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# 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))
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