Merge pull request #76 from jajimer/documentation

Rebranding Energym to Sinergym - version 1.3.0

.devcontainer/devcontainer.json

@@ -1,5 +1,5 @@
 {
-	"name": "energym",
+	"name": "sinergym",
 	"build": {
 		"args": {"ENERGYPLUS_VERSION":"9.5.0", "ENERGYPLUS_INSTALL_VERSION":"9-5-0", "ENERGYPLUS_SHA":"de239b2e5f"},
 	},

.github/workflows/create_release_docker.yml

@@ -17,15 +17,15 @@ jobs:
         uses: abatilo/[email protected]
         with:
           owner: jajimer
-          repo: energym
+          repo: sinergym
       - name: Build container image with release tag
         env:
           TAG: ${{ steps.tag_version.outputs.latest_tag }}
           #TAG_DATE: ${{ steps.tag_version.outputs.latest_tag_published_at }}
-        run: docker build . --file Dockerfile --build-arg ENERGYM_EXTRAS=[extras] --tag $DOCKER_USER/energym:$TAG
+        run: docker build . --file Dockerfile --build-arg SINERGYM_EXTRAS=[extras] --tag $DOCKER_USER/sinergym:$TAG
       - name: Login Docker Hub
         run: docker login -u $DOCKER_USER -p $DOCKER_PASSWORD
       - name: Push container built with release tag
         env:
           TAG: ${{ steps.tag_version.outputs.latest_tag }}
-        run: docker push $DOCKER_USER/energym:$TAG
+        run: docker push $DOCKER_USER/sinergym:$TAG

.github/workflows/testing.yml

@@ -16,10 +16,10 @@ jobs:
       - name: Checkout code
         uses: actions/checkout@v2
       - name: Build the latest Docker image
-        run: docker build . --file Dockerfile --build-arg ENERGYM_EXTRAS=[DRL,test] --tag $DOCKER_USER/energym:latest
+        run: docker build . --file Dockerfile --build-arg SINERGYM_EXTRAS=[DRL,test] --tag $DOCKER_USER/sinergym:latest
       - name: Execute tests from container
-        run: docker run -t $DOCKER_USER/energym:latest /bin/bash -c 'pytest tests/ -vv'
+        run: docker run -t $DOCKER_USER/sinergym:latest /bin/bash -c 'pytest tests/ -vv'
       - name: Login in Docker Hub account
         run: docker login -u $DOCKER_USER -p $DOCKER_PASSWORD
       - name: Push container used with test
-        run: docker push $DOCKER_USER/energym:latest
+        run: docker push $DOCKER_USER/sinergym:latest

.travis.yml

@@ -16,15 +16,15 @@ services:
   - docker
 
 before_script:
-  - docker build -t $DOCKER_USER/energym --build-arg ENERGYM_EXTRAS=[DRL,test] .
+  - docker build -t $DOCKER_USER/sinergym --build-arg SINERGYM_EXTRAS=[DRL,test] .
 
 script:
-  - docker run -it $DOCKER_USER/energym /bin/bash -c 'pytest tests -vv'
+  - docker run -it $DOCKER_USER/sinergym /bin/bash -c 'pytest tests -vv'
 
 after_success:
   - docker login -u $DOCKER_USER -p $DOCKER_PASSWORD  #Previusly set up in our own travis account
-  - docker tag $DOCKER_USER/energym $DOCKER_USER/energym:latest
-  - docker push $DOCKER_USER/energym:latest
+  - docker tag $DOCKER_USER/sinergym $DOCKER_USER/sinergym:latest
+  - docker push $DOCKER_USER/sinergym:latest
 
 
 

DRL_battery.py

@@ -1,14 +1,14 @@
 import gym
-import energym
+import sinergym
 import argparse
 import uuid
 import mlflow
 
 import numpy as np
 
-from energym.utils.callbacks import LoggerCallback, LoggerEvalCallback
-from energym.utils.wrappers import MultiObsWrapper, NormalizeObservation, LoggerWrapper
-from energym.utils.rewards import *
+from sinergym.utils.callbacks import LoggerCallback, LoggerEvalCallback
+from sinergym.utils.wrappers import MultiObsWrapper, NormalizeObservation, LoggerWrapper
+from sinergym.utils.rewards import *
 
 
 from stable_baselines3.common.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise
@@ -25,7 +25,7 @@
     required=True,
     type=str,
     dest='environment',
-    help='Environment name of simulation (see energym/__init__.py).')
+    help='Environment name of simulation (see sinergym/__init__.py).')
 parser.add_argument(
     '--episodes',
     '-ep',
@@ -64,7 +64,7 @@
     '-log',
     action='store_true',
     dest='logger',
-    help='Apply Energym CSVLogger class if this flag is specified.')
+    help='Apply Sinergym CSVLogger class if this flag is specified.')
 parser.add_argument(
     '--tensorboard',
     '-tens',
@@ -249,7 +249,7 @@
     callbacks.append(eval_callback)
 
 if args.tensorboard:
-    log_callback = LoggerCallback(energym_logger=bool(args.logger))
+    log_callback = LoggerCallback(sinergym_logger=bool(args.logger))
     callbacks.append(log_callback)
 
 callback = CallbackList(callbacks)

