diff --git a/xrft/tests/test_xrft.py b/xrft/tests/test_xrft.py
index f63144c9..ae248deb 100644
--- a/xrft/tests/test_xrft.py
+++ b/xrft/tests/test_xrft.py
@@ -1344,3 +1344,21 @@ def test_nondim_coords():
         xrft.power_spectrum(da)
 
     xrft.power_spectrum(da, dim=["time", "y"])
+
+
+def test_non_numerical_or_datetime_coords():
+    """Error should be raised if there are non-numerical or non-datetime coordinate"""
+    da = xr.DataArray(
+        np.random.rand(2, 5, 3),
+        dims=["time", "x", "y"],
+        coords={
+            "time": np.array(["2019-04-18", "2019-04-19"], dtype="datetime64"),
+            "x": range(5),
+            "y": ["a", "b", "c"],
+        },
+    )
+
+    with pytest.raises(ValueError):
+        xrft.power_spectrum(da)
+
+    xrft.power_spectrum(da, dim=["time", "x"])
diff --git a/xrft/xrft.py b/xrft/xrft.py
index 30d738e8..9ddb3c19 100644
--- a/xrft/xrft.py
+++ b/xrft/xrft.py
@@ -15,7 +15,7 @@
 import scipy.linalg as spl
 
 from .detrend import detrend as _detrend
-
+from pandas.api.types import is_numeric_dtype, is_datetime64_any_dtype
 
 __all__ = [
     "fft",
@@ -230,9 +230,9 @@ def _lag_coord(coord):
         decoded_time = cftime.date2num(lag, ref_units, calendar)
         return decoded_time
     elif pd.api.types.is_datetime64_dtype(v0):
-        return lag.astype("timedelta64[s]").astype("f8").data
+        return lag.astype("timedelta64[s]").astype("f8")
     else:
-        return lag.data
+        return lag
 
 
 def dft(
@@ -330,7 +330,6 @@ def fft(
     daft : `xarray.DataArray`
         The output of the Fourier transformation, with appropriate dimensions.
     """
-
     if dim is None:
         dim = list(da.dims)
     else:
@@ -352,6 +351,20 @@ def fft(
                 real_dim
             ]  # real dim has to be moved or added at the end !
 
+    if not np.all(
+        [
+            (
+                is_numeric_dtype(da.coords[d])
+                or is_datetime64_any_dtype(da.coords[d])
+                or bool(getattr(da.coords[d][0].item(), "calendar", False))
+            )
+            for d in dim
+        ]
+    ):  # checking if coodinates are numerical or datetime
+        raise ValueError(
+            "All transformed dimensions coordinates must be numerical or datetime."
+        )
+
     if chunks_to_segments:
         da = _stack_chunks(da, dim)
 
@@ -452,7 +465,7 @@ def fft(
                 dims=up_dim,
                 coords={up_dim: newcoords[up_dim]},
             )  # taking advantage of xarray broadcasting and ordered coordinates
-            daft[up_dim].attrs.update({"direct_lag": lag.obj})
+            daft[up_dim].attrs.update({"direct_lag": lag})
 
     if true_amplitude:
         daft = daft * np.prod(delta_x)
@@ -520,7 +533,6 @@ def ifft(
     da : `xarray.DataArray`
         The output of the Inverse Fourier transformation, with appropriate dimensions.
     """
-
     if dim is None:
         dim = list(daft.dims)
     else:
@@ -540,6 +552,21 @@ def ifft(
             dim = [d for d in dim if d != real_dim] + [
                 real_dim
             ]  # real dim has to be moved or added at the end !
+
+    if not np.all(
+        [
+            (
+                is_numeric_dtype(daft.coords[d])
+                or is_datetime64_any_dtype(daft.coords[d])
+                or bool(getattr(daft.coords[d][0].item(), "calendar", False))
+            )
+            for d in dim
+        ]
+    ):  # checking if coodinates are numerical or datetime
+        raise ValueError(
+            "All transformed dimensions coordinates must be numerical or datetime."
+        )
+
     if lag is None:
         lag = [daft[d].attrs.get("direct_lag", 0.0) for d in dim]
         msg = "Default ifft's behaviour (lag=None) changed! Default value of lag was zero (centered output coordinates) and is now set to transformed coordinate's attribute: 'direct_lag'."
@@ -898,8 +925,10 @@ def cross_phase(da1, da2, dim=None, true_phase=True, **kwargs):
     kwargs : dict : see xrft.fft for argument list
     """
 
-    cp = xr.ufuncs.angle(
-        cross_spectrum(da1, da2, dim=dim, true_phase=true_phase, **kwargs)
+    cp = xr.apply_ufunc(
+        np.angle,
+        cross_spectrum(da1, da2, dim=dim, true_phase=true_phase, **kwargs),
+        dask="allowed",
     )
 
     if da1.name and da2.name: