# Wendeling.core.bigarray | Basic tests # Copyright (C) 2014-2015 Nexedi SA and Contributors. # Kirill Smelkov # # This program is free software: you can Use, Study, Modify and Redistribute # it under the terms of the GNU General Public License version 3, or (at your # option) any later version, as published by the Free Software Foundation. # # You can also Link and Combine this program with other software covered by # the terms of any of the Open Source Initiative approved licenses and Convey # the resulting work. Corresponding source of such a combination shall include # the source code for all other software used. # # This program is distributed WITHOUT ANY WARRANTY; without even the implied # warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # # See COPYING file for full licensing terms. from wendelin.bigarray import BigArray from wendelin.bigfile import BigFile from wendelin.lib.mem import memcpy from wendelin.lib.calc import mul from numpy import ndarray, dtype, int32, uint32, uint8, all, zeros, arange, \ multiply, array_equal, asarray from pytest import raises # Synthetic bigfile that just loads zeros, and ignores writes (= a-la /dev/zero) class BigFile_Zero(BigFile): def loadblk(self, blk, buf): # Nothing to do here - the memory buf obtained from OS comes pre-cleared # XXX reenable once/if memory comes uninitialized here return def storeblk(self, blk, buf): return # Synthetic bigfile that loads/stores data from/to numpy array class BigFile_Data(BigFile): def __new__(cls, data, blksize): obj = BigFile.__new__(cls, blksize) obj.datab = data.view(uint8) return obj def loadblk(self, blk, buf): x = self.datab[self.blksize * blk : self.blksize * (blk+1)] memcpy(buf, x) def storeblk(self, blk, buf): memcpy(self.datab[self.blksize * blk : self.blksize * (blk+1)], buf) PS = 2*1024*1024 # FIXME hardcoded, TODO -> ram.pagesize # basic ndarray-compatibility attributes of BigArray def test_bigarray_basic(): Z = BigFile_Zero(PS) Zh = Z.fileh_open() A = BigArray((10,3), int32, Zh) raises(TypeError, "A.data") assert A.strides == (12, 4) assert A.dtype == dtype(int32) # XXX .flags? # XXX .flat? (non-basic) # XXX .imag? (non-basic) # XXX .real? (non-basic) assert A.size == 10*3 assert len(A) == 10 assert A.itemsize == 4 assert A.nbytes == 4*10*3 assert A.ndim == 2 assert A.shape == (10,3) # XXX .ctypes (non-basic) # TODO .base # DoubleGet(obj1, obj2)[key] -> obj1[key], obj2[key] class DoubleGet: def __init__(self, obj1, obj2): self.obj1 = obj1 self.obj2 = obj2 def __getitem__(self, key): return self.obj1[key], self.obj2[key] # getitem/setitem (1d case) def test_bigarray_indexing_1d(): Z = BigFile_Zero(PS) Zh = Z.fileh_open() A = BigArray((10*PS,), uint8, Zh) # ndarray of the same shape - we'll use it to get slices and compare result # shape/stride against BigArray.__getitem__ A_= ndarray ((10*PS,), uint8) # AA[key] -> A[key], A_[key] AA = DoubleGet(A, A_) # BigArray does not support advanced indexes # (in numpy they create _copy_ picking up elements) A_[0:5] = range(0,10,2) assert array_equal(A_[[0,1,2,3,4]], [0,2,4,6,8]) raises (TypeError, 'A[[0,1,2,3,4]]') # index out of range # - element access -> raises IndexError # - slice access -> empty A_[-1] = 0 assert AA[10*PS-1] == (0,0) raises(IndexError, 'A_[10*PS]') raises(IndexError, 'A [10*PS]') a, _ = AA[10*PS:10*PS+1] assert isinstance(a, ndarray) assert array_equal(a, _) assert a.dtype == dtype(uint8) assert a.shape == (0,) # "empty" slices assert A[10:5:1] .size == 0 assert A[5:10:-1] .size == 0 assert A[5:5] .size == 0 assert A[100*PS:200*PS] .size == 0 # whole array a, _ = AA[:] assert