change the website

docs/source/Contributor.rst

@@ -4,15 +4,15 @@ Call for Contributions
 Introduction
 ------------
 
-If you are interested in contributing to the **CompactObject** project, please send an email to `[email protected] <mailto:[email protected]>`_ to request collaboration. You can also open an issue on our GitHub repository at `Issue page <https://github.com/ChunHuangPhy/CompactOject/issues>`_ so that the community can track the status of collaborations.
+If you are interested in contributing to the **CompactObject** project, please send an email to `[email protected] <mailto:[email protected]>`_ to request collaboration. You can also open an issue on our GitHub repository at `Issue page <https://github.com/ChunHuangPhy/CompactObject/issues>`_ so that the community can track the status of collaborations.
 
 We are always eager to expand our scope to include the most equation of state (EOS) computations currently available in the community, as well as the most relevant constraints. If you have your own EOS and wish to contribute, please follow the steps outlined below.
 
 Contributing an Equation of State (EOS)
 ---------------------------------------
 
 1. **Open an Issue**  
-   Visit our GitHub issues page at `Issue page <https://github.com/ChunHuangPhy/CompactOject/issues>`_ and open a new issue to suggest the EOS you would like to contribute.
+   Visit our GitHub issues page at `Issue page <https://github.com/ChunHuangPhy/CompactObject/issues>`_ and open a new issue to suggest the EOS you would like to contribute.
 
 2. **Request Collaboration**  
    Send an email to `[email protected]` to request collaborator access to the repository.
@@ -21,7 +21,7 @@ Contributing an Equation of State (EOS)
    - Create a `.py` script that computes the EOS. The script should:
      - Input the EOS parameter values.
      - Output the pressure and density in CGS units.
-   - For unit conventions, please refer to the `UnitConventionForDeveloper <https://chunhuangphy.github.io/CompactOject/UnitConventionForDeveloper.html>`_.
+   - For unit conventions, please refer to the `UnitConventionForDeveloper <https://chunhuangphy.github.io/CompactObject/UnitConventionForDeveloper.html>`_.
    - Name the main compute function as `compute_EOS`.
    - Name the file as `(YourEOSName)_EOS.py`.
 
@@ -38,7 +38,7 @@ Contributing Inference Components
 If you have developed your own likelihood, prior, sampler, or any component related to inference that is suitable for addition to our functionalities, we highly recommend contacting us. Follow these steps to contribute:
 
 1. **Open an Issue**  
-   Visit our GitHub issues page at `Issue page <https://github.com/ChunHuangPhy/CompactOject/issues>`_ and open a new issue to suggest changes to the inference components.
+   Visit our GitHub issues page at `Issue page <https://github.com/ChunHuangPhy/CompactObject/issues>`_ and open a new issue to suggest changes to the inference components.
 
 2. **Request Collaboration**  
    Send an email to `[email protected]` to request collaborator access to the repository.

docs/source/starthere.rst

@@ -15,14 +15,14 @@ Depends on Your Scientific Goal
 
 If you are new to **CompactObject** and wish to familiarize yourself with its basic concepts and introduction:
 
-- **Index Page:** Explore the `Index Page <https://chunhuangphy.github.io/CompactOject/index.html>`_ for an overview of the package.
-- **README:** Review the `README <https://github.com/ChunHuangPhy/CompactOject/blob/main/README.md>`_ on GitHub to understand the package’s purpose and features.
+- **Index Page:** Explore the `Index Page <https://chunhuangphy.github.io/CompactObject/index.html>`_ for an overview of the package.
+- **README:** Review the `README <https://github.com/ChunHuangPhy/CompactObject/blob/main/README.md>`_ on GitHub to understand the package’s purpose and features.
 
  2. **Generating Equations of State (EOS)**
 
 To use **CompactObject** for generating equations of state:
 
-- **EOS Generators Tutorial:** Start with the `EOS Generators Tutorial <https://chunhuangphy.github.io/CompactOject/test_EOSgenerators.html>`_ which showcases all currently integrated EOS computations in the package. The supported EOS models include:
+- **EOS Generators Tutorial:** Start with the `EOS Generators Tutorial <https://chunhuangphy.github.io/CompactObject/test_EOSgenerators.html>`_ which showcases all currently integrated EOS computations in the package. The supported EOS models include:
   - **Relativistic Mean Field (RMF) Model**
   - **Strangeon Star Model**
   - **Quark Star Model:** MIT Bag Model
@@ -35,8 +35,8 @@ To use **CompactObject** for generating equations of state:
 
 To utilize the TOV solver integrated within **CompactObject**:
 
-- **TOV Solver Tutorial:** Refer to the `TOV Solver Tutorial <https://chunhuangphy.github.io/CompactOject/test_TOVsolver.html>`_. This tutorial demonstrates how to perform TOV computations using table-based EOS.
-- **Inputting an Array:** If you prefer to input an array instead of a table, refer back to the `EOS Generators Tutorial <https://chunhuangphy.github.io/CompactOject/test_EOSgenerators.html>`_. The quantities currently available are:
+- **TOV Solver Tutorial:** Refer to the `TOV Solver Tutorial <https://chunhuangphy.github.io/CompactObject/test_TOVsolver.html>`_. This tutorial demonstrates how to perform TOV computations using table-based EOS.
+- **Inputting an Array:** If you prefer to input an array instead of a table, refer back to the `EOS Generators Tutorial <https://chunhuangphy.github.io/CompactObject/test_EOSgenerators.html>`_. The quantities currently available are:
   - **Mass**
   - **Radius**
   - **Tidal Deformability**
@@ -49,26 +49,26 @@ To utilize the TOV solver integrated within **CompactObject**:
 
