Selecting/filtering data
The primary data filtering method of ModelSkill is the sel() method which is accesible on most ModelSkill data structures. The sel() method is a wrapper around xarray.Dataset.sel() and can be used to select data based on time, location and/or variable. The sel() method returns a new data structure of the same type with the selected data.
TimeSeries data
Point and track timeseries data of both observation and model result kinds are stored in TimeSeries objects which uses xarray.Dataset as data container. The sel() method can be used to select data based on time and returns a new TimeSeries object with the selected data.
>>> o = ms.observation('obs.nc', item='waterlevel')
>>> o_1month = o.sel(time=slice('2018-01-01', '2018-02-01'))
Comparer objects
Comparer and ComparerCollection contain matched data from observations and model results. The sel() method can be used to select data based on time, model, quantity or other criteria and returns a new comparer object with the selected data.
>>> cmp = ms.match(o, [m1, m2])
>>> cmp_1month = cmp.sel(time=slice('2018-01-01', '2018-02-01'))
>>> cmp_m1 = cmp.sel(model='m1')
Skill objects
The skill() and mean_skill() methods return a SkillTable object with skill scores from comparing observation and model result data using different metrics (e.g. root mean square error). The data of the SkillTable object is stored in a (MultiIndex) pandas.DataFrame which can be accessed via the data attribute. The sel() method can be used to select specific rows and returns a new SkillTable object with the selected data.