message_ix_models.model.transport.ldv.LDV

class message_ix_models.model.transport.ldv.LDV(source, source_kw)

Bases: MaybeAdaptR11Source

Provider of exogenous data on LDVs.

Parameters:

source_kw – Must include exactly the keys “measure” (must be one of “fuel economy”, “fix_cost”, or “inv_cost”), “nodes”, and “scenario”.

__init__(source, source_kw) → None

Handle source and source_kw.

An implementation must:

• Raise ValueError if it does not recognize or cannot handle the arguments in source or source_kw.

• Recognize and handle (if possible) a “measure” keyword in source_kw from MEASURES.

It may:

• Transform these into other values, for instance by mapping certain values to others, applying regular expressions, or other operations.

• Store those values as instance attributes for use in __call__().

• Set name and/or extra_dims to control the behaviour of prepare_computer().

• Log messages that give information that may help to debug a ValueError for source or source_kw that cannot be handled.

It should not actually load data or perform any time- or memory-intensive operations; these should only be triggered by __call__().

Methods

__init__(source, source_kw)	Handle source and source_kw.
get_keys()	Return the target keys for the (1) raw and (2) transformed data.
raise_on_extra_kw(kwargs)	Helper for subclasses to handle the source_kw argument.
transform(c, base_key)	Prepare c to transform raw data from base_key.

Attributes

aggregate	True if transform() should aggregate data on the $n$ dimension.
extra_dims	Optional additional dimensions for the returned Key/Quantity.
filename	Names of expected files given measure.
id	Identifier for this particular source.
interpolate	True if transform() should interpolate data on the $y$ dimension.
key	Key for the returned Quantity.
measures	Set of measures recognized by a subclass.
name

aggregate: bool = True

True if transform() should aggregate data on the $n$ dimension.

extra_dims: tuple[str, ...] = ()

Optional additional dimensions for the returned Key/Quantity. If not set by __init__(), the dimensions are $(n,y)$.

filename: Mapping[str, str] = {'fix_cost': 'ldv-fix_cost.csv', 'fuel economy': 'ldv-fuel-economy.csv', 'inv_cost': 'ldv-inv_cost.csv'}

Names of expected files given measure.

get_keys() → tuple[Key, Key]

Return the target keys for the (1) raw and (2) transformed data.

id: str = 'message_ix_models.model.transport.ldv'

Identifier for this particular source.

interpolate: bool = True

True if transform() should interpolate data on the $y$ dimension.

key: Key | None = None

Key for the returned Quantity. Optional. See get_keys().

measures: set[str] = {'fix_cost', 'fuel economy', 'inv_cost'}

Set of measures recognized by a subclass.

raise_on_extra_kw(kwargs) → None

Helper for subclasses to handle the source_kw argument.

1. Store aggregate and interpolate, if they remain in kwargs.

2. Raise ValueError if there are any other, unhandled keyword arguments in kwargs.

transform(c: Computer, base_key: Key) → Key

Prepare c to transform raw data from base_key.

base_key identifies the Quantity that is returned by __call__(). Before the data is returned, transform() allows the data source to add additional tasks or computations to c that further transform the data. (These operations may be done in __call__() directly, but transform() allows use of other genno operators and conveniences.)

The default implementation:

1. If aggregate is True, aggregates the data ( genno.operator.aggregate()) on the $n$ dimension using the key “n::groups”.

2. If interpolate is True, interpolates the data ( genno.operator.interpolate()) on the $y$ dimension using “y::coords”.