Fix evaluation of distribution methods on scalars (#39)

* fix dist method eval on scalars

* update changelog

* add helpful error for ndarray with ndim>=2

Author: OriolAbril

docs/source/changelog.md

@@ -7,6 +7,7 @@
 
 ### Maintenance and fixes
 * Fix issue in `linalg.svd` for non-square matrices {pull}`37`
+* Fix evaluation of distribution methods (e.g. `.pdf`) on scalars {pull}`38` and {pull}`39`
 
 ### Documentation
 * Ported NumPy tutorial on linear algebra with multidimensional arrays {pull}`37`

src/xarray_einstats/stats.py

@@ -91,7 +91,15 @@ def _asdataarray(x_or_q, dim_name):
     """
     if isinstance(x_or_q, xr.DataArray):
         return x_or_q
-    return xr.DataArray(np.asarray(x_or_q), dims=[dim_name], coords={dim_name: np.asarray(x_or_q)})
+    x_or_q_ary = np.asarray(x_or_q)
+    if x_or_q_ary.ndim == 0:
+        return xr.DataArray(x_or_q_ary)
+    if x_or_q_ary.ndim == 1:
+        return xr.DataArray(x_or_q_ary, dims=[dim_name], coords={dim_name: np.asarray(x_or_q)})
+    raise ValueError(
+        "To evaluate distribution methods on data with >=2 dims,"
+        " the input needs to be a xarray.DataArray"
+    )
 
 
 def _wrap_method(method):

tests/test_stats.py

@@ -32,6 +32,23 @@ def data():
 
 @pytest.mark.parametrize("wrapper", ("continuous", "discrete"))
 class TestRvWrappers:
+    @pytest.mark.parametrize(
+        "method", ("pxf", "logpxf", "cdf", "logcdf", "sf", "logsf", "ppf", "isf")
+    )
+    def test_eval_methods_scalar(self, data, wrapper, method):
+        if wrapper == "continuous":
+            dist = XrContinuousRV(stats.norm, data["mu"], data["sigma"])
+            if "pxf" in method:
+                method = method.replace("x", "d")
+        else:
+            dist = XrDiscreteRV(stats.poisson, data["mu"], data["sigma"])
+            if "pxf" in method:
+                method = method.replace("x", "m")
+        meth = getattr(dist, method)
+        out = meth(0.9)
+        assert out.ndim == 3
+        assert_dims_not_in_da(out, ["quantile", "point"])
+
     @pytest.mark.parametrize(
         "method", ("pxf", "logpxf", "cdf", "logcdf", "sf", "logsf", "ppf", "isf")
     )