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.

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.set_hgts([height_from, height_to, n])	Set generalized lib heights automatically
pywasp.wasp.set_z0s([z0meso_from, z0meso_to, n])	Set generalized lib roughness lengths automatically
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(genwc, topo_map, ...)	Calculate site_effects, downscaled wind climate, and meteorlogical fields in a single step
pywasp.wasp.downscale_from_site_effects(...)	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.add_met_fields(wco[, fields, ...])	Add additional fields to a weibull wind climate object
pywasp.wasp.probabilities(wwc, speed_bins[, ...])	Get the probabilities for all speed bins
pywasp.wasp.wtg_to_pywake(wtg)	Converts a PyWAsP WTG to a PyWake WTG
pywasp.wasp.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.wasp.potential_aep(wwc, wtg, /[, ...])	Calculate Potential Annual Energy Production using wind farm effects from PyWake.
pywasp.wasp.wind_farm_flow_map(wwc, ...[, ...])	Generate a flow map around a wind farm using py_wake for wake and blockage effects.
pywasp.wasp.get_air_density(elev[, source])	Calculate the air density at a given location from reanalysis data
pywasp.wasp.bwc_from_timeseries(ds[, hist, ...])	Add timeseries to histogram
pywasp.wasp.bwc_resample_like(source, target)	Resamples a histogram to different sector or wind speed bin structure.
pywasp.wasp.bwc_resample_sectors(source[, ...])	Resamples a histogram to different sector structure.
pywasp.wasp.bwc_resample_wsbins_like(source, ...)	Resamples a histogram to different wind speed bin structure.
pywasp.wasp.calc_temp_scale(ds[, ...])	Calculate temperature scale
pywasp.wasp.stability_histogram(ds[, hist, ...])	Add timeseries to existing histogram
pywasp.wasp.interpolate_gwc(gwc, /, output_locs)	Spatially interpolate GWC data to a grid of locations.
pywasp.wasp.get_climate(output_locs[, ...])	Get climatological parameters interpolated to the output locations
pywasp.wasp.get_climate_by_config(...[, ...])	Get climatological parameters for specified positions and a given config object.
pywasp.wasp.weibull_fit(bwc[, ...])	Returns sectorwise Weibull parameters using WAsP's fitting algorithm

I/O

pywasp.io	pywasp Input/Output routines
pywasp.io.WaspRasterMap(values, minx, miny, ...)	Stores a roughness or elevation map in a raster format usable by WAsP routines.
pywasp.io.rastermap_to_vectormap(da[, dz, ...])	Convert raster map to vector map
pywasp.io.rastermap_to_waspformat(da[, lctable])	Convert raster to WaspRasterMap expected for WAsP fortran routines.
pywasp.io.vectormap_to_rastermap(gdf, res[, ...])	Converts a geopandas.GeoDataFrame vectormap object to a xarray.DataArray rastermap object.
pywasp.io.vectormap_to_waspformat(gdf[, lctable])	Creates a WaspVectorMap object from a geodataframe.

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[, srs])	Reads grid from raster file
pywasp.lincom.grid_from_wasp_rastermap(rastermap)	Convert a WAsP RasterMap object to a Lincom Gridmap xarray object
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[, gewc_interp])	Apply lookup table to Generalized Extreme Wind Atlas
pywasp.lincom.create_lut(nsec, wind_speeds, ...)	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.config

Configuration for pywasp, saved as an ini file