Fix upstream-dev CI, silence warnings (#341)

* Fix upstream-dev CI

Closes #337

* silence warnings

* fix mypy

* Trigger upstream workflow

.github/workflows/upstream-dev-ci.yaml

@@ -10,6 +10,7 @@ on:
     paths:
       - ".github/workflows/upstream-dev-ci.yaml"
       - "ci/upstream-dev-env.yml"
+      - "flox/*"
   schedule:
     - cron: "0 0 * * *" # Daily “At 00:00” UTC
   workflow_dispatch: # allows you to trigger the workflow run manually

flox/xarray.py

@@ -259,7 +259,7 @@ def xarray_reduce(
     for var in maybe_drop:
         maybe_midx = ds._indexes.get(var, None)
         if isinstance(maybe_midx, PandasMultiIndex):
-            idx_coord_names = set(maybe_midx.index.names + [maybe_midx.dim])
+            idx_coord_names = set(tuple(maybe_midx.index.names) + (maybe_midx.dim,))
             idx_other_names = idx_coord_names - set(maybe_drop)
             more_drop.update(idx_other_names)
     maybe_drop.update(more_drop)

tests/test_core.py

@@ -880,7 +880,7 @@ def test_find_cohorts_missing_groups():
 
 @pytest.mark.parametrize("chunksize", [12, 13, 14, 24, 36, 48, 72, 71])
 def test_verify_complex_cohorts(chunksize: int) -> None:
-    time = pd.Series(pd.date_range("2016-01-01", "2018-12-31 23:59", freq="H"))
+    time = pd.Series(pd.date_range("2016-01-01", "2018-12-31 23:59", freq="h"))
     chunks = (chunksize,) * (len(time) // chunksize)
     by = np.array(time.dt.dayofyear.values)
 
@@ -1063,7 +1063,7 @@ def test_empty_bins(func, engine):
 
 
 def test_datetime_binning():
-    time_bins = pd.date_range(start="2010-08-01", end="2010-08-15", freq="24H")
+    time_bins = pd.date_range(start="2010-08-01", end="2010-08-15", freq="24h")
     by = pd.date_range("2010-08-01", "2010-08-15", freq="15min")
 
     (actual,) = _convert_expected_groups_to_index((time_bins,), isbin=(True,), sort=False)
@@ -1125,7 +1125,7 @@ def test_group_by_datetime(engine, method):
     if method == "blockwise":
         return None
 
-    edges = pd.date_range("1999-12-31", "2000-12-31", freq="M").to_series().to_numpy()
+    edges = pd.date_range("1999-12-31", "2000-12-31", freq="ME").to_series().to_numpy()
     actual, _ = groupby_reduce(daskarray, t.to_numpy(), isbin=True, expected_groups=edges, **kwargs)
     expected = data.resample("M").mean().to_numpy()
     assert_equal(expected, actual)
@@ -1520,7 +1520,7 @@ def test_validate_reindex() -> None:
 @requires_dask
 def test_1d_blockwise_sort_optimization():
     # Make sure for resampling problems sorting isn't done.
-    time = pd.Series(pd.date_range("2020-09-01", "2020-12-31 23:59", freq="3H"))
+    time = pd.Series(pd.date_range("2020-09-01", "2020-12-31 23:59", freq="3h"))
     array = dask.array.ones((len(time),), chunks=(224,))
 
     actual, _ = groupby_reduce(array, time.dt.dayofyear.values, method="blockwise", func="count")
@@ -1682,6 +1682,8 @@ def test_multiple_quantiles(q, chunk, func, by_ndim):
     actual, _ = groupby_reduce(array, labels, func=func, finalize_kwargs=dict(q=q), axis=axis)
     sorted_array = array[..., [0, 1, 2, 4, 3, 5, 6]]
     f = partial(getattr(np, func), q=q, axis=axis, keepdims=True)
+    if chunk:
+        sorted_array = sorted_array.compute()
     expected = np.concatenate((f(sorted_array[..., :4]), f(sorted_array[..., 4:])), axis=-1)
     if by_ndim == 2:
         expected = expected.squeeze(axis=-2)