BUG: Fix interpolation with mixed types

When given a DataArray and a numpy array, neither with units, a Quantity
would be passed into the core calculation, triggering an exception on
`apply_along_axis`. Instead, handle trying to convert and drop units on
a "dimensionless" Quantity, since that's what we get for a DataArray
with no units.

src/metpy/interpolate/one_dimension.py

@@ -239,4 +239,6 @@ def _strip_matching_units(*args):
     if all(hasattr(arr, 'units') for arr in args):
         return [arr.to(args[0].units).magnitude for arr in args]
     else:
-        return args
+        # Handle the case where we get mixed 'dimensionless' and bare array. This happens e.g.
+        # when you pass in a DataArray with no units for one arg.
+        return [arr.m_as('dimensionless') if hasattr(arr, 'units') else arr for arr in args]

tests/interpolate/test_one_dimension.py

@@ -5,6 +5,7 @@
 
 import numpy as np
 import pytest
+import xarray as xr
 
 from metpy.interpolate import interpolate_1d, interpolate_nans_1d, log_interpolate_1d
 from metpy.testing import assert_array_almost_equal
@@ -49,6 +50,15 @@ def test_log_interpolate_1d():
     assert_array_almost_equal(y_interp, y_interp_truth, 7)
 
 
+def test_log_interpolate_1d_mixed():
+    x_log = xr.DataArray([1e3, 1e4, 1e5, 1e6])
+    y_log = np.log(x_log) * 2 + 3
+    x_interp = np.array([5e3, 5e4, 5e5])
+    y_interp_truth = np.array([20.0343863828, 24.6395565688, 29.2447267548])
+    y_interp = log_interpolate_1d(x_interp, x_log, y_log)
+    assert_array_almost_equal(y_interp, y_interp_truth, 7)
+
+
 def test_log_interpolate_1d_units():
     """Test interpolating with log x-scale with units."""
     x_log = np.array([1e3, 1e4, 1e5, 1e6]) * units.hPa