doc_parameters_valuesdict.py

Out:

[[Fit Statistics]]
    # fitting method   = leastsq
    # function evals   = 63
    # data points      = 301
    # variables        = 4
    chi-square         = 10.9764657
    reduced chi-square = 0.03695780
    Akaike info crit   = -988.718387
    Bayesian info crit = -973.889945
[[Variables]]
    amp:    4.96174550 +/- 0.03830181 (0.77%) (init = 10)
    decay:  0.02571887 +/- 4.5357e-04 (1.76%) (init = 0.1)
    shift: -0.09714733 +/- 0.00991436 (10.21%) (init = 0)
    omega:  1.99750219 +/- 0.00321063 (0.16%) (init = 3)
[[Correlations]] (unreported correlations are < 0.100)
    C(shift, omega) = -0.785
    C(amp, decay)   =  0.584
    C(amp, shift)   = -0.120

##
import warnings
warnings.filterwarnings("ignore")
##
# <examples/doc_parameters_valuesdict.py>
import numpy as np

from lmfit import Minimizer, Parameters, report_fit

# create data to be fitted
x = np.linspace(0, 15, 301)
data = (5.0 * np.sin(2.0*x - 0.1) * np.exp(-x*x*0.025) +
        np.random.normal(size=x.size, scale=0.2))


# define objective function: returns the array to be minimized
def fcn2min(params, x, data):
    """Model a decaying sine wave and subtract data."""
    v = params.valuesdict()

    model = v['amp'] * np.sin(x * v['omega'] + v['shift']) * np.exp(-x*x*v['decay'])
    return model - data


# create a set of Parameters
params = Parameters()
params.add('amp', value=10, min=0)
params.add('decay', value=0.1)
params.add('shift', value=0.0, min=-np.pi/2., max=np.pi/2)
params.add('omega', value=3.0)

# do fit, here with the default leastsq algorithm
minner = Minimizer(fcn2min, params, fcn_args=(x, data))
result = minner.minimize()

# calculate final result
final = data + result.residual

# write error report
report_fit(result)

# try to plot results
try:
    import matplotlib.pyplot as plt
    plt.plot(x, data, 'k+')
    plt.plot(x, final, 'r')
    plt.show()
except ImportError:
    pass
# <end of examples/doc_parameters_valuesdict.py>