IncliNET

1. Description

2. IncliNET Tutorial

3. Code Repository & Issues

4. Basic Install

   1. on a local machine using Docker

   2. On a server using Docker

   3. directly from source codes

5. Bulging the Docker Package

6. API

7. Documentation

   1. Sphinx

   2. Swagger

8. Related Topics

9. Acknowledgments

   1. About the data

   2. Citation

   3. Author

   4. Disclaimer

1. Description

IncliNET is a Python application to determine the inclination of Spiral Galaxies

• This application is hosted on EDD: http://edd.ifa.hawaii.edu/inclinet/

2. IncliNET Tutorial

An application with and online web GUI and an easy to access API for determining the inclinations of the spiral galaxies using their optical images.

This program is powered by several deep convolutional neural networks (CNN) implemented in TensorFlow. All CNNs are constructed based on the well-known VGG structure, where the convolutional filters are of size 3x3.

• Tutorial in Jupyter Notebook: tutorial.ipynb

• Full Documentation: https://edd.ifa.hawaii.edu/static/html/index.html

3. Code Repository & Issues

https://github.com/ekourkchi/inclinet_deployment_repo

4. Basic Install

4.1. on a local machine using Docker

• First, you need to install Docker Compose.

pip install docker-compose

• Execute

$ docker run -it --entrypoint /inclinet/serverup.sh -p 3030:3030  ekourkchi/inclinet

• Open the Application: Once the service is running in the terminal, open a browser like Firefox or Google Chrome and enter the following url: http://0.0.0.0:3030/

4.2. On a server using Docker

• First, you need to install Docker Compose. How to install

• Execute

$ docker run -it --entrypoint /inclinet/serverup.sh --env="WEBROOT=/inclinet/" -p pppp:3030 -v /pathTO/public_html/static/:/inclinet/static ekourkchi/inclinet

where WEBROOT is an environmental variable that points to the root of the application in the URL path. pppp is the port number that the service would be available to the world. 3030 is the port number of the docker container that our application uses by default. /pathTO/public_html/static/ is the path to the public_html or any folder that the backend server uses to expose communicate with Internet. We basically need to mount /pathTO/public_html/static/ to the folder inclinet/static within the container which is used internally by the application.

URL: Following the above example, if the server host is accessible through www.example.com, then our application would be launched on www.example.com/inclinet:pppp. Remember http or https by default use ports 80 and 443, respectively.

4.3. directly from source codes

Just put the repository on the server or on a local machine and make sure that folder <repository>/static is linked to a folder that is exposed by the server to the outside world. Set WEBROOT prior to launching the application to point the application to the correct URL path.

Execution of server.py launches the application.

$ python server.py -h


- starting up the service on the desired host:port

- How to run:

    $ python server.py -t <host IP> -p <port_number> -d <debugging_mode>

- To get help
    $ python server.py -h



Options:
-h, --help            show this help message and exit
-p PORT, --port=PORT  the port number to run the service on
-t HOST, --host=HOST  service host
-d, --debug           debugging mode

Please consult the IncliNET code documentation for further details. For more thorough details, refer to the tutorial.

5. Bulging the Docker Package

In the code repository run:

docker build --tag myimage:x.x .
docker login -u <username>
docker push myimage:x.x .

where x.x is the package version.

• Docker simple cheat sheet

6. API

• See the API documentation here

1. URL query that reports all evaluated inclinations and other results in json format. <PGC_id> is the galaxy ID in the HyperLeda catalog.

$ curl http://edd.ifa.hawaii.edu/inclinet/api/pgc/<PGC_id>

• example:

