pocean.dsg.profile package

pocean.dsg.profile.profile_calculated_metadata(df, axes, geometries=True)

pocean.dsg.profile.profile_meta

alias of Profile

pocean.dsg.profile.profiles_meta

alias of ProfileCollection

Submodules

pocean.dsg.profile.im module

class pocean.dsg.profile.im.IncompleteMultidimensionalProfile

Bases: CFDataset

If there are the same number of levels in each profile, but they do not have the same set of vertical coordinates, one can use the incomplete multidimensional array representation, which the vertical coordinate variable is two-dimensional e.g. replacing z(z) in Example H.8, “Atmospheric sounding profiles for a common set of vertical coordinates stored in the orthogonal multidimensional array representation.” with alt(profile,z). This representation also allows one to have a variable number of elements in different profiles, at the cost of some wasted space. In that case, any unused elements of the data and auxiliary coordinate variables must contain missing data values (section 9.6).

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=True, clean_rows=True, **kwargs)

pocean.dsg.profile.om module

class pocean.dsg.profile.om.OrthogonalMultidimensionalProfile

Bases: CFDataset

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

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