def slicer(start, length, slicelen):
length = min(len(self) - start, length)
slicelen = min(length, slicelen)
buf = np.empty(slicelen, dtype=dtype)
if rightclosed:
return Seq.map((lambda s: g_rightclosed(s, buf, cuts)),
covariate.slicer(start, length, slicelen))
else:
return Seq.map(
(lambda s: g_leftclosed(s, buf, cuts, levelcount - 1)),
covariate.slicer(start, length, slicelen))
def __call__(self, start, length, slicelen):
assert start >= 0 and start < len(self.vector)
length = min(length, len(self.vector) - start)
slicelen = min(length, slicelen)
state = start, length, slicelen
def f(from_to):
return self.vector[from_to[0]:from_to[1]]
return Seq.map(f, Seq.from_next(state, next_slice_indices))
def __call__(self, start, length, slicelen):
dtypes = [f.slicer.dtype for f in self.factors]
alluint8 = all([d == np.uint8 for d in dtypes])
dtype = np.uint8 if alluint8 else np.uint16
k = len(self.factors)
buf = np.empty((k, slicelen), dtype=dtype)
def f(slices):
n = len(slices[0])
for i in range(k):
if dtypes[i] == dtype:
buf[i, :n] = slices[i]
else:
buf[i, :n] = slices[i].astype(dtype)
if n == slicelen:
return buf
else:
return buf[:, :n]
return Seq.map(
f,
Seq.zip(*[f.slicer(start, length, slicelen)
for f in self.factors]))
def slicer(start, length, slicelen):
length = min(self._length - start, length)
slicelen = min(length, slicelen)
buf = np.ones(slicelen, dtype = np.uint8)
return Seq.map((lambda x: buf[x[0]:x[1]]), Seq.from_next((start, length, slicelen), ch.next_slice_indices))
def f(start, length, slicelen):
buf = np.empty(slicelen, dtype=np.float32)
return Seq.map((lambda s: g(s, buf)), vslicer(start, length, slicelen))
def slice(start, length, slicelen):
buf = np.empty(slicelen, dtype = np.float32)
return Seq.map((lambda slice: g_parse(slice, buf, parsed)), basefactor.slicer(start, length, slicelen))
