Proposal to use pre-commit for continuous integration

.pre-commit-config.yaml

@@ -0,0 +1,41 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v3.2.0
+    hooks:
+    -   id: trailing-whitespace
+    -   id: end-of-file-fixer
+    -   id: check-yaml
+    -   id: check-added-large-files
+
+-   repo: https://github.com/psf/black
+    rev: 23.7.0
+    hooks:
+    -   id: black
+        args: [--safe, --line-length=100]
+
+-   repo: https://github.com/pycqa/docformatter
+    rev: v1.7.5
+    hooks:
+    -   id: docformatter
+
+-   repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.5.0
+    hooks:
+    -   id: mypy
+        additional_dependencies: [types-PyYAML, types-tqdm]
+
+-   repo: https://github.com/pycqa/doc8
+    rev: v1.1.1
+    hooks:
+    -   id: doc8
+        files: ^docs/.*\.(rst|md)$
+
+-   repo: https://github.com/pycqa/flake8
+    rev: 6.1.0
+    hooks:
+    -   id: flake8
+
+ci:
+    autoupdate_schedule: weekly

README.md

@@ -8,7 +8,8 @@ A tool to perform toyMC-based inference constructions
 [![PyPI version shields.io](https://img.shields.io/pypi/v/alea-inference.svg)](https://pypi.python.org/pypi/alea-inference/)
 [![Readthedocs Badge](https://readthedocs.org/projects/alea/badge/?version=latest)](https://alea.readthedocs.io/en/latest/?badge=latest)
 [![CodeFactor](https://www.codefactor.io/repository/github/xenonnt/alea/badge)](https://www.codefactor.io/repository/github/xenonnt/alea)
+[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/XENONnT/alea/main.svg)](https://results.pre-commit.ci/latest/github/XENONnT/alea/main)
 
-`alea` is a public package inherited the spirits of previously private XENON likelihood definition and inference construction code `binference` that based on the blueice repo https://github.com/JelleAalbers/blueice. 
+`alea` is a public package inherited the spirits of previously private XENON likelihood definition and inference construction code `binference` that based on the blueice repo https://github.com/JelleAalbers/blueice.
 
 Binference was developed for XENON1T WIMP searches by Knut Dundas Morå, and for the first XENONnT results by Robert Hammann, Knut Dundas Morå and Tim Wolf.

alea/__init__.py

@@ -1,4 +1,4 @@
-__version__ = '0.1.0'
+__version__ = "0.1.0"
 
 from .model import *
 

alea/examples/gaussian_model.py

@@ -1,4 +1,4 @@
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Union
 
 import numpy as np
 from scipy import stats
@@ -7,10 +7,9 @@
 
 
 class GaussianModel(StatisticalModel):
-    """
-    A model of a gaussian measurement, where the model has parameters mu and sigma.
-    For illustration, we show how required nominal parameters can be added
-    to the init sigma is fixed in this example.
+    """A model of a gaussian measurement, where the model has parameters mu and sigma. For
+    illustration, we show how required nominal parameters can be added to the init sigma is fixed in
+    this example.
 
     Args:
         parameter_definition (dict or list, optional (default=None)):
@@ -19,34 +18,34 @@ class GaussianModel(StatisticalModel):
     Caution:
         You must define the nominal values of the parameters (mu, sigma)
         in the parameters definition.
+
     """
 
     def __init__(
-            self,
-            parameter_definition: Optional[Dict or List] = None,
-            **kwargs,
-        ):
+        self,
+        parameter_definition: Optional[Union[Dict[str, dict], List[str]]] = None,
+        **kwargs,
+    ):
         """Initialise a gaussian model."""
         if parameter_definition is None:
             parameter_definition = ["mu", "sigma"]
         super().__init__(parameter_definition=parameter_definition, **kwargs)
 
     def _ll(self, mu=None, sigma=None):
-        """
-        Log-likelihood of the model.
+        """Log-likelihood of the model.
 
         Args:
             mu (float, optional (default=None)): mean of the gaussian,
                 if None, the nominal value is used
             sigma (float, optional (default=None)): standard deviation of the gaussian,
                 if None, the nominal value is used
+
         """
-        hat_mu = self.data[0]['hat_mu'][0]
+        hat_mu = self.data[0]["hat_mu"][0]
         return stats.norm.logpdf(x=hat_mu, loc=mu, scale=sigma)
 
     def _generate_data(self, mu=None, sigma=None):
-        """
-        Generate data from the model.
+        """Generate data from the model.
 
         Args:
             mu (float, optional (default=None)): mean of the gaussian,
@@ -56,7 +55,8 @@ def _generate_data(self, mu=None, sigma=None):
 
         Returns:
             list: data generated from the model
+
         """
         hat_mu = stats.norm(loc=mu, scale=sigma).rvs()
-        data = [np.array([(hat_mu,)], dtype=[('hat_mu', float)])]
+        data = [np.array([(hat_mu,)], dtype=[("hat_mu", float)])]
         return data