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README.md

Carpyncho web client

Carpyncho, a data mining catalog browser which we hope will reutilized to search for and characterize time variable data of the ~PiB size VVV/VVVx[1] survey.

Carpyncho is being developed for the detection and classification of periodic and non-periodic (or transient variables). For this purpose the stacked pawprint data from the VDFS CASU v 1.3 catalogues have been crossed matched with the VDFS CASU v1.3 tile catalogues into a PostgreSql data-base. The Carpyncho infraestructure http://carpyncho.jbcabral.org/ is being developed entirely in python on top of a Custom-Framework for data processing[2, 3] and a Django web-framework[4] (for the webapp).

For calculation purposes Carpyncho is layered on-top of a scientific-Python library stack that includes:

  • Numpy[5], Pandas[6] & Scipy[7]: for Numerical calculations
  • Astropy[8]: for Processing of Fits tables, astrometric and photometric calculations.
  • PyAstronomy[9]: for GLS, PDM and time conversion algorithms.
  • feets[10, 11]: for feature extraction.
  • Scikit-learn[12]: for machine learning algorithms.

Help & discussion mailing list

You can contact me at: jbc.develop@gmail.com

Code Repository & Issues

https://github.com/carpyncho/web

Citation

If you use Carpyncho in a scientific publication, we would appreciate citations to the following paper:

Cabral, J. B., ... Astronomy and Computing.

Bibtex entry

    @article{
    }

Full Publication: -

References

  • [1]: Minniti, D., Lucas, P. W., Emerson, J. P., Saito, R. K., Hempel, M., Pietrukowicz, P., ... & Bandyopadhyay, R. M. (2010). VISTA Variables in the Via Lactea (VVV): The public ESO near-IR variability survey of the Milky Way. New Astronomy, 15(5), 433-443.
  • [2]: Cabral, J. B., Sánchez, B., Beroiz, M., Domínguez, M., Lares, M., Gurovich, S., & Granitto, P. (2017). Corral framework: Trustworthy and fully functional data intensive parallel astronomical pipelines. Astronomy and computing, 20, 140-154.
  • [3]: Cabral, J., Sanchez, B., Beroiz, M., Dominguez, M., Lares, M., Gurovich, S., & Granitto, P. (2018). CPF: Corral Pipeline Framework. Astrophysics Source Code Library.
  • [4]: Forcier, J., Bissex, P., & Chun, W. J. (2008). Python web development with Django. Addison-Wesley Professional.
  • [5]: Van Der Walt, S., Colbert, S. C., & Varoquaux, G. (2011). The NumPy array: a structure for efficient numerical computation. Computing in Science & Engineering, 13(2), 22.
  • [6]: McKinney, W. (2012). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. " O'Reilly Media, Inc.".
  • [7]: Jones, E., Oliphant, T., & Peterson, P. (2014). SciPy: Open source scientific tools for Python.
  • [8]: Robitaille, T. P., Tollerud, E. J., Greenfield, P., Droettboom, M., Bray, E., Aldcroft, T., ... & Conley, A. (2013). Astropy: A community Python package for astronomy. Astronomy & Astrophysics, 558, A33.
  • [9]: This work made use of PyAstronomy. https://github.com/sczesla/PyAstronomy
  • [10]: Cabral, J. B., Sánchez, B., Ramos, F., Gurovich, S., Granitto, P. M., & Vanderplas, J. (2018). From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning. Astronomy and computing, 25, 213-220.
  • [11]: Cabral, J., Sanchez, B., Ramos, F., Gurovich, S., Granitto, P., & VanderPlas, J. (2018). feets: feATURE eXTRACTOR for tIME sERIES. Astrophysics Source Code Library.
  • [12]: Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Vanderplas, J. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12(Oct), 2825-2830.
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