def get_daily_plot(day):
"""Return matplotlib plot representing a day's plot."""
start, end = get_open_close(day)
desktops = list_desktops(public_only=True)
profiles = UtilizationProfile.from_hostnames(desktops, start, end).values()
desks_count = len(desktops)
now = datetime.now()
latest = min(end, now)
minute = timedelta(minutes=1)
times = [start + i * minute for i in range((latest - start) // minute + 1)]
if now >= end or now <= start:
now = None
sums = []
for t in times:
instant15 = t + timedelta(seconds=15)
instant45 = t + timedelta(seconds=45)
in_use = sum(
1 if profile.in_use(instant15) or profile.in_use(instant45) else 0
for profile in profiles)
sums.append(in_use)
# Do a weighted moving average to smooth out the data
processed = [0] * len(sums)
for i in range(len(sums)):
for delta_i, weight in AVERAGE_WEIGHTS:
m = i if (i + delta_i < 0
or i + delta_i >= len(sums)) else i + delta_i
# Don't use data that hasn't occurred yet
if now and times[i] <= now and times[m] >= now:
processed[i] += weight * sums[i]
elif now and times[i] > now:
processed[i] = 0
else:
processed[i] += weight * sums[m]
fig = Figure(figsize=(10, 4))
ax = fig.add_subplot(1, 1, 1)
ax.grid(True)
ax.plot_date(times, processed, fmt='b-', color='k', linewidth=1.5)
# Draw a vertical line, if applicable, showing current time
if now:
ax.axvline(now, linewidth=1.5)
# Draw a horizontal line for total desktops
ax.axhline(desks_count, ls='dashed')
ax.annotate(' Total desktops', xy=(start, desks_count + 1))
ax.xaxis.set_major_formatter(DateFormatter('%-I%P'))
ax.set_xlim(start, end)
ax.set_ylim(0, desks_count + 5)
ax.set_ylabel('Computers in Use')
ax.set_title(f'Lab Utilization {day:%a %b %d}')
return fig
def test_fast_slow_profiles_same():
start = datetime(2015, 11, 23)
end = start + timedelta(days=1)
slow_profiles = {
host + '.ocf.berkeley.edu': UtilizationProfile.from_hostname(host, start, end)
for host in list_desktops()
}
fast_profiles = UtilizationProfile.from_hostnames(list_desktops(), start, end)
assert set(slow_profiles.keys()) == set(fast_profiles.keys())
for host in slow_profiles.keys():
slow = slow_profiles[host]
fast = fast_profiles[host]
assert slow.hostname == fast.hostname
assert slow.start == fast.start
assert slow.end == fast.end
assert slow.sessions == fast.sessions
def test_fast_slow_profiles_same():
start = datetime(2015, 11, 23)
end = start + timedelta(days=1)
slow_profiles = {
host + '.ocf.berkeley.edu': UtilizationProfile.from_hostname(host, start, end)
for host in list_desktops()
}
fast_profiles = UtilizationProfile.from_hostnames(list_desktops(), start, end)
assert set(slow_profiles.keys()) == set(fast_profiles.keys())
for host in slow_profiles.keys():
slow = slow_profiles[host]
fast = fast_profiles[host]
assert slow.hostname == fast.hostname
assert slow.start == fast.start
assert slow.end == fast.end
assert slow.sessions == fast.sessions
def get_daily_plot(day):
"""Return matplotlib plot representing a day's plot."""
start, end = get_open_close(day)
profiles = UtilizationProfile.from_hostnames(list_desktops(public_only=True), start, end).values()
now = datetime.now()
latest = min(end, now)
minute = timedelta(minutes=1)
times = [start + i * minute for i in range((latest - start) // minute + 1)]
if now >= end or now <= start:
now = None
sums = []
for t in times:
instant15 = t + timedelta(seconds=15)
instant45 = t + timedelta(seconds=45)
in_use = sum(1 if profile.in_use(instant15) or profile.in_use(instant45) else 0 for profile in profiles)
sums.append(in_use)
# Do a weighted moving average to smooth out the data
processed = [0] * len(sums)
for i in range(len(sums)):
for delta_i, weight in AVERAGE_WEIGHTS:
m = i if (i + delta_i < 0 or i + delta_i >= len(sums)) else i + delta_i
# Don't use data that hasn't occurred yet
if now and times[i] <= now and times[m] >= now:
processed[i] += weight * sums[i]
elif now and times[i] > now:
processed[i] = 0
else:
processed[i] += weight * sums[m]
fig = Figure(figsize=(10, 4))
ax = fig.add_subplot(1, 1, 1)
ax.grid(True)
ax.plot_date(times, processed, fmt='b-', color='k', linewidth=1.5)
# Draw a vertical line, if applicable, showing current time
if now:
ax.axvline(now, linewidth=1.5)
ax.xaxis.set_major_formatter(DateFormatter('%-I%P'))
ax.set_xlim(start, end)
ax.set_ylim(0, len(profiles))
ax.set_ylabel('Computers in Use')
ax.set_title('Lab Utilization {:%a %b %d}'.format(day))
return fig
def desktop_profiles():
open_, close = get_open_close(date.today())
now = datetime.today()
# If the lab has opened, but hasn't closed yet, only count
# statistics until the current time. If the lab isn't open
# yet, then don't count anything, and if it is closed, show
# statistics from when it was open during the day.
if now > open_ and now < close:
end = now
elif now <= open_:
end = open_
else:
end = close
return sorted(
UtilizationProfile.from_hostnames(list_desktops(), open_, end).values(),
key=attrgetter('hostname'),
)
def desktop_profiles() -> List[Any]:
open_, close = get_open_close(date.today())
now = datetime.today()
# If the lab has opened, but hasn't closed yet, only count
# statistics until the current time. If the lab isn't open
# yet, then don't count anything, and if it is closed, show
# statistics from when it was open during the day.
if now > open_ and now < close:
end = now
elif now <= open_:
end = open_
else:
end = close
return sorted(
UtilizationProfile.from_hostnames(list_desktops(), open_, end).values(),
key=attrgetter('hostname'),
)
from datetime import datetime
from datetime import timedelta
from ocflib.lab.stats import list_desktops
from ocflib.lab.stats import UtilizationProfile
@contextmanager
def timeit():
start = time.time()
yield
print('Time taken: {}'.format(time.time() - start))
if __name__ == '__main__':
start = datetime(2015, 11, 23)
end = start + timedelta(days=1)
print('Testing naive time to create profiles.')
with timeit():
slow_profiles = {
host + '.ocf.berkeley.edu':
UtilizationProfile.from_hostname(host, start, end)
for host in list_desktops()
}
print('Testing optimized time to create profiles.')
with timeit():
fast_profiles = UtilizationProfile.from_hostnames(
list_desktops(), start, end)
import time
from contextlib import contextmanager
from datetime import datetime
from datetime import timedelta
from ocflib.lab.stats import list_desktops
from ocflib.lab.stats import UtilizationProfile
@contextmanager
def timeit():
start = time.time()
yield
print('Time taken: {}'.format(time.time() - start))
if __name__ == '__main__':
start = datetime(2015, 11, 23)
end = start + timedelta(days=1)
print('Testing naive time to create profiles.')
with timeit():
slow_profiles = {
host + '.ocf.berkeley.edu': UtilizationProfile.from_hostname(host, start, end)
for host in list_desktops()
}
print('Testing optimized time to create profiles.')
with timeit():
fast_profiles = UtilizationProfile.from_hostnames(list_desktops(), start, end)