  $ curl http://edd.ifa.hawaii.edu/inclinet/api/pgc/2557
  {
  "status": "success",
  "galaxy": {
      "pgc": "2557",
      "ra": "10.6848 deg",
      "dec": "41.2689 deg",
      "fov": "266.74 arcmin",
      "pa": "35.0 deg",
      "objname": "NGC0224"
  },
  "inclinations": {
      "Group_0": {
      "model4": 69.0,
      "model41": 72.0,
      "model42": 76.0,
      "model43": 71.0
      },
      "Group_1": {
      "model5": 73.0,
      "model51": 73.0,
      "model52": 74.0,
      "model53": 74.0
      },
      "Group_2": {
      "model6": 73.0,
      "model61": 76.0,
      "model62": 76.0,
      "model63": 67.0
      },
      "summary": {
      "mean": 72.83333333333333,
      "median": 73.0,
      "stdev": 2.6718699236468995
      }
  },
  "rejection_likelihood": {
      "model4-binary": 50.396937131881714,
      "model5-binary": 20.49814760684967,
      "model6-binary": 65.37048816680908,
      "summary": {
      "mean": 45.42185763518015,
      "median": 50.396937131881714,
      "stdev": 18.65378065042258
      }
  }
  }
  

1. Given the galaxy common name, the following URL reports all evaluated inclinations and other results in json format. <obj_name> is the galaxy galaxy name. Galaxy name is looked up on NASA/IPAC Extragalactic Database and the corresponding PGC number would be used for the purpose of our analysis.

$ curl http://edd.ifa.hawaii.edu/inclinet/api/objname/<obj_name>

• example:

  $ curl http://edd.ifa.hawaii.edu/inclinet/api/objname/M33
  {
  "status": "success",
  "galaxy": {
      "pgc": 5818,
      "ra": "23.4621 deg",
      "dec": "30.6599 deg",
      "fov": "92.49 arcmin",
      "pa": "22.5 deg",
      "objname": "M33"
  },
  "inclinations": {
      "Group_0": {
      "model4": 54.0,
      "model41": 58.0,
      "model42": 54.0,
      "model43": 52.0
      },
      "Group_1": {
      "model5": 54.0,
      "model51": 55.0,
      "model52": 52.0,
      "model53": 55.0
      },
      "Group_2": {
      "model6": 56.0,
      "model61": 57.0,
      "model62": 55.0,
      "model63": 53.0
      },
      "summary": {
      "mean": 54.583333333333336,
      "median": 54.5,
      "stdev": 1.753963764987432
      }
  },
  "rejection_likelihood": {
      "model4-binary": 41.28798842430115,
      "model5-binary": 4.068140685558319,
      "model6-binary": 55.70455193519592,
      "summary": {
      "mean": 33.68689368168513,
      "median": 41.28798842430115,
      "stdev": 21.754880259382322
      }
  }
  }
  

1. Given the galaxy image, the following API call reports all evaluated inclinations and other results in json format.

$ curl -F 'file=@/path/to/image/galaxy.jpg' http://edd.ifa.hawaii.edu/inclinet/api/file

where /path/to/image/galaxy.jpg would be replaced by the name of the galaxy image. The accepted suffixes are 'PNG', 'JPG', 'JPEG', 'GIF' and uploaded files should be smaller than 1 MB.

• example:

  $ curl -F 'file=@/path/to/image/NGC_4579.jpg' http://edd.ifa.hawaii.edu/inclinet/api/file
  {
  "status": "success",
  "filename": "NGC_4579.jpg",
  "inclinations": {
      "Group_0": {
      "model4": 47.0,
      "model41": 51.0,
      "model42": 50.0,
      "model43": 47.0
      },
      "Group_1": {
      "model5": 49.0,
      "model51": 49.0,
      "model52": 51.0,
      "model53": 52.0
      },
      "Group_2": {
      "model6": 50.0,
      "model61": 49.0,
      "model62": 49.0,
      "model63": 48.0
      },
      "summary": {
      "mean": 49.333333333333336,
      "median": 49.0,
      "stdev": 1.49071198499986
      }
  },
  "rejection_likelihood": {
      "model4-binary": 84.28281545639038,
      "model5-binary": 94.24970746040344,
      "model6-binary": 88.11054229736328,
      "summary": {
      "mean": 88.88102173805237,
      "median": 88.11054229736328,
      "stdev": 4.105278145778375
      }
  }
  }
  

7. Documentation

The full documentation of this application is available here.

7.1. Sphinx

Notes:

• A nice article on the Sphinx workflow.

• A short tutorial on how to easily get Sphinx to work.

• <code_repository>/docs: Run make html here to update the Sphinx documentations. Sphinx parses all docstrings into a set of html pages.

• • <code_repository>/docs/source/conf.py: Running make html executes conf.py, so any errors and/or conflict should be addressed here.

