curve-fitting

问题 I was adopting lmfit to do a curve fitting and use that fitted model to do prediction. However, the following code did not achieve what I want. Could you please help? Thanks. import numpy as np from lmfit import Model def linearModel(x, a0, a1): return a0+a1*x #main code begin here X=[1,2,4] # data for fitting y=[2,4,6] # data for fitting gmodel = Model(linearModel) #select model params = gmodel.make_params(a0=1, a1=1) # initial params result = gmodel.fit(y, params, x=X) # curve fitting x1=[1

问题 I would like to use scipy's ODR to fit a curve to a set of variables with variances. In this case, I am fitting a linear function with a set Y axis crossing point (e.g. a*x+100 ). Due to my inability to find an estimator (I asked about that here), I am using scipy.optimize curve_fit to estimate the initial a value. Now, the function works perfectly without standard deviation, but when I add it, the output makes completely no sense (the curve is far above all of the points). What could be the

问题 Im trying to follow and re-use a piece of code (with my own data) suggested by someone named @ThePredator (I couldn't comment on that thread since I don't currently have the required reputation of 50). The full code is as follows: import numpy as np # This is the Numpy module from scipy.optimize import curve_fit # The module that contains the curve_fit routine import matplotlib.pyplot as plt # This is the matplotlib module which we use for plotting the result """ Below is the function that

问题 How to fit a non linear data's using scipy.optimize import curve_fit in Python using following 3 methods: Gaussian. Lorentz fit. Langmuir fit. I am just able to link and plot from my data file. from matplotlib import pyplot as plt from matplotlib import style import numpy as np import pylab from scipy.optimize import curve_fit style.use('ggplot') data = np.genfromtxt('D:\csvtrail3.csv', delimiter=',', skiprows=1) x=data[:,0] y=data[:,1] data.fit_lorentzians() plt.plot(x, y) plt.title('Epic

问题 I have this plot: and want to flatten its baseline/reduce offset using Matlab. Basically like a baseline correction for a spectrum but here I've got a mesh and can't get my head around it how to flatten its baseline when dealing with a matrix? Basically the dot should stay but the surrounding is actually zero. The noise can stay, though. Here is the image: I am wondering if something like this works: for x=1:1201 for y=1:1201 Ibasetest = polyfit(x,y,1); end end Simply put do a baseline for

问题 I have this plot: and want to flatten its baseline/reduce offset using Matlab. Basically like a baseline correction for a spectrum but here I've got a mesh and can't get my head around it how to flatten its baseline when dealing with a matrix? Basically the dot should stay but the surrounding is actually zero. The noise can stay, though. Here is the image: I am wondering if something like this works: for x=1:1201 for y=1:1201 Ibasetest = polyfit(x,y,1); end end Simply put do a baseline for

问题 One of my algorithms performs automatic peak detection based on a Gaussian function, and then later determines the the edges based either on a multiplier (user setting) of the sigma or the 'full width at half maximum'. In the scenario where a user specified that he/she wants the peak limited at 2 Sigma, the algorithm takes -/+ 2*sigma from the peak center (mu). However, I noticed that the sigma returned by curve_fit can be negative, which is something that has been noticed before as can be

问题 With the curve_fit function in SciPy I'm able to determine the coefficients that represent the curve shown in the plot below. def func2(t, tau): return np.exp(-t / tau) t2 = np.linspace(0, 4, 50) y2 = func2(t2, 1.2) y2_noise = 0.2 * np.random.normal(size=t2.size) y2_curve_noise = y2 + y2_noise popt2, pcov2 = curve_fit(func2, t2, y2_curve_noise) tau2, = popt2 y2_fit = func2(t2, tau2) I would like to use a similar function to represent some data points. However, I'm unable to use this approach

问题 With the curve_fit function in SciPy I'm able to determine the coefficients that represent the curve shown in the plot below. def func2(t, tau): return np.exp(-t / tau) t2 = np.linspace(0, 4, 50) y2 = func2(t2, 1.2) y2_noise = 0.2 * np.random.normal(size=t2.size) y2_curve_noise = y2 + y2_noise popt2, pcov2 = curve_fit(func2, t2, y2_curve_noise) tau2, = popt2 y2_fit = func2(t2, tau2) I would like to use a similar function to represent some data points. However, I'm unable to use this approach

问题 I'd like to find the largest convex hull which fits in the interior of a set of points. I have a set of points which are roughly circular, with a large number of outlier points outside of the circle I'd like to fit. Imagine a circle with "solar flares"... I want to fit the circle and completely ignore the flares. I've tried various fit and culling strategies, but they aren't working well. I've searched quite a bit and not found a solution. Thanks in advance. 回答1: The notion you need may be