1/8/2023 0 Comments Scipy curve fitPlt.plot(x,y_fit2, color='y', label='constrained') Plt.plot(x,y_fit1, color='g', label='curve_fit') Return y - func1(x, p,p,p,p) - penalization Integral = quad( func1, 0, pi, args=(p,p,p,p)) # here you include the penalization factor If you test without the penalization you will see that what your are getting is the conventional curve_fit:įrom scipy.optimize import curve_fit, minimize, leastsq The curve fit is essential to find the optimal set of parameters for the defined function that best fits the provided set of observations. The curvefit method fits our model to the data. Create a list or numpy array of your independent variable (your x values). Python Scipy () function is used to find the best-fit parameters using a least-squares fit. You can define your own residuals function, including a penalization parameter, like detailed in the code below, where it is known beforehand that the integral along the interval must be 2. Data fitting Import the curvefit function from scipy.
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