它非常快，对于小型阵列（<2GB）也很容易使用。对于像您的示例这样的易于压缩的数据，通常可以更快地压缩数据以进行IO操作。（SATA-SSD：大约500 MB / s，PCIe-SSD：最高3500MB / s）在解压缩步骤中，阵列分配是最昂贵的部分。如果图像的形状相似，则可以避免重复分配内存。
对于以下示例，假定使用连续数组。
import blosc
import pickle
def compress(arr,Path):
#c = blosc.compress_ptr(arr.__array_interface__['data'][0], arr.size, arr.dtype.itemsize, clevel=3,cname='lz4',shuffle=blosc.SHUFFLE)
c = blosc.compress_ptr(arr.__array_interface__['data'][0], arr.size, arr.dtype.itemsize, clevel=3,cname='zstd',shuffle=blosc.SHUFFLE)
f=open(Path,"wb")
pickle.dump((arr.shape, arr.dtype),f)
f.write(c)
f.close()
return c,arr.shape, arr.dtype
def decompress(Path):
f=open(Path,"rb")
shape,dtype=pickle.load(f)
c=f.read()
#array allocation takes most of the time
arr=np.empty(shape,dtype)
blosc.decompress_ptr(c, arr.__array_interface__['data'][0])
return arr
#Pass a preallocated array if you have many similar images
def decompress_pre(Path,arr):
f=open(Path,"rb")
shape,dtype=pickle.load(f)
c=f.read()
#array allocation takes most of the time
blosc.decompress_ptr(c, arr.__array_interface__['data'][0])
return arr
#blosc.SHUFFLE, cname='zstd' -> 4728KB,
%timeit compress(arr,"Test.dat")
1.03 s ± 12.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
#611 MB/s
%timeit decompress("Test.dat")
146 ms ± 481 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
#4310 MB/s
%timeit decompress_pre("Test.dat",arr)
50.9 ms ± 438 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
#12362 MB/s
#blosc.SHUFFLE, cname='lz4' -> 9118KB,
%timeit compress(arr,"Test.dat")
32.1 ms ± 437 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
#19602 MB/s
%timeit decompress("Test.dat")
146 ms ± 332 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
#4310 MB/s
%timeit decompress_pre("Test.dat",arr)
53.6 ms ± 82.9 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
#11740 MB/s
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