SkyView Query and Visualization

This project provides a Python script for querying astronomical surveys from the SkyView service and visualizing the images in a dynamic grid layout. The script utilizes Astroquery to fetch images from multiple surveys, then displays them with various normalization techniques using matplotlib. The user can specify the target celestial position (e.g., a galaxy or star), and the script will query multiple surveys and plot them in a grid format.

Features

• Query SkyView: Query multiple surveys for a given celestial position (e.g., galaxy or coordinates).
• Grid Layout: Automatically determine the number of rows and columns for the plot based on the number of images.
• Normalization: Use different normalization methods (e.g., Logarithmic Normalization or Sqrt Normalization) for better visualization of astronomical images with varying intensities.
• Customizable: Adjust the grid layout size with a pixel_size parameter and choose from a variety of surveys.
• Color Mapping: Display images using grayscale with color bar for intensity reference.

Requirements

• astroquery: For querying the SkyView service and retrieving survey images.
• matplotlib: For plotting the images in a grid layout.
• numpy: For any necessary numerical operations (e.g., array manipulations).

Installation

To get started, you'll need to install the following dependencies:

pip install astroquery
pip install matplotlib

Usage

You can use the query_and_plot function to query a given celestial position and display images from multiple surveys in a grid layout.

Example

from astroquery.skyview import SkyView

# Define the target position (e.g., a galaxy or coordinates)
position = "M51"

# List of surveys to query (e.g., GALEX, DSS2, WISE, etc.)
surveys = ["GALEX Near UV", "DSS2 Red", "2MASS-J", "WISE 3.4"]

# Call the query_and_plot function
query_and_plot(position, surveys, pixel_size=5)

Parameters

• position (str): The target celestial position (e.g., "M51" for a galaxy, or coordinates like "00h42m44.3s +41d16m08s").
• surveys (list): A list of surveys to query (e.g., ["GALEX Near UV", "DSS2 Red", "2MASS-J"]).
• pixel_size (int): The pixel size (in inches) for each subplot. Adjust this to control the size of the final plot (default is 500).

Normalization Options

The script supports different normalization techniques:

• Logarithmic Normalization: Best for images with a wide range of intensities. Enhances the contrast in the lower intensity regions.
• Square Root Normalization: Ideal for images with moderate intensity variations. Enhances contrast while retaining details in brighter regions.

By default, the code uses LogNorm for visualization. You can adjust the normalization method within the code if needed.

Example Output

The output will be a grid layout of images from different surveys, each showing the queried position. The images will be normalized for better visualization with a color bar representing intensity.

Contribution

Contributions are welcome! Feel free to fork the repository, create issues, and submit pull requests for improvements or bug fixes.

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Acknowledgements

• Astroquery: For enabling access to astronomical data from SkyView and other services.
• matplotlib: For creating the visualization of the survey images in a customizable grid layout.