Static data

The foxes package comes with a set of data files whose main purpose is to serve for examples and tests. They are also a demonstration of the file formats that are required for input data. Such provided data files are often referred to as static data in foxes terminology.

Three different types of data are provided: Wind farm layout data, ambient states data, and power and thrust curve data.

import matplotlib.pyplot as plt
from plotly.offline import iplot

import foxes
import foxes.variables as FV

Wind farm layout data

test_farm_67.csv

This is a wind farm with 67 turbines with randomly generated turbine coordinates. The file starts as follows:

index,label,x,y
0,T0,101872.70,1004753.57
1,T1,103659.97,1002993.29
2,T2,100780.09,1000779.97
3,T3,100290.42,1004330.88
4,T4,103005.58,1003540.36
5,T5,100102.92,1004849.55
6,T6,104162.21,1001061.70
...

The random layout looks like this:

farm = foxes.WindFarm()
foxes.input.farm_layout.add_from_file(
    farm, "test_farm_67.csv", turbine_models=[], verbosity=0
)
foxes.output.FarmLayoutOutput(farm).get_figure()
plt.show()

Ambient states data

WRF-Timeseries-4464.csv.gz

This data represents a timeseries with 4464 entries as obtained by the mesoscale simulation software WRF at a single horizontal point with 8 different heights:

Time,WS-50,WS-75,...,WS-500,WD-50,WD-75,...,WD-500,TKE-50,TKE-75,...,TKE-500,RHO
2009-01-01 00:00:00,7.37214,7.42685,...,1.28838
...
2009-01-31 23:50:00,10.27767,10.36368,...,1.30095

At 100 m height the wind distribution looks like this:

states = foxes.input.states.MultiHeightTimeseries(
    data_source="WRF-Timeseries-4464.csv.gz",
    output_vars=[FV.WS, FV.WD, FV.TI, FV.RHO],
    heights=[50, 75, 90, 100, 150, 200, 250, 500],
    fixed_vars={FV.TI: 0.05},
)

o = foxes.output.StatesRosePlotOutput(states, point=[0., 0., 100.])
fig = o.get_figure(16, FV.AMB_WS, [0, 3.5, 6, 10, 15, 20])
iplot(fig)