To fit a curve onto a set of points, we can use ordinary least-squares regression. There is a solution page by MathWorks describing the process. As an example, let’s start with some random data: % some 3d points data = mvnrnd([0 0 0], [1 -0.5 0.8; -0.5 1.1 0; 0.8 0 1], 50); As @BasSwinckels … Read more
The solution here is to write a wrapper function that takes your argument list and translates it to variables that the fit function understands. This is really only necessary since I am working qwith someone else’s code, in a more direct application this would work without the wrapper layer. Basically def wrapper_fit_func(x, N, *args): a, … Read more
Instead of plotting datenums, use the associated datetimes. import numpy as np import matplotlib.pyplot as plt import matplotlib.dates as mdates import datetime as DT import time dates = [DT.datetime(1978, 7, 7), DT.datetime(1980, 9, 26), DT.datetime(1983, 8, 1), DT.datetime(1985, 8, 8)] y = [0.00134328779552718, 0.00155187668863844, 0.0039431374327427, 0.00780037563783297] yerr = [0.0000137547160254577, 0.0000225670232594083, 0.000105623642510075, 0.00011343121508] x = mdates.date2num(dates) … Read more
Your closed path can be considered as a parametric curve, x=f(u), y=g(u) where u is distance along the curve, bounded on the interval [0, 1). You can use scipy.interpolate.splprep with per=True to treat your x and y points as periodic, then evaluate the fitted splines using scipy.interpolate.splev: import numpy as np from scipy import interpolate … Read more
Apache Commons Math has a nice series of algorithms, in particular “SplineInterpolator”, see the API docs An example in which we call the interpolation functions for alpha(x), beta(x) from Groovy: package example.com import org.apache.commons.math3.analysis.interpolation.SplineInterpolator import org.apache.commons.math3.analysis.polynomials.PolynomialSplineFunction import statec.Extrapolate.Value; class Interpolate { enum Value { ALPHA, BETA } def static xValues = [ -284086, -94784, 31446, … Read more
I have similar problem and I have found “An algorithm for automatically fitting digitized curves” from Graphics Gems (1990) about Bezier curve fitting. Additionally to that I have found source code for that article. Unfortunately it is written in C which I don’t know very well. Also, the algorithm is quite hard to understand (at … Read more
After great help from @Brenlla the code was modified to: def sigmoid(x, L ,x0, k, b): y = L / (1 + np.exp(-k*(x-x0))) + b return (y) p0 = [max(ydata), np.median(xdata),1,min(ydata)] # this is an mandatory initial guess popt, pcov = curve_fit(sigmoid, xdata, ydata,p0, method=’dogbox’) The parameters optimized are L, x0, k, b, who are … Read more
I like loess() a lot for smoothing: x <- 1:10 y <- c(2,4,6,8,7,12,14,16,18,20) lo <- loess(y~x) plot(x,y) lines(predict(lo), col=”red”, lwd=2) Venables and Ripley’s MASS book has an entire section on smoothing that also covers splines and polynomials — but loess() is just about everybody’s favourite.
Scipy doesn’t produce multiple lines, the strange output is caused by the way you present your unsorted data to matplotlib. Sort your x-values and you get the desired output: from scipy.optimize import curve_fit from matplotlib import pyplot as plt def func(x, a, b, c): return a * x** b + c # my data pred_data … Read more