pycontrails.ext.bada.BADAGrid

class pycontrails.ext.bada.BADAGrid(met=None, params=None, **params_kwargs)

Bases: AircraftPerformanceGrid

Compute nominal BADA values for a large grid of independent points.

This model automatically corrects engine efficiency values to ensure that they remain realistic by clipping to a nominal grid of BADA-derived values.

Parameters:

• met (MetDataset | None, optional) – Dataset containing “air_temperature” variable. Only used if these variables are not already found on parameter source in eval(). By default None.

• params (dict[str, Any], optional) – Override model parameters with dictionary. See BADAGridParams for model parameters.

• **params_kwargs – Override model parameters with keyword arguments. See BADAFlightParams for model parameters.

See also

-

meth:eval

-

class:BADAGridParams

__init__(met=None, params=None, **params_kwargs)

Methods

__init__([met, params])
downselect_met()	Downselect met domain to the max/min bounds of source.
ecmwf_met_variables()	Return an ECMWF-specific list of required meteorology variables.
eval([source])	Extract aircraft properties and calculate the fuel consumption.
generic_met_variables()	Return a model-agnostic list of required meteorology variables.
get_data_param(other, key[, default, set_attr])	Get data from other source-compatible object with default set by model parameter key.
get_source_param(key[, default, set_attr])	Get source data with default set by parameter key.
gfs_met_variables()	Return a GFS-specific list of required meteorology variables.
require_met()	Ensure that met is a MetDataset.
require_source_type(type_)	Ensure that source is type_.
set_source([source])	Attach original or copy of input source to source.
set_source_met([optional, variable])	Ensure or interpolate each required met_variables on source .
transfer_met_source_attrs([source])	Transfer met source metadata from met to source.
update_params([params])	Update model parameters on params.

Attributes

met	Meteorology data
params	Instantiated model parameters, in dictionary form
source	Evaluated data source
hash	Generate a unique hash for model instance.
interp_kwargs	Shortcut to create interpolation arguments from params.
long_name
met_required	Require meteorology is not None on __init__()
met_variables	Required meteorology pressure level variables.
name
optional_met_variables	Optional meteorology variables
processed_met_variables	Set of required parameters if processing already complete on met input.

default_params

alias of BADAGridParams

downselect_met()

Downselect met domain to the max/min bounds of source.

Override this method if special handling is needed in met down-selection.

• source must be defined before calling downselect_met().

• This method copies and re-assigns met using met.copy() to avoid side-effects.

Raises:

• ValueError – Raised if source is not defined. Raised if source is not a GeoVectorDataset.

• TypeError – Raised if met is not a MetDataset.

classmethod ecmwf_met_variables()

Return an ECMWF-specific list of required meteorology variables.

Returns:

tuple[MetVariable] – List of ECMWF-specific variants of required variables

eval(source=None, **params)

Extract aircraft properties and calculate the fuel consumption.

Parameters:

• source (GeoVectorDataset | None, optional) – Vector dataset defining coordinates to evaluate model. If None, the coordinates of met are used as evaluation points.

• **params (Any) – Overwrite model parameters before eval

Returns:

GeoVectorDataset –

Data with variables:

• ”engine_efficiency”

• ”true_airspeed”

• ”fuel_flow”

• ”thrust”

• ”aircraft_mass”

and attributes:

• ”aircraft_type”

• ”bada_model”

• ”aircraft_type_bada”

• ”wingspan”

• ”max_mach”

• ”max_altitude”

• ”engine_name”

• ”n_engine”

classmethod generic_met_variables()

Return a model-agnostic list of required meteorology variables.

Returns:

tuple[MetVariable] – List of model-agnostic variants of required variables

get_data_param(other, key, default=<object object>, *, set_attr=True)

Get data from other source-compatible object with default set by model parameter key.

Retrieves data with the following hierarchy:

1. other.data[key]. Returns np.ndarray | xr.DataArray.

2. other.attrs[key]

3. params[key]

4. default

In case 3., the value of params[key] is attached to other.attrs[key] unless set_attr is set to False.

Parameters:

• key (str) – Key to retrieve

• default (Any, optional) – Default value if key is not found.

• set_attr (bool, optional) – If True (default), set source.attrs[key] to params[key] if found. This allows for better post model evaluation tracking.

Returns:

Any – Value(s) found for key in other data, other attrs, or model params

Raises:

KeyError – Raises KeyError if key is not found in any location and default is not provided.

See also

get_source_param, pycontrails.core.vector.GeoVectorDataset.get_data_or_attr

get_source_param(key, default=<object object>, *, set_attr=True)

Get source data with default set by parameter key.

Retrieves data with the following hierarchy:

1. source.data[key]. Returns np.ndarray | xr.DataArray.

