使用curve_fit来拟合数据

Question

I'm new to scipy and matplotlib, and I've been trying to fit functions to data. The first example in the Scipy Cookbook works fantastically, but when I am trying it with points read from a file, the initial coefficients I give (p0 below) never seem to actually change, and the covariance matrix is always INF.

I've tried to fit even data following a line, to no avail. Is it a problem with the way I am importing the data? If so, is there a better way to do it?

import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import scipy as sy

with open('data.dat') as f:
    noms = f.readline().split('\t')

    dtipus = [('x', sy.float32)] + [('y', sy.float32)]

    data = sy.loadtxt(f,delimiter='\t',dtype=dtipus)

    x = data['x']
    y = data['y']

    def func(x, a, b, c):
        return a*x**b + c

    p0 = sy.array([1,1,1])

    coeffs, matcov = curve_fit(func, x, y, p0)

    yaj = func(x, coeffs[0], coeffs[1], coeffs[2])

    print(coeffs)
    print(matcov)

    plt.plot(x,y,'x',x,yaj,'r-')
    plt.show()

Thanks!

Answer 1

It seems to me that the problem is indeed in how you import your data. Faking this datafile:

$:~/temp$ cat data.dat
1.0  2.0
2.0  4.2
3.0  8.4
4.0  16.1

and using the pylab 's loadtxt function for reading:

import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import scipy as sy
import pylab as plb  

data = plb.loadtxt('data.dat')  
x = data[:,0]
y= data[:,1]

def func(x, a, b, c):
  return a*x**b + c

p0 = sy.array([1,1,1])
coeffs, matcov = curve_fit(func, x, y, p0)

yaj = func(x, coeffs[0], coeffs[1], coeffs[2])
print(coeffs)
print(matcov)

plt.plot(x,y,'x',x,yaj,'r-')
plt.show()

works for me. By the way, you can use dtypes to name the columns.

Answer 2

The underlying problem with your load data is that you cast it to float32, but in scipy 0.10.1, curve_fit works with float64 but not float32 (it's a bug, not a feature). Your example works with float64.