Merge pull request scipy#22158 from lesteve/dierckx-less-strict-input-check

MAINT: accept ndarray subclasses in interpolate._dierckx

Author: ev-br

scipy/interpolate/src/_dierckxmodule.cc

@@ -15,7 +15,7 @@
 static int
 check_array(PyObject *obj, npy_intp ndim, int typenum) {
 
-    int cond = (PyArray_CheckExact(obj) &&
+    int cond = (PyArray_Check(obj) &&
                (PyArray_TYPE((PyArrayObject*)obj) == typenum) &&
                (PyArray_NDIM((PyArrayObject*)obj) == ndim) &&
                 PyArray_CHKFLAGS((PyArrayObject*)obj, NPY_ARRAY_ALIGNED | NPY_ARRAY_C_CONTIGUOUS)

scipy/interpolate/tests/test_bsplines.py

@@ -647,6 +647,28 @@ def worker_fn(_, b):
         _run_concurrent_barrier(10, worker_fn, b)
 
 
+    def test_memmap(self, tmpdir):
+        # Make sure that memmaps can be used as t and c atrributes after the
+        # spline has been constructed. This is similar to what happens in a
+        # scikit-learn context, where joblib can create read-only memmap to
+        # share objects between workers. For more details, see
+        # https://github.com/scipy/scipy/issues/22143
+        b = _make_random_spline()
+        xx = np.linspace(0, 1, 10)
+
+        expected = b(xx)
+
+        t_mm = np.memmap(
+            str(tmpdir.join('t.dat')), mode='w+', dtype=b.t.dtype, shape=b.t.shape)
+        t_mm[:] = b.t
+        c_mm = np.memmap(
+            str(tmpdir.join('c.dat')), mode='w+', dtype=b.c.dtype, shape=b.c.shape)
+        c_mm[:] = b.c
+        b.t = t_mm
+        b.c = c_mm
+
+        xp_assert_close(b(xx), expected)
+
 class TestInsert:
 
     @pytest.mark.parametrize('xval', [0.0, 1.0, 2.5, 4, 6.5, 7.0])