Commit 96005917 authored by csongor's avatar csongor
Browse files

partial but running solution

parent 1e1feb44
......@@ -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)
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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