Added ruff

.github/workflows/ci.yml

@@ -20,4 +20,7 @@ jobs:
       run: pytest ./tests
 
     - name: Type checking with mypy
-      run: mypy building_energy_storage_simulation
+      run: mypy building_energy_storage_simulation
+
+    - name: Code style checking with ruff
+      run: ruff check .

.ruff.toml

@@ -0,0 +1,12 @@
+# Same as Black.
+line-length = 127
+# Assume Python 3.8
+target-version = "py38"
+# See https://beta.ruff.rs/docs/rules/
+select = ["E", "F", "B", "UP", "C90", "RUF", "I", "N", "B", "SIM"]
+# B028: Ignore explicit stacklevel`
+# RUF013: Too many false positives (implicit optional)
+ignore = ["B028", "RUF013"]
+
+[extend-per-file-ignores]
+"__init__.py" = ["F401", "F403"]

building_energy_storage_simulation/building_simulation.py

@@ -1,4 +1,4 @@
-from typing import Tuple, Iterable, Union
+from typing import Iterable, Tuple, Union
 
 import numpy as np
 
@@ -26,7 +26,8 @@ class BuildingSimulation:
     def __init__(self,
                  electricity_load_profile: Iterable = load_profile('electricity_load_profile.csv', 'Load [kWh]'),
                  solar_generation_profile: Iterable = load_profile('solar_generation_profile.csv', 'Generation [kWh]'),
-                 electricity_price: Union[Iterable, float] = load_profile('electricity_price_profile.csv', 'Day Ahead Auction'),
+                 electricity_price: Union[Iterable, float] = load_profile('electricity_price_profile.csv',
+                                                                          'Day Ahead Auction'),
                  battery_capacity: float = 100,
                  max_battery_charge_per_timestep: float = 20
                  ):

building_energy_storage_simulation/environment.py

@@ -1,9 +1,9 @@
-from typing import Tuple, Optional, Union, List, Iterable, Any
-from collections.abc import MutableSequence
+from typing import Any, Iterable, List, Optional, Tuple, Union
 
 import gymnasium as gym
 import numpy as np
-from gymnasium.core import ActType, ObsType, RenderFrame
+from gymnasium.core import RenderFrame
+
 from building_energy_storage_simulation.building_simulation import BuildingSimulation
 
 

building_energy_storage_simulation/utils.py

@@ -1,5 +1,5 @@
-import pkg_resources
 import pandas as pd
+import pkg_resources
 
 
 def load_profile(filename: str, column_name: str) -> pd.Series:

example_solutions/deep_reinforcement_learning/evaluate.py

@@ -5,9 +5,15 @@
 from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
 
 from building_energy_storage_simulation import BuildingSimulation, Environment
-from example_solutions.deep_reinforcement_learning.train import RESULT_PATH, NUM_FORECAST_STEPS
-from example_solutions.helper import read_data, TEST_INDEX_START, TEST_INDEX_END, BATTERY_POWER, BATTERY_CAPACITY, \
-    plot_control_trajectory
+from example_solutions.deep_reinforcement_learning.train import NUM_FORECAST_STEPS, RESULT_PATH
+from example_solutions.helper import (
+    BATTERY_CAPACITY,
+    BATTERY_POWER,
+    TEST_INDEX_END,
+    TEST_INDEX_START,
+    plot_control_trajectory,
+    read_data,
+)
 from example_solutions.observation_wrapper import ObservationWrapper
 
 
@@ -17,7 +23,7 @@ def evaluate(env, agent=None):
     done = False
     obs = env.reset()
     while not done:
-        if agent is None:
+        if agent is None:  # noqa
             action = [[0]]
         else:
             action = [agent.predict(obs, deterministic=True)[0][0]]
@@ -44,7 +50,7 @@ def evaluate(env, agent=None):
     # Plot evolution of reward during training
     try:
         plot_results(RESULT_PATH, x_axis='timesteps', task_name='title', num_timesteps=None)
-    except:
+    except:  # noqa
         print('Training Reward Plot could not be created')
 
     load, price, generation = read_data()

example_solutions/deep_reinforcement_learning/train.py

@@ -5,7 +5,7 @@
 from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
 
 from building_energy_storage_simulation import BuildingSimulation, Environment
-from example_solutions.helper import read_data, TEST_INDEX_START, BATTERY_CAPACITY, BATTERY_POWER
+from example_solutions.helper import BATTERY_CAPACITY, BATTERY_POWER, TEST_INDEX_START, read_data
 from example_solutions.observation_wrapper import ObservationWrapper
 
 NUM_FORECAST_STEPS = 8
@@ -41,6 +41,7 @@
     # Train :-)
     model = SAC("MlpPolicy", env, verbose=1, gamma=0.95)
     model.learn(total_timesteps=200_000)
-    # Store the trained Model and environment stats (which are needed as we are standardizing the observations and reward using VecNormalize())
+    # Store the trained Model and environment stats (which are needed as we are standardizing the observations and
+    # reward using VecNormalize())
     model.save(RESULT_PATH + 'model')
     env.save(RESULT_PATH + 'env.pkl')

example_solutions/helper.py

@@ -1,9 +1,8 @@
 from pathlib import Path
-
-import pandas as pd
-import numpy as np
 from typing import Tuple
 
