I've done a spline interpolation of a 3D path using 2 2D fits. Using the interpolation condition as well as the requirement to be 2 times differentiable I got the required equations to interpolate my 3D path. However I came to realize, that I disregarded the fact, that the paths...

I am currently trying to use gnuplot to fit the a simple data set using the beneath commands, however I am having no luck despite including good initial guesses. Commands: gnuplot> f(x) = 1/(2*pi) * 1/m1 * 1/(b + x**2)**(-3/2) gnuplot> m1 = 150 gnuplot> m2 = 17 gnuplot> fit...

Forum I've got a set of data that apparently forms an ellipse in 3D space (not an ellipsoid, but a curve in 3D). Being inspired by following thread http://au.mathworks.com/matlabcentral/newsreader/view_thread/65773 and with the help from someone ,I manage to get the optimization code running and outputs a set of best parameters...

I have data like this: y = [0.001 0.0042222222 0.0074444444 0.0106666667 0.0138888889 0.0171111111 0.0203333333 0.0235555556 0.0267777778 0.03] and x = [3.52E-06 9.72E-05 0.0002822918 0.0004929136 0.0006759156 0.0008199029 0.0009092797 0.0009458332 0.0009749509 0.0009892005] and I want y to be a function of x with y = a(0.01 − b*n^−cx). What is the best...

I am trying to fit resistivity vs temperature data to Bloch-Gruneisen formula for resistivity in metals: function as you can see there is an integral function with a parametric limit. I don't know how to implement an algorithm to run a least squares fit. I came up with: import matplotlib.pyplot...

Initial Question (Partially Answered) I am using gnuplot's fitting routines to fit a function to some data, and extract a "characteristic decay time constant". (I call this parameter d in my fitting function.) I have used the script code set fit quiet to prevent reams of text being printed to...

I'm trying to fit Einstein approximation of resistivity in a solid in a set of experimental data. I have resistivity vs temperature (from 200 to 4 K) import xlrd as xd import matplotlib.pyplot as plt import numpy as np import pylab as pl import scipy as sp from scipy.optimize import...

Say I want to fit two arrays x_data_one and y_data_one with an exponential function. In order to do that I might use the following code (in which x_data_one and y_data_one are given dummy definitions): import numpy as np from scipy.optimize import curve_fit def power_law(x, a, b, c): return a *...

Given that the fitting function is of type: I intend to fit such function to the experimental data (x,y=f(x)) that I have. But then I have some doubts: How do I define my fitting function when there's a summation involved? Once the function defined, i.e. def func(..) return ... is...