|
3 | 3 | import pytest |
4 | 4 | from pandas.util.testing import assert_series_equal |
5 | 5 |
|
6 | | -from windpowerlib.wake_losses import reduce_wind_speed, get_wind_efficiency_curve, display_wind_efficiency_curves |
| 6 | +from windpowerlib.wake_losses import reduce_wind_speed |
7 | 7 | import windpowerlib.wind_turbine as wt |
8 | 8 |
|
9 | 9 | class TestWakeLosses: |
10 | 10 |
|
11 | 11 | def test_reduce_wind_speed(self): |
12 | | - parameters = {'wind_speed': pd.Series(np.arange(0, 26, 1.0)), 'wind_efficiency_curve_name': 'dena_mean'} |
| 12 | + parameters = {'wind_speed': pd.Series(np.arange(0, 26, 1.0)), |
| 13 | + 'wind_efficiency_curve_name': 'dena_mean'} |
13 | 14 | wind_speed_exp = pd.Series([ |
14 | | - 0.0, 0.9949534234119396, 1.9897327884892086, 2.9843374545454546, 3.807636264984227, 4.714931284760845, |
15 | | - 5.642507531914893, 6.607021108049704, 7.592423167192429, 8.59498170212766, 9.606135658475111, |
16 | | - 10.619828799086758, 11.641291957894737, 12.674012890137966, 13.709490666666666, 14.742508260567297, |
17 | | - 15.773293013157893, 16.794615009724474, 17.817683032858028, 18.85294996704484, 19.86509539493748, |
18 | | - 20.858807854510186, 21.854369681134507, 22.850700350710902, 23.85962037735849, 24.958125]) |
| 15 | + 0.0, 0.9949534234119396, 1.9897327884892086, 2.9843374545454546, |
| 16 | + 3.807636264984227, 4.714931284760845, 5.642507531914893, |
| 17 | + 6.607021108049704, 7.592423167192429, 8.59498170212766, |
| 18 | + 9.606135658475111, 10.619828799086758, 11.641291957894737, |
| 19 | + 12.674012890137966, 13.709490666666666, 14.742508260567297, |
| 20 | + 15.773293013157893, 16.794615009724474, 17.817683032858028, |
| 21 | + 18.85294996704484, 19.86509539493748, 20.858807854510186, |
| 22 | + 21.854369681134507, 22.850700350710902, 23.85962037735849, |
| 23 | + 24.958125]) |
19 | 24 | assert_series_equal(reduce_wind_speed(**parameters), wind_speed_exp) |
20 | 25 |
|
21 | 26 | # Raise ValueError - misspelling |
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