modifications of the documentation

mohammedazzouzi15 · Sep 19, 2024 · caa0294 · caa0294
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diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -6,8 +6,8 @@
 # -- Project information -----------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
-project = "stk_search"
-project_copyright = "2024, Mohammed Azzouzi"
+project = "stk-search"
+project_copyright = "2023, Mohammed Azzouzi"
 author = "Mohammed Azzouzi"
 
 # -- General configuration ---------------------------------------------------
@@ -20,15 +20,17 @@
     "sphinx.ext.intersphinx",
     "sphinx.ext.viewcode",
     "sphinx_copybutton",
-    "sphinx.ext.autodoc",
 ]
 
 autosummary_imported_members = True
 
 autodoc_typehints = "description"
 autodoc_member_order = "groupwise"
 autoclass_content = "class"
-
+autodoc_type_aliases = {
+    "Properties": "dict[str, Json]",
+    "Json": "Json",
+}
 
 intersphinx_mapping = {
     "python": ("https://docs.python.org/3", None),

diff --git a/docs/source/index copy.md b/docs/source/index copy.md
@@ -0,0 +1,95 @@
+Welcome to stk_search's documentation!
+===================================
+
+skt_search is a Python library for searching the chemical space of molecules formed by stk. It is built on top of stk and stk_optim.
+<p align="center">
+  <img src="../../overview.svg" alt="Overview Image" />
+</p>
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   Calculators <_autosummary/stk_search.Calculators>
+   utils <_autosummary/stk_search.utils>
+   Search_algorithm <_autosummary/stk_search.Search_algorithm>
+   Representation <_autosummary/stk_search.Representation>
+   SearchAlgorithm <_autosummary/stk_search.Search_algorithm>
+   SearchSpace <_autosummary/stk_search.SearchSpace>
+   Modules <modules>
+
+
+.. tip::
+
+  ⭐ Star us on GitHub! ⭐
+
+GitHub: https://GitHub.com/mohammedazzouzi15/stk_search
+
+## Overview
+
+`stk_search` is a Python package for searching the chemical space of molecules formed by `stk`. It is built on top of `stk` and `stko`. For more details on the use of the package, please refer to the corresponding publication as well as the documentation associated with it.
+
+We use [stk](https://github.com/lukasturcani/stk) and [stko](https://github.com/JelfsMaterialsGroup/stko) for building and calculating the properties of the molecules.
+
+We use [BoTorch](https://botorch.org/) for the implementation of the Bayesian optimization.
+
+For the implementation of the geometric modeling on 3D structure, we use the implementation of models in [GEOM3D](https://github.com/chao1224/Geom3D).
+
+## Installation
+
+To install the package, follow these steps:
+
+1. **Open a terminal and change to the directory where the `pyproject.toml` file is located.**
+    ```bash
+    cd path/to/directory
+    ```
+
+2. **Create a new conda environment**
+    ```bash
+    conda create -n stk_search python=3.8
+    ```
+
+3. **Activate the environment**
+    ```bash
+    conda activate stk_search
+    ```
+
+4. **Run the following command to install the package:**
+    In some cases, you may need to install `gcc` before installing the package.
+    ```bash
+    pip install -e .
+    ```
+
+5. **Install additional packages to use the GNN model:**
+
+    **For GPU:**
+    ```bash
+    pip install torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.3.0+cu121.html
+    # Make sure the torch version is the right one
+    ```
+
+    **For CPU:**
+    ```bash
+    pip install torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.3.0+cpu.html
+
+## Usage
+
+Refer to the example notebooks where we show a step-by-step use of the package to search a space of oligomers formed of 6 building blocks.
+
+1. **Notebook 0: Generate Building Blocks**
+    - Shows how to go from a list of SMILES to generate a list of building blocks.
