Abstract : Flexible modeling and fitting environment

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Homepage : http://cxc.harvard.edu/contrib/sherpa47b

Blog: http://pysherpa.blogspot.com/

Sherpa is a modeling and fitting application for Python. It contains a powerful language for combining simple models into complex expressions that can be fit to the data using a variety of statistics and optimization methods. It is easily extensible to include user models, statistics and optimization methods.

What can you do with Sherpa?

  • Fit 1D (multiple) data including: spectra, surface brightness profiles, light curves, general ASCII arrays
  • Fit 2D images/surfaces in Poisson/Gaussian regime
  • Build complex model expressions
  • Import and use your own models
  • Use appropriate statistics for modeling Poisson or Gaussian data
  • Import new statistics, with priors if required by analysis
  • Visualize a parameter space with simulations or using 1D/2D cuts of the parameter space
  • Calculate confidence levels on the best fit model parameters
  • Choose a robust optimization method for the fit: Levenberg-Marquardt, Nelder-Mead Simplex or Monte Carlo/Differential Evolution.

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