pocean.dsg.timeseries package

Submodules

pocean.dsg.timeseries.cr module

class pocean.dsg.timeseries.cr.ContiguousRaggedTimeseries

Bases: CFDataset

calculated_metadata(df=None, geometries=True, clean_cols=True, clean_rows=True)

from_dataframe(df, output, **kwargs)

classmethod is_mine(dsg, strict=False)

to_dataframe()

pocean.dsg.timeseries.im module

class pocean.dsg.timeseries.im.IncompleteMultidimensionalTimeseries

Bases: CFDataset

calculated_metadata(df=None, geometries=True, clean_cols=True, clean_rows=True)

from_dataframe(df, output, **kwargs)

classmethod is_mine(dsg, strict=False)

to_dataframe()

pocean.dsg.timeseries.ir module

class pocean.dsg.timeseries.ir.IndexedRaggedTimeseries

Bases: CFDataset

calculated_metadata(df=None, geometries=True, clean_cols=True, clean_rows=True)

from_dataframe(df, output, **kwargs)

classmethod is_mine(dsg, strict=False)

to_dataframe()

pocean.dsg.timeseries.om module

class pocean.dsg.timeseries.om.OrthogonalMultidimensionalTimeseries

Bases: CFDataset

H.2.1. Orthogonal multidimensional array representation of time series

If the time series instances have the same number of elements and the time values are identical for all instances, you may use the orthogonal multidimensional array representation. This has either a one-dimensional coordinate variable, time(time), provided the time values are ordered monotonically, or a one-dimensional auxiliary coordinate variable, time(o), where o is the element dimension. In the former case, listing the time variable in the coordinates attributes of the data variables is optional.

calculated_metadata(df=None, geometries=True, clean_cols=True, clean_rows=True, **kwargs)

classmethod from_dataframe(df, output, **kwargs)

classmethod is_mine(dsg, strict=False)

nc_attributes(axes, daxes)

to_dataframe(clean_cols=False, clean_rows=False, **kwargs)