isinstance(a, ndarray) assert a.dtype == dtype(uint8) assert a.shape == _.shape assert a.strides == _.strides assert a[0] == 0 assert a[5*PS] == 0 assert a[10*PS-1] == 0 # overlaps with a b, _ = AA[4*PS:] assert isinstance(b, ndarray) assert b.dtype == dtype(uint8) assert b.shape == _.shape assert b.strides == _.strides assert b[0] == 0 assert b[1*PS] == 0 assert b[5*PS-1] == 0 # a <-> b assert b[1*PS] == 0 a[5*PS] = 1 assert b[1*PS] == 1 # non-pagesize aligned slice c, _ = AA[4*PS+3 : 9*PS-3] assert isinstance(c, ndarray) assert c.dtype == dtype(uint8) assert c.shape == _.shape assert c.strides == _.strides assert c[0] == 0 assert c[-1] == 0 # a <-> b <-> c assert b[3] == 0 assert a[4*PS+3] == 0 c[0] = 3 assert b[3] == 3 assert a[4*PS+3] == 3 assert b[5*PS-4] == 0 assert a[9*PS-4] == 0 c[-1] = 99 assert b[5*PS-4] == 99 assert a[9*PS-4] == 99 # negative stride d, _ = AA[9*PS+1:4*PS-1:-1] assert isinstance(d, ndarray) assert d.dtype == dtype(uint8) assert d.shape == _.shape assert d.strides == _.strides assert all(d[:5] == 0) assert d[5] == 99 # c[-1] assert all(d[6:-(PS+1)] == 0) assert d[-(PS+1)] == 1 # a[5*PS] assert all(d[-PS:-4] == 0) assert d[-4] == 3 # c[0] assert all(d[-3:] == 0) # like c, but stride > 1 e, _ = AA [4*PS+3 : 9*PS-3 : 7] assert isinstance(e, ndarray) assert e.dtype == dtype(uint8) assert e.shape == _.shape assert e.strides == _.strides c[0] = 4 assert e[0] == c[0] c[0] = 5 assert e[0] == c[0] c[7] = 7 assert e[1] == c[7] c[7] = 8 assert e[1] == c[7] # TODO check more # like d, but stride < -1 f, _ = AA[9*PS+1:4*PS-1:-11] assert isinstance(f, ndarray) assert f.dtype == dtype(uint8) assert f.shape == _.shape assert f.strides == _.strides d[0] = 11 assert f[0] == d[0] d[0] = 12 assert f[0] == d[0] d[11] = 13 assert f[1] == d[11] d[11] = 14 assert f[1] == d[11] # TODO check more # setitem A[2*PS+1:3*PS+2] = 5 assert all(a[2*PS+1 : 3*PS+2] == 5) assert a[2*PS] == 0 assert a[3*PS+3] == 0 A[2*PS+2:2*PS+5] = [6,7,8] assert a[2*PS+0] == 0 assert a[2*PS+1] == 5 assert a[2*PS+2] == 6 assert a[2*PS+3] == 7 assert a[2*PS+4] == 8 assert a[2*PS+5] == 5 assert a[2*PS+6] == 5 assert raises(ValueError, 'A[:4] = range(5)') # given dimension length n, yield index variants to test def indices_to_test(n): # ":" yield slice(None) # int yield 0 yield -1 yield n//2 # start:stop:stride yield slice(1,-1) yield slice(n//4+1, n*3//4-1, 2) yield slice(n//5+1, n*4//5-1, 3) # geven shape, yield all Nd idx variant, where every index iterates full indices_to_test def idx_to_test(shape, idx_prefix=()): leaf = len(shape) <= 1 for i in indices_to_test(shape[0]): idx = idx_prefix + (i,) if leaf: yield idx else: # = yield from for _ in idx_to_test(shape[1:], idx): yield _ # getitem/setitem (Nd case) def test_bigarray_indexing_Nd(): # shape of tested array - all primes, total size for uint32 ~ 7 2M pages # XXX even less dimensions (to speed up tests)? shape = tuple(reversed( (17,23,101,103) )) # test data - all items are unique - so we can check array by content # NOTE +PS so that BigFile_Data has no problem loading last blk # (else data slice will be smaller than buf) data = arange(mul(shape) + PS, dtype=uint32) # synthetic bigfile that only loads data from numpy array class BigFile_Data_RO(BigFile_Data): def storeblk(self, blk, buf): raise RuntimeError('tests should not try to change test data') f = BigFile_Data_RO(data, PS) fh = f.fileh_open() A = BigArray(shape, uint32, fh) # bigarray with test data and shape A_ = data[:mul(shape)].reshape(shape) # ndarray ----//---- # AA[key] -> A[key], A_[key] AA = DoubleGet(A, A_) # now just go over combinations of various slice