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"""
Fit Using Bounds
================
A major advantage of using lmfit is that one can specify boundaries on fitting
parameters, even if the underlying algorithm in SciPy does not support this.
For more information on how this is implemented, please refer to:
https://lmfit.github.io/lmfit-py/bounds.html
The example below shows how to set boundaries using the ``min`` and ``max``
attributes to fitting parameters.
"""
import matplotlib.pyplot as plt
from numpy import exp, linspace, pi, random, sign, sin
from lmfit import Parameters, minimize
from lmfit.printfuncs import report_fit
p_true = Parameters()
p_true.add('amp', value=14.0)
p_true.add('period', value=5.4321)
p_true.add('shift', value=0.12345)
p_true.add('decay', value=0.01000)
def residual(pars, x, data=None):
argu = (x * pars['decay'])**2
shift = pars['shift']
if abs(shift) > pi/2:
shift = shift - sign(shift)*pi
model = pars['amp'] * sin(shift + x/pars['period']) * exp(-argu)
if data is None:
return model
return model - data
random.seed(0)
x = linspace(0, 250, 1500)
noise = random.normal(scale=2.80, size=x.size)
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(out.params, x)
###############################################################################
# This gives the following fitting results:
report_fit(out, show_correl=True, modelpars=p_true)
###############################################################################
# and shows the plot below:
#
plt.plot(x, data, 'ro')
plt.plot(x, fit, 'b')
plt.show()
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