Calling fit from scripts
========================

Revisiting our curve fit example, let's call the optimizer directly from
the script.

Setting up the problem remains the same:

::

    from bumps.names import *
    
    x = [1, 2, 3, 4, 5, 6]
    y = [2.1, 4.0, 6.3, 8.03, 9.6, 11.9]
    dy = [0.05, 0.05, 0.2, 0.05, 0.2, 0.2]
    
    
    def line(x, m, b=0):
        return m * x + b
    
    
    M = Curve(line, x, y, dy, m=2, b=2)
    M.m.range(0, 4)
    M.b.range(-5, 5)
    
    problem = FitProblem(M)
    
With the problem defined, we can now call the fitter.  The following
uses the minimalist fit interface defined in bumps, which takes a problem
definition and returns a results object with x, dx attributes for the
best value and the estimated uncertainty.  The 'dream' fitter will
additionally return the dream state, which allows for more detailed
uncertainty analysis.

::

    from bumps.fitters import fit
    from bumps.formatnum import format_uncertainty
    
    # Allow choice of fitter from the command line
    method = "amoeba" if len(sys.argv) < 2 else sys.argv[1]
    
    print("initial chisq", problem.chisq_str())
    result = fit(problem, method=method, xtol=1e-6, ftol=1e-8)
    print("final chisq", problem.chisq_str())
    for k, v, dv in zip(problem.labels(), result.x, result.dx):
        print(k, ":", format_uncertainty(v, dv))


.. only:: html

   Download: :download:`direct_call.py <direct_call.py>`.
