Category : curve-fitting

”’ D_new = np.array([0,20,40,60,80,100,120,140,160,180,200,220,240,260,280,300,320,340,360]) D_curve_Adjusted = np.array(Date_Adjusted) def obj_func(x, a, b, c, d): return a*np.cos((b*np.pi*(x+c)))+d k = sc.curve_fit(obj_func, Day_Of_Year,Dec) a_fit = k[0][0] b_fit = k[0][1] c_fit = k[0][2] d_fit = k[0][3] curve = obj_func(D_new,a_fit,b_fit,c_fit,d_fit) D_curve_model = obj_func(D_curve_Adjusted,a_fit,b_fit,c_fit,d_fit) plt.scatter(Day_Of_Year, Dec, color=’k’, label=’Original Data Points’) plt.plot(D_curve_Adjusted,D_curve_model, ‘:’, color = ‘g’, label = ‘Model Curve Fit Line’) #plt.plot(D_new, ..

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I am using this gompertz function to fit my curve but the curve shape not smooth, ` def gompartz(time, alpha0, beta): Vc = 1e-6 VI = 1 V = Vc*(VI/Vc)**(np.exp(-beta*time))*np.exp(alpha0/beta*(1-np.exp(-beta*time))) V r3= 1/np.array(query[‘Repetitions’])**2 popt, pcov = curve_fit(gompartz, xdata, ydata,method="trf",sigma=r3,absolute_sigma=True, maxfev=10000000,gtol=1e-10) The curve picture enter image description here Source: Python..

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I am using SciPy.optimize.curve_fit https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html to get the coefficients of curve fitting function, the SciPy function takes the model function as its first argument so if I want to make a linear curve fit, I pass to it the following function: def objective(x, a, b): return a * x + b If I want polynomial ..

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