Parameter(name='line_off', value=0.0)]
sigma = 0.021 # estimate of data error (for all data points)
myfit = Minimizer(residual, pfit,
fcn_args=(x,), fcn_kws={'sigma':sigma, 'data':data},
scale_covar=True)
myfit.prepare_fit()
init = residual(myfit.params, x)
if HASPYLAB:
pylab.plot(x, init, 'b--')
myfit.leastsq()
print(' Nfev = ', myfit.nfev)
print( myfit.chisqr, myfit.redchi, myfit.nfree)
report_errors(myfit.params)
fit = residual(myfit.params, x)
if HASPYLAB:
pylab.plot(x, fit, 'k-')
pylab.show()
xmin = 0.
xmax = 250.0
random.seed(0)
noise = random.normal(scale=2.80, size=n)
x = linspace(xmin, xmax, n)
data = residual(p_true, x) + noise
fit_params = Parameters()
fit_params.add('amp', value=13.0, max=20, min=0.0)
fit_params.add('period', value=2, max=10)
fit_params.add('shift', value=0.0, max=pi/2., min=-pi/2.)
fit_params.add('decay', value=0.02, max=0.10, min=0.00)
out = minimize(residual, fit_params, args=(x,), kws={'data':data})
fit = residual(fit_params, x)
print '# N_func_evals, N_free = ', out.nfev, out.nfree
print '# chi-square, reduced chi-square = % .7g, % .7g' % (out.chisqr, out.redchi)
report_errors(fit_params, show_correl=True, modelpars=p_true)
print 'Raw (unordered, unscaled) Covariance Matrix:'
print out.covar
if HASPYLAB:
pylab.plot(x, data, 'ro')
pylab.plot(x, fit, 'b')
pylab.show()
