Annual time series¶
from warsa.timeseries import annually
Annual maxima¶
Starting on 1st January at 00:00:00
annually.annual_maxima(sr)
Output:
1979 4.078448
1980 10.036425
1981 15.106880
...
2012 7.410849
2013 22.590010
2014 10.074438
If not otherwise stated, the year starts on 1st January at 00:00:00. Hydrological years normally start in another month and at another time, e.g., on 1st November at 07:30am in Germany. In order to get maxima from 1st November 07:30 to 31th October before 07:30 of the next year:
annually.annual_maxima(sr, datetime.datetime(2000, 11, 1, 7, 30))
Note that the years are shifted and some values are different when compared to the previous example:
1979 6.514527
1980 15.106880
1981 11.751186
....
2011 5.625469
2012 22.590010
2013 10.074438
2014 5.337633
Labeling according to the finishing year (instead of the starting year):
annually.annual_maxima(sr, datetime.datetime(2000, 11, 1, 7, 30), left_labeled=False)
Output:
1980 6.514527
1981 15.106880
1982 11.751186
...
2013 22.590010
2014 10.074438
2015 5.337633
The procedures to obtain annual minima, means, and media are:
annually.annual_minima(sr, datetime.datetime(2000, 11, 1, 7, 30)
annually.annual_means(sr, datetime.datetime(2000, 11, 1, 7, 30))
annually.annual_media(sr, datetime.datetime(2000, 11, 1, 7, 30))
Furthermore, there is a procedure to return a data frame with max, min, and mean:
annually.annual_min_max_mean(sr, datetime.datetime(2000, 11, 1, 7, 30))
Output:
min max mean
Year
1979 0.015055 6.514527 0.714102
1980 0.301246 15.106880 0.945218
1981 0.060179 11.751186 0.576625
... ... ... ...
2012 0.283987 22.590010 0.804904
2013 0.260308 10.074438 0.681222
2014 0.005430 5.337633 0.519517
Finally, there is also a procedure to return a data frame with more statistics:
annually.annual_statistics(sr, datetime.datetime(2000, 11, 1, 7, 30))
Output:
min max mean std median gaps count
Year
1979 0.015055 6.514527 0.714102 0.762369 0.486472 0.0 34618.0
1980 0.301246 15.106880 0.945218 1.375311 0.515939 0.0 34598.0
1981 0.060179 11.751186 0.576625 0.891372 0.400287 0.0 34642.0
... ... ... ... ... ... ... ...
2012 0.283987 22.590010 0.804904 1.167109 0.487111 0.0 34620.0
2013 0.260308 10.074438 0.681222 0.625493 0.566514 0.0 34613.0
2014 0.005430 5.337633 0.519517 0.641135 0.426398 0.0 32469.0