Address comments

floris/wind_data.py

@@ -145,6 +145,26 @@ 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
+        # increasing and evenly spaced
+        if len(wind_directions) > 1:
+            # Check monotonically increasing
+            if not np.all(np.diff(wind_directions) > 0):
+                raise ValueError("wind_directions must be monotonically increasing")
+
+            # Check evenly spaced
+            if not np.allclose(np.diff(wind_directions), wind_directions[1] - wind_directions[0]):
+                raise ValueError("wind_directions must be evenly spaced")
+
+        if len(wind_speeds) > 1:
+            # Check monotonically increasing
+            if not np.all(np.diff(wind_speeds) > 0):
+                raise ValueError("wind_speeds must be monotonically increasing")
+
+            # Check evenly spaced
+            if not np.allclose(np.diff(wind_speeds), wind_speeds[1] - wind_speeds[0]):
+                raise ValueError("wind_speeds must be evenly spaced")
+
         # Save the wind speeds and directions
         self.wind_directions = wind_directions
         self.wind_speeds = wind_speeds
@@ -469,12 +489,28 @@ def upsample(self, wd_step=None, ws_step=None, method="linear", inplace=False):
         if wd_step is None:
             wd_step = wd_step_current
 
+        # Make sure upsampling is appropriate
+        if wd_step > wd_step_current:
+            raise ValueError(
+                f"Provided wd_step ({wd_step}) is larger than the current "
+                f" wind direction step size.  ({wd_step_current} degrees)"
+                " Use the downsample method."
+            )
+
+        if ws_step > ws_step_current:
+            raise ValueError(
+                f"Provided ws_step ({ws_step}) is larger than "
+                f"the current wind speed step size.  ({ws_step_current} m/s)"
+                " Use the downsample method."
+            )
+
         # Identify the covered range of wind directions
         wd_range_min_current = np.min(self.wind_directions) - wd_step_current / 2.0
         wd_range_max_current = np.max(self.wind_directions) + wd_step_current / 2.0
 
         # Look for unlikely case where for example wind directions are 8, 28, ... 358
         if wd_range_max_current > 360:
+            # TODO: Handle this case without an error
             raise ValueError(
                 "Cannot upsample wind rose for case when wind directions are defined"
                 " such that 0 degrees is included by bins to the left of 0 degrees. "
@@ -1041,6 +1077,39 @@ def __init__(
         if not isinstance(turbulence_intensities, np.ndarray):
             raise TypeError("turbulence_intensities must be a NumPy array")
 
+        # Confirm that both wind_directions and wind_speeds
+        # and turbulence intensities are monotonically
+        # increasing and evenly spaced
+        if len(wind_directions) > 1:
+            # Check monotonically increasing
+            if not np.all(np.diff(wind_directions) > 0):
+                raise ValueError("wind_directions must be monotonically increasing")
+
+            # Check evenly spaced
+            if not np.allclose(np.diff(wind_directions), wind_directions[1] - wind_directions[0]):
+                raise ValueError("wind_directions must be evenly spaced")
+
+        if len(wind_speeds) > 1:
+            # Check monotonically increasing
+            if not np.all(np.diff(wind_speeds) > 0):
+                raise ValueError("wind_speeds must be monotonically increasing")
+
+            # Check evenly spaced
+            if not np.allclose(np.diff(wind_speeds), wind_speeds[1] - wind_speeds[0]):
+                raise ValueError("wind_speeds must be evenly spaced")
+
+        if len(turbulence_intensities) > 1:
+            # Check monotonically increasing
+            if not np.all(np.diff(turbulence_intensities) > 0):
+                raise ValueError("turbulence_intensities must be monotonically increasing")
+
+            # Check evenly spaced
+            if not np.allclose(
+                np.diff(turbulence_intensities),
+                turbulence_intensities[1] - turbulence_intensities[0],
+            ):
+                raise ValueError("turbulence_intensities must be evenly spaced")
+
         # Save the wind speeds and directions
         self.wind_directions = wind_directions
         self.wind_speeds = wind_speeds

tests/wind_data_integration_test.py

@@ -69,6 +69,18 @@ def test_wind_rose_init():
     The wind directions and wind speeds can have any length, but the frequency
     array must have shape (n wind directions, n wind speeds)
     """
+
+    # Test that passing in unevenly spaced wind directions or wind speeds raises an error
+    with pytest.raises(ValueError):
+        WindRose(np.array([270, 280, 290, 310]), np.array([6, 7]), 0.06)
+
+    with pytest.raises(ValueError):
+        WindRose(np.array([270, 280, 290]), np.array([6, 7, 10]), 0.06)
+
+    # Test that passing in decreasing wind directions raises an error
+    with pytest.raises(ValueError):
+        WindRose(np.array([290, 280, 270]), np.array([6, 7]), 0.06)
+
     wind_directions = np.array([270, 280, 290])
     wind_speeds = np.array([6, 7])