gnumpy.py 文件源码

def tile(a, reps):
 if type(reps) in _numberTypes: reps = (reps,)
 reps = tuple(reps) # for generator expressions
 if type(a) in _numberTypes:
  ret = empty(reps)
  ret._base.assign(a)
  return ret
 a = as_garray(a)
 if len(reps) > a.ndim: a = a._add_axes(len(reps))
 if len(reps) < a.ndim: reps = _extend_shape(reps, a.ndim) # now len(reps)==a.ndim
 retShape = tuple([ a.shape[i] * reps[i] for i in tuple(xrange(len(reps)))])
 if _prodT(retShape)==0: return zeros(retShape)
 if _prodT(reps)==1: return a
 for i in range(a.ndim-1): # merge replication requests on adjacent axes, for efficiency.
  if reps[i]!=1 and reps[i+1]!=1 and a.shape[i]==1: return a.reshape(_deleteT2(a.shape, i)).tile(reps[:i]+(_prodT(reps[i:i+2]),)+reps[i+2:]).reshape(map(operator.mul, a.shape, reps))
 def dataIDone(nextA, i): return nextA.reshape(_modifyT(a.shape, i, a.shape[i]*reps[i])).tile(_modifyT(reps, i, 1))
 if reps[0]!=1: # replicating rows is easy and efficient: just repeat the data a number of times.
  temp = empty((reps[0], a.size)) # shape doesn't matter because dataIDone changes it
  tempCm = temp._base_shaped(1)
  if reps[0]>=1:
   _cm_row_slice_read(tempCm, 0, 1).assign(a._base_as_row())
   nCopiesDone = 1
   while nCopiesDone < reps[0]:
    nNow = __builtin__.min(nCopiesDone, reps[0]-nCopiesDone)
    _cm_row_slice_read(tempCm, nCopiesDone, nCopiesDone + nNow).assign(_cm_row_slice_read(tempCm, 0, nNow))
    nCopiesDone += nNow
  return dataIDone(temp, 0)
 # the general case is repeating a subset (aot the whole array) n times, before moving on to the next subset
 # using a transpose with the right shape, the subsets can become columns. those can be lengthened because that is replicating rows; a second transpose makes them now-lengthened subsets again
 axis = __builtin__.min( i for i in range(a.ndim) if reps[i]!=1)
 return dataIDone(a.reshape_2d(axis).T.tile((reps[axis], 1)).T, axis)