Updates main index.rst file

README.md

@@ -9,19 +9,28 @@ https://dimtsap.github.io/ShockPy/
 * [Getting started](#getting-started)
 
 ## General info
+This repository contains a library for modeling uncertainties in shock-wave experiments. 
+Specifically, using available experimental shock velocity and particle velocity measurements derived from shock-wave experiments,
+Bayesian parameter estimation is performed to enhance the until now deterministic analytical Hugoniot formulas with uncertainty measures.
 
+By combining multiple materials with uncertain Hugoniot equations and the Impedance Matching technique, shock-wave experiments can now be replicated using analytical formulas.
+This approach provides experimentalists with statistical data about the possible experimental outcomes and thus expedites the experimentla deisgn process.
 
 ## Method
-
+Details of the methodology can be found in the paper [here](TBD).
 
 ## Application
+Inside the [documentation](https://dimtsap.github.io/ShockPy/auto_examples/index.html) a set of examples can be found that illustrate the use of the code 
+for both forward and backward propagation of the shock-wave experiments allowing the option of including uncertainty to the predicted outcomes. 
+An illustration of forward experiment propagation for a three material experimental setup is provided below.  
+
+<img src="docs/source/_static/forward_experiment.png" width="900">
 
-
 ## Contents
 
 
 ## Getting started
-
+### Users
 **1.** Create an Anaconda Python 3.9 virtual environment:
 ```
 conda create -n shock_wave python==3.9
@@ -34,6 +43,16 @@ conda activate shock_wave
 pip install ShockPy
 ```
 
+### Developers
+Given that the goal of this library is to integrate a wide variety of material with uncertain Hugoniot representations,
+all contributions to the library are encouraged. To assist this process a :code:`.devcontainer` is include in the library that 
+allows for a replicable environment to be generated. This is environment is based on Centos 8 distribution due fortran 
+dependencies of one of the first-principles libraries integrated. Despite that the process is streamlined and requires no 
+further knowledge from the developers. 
+
+Developers are encouraged to use Visual Studio code as it offers a seamless integration with DevContainer as they are auto-detected and the user is directly prompted to open and work on them.
+For further info on DevContainer you can refer [here](https://code.visualstudio.com/docs/devcontainers/containers).
+
 ### Mainteners
 [Dimitris Tsapetis](https://github.com/dimtsap)
 

docs/source/index.rst

@@ -6,8 +6,6 @@
 Welcome to ShockPy's documentation!
 =============================================================
 
-https://dimtsap.github.io/ShockPy/
-
 Table of contents
 --------------------
 * :ref:`General Info <General>`
@@ -19,26 +17,44 @@ Table of contents
 .. _General:
 General info
 --------------------
+This repository contains a library for modeling uncertainties in shock-wave experiments.
+Specifically, using available experimental shock velocity and particle velocity measurements derived from shock-wave experiments,
+Bayesian parameter estimation is performed to enhance the until now deterministic analytical Hugoniot formulas with uncertainty measures.
+
+By combining multiple materials with uncertain Hugoniot equations and the Impedance Matching technique, shock-wave experiments can now be replicated using analytical formulas.
+This approach provides experimentalists with statistical data about the possible experimental outcomes and thus expedites the experimentla deisgn process.
 
 
 .. _Method:
 Method
 --------------------
-
+Details of the methodology can be found in the `paper <TBD>`_.
 
 .. _Examples:
 Examples
 --------------------
+Inside the `documentation <https://dimtsap.github.io/ShockPy/auto_examples/index.html>`_ a set of examples can be found that illustrate the use of the code
+for both forward and backward propagation of the shock-wave experiments allowing the option of including uncertainty to the predicted outcomes.
+An illustration of forward experiment propagation for a three material experimental setup is provided below.
+
+.. image:: _static/forward_experiment.png
+   :scale: 20 %
+   :alt: Forward propagation of shock-wave experiment using analytical equations
+   :align: center
 
 
 .. _Contents:
 Contents
 --------------------
+The :code:`src` folder contains the implementation of the aforementioned methodology.
+The are contained inside the :code:`material_database` module inside the :code:`src`.
+There all the sample generated using the
 
 .. _GettingStarted:
 Getting started
 --------------------
-
+Users
+^^^^^^^^^^^^^^
 **1.** Create an Anaconda Python 3.9 virtual environment:
 
 :code:`conda create -n shock_wave python==3.9`
@@ -50,6 +66,18 @@ Getting started
 
 :code:`pip install ShockPy`
 
+Developers
+^^^^^^^^^^^^^^
+Given that the goal of this library is to integrate a wide variety of material with uncertain Hugoniot representations,
+all contributions to the library are encouraged. To assist this process a :code:`.devcontainer` is include in the library that
+allows for a replicable environment to be generated. This is environment is based on Centos 8 distribution due fortran
+dependencies of one of the first-principles libraries integrated. Despite that the process is streamlined and requires no
+further knowledge from the developers.
+
+Developers are encouraged to use Visual Studio code as it offers a seamless integration with DevContainer as they are auto-detected and the user is directly prompted to open and work on them.
+For further info on DevContainer you can refer `here <https://code.visualstudio.com/docs/devcontainers/containers>`_.
+
+
 Mainteners
 ^^^^^^^^^^^^^^
 Dimitris Tsapetis