Add test of nans in inputs to wind data objects

floris/wind_data.py

@@ -174,7 +174,14 @@ def __init__(
         if not isinstance(wind_speeds, np.ndarray):
             raise TypeError("wind_speeds must be a NumPy array")
 
-        # Confirm that both wind_directions and wind_speeds are monitonically
+        # Confirm that none of wind_directions or wind_speeds contain NaN values
+        if np.isnan(wind_directions).any():
+            raise ValueError("wind_directions must not contain NaNs")
+
+        if np.isnan(wind_speeds).any():
+            raise ValueError("wind_speeds must not contain NaNs")
+
+        # Confirm that both wind_directions and wind_speeds are monotonically
         # increasing and evenly spaced
         if len(wind_directions) > 1:
             # Check monotonically increasing
@@ -1125,6 +1132,14 @@ def __init__(
         if not isinstance(turbulence_intensities, np.ndarray):
             raise TypeError("turbulence_intensities must be a NumPy array")
 
+        # Check that wind_directions, wind_speeds, and turbulence_intensities do not contain NaNs
+        if np.isnan(wind_directions).any():
+            raise ValueError("wind_directions must not contain NaNs")
+        if np.isnan(wind_speeds).any():
+            raise ValueError("wind_speeds must not contain NaNs")
+        if np.isnan(turbulence_intensities).any():
+            raise ValueError("turbulence_intensities must not contain NaNs")
+
         # Confirm that both wind_directions and wind_speeds
         # and turbulence intensities are monotonically
         # increasing and evenly spaced

tests/wind_data_integration_test.py

@@ -37,6 +37,21 @@ def test_time_series_instantiation():
     with pytest.raises(TypeError):
         TimeSeries(wind_directions, wind_speeds)
 
+    # Test instantiation with NaN values
+    with pytest.raises(ValueError, match="contain NaNs"):
+       TimeSeries(np.array([270, 280, np.nan]), wind_speeds, turbulence_intensities=0.06)
+    with pytest.raises(ValueError, match="contain NaNs"):
+        TimeSeries(wind_directions, np.array([np.nan, 5, 5,]), turbulence_intensities=0.06)
+    with pytest.raises(ValueError, match="contain NaNs"):
+        TimeSeries(wind_directions, wind_speeds, np.array([0.06, np.nan, 0.06]))
+    with pytest.raises(ValueError, match="contain NaNs"):
+        TimeSeries(
+            wind_directions,
+            wind_speeds,
+            0.06*np.ones_like(wind_speeds),
+            np.array([1.0, np.nan, 3.0])
+        )
+
     # Test that passing a float TI returns a list of length matched to wind directions
     time_series = TimeSeries(wind_directions, wind_speeds, turbulence_intensities=0.06)
     np.testing.assert_allclose(time_series.turbulence_intensities, [0.06, 0.06, 0.06])
@@ -81,6 +96,12 @@ def test_wind_rose_init():
     with pytest.raises(ValueError):
         WindRose(np.array([290, 280, 270]), np.array([6, 7]), 0.06)
 
+    # Test that passing in a NaN value raises an error
+    with pytest.raises(ValueError, match="contain NaNs"):
+        WindRose(np.array([270, 280, np.nan]), np.array([6, 7]), 0.06)
+    with pytest.raises(ValueError, match="contain NaNs"):
+        WindRose(np.array([270, 280, 290]), np.array([np.nan, 7]), 0.06)
+
     wind_directions = np.array([270, 280, 290])
     wind_speeds = np.array([6, 7])
 
@@ -750,6 +771,13 @@ def test_wind_ti_rose_init():
     with pytest.raises(ValueError):
         WindTIRose(wind_directions, wind_speeds, turbulence_intensities, np.ones((3, 3, 3)))
 
+    # Test that passing in a NaN value raises an error
+    with pytest.raises(ValueError, match="contain NaNs"):
+        WindTIRose(np.array([270, 280, np.nan, 300]), wind_speeds, turbulence_intensities)
+    with pytest.raises(ValueError, match="contain NaNs"):
+        WindTIRose(wind_directions, np.array([6, np.nan, 8]), turbulence_intensities)
+    with pytest.raises(ValueError, match="contain NaNs"):
+        WindTIRose(wind_directions, wind_speeds, np.array([0.05, np.nan]))
 
 def test_wind_ti_rose_grid():
     wind_directions = np.array([270, 280, 290, 300])