Question

我是编程和尝试分析物理类数据的新手。到目前为止，我有：

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

def curve_fit_custom(f, xdata, ydata, p0=None, sigma=None, **kw):
    """
    Pass all arguments to curve_fit, which uses non-linear least squares
    to fit a function, f, to data.  Calculate the uncertaities in the
    fit parameters from the covariance matrix.
    """
    (popt, pcov) = curve_fit(f, xdata, ydata, p0, sigma, **kw)

    if sigma is None:
        chi2 = sum(((f(xdata,*popt)-ydata))**2)
    else:
        chi2 = sum(((f(xdata,*popt)-ydata)/sigma)**2)
    dof = len(ydata) - len(popt)
    rchi2 = chi2/dof
    print 'results of general_fit:'
    print '   chi squared = ', chi2
    print '   degrees of freedom = ', dof
    print '   reduced chi squared = ', rchi2

    # The uncertainties are the square roots of the diagonal elements
    punc = zeros(len(popt))
    for i in arange(0,len(popt)):
        punc[i] = sqrt(pcov[i,i])
    return popt, punc, rchi2, dof

(turnsmm, voltage) = loadtxt('Lab2 mm vs. voltage.csv',delimiter=',', unpack=True, usecols=[0,1])

def P(P0, Pmax, x, x0, w):
    return P0+(Pmax/2.0)(1.0-math.erf((sqrt(2.0)(x-x0))/w))

yuncertainty=0.01
yuncertaintyvector=ones(len(turnsmm))*yuncertainty

pguess = [18, 8, 3, 5] 

(popt, punc, rc, d) = curve_fit_custom(P, turnsmm, voltage, pguess, yuncertaintyvector)

但是我收到了最后一部分的错误消息：

TypeError                                 Traceback (most recent call last)
<ipython-input-103-bd94f34028f4> in <module>()
      1 pguess = [18, 8, 3, 5]
      2 
----> 3 (popt, punc, rc, d) = curve_fit_custom(P, turnsmm, voltage, pguess, yuncertaintyvector)

<ipython-input-98-6c1057de6870> in curve_fit_custom(f, xdata, ydata, p0, sigma, **kw)
     12     fit parameters from the covariance matrix.
     13     """
---> 14     (popt, pcov) = curve_fit(f, xdata, ydata, p0, sigma, **kw)
     15 
     16     if sigma is None:

/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/optimize/minpack.pyc in curve_fit(f, xdata, ydata, p0, sigma, absolute_sigma, **kw)
    553     # Remove full_output from kw, otherwise we're passing it in twice.
    554     return_full = kw.pop('full_output', False)
--> 555     res = leastsq(func, p0, args=args, full_output=1, **kw)
    556     (popt, pcov, infodict, errmsg, ier) = res
    557 

/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/optimize/minpack.pyc     in leastsq(func, x0, args, Dfun, full_output, col_deriv, ftol, xtol, gtol, maxfev, epsfcn, factor, diag)
    367     if not isinstance(args, tuple):
    368         args = (args,)
--> 369     shape, dtype = _check_func('leastsq', 'func', func, x0, args, n)
    370     m = shape[0]
    371     if n > m:

/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/optimize/minpack.pyc in _check_func(checker, argname, thefunc, x0, args, numinputs, output_shape)
     18 def _check_func(checker, argname, thefunc, x0, args, numinputs,
     19                 output_shape=None):
---> 20     res = atleast_1d(thefunc(*((x0[:numinputs],) + args)))
     21     if (output_shape is not None) and (shape(res) != output_shape):
     22         if (output_shape[0] != 1):

/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/optimize/minpack.pyc in _weighted_general_function(params, xdata, ydata, function, weights)
    447 
    448 def _weighted_general_function(params, xdata, ydata, function, weights):
--> 449     return weights * (function(xdata, *params) - ydata)
    450 
    451 

<ipython-input-101-c072ef1a8e7e> in P(P0, Pmax, x, x0, w)
      1 def P(P0, Pmax, x, x0, w):
----> 2     return P0+(Pmax/2.0)(1.0-math.erf((sqrt(2.0)(x-x0))/w))

TypeError: 'numpy.float64' object is not callable

我该如何解决这个问题？

Answer 1

看起来你在线上错过了一个算术运算符

P0+(Pmax/2.0)(1.0-math.erf((sqrt(2.0)(x-x0))/w))

如果要在Python中乘以两个数字，则必须明确使用运算符，例如*。 Python不会将(4-1)(4)等表达式计算为12;你必须写(4-1)*(4)。

(Pmax/2.0)是一个浮点数，通过在它后面直接放置括号，Python认为你试图把它当作一个可调用的对象（就像一个函数），它不是。

也许这条线应该是这样的：

P0 + (Pmax/2.0) * (1.0 - math.erf((sqrt(2.0)*(x-x0))/w))

带有curve_fit的NameError popt

1 个答案: