curve-fitting

This is the data I am using for Y data: 0.577032413537833 0.288198874369377 0.192282280031568 0.143824619265244 0.114952782524097 0.0960518606520442 0.0824041879978560 0.0719078360110914 0.0640919744028295 0.0572120310249072 0.0519630635470660 0.0479380073164273 0.0443712721513307 X is simply the integer value from 1 to 13 and I know that this is power function of form a*x^b+c from running GUI cftool on MATLAB with rather high R-square value (1) To perform the fit on command line, I used: >> g = fittype('a*x^b+c','coeff',{'a','b','c'}) >> x=1:13; >> [c3,gof3] = fit(x',B3(:,1),g) This results

I do have a bezier curve, and at a certain point, I want a second bezier curve "branching off" the first curve in a smooth manner. Together with calculating the intersection point (with a percentage following the Bezier curve), I need also the control point (the tangent and weight). The intersection point is calculated with the following piece of javascript: getBezier = function getBez(percent,p1,cp1,cp2,p2) { function b1(t) { return t*t*t } function b2(t) { return 3*t*t*(1-t) } function b3(t) { return 3*t*(1-t)*(1-t) } function b4(t) { return (1-t)*(1-t)*(1-t) } var pos = {x:0,y:0}; pos.x =

I am trying to deblend the emission lines of low resolution spectrum in order to get the gaussian components. This plot represents the kind of data I am using: After searching a bit, the only option I found was the application of the gauest function from the kmpfit package ( http://www.astro.rug.nl/software/kapteyn/kmpfittutorial.html#gauest ). I have copied their example but I cannot make it work. I wonder if anyone could please offer me any alternative to do this or how to correct my code: import numpy as np import matplotlib.pyplot as plt from scipy import optimize def CurveData(): x = np

I have a data set with some 300 columns, each of them depth-dependent. The simplified version of the Pandas DataFrame would look something like this: import matplotlib.pyplot as plt import numpy as np import pandas as pd from scipy_optimize import curve_fit df1 = pd.DataFrame({'depth': [1.65, 2.15, 2.65, 3.15, 3.65, 4.15, 4.65, 5.15, 5.65, 6.15, 6.65, 7.15, 7.65, 8.15, 8.65], '400.0': [13.909261, 7.758734, 3.513627, 2.095409, 1.628918, 0.782643, 0.278548, 0.160153, -0.155895, -0.152373, -0.147820, -0.023997, 0.010729, 0.006050, 0.002356], '401.0': [14.581624, 8.173803, 3.757856, 2.223524, 1

I am trying to fit exponential decay functions on data which has only few time points. I would like to use the exponential decay equation y = y0*e^(-r*time) in order to compare r (or eventually half-life) between datasets and factors. I have understood that using a linear fit instead of nls is a better alternative for this particular function [ 1 , 2 ], if I want to estimate the confidence intervals (which I do). Copy this to get some example data: x <- structure(list(Factor = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L