AstroPhotoPy

Astrophotography processing tools

AstroPhotoPy

Flexible image processing with the astrophotographer in mind

Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Changelog
5. Roadmap
6. Contributing
7. License
8. Contact

About The Project

Once I approached astrophotography several months ago, I searched for a tool grouping all my needs in term of image processing workflow. Much of those instruments are counterintuitive and/or platform/OS restricted (and not less important not free). So I wrote this simple library including all I need at the moment for a flexible processing workflow.
Remember that the AstroPhotoPy library can only be used inside a Jupiter / IPython notebook. I hope you'll enjoy it!

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Built With

This library is based essentially on two image processing libraries and a front-end library:

• opencv
• skimage
• IPython

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Getting Started

This is an example of how you can setting up your project locally. To get a local copy up and running, follow these simple steps:

Prerequisites

To install the things you need first:

• numpy

  pip install numpy

• matplotlib

  pip install matplotlib
  

• pyfits

  pip install pyfits

• opencv

  pip install opencv-python

• skimage

  pip install -U scikit-image

• IPython

  pip install ipython

Installation

After installing dependencies, you can install the library with pip:

pip install astrophotopy

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Simple usage

1. Open your Jupyter / IPython notebook and import the AstroPhotoPy library. You can create a setup (optics + sensor) and an observation site first, then you have to create your project:

  import astrophotopy as ap 
  my_optics = ap.Optics(model="RC 8", diameter = 203, f_len = 1604, f_len_ep = 22)
  my_sensor = ap.Sensor(model = "Sony IMX", px_dim = 2.9, sens_diag = 23)
  my_site = ap.Observation_Site(location_name = "Tre cime di Lavaredo", altitude = 2320, latitude = 46.6, air_temp = -5.5, fwhm = 1)
  crab = ap.Project(subject="M1", path="My_folder", optics = my_optics, sensor = my_sensor, observation_site = my_site)

2. After copying your images (rough, dark and flat frames) in their respective folders, you have to load them (only .jpg, .png, .bmp, .tiff and .fits can be imported), possibly debayer (in case of color camera acquisition software can't make it properly) and then perform set cleaning by means of master dark and flat frame subtraction:

   crab.load()
   crab.debayering()
   crab.subtract_master_dark_and_flat()

3. Now perform an automatic selection of the best images (i.e. dropping those frames in which a damn Starlink passed) and then align the retained frames:

   crab.show_select_and_drop()
   crab.align_frames()

4. After prior calculation of total exposure and creation of integration sets, sets' integration can be performed:

   crab.create_integration_sets()
   crab.integrate_over_sets()

For a better description of the above (and others) astrophotopy methods, please refer to the Documentation

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Changelog

2022 feb 25 - v0.2.2

• added multiprocessing support for speedup of cpu intensive tasks
• added Changelog

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Roadmap

• Add Changelog
• Add multiprocessing
• Add monochromatic camera workflow
• Write exhaustive documentation
• Add .ser file import/export
• CUDA/OpenCL aligning code implementation

See the open issues for a full list of proposed features (and known issues).

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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!

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License

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

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Contact

Davidia - d.costantini.stcerello@gmail.com

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