data-fitting

fitting data with numpy

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Unfortunately, np.polynomial.polynomial.polyfit returns the coefficients in the opposite order of that for np.polyfit and np.polyval (or, as you used np.poly1d). To illustrate: In [40]: np.polynomial.polynomial.polyfit(x, y, 4) Out[40]: array([ 84.29340848, -100.53595376, 44.83281408, -8.85931101, 0.65459882]) In [41]: np.polyfit(x, y, 4) Out[41]: array([ 0.65459882, -8.859311 , 44.83281407, -100.53595375, 84.29340846]) In general: np.polynomial.polynomial.polyfit returns coefficients [A, B, C] … Read more

Getting standard errors on fitted parameters using the optimize.leastsq method in python

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Updated on 4/6/2016 Getting the correct errors in the fit parameters can be subtle in most cases. Let’s think about fitting a function y=f(x) for which you have a set of data points (x_i, y_i, yerr_i), where i is an index that runs over each of your data points. In most physical measurements, the error … Read more

Fitting a closed curve to a set of points

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Actually, you were not far from the solution in your question. Using scipy.interpolate.splprep for parametric B-spline interpolation would be the simplest approach. It also natively supports closed curves, if you provide the per=1 parameter, import numpy as np from scipy.interpolate import splprep, splev import matplotlib.pyplot as plt # define pts from the question tck, u … Read more

Fitting a 2D Gaussian function using scipy.optimize.curve_fit – ValueError and minpack.error

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The output of twoD_Gaussian needs to be 1D. What you can do is add a .ravel() onto the end of the last line, like this: def twoD_Gaussian((x, y), amplitude, xo, yo, sigma_x, sigma_y, theta, offset): xo = float(xo) yo = float(yo) a = (np.cos(theta)**2)/(2*sigma_x**2) + (np.sin(theta)**2)/(2*sigma_y**2) b = -(np.sin(2*theta))/(4*sigma_x**2) + (np.sin(2*theta))/(4*sigma_y**2) c = (np.sin(theta)**2)/(2*sigma_x**2) + … Read more