Fix the turbine.power function unit test

[paulf81]

Fix the power unit test

The unit test of the power() function in turbine.py, test_power() in turbine_unit_test.py contained several errors which were masked by the fact that the tolerance value passed assert_allclose() function was very large. This pull request fixes these issues. Specifically:

• Previously, the cp_interp function was passed to power, yielding a cp value multiplied by air_density. However, this is not expected. get_turbine_powers() in floris_interface.py passes self.floris.farm.turbine_power_interps, which are in W. Correcting this yields a p, and a power_truth both in W
• The "truth" calculation of p was missing the air density term
• The new test compares the successfully with a very small, default tolerance.
• Added to the unit test are checks that in high wind speeds the power is 5MW (since the test case is the reference 5MW turbine) and at near 0 wind speeds, power should be close to 0
• Both the test_function, and the doc-string for power implied that rotor_effective_velocities should be 5D (including the last 2 dimensions for grid). However, since these are rotor-averaged quantities they should be only 3D, and this pull request fixes that in the test, and in the doc-string of power() (it was not necessary to change the code)
• Finally, a block of commented out code in turbine_unit_test.py was deleted

Impacted areas of the software

turbine.py
turbine_unit_test.py

[rafmudaf]

Well done @paulf81! Here are a few comments to expand the tests and add a little more clarity. Overall, this is great, thanks for fixing this.

[paulf81]

Improvements made in commit: "Add github revisions":

1. Include multiple turbine test
2. Include test with grid dimension
3. Include comment that test turbine is NREL 5MW
4. Lower tolerance for 0 power test to 1 W

[rafmudaf]

I changed the array comparisons here to assert np.allclose(..., ...) instead of np.testing.assert_allclose(..., ...) because the defaults for relative and absolute tolerance are different:

• np.allclose: rtol=1e-05, atol=1e-08
• np.testing.assert_allclose: rtol=1e-07, atol=0

This means np.allclose tests for 5 significant digits until numbers very close to zero and then it checks for the absolute tolerance. On the other hand, np.testing.assert_allclose always tests for 7 significant digits. In this particular case, it's the rtol that fails in the power calculation, so switching comparison methods also relaxes the relative tolerance.