ValueError: ndarray is not C-contiguous in cython

Question

I have written the following function in cython to estimate the log-likelihood

@cython.boundscheck(False)
@cython.wraparound(False)
def likelihood(double m,
               double c,
               np.ndarray[np.double_t, ndim=1, mode='c'] r_mpc not None,
               np.ndarray[np.double_t, ndim=1, mode='c'] gtan not None,
               np.ndarray[np.double_t, ndim=1, mode='c'] gcrs not None,
               np.ndarray[np.double_t, ndim=1, mode='c'] shear_err not None,
               np.ndarray[np.double_t, ndim=1, mode='c'] beta not None,
               double rho_c,
               np.ndarray[np.double_t, ndim=1, mode='c'] rho_c_sigma not None):
    cdef double rscale = rscaleConstM(m, c,rho_c, 200)

    cdef Py_ssize_t ngals = r_mpc.shape[0]

    cdef np.ndarray[DTYPE_T, ndim=1, mode='c'] gamma_inf = Sh(r_mpc, c, rscale, rho_c_sigma)
    cdef np.ndarray[DTYPE_T, ndim=1, mode='c'] kappa_inf = Kap(r_mpc, c, rscale, rho_c_sigma)


    cdef double delta = 0.
    cdef double modelg = 0.
    cdef double modsig = 0.

    cdef Py_ssize_t i
    cdef DTYPE_T logProb = 0.


    #calculate logprob
    for i from ngals > i >= 0:

        modelg = (beta[i]*gamma_inf[i] / (1 - beta[i]*kappa_inf[i]))

        delta = gtan[i] - modelg

        modsig = shear_err[i]

        logProb = logProb -.5*(delta/modsig)**2  - logsqrt2pi - log(modsig)


    return logProb

but when I run the compiled version of this function, I get the following error message:

  File "Tools.pyx", line 3, in Tools.likelihood 
    def likelihood(double m,
ValueError: ndarray is not C-contiguous

I could not quite understand why this problem occurs??!!! I will appreciate to get any useful tips.

Solution

Just before you get the error, try printing the flags attribute of the numpy array(s) you're passing to likelihood. You'll probably see something like:

In [2]: foo.flags
Out[2]: 
  C_CONTIGUOUS : False
  F_CONTIGUOUS : True
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  UPDATEIFCOPY : False

Note where it says C_CONTIGUOUS : False, because that's the issue. To fix it, simply convert it to C-order:

In [6]: foo = foo.copy(order='C')

In [7]: foo.flags
Out[7]: 
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  UPDATEIFCOPY : False

Comments

[S.Kozlov]

Is it full source? May be adding of cimport numpy as np would help you?

[Dalek]

@S.Kozlov ofcourse it is not! It is a long code and I posted the part of code that raises error message.

[Dalek]

@S.Kozlov Indeed it is on the top of the long code.