partial but running solution

nifty/operators/smoothing_operator/Cython/extended.pyx

@@ -17,12 +17,6 @@ cpdef np.ndarray[np.float64_t, ndim=1] apply_kernel_along_array(np.ndarray[np.fl
         # No smoothing requested, just return the input array.
         return power[startindex:endindex]
 
-    #excluded_power = np.array([])
-    #if (exclude > 0):
-    #    distances = distances[exclude:]
-    #    excluded_power = np.copy(power[:exclude])
-    #    power = power[exclude:]
-
     if (smooth_length is None) or (smooth_length < 0):
         smooth_length = distances[1]-distances[0]
 
@@ -32,39 +26,41 @@ cpdef np.ndarray[np.float64_t, ndim=1] apply_kernel_along_array(np.ndarray[np.fl
         l = max(i-int(2*smooth_length)-1,0)
         u = min(i+int(2*smooth_length)+2,len(p_smooth))
         p_smooth[i-startindex] = GaussianKernel(power[l:u], distances[l:u], distances[i], smooth_length)
-    #if (exclude > 0):
-    #    p_smooth = np.r_[excluded_power,p_smooth]
+
     return p_smooth
 
 def getShape(a):
     return tuple(a.shape)
 
-cpdef np.ndarray[np.float64_t, ndim=1] apply_along_axis(tuple axis, np.ndarray[np.float64_t] arr, np.int_t startindex,  np.int_t endindex, np.ndarray[np.float64_t, ndim=1] distances, np.float64_t smooth_length):
-    cdef tuple nd = arr.ndim
+cpdef np.ndarray[np.float64_t, ndim=1] apply_along_axis(np.int_t axis, np.ndarray arr, np.int_t startindex,  np.int_t endindex, np.ndarray distances, np.float64_t smooth_length):
+    cdef np.int_t nd = arr.ndim
     if axis < 0:
         axis += nd
     if (axis >= nd):
         raise ValueError("axis must be less than arr.ndim; axis=%d, rank=%d."
             % (axis, nd))
-    cdef np.ndarray[np.int_t, ndim=1] ind = np.zeros(nd-1)
-    cdef np.ndarray[np.int_t, ndim=1] i = np.zeros(nd, 'O')
+    cdef np.ndarray[np.int_t, ndim=1] ind = np.zeros(nd-1, dtype=np.int)
+    cdef np.ndarray i = np.zeros(nd, dtype=object)
     cdef tuple shape = getShape(arr)
-    cdef np.ndarray[np.int_t, ndim=1] indlist = list(range(nd))
-    indlist.remove(axis)
+    cdef np.ndarray[np.int_t, ndim=1] indlist = np.asarray(range(nd))
+    indlist = np.delete(indlist,axis)
     i[axis] = slice(None, None)
-    cdef np.ndarray[np.int_t, ndim=1]  outshape = np.asarray(shape).take(indlist)
+    cdef np.ndarray[np.int_t, ndim=1] outshape = np.asarray(shape).take(indlist)
+
     i.put(indlist, ind)
-    cdef np.ndarray[np.float64_t, ndim=1] slicedArr = arr[tuple(i.tolist())]
-    cdef np.ndarray[np.float64_t, ndim=1] res = apply_kernel_along_array(slicedArr, startindex, endindex, distances, smooth_length)
 
-    '''
-    Ntot = np.product(outshape)
-    holdshape = outshape
-    outshape = list(arr.shape)
-    outshape[axis] = len(res)
-    outarr = np.zeros(outshape, np.asarray(res).dtype)
+    cdef np.ndarray[np.float64_t, ndim=1] slicedArr
+    cdef np.ndarray[np.float64_t, ndim=1] res
+
+    cdef np.int_t Ntot = np.product(outshape)
+    cdef np.ndarray[np.int_t, ndim=1] holdshape = outshape
+    slicedArr = arr[tuple(i.tolist())]
+    res = apply_kernel_along_array(slicedArr, startindex, endindex, distances, smooth_length)
+    outshape = np.asarray(getShape(arr))
+    outshape[axis] = endindex - startindex
+    outarr = np.zeros(outshape, dtype=np.float64)
     outarr[tuple(i.tolist())] = res
-    k = 1
+    cdef np.int_t n, k = 1
     while k < Ntot:
         # increment the index
         ind[-1] += 1
@@ -74,9 +70,9 @@ cpdef np.ndarray[np.float64_t, ndim=1] apply_along_axis(tuple axis, np.ndarray[n
             ind[n] = 0
             n -= 1
         i.put(indlist, ind)
-        res = apply_kernel_along_array(arr[tuple(i.tolist())], startindex=startindex, endindex=endindex, distances=distances, smooth_length=smooth_length)
+        slicedArr = arr[tuple(i.tolist())]
+        res = apply_kernel_along_array(slicedArr, startindex, endindex, distances, smooth_length)
         outarr[tuple(i.tolist())] = res
         k += 1
 
     return outarr
-    '''
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