More accurate coverage (#62)

* Update codecov.yml

* More coverage

* Add lxml

* Remove unused result_type function

* Fix syntax

* Add yaml checker

* Actually save coverage

* Don't report xarray/groupby tests.

We want flox coverage, not xarray.

Author: dcherian

.github/workflows/ci-additional.yaml

@@ -71,7 +71,15 @@ jobs:
           conda list
       - name: Run doctests
         run: |
-          python -m pytest --doctest-modules flox --ignore flox/tests
+          python -m pytest --doctest-modules flox --ignore flox/tests --cov=./ --cov-report=xml
+      - name: Upload code coverage to Codecov
+        uses: codecov/codecov-action@v3.1.1
+        with:
+          file: ./coverage.xml
+          flags: unittests
+          env_vars: RUNNER_OS
+          name: codecov-umbrella
+          fail_ci_if_error: false
 
   mypy:
     name: Mypy
@@ -97,12 +105,12 @@ jobs:
         uses: mamba-org/provision-with-micromamba@v14
         with:
           environment-file: ${{env.CONDA_ENV_FILE}}
-          environment-name: xarray-tests
+          environment-name: flox-tests
           extra-specs: |
             python=${{env.PYTHON_VERSION}}
           cache-env: true
           cache-env-key: "${{runner.os}}-${{runner.arch}}-py${{env.PYTHON_VERSION}}-${{env.TODAY}}-${{hashFiles(env.CONDA_ENV_FILE)}}"
-      - name: Install xarray
+      - name: Install flox
         run: |
           python -m pip install --no-deps -e .
       - name: Version info
@@ -115,4 +123,13 @@ jobs:
 
       - name: Run mypy
         run: |
-          python -m mypy --install-types --non-interactive
+          python -m mypy --install-types --non-interactive --cobertura-xml-report mypy_report
+
+      - name: Upload mypy coverage to Codecov
+        uses: codecov/codecov-action@v3.1.1
+        with:
+          file: mypy_report/cobertura.xml
+          flags: mypy
+          env_vars: PYTHON_VERSION
+          name: codecov-umbrella
+          fail_ci_if_error: false

.github/workflows/ci.yaml

@@ -89,7 +89,15 @@ jobs:
           python -m pip install --no-deps -e .
       - name: Run tests
         run: |
-          python -m pytest -n auto
+          python -m pytest -n auto --cov=./ --cov-report=xml
+      - name: Upload code coverage to Codecov
+        uses: codecov/codecov-action@v3.1.1
+        with:
+          file: ./coverage.xml
+          flags: unittests
+          env_vars: RUNNER_OS
+          name: codecov-umbrella
+          fail_ci_if_error: false
 
   upstream-dev:
     name: upstream-dev

.pre-commit-config.yaml

@@ -5,6 +5,7 @@ repos:
     - repo: https://github.com/pre-commit/pre-commit-hooks
       rev: v4.4.0
       hooks:
+        - id: check-yaml
         - id: trailing-whitespace
         - id: end-of-file-fixer
         - id: check-docstring-first

ci/environment.yml

@@ -9,6 +9,7 @@ dependencies:
   - netcdf4
   - pandas
   - numpy>=1.20
+  - lxml  # for mypy coverage report
   - matplotlib
   - pip
   - pytest

codecov.yml

@@ -5,6 +5,7 @@ codecov:
 comment: false
 
 ignore:
+  - 'benchmarks/*.py'
   - 'tests/*.py'
   - 'setup.py'
 

flox/xrdtypes.py

@@ -152,28 +152,3 @@ def get_neg_infinity(dtype, min_for_int=False):
 def is_datetime_like(dtype):
     """Check if a dtype is a subclass of the numpy datetime types"""
     return np.issubdtype(dtype, np.datetime64) or np.issubdtype(dtype, np.timedelta64)
-
-
-def result_type(*arrays_and_dtypes):
-    """Like np.result_type, but with type promotion rules matching pandas.
-
-    Examples of changed behavior:
-    number + string -> object (not string)
-    bytes + unicode -> object (not unicode)
-
-    Parameters
-    ----------
-    *arrays_and_dtypes : list of arrays and dtypes
-        The dtype is extracted from both numpy and dask arrays.
-
-    Returns
-    -------
-    numpy.dtype for the result.
-    """
-    types = {np.result_type(t).type for t in arrays_and_dtypes}
-
-    for left, right in PROMOTE_TO_OBJECT:
-        if any(issubclass(t, left) for t in types) and any(issubclass(t, right) for t in types):
-            return np.dtype(object)
-
-    return np.result_type(*arrays_and_dtypes)