Question

我在cython编写了以下函数来估算对数似然

@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

但是当我运行此函数的编译版本时，我收到以下错误消息：

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

我不太明白为什么会出现这个问题?? !!!我将很高兴获得任何有用的提示。

Answer 1

在您收到错误之前，请尝试打印您传递给flags的numpy数组的likelihood属性。你可能会看到类似的东西：

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

请注意C_CONTIGUOUS : False的位置，因为这是问题所在。要修复它，只需将其转换为C顺序：

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

ValueError：ndarray在cython中不是C-contiguous

1 个答案: