import sys
import glob
from datetime import datetime, timedelta
import traces
from traces.utils import datetime_range
def parse_iso_datetime(value):
return datetime.strptime(value, "%Y-%m-%dT%H:%M:%S")
def read_all(pattern='data/lightbulb-*.csv'):
"""Read all of the CSVs in a directory matching the filename pattern
as TimeSeries.
"""
result = []
for filename in glob.iglob(pattern):
print('reading', filename, file=sys.stderr)
ts = traces.TimeSeries.from_csv(
filename,
time_column=0,
time_transform=parse_iso_datetime,
value_column=1,
value_transform=int,
default=0,
)
ts.compact()
result.append(ts)
return result
ts_list = read_all()
total_watts = traces.TimeSeries.merge(ts_list, operation=sum)
# use distribution to look at the distribution of number of lights on
# over a month
histogram = total_watts.distribution(
start=datetime(2016, 1, 1),
end=datetime(2016, 2, 1),
)
print(histogram.mean())
# use distribution with mask to look at the median/lower/upper of
# lights on by hour of day, plot with your tool of choice
for hour, distribution in total_watts.distribution_by_hour_of_day():
print(hour, distribution.quantiles([0.25, 0.5, 0.75]))
for day, distribution in total_watts.distribution_by_day_of_week():
print(day, distribution.quantiles([0.25, 0.5, 0.75]))
# look at the typical number of lights on during business hours
# (8am-6pm) for each day in january
for t in datetime_range(datetime(2016, 1, 1), datetime(2016, 2, 1), 'days'):
biz_start = t + timedelta(hours=8)
biz_end = t + timedelta(hours=18)
histogram = total_watts.distribution(start=biz_start, end=biz_end)
print(t, histogram.quantiles([0.25, 0.5, 0.75]))
# transform time series to evenly spaced version using moving average
# instead of just sampling to avoid aliasing, and proceed to use
# statsmodels/pandas to forecast electricity usage "Modeling Time
# Series" http://tomaugspurger.github.io/modern-7-timeseries.html, in
# the Jupyter notebook it's `In [17]`.
regular = total_watts.moving_average(300, pandas=True)
print(regular)
