我试图通过使用memoryview将以前的python代码转换为cython，并且我从第一个bolo线（靠近底部）继续编译器崩溃：
from __future__ import print_function
from builtins import range
from builtins import object
import cython
from cython.view cimport array as cvarray
from cpython cimport bool
from libc.math cimport round
from libc.stdlib cimport malloc, free
import numpy as np
cimport numpy as np
class LegCache(object):
def __init__(self):
self.d = {}
pass
def prep_legendre(self, n, polyorder):
p = (n, polyorder)
if p not in self.d:
self.d[p] = prep_legendre(n, polyorder)
return self.d[p]
@cython.boundscheck(False)
@cython.wraparound(False)
cdef prep_legendre(int n, int polyorder):
'''make array of legendre's'''
assert type(n) == int and type(polyorder) == int
cdef int[:,:] legendres = np.empty([n, polyorder + 1], dtype=int)
cdef int l = 0
legendres[:, 0] = np.ones(n)
if polyorder > 0:
legendres[:, 1] = np.linspace(-1, 1, n)
for i in range(polyorder - 1):
l = i + 1
np.multiply(l/(l+1), legendres[:,l-1])
cdef double[:,:] q = np.empty([polyorder + 1, polyorder + 1], dtype=double)
cdef double[:,:] r = np.empty([n, polyorder + 1], dtype=double)
cdef double[:,:] qt = np.empty([polyorder+1, polyorder+1], dtype=double)
cdef double[:,:] rinv = np.empty([polyorder + 1, n], dtype=double)
q, r = np.linalg.qr(legendres)
rinv = np.linalg.inv(r)
qt = q.T.copy()
return legendres, rinv, qt
def filter_slice_legendre_qr_mask_precalc(bolo,mask,legendres):
m=legendres.shape[1]
n=legendres.shape[0]
l2 = legendres*np.tile(mask.reshape(n,1),[1,m])
q,r=np.linalg.qr(l2)
rinv = np.linalg.inv(r)
p = np.dot(q.T,bolo)
coeff=np.dot(rinv,p)
out=bolo-np.dot(legendres,coeff)
return out,coeff
@cython.boundscheck(False)
@cython.wraparound(False)
cdef poly_filter_array(
double[:,:] array,
np.ndarray[DTYPE3_t, cast=True, ndim=2] mask_remove, # I think this casting should still work like this
np.ndarray[DTYPE3_t, cast=True, ndim=2] mask,
int[:] scan_list,
int ibegin,
int polyorder,
double minfrac=.75):
""" writes over input array
"""
cdef double nold = -1
# do nothing
if polyorder < 0:
return array
#damn, work
cdef int nch = array.shape[0]
cdef int nt = array.shape[1]
cdef int ns = len(scan_list)
cdef double[:,:,:] coeff_out = np.empty([nch, ns, nt], dtype = double)
legcache = LegCache()
cdef int istart = 0
cdef int n = 0
cdef int start = 0
cdef double mean = 0.0
cdef int[:,:] legendres = np.empty([n, polyorder + 1], dtype=int)
cdef double[:,:] qt = np.empty([polyorder+1, polyorder+1], dtype=double)
cdef double[:,:] rinv = np.empty([polyorder + 1, n], dtype=double)
#cdef double[:,:] bolo # I think you can get away without giving it a value. bolo changes in size throughout the loop
cdef int[:] goodhits = np.empty(np.shape(mask)[1], dtype = int)
# I'm not sure about the size of these
cdef double[:,:] coeff = np.empty([]) # I can't remember how dot product work right now but this should be easy to sort out
# remove mean
if polyorder == 0:
for s in range(len(scan_list)):
istart, n = scan_list[s]
start = istart - ibegin
for i in range(nch):
if np.any(mask[i, start:start + n]):
mean = np.average(
array[i, start:start + n], weights=mask[i, start:start + n])
array[i, start:start + n] -= mean
coeff_out[i, s, 0] = mean
# other cases
if polyorder > 0:
for s in range(len(scan_list)):
istart, n = scan_list[s]
start = istart - ibegin
if n <= polyorder: # otherwise cannot compute legendre polynomials
for i in range(nch):
mask[i, start:start + n] = 0 # flag it
# remove this region from actual data as well
mask_remove[i, start:start + n] = 0
print('Not enough points (%d) to build legendre of order (%d)' % (n, polyorder))
continue
goodhits = np.sum(mask[:, start:start + n], axis=1)
if n != nold:
legendres, rinv, qt = legcache.prep_legendre(n, polyorder)
rinvqt = np.dot(rinv, qt)
nold = n
# handle no masked ones
for i in range(nch):
if goodhits[i] != n:
continue # skip for now
bolo[i, :] = array[i, start:start + n] #where problem starts
coeff = np.dot(rinvqt, bolo)
coeff_out[i, s, :] = coeff
bolo -= np.dot(legendres, coeff)
for i in range(nch):
if goodhits[i] == n:
continue # skip since dealt with above
if goodhits[i] < minfrac * n: # not enough points
mask[i, start:start + n] = 0 # flag it
# remove this region from actual data as well
mask_remove[i, start:start + n] = 0
continue
bolo, coeff = filter_slice_legendre_qr_mask_precalc(
array[i, start:start + n], mask[i, start:start + n], legendres)
array[i, start:start + n] = bolo
coeff_out[i, s, :] = coeff
return coeff_out
它引发了非特定错误＆＃34;编译器崩溃在ExpandInplaceOperators＆＃34;当我尝试编译代码时。我完全迷失了。
答案 0 :(得分：1)
生成错误的最小代码不需要运行。它只需要足以生成错误消息。我一直在删除代码，直到我留下了生成错误消息的最小块，这就是
cdef poly_filter_array(
double[:,:] array):
cdef double mean = 0.0
cdef int i =0
array[i, :] -= mean
编译此代码会给出
Compiler crash in ExpandInplaceOperators
ModuleNode.body = StatListNode(cycrashdelme.pyx:7:5)
StatListNode.stats[0] = CFuncDefNode(cycrashdelme.pyx:7:5,
args = [...]/1,
modifiers = [...]/0,
visibility = 'private')
CFuncDefNode.body = StatListNode(cycrashdelme.pyx:10:4)
StatListNode.stats[2] = InPlaceAssignmentNode(cycrashdelme.pyx:14:9,
operator = '-')
File 'UtilNodes.py', line 146, in __init__: ResultRefNode(may_hold_none = True,
result_is_used = True,
use_managed_ref = True)
Compiler crash traceback from this point on:
File "<some path on my computer>\lib\site-packages\Cython\Compiler\UtilNodes.py", line 146, in __init__
assert self.pos is not None
AssertionError:
这样的事情就是用...提出问题的一个更好的例子。
这几乎告诉您问题在于行array[i,:] -= mean（原始版本中我认为是array[:,start:start+n]）。
值得尝试一些简化版本，看看会发生什么：
array -= mean
array[i,:] = array[i,:] - mean
给出
Invalid operand types for '-' (double[:, :]; double)
Invalid operand types for '-' (double[:]; double)
分别为两行。所以我们知道问题是Cython内存视图不支持整个视图的算术（你可以对单个元素进行操作）。
您可以通过暂时将其转换为numpy数组来进行算术运算
tmp_as_array = np.asarray(array[i,:])
tmp_as_array -= mean
（输入tmp_as_array）没有速度优势。这会修改现有数据，而不是复制它，您可以通过打印tmp_as_array.owndata False进行验证。
总结 -