pocean.dsg package

class pocean.dsg.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()
class pocean.dsg.ContiguousRaggedTrajectory

Bases: CFDataset

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)
class pocean.dsg.ContiguousRaggedTrajectoryProfile

Bases: CFDataset

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)
class pocean.dsg.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)
class pocean.dsg.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()
class pocean.dsg.IncompleteMultidimensionalTimeseriesProfile

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()
class pocean.dsg.IncompleteMultidimensionalTrajectory

Bases: CFDataset

When storing multiple trajectories in the same file, and the number of elements in each trajectory is the same, one can use the multidimensional array representation. This representation also allows one to have a variable number of elements in different trajectories, 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)
class pocean.dsg.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()
class pocean.dsg.IndexedRaggedTrajectory

Bases: CFDataset

calculated_metadata(df=None, geometries=True, clean_cols=True, clean_rows=True)
from_dataframe(df, output, **kwargs)
to_dataframe()
class pocean.dsg.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)
class pocean.dsg.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)
class pocean.dsg.OrthogonalMultidimensionalTimeseriesProfile

Bases: CFDataset

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)
class pocean.dsg.RaggedTimeseriesProfile

Bases: CFDataset

calculated_metadata(df=None, geometries=True, clean_cols=True, clean_rows=True)
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.get_calculated_attributes(df, axes=None, history=None)

Functions to automate netCDF attribute generation from the data itself This is a wrapper for the other four functions, which could be called separately.

Parameters:

  • df – data (Pandas DataFrame)

  • axes – keys (x,y,z,t) are associated with actual column names (dictionary)

  • history – history: text initializing audit trail for modifications to the original data (optional, string)

Returns:

dictionary of global attributes

pocean.dsg.get_creation_attributes(history=None)

Query system for netCDF file creation times

Parameters:

history – text initializing audit trail for modifications to the original data (optional, string)

Returns:

dictionary of global attributes

pocean.dsg.get_geographic_attributes(df, axes=None)

Use values in a dataframe to set geographic attributes for the eventual netCDF file Attribute names come from https://www.ncei.noaa.gov/data/oceans/ncei/formats/netcdf/v2.0/index.html The coordinate reference system (CRS) is assumed to be EPSG:4326, which is WGS84 and is used with GPS satellite navigation (http://spatialreference.org/ref/epsg/wgs-84/). This is NCEI’s default. Coordinate values are latitude (decimal degrees_north) and longitude (decimal degrees_east). Longitude values are limited to [-180, 180).

Parameters:

  • df – data (Pandas DataFrame)

  • axes – keys (x,y,z,t) are associated with actual column names (dictionary)

Returns:

nested dictionary of variable and global attributes

pocean.dsg.get_temporal_attributes(df, axes=None)

Use values in a dataframe to set temporal attributes for the eventual netCDF file Attribute names come from https://www.ncei.noaa.gov/data/oceans/ncei/formats/netcdf/v2.0/index.html

Parameters:

  • df – data (Pandas DataFrame)

  • axes – keys (x,y,z,t) are associated with actual column names (dictionary). z in meters.

Returns:

nested dictionary of variable and global attributes

pocean.dsg.get_vertical_attributes(df, axes=None)

Use values in a dataframe to set vertical attributes for the eventual netCDF file Attribute names come from https://www.ncei.noaa.gov/data/oceans/ncei/formats/netcdf/v2.0/index.html The CRS, geospatial_bounds_vertical_crs, cannot be assumed because NCEI suggests any of * ‘EPSG:5829’ (instantaneous height above sea level), * ‘EPSG:5831’ (instantaneous depth below sea level), or * ‘EPSG:5703’ (NAVD88 height). Likewise, geospatial_vertical_positive cannot be assumed to be either ‘up’ or ‘down’. Set these attributes separately according to the dataset. Note: values are cast from numpy.int to float

Parameters:

  • df – data (Pandas DataFrame)

  • axes – keys (x,y,z,t) are associated with actual column names (dictionary). z in meters.

Returns:

nested dictionary of variable and global attributes

Subpackages

Submodules

pocean.dsg.utils module

pocean.dsg.utils.get_calculated_attributes(df, axes=None, history=None)

Functions to automate netCDF attribute generation from the data itself This is a wrapper for the other four functions, which could be called separately.

Parameters:

  • df – data (Pandas DataFrame)

  • axes – keys (x,y,z,t) are associated with actual column names (dictionary)

  • history – history: text initializing audit trail for modifications to the original data (optional, string)

Returns:

dictionary of global attributes

pocean.dsg.utils.get_creation_attributes(history=None)

Query system for netCDF file creation times

Parameters:

history – text initializing audit trail for modifications to the original data (optional, string)

Returns:

dictionary of global attributes

pocean.dsg.utils.get_geographic_attributes(df, axes=None)

Use values in a dataframe to set geographic attributes for the eventual netCDF file Attribute names come from https://www.ncei.noaa.gov/data/oceans/ncei/formats/netcdf/v2.0/index.html The coordinate reference system (CRS) is assumed to be EPSG:4326, which is WGS84 and is used with GPS satellite navigation (http://spatialreference.org/ref/epsg/wgs-84/). This is NCEI’s default. Coordinate values are latitude (decimal degrees_north) and longitude (decimal degrees_east). Longitude values are limited to [-180, 180).

Parameters:

  • df – data (Pandas DataFrame)

  • axes – keys (x,y,z,t) are associated with actual column names (dictionary)

Returns:

nested dictionary of variable and global attributes

pocean.dsg.utils.get_temporal_attributes(df, axes=None)

Use values in a dataframe to set temporal attributes for the eventual netCDF file Attribute names come from https://www.ncei.noaa.gov/data/oceans/ncei/formats/netcdf/v2.0/index.html

Parameters:

  • df – data (Pandas DataFrame)

  • axes – keys (x,y,z,t) are associated with actual column names (dictionary). z in meters.

Returns:

nested dictionary of variable and global attributes

pocean.dsg.utils.get_vertical_attributes(df, axes=None)

Use values in a dataframe to set vertical attributes for the eventual netCDF file Attribute names come from https://www.ncei.noaa.gov/data/oceans/ncei/formats/netcdf/v2.0/index.html The CRS, geospatial_bounds_vertical_crs, cannot be assumed because NCEI suggests any of * ‘EPSG:5829’ (instantaneous height above sea level), * ‘EPSG:5831’ (instantaneous depth below sea level), or * ‘EPSG:5703’ (NAVD88 height). Likewise, geospatial_vertical_positive cannot be assumed to be either ‘up’ or ‘down’. Set these attributes separately according to the dataset. Note: values are cast from numpy.int to float

Parameters:

  • df – data (Pandas DataFrame)

  • axes – keys (x,y,z,t) are associated with actual column names (dictionary). z in meters.

Returns:

nested dictionary of variable and global attributes