Add support for non-ndarrays as Hypercubes

That way we can use CubeMath functions on non-ndarrays, for example.

python/pjrmi/__init__.py

@@ -3541,12 +3541,20 @@ def _format_by_class(self, klass, value,
                         self._format_int32(self._get_object_id(value)))
 
             elif (klass._type_id == self._com_deshaw_hypercube_Hypercube._type_id and
-                  type(value) is numpy.ndarray):
+                  (type(value) is numpy.ndarray or hasattr(value, '__iter__'))):
+                # Using asarray() will ensure that we share the same semantics
+                # as how numpy would convert a value to an ndarray. However,
+                # this might differ from how PJRmi handles integer values (i.e.
+                # everything winds up being a long).
+                arr = numpy.asarray(value)
                 return (self._ARGUMENT_VALUE +
                         self._format_int32(klass._type_id) +
-                        self._format_by_class(self._L_java_lang_long, value.shape) +
+                        self._format_by_class(self._L_java_lang_long, arr.shape) +
                         self._format_int32(1) + # num arrays to follow
-                        self._format_by_class(self._java_lang_Object, value.flatten()))
+                        self._format_by_class(
+                            self._java_lang_Object,
+                            arr.flatten() if len(arr.shape) > 1 else arr)
+                        )
 
             elif isinstance(value, JavaMethod) and value._can_format_as(klass):
                 return self._format_method_as(value, klass)

python/tests/pjrmi_tests.py

@@ -1354,6 +1354,13 @@ def test_cubemath(self):
         CubeMath                 = get_pjrmi().class_for_name('com.deshaw.hypercube.CubeMath')
         IllegalArgumentException = get_pjrmi().class_for_name('java.lang.IllegalArgumentException')
 
+        # Ensure that calling sum() and copy() for non-ndarrays works. This
+        # tests marshalling non-ndarrays as Hypercubes.
+        l22 = [[0,1],
+               [2,3]]
+        self.assertEqual(CubeMath.sum(range(10)), 45)
+        self.assertTrue(numpy.all(CubeMath.copy(l22) == numpy.asarray(l22)))
+
         # Create an ndarray to work on. We futz with the values a little so that
         # floating point noise doesn't cause some of the comparisons (e.g. the
         # trig ones) to fail.
@@ -1387,8 +1394,8 @@ def test_cubemath(self):
         self.assertTrue(numpy.all((dac / dac) == (nda / nda)))
 
         # We get floating point noise for some of the tests in the below since
-        # CubeMath and numpy work slightly differently and we get floating point
-        # noise. Compare against an epsilon value in order to handle this.
+        # CubeMath and numpy work slightly differently. Compare against an
+        # epsilon value in order to handle this.
         EPS = 1e-15
 
         # Hyperbolic and trig