- class foxes.input.states.FieldDataNC(foxes.core.States)[source]
Heterogeneous ambient states on a regular horizontal grid in NetCDF format.
Attributes¶
- data_source: str or xarray.Dataset
The data or the file search pattern, should end with suffix ‘.nc’. One or many files.
- ovars: list of str
The output variables
- var2ncvar: dict
Mapping from variable names to variable names in the nc file
- fixed_vars: dict
Uniform values for output variables, instead of reading from data
- states_coord: str
The states coordinate name in the data
- x_coord: str
The x coordinate name in the data
- y_coord: str
The y coordinate name in the data
- h_coord: str
The height coordinate name in the data
- pre_load: bool
Flag for loading all data into memory during initialization
- weight_ncvar: str
Name of the weight data variable in the nc file(s)
- bounds_error: bool
Flag for raising errors if bounds are exceeded
- fill_value: number
Fill value in case of exceeding bounds, if no bounds error
- time_format: str
The datetime parsing format string
- interp_nans: bool
Linearly interpolate nan values
- interpn_pars: dict, optional
Additional parameters for scipy.interpolate.interpn
Public members¶
-
FieldDataNC(data_source, output_vars, var2ncvar=
{}
, ...)[source] Constructor.
- output_point_vars(algo)[source]
The variables which are being modified by the model.
- 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.
- property initialized
Initialization flag.
- sub_models()[source]
List of all sub-models
-
initialize(algo, verbosity=
0
, force=False
)[source] Initializes the model.
-
get_data(variable, target, lookup=
'smfp'
, mdata=None
, ...)[source] Getter for a data entry in the model object or provided data sources
- data_to_store(name, algo, data)[source]
Adds data from mdata to the local store, intended for iterative runs.
-
from_data_or_store(name, algo, data, ret_dims=
False
, safe=False
)[source] Get data from mdata or local store