Updated script that can be controled by Nodejs web app

lib/python3.13/site-packages/pandas/tests/window/test_online.py

@@ -0,0 +1,103 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Series,
+)
+import pandas._testing as tm
+
+pytestmark = pytest.mark.single_cpu
+
+pytest.importorskip("numba")
+
+
+@pytest.mark.filterwarnings("ignore")
+# Filter warnings when parallel=True and the function can't be parallelized by Numba
+class TestEWM:
+    def test_invalid_update(self):
+        df = DataFrame({"a": range(5), "b": range(5)})
+        online_ewm = df.head(2).ewm(0.5).online()
+        with pytest.raises(
+            ValueError,
+            match="Must call mean with update=None first before passing update",
+        ):
+            online_ewm.mean(update=df.head(1))
+
+    @pytest.mark.slow
+    @pytest.mark.parametrize(
+        "obj", [DataFrame({"a": range(5), "b": range(5)}), Series(range(5), name="foo")]
+    )
+    def test_online_vs_non_online_mean(
+        self, obj, nogil, parallel, nopython, adjust, ignore_na
+    ):
+        expected = obj.ewm(0.5, adjust=adjust, ignore_na=ignore_na).mean()
+        engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
+
+        online_ewm = (
+            obj.head(2)
+            .ewm(0.5, adjust=adjust, ignore_na=ignore_na)
+            .online(engine_kwargs=engine_kwargs)
+        )
+        # Test resetting once
+        for _ in range(2):
+            result = online_ewm.mean()
+            tm.assert_equal(result, expected.head(2))
+
+            result = online_ewm.mean(update=obj.tail(3))
+            tm.assert_equal(result, expected.tail(3))
+
+            online_ewm.reset()
+
+    @pytest.mark.xfail(raises=NotImplementedError)
+    @pytest.mark.parametrize(
+        "obj", [DataFrame({"a": range(5), "b": range(5)}), Series(range(5), name="foo")]
+    )
+    def test_update_times_mean(
+        self, obj, nogil, parallel, nopython, adjust, ignore_na, halflife_with_times
+    ):
+        times = Series(
+            np.array(
+                ["2020-01-01", "2020-01-05", "2020-01-07", "2020-01-17", "2020-01-21"],
+                dtype="datetime64[ns]",
+            )
+        )
+        expected = obj.ewm(
+            0.5,
+            adjust=adjust,
+            ignore_na=ignore_na,
+            times=times,
+            halflife=halflife_with_times,
+        ).mean()
+
+        engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
+        online_ewm = (
+            obj.head(2)
+            .ewm(
+                0.5,
+                adjust=adjust,
+                ignore_na=ignore_na,
+                times=times.head(2),
+                halflife=halflife_with_times,
+            )
+            .online(engine_kwargs=engine_kwargs)
+        )
+        # Test resetting once
+        for _ in range(2):
+            result = online_ewm.mean()
+            tm.assert_equal(result, expected.head(2))
+
+            result = online_ewm.mean(update=obj.tail(3), update_times=times.tail(3))
+            tm.assert_equal(result, expected.tail(3))
+
+            online_ewm.reset()
+
+    @pytest.mark.parametrize("method", ["aggregate", "std", "corr", "cov", "var"])
+    def test_ewm_notimplementederror_raises(self, method):
+        ser = Series(range(10))
+        kwargs = {}
+        if method == "aggregate":
+            kwargs["func"] = lambda x: x
+
+        with pytest.raises(NotImplementedError, match=".* is not implemented."):
+            getattr(ser.ewm(1).online(), method)(**kwargs)