Here you have an example working on py2.6 and py3.2: from scipy.stats import norm import matplotlib.mlab as mlab import matplotlib.pyplot as plt # read data from a text file. One number per line arch = “test/Log(2)_ACRatio.txt” datos = [] for item in open(arch,’r’): item = item.strip() if item != ”: try: datos.append(float(item)) except ValueError: pass … Read more
A very late reply, but just in case someone needs a ready function for this: scipy.stats.linregress i.e. slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y) as in @Adam Marples’s answer.
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
There are several problems: formula is a parameter of nls and you need to pass a formula object to it and not a character. ggplot2 passes y and x to nls and not fold and t. By default, stat_smooth tries to get the confidence interval. That isn’t implemented in predict.nls. In summary: d <- ggplot(test,aes(x=t, … Read more
Thanks David, I converted your pseudocode to the following C# code: public static void CalcParabolaVertex(int x1, int y1, int x2, int y2, int x3, int y3, out double xv, out double yv) { double denom = (x1 – x2) * (x1 – x3) * (x2 – x3); double A = (x3 * (y2 – y1) … Read more
Take a look at this answer for fitting arbitrary curves to data. Basically you can use scipy.optimize.curve_fit to fit any function you want to your data. The code below shows how you can fit a Gaussian to some random data (credit to this SciPy-User mailing list post). import numpy from scipy.optimize import curve_fit import matplotlib.pyplot … Read more
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
You can define your own residuals function, including a penalization parameter, like detailed in the code below, where it is known beforehand that the integral along the interval must be 2.. If you test without the penalization you will see that what your are getting is the conventional curve_fit: import matplotlib.pyplot as plt import scipy … Read more
I think you’re most of the way there. You need to put the data sets into an array or structure that can be used in a single, global objective function that you give to minimize() and fits all data sets with a single set of Parameters for all the data sets. You can share this … Read more
Be careful with numpy-arrays, operations working on arrays and operations working on scalars! Scipy optimize assumes the input (initial-point) to be a 1d-array and often things go wrong in other cases (a list for example becomes an array and if you assumed to work on lists, things go havoc; those kind of problems are common … Read more