Revert "Drop Python 3.9 support (#720)"

This reverts commit d8d91e1.

.github/workflows/build_workflow.yml

@@ -36,10 +36,10 @@ jobs:
             - name: Checkout Code Repository
               uses: actions/checkout@v3
 
-            - name: Set up Python 3.11
+            - name: Set up Python 3.10
               uses: actions/setup-python@v3
               with:
-                  python-version: "3.11"
+                  python-version: "3.10"
 
             - name: Install and Run Pre-commit
               uses: pre-commit/[email protected]
@@ -55,7 +55,7 @@ jobs:
                 shell: bash -l {0}
         strategy:
             matrix:
-                python-version: ["3.10", "3.11", "3.12"]
+                python-version: ["3.9", "3.10", "3.11", "3.12"]
         steps:
             - uses: actions/checkout@v3
 

conda-env/ci.yml

@@ -6,7 +6,7 @@ channels:
 dependencies:
     # Base - required for building the package.
     # =========================================
-    - python >=3.10
+    - python >=3.9
     - cf_xarray >=0.9.1
     - cftime
     - dask

conda-env/dev.yml

@@ -6,7 +6,7 @@ channels:
 dependencies:
     # Base - required for building the package.
     # =========================================
-    - python >=3.10
+    - python >=3.9
     - cf_xarray >=0.9.1
     - cftime
     - dask

docs/getting-started-guide/installation.rst

@@ -66,7 +66,7 @@ Instructions
 
    We recommend using the Conda environment creation procedure to install ``xcdat``.
    The advantage with following this approach is that Conda will attempt to resolve
-   dependencies for compatibility.
+   dependencies (e.g. ``python >= 3.9``) for compatibility.
 
    To create an ``xcdat`` Conda environment, run:
 

pyproject.toml

@@ -7,7 +7,7 @@ name = "xcdat"
 dynamic = ["version"]
 description = "Xarray Climate Data Analysis Tools"
 readme = "README.rst"
-requires-python = ">=3.10"
+requires-python = ">=3.9"
 license = { text = "Apache-2.0" }
 authors = [{ name = "xCDAT developers" }]
 classifiers = [
@@ -16,6 +16,7 @@ classifiers = [
   "License :: OSI Approved :: Apache-2.0 License",
   "Natural Language :: English",
   "Programming Language :: Python :: 3",
+  "Programming Language :: Python :: 3.9",
   "Programming Language :: Python :: 3.10",
   "Programming Language :: Python :: 3.11",
   "Programming Language :: Python :: 3.12",

tests/test_regrid.py

@@ -664,10 +664,10 @@ def test_map_latitude_coarse_to_fine(self):
             [[0.29289322]],
         ]
 
-        for x, y in zip(mapping, expected_mapping, strict=False):
+        for x, y in zip(mapping, expected_mapping):
             np.testing.assert_allclose(x, y)
 
-        for x2, y2 in zip(weights, expected_weigths, strict=False):
+        for x2, y2 in zip(weights, expected_weigths):
             np.testing.assert_allclose(x2, y2)
 
     def test_map_latitude_fine_to_coarse(self):

xcdat/dataset.py

@@ -652,7 +652,7 @@ def _get_cftime_coords(offsets: np.ndarray, units: str, calendar: str) -> np.nda
 
     # Convert offsets to `np.float64` to avoid "TypeError: unsupported type
     # for timedelta days component: numpy.int64".
-    flat_offsets = flat_offsets.astype("float")  # type: ignore
+    flat_offsets = flat_offsets.astype("float")
 
     # We don't need to do calendar arithmetic here because the units and
     # offsets are in "months" or "years", which means leap days should not

xcdat/regridder/grid.py

@@ -140,7 +140,7 @@ def _create_gaussian_axis(nlats: int) -> Tuple[xr.DataArray, xr.DataArray]:
         },
     )
 
-    bounds = (180.0 / np.pi) * np.arcsin(bounds)  # type: ignore
+    bounds = (180.0 / np.pi) * np.arcsin(bounds)
 
     bounds_data = np.zeros((points.shape[0], 2))
     bounds_data[:, 0] = bounds[:-1]

xcdat/temporal.py

@@ -1892,7 +1892,7 @@ def _convert_df_to_dt(self, df: pd.DataFrame) -> np.ndarray:
         dates = [
             self.date_type(year, month, day, hour)
             for year, month, day, hour in zip(
-                df_new.year, df_new.month, df_new.day, df_new.hour, strict=False
+                df_new.year, df_new.month, df_new.day, df_new.hour
             )
         ]