spaemis.project

Project a set of emissions into the future according to a set of scaling methods

scale_inventory

scale_inventory(cfg, inventory, target_year, timeseries)

Scale a given variable/sector

Parameters:

• cfg (spaemis.config.VariableScalerConfig) – Configuration used to determine how the scaling is performed

• inventory (spaemis.inventory.EmissionsInventory) – Emissions inventory

• target_year (int) – Year the data will be scaled according to

• timeseries (dict[str, scmdata.run.ScmRun]) – Timeseries for use by proxies

Returns:

xarray.core.dataset.Dataset – Dataset with a single variable with dimensions of (sector, year, lat, lon)

calculate_projections

calculate_projections(config, inventory, timeseries)

Calculate a projected set of emissions according to some configuration

Parameters:

• config (spaemis.config.DownscalingScenarioConfig) –

• inventory (spaemis.inventory.EmissionsInventory) –

• timeseries (dict[str, scmdata.run.ScmRun]) – Optional timeseries

Returns:

xarray.core.dataset.Dataset – Dataset containing the requested projections.

The dimensionality of the output variables is (sector, year, lat, lon)

calculate_point_sources

calculate_point_sources(config, inventory)

Generate grids for point sources

Each point source has a total quantity. This quantity is split evenly over all locations of that source. Values are excluded if the location falls outside of the domain, but still contribute to the set of locations that the quantity is spread over.

Parameters:

• config (spaemis.config.DownscalingScenarioConfig) – Scenario configuration

• inventory (spaemis.inventory.EmissionsInventory) –

  Loaded inventory data

  These data are used for generating the latitude and longitude coordinates of the output grid

Returns:

xarray.core.dataset.Dataset – * Dataset containing a gridded representation of the point source data. This Dataset * contains the variables and sectors covered by the point sources. The sectors * do not necessarily need to be the inventory data.