simplerecipe.py

#!/usr/bin/env python
########################################################################
#
# diffpy.srfit      by DANSE Diffraction group
#                   Simon J. L. Billinge
#                   (c) 2009 The Trustees of Columbia University
#                   in the City of New York.  All rights reserved.
#
# File coded by:    Chris Farrow
#
# See AUTHORS.txt for a list of people who contributed.
# See LICENSE_DANSE.txt for license information.
#
########################################################################

"""Example of simplified fitting.

This is like gaussianrecipe.py, but it uses the SimpleRecipe, which
integrates the FitContribution and Profile objects for simple recipe
creation.
"""

from diffpy.srfit.fitbase import SimpleRecipe

####### Example Code

def main():
    """Set up a simple recipe in a few lines."""

    # The SimpleRecipe class is a type of FitRecipe. It provides attribute-like
    # access to variables and a residual function that can be minimized.
    recipe = SimpleRecipe()

    # Load text from file.
    recipe.loadtxt("data/gaussian.dat")

    # Set the equation. The variable "x" is taken from the data that was just
    # loaded. The other variables, "A", "x0" and "sigma" are turned into
    # attributes with an initial value of 0.
    recipe.setEquation("A * exp(-0.5*(x-x0)**2/sigma**2)")

    # We can give them other values here.
    recipe.A = 1
    recipe.x0 = 5
    recipe.sigma = 1

    # We explicitly optimize the residual method of the SimpleRecipe
    from scipy.optimize import leastsq
    leastsq(recipe.residual, recipe.values)

    # Print the results
    recipe.printResults()

    return

if __name__ == "__main__":

    main()

# End of file