at each dimension, and see # whether slicing result is the same ndarray would do. for idx in idx_to_test(shape): a, a_ = AA[idx] assert array_equal(a, a_) # any part of index out of range # - element access -> raises IndexError # - slice access -> empty for idxpos in range(len(shape)): idx = [0]*len(shape) # idx -> tuple(idx) # ( list would mean advanced indexing - not what we want ) idxt = lambda : tuple(idx) # valid access element access idx[idxpos] = shape[idxpos] - 1 # 0, 0, 0, Ni-1, 0 ,0, 0 a, a_ = AA[idxt()] assert array_equal(a, a_) # out-of-range element access idx[idxpos] = shape[idxpos] # 0, 0, 0, Ni , 0 ,0, 0 raises(IndexError, 'A [idxt()]') raises(IndexError, 'A_[idxt()]') # out-of-range slice access idx[idxpos] = slice(shape[idxpos], # 0, 0, 0, Ni:Ni+1 , 0 ,0, 0 shape[idxpos]+1) a, a_ = AA[idxt()] assert array_equal(a, a_) assert a .size == 0 assert a_.size == 0 # TODO ... -> expanded (0,1,2,negative), rejected if many # TODO newaxis # TODO nidx < len(shape) # TODO empty slice in major row, empty slice in secondary row """ # ellipsis - take some idx[a:b] and replace it by ... for ellipsis in range(2): # 0 - no ellipsis # newaxis - added after at some position(s) for newaxis in range(3): # 0 - no newaxis """ def test_bigarray_resize(): data = zeros(8*PS, dtype=uint32) f = BigFile_Data(data, PS) fh = f.fileh_open() # set first part & ensure it is set correctly A = BigArray((10,3), uint32, fh) A[:,:] = arange(10*3, dtype=uint32).reshape((10,3)) a = A[:] assert array_equal(a.ravel(), arange(10*3, dtype=uint32)) # grow array A.resize((11,3)) # a as already mapped, should stay the same assert array_equal(a.ravel(), arange(10*3, dtype=uint32)) # mapping it once again maps it whole with new size b = A[:] assert isinstance(b, ndarray) assert b.shape == (11,3) assert b.dtype == dtype(uint32) # head data is the same as a assert array_equal(a, b[:10,:]) # tail is zeros assert array_equal(b[10,:], zeros(3, dtype=uint32)) # old mapping stays valid and changes propageate to/from it assert a[0,0] == 0 assert b[0,0] == 0 a[0,0] = 1 assert b[0,0] == 1 b[0,0] = 2 assert a[0,0] == 2 a[0,0] = 0 assert b[0,0] == 0 assert a[ -1,-1] == 10*3-1 assert b[10-1,-1] == 10*3-1 a[ -1,-1] = 1 assert b[10-1,-1] == 1 b[10-1,-1] = 2 assert a[ -1,-1] == 2 a[ -1,-1] = 10*3-1 assert b[10-1,-1] == 10*3-1 # we cannot access old mapping beyond it's end assert raises(IndexError, 'a[10,:]') # we can change tail b[10,:] = arange(10*3, (10+1)*3) # map it whole again and ensure we have correct data c = A[:] assert array_equal(c.ravel(), arange(11*3, dtype=uint32)) def test_bigarray_list(): Z = BigFile_Zero(PS) Zh = Z.fileh_open() A = BigArray((10,), uint8, Zh) # the IndexError for out-of-bound scalar access should allow, though # inefficient, for list(A) to work (instead of looping inside forever) l = list(A) assert isinstance(l, list) assert l == [0]*10 def test_bigarray_to_ndarray(): Z = BigFile_Zero(PS) Zh = Z.fileh_open() A = BigArray((10,), uint8, Zh) # without IndexError on out-of-bound scalar access, the following # - would work with numpy-1.8 # - would loop forever eating memory with numpy-1.9 a = asarray(A) assert array_equal(a, A[:]) # "medium"-sized array of 1TB. converting it to ndarray should work here # without hanging, becuse initially all data are unmapped, and we don't # touch mapped memory. B = BigArray((1<<40,), uint8, Zh) b = asarray(B) assert isinstance(b, ndarray) assert b.nbytes == 1<<40 # array of size larger than virtual address space (~ 2^47 on linux/amd64) # converting it to ndarray is should be not possible for i in range(48,65): C = BigArray(((1<