curve-fitting

问题 I'm trying to fit a sigmoid function to some data I have but I keep getting: ValueError: Unable to determine number of fit parameters. My data looks like this: My code is: from scipy.optimize import curve_fit def sigmoid(x): return (1/(1+np.exp(-x))) popt, pcov = curve_fit(sigmoid, xdata, ydata, method='dogbox') Then I get: --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-5-78540a3a23df> in <module> 2

问题 I have an array of points, each point consists of a x and y, the x is a timestamp, y is the actual data. I have created a time series plot in Javascript and I would like to curve fit the data and draw the curve. Can anyone help me to achieve this? My list of points: {fltX: 0, fltY: 55.932203389830505} {fltX: 1.8237082066869301, fltY: 55.932203389830505} {fltX: 3.6474164133738602, fltY: 55.932203389830505} {fltX: 5.47112462006079, fltY: 55.932203389830505} {fltX: 7.2948328267477205, fltY: 81

问题 have a question on curve fitting / optimizing. I have three coupled ODEs that descibe a biochemical reaction with a disappearing substrate and two products being formed. I've found examples that have helped me create code to solve the ODEs (below). Now I want to optimize the unknown rate constants (k, k3 and k4) to fit to the experimental data, P, which is a signal from product y[1]. What would be the easiest way of doing this? Thanks. import numpy as np from scipy.integrate import odeint

问题 have a question on curve fitting / optimizing. I have three coupled ODEs that descibe a biochemical reaction with a disappearing substrate and two products being formed. I've found examples that have helped me create code to solve the ODEs (below). Now I want to optimize the unknown rate constants (k, k3 and k4) to fit to the experimental data, P, which is a signal from product y[1]. What would be the easiest way of doing this? Thanks. import numpy as np from scipy.integrate import odeint

问题 I am trying to fit x y data which look something like x = np.linspace(-2, 2, 1000) a = 0.5 yl = np.ones_like(x[x < a]) * -0.4 + np.random.normal(0, 0.05, x[x < a].shape[0]) yr = np.ones_like(x[x >= a]) * 0.4 + np.random.normal(0, 0.05, x[x >= a].shape[0]) y = np.concatenate((yl, yr)) plt.scatter(x, y, s=2, color='k') I'm using a variation of the Heaviside step function def f(x, a, b): return 0.5 * b * (np.sign(x - a)) and fitting with popt, pcov = curve_fit(f, x, y, p0=p) where p is some

问题 I have an array of data which, when plotted, looks like this. I need to use the polyfit command to determine the best fitting exponential for the time roughly between 1.7 and 2.3 . I must also compare this exponential fit to a simple linear fit. I'm given the equation Temp(t) = Temp0 * exp(-(t-t0)/tau) , where t0 is the time corresponding to temperature Temp0 (I can select where to begin my curve-fitting, but it must be confined to the area roughly between 1.7 and 2.3). Here is my attempt. %

问题 I am trying to fit the power curve into my data with following gnuplot code. set termoption enhanced f(x) = a*x**b; fit f(x) 'data.txt' via a,b plot 'data.txt' with points title 'data points', \ f(x) with lines title sprintf('power fit curve f(x) = %.2f·x^{%.2f}', a, b) It works fine for non-redundant data for x axis. (no repeats). But for following type of data: It is fitting the curve only to points of first x value i.e. 1, ( Starred ). and not to the whole dataset. Data: 1 2194* 1 2675* 1

问题 I am trying to fit a gaussian to my data which is taken in a pretty narrow spectral window. We got about 2 points of continuum and then about 10-11 that are part of the line. It should still be possible to fit it I think, but the curve fit is failing each time, and I am not sure why. When running I get RuntimeError: Optimal parameters not found: Number of calls to function has reached maxfev = 800. Code and data: import matplotlib.pyplot as plt from scipy.optimize import curve_fit import

问题 Is there any way to use cftool non-interactively. For example, given x, y and the fitting function, calling cftool to generate and returned the fitted data without using opening the toolbox GUI. Thanks 回答1: You can use the fit function that comes with Curve Fitting Toolbox. To find out more, type doc fit . Or you can use cftool interactively, then use Generate Code from the File menu to create a function that uses the fit command to repeat your interactive work programmatically. Use this as a

问题 I am trying to apply bounds onto some of the parameters during curve fitting but I'm getting the following error message when I tried to do so: ValueError: too many values to unpack Doesn't each 2-tuple in the bound command corresponds to x0, k, lapse, guess in the sigmoidscaled function in my case respectively (Ie. corresponding to p0 too)? I then tried playing around in hopes of trying to figure out how it works by reducing the bound command to the following to rid the 'too many values':