Annual time series
==================

.. code::

    from warsa.timeseries import annually

Annual maxima
-------------

Starting on 1st January at 00:00:00

.. code::

    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:

.. code::

    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):

.. code::

    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:

.. code::

    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:

.. code::

    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:

.. code::

    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