Minor refactor in attempt to speed up site
This commit is contained in:
Peter Stockings
59
db.py
59
db.py
@@ -534,7 +534,7 @@ class DataBase():
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w.workout_id,
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to_char(w.start_date, 'Mon DD YYYY') AS formatted_start_date,
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w.note,
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t.filter as tag_filter,
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t.filter AS tag_filter,
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t.name AS tag_name
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FROM person p
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LEFT JOIN workout w ON p.person_id = w.person_id AND w.note IS NOT NULL AND w.note <> ''
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@@ -545,20 +545,20 @@ class DataBase():
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"""
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# Execute the SQL query
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raw_workout_notes = self.execute(sql_query, [person_id])
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raw_data = self.execute(sql_query, [person_id])
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# Initialize variables to hold the person's name and the workouts
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person_name = None
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if not raw_data:
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return None, []
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# Extract person name from the first row (all rows have the same person name)
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person_name = raw_data[0]['person_name']
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# Process the workout notes
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workout_notes = {}
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for row in raw_workout_notes:
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# Update person_name (it will be the same for all rows)
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if person_name is None:
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person_name = row['person_name']
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# Process workout notes and tags if there's a note associated with the workout
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if row['workout_id'] and row['note']: # Check if workout_id exists and note is not None or empty
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for row in raw_data:
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workout_id = row['workout_id']
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if workout_id and row['note']:
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# Initialize the workout entry if it doesn't exist
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if workout_id not in workout_notes:
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workout_notes[workout_id] = {
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'workout_id': workout_id,
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@@ -566,37 +566,38 @@ class DataBase():
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'note': row['note'],
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'tags': []
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}
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if row['tag_name']: # Only add the tag if it is not None
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workout_notes[workout_id]['tags'].append({'tag_filter': row['tag_filter'], 'tag_name': row['tag_name'], 'person_id': person_id})
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# Add tags if present
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if row['tag_name']:
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workout_notes[workout_id]['tags'].append({
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'tag_filter': row['tag_filter'],
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'tag_name': row['tag_name'],
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'person_id': person_id
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})
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# Convert the workout_notes dictionary back into a list as the final result
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# Convert to a list for the final output
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workout_notes_list = list(workout_notes.values())
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return person_name, workout_notes_list
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# Return a tuple containing the person's name and their workout notes
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return (person_name, workout_notes_list)
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def get_exercise_earliest_and_latest_dates(self, person_id, exercise_id):
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sql_query = """
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SELECT
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w.start_date
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MIN(w.start_date) AS earliest_date,
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MAX(w.start_date) AS latest_date
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FROM workout w
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INNER JOIN topset t on w.workout_id = t.workout_id
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INNER JOIN exercise e on t.exercise_id = e.exercise_id
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WHERE w.person_id = %s AND e.exercise_id = %s
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ORDER BY w.start_date DESC;
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INNER JOIN topset t ON w.workout_id = t.workout_id
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INNER JOIN exercise e ON t.exercise_id = e.exercise_id
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WHERE w.person_id = %s AND e.exercise_id = %s;
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"""
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# Execute the SQL query
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workout_exercise_dates = self.execute(sql_query, [person_id, exercise_id])
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result = self.execute(sql_query, [person_id, exercise_id])
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if not workout_exercise_dates:
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if not result or not result[0]:
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return None, None
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latest_date = workout_exercise_dates[0]['start_date']
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earliest_date = workout_exercise_dates[-1]['start_date']
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return earliest_date, latest_date
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return result[0]['earliest_date'], result[0]['latest_date']
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75
utils.py
75
utils.py
@@ -217,83 +217,71 @@ def flatten(lst):
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return result
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def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_dates, messages, epoch, person_id, exercise_id, min_date=None, max_date=None):
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# Precompute ranges
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min_date, max_date = min(start_dates), max(start_dates)
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total_span = (max_date - min_date).days or 1
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min_e1rm, max_e1rm = min(estimated_1rm), max(estimated_1rm)
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min_reps, max_reps = min(repetitions), max(repetitions)
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min_weight, max_weight = min(weight), max(weight)
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e1rm_range = max_e1rm - min_e1rm or 1
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reps_range = max_reps - min_reps or 1
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weight_range = max_weight - min_weight or 1
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# Calculate viewBox dimensions
