estimated_1rm = round((100 * int(weight)) / (101.3 - 2.67123 * repetitions), 0)
+    return int(estimated_1rm)