bump enda to version 0.1.0

.gitignore

@@ -148,3 +148,4 @@ data/
 
 
 
+.vscode/settings.json

.pre-commit-config.yaml

@@ -0,0 +1,29 @@
+---
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.4.0
+    hooks:
+      - id: check-yaml
+        args: ['--unsafe']
+      - id: trailing-whitespace
+      - id: detect-private-key
+      - id: name-tests-test
+        args: ["--pytest-test-first"]
+  - repo: https://github.com/psf/black
+    rev: 22.12.0
+    hooks:
+      - id: black
+  - repo: https://github.com/pycqa/isort
+    rev: 5.11.5
+    hooks:
+      - id: isort
+        name: isort (python)
+        args: ["--profile", "black", "--filter-files"]
+  - repo: https://github.com/charliermarsh/ruff-pre-commit
+    # Ruff version.
+    rev: "v0.0.261"
+    hooks:
+      - id: ruff
+fail_fast: false
+files: ".*"
+exclude: "desktop.ini" # Windows

.prettierrc

@@ -0,0 +1,4 @@
+{
+  "tabWidth": 2,
+  "useTabs": false
+}

CONTRIBUTING.md

@@ -0,0 +1,62 @@
+# Contributing
+
+The project can be built using [Poetry](https://python-poetry.org/). You can install it following the instructions here: <https://python-poetry.org/docs/#installation>.
+
+To develop `enda`, you can clone the repository and install the dependencies with poetry:
+
+```sh
+git clone https://github.com/enercoop/enda.git
+cd enda
+poetry install --with dev
+```
+
+If you are not using `Poetry` you can install the dependencies with `pip`:
+
+```sh
+pip install enda[dev]
+```
+
+To run the tests, you can use the following command:
+
+```sh
+poetry run pytest # or just pytest if you have activated the virtual environment
+```
+
+To run tests using tox, you can use the following command:
+
+```sh
+poetry run tox # or just tox if you have activated the virtual environment
+```
+
+## Building and publishing
+
+After you have built a new feature, upgraded a dependency or fixed a bug, you need to update the version number in `pyproject.toml` and `enda/__init__.py` and publish the new version to PyPI.
+
+If you are using `Poetry`, you can add the poetry plugin `poetry-bumpversion` to help you with this:
+
+```sh
+poetry self add poetry-bumpversion
+```
+
+and then you can use the following commands to update the version number:
+
+```sh
+poetry version patch # or minor or major
+```
+
+See the [`poetry-bumpversion`](https://pypi.org/project/poetry-bumpversion/) documentation for more details:
+
+After that you will need to build the package with:
+
+```sh
+poetry build
+```
+
+and then you can publish the new version to PyPI:
+
+```sh
+poetry publish
+```
+
+You will need to setup your PyPI credentials for this to work. See the [Poetry documentation](https://python-poetry.org/docs/repositories/#configuring-credentials) for more details.
+

README.md

@@ -1,22 +1,25 @@
 # enda
 
+![PyPI](https://img.shields.io/pypi/v/enda?link=https%3A%2F%2Fpypi.org%2Fproject%2Fenda%2F) [![Poetry](https://img.shields.io/endpoint?url=https://python-poetry.org/badge/v0.json)](https://python-poetry.org/) [![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit)
 
 ## What is it?
 
-**enda** is a Python package that provides tools to manipulate **timeseries** data in conjunction with **contracts** data for analysis and **forecasts**. 
+**enda** is a Python package that provides tools to manipulate **timeseries** data in conjunction with **contracts** data for analysis and **forecasts**.
 
 Its main goal is to help [Rescoop.eu](https://www.rescoop.eu/) members build various applications, such as short-term electricity load and production forecasts, specifically for the [RescoopVPP](https://www.rescoopvpp.eu/) project. Hence some tools in this package perform TSO (transmission network operator) and DNO (distribution network operator) data wrangling as well as weather data management. enda is mainly developed by [Enercoop](https://www.enercoop.fr/).
 
 ## Main Features
-Here are some things **enda** does well :
 
-  - Provide robust machine learning algorithms for short-term electricty load and production forecasts, developed by Enercoop. The load forecast was originally based on Komi Nagbe's thesis (http://www.theses.fr/s148364).
-  - Manipulate **contracts** data coming from your ERP and turn it into timeseries you can use for analysis, visualisation and machine learning.  
-  - Timeseries-specific detection of missing data, like time gaps and frequency changes.
-  - Date-time feature engineering robust to timezone hazards.
+Here are some things **enda** does well:
+
+- Provide robust machine learning algorithms for short-term electricty load and production forecasts, developed by Enercoop. The load forecast was originally based on Komi Nagbe's thesis (<http://www.theses.fr/s148364>).
+- Manipulate **contracts** data coming from your ERP and turn it into timeseries you can use for analysis, visualisation and machine learning.
+- Timeseries-specific detection of missing data, like time gaps and frequency changes.
+- Date-time feature engineering robust to timezone hazards.
 