2. source.attrs[key]

3. params[key]

4. default

In case 3., the value of params[key] is attached to source.attrs[key] unless set_attr is set to False.

Parameters:

• key (str) – Key to retrieve

• default (Any, optional) – Default value if key is not found.

• set_attr (bool, optional) – If True (default), set source.attrs[key] to params[key] if found. This allows for better post model evaluation tracking.

Returns:

Any – Value(s) found for key in source data, source attrs, or model params

Raises:

KeyError – Raises KeyError if key is not found in any location and default is not provided.

See also

get_data_param, pycontrails.core.vector.GeoVectorDataset.get_data_or_attr

classmethod gfs_met_variables()

Return a GFS-specific list of required meteorology variables.

Returns:

tuple[MetVariable] – List of GFS-specific variants of required variables

property hash

Generate a unique hash for model instance.

Returns:

str – Unique hash for model instance (sha1)

property interp_kwargs

Shortcut to create interpolation arguments from params.

The output of this is useful for passing to interpolate_met().

Returns:

dict[str, Any] – Dictionary with keys

• ”method”

• ”bounds_error”

• ”fill_value”

• ”localize”

• ”use_indices”

• ”q_method”

as determined by params.

long_name = 'Base of aircraft data evaluated at arbitrary points'

met

Meteorology data

met_required = False

Require meteorology is not None on __init__()

met_variables = (MetVariable(short_name='t', standard_name='air_temperature', long_name='Air Temperature', level_type='isobaricInhPa', ecmwf_id=130, grib1_id=11, grib2_id=(0, 0, 0), units='K', amip='ta', description='Air temperature is the bulk temperature of the air, not the surface (skin) temperature.'),)

Required meteorology pressure level variables. Each element in the list is a MetVariable or a tuple[MetVariable]. If element is a tuple[MetVariable], the variable depends on the data source and the tuple must include entries for a model-agnostic variable, an ECMWF-specific variable, and a GFS-specific variable. Only one of the three variable in the tuple is required for model evaluation.

name = 'bada-points'

optional_met_variables = (MetVariable(short_name='u', standard_name='eastward_wind', long_name='Eastward Wind', level_type='isobaricInhPa', ecmwf_id=131, grib1_id=33, grib2_id=(0, 2, 2), units='m s**-1', amip='ua', description='"Eastward" indicates a vector component which is positive when directed eastward (negative westward). Wind is defined as a two-dimensional (horizontal) air velocity vector, with no vertical component.'), MetVariable(short_name='v', standard_name='northward_wind', long_name='Northward Wind', level_type='isobaricInhPa', ecmwf_id=132, grib1_id=34, grib2_id=(0, 2, 3), units='m s**-1', amip='va', description='"Northward" indicates a vector component which is positive when directed northward (negative southward). Wind is defined as a two-dimensional (horizontal) air velocity vector, with no vertical component.'))

Optional meteorology variables

params

Instantiated model parameters, in dictionary form

processed_met_variables

Set of required parameters if processing already complete on met input.

require_met()

Ensure that met is a MetDataset.

Returns:

MetDataset – Returns reference to met. This is helpful for type narrowing met when meteorology is required.

Raises:

ValueError – Raises when met is None.

require_source_type(type_)

Ensure that source is type_.

Returns:

_Source – Returns reference to source. This is helpful for type narrowing source to specific type(s).

Raises:

ValueError – Raises when source is not _type_.

set_source(source=None)

Attach original or copy of input source to source.

Parameters:

source (MetDataset | GeoVectorDataset | Flight | Iterable[Flight] | None) – Parameter source passed in eval(). If None, an empty MetDataset with coordinates like met is set to source.

See also

eval

set_source_met(optional=False, variable=None)

Ensure or interpolate each required met_variables on source .

For each variable in met_variables, check source for data variable with the same name.

For GeoVectorDataset sources, try to interpolate met if variable does not exist.

For MetDataset sources, try to get data from met if variable does not exist.

Parameters:

• optional (bool, optional) – Include optional_met_variables

• variable (MetVariable | Sequence[MetVariable] | None, optional) – MetVariable to set, from met_variables. If None, set all variables in met_variables and optional_met_variables if optional is True.

Raises:

• ValueError – Variable does not exist and source is a MetDataset.

• KeyError – Variable not found in source or met.

source

Evaluated data source

transfer_met_source_attrs(source=None)

Transfer met source metadata from met to source.

update_params(params=None, **params_kwargs)

Update model parameters on params.

Parameters:

• params (dict[str, Any], optional) – Model parameters to update, as dictionary. Defaults to {}

• **params_kwargs (Any) – Override keys in params with keyword arguments.