pycif.plugins.transforms.basic.vertical_interpolation — API reference

Configuration reference: vertical_interpolation plugin

pycif.plugins.transforms.basic.vertical_interpolation.adjoint.adjoint(transf, inout_datastore, controlvect, obsvect, mapper, di, df, mode, runsubdir, workdir, onlyinit=False, **kwargs)

Adjoint vertical interpolation: scatter observation sensitivities back to model levels.

Dispatches to the sparse or array adjoint implementation. For sparse output (observation-indexed data), scatters adj_out from each observation back to the model level(s) it corresponds to. For array output, applies the transposed interpolation weights.

Parameters:

• transf (Plugin) – vertical_interpolation instance (carries sparse_out, sampled_out).

• inout_datastore (dict) – mutable datastore.

• controlvect – unused.

• obsvect – unused.

• mapper (dict) – transform mapper.

• di (datetime) – sub-simulation start date.

• df (datetime) – sub-simulation end date.

• mode (str) – 'adj'.

• runsubdir (str) – unused.

• workdir (str) – unused.

• onlyinit (bool) – if True (array path), return immediately.

• **kwargs – forwarded to the sparse/array implementations.

pycif.plugins.transforms.basic.vertical_interpolation.forward.forward(transf, inout_datastore, controlvect, obsvect, mapper, di, df, mode, runsubdir, workdir, onlyinit=False, save_debug=False, **kwargs)

Vertically interpolate model fields to observation pressure/height levels.

Dispatches to either the sparse/sampled or gridded (array) implementation:

• Sparse/sampled output — sparse_forward(): extracts model values at the observation vertical coordinate.

• Array output — array_forward(): applies the configured interpolation method (static-levels, linear, closest, match-layer, or layer-weighted) to the full gridded field.

Parameters:

• transf (Plugin) – vertical_interpolation instance (carries method, coord_in, coord_out, ignore_level, and optional file_statlev).

• inout_datastore (dict) – mutable datastore.

• controlvect – unused.

• obsvect – unused.

• mapper (dict) – transform mapper (carries sparse_data, sampled, and domain objects).

• di (datetime) – sub-simulation start date.

• df (datetime) – sub-simulation end date.

• mode (str) – 'fwd' or 'tl'.

• runsubdir (str) – unused.

• workdir (str) – unused.

• onlyinit (bool) – passed to the sparse/array implementations.

• save_debug (bool) – if True, save intermediate results.

• **kwargs – forwarded to the sparse/array implementations.

pycif.plugins.transforms.basic.vertical_interpolation.utils.array.adjoint.array_adjoint(transf, mapper, inout_datastore, ddi, onlyinit, **kwargs)

pycif.plugins.transforms.basic.vertical_interpolation.utils.array.closest.closest_fwd(transf, pres_in, pres_out, mode, inout_datastore, trid, ddi, mapper)

pycif.plugins.transforms.basic.vertical_interpolation.utils.array.forward.array_forward(transf, mapper, inout_datastore, ddi, mode, onlyinit, **kwargs)

pycif.plugins.transforms.basic.vertical_interpolation.utils.array.linear.linear_fwd(transf, pres_in, pres_out, mode, inout_datastore, trid, ddi)

pycif.plugins.transforms.basic.vertical_interpolation.utils.array.weight.weight_fwd(transf, pres_in, pres_out, mode, inout_datastore, trid, ddi, mapper)

pycif.plugins.transforms.basic.vertical_interpolation.utils.sparse.adjoint.sparse_adjoint(transf, mapper, inout_datastore, ddi, onlyinit, **kwargs)

pycif.plugins.transforms.basic.vertical_interpolation.utils.sparse.forward.sparse_forward(transf, mapper, inout_datastore, ddi, onlyinit, mode, **kwargs)

pycif.plugins.transforms.basic.vertical_interpolation.utils.sparse.vcoordfromfile.vcoordfromfile(datastore, file_lev, **kwargs)

Computes vertical levels from pre-defined file

Parameters:

• datastore (dict) – dictionary of pd.Dataframes containing measurement informations

• file_lev (str) – path to the file defining station levels