Dockerfile

@@ -8,8 +8,8 @@ ARG ENERGYPLUS_VERSION=9.5.0
 ARG ENERGYPLUS_INSTALL_VERSION=9-5-0
 ARG ENERGYPLUS_SHA=de239b2e5f
 
-# Argument for Energym extras libraries
-ARG ENERGYM_EXTRAS=[extras]
+# Argument for Sinergym extras libraries
+ARG SINERGYM_EXTRAS=[extras]
 
 ENV ENERGYPLUS_VERSION=$ENERGYPLUS_VERSION
 ENV ENERGYPLUS_TAG=v$ENERGYPLUS_VERSION
@@ -60,13 +60,13 @@ WORKDIR /code
 COPY requirements.txt .
 COPY setup.py .
 COPY DRL_battery.py .
-COPY energym /code/energym
+COPY sinergym /code/sinergym
 COPY tests /code/tests
 COPY examples /code/examples
 COPY check_run_times.py .
-RUN pip3 install -e .${ENERGYM_EXTRAS}
+RUN pip3 install -e .${SINERGYM_EXTRAS}
 
 CMD ["/bin/bash"]
 
-# Build: docker build -t energym:1.1.0 --build-arg ENERGYPLUS_VERSION=9.5.0 --build-arg ENERGYPLUS_INSTALL_VERSION=9-5-0 --build-arg ENERGYPLUS_SHA=de239b2e5f .
-# Run: docker run -it --rm -p 5005:5005 energym:1.1.0
+# Build: docker build -t sinergym:1.1.0 --build-arg ENERGYPLUS_VERSION=9.5.0 --build-arg ENERGYPLUS_INSTALL_VERSION=9-5-0 --build-arg ENERGYPLUS_SHA=de239b2e5f .
+# Run: docker run -it --rm -p 5005:5005 sinergym:1.1.0

README.md

@@ -1,18 +1,16 @@
-# energym
-
-> :warning: **We are in the process of rebranding this project. Stay tuned for new updates!**
+# sinergym
 
 <div align="center">
   <img src="images/logo.png" width=40%><br><br>
 </div>
 
-**Welcome to energym!**
+**Welcome to sinergym!**
 
 This is a project based on Zhiang Zhang and Khee Poh Lam [Gym-Eplus](https://github.com/zhangzhizza/Gym-Eplus).
 
 The goal of this project is to create an environment following OpenAI Gym interface for wrapping simulation engines for building control using **deep reinforcement learning**.
 
-The main functionalities of Energym are the following :
+The main functionalities of Sinergym are the following :
 
   - **Benchmark environments**. Similarly to Atari or Mujoco environments for RL community, we are designing a set of environments for benchmarking and testing deep RL algorithms. These environments may include different buildings, weathers or action spaces.
   - **Develop different experimental settings**. We aim to provide a package that allows to modify experimental settings in an easy manner. For example, several reward functions or observation variables may be defined. 
@@ -60,26 +58,26 @@ _This is a work in progress project. Stay tuned for upcoming releases._
 
 ## Installation process
 
-To install energym, follow these steps.
+To install sinergym, follow these steps.
 
 First, it is recommended to create a virtual environment. You can do so by:
 
 ```sh
 $ sudo apt-get install python-virtualenv virtualenv
-$ virtualenv env_energym --python=python3.7
-$ source env_energym/bin/activate
+$ virtualenv env_sinergym --python=python3.7
+$ source env_sinergym/bin/activate
 ```
 
 Then, clone this repository using this command:
 ```
-$ git clone https://github.com/jajimer/energym.git
+$ git clone https://github.com/jajimer/sinergym.git
 ```
 
 ### Docker container
 
-We include a **Dockerfile** for installing all dependencies and setting up the image for running energym. If you use [Visual Studio Code](https://code.visualstudio.com/), by simply opening the root directory and clicking on the pop-up button "_Reopen in container_", dependencies will be installed automatically and you will be able to run energym in an isolated environment.
+We include a **Dockerfile** for installing all dependencies and setting up the image for running sinergym. If you use [Visual Studio Code](https://code.visualstudio.com/), by simply opening the root directory and clicking on the pop-up button "_Reopen in container_", dependencies will be installed automatically and you will be able to run sinergym in an isolated environment.
 
