exponential curve fitting with python

Question

I am trying to convert some Matlab code I have for curve fitting my data into python code but am having trouble getting similar answers. The data is:

x = array([   0.  ,   12.5 ,   24.5 ,   37.75,   54.  ,   70.25,   87.5 ,
    108.5 ,  129.5 ,  150.5 ,  171.5 ,  193.75,  233.75,  273.75])
y = array([-8.79182857, -5.56347794, -5.45683824, -4.30737662, -1.4394612 ,
   -1.58047016, -0.93225927, -0.6719836 , -0.45977157, -0.37622436,
   -0.56115757, -0.3038559 , -0.26594558, -0.26496367])

The Matlab code is:

function [estimates, model] = curvefit(xdata, ydata)
% fits data to the curve y(x)=A-B*e(-lambda*x)

start_point = rand(1,3);

model =@efun;
options = optimset('Display','off','TolFun',1e-16,'TolX',1e-16);
estimates = fminsearch(model, start_point,options);
% expfun accepts curve parameters as inputs, and outputs sse,
% the sum of squares error for A -B* exp(-lambda * xdata) - ydata, 
% and the FittedCurve. 
    function [sse,FittedCurve] = efun(v)
        A=v(1);
        B=v(2);
        lambda=v(3);
        FittedCurve =A - B*exp(-lambda*xdata);
        ErrorVector=FittedCurve-ydata;
        sse = sum(ErrorVector .^2);
    end
end
err = Inf;
numattempts = 100;
for k=1:numattempts
[intermed,model]=curvefit(x, y));
[thiserr,thismodel]=model(intermed);
if thiserr<err
    err = thiserr;
    coeffs = intermed;
    ymodel = thismodel;
end

and so far in Python I have:

import numpy as np
from pandas import Series, DataFrame
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
from scipy.optimize import curve_fit
import pickle

def fitFunc(A, B, k, t):
    return A - B*np.exp(-k*t)
init_vals = np.random.rand(1,3)
fitParams, fitCovariances = curve_fit(fitFunc, y, x], p0=init_vals)

I think I have to do something with running 100 attempts on the p0, but the curve only converges about 1/10 times and it converges to a straight line, way off from the value I get in Matlab. Also most questions regarding curve fitting that I have seen use Bnp.exp(-kt) + A, but the exponential formula I have above is the one I have to use for this data. Any thoughts? Thank you for your time!

plasmon360 · Accepted Answer · 2017-03-16 02:20:31Z

1

curve_fit(fitFunc, y, x], p0=init_vals) should be curve_fit(fitFunc, x,y, p0=init_vals) ie, x goes before y . fitFunc(A, B, k, t) should be fitFunc(t,A, B, k). The independent variable goes first. See the code below:

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

x = np.array([   0.  ,   12.5 ,   24.5 ,   37.75,   54.  ,   70.25,   87.5 ,
    108.5 ,  129.5 ,  150.5 ,  171.5 ,  193.75,  233.75,  273.75])
y = np.array([-8.79182857, -5.56347794, -5.45683824, -4.30737662, -1.4394612 ,
   -1.58047016, -0.93225927, -0.6719836 , -0.45977157, -0.37622436,
   -0.56115757, -0.3038559 , -0.26594558, -0.26496367])

def fitFunc(t, A, B, k):
    return A - B*np.exp(-k*t)
init_vals = np.random.rand(1,3)

fitParams, fitCovariances = curve_fit(fitFunc, x, y, p0=init_vals)
print fitParams
plt.plot(x,y)
plt.plot(x,fitFunc(x,*fitParams))
plt.show()

answered Mar 16, 2017 at 2:20

plasmon360

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2 Comments

plasmon360 Over a year ago

Welcome to python! please read the documentation for these functions and examples. they are very useful.

plasmon360 Over a year ago

Can you pick one of the answers and accept it so that people know the question is done?

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