Crash of interpolate.splprep(task=-1)

[ebw44]

Running the circle example but with task = -1 parameter for splprep
http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html#spline-interpolation-in-1-d-procedural-interpolate-splxxx

I got a crash in firpack.py at
t,c,o = _fitpack._parcur(ravel(transpose(x)),w,u,ub,ue,k,task,ipar,s,t, nest,wrk,iwrk,per)

Here is my code:

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

""" tutorial example """
t = np.arange(0, 1.1, .1)
x = np.sin(2*np.pi*t)
y = np.cos(2*np.pi*t)
tck,u = interpolate.splprep([x,y], s=0)
unew = np.arange(0, 1.01, 0.01)
out = interpolate.splev(unew, tck)
plt.figure()
plt.plot(x, y, 'x', out[0], out[1], np.sin(2*np.pi*unew), np.cos(2*np.pi*unew), x, y, 'b')
plt.legend(['Linear', 'Cubic Spline', 'True'])
plt.axis([-1.05, 1.05, -1.05, 1.05])
plt.title('Spline of parametrically-defined curve')
plt.show()

"""test with splprep(task=-1) """
uknots = tck[0]                                    # using the knots from the previous fitting
tck,u = interpolate.splprep([x,y], task=-1, t=uknots)               # here is the crash
plt.figure()
plt.plot(x, y, 'x', out[0], out[1], np.sin(2*np.pi*unew), np.cos(2*np.pi*unew), x, y, 'b')
plt.legend(['Linear', 'Cubic Spline', 'True'])
plt.axis([-1.05, 1.05, -1.05, 1.05])
plt.title('Spline of parametrically-defined curve')
plt.show()

Here are the parameters for _fitpack._parcur:

1. in the first case

[  0.00000000e+00   1.00000000e+00   5.87785252e-01   8.09016994e-01
   9.51056516e-01   3.09016994e-01   9.51056516e-01  -3.09016994e-01
   5.87785252e-01  -8.09016994e-01   1.22464680e-16  -1.00000000e+00
  -5.87785252e-01  -8.09016994e-01  -9.51056516e-01  -3.09016994e-01
  -9.51056516e-01   3.09016994e-01  -5.87785252e-01   8.09016994e-01
  -2.44929360e-16   1.00000000e+00] [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.] [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.] 0 1 3 0 False 0 [] 15 [] [] 0

2. in the second case (task =-1)

[  0.00000000e+00   1.00000000e+00   5.87785252e-01   8.09016994e-01
   9.51056516e-01   3.09016994e-01   9.51056516e-01  -3.09016994e-01
   5.87785252e-01  -8.09016994e-01   1.22464680e-16  -1.00000000e+00
  -5.87785252e-01  -8.09016994e-01  -9.51056516e-01  -3.09016994e-01
  -9.51056516e-01   3.09016994e-01  -5.87785252e-01   8.09016994e-01
  -2.44929360e-16   1.00000000e+00] [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.] [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.] 0 1 3 -1 False 6.30958424018 [ 0.   0.   0.   0.   0.2  0.3  0.4  0.5  0.6  0.7  0.8  1.   1.   1.   1. ] 17 [] [] 0

I'm, using scipy '0.14.0'.