Add gitlab ci pipeline

.gitlab-ci.yml

+stages:
+  - test
+  - report
+
+.template: &pyt_test
+  stage: test
+  tags:
+    - conda
+
+lint:
+  << : *py_test
+  before_script:
+    - pip install .[test]
+  script:
+    - mypy
+    - black --check src
+    - pylint src/rechunk_data/__init__.py
+
+test_35:
+  << : *py_test
+  before_script:
+    - conda env create -q -n test python=3.5 pip
+    - conda run -n python -m pip install .[test]
+  script:
+    - pytest -vv
+
+test_36:
+  << : *py_test
+  before_script:
+    - conda env create -q -n test python=3.6 pip
+    - conda run -n python -m pip install .[test]
+  script:
+    - pytest -vv
+
+test_37:
+  << : *py_test
+  before_script:
+    - conda env create -q -n test python=3.7 pip
+    - conda run -n python -m pip install .[test]
+  script:
+    - pytest -vv
+
+test_38:
+  << : *py_test
+  before_script:
+    - conda env create -q -n test python=3.8 pip
+    - conda run -n python -m pip install .[test]
+  script:
+    - pytest -vv
+
+test_39:
+  << : *py_test
+  before_script:
+    - conda env create -q -n test python=3.9 pip
+    - conda run -n python -m pip install .[test]
+  script:
+    - pytest -vv
+
+test_latest:
+  << : *py_test
+  before_script:
+    - conda env create -q -n test python=3.10 pip
+    - conda run -n python -m pip install .[test]
+  script:
+    - pytest -vv
+  artifacts:
+      when: always
+      reports:
+        junit: report.xml
+      paths:
+        - coverage_report
+        - report.xml
+pages:
+  stage: report
+  tags:
+    - conda
+  dependencies:
+    - test_latest
+  before_script:
+    - mv coverage_report public/
+  artifacts:
+    when: always
+    paths:
+      - public/
+    reports:
+      junit: report.xml

setup.py

@@ -35,10 +35,24 @@ setup(
     packages=find_packages("src"),
     package_dir={"": "src"},
     entry_points={
-        "console_scripts": [f"{find_key(key='ProgramName')} = rechunk_data:cli"]
+        "console_scripts": [f"{find_key(key='PROGRAM_NAME')} = rechunk_data:cli"]
     },
     install_requires=["argparse", "dask", "xarray", "h5netcdf", "netCDF4"],
-    python_requires=">=3.6",
+    extras_require={
+        "test": [
+            "black",
+            "mock",
+            "mypy",
+            "nbformat",
+            "pytest",
+            "pytest-html",
+            "pytest-env",
+            "pytest-cov",
+            "testpath",
+            "types-mock",
+        ],
+    },
+    python_requires=">=3.5",
     classifiers=[
         "Development Status :: 3 - Alpha",
         "Environment :: Console",

src/rechunk_data/__init__.py

@@ -11,19 +11,19 @@ from dask.array.core import Array
 import xarray as xr
 
 __version__ = "2206.0.2"
-ProgramName = "rechunk-data"
+PROGRAM_NAME = "rechunk-data"
 
 logging.basicConfig(
     format="%(name)s - %(levelname)s - %(message)s", level=logging.ERROR
 )
-logger = logging.getLogger(ProgramName)
+logger = logging.getLogger(PROGRAM_NAME)
 
 
 def parse_args(argv: Optional[List[str]]) -> argparse.Namespace:
     """Parse arguments for the command line parser."""
 
     parser = argparse.ArgumentParser(
-        prog=ProgramName,
+        prog=PROGRAM_NAME,
         description=(
             "Rechunk input netcdf data to optimal chunk-size."
             " approx. 126 MB per chunk"
@@ -57,7 +57,9 @@ def parse_args(argv: Optional[List[str]]) -> argparse.Namespace:
         type=str,
     )
     parser.add_argument(
-        "-v", action="count", default=0,
+        "-v",
+        action="count",
+        default=0,
     )
     parser.add_argument(
         "-V",
@@ -98,7 +100,9 @@ def _save_dataset(
     logger.debug("Saving file ot %s", file_name)
     try:
         dset.to_netcdf(
-            file_name, engine=engine, encoding=encoding,
+            file_name,
+            engine=engine,
+            encoding=encoding,
         )
     except Exception as error:
         logger.error("Saving to file failed: %s", error.__str__())

src/rechunk_data/tests/conftest.py

@@ -16,7 +16,10 @@ def create_data(
     """Create a netcdf dataset."""
     coords = {d: np.ones(chunk_size[n]) for (n, d) in enumerate(dims)}
     dset = xr.DataArray(
-        np.zeros(chunk_size), dims=dims, coords=coords, name=variable_name,
+        np.zeros(chunk_size),
+        dims=dims,
+        coords=coords,
+        name=variable_name,
     ).chunk(dict(zip(dims, chunk_size)))
     dset.encoding = {"chunksizes": chunk_size}
     return xr.Dataset({variable_name: dset})