+    - Introduces a way to run calculations using [`xtb`](command:_github.copilot.openSymbolFromReferences?%5B%22%22%2C%5B%7B%22uri%22%3A%7B%22scheme%22%3A%22file%22%2C%22authority%22%3A%22%22%2C%22path%22%3A%22%2Fc%3A%2FUsers%2Fma11115%2FOneDrive%20-%20Imperial%20College%20London%2Fgithub_folder%2FSTK_search%2FREADME.md%22%2C%22query%22%3A%22%22%2C%22fragment%22%3A%22%22%7D%2C%22pos%22%3A%7B%22line%22%3A55%2C%22character%22%3A74%7D%7D%5D%2C%220faed8b2-e29e-4f60-b965-c22999e98b01%22%5D "Go to definition") and [`xtb_stda`](command:_github.copilot.openSymbolFromReferences?%5B%22%22%2C%5B%7B%22uri%22%3A%7B%22scheme%22%3A%22file%22%2C%22authority%22%3A%22%22%2C%22path%22%3A%22%2Fc%3A%2FUsers%2Fma11115%2FOneDrive%20-%20Imperial%20College%20London%2Fgithub_folder%2FSTK_search%2FREADME.md%22%2C%22query%22%3A%22%22%2C%22fragment%22%3A%22%22%7D%2C%22pos%22%3A%7B%22line%22%3A55%2C%22character%22%3A82%7D%7D%5D%2C%220faed8b2-e29e-4f60-b965-c22999e98b01%22%5D "Go to definition") to get the properties of the building blocks and save them in a database.
+    - Demonstrates how to generate a dataframe with the necessary data to form a representation of the constructed molecules for Bayesian optimization.
+
+2. **Notebook 1: Define Search Space**
+    - Shows how to define the search space and generate a search space pickle that can be loaded later to run the search algorithm.
+
+3. **Notebook 2: Run Search Algorithm**
+    - Shows how to run the search algorithm on the search space using different search algorithms: BO, EA, SUEA.
+
+4. **Notebook 3: Representation Learning**
+    - Shows how to run a representation learning using a 3D geometry-based GNN.
+
+## Contact
+
+For questions, please contact `[email protected]'
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -2,6 +2,9 @@ Welcome to stk_search's documentation!
 ===================================
 
 skt_search is a Python library for searching the chemical space of molecules formed by stk. It is built on top of stk and stk_optim.
+<p align="center">
+  <img src="../../overview.svg" alt="Overview Image" />
+</p>
 
 .. toctree::
    :maxdepth: 2
@@ -22,44 +25,71 @@ skt_search is a Python library for searching the chemical space of molecules for
 
 GitHub: https://GitHub.com/mohammedazzouzi15/stk_search
 
-Installation
-============
+## Overview
 
-To install the package, follow these steps:
+`stk_search` is a Python package for searching the chemical space of molecules formed by `stk`. It is built on top of `stk` and `stko`. For more details on the use of the package, please refer to the corresponding publication as well as the documentation associated with it.
+
+We use [stk](https://github.com/lukasturcani/stk) and [stko](https://github.com/JelfsMaterialsGroup/stko) for building and calculating the properties of the molecules.
+
+We use [BoTorch](https://botorch.org/) for the implementation of the Bayesian optimization.
 
+For the implementation of the geometric modeling on 3D structure, we use the implementation of models in [GEOM3D](https://github.com/chao1224/Geom3D).
+
+## Installation
+
+To install the package, follow these steps:
 
 1. **Open a terminal and change to the directory where the `pyproject.toml` file is located.**
-        cd path/to/directory
-2. create a new conda environment
-        conda create -n stk_search python=3.8 
-3. activate the environment
-        conda activate stk_search
-4. Run the following command to install the package:
-        pip install -e .
-5. install additional package to use the GNN model:
-    for GPU:
-       pip install torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.3.0+cu121.html
-       Make sure the torch version is the right one
-    for CPU:
-        pip install torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.3.0+cpu.html
+    ```bash
+    cd path/to/directory
+    ```
+
+2. **Create a new conda environment**
+    ```bash
+    conda create -n stk_search python=3.8
+    ```
+
+3. **Activate the environment**
+    ```bash
+    conda activate stk_search
+    ```
+
+4. **Run the following command to install the package:**
+    In some cases, you may need to install `gcc` before installing the package.
+    ```bash
+    pip install -e .
+    ```
+
+5. **Install additional packages to use the GNN model:**
+
+    **For GPU:**
+    ```bash
+    pip install torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.3.0+cu121.html
+    # Make sure the torch version is the right one
+    ```
+
+    **For CPU:**
+    ```bash
+    pip install torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.3.0+cpu.html
 
