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
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
You can wrap func in a lambda, as follows: def func(x, a, b): return a*x*x + b for b in xrange(10): popt, pcov = curve_fit(lambda x, a: func(x, a, b), x1, x2) A lambda is an anonymous function, which in Python can only be used for simple one line functions. Basically, it’s normally used to … Read more
Scipy’s curve_fit() uses iterations to search for optimal parameters. If the number of iterations exceeds the default number of 800, but the optimal parameters are still not found, then this error will be raised. Optimal parameters not found: Number of calls to function has reached maxfev = 800 You can provide some initial guess parameters … Read more
Here is a parameter-free fitting function fit_sin() that does not require manual guess of frequency: import numpy, scipy.optimize def fit_sin(tt, yy): ”’Fit sin to the input time sequence, and return fitting parameters “amp”, “omega”, “phase”, “offset”, “freq”, “period” and “fitfunc””’ tt = numpy.array(tt) yy = numpy.array(yy) ff = numpy.fft.fftfreq(len(tt), (tt[1]-tt[0])) # assume uniform spacing Fyy … 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
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
You can pass curve_fit a multi-dimensional array for the independent variables, but then your func must accept the same thing. For example, calling this array X and unpacking it to x, y for clarity: import numpy as np from scipy.optimize import curve_fit def func(X, a, b, c): x,y = X return np.log(a) + b*np.log(x) + … Read more
I suggest you to start with simple polynomial fit, scipy.optimize.curve_fit tries to fit a function f that you must know to a set of points. This is a simple 3 degree polynomial fit using numpy.polyfit and poly1d, the first performs a least squares polynomial fit and the second calculates the new points: import numpy as … Read more
An easy solution, as you mentioned, is to simply connect the points with a straight line. Here’s the code to do so: public void onDraw(Canvas canvas) { Path path = new Path(); boolean first = true; for(Point point : points){ if(first){ first = false; path.moveTo(point.x, point.y); } else{ path.lineTo(point.x, point.y); } } canvas.drawPath(path, paint); } … Read more