'numpy.ndarray' object is not callable error with optimize.minimize

Question

I want to fit a set of data with a simple sin^2 function and want to determine its minima based on the fitted parameters.

Here's my code:

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

data = np.loadtxt('data.txt', usecols=(0,1))
x = data[:,0]*np.pi/180
y = data[:,1]

plt.scatter(x, y, c='red')

def sine(t,a,b,c):
    return a*(np.sin(b*(t-c)))**2

params, cov = optimize.curve_fit(sine, x, y, p0=[9500, 0.5, 0])
print(params)

t = np.linspace(0, 2*np.pi/3, 120) 
plt.plot(t, sine(t, *params), 'black')

plt.show()

optimize.minimize(sine(t, *params), x0=0)

Everything is fine except for the minimize call as I get the following error (with a full traceback):

TypeError                                 Traceback (most recent call last)
~\Documents\CNR\Calibrazione_lamine_20181112\Fit.py in <module>()
     23 plt.show()
     24 
---> 25 optimize.minimize(sine(t, *params), x0=0)

~\Anaconda3\lib\site-packages\scipy\optimize\_minimize.py in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
    442         return _minimize_cg(fun, x0, args, jac, callback, **options)
    443     elif meth == 'bfgs':
--> 444         return _minimize_bfgs(fun, x0, args, jac, callback, **options)
    445     elif meth == 'newton-cg':
    446         return _minimize_newtoncg(fun, x0, args, jac, hess, hessp, callback,

~\Anaconda3\lib\site-packages\scipy\optimize\optimize.py in _minimize_bfgs(fun, x0, args, jac, callback, gtol, norm, eps, maxiter, disp, return_all, **unknown_options)
    911     else:
    912         grad_calls, myfprime = wrap_function(fprime, args)
--> 913     gfk = myfprime(x0)
    914     k = 0
    915     N = len(x0)

~\Anaconda3\lib\site-packages\scipy\optimize\optimize.py in function_wrapper(*wrapper_args)
    290     def function_wrapper(*wrapper_args):
    291         ncalls[0] += 1
--> 292         return function(*(wrapper_args + args))
    293 
    294     return ncalls, function_wrapper

~\Anaconda3\lib\site-packages\scipy\optimize\optimize.py in approx_fprime(xk, f, epsilon, *args)
    686 
    687     """
--> 688     return _approx_fprime_helper(xk, f, epsilon, args=args)
    689 
    690 

~\Anaconda3\lib\site-packages\scipy\optimize\optimize.py in _approx_fprime_helper(xk, f, epsilon, args, f0)
    620     """
    621     if f0 is None:
--> 622         f0 = f(*((xk,) + args))
    623     grad = numpy.zeros((len(xk),), float)
    624     ei = numpy.zeros((len(xk),), float)

~\Anaconda3\lib\site-packages\scipy\optimize\optimize.py in function_wrapper(*wrapper_args)
    290     def function_wrapper(*wrapper_args):
    291         ncalls[0] += 1
--> 292         return function(*(wrapper_args + args))
    293 
    294     return ncalls, function_wrapper

TypeError: 'numpy.ndarray' object is not callable.

I'm missing something but I don't know what.

I'm adding the data file to make this program run, as suggested

I guess you just need x0=[0] or something like this (depending on how many variables you use). Just make sure to provide a list of same length as the number of variables. — Cleb
– Cleb, Commented Nov 23, 2018 at 11:06
Ok, could you provide some data then which reproduce the error?! That makes it easier to help. — Cleb
– Cleb, Commented Nov 23, 2018 at 11:52
Could you explain what exactly you would like to achieve by this minimize call? You already fitted the parameters using curve_fit. — Cleb
– Cleb, Commented Nov 23, 2018 at 12:39
I want to find the values of the angles which minimize my function and optimize.minimize(sine, x0=[0], args=(params[0], params[1], params[2])) is apparently working fine. Thank you! — R. Panico
– R. Panico, Commented Nov 23, 2018 at 12:56

Cleb · Accepted Answer · 2018-11-23 13:01:58Z

1

minimize expects a function as first argument, however, you currently pass

sine(t, *params)

which is a numpy array.

You can fix this and do:

print(optimize.minimize(sine, x0=[0], args=tuple(params)))

This will print

      fun: 2.4080485986582715e-12
 hess_inv: array([[1.15258817e-05]])
      jac: array([8.19961349e-09])
  message: 'Optimization terminated successfully.'
     nfev: 18
      nit: 4
     njev: 6
   status: 0
  success: True
        x: array([0.09203053])

answered Nov 23, 2018 at 13:01

Cleb

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Rarblack · Accepted Answer · 2018-11-23 10:27:19Z

0

In the documentation of scipy, optimize.minimize function takes ndarray or shape(n) as an input for x, not an integer. I think the error is raised from there because in their error trace

--> 913     gfk = myfprime(x0)

the error is raised form this function.

Documentation link.

answered Nov 23, 2018 at 10:27

Rarblack

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1 Comment

R. Panico Over a year ago

I verified that the input for x, is indeed an ndarray of shape(n,), so the problem is probably something else

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