-You can also use our [Docker Hub repository](https://hub.docker.com/repository/docker/alejandrocn7/energym) with the desired version directly.
+You can also use our [Docker Hub repository](https://hub.docker.com/repository/docker/alejandrocn7/sinergym) with the desired version directly.
 
 However, if you prefer installing it manually, follow the steps below.
 
@@ -88,9 +86,9 @@ However, if you prefer installing it manually, follow the steps below.
 #### 1. Install Energy Plus 9.5.0
 
 Firstly, install EnergyPlus. Currently it has been update compability to 9.5.0 and it has
-been tested, but code may also work with other versions. Energym ensure this support:
+been tested, but code may also work with other versions. Sinergym ensure this support:
 
-| Energym Version  | EnergyPlus version |
+| Sinergym Version  | EnergyPlus version |
 |------------------|--------------------|
 | 1.0.0            | 8.6.0              | 
 | 1.1.0            | 9.5.0              | 
@@ -111,34 +109,34 @@ Two environment variables must be set: ``EPLUS_PATH`` and ``BCVTB_PATH``, with t
 
 #### 4. Install the package
 
-Finally, energym can be installed by running this command at the repo root folder:
+Finally, sinergym can be installed by running this command at the repo root folder:
 
 ```sh
 pip install -e .
 ```
 
-Extra libraries can be installed by typing ``pip install -e .[extras]``. They are intended for running and analyzing DRL algorithms over energym, but they are
+Extra libraries can be installed by typing ``pip install -e .[extras]``. They are intended for running and analyzing DRL algorithms over sinergym, but they are
 not a requirement of the package.   
 
 And that's all!
 
 ## Check Installation
 
-This project is automatically supervised using tests developed specifically for it. If you want to check energym has been installed successfully, run next command:
+This project is automatically supervised using tests developed specifically for it. If you want to check sinergym has been installed successfully, run next command:
 
 ```sh
 $ pytest tests/ -vv
 ```
-Anyway, every time energym repository is updated, the tests will run automatically in a remote container using the Dockerfile to build it. `Github Action <https://docs.github.com/es/actions/>`__ will do that job (see our documentation for more information).
+Anyway, every time sinergym repository is updated, the tests will run automatically in a remote container using the Dockerfile to build it. `Github Action <https://docs.github.com/es/actions/>`__ will do that job (see our documentation for more information).
 
