fix(axes): Handle np.ravel and using poorly-initialized arrays

pysindy/utils/axes.py

@@ -346,11 +346,23 @@ def __array_finalize__(self, obj) -> None:
         elif all(
             (
                 isinstance(obj, AxesArray),
+                hasattr(obj, "_ax_map"),
                 not hasattr(self, "_ax_map"),
                 self.shape == obj.shape,
             )
         ):
             self._ax_map = _AxisMapping(obj.axes, obj.ndim)
+        # Using a poorly-initialized AxesArray
+        # Occurs in MaskedArray.ravel, used in some plotting.  MaskedArray views
+        # of AxesArray lose the axes attributes, and then the _ax_map attributes.
+        # See numpy.ma.core:asanyarray
+        elif all(
+            (
+                isinstance(obj, AxesArray),
+                not hasattr(obj, "_ax_map"),
+            )
+        ):
+            self._ax_map = _AxisMapping({"ax_unk": 0}, in_ndim=1)
         # maybe add errors for incompatible views?
 
     def __array_ufunc__(
@@ -418,6 +430,16 @@ def decorator(func):
     return decorator
 
 
+@_implements(np.ravel)
+def ravel(a, order="C"):
+    out = np.ravel(np.asarray(a), order=order)
+    is_1d_already = len(a.shape) == 1
+    if is_1d_already:
+        return AxesArray(out, a.axes)
+    else:
+        return AxesArray(out, {"ax_unk": 0})
+
+
 @_implements(np.ix_)
 def ix_(*args: AxesArray):
     calc = np.ix_(*(np.asarray(arg) for arg in args))

test/utils/test_axes.py

@@ -629,6 +629,27 @@ def test_tensordot_list_axes():
     assert_array_equal(result, super_result)
 
 
+def test_ravel_1d():
+    arr = AxesArray(np.array([1, 2]), axes={"ax_a": 0})
+    result = np.ravel(arr)
+    assert_array_equal(result, arr)
+    assert result.axes == arr.axes
+
+
+def test_ravel_nd():
+    arr = AxesArray(np.array([[1, 2], [3, 4]]), axes={"ax_a": 0, "ax_b": 1})
+    result = np.ravel(arr)
+    expected = np.ravel(np.asarray(arr))
+    assert_array_equal(result, expected)
+    assert result.axes == {"ax_unk": 0}
+
+
+def test_ma_ravel():
+    arr = AxesArray(np.array([1, 2]), axes={"ax_a": 0})
+    marr = np.ma.MaskedArray(arr)
+    np.ma.ravel(marr)
+
+
 @pytest.mark.skip
 def test_einsum_implicit():
     ...