Merge pull request #10 from robinhenry/update-requirements

Update requirements and CI checks

.github/workflows/ci_checks.yml

@@ -0,0 +1,33 @@
+name: Tests pip
+on: [push]
+
+jobs:
+  checks:
+    strategy:
+      max-parallel: 6
+      matrix:
+        python-version: ["3.8", "3.9", "3.10"]
+        poetry-version: ["1.2"]
+        os: [ubuntu-latest]
+    runs-on: ${{ matrix.os }}
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install poetry ${{ matrix.poetry-version }}
+        uses: abatilo/actions-poetry@v2
+        with:
+          poetry-version: ${{ matrix.poetry-version }}
+      - name: Install dependencies
+        run: poetry install
+      - name: Run black
+        run: poetry run black --check .
+      - name: Test with pytest
+        run: poetry run pytest --cov --cov-report=xml --cov-report=html
+      - name: Upload coverage
+        uses: codecov/codecov-action@v3
+        with:
+          token: ${{ secrets.CODECOV_TOKEN }}

.github/workflows/ci_release.yml

@@ -0,0 +1,32 @@
+name: Release
+on:
+  release:
+    types:
+      - created
+
+jobs:
+  publish:
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: [3.10]
+        poetry-version: [1.2]
+        os: [ubuntu-latest]
+    runs-on: ${{ matrix.os }}
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install poetry ${{ matrix.poetry-version }}
+        uses: abatilo/[email protected]
+        with:
+          poetry-version: ${{ matrix.poetry-version }}
+      - name: Publish to pypi
+        env:
+          PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }}
+        run: |
+          poetry config pypi-token.pypi $PYPI_TOKEN
+          poetry publish --build

README.md

@@ -9,15 +9,15 @@
 `gym-anm` is a framework for designing reinforcement learning (RL) environments that model Active Network
 Management (ANM) tasks in electricity distribution networks. It is built on top of the
 [OpenAI Gym](https://github.com/openai/gym) toolkit.
-  
+
 The `gym-anm` framework was designed with one goal in mind: **bridge the gap between research in RL and in
 the management of power systems**. We attempt to do this by providing RL researchers with an easy-to-work-with
 library of environments that model decision-making tasks in power grids.
 
-**Papers:** 
+**Papers:**
 *  [Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systems](https://doi.org/10.1016/j.egyai.2021.100092)
 *  [Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and education](https://doi.org/10.1016/j.simpa.2021.100092)
- 
+
 ## Key features
 *  Very little background in electricity systems modelling it required. This makes `gym-anm` an ideal starting point
    for RL students and researchers looking to enter the field.
@@ -26,14 +26,14 @@ library of environments that model decision-making tasks in power grids.
 *  The flexibility of `gym-anm`, with its different customizable components, makes it a suitable framework
    to model a wide range of ANM tasks, from simple ones that can be used for educational purposes, to complex ones
    designed to conduct advanced research.
-   
+
 ## Documentation
 Documentation is provided online at [https://gym-anm.readthedocs.io/en/latest/](https://gym-anm.readthedocs.io/en/latest/).
 
 ## Installation
 
 ### Requirements
-`gym-anm` requires Python 3.7+ and can run on Linux, MaxOS, and Windows.
+`gym-anm` requires Python 3.8+ and can run on Linux, MaxOS, and Windows.
 
 We recommend installing `gym-anm` in a Python environment (e.g., [virtualenv](https://virtualenv.pypa.io/en/latest/)
 or [conda](https://conda.io/en/latest/#)).
@@ -63,13 +63,13 @@ import time
 def run():
    env = gym.make('gym_anm:ANM6Easy-v0')
    o = env.reset()
-   
+
    for i in range(100):
        a = env.action_space.sample()
        o, r, done, info = env.step(a)
        env.render()
        time.sleep(0.5)  # otherwise the rendering is too fast for the human eye.
-       
+
    env.close()
 
 if __name__ == '__main__':
@@ -82,8 +82,8 @@ Additional example scripts can be found in [examples/](examples).
 
