Calculate Resource grid

Example of using PyWAsP to calculate a resource grid

Prepare TopographyMap

First, we need to prepare the topography map. This is done by reading the elevation and roughness maps and creating a TopographyMap object.

import numpy as np
import windkit as wk
import pywasp as pw

ssl = wk.load_tutorial_data("serra_santa_luzia")
# The wind directions are relative to the grid north of the UTM 29N maps
bwc = ssl.bwc.assign_attrs(wind_dir_crs="EPSG:32629")

topo_map = pw.wasp.TopographyMap(ssl.elev, ssl.rgh)

Define output locations

Second, we need to define the output locations. This is done by creating a dataset with the coordinates of the output locations.

dx, dy = 500, 500
nx, ny = 31, 31
x0, y0 = 510500, 4613500
steps = np.arange(nx) * dx
x = x0 + steps
y = y0 + steps
xmin, xmax = x.min(), x.max()
ymin, ymax = y.min(), y.max()

output_locs = wk.spatial.create_cuboid(
    west_east=x,
    south_north=y,
    height=[50],
    crs="EPSG:32629",
)

Calculate resource grid

Finally, we can calculate the resource grid. This is done by calling the predict_wwc function. This function takes the output locations, the boundary conditions, and the topography map as input. The output is a weibull wind climate dataset at the output locations.

wwc = pw.wasp.predict_wwc(bwc, topo_map, output_locs)

Plot the mean wind speed

We can plot the mean wind speed to see the result.

wwc["wspd"].plot()