With using lmfit module in python, how to call the parameters in the model?

Question

import numpy as np

import matplotlib.pyplot as plt

from lmfit import minimize, Parameters, Parameter, report_fit


# create data to be fitted


x = np.linspace(0, 15, 301)

data = (5. * np.sin(2 * x - 0.1) * np.exp(-x*x*0.025) +

np.random.normal(size=len(x), scale=0.2) )

# define objective function: returns the array to be minimized


def fcn2min(params, x, data):

    """ model decaying sine wave, subtract data"""
    amp = params['amp'].value
    shift = params['shift'].value
    omega = params['omega'].value
    decay = params['decay'].value
    model = amp * np.sin(x * omega + shift) * np.exp(-x*x*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= 5.0)


# do fit, here with leastsq model

result = minimize(fcn2min, params, args=(x, data))


# calculate final result

final = data + result.residual


# try to plot results
plt.plot(x,data,'k+')

plt.plot(x,final,'r')

plt.show()

In this code, I want to call the parameters like 'amp', 'shift' in the python. Print(amp).. kinds of things How to call these parameters in the python after fitting? When I use print(amp), the error message is shown; name 'amp' is not defined. How to print these fitted parameters using print function? (etc. print(amp))

Answer 1

You are likely trying to print that data outside of the function. The amp , shift , omega and decay variables are in fc2min 's local scope and are therefore only accessible inside the function. Your data analysis skills seem to far outmatch your Python know-how, so I added some helpful tips inside this code:

import numpy as np
import matplotlib.pyplot as plt
from lmfit import minimize, Parameters, Parameter, report_fit

# create data to be fitted
x = np.linspace(0, 15, 301)
data = (5. * np.sin(2 * x - 0.1) * np.exp(-x*x*0.025) +
np.random.normal(size=len(x), scale=0.2) )

# define objective function: returns the array to be minimized
def fcn2min(params, x, data):

    """ model decaying sine wave, subtract data"""
    amp = params['amp'].value
    shift = params['shift'].value
    omega = params['omega'].value
    decay = params['decay'].value
    model = amp * np.sin(x * omega + shift) * np.exp(-x*x*decay)

    # tell Python we're modifying the model_data list
    # that was declared outside of this function
    global model_data

    # store the model data produced by this function call
    # add any data you want to display later to this "dictionary"
    model_data += [{
        "amp": amp,
        "shift": shift,
        "omega": omega,
        "decay": 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= 5.0)

# declare an empty list to hold the model data
model_data = []

# do fit, here with leastsq model
result = minimize(fcn2min, params, args=(x, data))

# print each item in the model data list
for datum in model_data:
    for key in datum:

        #the 5 in %.5f controls the precision of the floating point value
        print("%s: %.5f  ") % (key, datum[key]),
    print


# calculate final result
final = data + result.residual

# try to plot results
plt.plot(x,data,'k+')
plt.plot(x,final,'r')
plt.show()