

In [1]:

    
%matplotlib inline
import numpy as np
from matplotlib import pylab

from curve_fit import *

Create dataset



In [2]:

    
def linear_fit(x, a, b):
    return a*x + b



In [3]:

    
x = np.arange(0, 1000, 1)
y = 1.5*x+10 + np.random.uniform(-100, 100, len(x))



In [4]:

    
pylab.plot(x, y, '.')









    Out[4]:





[<matplotlib.lines.Line2D at 0x7f70bf162358>]

Create parameters



In [13]:

    
parameters = Parameters([Parameter("a", 1), Parameter("b", 0)])
data = SimpleFitInputData(x, y, id="Example Fit")
fit_performer = SimpleMinimizeFit(data, parameters, fit_func=linear_fit)

The perform_fit method does fit and returns a fitted function.



In [18]:

    
fitted_func = fit_performer.perform_fit()

Parameter values are accessible using:



In [20]:

    
fit_performer.fit_result.params.param_values









    Out[20]:





{'b': 9.8271467541218485, 'a': 1.5076467079644329}



In [21]:

    
pylab.plot(x, y, '.')
pylab.plot(fit_performer.fit_result.plot_geometry, fit_performer.fit_result.plot_profile, linewidth=3)









    Out[21]:





[<matplotlib.lines.Line2D at 0x7f70bf0e3160>]

Use disabled parameters

Let's assume that we know that in this particular case "true" value of parameter b is 10. In this case we still pass "b" parameter to the fitter, but we mark it as disabled --- it will still be passed to linear_fit but will not take part in fitting at all.



In [22]:

    
parameters = Parameters([Parameter("a", 1), Parameter("b", 10, enabled=False)])
data = SimpleFitInputData(x, y, id="Example Fit")
fit_performer = SimpleMinimizeFit(data, parameters, fit_func=linear_fit)



In [23]:

    
fit_performer.perform_fit()









    Out[23]:





functools.partial(<bound method SimpleMinimizeFit.__eval_function_wrapper of <curve_fit.api.SimpleMinimizeFit object at 0x7f70bf0239e8>>, array([ 1.50738729]))



In [24]:

    
fit_performer.fit_result.params.param_values









    Out[24]:





{'b': 10, 'a': 1.5073872949958238}

Let's use bounds



In [27]:

    
parameters = Parameters([Parameter("a", 1, min_bound=0), Parameter("b", 10, min_bound=5, max_bound=15)])
data = SimpleFitInputData(x, y, id="Example Fit")
fit_performer = SimpleMinimizeFit(data, parameters, fit_func=linear_fit)



In [28]:

    
fitted_func = fit_performer.perform_fit()



In [29]:

    
fit_performer.fit_result.params.param_values









    Out[29]:





{'b': 9.8267684715858348, 'a': 1.5076472069299807}



In [30]:

    
pylab.plot(x, y, '.')
pylab.plot(fit_performer.fit_result.plot_geometry, fit_performer.fit_result.plot_profile, linewidth=3)









    Out[30]:





[<matplotlib.lines.Line2D at 0x7f70bf12d908>]



In [ ]: