# 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 __future__ import print_function
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))


Download: direct_call.py.