 If you wish to perform Bayesian inference using the EOS models already integrated into **CompactObject**, follow these examples:
 
-1. **RMF EOS Inference Pipeline:** Explore the `RMF EOS Inference Pipeline <https://chunhuangphy.github.io/CompactOject/test_Inference.html>`_.
-2. **Strangeon EOS Inference Pipeline:** Check out the `Strangeon EOS Inference Pipeline <https://chunhuangphy.github.io/CompactOject/test_Bayesian_inference_Strangeon_EOS.html>`_.
+1. **RMF EOS Inference Pipeline:** Explore the `RMF EOS Inference Pipeline <https://chunhuangphy.github.io/CompactObject/test_Inference.html>`_.
+2. **Strangeon EOS Inference Pipeline:** Check out the `Strangeon EOS Inference Pipeline <https://chunhuangphy.github.io/CompactObject/test_Bayesian_inference_Strangeon_EOS.html>`_.
 3. *...and more examples*
 
  b. **Using Your Own EOS for Inference**
 
 If you have developed your own EOS and wish to perform Bayesian inference:
 
-1. **Contributor Instructions:** Follow the `Instructions for Contributors <https://chunhuangphy.github.io/CompactOject/Contributor.html>`_ to define your EOS without conflicts and understand the necessary tests to ensure computational accuracy.
-2. **Constructing a New Pipeline:** Utilize the existing inference pipelines, such as the `RMF EOS Inference Pipeline <https://chunhuangphy.github.io/CompactOject/test_Inference.html>`_ and `Strangeon EOS Inference Pipeline <https://chunhuangphy.github.io/CompactOject/test_Bayesian_inference_Strangeon_EOS.html>`_, as reference points to build your own inference workflow based on **CompactObject**.
+1. **Contributor Instructions:** Follow the `Instructions for Contributors <https://chunhuangphy.github.io/CompactObject/Contributor.html>`_ to define your EOS without conflicts and understand the necessary tests to ensure computational accuracy.
+2. **Constructing a New Pipeline:** Utilize the existing inference pipelines, such as the `RMF EOS Inference Pipeline <https://chunhuangphy.github.io/CompactObject/test_Inference.html>`_ and `Strangeon EOS Inference Pipeline <https://chunhuangphy.github.io/CompactObject/test_Bayesian_inference_Strangeon_EOS.html>`_, as reference points to build your own inference workflow based on **CompactObject**.
 3. **Performance Considerations:** Ensure that the computation time for a single EOS is within the sub-second range to handle millions of samples required for Bayesian inference efficiently.
 
 **Contribution Invitation:**  
-Consider contributing your EOS to our community if you are using our package! This will boost the influence of your work and make you a collaborator on this project. We can also promote your results on our page. Please check the `Instructions for Contributors <https://chunhuangphy.github.io/CompactOject/Contributor.html>`_ if you wish to contribute.
+Consider contributing your EOS to our community if you are using our package! This will boost the influence of your work and make you a collaborator on this project. We can also promote your results on our page. Please check the `Instructions for Contributors <https://chunhuangphy.github.io/CompactObject/Contributor.html>`_ if you wish to contribute.
 
  5. **Performing Bayesian Inference in Other Fields**
 
 If you aim to use the inference modules of **CompactObject** for Bayesian inference outside of EOS studies, such as in high-energy physics or other astrophysical fields:
 
-- **Example Notebook:** Refer to the `Sample Glitch Bayesian Inference Notebook <https://github.com/ChunHuangPhy/CompactOject/blob/main/Test_Case/Sample_glitchBayesian.ipynb>`_ for an example of how to apply **CompactObject** to different inference scenarios.
+- **Example Notebook:** Refer to the `Sample Glitch Bayesian Inference Notebook <https://github.com/ChunHuangPhy/CompactObject/blob/main/Test_Case/Sample_glitchBayesian.ipynb>`_ for an example of how to apply **CompactObject** to different inference scenarios.
 
  6. **Advanced Usage: Modifying Solvers or APIs**
 
@@ -80,7 +80,7 @@ If the existing tutorials do not cover your specific needs, such as:
 You can:
 
 - **API Documentation:** Consult the detailed API documentation in this documentation to identify and utilize the functions you need.
-- **GitHub Repository:** Visit our `GitHub Page <https://github.com/ChunHuangPhy/CompactOject/tree/main>`_ to explore the source code and understand the internal workings of **CompactObject**.
+- **GitHub Repository:** Visit our `GitHub Page <https://github.com/ChunHuangPhy/CompactObject/tree/main>`_ to explore the source code and understand the internal workings of **CompactObject**.
 
 **Note:**  
 The key functions, including the **TOVsolver module** code, form the foundation for all inference and computations. Modifying them may require substantial code restructuring. Therefore, if you clone the repository locally and need to make changes to the **TOVsolver**, proceed with caution to avoid extensive code rebuilding. Only original contributors have permission to modify the **TOVsolver** module code, and such commits must undergo detailed reviews by the original members of the project.