Merge pull request #103 from alliander-opensource/feature/pre-commit

Feature/pre commit

Author: bramstoeller

.pre-commit-config.yaml

@@ -0,0 +1,29 @@
+# SPDX-FileCopyrightText: 2022 Contributors to the Power Grid Model project <[email protected]>
+#
+# SPDX-License-Identifier: MPL-2.0
+
+repos:
+  - repo: https://github.com/fsfe/reuse-tool
+    rev: v1.0.0
+    hooks:
+      - id: reuse
+  - repo: https://github.com/psf/black
+    rev: 22.6.0
+    hooks:
+      - id: black
+        language_version: python3.8
+  - repo: local
+    hooks:
+      - id: pylint
+        name: pylint
+        entry: pylint
+        files: ^src/.+\.py$
+        language: system
+        types: [ python ]
+        args: [ "--rcfile=pyproject.toml" ]
+      - id: pytest
+        name: pytest
+        entry: pytest
+        language: system
+        pass_filenames: false
+        always_run: true

CONTRIBUTING.md

@@ -58,6 +58,32 @@ This project uses Google Format Style (NOTE: not Google C++ Programming Style) t
 
 Tip: Use [clang-format](https://clang.llvm.org/docs/ClangFormat.html) to format your C++ code.
 
+## pre-commit hooks
+This project uses [pre-commit](https://pre-commit.com/) to run a list of checks (and perform some automatic
+corrections) to your code (style) before each commit. It is up to the developer to choose whether you would like to 
+use this tool or not. The goal is to make sure that each commit will pass the quality checks in the github actions
+workflow. Currently, these hooks are defined in [`.pre-commit-config.yaml`](.pre-commit-config.yaml):
+* **reuse**: check if all licence headers and files are in place
+* **black**: check and correct code style
+* **pylint**: check code style
+* **pytest**: run all unit tests
+
+You can manually run pre-commit whenever you like:
+```bash
+pre-commit run
+```
+
+Or you can install it as a git pre-commit hook. In this case a commit will be aborted whenever one of the hooks fail.
+```bash
+pre-commit install
+```
+
+As using the pre-commit tool is not mandatory, you can always skip the tool:
+
+```bash
+git commit ... --no-verify
+```
+
 ## REUSE Compliance
 
 All the files in the repository need to be [REUSE compliant](https://reuse.software/). 

pyproject.toml

@@ -9,7 +9,7 @@ requires = [
     "cython",
     "oldest-supported-numpy",
     "requests",
-    "pybuild-header-dependency"
+    "pybuild-header-dependency",
 ]
 build-backend = "setuptools.build_meta"
 
@@ -38,15 +38,16 @@ classifiers = [
 ]
 requires-python = ">=3.8"
 dependencies = [
-    "numpy>=1.21.0"
+    "numpy>=1.21.0",
 ]
 dynamic = ["version"]
 
 
 [project.optional-dependencies]
 dev = [
+    "pre-commit",
     "pytest",
-    "pytest-cov"
+    "pytest-cov",
 ]
 
 [project.urls]
@@ -66,3 +67,6 @@ addopts = ["--cov=power_grid_model", "--cov-report", "term", "--cov-report", "ht
 [tool.black]
 line-length = 120
 target-version = ['py38']
+
+[tool.pylint]
+max-line-length = 120

src/power_grid_model/__init__.py

@@ -2,12 +2,9 @@
 #
 # SPDX-License-Identifier: MPL-2.0
 
+"""Power Grid Model"""
+
 # pylint: disable=no-name-in-module
 
-# Helper functions
-# Power Grid metadata
-# Power Grid Model
 from ._power_grid_core import PowerGridModel, initialize_array, power_grid_meta_data
-
-# Enumerations
 from .enum import BranchSide, CalculationMethod, CalculationType, LoadGenType, MeasuredTerminalType, WindingType

src/power_grid_model/manual_testing.py

@@ -121,26 +121,26 @@ def convert_python_to_numpy(
         dataset[component] = initialize_array(data_type, component, len(objects))
 
         for i, obj in enumerate(objects):
-            # As each object is a separate dictionary, and the properties may differ per object, we need to check
-            # all properties. Non-existing properties
-            for prop, value in obj.items():
-                if prop == "extra":
-                    # The "extra" property is a special one. It can store any type of information associated with
+            # As each object is a separate dictionary, and the attributes may differ per object, we need to check
+            # all attributes. Non-existing attributes
+            for attribute, value in obj.items():
+                if attribute == "extra":
+                    # The "extra" attribute is a special one. It can store any type of information associated with
                     # an object, but it will not be used in the calculations. Therefore it is not included in the
-                    # numpy array, so we can skip this property
+                    # numpy array, so we can skip this attribute
                     continue
 
-                if prop not in dataset[component].dtype.names:
-                    # If a property doen't exist, the user made a mistake. Let's be merciless in that case,
+                if attribute not in dataset[component].dtype.names:
+                    # If a attribute doen't exist, the user made a mistake. Let's be merciless in that case,
                     # for their own good.
-                    raise ValueError(f"Invalid property '{prop}' for {component} {data_type} data.")
+                    raise ValueError(f"Invalid attribute '{attribute}' for {component} {data_type} data.")
 
                 # Now just assign the value and raise an error if the value cannot be stored in the specific
-                # numpy array data format for this property.
+                # numpy array data format for this attribute.
                 try:
-                    dataset[component][i][prop] = value
+                    dataset[component][i][attribute] = value
                 except ValueError as ex:
-                    raise ValueError(f"Invalid '{prop}' value for {component} {data_type} data: {ex}") from ex
+                    raise ValueError(f"Invalid '{attribute}' value for {component} {data_type} data: {ex}") from ex
     return dataset
 
 
@@ -226,11 +226,11 @@ def convert_numpy_to_python(
     if not isinstance(example_data, np.ndarray) or example_data.ndim != 1:
         raise ValueError("Invalid data format")
 
-    # Convert each numpy array to a list of objects, which contains only the non-NaN properties:
+    # Convert each numpy array to a list of objects, which contains only the non-NaN attributes:
     # For example: {"node": [{"id": 0, ...}, {"id": 1, ...}], "line": [{"id": 2, ...}]}
     return {
         component: [
-            {property: obj[property].tolist() for property in objects.dtype.names if not is_nan(obj[property])}
+            {attribute: obj[attribute].tolist() for attribute in objects.dtype.names if not is_nan(obj[attribute])}
             for obj in objects
         ]
         for component, objects in data.items()

tests/unit/test_manual_testing.py

@@ -113,7 +113,7 @@ def test_round_trip_json_numpy_json(two_nodes_one_line, two_nodes_two_lines):
 
 
 def test_convert_python_to_numpy__raises_value_error():
-    with pytest.raises(ValueError, match="Invalid property 'u' for line input data."):
+    with pytest.raises(ValueError, match="Invalid attribute 'u' for line input data."):
         convert_python_to_numpy({"line": [{"id": 1, "u": 10.5e3}]}, "input")
     with pytest.raises(ValueError, match="Invalid 'id' value for line input data."):
         convert_python_to_numpy({"line": [{"id": "my_line", "u_rated": 10.5e3}]}, "input")