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date_range = max_date - min_date
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total_span = date_range.days or 1
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e1rm_range = (max_e1rm - min_e1rm) or 1
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reps_range = (max_reps - min_reps) or 1
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weight_range = (max_weight - min_weight) or 1
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vb_width, vb_height = total_span, e1rm_range
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vb_width *= 200 / vb_width # Scale to 200px width
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vb_height *= 75 / vb_height # Scale to 75px height
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# Scale estimated_1rm values for SVG plotting
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estimated_1rm_scaled = [((value - min_e1rm) / e1rm_range) * vb_height for value in estimated_1rm]
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repetitions_scaled = [((value - min_reps) / reps_range) * vb_height for value in repetitions]
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weight_scaled = [((value - min_weight) / weight_range) * vb_height for value in weight]
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relative_positions = [(date - min_date).days / total_span for date in start_dates]
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# Convert the slope from scaled units per day to kg per day
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slope_kg_per_day = (max_e1rm - min_e1rm) / total_span
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# Use NumPy arrays for efficient scaling
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relative_positions = np.array([(date - min_date).days / total_span for date in start_dates])
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estimated_1rm_scaled = ((np.array(estimated_1rm) - min_e1rm) / e1rm_range) * vb_height
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repetitions_scaled = ((np.array(repetitions) - min_reps) / reps_range) * vb_height
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weight_scaled = ((np.array(weight) - min_weight) / weight_range) * vb_height
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# Calculate slope and line of best fit
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slope_kg_per_day = e1rm_range / total_span
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best_fit_formula = {
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'kg_per_week': round(slope_kg_per_day * 7, 1), # Convert to kg/week
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'kg_per_month': round(slope_kg_per_day * 30, 1) # Convert to kg/month
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'kg_per_week': round(slope_kg_per_day * 7, 1),
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'kg_per_month': round(slope_kg_per_day * 30, 1)
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}
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best_fit_points = []
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# Catch LinAlgError
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try:
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# Convert relative positions and scaled estimated 1RM values to numpy arrays
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x = np.array(relative_positions)
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y = np.array(estimated_1rm_scaled)
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# Calculate the slope (m) and y-intercept (b) of the line of best fit
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m, b = np.polyfit(x, y, 1)
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# Generate points along the line of best fit
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y_best_fit = [m * xi + b for xi in x]
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best_fit_points = list(zip(y_best_fit, relative_positions))
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except:
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# Calculate line of best fit using NumPy
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m, b = np.polyfit(relative_positions, estimated_1rm_scaled, 1)
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y_best_fit = m * relative_positions + b
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best_fit_points = list(zip(y_best_fit.tolist(), relative_positions.tolist()))
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except np.linalg.LinAlgError:
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pass
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# Create messages and zip data for SVG plotting
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estimated_1rm_points = zip(estimated_1rm_scaled, relative_positions)
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repetitions_points = zip(repetitions_scaled, relative_positions)
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weight_points = zip(weight_scaled, relative_positions)
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repetitions = {
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# Prepare data for plots
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repetitions_data = {
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'label': 'Reps',
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'color': '#388fed',
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'points': list(repetitions_points)
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'points': list(zip(repetitions_scaled.tolist(), relative_positions.tolist()))
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}
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weight = {
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weight_data = {
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'label': 'Weight',
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'color': '#bd3178',
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'points': list(weight_points)
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'points': list(zip(weight_scaled.tolist(), relative_positions.tolist()))
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}
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estimated_1rm = {
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estimated_1rm_data = {
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'label': 'E1RM',
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'color': '#2ca02c',
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'points': list(estimated_1rm_points)
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'points': list(zip(estimated_1rm_scaled.tolist(), relative_positions.tolist()))
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}
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plot_labels = zip(relative_positions, messages)
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# Prepare plot labels
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plot_labels = list(zip(relative_positions.tolist(), messages))
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# Return exercise data with SVG dimensions and data points
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return {
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'title': title,
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'vb_width': vb_width,
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'vb_height': vb_height,
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'plots': [repetitions, weight, estimated_1rm],
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'plots': [repetitions_data, weight_data, estimated_1rm_data],
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'best_fit_points': best_fit_points,
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'best_fit_formula': best_fit_formula,
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'plot_labels': plot_labels,
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@@ -305,6 +293,7 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
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'max_date': max_date
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}
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def get_workout_counts(workouts, period='week'):
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df = pd.DataFrame(workouts)
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