metadata#

Manage GRIB metadata.

Functions

`compute_origin`(ref_grid, field)	Compute horizontal components of the origin dict.
`deserialise_field`(value)	Deserialise a base64-encoded GRIB message to a Field object.
`extract`(field)	Extract GRIB field metadata.
`extract_hcoords`(message_b64)	Extract horizontal coordinates.
`is_staggered_horizontal`(field)	Determine if the field is on a staggered horizontal grid.
`load_grid_reference`(field)	Construct a grid from a reference parameter.
`override`(message, **kwargs)	Override GRIB metadata.
`serialise_field`(field)	Serialise a GRIB field to a base64-encoded string.
`set_origin_xy`(ds, ref_param)	Set horizontal components of the origin attribute.

Classes

Grid(lon_first_grid_point, lat_first_grid_point)

Coordinates of the reference grid.

class meteodatalab.metadata.Grid(lon_first_grid_point: float, lat_first_grid_point: float)[source]#

Coordinates of the reference grid.

lon_first_grid_point#

longitude of first grid point in rotated lat-lon CRS

Type:: float

lat_first_grid_point#

latitude of first grid point in rotated lat-lon CRS

Type:: float

meteodatalab.metadata.compute_origin(ref_grid: Grid, field: DataArray) → dict[str, float][source]#

Compute horizontal components of the origin dict.

Parameters:

ref_grid (Grid) – reference grid
field (xarray.DataArray) – field for which to compute the origin

Returns:

Horizontal components of the origin

Return type:

dict[str, float]

meteodatalab.metadata.deserialise_field(value: str) → Field[source]#

Deserialise a base64-encoded GRIB message to a Field object.

Parameters:: value (str) – Base64-encoded GRIB message.
Returns:: GRIB field with no values loaded.
Return type:: earthkit.data.Field

meteodatalab.metadata.extract(field: Field) → dict[str, Any][source]#

Extract GRIB field metadata.

Parameters:: field (earthkit.data.Field) – GRIB field from which to extract metadata.
Returns:: Dictionary containing parameter, geography, vref, vcoord_type, and origin_z metadata extracted from the field.
Return type:: dict[str, Any]

meteodatalab.metadata.extract_hcoords(message_b64: str) → dict[str, DataArray][source]#

Extract horizontal coordinates.

Parameters:: message_b64 (str) – Serialised GRIB message containing the grid definition.
Returns:: Horizontal coordinates in geolatlon.
Return type:: dict[str, xarray.DataArray]

meteodatalab.metadata.is_staggered_horizontal(field: DataArray) → bool[source]#

Determine if the field is on a staggered horizontal grid.

Parameters:: field (xr.DataArray) – Field containing the grid definition.
Raises:: ValueError – if the field is a on regular grid without origin_x and origin_y set.
Returns:: True if the field is on a staggered horizontal grid.
Return type:: bool

meteodatalab.metadata.load_grid_reference(field: Field) → Grid[source]#

Construct a grid from a reference parameter.

Parameters:: field (earthkit.data.Field) – Field defining the reference grid.
Returns:: reference grid
Return type:: Grid

meteodatalab.metadata.override(message: str, **kwargs: Any) → dict[str, Any][source]#

Override GRIB metadata.

Note that no special consideration is made for maintaining consistency when overriding template definition keys such as productDefinitionTemplateNumber.

Parameters:

message (str) – Serialised GRIB message with original values
kwargs (Any) – Metadata keys and values that are overridden in the output

Returns:

Updated metadata along with the geography and parameter namespaces

Return type:

dict[str, Any]

meteodatalab.metadata.serialise_field(field: Field) → str[source]#

Serialise a GRIB field to a base64-encoded string.

Parameters:: field (earthkit.data.Field) – GRIB field to serialise.
Returns:: Base64-encoded GRIB message.
Return type:: str

meteodatalab.metadata.set_origin_xy(ds: dict[str, DataArray], ref_param: str) → None[source]#

Set horizontal components of the origin attribute.

Parameters:

ds (dict[str, xarray.DataArray]) – Dataset of fields to update.
ref_param (str) – Name of the parameter field to use as a reference. Must be a key of ds.

Raises:

KeyError – if the ref_param key is not found in the input dataset