 ## Where to get it
-The source code is currently hosted on GitHub at : https://github.com/enercoop/enda
+
+The source code is currently hosted on GitHub at: <https://github.com/enercoop/enda>
 
 Binary installers for the latest released version are available at the [Python
 Package Index (PyPI)](https://pypi.org/project/enda) (for now it is not directly on [Conda](https://docs.conda.io/en/latest/)).
@@ -26,36 +29,69 @@ Package Index (PyPI)](https://pypi.org/project/enda) (for now it is not directly
 pip install enda
 ```
 
-## How to get started ?
+If you wish to install the dependencies needed to run the examples, you can install `enda` with the `examples` extra:
+
+```sh
+pip install enda[examples]
+```
+
+You can install all the optional dependencies with the `all` extra:
+
+```sh
+pip install enda[all]
+```
+
+or using poetry:
+
+```sh
+poetry add enda[all]
+```
+
+## How to get started?
 
-Check out the guides : https://github.com/enercoop/enda/tree/main/guides .
+Check out the guides: <https://github.com/enercoop/enda/tree/main/guides>.
 
 ## Hard dependencies
+
 - [Pandas - the main dataframe manipulation tool for python, advanced timeseries management included.](https://pandas.pydata.org/)
-- Pandas itself has hard dependencies and optional dependencies, checkout https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html . Hard dependencies of pandas include : `setuptools`, `NumPy`, `python-dateutil`, `pytz`.
+- Pandas itself has hard dependencies and optional dependencies, checkout <https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html> . Hard dependencies of pandas include: `setuptools`, `NumPy`, `python-dateutil`, `pytz`.
 
-## Optional dependencies 
+## Optional dependencies
 
-Optional dependencies are used only for specific methods. Enda will give an error if the method called requires a dependency that is not installed. 
+Optional dependencies are used only for specific methods. Enda will give an error if the method called requires a dependency that is not installed.
 
 Enda can work with different machine learning "backends" :
+
 - [Scikit-learn](https://scikit-learn.org/stable/)
 - [H2O - an efficient machine learning framework](https://docs.h2o.ai/)
 
-You can also easily implement your own ml-backend by implementing enda's ModelInterface. Checkout `enda.ml_backends.sklearn_linreg.py` for an example with `SKLearnLinearRegression`. 
+You can also easily implement your own ml-backend by implementing enda's ModelInterface. Checkout `enda.ml_backends.sklearn_linreg.py` for an example with `SKLearnLinearRegression`.
+
+Other optional dependencies:
 
-Other optional dependencies :
 - [statsmodel](https://pypi.org/project/statsmodels/)
 
 Furthermore, don't hesitate to install pandas "Recommended dependencies" for speed-ups : `numexpr` and `bottleneck`.
 
-If you want to save your trained models, we recommend `joblib`. See Scikit-learn's recommendations here : https://scikit-learn.org/stable/modules/model_persistence.html . 
+If you want to save your trained models, we recommend `joblib`. See Scikit-learn's recommendations here: <https://scikit-learn.org/stable/modules/model_persistence.html>.
+
+All these dependencies can be installed along `enda` with the following command:
 
-An almost complete install looks like :
+```sh
+pip install enda[examples]
 ```
+
+Or you can install them manually:
+
+```sh
 pip install numexpr bottleneck pandas enda jupyter h2o scikit-learn statsmodels joblib matplotlib
 ```
 
+## About `numpy` support for python 3.7
+
+Support for `numpy` and python 3.7 according to <https://numpy.org/neps/nep-0029-deprecation_policy.html#support-table>
+and <https://github.com/scipy/oldest-supported-numpy>
 
 ## License
+
 [MIT](LICENSE)

enda/__init__.py

@@ -1,3 +1,5 @@
+__version__ = "0.1.0"
+
 # Let users know if they're missing any of our hard dependencies
 hard_dependencies = ("pandas",)
 # note that we also need python-dateutil and pytz but pandas already depends on them, so importing pandas is enough
@@ -6,21 +8,25 @@
 for dependency in hard_dependencies:
     try:
         __import__(dependency)
-    except ImportError as e:
+    except ImportError:
         missing_dependencies.append(dependency)
 
 if len(missing_dependencies) > 0:
     raise ImportError(
-        "Unable to import required dependencies:\n" + "\n".join(missing_dependencies)
+        "Unable to import required dependencies:\n"
+        + "\n".join(
+            missing_dependencies,
+        )
     )
 del hard_dependencies, dependency, missing_dependencies
 
+from enda.backtesting import BackTesting  # noqa
+
 # import some subclasses here so users can use for instance :
 # 'enda.Contracts' without knowing the internal structure.
 # Do not import classes that need a specific packages like "H2OModel".
-from enda.contracts import (Contracts)
-from enda.feature_engineering.datetime_features import (DatetimeFeature)
-from enda.timeseries import (TimeSeries)
-from enda.backtesting import (BackTesting)
-from enda.power_stations import (PowerStations)
-from enda.power_predictor import (PowerPredictor)
+from enda.contracts import Contracts  # noqa
+from enda.feature_engineering.datetime_features import DatetimeFeature  # noqa
+from enda.power_predictor import PowerPredictor  # noqa
+from enda.power_stations import PowerStations  # noqa
+from enda.timeseries import TimeSeries  # noqa