curve-fitting

I have a fitting function which has the form: def fit_func(x_data, a, b, c, N) where a, b, c are lists of lenth N, every entry of which is a variable parameter to be optimized in scipy.optimize.curve_fit(), and N is a fixed number used for loop index control. Following this question I think I am able to fix N, but I currently am calling curve_fit as follows: params_0 = [a_init, b_init, c_init] popt, pcov = curve_fit(lambda x, a, b, c: fit_func(x, a, b, c, N), x_data, y_data, p0=params_0) I get an error: lambda() takes exactly Q arguments (P given) where Q and P vary depending on how I am

I'm trying to fit a piecewise defined function to a data set in Python. I've searched for quite a while now, but I haven't found an answer whether it is possible or not. To get an impression of what I am trying to do, look at the following example (which is not working for me). Here I'm trying to fit a shifted absolute value function (f(x) = |x-p|) to a dataset with p as the fit parameter. import scipy.optimize as so import numpy as np def fitfunc(x,p): if x>p: return x-p else: return -(x-p) fitfunc = np.vectorize(fitfunc) #vectorize so you can use func with array x=np.arange(1,10) y=fitfunc(x

I'm writing a python script to interpolate a given set of points with splines. The points are defined by their [x, y] coordinates. I tried to use this code: x = np.array([23, 24, 24, 25, 25]) y = np.array([13, 12, 13, 12, 13]) tck, u = scipy.interpolate.splprep([x,y], s=0) unew = np.arange(0, 1.00, 0.005) out = scipy.interpolate.splev(unew, tck) which gives me a curve like this: However, I need to have a smooth closed curve - on the picture above the derivatives at one of the points are obviously not the same. How can I achieve this? ali_m Your closed path can be considered as a parametric

I'm new to scipy and matplotlib, and I've been trying to fit functions to data. The first example in the Scipy Cookbook works fantastically, but when I am trying it with points read from a file, the initial coefficients I give (p0 below) never seem to actually change, and the covariance matrix is always INF. I've tried to fit even data following a line, to no avail. Is it a problem with the way I am importing the data? If so, is there a better way to do it? import matplotlib.pyplot as plt from scipy.optimize import curve_fit import scipy as sy with open('data.dat') as f: noms = f.readline()

I have the following data in my thesis: 28 45 91 14 102 11 393 5 4492 1.77 I need to fit a curve into this. If I plot it, then this is what I get. I think some kind of exponential curve should fit this data. I am using GNUplot. Can someone tell me what kind of curve will fit this and what initial parameters I can use? Ben Just in case R is an option, here's a sketch of two methods you might use. First method: evaluate the goodness of fit of a set of candidate models This is probably the best way as it takes advantage of what you might already know or expect about the relationship between the