States with multiple heights data per entry.

The input data is taken from a csv file or pandas data frame with columns. The format of the data columns is as in the following example for wind speed at heights 50, 60, 100 m:

WS-50, WS-60, WS-100, …

Attributes¶

data_source: str or pandas.DataFrame

Either path to a file or data

ovars: list of str

The output variables

heights: list of float

The heights at which to search data

var2col: dict, optional

Mapping from variable names to data column names

fixed_vars: dict, optional

Fixed uniform variable values, instead of reading from data

pd_read_pars: dict, optional

pandas file reading parameters

states_sel: slice or range or list of int

States subset selection

states_loc: list

State index selection via pandas loc function

RDICT: dict

Default pandas file reading parameters

Public members¶

RDICT = {'index_col': 0}

MultiHeightStates(data_source, output_vars, heights, ...)[source]

Constructor.

__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

load_data(algo, verbosity=0)[source]

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

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.

weights(algo)[source]

The statistical weights of all states.

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

The main model calculation.

finalize(algo, verbosity=0)[source]

Finalizes the model.

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

Run-time states factory.

output_coords()[source]

Gets the coordinates of all output arrays

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

Add variables to tdata, initialized with NaN

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

Starts the model calculation in parallel, via xarray’s apply_ufunc.

property model_id

Unique id based on the model type.

var(v)[source]

Creates a model specific 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