MAINT: Don't use PyInstance_Check to detect object type

That function does not exist in python3, causing differing
behavior in python2 vs python3.

This will slightly affect dtype detection when creating arrays from
user-supplied object, in cases where an object array might be produced.

3 files changed, 7 insertions, 44 deletions

diff --git a/numpy/core/src/multiarray/common.c b/numpy/core/src/multiarray/common.c
index 1948b8b61..5db6b85bd 100644
--- a/numpy/core/src/multiarray/common.c
+++ b/numpy/core/src/multiarray/common.c
@@ -128,30 +128,6 @@ _array_find_python_scalar_type(PyObject *op)
     return NULL;
 }
 
-#if !defined(NPY_PY3K)
-static PyArray_Descr *
-_use_default_type(PyObject *op)
-{
-    int typenum, l;
-    PyObject *type;
-
-    typenum = -1;
-    l = 0;
-    type = (PyObject *)Py_TYPE(op);
-    while (l < NPY_NUMUSERTYPES) {
-        if (type == (PyObject *)(userdescrs[l]->typeobj)) {
-            typenum = l + NPY_USERDEF;
-            break;
-        }
-        l++;
-    }
-    if (typenum == -1) {
-        typenum = NPY_OBJECT;
-    }
-    return PyArray_DescrFromType(typenum);
-}
-#endif
-
 /*
  * These constants are used to signal that the recursive dtype determination in
  * PyArray_DTypeFromObject encountered a string type, and that the recursive
@@ -490,19 +466,6 @@ PyArray_DTypeFromObjectHelper(PyObject *obj, int maxdims,
         }
     }
 
-    /* Not exactly sure what this is about... */
-#if !defined(NPY_PY3K)
-    if (PyInstance_Check(obj)) {
-        dtype = _use_default_type(obj);
-        if (dtype == NULL) {
-            goto fail;
-        }
-        else {
-            goto promote_types;
-        }
-    }
-#endif
-
     /*
      * If we reached the maximum recursion depth without hitting one
      * of the above cases, the output dtype should be OBJECT
@@ -519,8 +482,9 @@ PyArray_DTypeFromObjectHelper(PyObject *obj, int maxdims,
     }
 
     /*
-     * fails if convertable to list but no len is defined which some libraries
-     * require to get object arrays
+     * If we get here, it may be a sequence. However some libraries define
+     * sequence-like classes but want them to be treated as objects, and they
+     * expect numpy to treat it as an object if __len__ is not defined.
      */
     size = PySequence_Size(obj);
     if (size < 0) {
diff --git a/numpy/core/src/multiarray/ctors.c b/numpy/core/src/multiarray/ctors.c
index 785b3073a..79c2b16b1 100644
--- a/numpy/core/src/multiarray/ctors.c
+++ b/numpy/core/src/multiarray/ctors.c
@@ -695,11 +695,6 @@ discover_dimensions(PyObject *obj, int *maxndim, npy_intp *d, int check_it,
 
     /* obj is not a Sequence */
     if (!PySequence_Check(obj) ||
-#if defined(NPY_PY3K)
-        /* FIXME: XXX -- what is the correct thing to do here? */
-#else
-            PyInstance_Check(obj) ||
-#endif
             PySequence_Length(obj) < 0) {
         *maxndim = 0;
         PyErr_Clear();
diff --git a/numpy/core/tests/test_dtype.py b/numpy/core/tests/test_dtype.py
index 6d898eaa1..a6cb66b7d 100644
--- a/numpy/core/tests/test_dtype.py
+++ b/numpy/core/tests/test_dtype.py
@@ -590,6 +590,10 @@ def test_rational_dtype():
     a = np.array([1111], dtype=rational).astype
     assert_raises(OverflowError, a, 'int8')
 
+    # test that dtype detection finds user-defined types
+    x = rational(1)
+    assert_equal(np.array([x,x]).dtype, np.dtype(rational))
+
 
 if __name__ == "__main__":
     run_module_suite()