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Refactor more functions in utils, trying to speed up site

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Peter Stockings
2024-04-07 22:23:26 +10:00
parent 916fc47d26
commit ff422e9536
1 changed files with 64 additions and 40 deletions
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98
utils.py
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@@ -47,59 +47,76 @@ def get_all_exercises_from_topsets(topsets):
return list(exercises_dict.values())
def get_topsets_for_person(person_topsets):
person_exercises = get_all_exercises_from_topsets(person_topsets)
# Group topsets by ExerciseId
grouped_topsets = {}
for topset in person_topsets:
exercise_id = topset['ExerciseId']
if exercise_id in grouped_topsets:
grouped_topsets[exercise_id].append(topset)
else:
grouped_topsets[exercise_id] = [topset]
# Process each group of topsets
exercises_topsets = []
for e in person_exercises:
exercise_topsets = [t for t in person_topsets if t['ExerciseId'] == e['ExerciseId']]
for exercise_id, topsets in grouped_topsets.items():
# Sort topsets by StartDate in descending order
sorted_topsets = sorted(exercise_topsets, key=lambda x: x['StartDate'], reverse=True)
sorted_topsets = sorted(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]
estimated_1rm = [t['Estimated1RM'] for t in sorted_topsets]
repetitions = [t['Repetitions'] for t in sorted_topsets]
weight = [t['Weight'] for t in sorted_topsets]
start_dates = [t['StartDate'] for t in sorted_topsets]
messages = [f'{t["Repetitions"]} x {t["Weight"]}kg ({t["Estimated1RM"]}kg E1RM) on {t["StartDate"].strftime("%d %b %y")}' for t in sorted_topsets]
epoch = 'All'
person_id = exercise_topsets[0]['PersonId']
exercise_id = exercise_topsets[0]['ExerciseId']
person_id = sorted_topsets[0]['PersonId']
exercise_name = sorted_topsets[0]['ExerciseName']
exercise_progress = get_exercise_graph_model(exercise_topsets[0]['ExerciseName'], estimated_1rm, repetitions, weight, start_dates, messages, epoch, person_id, exercise_id)
exercise_progress = get_exercise_graph_model(exercise_name, estimated_1rm, repetitions, weight, start_dates, messages, epoch, person_id, exercise_id)
exercises_topsets.append({
'ExerciseId': e['ExerciseId'],
'ExerciseName': e['ExerciseName'],
'ExerciseId': exercise_id,
'ExerciseName': exercise_name,
'Topsets': sorted_topsets,
'ExerciseProgressGraph': exercise_progress
})
return exercises_topsets
def get_people_and_exercise_rep_maxes(topsets, selected_person_ids, selected_exercise_ids, min_date, max_date):
# Get all unique workout_ids (No duplicates)
people_ids = set([t['PersonId']
for t in topsets])
filtered_people_ids = [p for p in people_ids if p in selected_person_ids]
# Filter topsets once based on the criteria
filtered_topsets = [
t for t in topsets if t['PersonId'] in selected_person_ids
and t['ExerciseId'] in selected_exercise_ids
and min_date <= t['StartDate'] <= max_date
]
# Group the filtered topsets by PersonId
grouped_by_person = {}
for t in filtered_topsets:
person_id = t['PersonId']
if person_id in grouped_by_person:
grouped_by_person[person_id].append(t)
else:
grouped_by_person[person_id] = [t]
# Group topsets into workouts
people = []
for person_id in filtered_people_ids:
workouts_for_person = [
t for t in topsets if t['PersonId'] == person_id and t['ExerciseId'] in selected_exercise_ids and t['StartDate'] >= min_date and t['StartDate'] <= max_date]
if workouts_for_person:
for person_id, person_topsets in grouped_by_person.items():
person_name = person_topsets[0]['PersonName']
workout_ids = {t['WorkoutId'] for t in person_topsets if t['WorkoutId']}
number_of_workouts = len(workout_ids)
people.append({
'PersonId': person_id,
'PersonName': workouts_for_person[0]['PersonName'],
'NumberOfWorkouts': len(list(set([t['WorkoutId'] for t in workouts_for_person if t['WorkoutId'] is not None]))),
'Exercises': get_topsets_for_person(workouts_for_person)
'PersonName': person_name,
'NumberOfWorkouts': number_of_workouts,
'Exercises': get_topsets_for_person(person_topsets)
})
return {"People": people, "Stats": get_stats_from_topsets(topsets)}
def get_stats_from_topsets(topsets):
workout_count = len(set([t['WorkoutId']
for t in topsets if t['WorkoutId'] is not None]))
@@ -147,9 +164,21 @@ def convert_str_to_date(date_str, format='%Y-%m-%d'):
def get_earliest_and_latest_workout_date(person):
if len(person['Workouts']) > 0:
return (min(person['Workouts'], key=lambda x: x['StartDate'])['StartDate'], max(person['Workouts'], key=lambda x: x['StartDate'])['StartDate'])
return (datetime.now().date(), datetime.now().date())
workouts = person.get('Workouts', [])
if workouts:
# Initialize earliest and latest dates with the first workout's start date
earliest_date = latest_date = workouts[0]['StartDate']
for workout in workouts[1:]:
date = workout['StartDate']
if date < earliest_date:
earliest_date = date
if date > latest_date:
latest_date = date
return (earliest_date, latest_date)
# Return the current date for both if no workouts are present
current_date = datetime.now().date()
return (current_date, current_date)
def filter_workout_topsets(workout, selected_exercise_ids):
@@ -158,11 +187,6 @@ def filter_workout_topsets(workout, selected_exercise_ids):
return workout
def get_exercise_ids_from_workouts(workouts):
return list(set(flatten_list(list(map(lambda x: list(
map(lambda y: y['ExerciseId'], x['TopSets'])), workouts)))))
def flatten_list(list_of_lists):
return [item for sublist in list_of_lists for item in sublist]