To Optimize?

cpdef DTYPE_t grad1Timbre(np.ndarray[DTYPE_t,ndim=2] t):
    cdef DTYPE_t runningGrad1
    runningGrad1 = 0.0
    cdef unsigned int length = np.shape(t)[0]
    cdef unsigned int i
    cdef unsigned int j
    for i in range(length):
        for j in range(i+1,length):
            runningGrad1 += grad2Partials(t[i], t[j])
    return runningGrad1

cpdef DTYPE_t grad2Partials(np.ndarray[DTYPE_t,ndim=1] p1, np.ndarray[DTYPE_t,ndim=1] p2):
    if ((p1[2] < 0.5) and (p2[2] < 0.5)): return 0.0

    if (p1[2] > .5) and (p2[2] < .5):
        return float_min(abs(p1[1]),abs(p2[1]))*gradF1(p1[0],p2[0])*p1[2]
    elif (p1[2] < .5) and (p2[2] > .5):
        return float_min(abs(p1[1]),abs(p2[1]))*gradF2(p1[0],p2[0])*p2[2]
    else:
        return float_min(abs(p1[1]),abs(p2[1]))*(p1[2]*gradF1(p1[0],p2[0]) + p2[2]*gradF2(p1[0],p2[0]))