 ## Testing the installation
 All unit tests in `gym-anm` can be ran from the project root directory with:
-``` 
-python -m tests
+```
+python -m pytest tests
 ```
 
 ## Contributing
@@ -120,5 +120,4 @@ All publications derived from the use of `gym-anm` should cite the following two
 `gym-anm` is currently maintained by [Robin Henry](https://www.robinxhenry.com/).
 
 ## License
-
 This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.

docs/source/conf.py

@@ -12,17 +12,18 @@
 
 import os
 import sys
-sys.path.insert(0, os.path.abspath('../..'))
+
+sys.path.insert(0, os.path.abspath("../.."))
 
 
 # -- Project information -----------------------------------------------------
 
-project = 'gym-anm'
-copyright = '2020, Robin Henry'
-author = 'Robin Henry'
+project = "gym-anm"
+copyright = "2020, Robin Henry"
+author = "Robin Henry"
 
 # The full version, including alpha/beta/rc tags
-release = 'v1'
+release = "v1"
 
 
 # -- General configuration ---------------------------------------------------
@@ -37,32 +38,32 @@
     "sphinx.ext.ifconfig",
     "sphinx.ext.viewcode",
     "sphinx.ext.napoleon",
-    "sphinx_rtd_theme"
+    "sphinx_rtd_theme",
 ]
 autosummary_generate = True  # Turn on sphinx.ext.autosummary
 
 # Autodoc settings
-autodoc_member_order = 'groupwise'
+autodoc_member_order = "groupwise"
 
 # Napoleon settings
 napoleon_google_docstring = False
 napoleon_numpy_docstring = True
 
 # Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
+templates_path = ["_templates"]
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ['topics/archive']
+exclude_patterns = ["topics/archive"]
 
 
 # -- Options for HTML output -------------------------------------------------
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = 'sphinx_rtd_theme'
+html_theme = "sphinx_rtd_theme"
 
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,

examples/custom_anm6.py

@@ -16,38 +16,38 @@
 import numpy as np
 from gym_anm.envs import ANM6
 
+
 class CustomANM6Environment(ANM6):
     """A gym-anm task built on top of the ANM6 grid."""
 
     def __init__(self):
-        observation = 'state'             # fully observable environment
-        K = 1                             # 1 auxiliary variable
-        delta_t = 0.25                    # 15min intervals
-        gamma = 0.9                       # discount factor
-        lamb = 100                        # penalty weighting hyperparameter
+        observation = "state"  # fully observable environment
+        K = 1  # 1 auxiliary variable
+        delta_t = 0.25  # 15min intervals
+        gamma = 0.9  # discount factor
+        lamb = 100  # penalty weighting hyperparameter
         aux_bounds = np.array([[0, 10]])  # bounds on auxiliary variable
-        costs_clipping = (1, 100)         # reward clipping parameters
-        seed = 1                          # random seed
+        costs_clipping = (1, 100)  # reward clipping parameters
+        seed = 1  # random seed
 
-        super().__init__(observation, K, delta_t, gamma, lamb,
-                         aux_bounds, costs_clipping, seed)
+        super().__init__(observation, K, delta_t, gamma, lamb, aux_bounds, costs_clipping, seed)
 
     def init_state(self):
         """Return a state vector with random values in [0, 1]."""
-        n_dev = self.simulator.N_device                # number of devices
-        n_des = self.simulator.N_des                   # number of DES units
-        n_gen = self.simulator.N_non_slack_gen         # number of non-slack generators
+        n_dev = self.simulator.N_device  # number of devices
+        n_des = self.simulator.N_des  # number of DES units
+        n_gen = self.simulator.N_non_slack_gen  # number of non-slack generators
         s = np.random.rand(2 * n_dev + n_des + n_gen)  # random state vector
 
         # Let the auxiliary variable be a time of day index where increments
         # represent `self.delta_t` time durations.
         # Initial time: 00:00.
         aux = 0
 
-        return np.hstack((s, aux))                 # initial state vector s0
+        return np.hstack((s, aux))  # initial state vector s0
 
     def next_vars(self, s_t):
-        """ Generate the next stochastic variables and auxiliary variables."""
+        """Generate the next stochastic variables and auxiliary variables."""
         next_var = []
 
         # Random demand for residential area in [-10, 0] MW.
@@ -71,11 +71,12 @@ def next_vars(self, s_t):
 
         return np.array(next_var)
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     env = CustomANM6Environment()
     env.reset()
 
     for t in range(10):
         a = env.action_space.sample()
         o, r, done, _ = env.step(a)
-        print(f't={t}, r_t={r:.3}')
+        print(f"t={t}, r_t={r:.3}")

examples/mpc_constant.py

@@ -11,19 +11,20 @@
 import gym
 from gym_anm import MPCAgentConstant
 
+
 def run():
-    env = gym.make('ANM6Easy-v0')
+    env = gym.make("ANM6Easy-v0")
     o = env.reset()
 
     # Initialize the MPC policy.
-    agent = MPCAgentConstant(env.simulator, env.action_space, env.gamma,
-                             safety_margin=0.96, planning_steps=10)
+    agent = MPCAgentConstant(env.simulator, env.action_space, env.gamma, safety_margin=0.96, planning_steps=10)
 
     # Run the policy.
     for t in range(100):
         a = agent.act(env)
         obs, r, done, _ = env.step(a)
-        print(f't={t}, r_t={r:.3}')
+        print(f"t={t}, r_t={r:.3}")
+
 
-if __name__ == '__main__':
-    run()
+if __name__ == "__main__":
+    run()

examples/mpc_perfect.py

@@ -10,19 +10,20 @@
 import gym
 from gym_anm import MPCAgentPerfect
 
+
 def run():
-    env = gym.make('ANM6Easy-v0')
+    env = gym.make("ANM6Easy-v0")
     o = env.reset()
 
     # Initialize the MPC policy.
-    agent = MPCAgentPerfect(env.simulator, env.action_space, env.gamma,
-                            safety_margin=0.96, planning_steps=10)
+    agent = MPCAgentPerfect(env.simulator, env.action_space, env.gamma, safety_margin=0.96, planning_steps=10)
 
     # Run the policy.
     for t in range(100):
         a = agent.act(env)
         obs, r, done, _ = env.step(a)
-        print(f't={t}, r_t={r:.3}')
+        print(f"t={t}, r_t={r:.3}")
+
 
-if __name__ == '__main__':
-    run()
+if __name__ == "__main__":
+    run()