• <code_repository>/docs/source: *.rsd files contain lists of links and bookmarks that appear on the left side of html documentations. I have manually revised these index files after the first round of executing sphinx-apidoc.

7.2. Swagger

The documentation of the REST API of our application is available here.

Notes: You may follow this tutorial to easily setup the Swagger API documentation under Flask.

8. Related Topics

• For further details on various VGG models we considered in this project click here.

• Visit here to get the full picture of the deployment plan.

• The Production Pipeline

• Project proposal and motivations

• Data Preprocessing in order to get reliable labels

• On how to download data from the SDSS image service and preprocess them click here

9. Acknowledgments

9.1. About the data

All data exposed by the IncliNET project belongs to

• Cosmicflows-4 program

• Copyright (C) Cosmicflows

• Team - The Extragalactic Distance Database (EDD)

9.2. Citation

Please cite the following paper and the gitHub repository of this project.

• Cosmicflows-4: The Catalog of ∼10,000 Tully-Fisher Distances

@ARTICLE{2020ApJ...902..145K,
       author = {{Kourkchi}, Ehsan and {Tully}, R. Brent and {Eftekharzadeh}, Sarah and {Llop}, Jordan and {Courtois}, H{\'e}l{\`e}ne M. and {Guinet}, Daniel and {Dupuy}, Alexandra and {Neill}, James D. and {Seibert}, Mark and {Andrews}, Michael and {Chuang}, Juana and {Danesh}, Arash and {Gonzalez}, Randy and {Holthaus}, Alexandria and {Mokelke}, Amber and {Schoen}, Devin and {Urasaki}, Chase},
        title = "{Cosmicflows-4: The Catalog of {\ensuremath{\sim}}10,000 Tully-Fisher Distances}",
      journal = {\apj},
     keywords = {Galaxy distances, Spiral galaxies, Galaxy photometry, Hubble constant, H I line emission, Large-scale structure of the universe, Inclination, Sky surveys, Catalogs, Distance measure, Random Forests, 590, 1560, 611, 758, 690, 902, 780, 1464, 205, 395, 1935, Astrophysics - Astrophysics of Galaxies},
         year = 2020,
        month = oct,
       volume = {902},
       number = {2},
          eid = {145},
        pages = {145},
          doi = {10.3847/1538-4357/abb66b},
archivePrefix = {arXiv},
       eprint = {2009.00733},
 primaryClass = {astro-ph.GA},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2020ApJ...902..145K},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

9.3. Author

• Ehsan Kourkchi - ekourkchi@gmail.com

9.4. Disclaimer

• All rights reserved. The material may not be used, reproduced or distributed, in whole or in part, without the prior agreement.

Contents:

• 1. The IncliNET Tutorial
  • 1.1. Introduction
    • 1.1.1. Problem
    • 1.1.2. Objectives
• 2. Web Application
  • 2.1. Galaxy PGC ID
  • 2.2. Galaxy Name
  • 2.3. Galaxy Coordinates
  • 2.4. Galaxy Image
• 3. API
  • 3.1. Example 1: /api/pgc/
    • 3.1.1. Sending a request with the galaxy ID
    • 3.1.2. Erroneous PGC ID
  • 3.2. Example 2: /api/objname
    • 3.2.1. Sending a request with the galaxy name
    • 3.2.2. Wrong galaxy name
  • 3.3. Example 3: /api/file
    • 3.3.1. Sending a request with the galaxy image
    • 3.3.2. Wrong file name
    • 3.3.3. Wrong file format
  • 3.4. Example 4: /getPGC
    • 3.4.1. Get information about the galaxy providing the PGC ID
    • 3.4.2. Generating a wrong API call
  • 3.5. Example 5: /getObj
    • 3.5.1. Get information about the galaxy providing its common name
    • 3.5.2. Generating a wrong API call
  • 3.6. Related Topics
• 4. Acknowledgments
  • 4.1. About the data
  • 4.2. Citation
  • 4.3. Author
  • 4.4. Disclaimer
• 5. Python Modules
  • 5.1. server.py
  • 5.2. incNET.py
  • 5.3. CNN_models.py

Indices and tables

• Index

• Module Index

• Search Page