 ## Usage example
 
-Energym uses the standard OpenAI gym API. So basic loop should be something like:
+Sinergym uses the standard OpenAI gym API. So basic loop should be something like:
 
 ```python
 
 import gym
-import energym
+import sinergym
 
 env = gym.make('Eplus-demo-v1')
 obs = env.reset()

check_run_times.py

@@ -1,5 +1,5 @@
 import gym
-from energym.utils.wrappers import NormalizeObservation, LoggerWrapper, MultiObsWrapper
+from sinergym.utils.wrappers import NormalizeObservation, LoggerWrapper, MultiObsWrapper
 from datetime import datetime
 import argparse
 import os
@@ -49,6 +49,6 @@
     shutil.rmtree(directory)
 
 # Deleting new random weather files once it has been checked
-files = glob('energym/data/weather/*Random*.epw')
+files = glob('sinergym/data/weather/*Random*.epw')
 for file in files:
     os.remove(file)

cloud_manager.py

@@ -1,7 +1,7 @@
 import argparse
 from time import sleep
 from pprint import pprint
-import energym.utils.gcloud as gcloud
+import sinergym.utils.gcloud as gcloud
 
 parser = argparse.ArgumentParser(
     description='Process for run experiments in Google Cloud')
@@ -22,14 +22,14 @@
     '--template_name',
     '-tem',
     type=str,
-    default='energym-template',
+    default='sinergym-template',
     dest='template_name',
     help='Name of template previously created in gcloud account to generate VM copies.')
 parser.add_argument(
     '--group_name',
     '-group',
     type=str,
-    default='energym-group',
+    default='sinergym-group',
     dest='group_name',
     help='Name of instance group(MIG) will be created during experimentation.')
 parser.add_argument(
@@ -110,8 +110,8 @@
 print('All VM\'s are working correctly, see Google Cloud Platform Console.')
 # Close VM when finished with google cloud alerts?
 
-# python  cloud_manager.py --project_id energym-314709
+# python  cloud_manager.py --project_id sinergym
 # --experiments_commands
 # 'python 3 DRL_battery.py --environment Eplus-5Zone-hot-discrete-v1 --episodes 3 --algorithm DQN --logger log_interval 1 --seed 54'
 # 'python3 DRL_battery.py --environment Eplus-5Zone-hot-continuous-stochastic-v1 --episodes 3 --algorithm PPO --logger --log_interval 1 --tensorboard --normalization --seed 54'
-# --template_name energym_template --group_name sinergym-vm
+# --template_name sinergym_template --group_name sinergym-vm

cloudbuild.yaml

@@ -2,22 +2,22 @@ steps:
   # Write in cache for quick updates
 - name: 'gcr.io/google.com/cloudsdktool/cloud-sdk'
   entrypoint: 'bash'
-  args: ['-c', 'docker pull gcr.io/${PROJECT_ID}/energym:latest || exit 0']
+  args: ['-c', 'docker pull gcr.io/${PROJECT_ID}/sinergym:latest || exit 0']
   # Build image (using cache if it's possible)
 - name: 'gcr.io/google.com/cloudsdktool/cloud-sdk'
   entrypoint: 'docker'
   args: [
             'build',
-            '-t', 'gcr.io/${PROJECT_ID}/energym:latest',
-            '--cache-from', 'gcr.io/${PROJECT_ID}/energym:latest',
-            '--build-arg', 'ENERGYM_EXTRAS=[DRL]',
+            '-t', 'gcr.io/${PROJECT_ID}/sinergym:latest',
+            '--cache-from', 'gcr.io/${PROJECT_ID}/sinergym:latest',
+            '--build-arg', 'SINERGYM_EXTRAS=[DRL]',
             '.'
         ]
 
   # Push image built to container registry
 - name: 'gcr.io/google.com/cloudsdktool/cloud-sdk'
   entrypoint: 'docker'
-  args: ['push', 'gcr.io/${PROJECT_ID}/energym:latest']
+  args: ['push', 'gcr.io/${PROJECT_ID}/sinergym:latest']
 
   # This container is going to be public (Change command in other case)
 - name: 'gcr.io/cloud-builders/gsutil'
@@ -27,6 +27,6 @@ options:
   diskSizeGb: '10'
   machineType: 'E2_HIGHCPU_8'
 timeout: 86400s
-images: ['gcr.io/${PROJECT_ID}/energym:latest']
+images: ['gcr.io/${PROJECT_ID}/sinergym:latest']
 
 

doc/modules.rst

@@ -1,7 +1,7 @@
-energym
-=======
+sinergym
+=========
 
 .. toctree::
    :maxdepth: 4
 
-   energym
+   sinergym

doc/source/conf.py

@@ -17,7 +17,7 @@
 
 # -- Project information -----------------------------------------------------
 
-project = 'energym'
+project = 'sinergym'
 copyright = '2021, J. Jiménez, J. Gómez, M. Molina, A. Manjavacas, A. Campoy'
 author = 'J. Jiménez, J. Gómez, M.l Molina, A. Manjavacas, A. Campoy'
 

doc/source/index.rst

@@ -1,5 +1,5 @@
-Welcome to **energym**!
-=======================
+Welcome to **sinergym**!
+========================
 
 .. include:: pages/introduction.rst
 

doc/source/pages/API-reference.rst

@@ -9,7 +9,7 @@ API reference
    :template: custom-module-template.rst
    :recursive:
 
-   energym.utils
-   energym.envs
-   energym.simulators
+   sinergym.utils
+   sinergym.envs
+   sinergym.simulators
 

doc/source/pages/controllers.rst

@@ -2,11 +2,11 @@
 Controllers
 ############
 
-Energym has a section to implement your own controllers. Currently, we have developed a **random agent** and a **rule-based agent**.
-You can find this code in `energym/energym/utils/controllers.py <https://github.com/jajimer/energym/blob/main/energym/utils/controllers.py>`__.
+Sinergym has a section to implement your own controllers. Currently, we have developed a **random agent** and a **rule-based agent**.
+You can find this code in `sinergym/sinergym/utils/controllers.py <https://github.com/jajimer/sinergym/blob/main/sinergym/utils/controllers.py>`__.
 it is very useful in order to perform benchmarks as a reference point to study DRL algorithms:
 
-.. literalinclude:: ../../../energym/utils/controllers.py
+.. literalinclude:: ../../../sinergym/utils/controllers.py
     :language: python
 
 The functionality is very simple; given an environment observation, these instances return an action to interact with the environment. You can develop your own