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##############################################################################
# Copyright (c) 2020 Parker Berberian, Sawyer Bergeron, Sean Smith and others.
#
# All rights reserved. This program and the accompanying materials
# are made available under the terms of the Apache License, Version 2.0
# which accompanies this distribution, and is available at
# http://www.apache.org/licenses/LICENSE-2.0
##############################################################################
from booking.models import Booking
from datetime import datetime, timedelta
import pytz
class StatisticsManager(object):
@staticmethod
def getContinuousBookingTimeSeries(span=28):
"""
Calculate Booking usage data points.
Gathers all active bookings that fall in interval [(now - span), (now + 1 week)].
x data points are every 12 hours
y values are the integer number of bookings/users active at time
"""
x = []
y = []
users = []
now = datetime.now(pytz.utc)
delta = timedelta(days=span)
start = now - delta
end = now + timedelta(weeks=1)
bookings = Booking.objects.filter(
start__lte=end,
end__gte=start
).prefetch_related("collaborators")
# get data
while start <= end:
active_users = 0
books = bookings.filter(
start__lte=start,
end__gte=start
).prefetch_related("collaborators")
for booking in books:
active_users += booking.collaborators.all().count() + 1
x.append(str(start))
y.append(books.count())
users.append(active_users)
start += timedelta(hours=12)
return {"booking": [x, y], "user": [x, users]}
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