fix docs and add docker install

Dockerfile

@@ -21,4 +21,5 @@ RUN chown -R admin /src /home/admin
 USER admin
 RUN pip install -e .
 ENV PYTHONUNBUFFERED=1
+EXPOSE 9099
 ENTRYPOINT ["assume"]

assume/world.py

@@ -39,7 +39,7 @@
 class World:
     def __init__(
         self,
-        ifac_addr: str = "localhost",
+        ifac_addr: str = "0.0.0.0",
         port: int = 9099,
         database_uri: str = "",
         export_csv_path: str = "",

compose.yml

@@ -60,11 +60,6 @@ services:
     image: ghcr.io/assume-framework/assume:latest
     profiles: ["cli-only"]
     build: .
-    environment:
-      DATABASE_URI: 'postgresql://assume:assume@assume_db/assume'
-      EXPORT_CSV_PATH: ''
-    ports:
-      - 5000:5000
     depends_on:
       - assume_db
-    command: -s example_02 -c base -db "sqlite:///"
+    command: -s example_02 -db "postgresql://assume:assume@assume_db:5432/assume"

docs/source/installation.rst

@@ -2,32 +2,88 @@
  Installation
 ################
 
-Getting Python
+You can install ASSUME using pip or docker. Choose the appropriate
+installation method based on your needs.
+
+Using Learning Capabilities
+---------------------------
+
+If you intend to use the reinforcement learning capabilities of
+ASSUME and train your agents, make sure to install Torch. Detailed
+installation instructions can be found `here <https://pytorch.org/get-started/locally/>`_.
+
+Using Pip/Python
 ==============
 
+In general most people use an environment manager for python.
+Here, Install instructions for Conda and Venv are given.
+
 If it is your first time with Python, we recommend `conda
 <https://docs.conda.io/en/latest/miniconda.html>`_ as easy-to-use package managers. It is
 available for Windows, Mac OS X and GNU/Linux.
 
-Installation
-============
+With Conda
+----------
+
+After installing conda, you can create an environment for assume like::
+
+    conda create -n assume python=3.11
+
+accept the installed packages and activate the newly created environment::
+
+    conda activate assume
+
+Now you can proceed with the assume installation using pip below.
+
+With Venv
+-----------------
+
+Venvs are the python native way of handling environments using the same python version.
+To activate a venv, you have to be in the same folder where you created it.
+
+Using venvs, we can create a new environment::
+
+    python -m venv venv
 
-You can install ASSUME using pip. Choose the appropriate
-installation method based on your needs:
+And activate it::
 
-Using pip
----------
+    source venv/bin/activate
+
+Now you can proceed with the assume installation using pip below.
+
+
+Install using pip
+-----------------
 
 To install the core package::
 
     pip install assume-framework
 
+To install with learning capabilities::
+
+    pip install assume-framework[learning]
+
+To install with optimization capabilities::
+
+    pip install assume-framework[optimize]
+
 To install with testing capabilities::
 
     pip install assume-framework[test]
 
+Install using Docker
+=========================================
+
+After installing `Docker Engine <https://docs.docker.com/engine/install/>`_ you can run::
+    docker compose up -d assume_db grafana
+    docker compose run simulation -s example_01a -db "postgresql://assume:assume@assume_db:5432/assume"
+
+Now you can see the simulation results directly on grafana at `http://localhost:3000`
+
 Timescale Database and Grafana Dashboards
------------------------------------------
+=========================================
+
+ASSUME supports writing into Databases supported by `SQLAlchemy <https://docs.sqlalchemy.org/en/latest/dialects/index.html>`_ and CSV files.
 
 If you want to benefit from a supported database and integrated
 Grafana dashboards for scenario analysis, you can use the provided
@@ -42,15 +98,8 @@ Follow these steps:
 
 2. Start the database and Grafana using the following command::
 
-    docker-compose up -d
+    docker compose up -d
 
 This will launch a container for TimescaleDB and Grafana with
 preconfigured dashboards for analysis. You can access the Grafana
 dashboards at `http://localhost:3000`.
-
-Using Learning Capabilities
----------------------------
-
-If you intend to use the reinforcement learning capabilities of
-ASSUME and train your agents, make sure to install Torch. Detailed
-installation instructions can be found `here <https://pytorch.org/get-started/locally/>`_.

pyproject.toml

@@ -34,6 +34,7 @@ python-dateutil = "^2.8.2"
 sqlalchemy = "^2.0.9"
 pandas = {version = "^2.0.0"}
 psycopg2-binary = "^2.9.5"
+pyomo = "^6.6.1"
 pyyaml = "^6.0"
 nest-asyncio = "^1.5.6"
 black = {version = "^23.3.0", optional = true}
@@ -44,7 +45,7 @@ pytest = {version = "^7.2.2", optional = true}
 pytest-cov = {version = "^4.1.0", optional = true}
 pytest-asyncio = {version = "^0.21.1", optional = true}
 torch = {version = "^2.0.1", optional = true}
-pyomo = {version = "^6.6.1", optional = true}
+
 glpk = {version = "^0.4.7", optional = true}
 
 [tool.poetry.group.dev.dependencies]
@@ -58,8 +59,8 @@ pytest-asyncio = "^0.21.1"
 
 [tool.poetry.extras]
 learning = ["torch"]
-optimize = ["pyomo", "glpk"]
-test = ["black", "isort", "matplotlib", "pytest", "pytest-cov", "pytest-asyncio", "pyomo", "glpk"]
+optimize = ["glpk"]
+test = ["black", "isort", "matplotlib", "pytest", "pytest-cov", "pytest-asyncio", "glpk"]
 
 
 [tool.poetry.scripts]