curve-fitting

问题 I have 40 points for each curve and I would like to smooth the function and estimate when the curve crosses a threshold on the y axis. Is there a fitting function that I can easily apply this to, I can use interpolate to plot the new function but I can't figure out how to request the x value for which y = threshold. Unfortunately the curves don't all have the same shape so I can't use scipy.optimize.curve_fit. Thanks! Update: Adding two curves: Curve 1 [942.153,353.081,53.088,125.110,140.851

问题 I have 40 points for each curve and I would like to smooth the function and estimate when the curve crosses a threshold on the y axis. Is there a fitting function that I can easily apply this to, I can use interpolate to plot the new function but I can't figure out how to request the x value for which y = threshold. Unfortunately the curves don't all have the same shape so I can't use scipy.optimize.curve_fit. Thanks! Update: Adding two curves: Curve 1 [942.153,353.081,53.088,125.110,140.851

问题 # import packages we need later import matplotlib.pyplot as plt import numpy as np What I am doing Inspired by this question & answer, I am fitting a series of Legendre polynomials to a time series: curve1 = \ np.asarray([942.153,353.081,53.088,125.110,140.851,188.170,70.536,-122.473,-369.061,-407.945,88.734,484.334,267.762,65.831,74.010,-55.781,-260.024,-466.830,-524.511,-76.833,-36.779,-117.366,218.578,175.662,185.653,299.285,215.276,546.048,1210.132,3087.326,7052.849,13867.824,27156.939

问题 # import packages we need later import matplotlib.pyplot as plt import numpy as np What I am doing Inspired by this question & answer, I am fitting a series of Legendre polynomials to a time series: curve1 = \ np.asarray([942.153,353.081,53.088,125.110,140.851,188.170,70.536,-122.473,-369.061,-407.945,88.734,484.334,267.762,65.831,74.010,-55.781,-260.024,-466.830,-524.511,-76.833,-36.779,-117.366,218.578,175.662,185.653,299.285,215.276,546.048,1210.132,3087.326,7052.849,13867.824,27156.939

问题 I want to fit complex data set with a two functions which shared the same parameters. For this I used def funcReal(x,a,b,c,d): return np.real((a + 1j*b)*(np.exp(1j*k*x - kappa1*x) - np.exp(kappa2*x)) + (c + 1j*d)*(np.exp(-1j*k*x - kappa1*x) - np.exp(-kappa2*x))) def funcImag(x,a,b,c,d): return np.imag((a + 1j*b)*(np.exp(1j*k*x - kappa1*x) - np.exp(kappa2*x)) + (c + 1j*d)*(np.exp(-1j*k*x - kappa1*x) - np.exp(-kappa2*x)))` poptReal, pcovReal = curve_fit(funcReal, x, yReal) poptImag, pcovImag =

问题 I am trying to create a multible voigt/Gaussian/Lorentizan-peak fit function with lmfit. Therefore, I wrote the following Function: def apply_fit_mix_multy(data,modelPeak,peakPos,amplitud,**kwargs): peakPos=np.array(peakPos) Start=kwargs.get('Start',data[0,0]) length_data=len(data)-1 End=kwargs.get('End',data[length_data,0]) StartPeak=kwargs.get('StartPeak',data[0,0]) EndPeak=kwargs.get('EndPeak',data[length_data,0]) BackFunc=kwargs.get('BackFunc',False) BackCut=kwargs.get('BackCut',False)

问题 I have a 3-D dataframe representing family of curves: yi=f(xi) which are also depending on a third variable lets say zi (yi=g(xi,zi)) as shown in the graphic below: Family Of Curves The yi data are measurements and each curve is valid on its own. Now I would like to find out a proper way to fit a plane or surface to this data in order to avoid 2-D bilinear data interpolation. In general I am trying to do a similar approach as in the link below: 2D polynomial in Python With the difference that

问题 I have some data that I want to fit so I can make some estimations for the value of a physical parameter given a certain temperature. I used numpy.polyfit for a quadratic model, but the fit isn't quite as nice as I'd like it to be and I don't have much experience with regression. I have included the scatter plot and the model provided by numpy: S vs Temperature; blue dots are experimental data, black line is the model The x axis is temperature (in C) and the y axis is the parameter, which we

问题 I have some data that I want to fit so I can make some estimations for the value of a physical parameter given a certain temperature. I used numpy.polyfit for a quadratic model, but the fit isn't quite as nice as I'd like it to be and I don't have much experience with regression. I have included the scatter plot and the model provided by numpy: S vs Temperature; blue dots are experimental data, black line is the model The x axis is temperature (in C) and the y axis is the parameter, which we

问题 I have data provided in the code which have negative and positive slopes as shown in figure: Using the code applied in this post Fit a curve for data made up of two distinct regimes, I created this code. It works for same slopes either both positive or both negative, but when one is positive and other negative, it is not able to fit the lines properly. from scipy import optimize from scipy import optimize, interpolate from scipy.optimize import curve_fit import matplotlib.pyplot as plt import