+## Usage
 
-Usage
-============
-for the usage of the package, please refer to the example notebooks in the notebooks folder.
+Refer to the example notebooks where we show a step-by-step use of the package to search a space of oligomers formed of 6 building blocks.
 
-Contributing
-============
-[Provide guidelines for contributing to the project]
+1. **Notebook 0: Generate Building Blocks**
+    - Shows how to go from a list of SMILES to generate a list of building blocks.
+    - Introduces a way to run calculations using [`xtb`](command:_github.copilot.openSymbolFromReferences?%5B%22%22%2C%5B%7B%22uri%22%3A%7B%22scheme%22%3A%22file%22%2C%22authority%22%3A%22%22%2C%22path%22%3A%22%2Fc%3A%2FUsers%2Fma11115%2FOneDrive%20-%20Imperial%20College%20London%2Fgithub_folder%2FSTK_search%2FREADME.md%22%2C%22query%22%3A%22%22%2C%22fragment%22%3A%22%22%7D%2C%22pos%22%3A%7B%22line%22%3A55%2C%22character%22%3A74%7D%7D%5D%2C%220faed8b2-e29e-4f60-b965-c22999e98b01%22%5D "Go to definition") and [`xtb_stda`](command:_github.copilot.openSymbolFromReferences?%5B%22%22%2C%5B%7B%22uri%22%3A%7B%22scheme%22%3A%22file%22%2C%22authority%22%3A%22%22%2C%22path%22%3A%22%2Fc%3A%2FUsers%2Fma11115%2FOneDrive%20-%20Imperial%20College%20London%2Fgithub_folder%2FSTK_search%2FREADME.md%22%2C%22query%22%3A%22%22%2C%22fragment%22%3A%22%22%7D%2C%22pos%22%3A%7B%22line%22%3A55%2C%22character%22%3A82%7D%7D%5D%2C%220faed8b2-e29e-4f60-b965-c22999e98b01%22%5D "Go to definition") to get the properties of the building blocks and save them in a database.
+    - Demonstrates how to generate a dataframe with the necessary data to form a representation of the constructed molecules for Bayesian optimization.
 
-License
-============
-[Specify the license under which the package is distributed]
+2. **Notebook 1: Define Search Space**
+    - Shows how to define the search space and generate a search space pickle that can be loaded later to run the search algorithm.
 
-Contact
-============
+3. **Notebook 2: Run Search Algorithm**
+    - Shows how to run the search algorithm on the search space using different search algorithms: BO, EA, SUEA.
 
-[Provide contact information or links to relevant resources]
+4. **Notebook 3: Representation Learning**
+    - Shows how to run a representation learning using a 3D geometry-based GNN.
 
-```
+## Contact
 
+For questions, please contact `[email protected]'
diff --git a/src/dev_scripts/get_frag_encoding.py b/src/dev_scripts/get_frag_encoding.py
@@ -9,17 +9,19 @@
 
 to run it from the command line:
 python get_frag_encoding.py --config_dir config_dir.
+
 """
 
 
 
 import os
-import Path
-from pathlib import Path
+import pathlib import Path
 
 import numpy as np
 import pandas as pd
+import Path
 import torch
+
 from stk_search.geom3d import (
     dataloader,
     oligomer_encoding_with_transformer,