Test with numpy 1.*

.github/workflows/ci.yml

@@ -39,6 +39,7 @@ jobs:
           - py310
           - py311
           - py312
+          - np1x
     steps:
       - name: Checkout branch
         uses: actions/checkout@v4

pixi.toml

@@ -66,12 +66,16 @@ python = "3.10.*"
 python = "3.11.*"
 [feature.py312.dependencies]
 python = "3.12.*"
+[feature.np1x.dependencies]
+python = "3.11.*"
+numpy = "1.*"
 
 [environments]
 default = ["test", "lint"]
 py310 = ["py310", "test"]
 py311 = ["py311", "test"]
 py312 = ["py312", "test"]
+np1x = ["np1x", "test"]
 docs = ["docs"]
 build = ["build"]
 lint = { features = ["lint"], no-default-feature = true }

tests/ndonnx/test_core.py

@@ -13,7 +13,7 @@
 import ndonnx.additional as nda
 from ndonnx import _data_types as dtypes
 
-from .utils import run
+from .utils import get_numpy_array_api_namespace, run
 
 
 def numpy_to_graph_input(arr, eager=False):
@@ -365,8 +365,9 @@ def test_matrix_transpose():
     b = ndx.matrix_transpose(a)
 
     model = ndx.build({"a": a}, {"b": b})
+    npx = get_numpy_array_api_namespace()
     np.testing.assert_equal(
-        np.matrix_transpose(np.reshape(np.arange(3 * 2 * 3), (3, 2, 3))),
+        npx.matrix_transpose(npx.reshape(npx.arange(3 * 2 * 3), (3, 2, 3))),
         run(model, {"a": np.arange(3 * 2 * 3, dtype=np.int64).reshape(3, 2, 3)})["b"],
     )
 
@@ -376,8 +377,11 @@ def test_matrix_transpose_attribute():
     b = a.mT
 
     model = ndx.build({"a": a}, {"b": b})
+    npx = get_numpy_array_api_namespace()
+    expected = npx.reshape(npx.arange(3 * 2 * 3), (3, 2, 3)).mT
+
     np.testing.assert_equal(
-        np.reshape(np.arange(3 * 2 * 3), (3, 2, 3)).mT,
+        expected,
         run(model, {"a": np.arange(3 * 2 * 3, dtype=np.int64).reshape(3, 2, 3)})["b"],
     )
 

tests/ndonnx/test_masked.py

@@ -9,7 +9,7 @@
 import ndonnx as ndx
 import ndonnx.additional as nda
 
-from .utils import run
+from .utils import get_numpy_array_api_namespace, run
 
 
 def testfill_null():
@@ -104,7 +104,7 @@ def test_reduce_ops_none_filling(fn_name, default_value):
 )
 def test_unary_none_propagation(fn_name, args, kwargs):
     fn = getattr(ndx, fn_name)
-    np_fn = getattr(np, fn_name)
+    # If testing with NumPy 1.x, we need to get the function from the numpy.array_api
 
     a = ndx.array(shape=(1, 3), dtype=ndx.nfloat32)
     b = fn(a, *args)
@@ -114,7 +114,10 @@ def test_unary_none_propagation(fn_name, args, kwargs):
     inp_a = np.ma.masked_array([[0, -2.0, 3.0]], mask=[[1, 0, 0]], dtype=np.float32)
     ret_b = run(model, {"a": inp_a})["b"]
     missing_a = inp_a.mask
-    expected_b = np_fn(np.ma.filled(inp_a, np.nan), *args, **kwargs)
+    npx = get_numpy_array_api_namespace()
+    np_fn = getattr(npx, fn_name)
+    inp_a = npx.asarray(np.ma.filled(inp_a, np.nan))
+    expected_b = np_fn(inp_a, *args, **kwargs)
     np.testing.assert_almost_equal(
         np.ma.masked_array(expected_b, mask=missing_a),
         ret_b,

tests/ndonnx/utils.py

@@ -61,3 +61,12 @@ def _get_dtypes(
         output_schema[name] = _get_dtype(schema["type_name"], version)
 
     return input_schema, output_schema
+
+
+def get_numpy_array_api_namespace():
+    if np.__version__ < "2":
+        import numpy.array_api as npx
+
+        return npx
+    else:
+        return np