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python Programming Glossary: curve_fithttp://stackoverflow.com/questions/11507028/fit-a-gaussian-function curves to data. Basically you can use scipy.optimize.curve_fit to fit any function you want to your data. The code below shows.. list post . import numpy from scipy.optimize import curve_fit import matplotlib.pyplot as plt # Define some test data which.. A mu and sigma above p0 1. 0. 1. coeff var_matrix curve_fit gauss bin_centres hist p0 p0 # Get the fitted curve hist_fit..
Curve fitting in Scipy with 3d data and parameters http://stackoverflow.com/questions/17934198/curve-fitting-in-scipy-with-3d-data-and-parameters function however it's significantly easier to just use curve_fit which does it for you and creates the necessary equations For.. creates the necessary equations For fitting you should use curve_fit if you are ok with the generic residual they use which is actually.. in 2d and guess the y parameter from scipy.optimize import curve_fit from itertools import imap import numpy as np # use only an..
Solve equation with a set of points http://stackoverflow.com/questions/18619131/solve-equation-with-a-set-of-points f59Eo29.png import numpy as np from scipy.optimize import curve_fit from matplotlib.pyplot import import xlrd def myFunc t V W k.. best fit parameters and the covariance matrix #popt pcov curve_fit myFunc x yn popt pcov curve_fit myFunc x y print popt print.. matrix #popt pcov curve_fit myFunc x yn popt pcov curve_fit myFunc x y print popt print pcov #plot the data clf #matplotlib..
Solving non-linear equations in python http://stackoverflow.com/questions/19542801/solving-non-linear-equations-in-python accessible function in scipy.optimize is scipy.optimize.curve_fit which uses a Levenberg Marquardt method by default. Levenberg.. leaving it out below just pass it in as the p0 kwarg to curve_fit because the default a b c 1 1 1 is a fairly accurate guess for.. guess for a b c 3 2 1 . With the caveats out of the way curve_fit expects to be passed a function a set of points where the observations..
fitting exponential decay with no initial guessing http://stackoverflow.com/questions/3938042/fitting-exponential-decay-with-no-initial-guessing to be pre specified which is not always possible EDIT curve_fit works but it can fail quite miserably with no initial guess.. sp import pylab as pl from scipy.optimize.minpack import curve_fit x np.array 50. 110. 170. 230. 290. 350. 410. 470. 530. 590... exp_decay lambda x A t y0 A np.exp x t y0 params cov curve_fit exp_decay x y p0 guess A t y0 params print A s nt s ny0 s n..
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