API

Data

data.cmip.postprocess(ds[, to_standard_calendar])	Post process input experiment
data.cmip.load_cmip([activity_ids, ...])	Loads CMIP6 GCM dataset based on input criteria.
data.cmip.get_gcm(scenario, member_id, ...)	Load historical or future GCM into one dataset.
data.observations.open_era5(variables, ...)	Open ERA5 daily data for one or more variables for period 1979-2021
data.utils.to_standard_calendar(obj)	Convert a Dataset's calendar to the "standard calendar"
data.utils.lon_to_180(ds)	Converts longitude values to (-180, 180)

Downscaling Methods

BCSD

bcsd.tasks.spatial_anomalies	Returns spatial anomalies Calculate the seasonal cycle (12 timesteps) spatial anomaly associated with aggregating the fine_obs to a given coarsened scale and then reinterpolating it back to the original spatial resolution.
bcsd.tasks.fit_and_predict	Fit bcsd model on prepared CMIP data with obs at corresponding spatial scale.
bcsd.tasks.postprocess_bcsd	Downscale the bias-corrected data (fit_and_predict results) by interpolating and then adding the spatial anomalies back in.
bcsd.utils.reconstruct_finescale(ds[, ...])	Add the spatial anomalies back into the interpolated fine scale dataset.

GARD

gard.tasks.coarsen_and_interpolate	Coarsen up obs and then interpolate it back to the original finescale grid.
gard.tasks.fit_and_predict	Prepare inputs (e.g. normalize), use them to fit a GARD model based upon specified parameters and then use that fitted model to make a prediction.
gard.tasks.read_scrf	Read spatial-temporally correlated random fields on file and subset into the correct spatial/temporal domain according to model_output.
gard.utils.get_gard_model(model_type, ...)	Based on input, return the corresponding GARD model instance
gard.utils.add_random_effects(model_output, ...)

DEEPSD

deepsd.tasks.shift	Interpolate obs data to grid specs in xe.util.grid_global.
deepsd.tasks.normalize_gcm	Normalize gcm data based on historical data.
deepsd.tasks.inference	Run inference on normalized gcm data.
deepsd.tasks.rescale	Rescale GCM data that has been normalized based on data in obs_path.
deepsd.tasks.bias_correction	Bias correct downscaled data product.
deepsd.utils.bilinear_interpolate(ds, ...)	Bilinear inperpolate dataset to a global grid with specified step size
deepsd.utils.conservative_interpolate(ds, ...)	Conservative inperpolate dataset to a global grid with specified spacing
deepsd.utils.normalize(ds[, dims, epsilon])	Normalize dataset

MACA

maca.tasks.bias_correction	MACA bias correction task
maca.tasks.epoch_trend	Task to calculate the epoch trends in MACA.
maca.tasks.construct_analogs	MACA analog construction
maca.tasks.split_by_region	Split dataset into separate regions
maca.tasks.combine_regions	Combine regions
maca.tasks.replace_epoch_trend	Replace epoch trend

Common Tasks

common.tasks.make_run_parameters	Prefect task wrapper for RunParameters
common.tasks.get_obs	Task to return observation data subset from input parameters.
common.tasks.get_experiment	Prefect task that returns cmip GCM data from input run parameters.
common.tasks.rechunk	Use rechunker package to adjust chunks of dataset to a form conducive for your processing.
common.tasks.time_summary	Prefect task to create resampled data.
common.tasks.get_weights	Retrieve pre-generated regridding weights.
common.tasks.get_pyramid_weights	Retrieve pre-generated regridding pyramids weights.
common.tasks.regrid	Task to regrid a dataset to target grid.
common.tasks.pyramid	Task to create a data pyramid from an xarray Dataset
common.tasks.run_analyses	Prefect task to run the analyses on results from a downscaling run.
common.tasks.finalize	Prefect task to finalize the downscaling run.