132 lines
5.3 KiB
Python
132 lines
5.3 KiB
Python
from collections import defaultdict
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from datetime import date
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class Stats:
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def __init__(self, db_connection_method):
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self.execute = db_connection_method
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def get_stats_from_topsets(self, topsets):
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if not topsets:
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return []
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# Extract necessary fields
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workout_ids = [t["WorkoutId"] for t in topsets if t["WorkoutId"]]
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person_ids = [t["PersonId"] for t in topsets if t["PersonId"]]
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start_dates = [t["StartDate"] for t in topsets if t["StartDate"]]
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exercise_ids = [t["ExerciseId"] for t in topsets if t["ExerciseId"]]
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workout_count = len(set(workout_ids))
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people_count = len(set(person_ids))
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total_sets = len(topsets)
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exercise_count = len(set(exercise_ids))
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# Group sets by workout and exercise
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sets_per_exercise_per_workout = defaultdict(lambda: defaultdict(int))
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for t in topsets:
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if t["WorkoutId"] and t["ExerciseId"]:
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sets_per_exercise_per_workout[t["WorkoutId"]][t["ExerciseId"]] += 1
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# Calculate the average sets per exercise across all workouts
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total_sets_per_exercise = []
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for workout_exercises in sets_per_exercise_per_workout.values():
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total_sets_per_exercise.extend(workout_exercises.values())
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average_sets_per_exercise = round(sum(total_sets_per_exercise) / len(total_sets_per_exercise), 1) if total_sets_per_exercise else 0
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# Group exercises by workout
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exercises_by_workout = defaultdict(set)
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for t in topsets:
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if t["WorkoutId"] and t["ExerciseId"]:
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exercises_by_workout[t["WorkoutId"]].add(t["ExerciseId"])
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# Calculate average exercises per workout
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average_exercises_per_workout = round(
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sum(len(exercises) for exercises in exercises_by_workout.values()) / workout_count, 1
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) if workout_count > 0 else 0
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# Stats
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stats = [
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{"Text": "Total Workouts", "Value": workout_count},
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{"Text": "Total Sets", "Value": total_sets},
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{"Text": "Average Sets Per Exercise", "Value": average_sets_per_exercise}
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]
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if exercise_count > 1:
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stats.append({"Text": "Total Exercises", "Value": exercise_count})
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stats.append({"Text": "Average Exercises Per Workout", "Value": average_exercises_per_workout})
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if people_count > 1:
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stats.append({"Text": "People Tracked", "Value": people_count})
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if workout_count > 0:
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first_workout_date = min(start_dates)
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last_workout_date = max(start_dates)
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current_date = date.today()
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stats.append({"Text": "Days Since First Workout",
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"Value": (current_date - first_workout_date).days})
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if workout_count >= 2:
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stats.append({"Text": "Days Since Last Workout",
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"Value": (current_date - last_workout_date).days})
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average_sets_per_workout = round(total_sets / workout_count, 1)
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stats.append({"Text": "Average Sets Per Workout",
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"Value": average_sets_per_workout})
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training_duration = last_workout_date - first_workout_date
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if training_duration.days > 0:
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average_workouts_per_week = round(
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workout_count / (training_duration.days / 7), 1)
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stats.append({"Text": "Average Workouts Per Week",
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"Value": average_workouts_per_week})
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return stats
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def fetch_stats(self, selected_people_ids=None, min_date=None, max_date=None, selected_exercise_ids=None):
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# Base query
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query = """
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SELECT
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t.workout_id AS "WorkoutId",
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w.person_id AS "PersonId",
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w.start_date AS "StartDate",
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e.exercise_id AS "ExerciseId"
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FROM
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topset t
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JOIN workout w ON t.workout_id = w.workout_id
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JOIN person p ON w.person_id = p.person_id
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JOIN exercise e ON t.exercise_id = e.exercise_id
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"""
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# Parameters for the query
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params = []
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# Add optional filters
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conditions = [] # Collect conditions dynamically
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if selected_people_ids:
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placeholders = ", ".join(["%s"] * len(selected_people_ids))
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conditions.append(f"p.person_id IN ({placeholders})")
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params.extend(selected_people_ids)
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if min_date:
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conditions.append("w.start_date >= %s")
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params.append(min_date)
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if max_date:
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conditions.append("w.start_date <= %s")
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params.append(max_date)
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if selected_exercise_ids:
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placeholders = ", ".join(["%s"] * len(selected_exercise_ids))
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conditions.append(f"e.exercise_id IN ({placeholders})")
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params.extend(selected_exercise_ids)
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# Add conditions to the query
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if conditions:
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query += " WHERE " + " AND ".join(conditions)
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# Execute the query
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workouts_data = self.execute(query, params)
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# Generate stats from the retrieved data
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stats = self.get_stats_from_topsets(workouts_data)
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return stats
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