Table of Contents

1. About The Project
2. Getting Started
   • Prerequisites
   • Installation
   • Structure
3. Usage
4. Methods
5. Contributing
6. License
7. Contact
8. Acknowledgments

About The Project

This is a class final project.

For our project, we have decided to build our own AI astronomer which determines the most searched objects in a given day and gets the coordinates of said objects through a DSS (Digitized Sky Survey) query. The project itself can be navigated through a simple and easily understandable website.

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Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Prerequisites

pip install -r requirements.txt

Installation

1. Clone the repo

   git clone https://github.com/ngan-110/OA_RobotAstro.git

2. Install packages

   https://github.com/ngan-110/OA_RobotAstro.git

3. Run

   python main.py

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Structure:

1. COMPONENTS: All Python scripts
2. DATA: All data files
3. IMAGES: Where all images used for websites and images downloaded from STScI database are stored
4. PAGES: All HTML files
5. SCRIPTS: All javascript files
6. STYLES: All CSS files
7. UNIVERSAL: All common header and footer across all pages

Usage

Use this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources.

For more examples, please refer to the Documentation

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Methods:

• Collect data from space news sites: website_scrapping.py:
  • Websites: https://www.space.com/science-astronomy, https://www.sciencenews.org/topic/astronomy, https://www.nature.com/natastron/news-and-comment, https://phys.org/space-news/
  • Write article headlines and links to headlines.txt and links.txt
• Process keywords keyword_processing.py
  • Use nltk for language and POS tagging
  • Find most popular topics:
    • Read headlines.txt
    • Get nouns with nltk
    • Write nouns in a dictionary and track count in headlines.
    • Exclude generic words, websites, org names
    • Get the top 10 words with the highest counts
    • Write these words in popular_topics.txt and their occurrences
• Find object names from popular topics list: back_search.py
  • Use genism for summaries and object locating
    • Search all headlines for top words
    • Scrape articles from their corresponding links
    • Create 40% sized summaries of each article
    • Filter through summaries with a large list of object names accumulated from multiple databases
    • Create list of final object names list_objects.txt
• Search top topics in databases to find more information about the objects, the coordinates, and populate to HTML: popular_object_to_html.py:
  • Reverse search using keywords on websites
  • Get exact objects names
  • Get objects' coordinates from databases, can feed object name to https://archive.stsci.edu/cgi-bin/dss_form, use Astropy
  • Determine if objects are observable, flag, and move to the next object if not observable
  • Use coordinates RA and Dec to get image from https://archive.stsci.edu/cgi-bin/dss_form
  • Save information about objects: coordinates and images and update on HTML files
• Create a user interface (website) that:
  • Displays images of the most-searched objects (catalog and originally taken), general information about each object (ie. each's location in the sky)
  • Includes a link to GitHub documentation
  • Includes an 'About Us' page, a page about the history of the stellarium telescope, a project 'README' page, and possibly additional informational pages

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Contact

Ngan Nguyen - Alexandra Savino - Rhessa Weber Langstaff

Project Link: https://github.com/ngan-110/OA_RobotAstro

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Acknowledgments

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