diff --git a/db.py b/db.py index 57398a5..609611d 100644 --- a/db.py +++ b/db.py @@ -534,7 +534,7 @@ class DataBase(): w.workout_id, to_char(w.start_date, 'Mon DD YYYY') AS formatted_start_date, w.note, - t.filter as tag_filter, + t.filter AS tag_filter, t.name AS tag_name FROM person p LEFT JOIN workout w ON p.person_id = w.person_id AND w.note IS NOT NULL AND w.note <> '' @@ -545,20 +545,20 @@ class DataBase(): """ # Execute the SQL query - raw_workout_notes = self.execute(sql_query, [person_id]) + raw_data = self.execute(sql_query, [person_id]) + + if not raw_data: + return None, [] + + # Extract person name from the first row (all rows have the same person name) + person_name = raw_data[0]['person_name'] - # Initialize variables to hold the person's name and the workouts - person_name = None + # Process the workout notes workout_notes = {} - - for row in raw_workout_notes: - # Update person_name (it will be the same for all rows) - if person_name is None: - person_name = row['person_name'] - - # Process workout notes and tags if there's a note associated with the workout - if row['workout_id'] and row['note']: # Check if workout_id exists and note is not None or empty - workout_id = row['workout_id'] + for row in raw_data: + workout_id = row['workout_id'] + if workout_id and row['note']: + # Initialize the workout entry if it doesn't exist if workout_id not in workout_notes: workout_notes[workout_id] = { 'workout_id': workout_id, @@ -566,37 +566,38 @@ class DataBase(): 'note': row['note'], 'tags': [] } - if row['tag_name']: # Only add the tag if it is not None - workout_notes[workout_id]['tags'].append({'tag_filter': row['tag_filter'], 'tag_name': row['tag_name'], 'person_id': person_id}) + # Add tags if present + if row['tag_name']: + workout_notes[workout_id]['tags'].append({ + 'tag_filter': row['tag_filter'], + 'tag_name': row['tag_name'], + 'person_id': person_id + }) - - # Convert the workout_notes dictionary back into a list as the final result + # Convert to a list for the final output workout_notes_list = list(workout_notes.values()) + return person_name, workout_notes_list - # Return a tuple containing the person's name and their workout notes - return (person_name, workout_notes_list) def get_exercise_earliest_and_latest_dates(self, person_id, exercise_id): sql_query = """ SELECT - w.start_date + MIN(w.start_date) AS earliest_date, + MAX(w.start_date) AS latest_date FROM workout w - INNER JOIN topset t on w.workout_id = t.workout_id - INNER JOIN exercise e on t.exercise_id = e.exercise_id - WHERE w.person_id = %s AND e.exercise_id = %s - ORDER BY w.start_date DESC; + INNER JOIN topset t ON w.workout_id = t.workout_id + INNER JOIN exercise e ON t.exercise_id = e.exercise_id + WHERE w.person_id = %s AND e.exercise_id = %s; """ # Execute the SQL query - workout_exercise_dates = self.execute(sql_query, [person_id, exercise_id]) + result = self.execute(sql_query, [person_id, exercise_id]) - if not workout_exercise_dates: + if not result or not result[0]: return None, None - latest_date = workout_exercise_dates[0]['start_date'] - earliest_date = workout_exercise_dates[-1]['start_date'] - - return earliest_date, latest_date + return result[0]['earliest_date'], result[0]['latest_date'] + diff --git a/utils.py b/utils.py index c8ef209..a56c1f4 100644 --- a/utils.py +++ b/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)