Multi-height states from xarray Dataset.

Attributes¶

data_source: str or xarray.Dataset

Either path to a file or data

state_coord: str

Name of the state coordinate

h_coord: str

Name of the height coordinate

xr_read_pars: dict

Parameters for xarray.open_dataset

Examples¶

Example of the NetCDF structure:

>>>    Dimensions:  (Time: 3000, height: 8)
>>>    Coordinates:
>>>    * Time     (Time) <U19 228kB '2009-01-01 00:00:00' ... '2009-01-21 19:50:00'
>>>    * height   (height) float32 32B 50.0 75.0 90.0 100.0 150.0 200.0 250.0 500.0
>>>    Data variables:
>>>        ws       (Time, height) float32 96kB ...
>>>        wd       (Time, height) float32 96kB ...
>>>        ti       (Time, height) float32 96kB ...
>>>        rho      (Time) float32 12kB ...

Public members¶

MultiHeightNCStates(data_source, *args, state_coord='state', ...)[source]

Constructor.

load_data(algo, verbosity=0)[source]

Load and/or create all model data that is subject to chunking.

RDICT = {'index_col': 0}

__repr__()[source]

Return repr(self).

property data_source

The data source

reset(algo=None, states_sel=None, states_loc=None, verbosity=0)[source]

Reset the states, optionally select states

set_running(algo, data_stash, sel=None, isel=None, verbosity=0)[source]

Sets this model status to running, and moves all large data to stash.

unset_running(algo, data_stash, sel=None, isel=None, verbosity=0)[source]

Sets this model status to not running, recovering large data from stash

size()[source]

The total number of states.

index()[source]

The index list

output_point_vars(algo)[source]

The variables which are being modified by the model.

calculate(algo, mdata, fdata, tdata)[source]

The main model calculation.

finalize(algo, verbosity=0)[source]

Finalizes the model.

gen_states_split_size()[source]

Generator for suggested states split sizes for output writing.

classmethod new(states_type, *args, **kwargs)[source]

Run-time states factory.

output_coords()[source]

Gets the coordinates of all output arrays

ensure_output_vars(algo, tdata)[source]

Ensures that the output variables are present in the target data.

run_calculation(algo, *data, out_vars, **calc_pars)[source]

Starts the model calculation in parallel.

property model_id

Unique id based on the model type.

var(v)[source]

Creates a model specific variable name.

unvar(vnm)[source]

Translates model specific variable name to origninal variable name.

property initialized

Initialization flag.

sub_models()[source]

List of all sub-models

initialize(algo, verbosity=0, force=False)[source]

Initializes the model.

property running

Flag for currently running models

get_data(variable, target, lookup='smfp', mdata=None, ...)[source]

Getter for a data entry in the model object or provided data sources