Fixed type errors in mypy GitHub Action (#6963)

* fixed mypy type errors

* add mypy with python3.8 to ci

* fix mypy python 3.8 problems

* add verbosity flag to mypy action

* silence import errors in mypy python 3.8

* Test copying the entire workflow.

* Update ci-additional.yaml

* Last test running 3.8 inside mypy workflow

* Update ci-additional.yaml

* Use the copy/pasted workflow only

Co-authored-by: Deepak Cherian <[email protected]>
Co-authored-by: Michael Niklas <[email protected]>
Co-authored-by: Illviljan <[email protected]>

Author: 4 people

.github/workflows/ci-additional.yaml

@@ -134,6 +134,64 @@ jobs:
           name: codecov-umbrella
           fail_ci_if_error: false
 
+  mypy38:
+    name: Mypy 3.8
+    runs-on: "ubuntu-latest"
+    needs: detect-ci-trigger
+    # temporarily skipping due to https://github.com/pydata/xarray/issues/6551
+    if: needs.detect-ci-trigger.outputs.triggered == 'false'
+    defaults:
+      run:
+        shell: bash -l {0}
+    env:
+      CONDA_ENV_FILE: ci/requirements/environment.yml
+      PYTHON_VERSION: "3.8"
+
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0 # Fetch all history for all branches and tags.
+
+      - name: set environment variables
+        run: |
+          echo "TODAY=$(date +'%Y-%m-%d')" >> $GITHUB_ENV
+      - name: Setup micromamba
+        uses: mamba-org/provision-with-micromamba@v14
+        with:
+          environment-file: ${{env.CONDA_ENV_FILE}}
+          environment-name: xarray-tests
+          extra-specs: |
+            python=${{env.PYTHON_VERSION}}
+            conda
+          cache-env: true
+          cache-env-key: "${{runner.os}}-${{runner.arch}}-py${{env.PYTHON_VERSION}}-${{env.TODAY}}-${{hashFiles(env.CONDA_ENV_FILE)}}"
+      - name: Install xarray
+        run: |
+          python -m pip install --no-deps -e .
+      - name: Version info
+        run: |
+          conda info -a
+          conda list
+          python xarray/util/print_versions.py
+      - name: Install mypy
+        run: |
+          python -m pip install 'mypy<0.990'
+
+      - name: Run mypy
+        run: |
+          python -m mypy --install-types --non-interactive --cobertura-xml-report mypy_report
+
+      - name: Upload mypy coverage to Codecov
+        uses: codecov/[email protected]
+        with:
+          file: mypy_report/cobertura.xml
+          flags: mypy38
+          env_vars: PYTHON_VERSION
+          name: codecov-umbrella
+          fail_ci_if_error: false
+
+
+
   min-version-policy:
     name: Minimum Version Policy
     runs-on: "ubuntu-latest"

xarray/backends/api.py

@@ -9,6 +9,7 @@
     TYPE_CHECKING,
     Any,
     Callable,
+    Dict,
     Final,
     Hashable,
     Iterable,
@@ -62,7 +63,7 @@
         str,  # no nice typing support for custom backends
         None,
     ]
-    T_Chunks = Union[int, dict[Any, Any], Literal["auto"], None]
+    T_Chunks = Union[int, Dict[Any, Any], Literal["auto"], None]
     T_NetcdfTypes = Literal[
         "NETCDF4", "NETCDF4_CLASSIC", "NETCDF3_64BIT", "NETCDF3_CLASSIC"
     ]

xarray/core/merge.py

@@ -40,9 +40,9 @@
     ArrayLike = Any
     VariableLike = Union[
         ArrayLike,
-        tuple[DimsLike, ArrayLike],
-        tuple[DimsLike, ArrayLike, Mapping],
-        tuple[DimsLike, ArrayLike, Mapping, Mapping],
+        Tuple[DimsLike, ArrayLike],
+        Tuple[DimsLike, ArrayLike, Mapping],
+        Tuple[DimsLike, ArrayLike, Mapping, Mapping],
     ]
     XarrayValue = Union[DataArray, Variable, VariableLike]
     DatasetLike = Union[Dataset, Mapping[Any, XarrayValue]]

xarray/core/types.py

@@ -6,10 +6,12 @@
     Callable,
     Hashable,
     Iterable,
+    List,
     Literal,
     Protocol,
     Sequence,
     SupportsIndex,
+    Tuple,
     TypeVar,
     Union,
 )
@@ -70,13 +72,13 @@ def dtype(self) -> np.dtype:
         # character codes, type strings or comma-separated fields, e.g., 'float64'
         str,
         # (flexible_dtype, itemsize)
-        tuple[_DTypeLikeNested, int],
+        Tuple[_DTypeLikeNested, int],
         # (fixed_dtype, shape)
-        tuple[_DTypeLikeNested, _ShapeLike],
+        Tuple[_DTypeLikeNested, _ShapeLike],
         # (base_dtype, new_dtype)
-        tuple[_DTypeLikeNested, _DTypeLikeNested],
+        Tuple[_DTypeLikeNested, _DTypeLikeNested],
         # because numpy does the same?
-        list[Any],
+        List[Any],
         # anything with a dtype attribute
         _SupportsDType,
     ]