+import numpy as np
+import pandas as pd
 from matplotlib import pyplot as plt
 
 # Start and end Index of data used for testing

example_solutions/model_predictive_control.py

@@ -1,9 +1,9 @@
-import pyomo.environ as pyo
 import numpy as np
+import pyomo.environ as pyo
+from helper import BATTERY_CAPACITY, BATTERY_POWER, TEST_INDEX_END, TEST_INDEX_START, plot_control_trajectory, read_data
+from optimal_control_problem import build_optimization_problem
 
 from building_energy_storage_simulation import BuildingSimulation, Environment
-from optimal_control_problem import build_optimization_problem
-from helper import read_data, TEST_INDEX_END, TEST_INDEX_START, BATTERY_POWER, BATTERY_CAPACITY, plot_control_trajectory
 
 FORECAST_LENGTH = 24
 
@@ -43,7 +43,8 @@ def normalize_to_minus_one_to_one(x, min_value, max_value):
 
     optimization_problem = build_optimization_problem(residual_fixed_load=residual_load_forecast,
                                                       price=price_forecast,
-                                                      soc=soc / BATTERY_CAPACITY * 100,  # Convert SOC due to different SOC definitions
+                                                      # Convert SOC due to different SOC definitions
+                                                      soc=soc / BATTERY_CAPACITY * 100,
                                                       battery_capacity=BATTERY_CAPACITY,
                                                       battery_power=BATTERY_POWER)
     solver.solve(optimization_problem, tee=True)

example_solutions/optimal_control_problem.py

@@ -1,7 +1,6 @@
-import pyomo.environ as pyo
 import numpy as np
-
-from helper import read_data, TEST_INDEX_END, TEST_INDEX_START, BATTERY_CAPACITY, BATTERY_POWER, plot_control_trajectory
+import pyomo.environ as pyo
+from helper import BATTERY_CAPACITY, BATTERY_POWER, TEST_INDEX_END, TEST_INDEX_START, plot_control_trajectory, read_data
 
 
 def build_optimization_problem(residual_fixed_load, price, soc, battery_power, battery_capacity, delta_time_hours=1):
@@ -20,7 +19,7 @@ def build_optimization_problem(residual_fixed_load, price, soc, battery_power, b
 
     def obj_expression(m):
         # pyo.log to make the objective expression smooth and therefore solvable
-        return sum([price[i] * pyo.log(1 + pyo.exp((m.power[i] + residual_fixed_load[i]))) for i in time])
+        return sum([price[i] * pyo.log(1 + pyo.exp(m.power[i] + residual_fixed_load[i])) for i in time])
 
     m.OBJ = pyo.Objective(rule=obj_expression, sense=pyo.minimize)
 

setup.py

@@ -1,10 +1,11 @@
 from distutils.core import setup
 from pathlib import Path
+
 this_directory = Path(__file__).parent
 long_description = (this_directory / "README.md").read_text()
 
 setup(name='building_energy_storage_simulation',
-      version='0.9.2',
+      version='0.9.3',
       description='A simulation of a building to optimize energy storage utilization.',
       long_description=long_description,
       long_description_content_type='text/markdown',
@@ -31,7 +32,8 @@
               "pytest",
               "mypy",
               "pandas-stubs",
-              "types-setuptools"
+              "types-setuptools",
+              "ruff"
           ]
       }
       )

tests/test_building_simulation.py

@@ -5,7 +5,7 @@
 
 def test_energy_consumption_is_trimmed_to_0():
     sim = BuildingSimulation(electricity_load_profile=[0], solar_generation_profile=[100], electricity_price=[1])
-    # Don´t charge the battery, meaning don´t do anything with the 100kWh energy we gained from the solar system.
+    # Don't charge the battery, meaning don't do anything with the 100kWh energy we gained from the solar system.
     electricity_consumption, excess_energy = sim.simulate_one_step(0)
     # Still the consumption is 0, as we loose excess electricity which we do not use to charge the battery.
     assert electricity_consumption == 0
@@ -29,6 +29,6 @@ def test_simulation_scalar_electricity_price_converted_into_profile(electricity_
 
 def test_simulation_throws_when_data_profiles_have_unequal_length():
     with pytest.raises(Exception) as exception_info:
-        sim = BuildingSimulation(electricity_load_profile=[0, 0, 0],
+        BuildingSimulation(electricity_load_profile=[0, 0, 0],
                                  solar_generation_profile=[0, 0])
     assert exception_info.errisinstance(ValueError)

tests/test_environment.py

@@ -1,8 +1,8 @@
-from building_energy_storage_simulation.building_simulation import BuildingSimulation
+import numpy as np
+import pytest
 
 from building_energy_storage_simulation import Environment
-import pytest
-import numpy as np
+from building_energy_storage_simulation.building_simulation import BuildingSimulation
 
 
 @pytest.fixture(scope='module')
@@ -37,7 +37,7 @@ def test_terminated_at_timelimit_reached(data_profile_length, num_forecasting_st
                       max_timesteps=data_profile_length - num_forecasting_steps)
     env.reset()
     print(range(data_profile_length - num_forecasting_steps))
-    for i in range(data_profile_length - num_forecasting_steps):
+    for _ in range(data_profile_length - num_forecasting_steps):
         obs, reward, terminated, trunc, info = env.step(0)
     assert terminated is True