API Reference

This page provides an auto-generated summary of pywasps’s API. For more details and examples, refer to the relevant chapters in the main part of the documentation.

General-purpose functions

pywasp.raster_to_vector(da[, dz, gsize, ...])	Convert raster map to vector map
pywasp.vector_to_raster(gdf, res[, bounds, ...])	Converts a geopandas.GeoDataFrame vector map object to a xarray.DataArray raster map object.
pywasp.LandCoverTable(*args, **kwargs)	Class with methods to work with landcover tables
pywasp.polygons_to_lines(gdf[, lctable, ...])	Converts a geopandas.GeoDataFrame vectormap object to a xarray.DataArray rastermap object.
pywasp.add_met_fields(wco[, fields, air_density])	Add additional fields to a weibull wind climate object
pywasp.estimate_sensitivity_factor(pwc, wtg)	Calculate the sensitivity factor that multiplies the wind uncertainty terms.
pywasp.get_air_density(elev[, source])	Calculate the air density at a given location from reanalysis data
pywasp.bwc_from_tswc(ds[, hist, normalize, ...])	Add timeseries to histogram
pywasp.bwc_resample_like(source, target)	Resamples a histogram to different sector or wind speed bin structure.
pywasp.bwc_resample_sectors(source[, ...])	Resamples a histogram to different sector structure.
pywasp.bwc_resample_wsbins_like(source, target)	Resamples a histogram to different wind speed bin structure.
pywasp.calc_temp_scale(ds[, ...])	Calculate temperature scale
pywasp.stability_histogram(ds[, hist, ...])	Add timeseries to existing histogram
pywasp.gross_aep(wwc, wtg, /[, ...])	Calculate annual energy production (AEP), using the WAsP core Fortran implementation, from Weibull wind climate(s) and Wind Turbine Generator(s) or WindTurbines object.
pywasp.net_aep(loss_table, ds_potential_aep)	Calculates the Net Annual Energy Production (AEP) by applying the losses from a loss table to the potential AEP values provided in the dataset.
pywasp.potential_aep(wwc, wtg, /[, ...])	Calculate Potential Annual Energy Production using wind farm effects from PyWake.
pywasp.px_aep(uncertainty_table, ds_net_aep)	Calculates the Annual Energy Production (AEP) level reached with a given probability.
pywasp.wind_farm_flow_map(wwc, wtg, ...[, ...])	Generate a flow map around a wind farm using py_wake for wake and blockage effects.
pywasp.weibull_fit(bwc[, ...])	Returns sectorwise Weibull parameters using WAsP's fitting algorithm

WAsP

pywasp.wasp	Configuration modules for working with WAsP.
pywasp.wasp.Config([par_set])	Configuration class for the WAsP model parameters
pywasp.wasp.TopographyMap(elev_map, rou_map)	Class for topography maps
pywasp.wasp.get_site_effects_cfd(cfd_files, ...)	Calculate speedups at all points provided
pywasp.wasp.predict_wwc(bwc, topo_map, ...)	Predict a weibull wind climate from a binned wind climate using a topography map
pywasp.wasp.predict_wwc_from_site_effects(...)	Predict a weibull wind climate from a binned wind climate using precalculated site effects
pywasp.wasp.predict_bwc(bwc, topo_map, ...)	Predict a binned wind climate from a binned wind climate using a topography map for given output locations
pywasp.wasp.predict_bwc_from_site_effects(...)	Creates a generalized wind climate using atlas_nt
pywasp.wasp.downscale(gwc, topo_map, output_locs)	Calculate site_effects, downscaled wind climate, and meteorlogical fields in a single step
pywasp.wasp.downscale_from_site_effects(gwc, ...)	Downscale a generalized wind climate using precalculated site effects
pywasp.wasp.downscale_from_geostrophic_and_site_effects_to_wwc(...)	Downscale a geostrophic wind climate using precalculated site effects
pywasp.wasp.downscale_from_geostrophic_and_site_effects_to_bwc(...)	Downscale a geostrophic wind climate using precalculated site effects
pywasp.wasp.generalize(bwc, topo_map[, ...])	Generalizes the wind climate using either the BZ model or a CFD volume.
pywasp.wasp.generalize_from_site_effects(...)	Creates a generalized wind climate using atlas_nt
pywasp.wasp.generalize_from_site_effects_to_geowc(...)	Creates a generalized wind climate using atlas_nt
pywasp.wasp.interpolate_gwc(gwc, /, output_locs)	Spatially interpolate GWC data to a grid of locations.

LINCOM

pywasp.lincom	Routines for running LINCOM and calculating shear exponent
pywasp.lincom.calculate_fetchmap(...)	Calculates a fetchmap for a given domain and wind
pywasp.lincom.roughness_to_landmask(lc_map, ...)	Convert a roughness map to a landmask map
pywasp.lincom.FourierSpace(nx_proj, ny_proj, ...)	FourierSpace object
pywasp.lincom.interpolate_gewc(gewc, target)	Interpolate GEWC file
pywasp.lincom.open_raster(mapfilename[, crs])	Reads grid from raster file
pywasp.lincom.shear_exp_from_wspd(wspd, ...)	Calculate shear exponent using a linear log-log fit from a number of heights
pywasp.lincom.get_spec_corr_fac(wind_ts[, ...])	Calculate spectral correction factor for model time-series
pywasp.lincom.get_return_wind(pewc[, ...])	Use the Gumbel distribution to find the return wind and uncertainty
pywasp.lincom.apply_lut(lut, gewc[, ...])	Apply lookup table to Generalized Extreme Wind Atlas
pywasp.lincom.create_lut(n_sectors, ...)	Run LINCOM to create lookup table for extreme wind estimation
pywasp.lincom.create_wind(wind_type, speed, ...)	Creates an xarray object defining the input wind for a LINCOM simulation
pywasp.lincom.WindLevel(fou_space, ...)	WindLevel object
pywasp.lincom.get_wind_points(wind_level, out_ds)	Calculate point results from wind level for requested domain

User configuration

pywasp.user_config

Configuration for pywasp, saved as an ini file