zarr 0.2.7

A minimal implementation of chunked, compressed, N-dimensional arrays for Python.

A minimal implementation of chunked, compressed, N-dimensional arrays for Python.

• Source code: https://github.com/alimanfoo/zarr

• Download: https://pypi.python.org/pypi/zarr

Installation

Installation currently requires NumPy and Cython pre-installed. Currently only compatible with Python >= 3.4.

Install from PyPI:

$ pip install -U zarr

Install from GitHub:

$ pip install -U git+https://github.com/alimanfoo/zarr.git@master

Status

Highly experimental, pre-alpha. Bug reports and pull requests very welcome.

Design goals

• Chunking in multiple dimensions

• Resize any dimension

• Concurrent reads

• Concurrent writes

• Release the GIL during compression and decompression

Usage

Create an array:

>>> import numpy as np
>>> import zarr
>>> z = zarr.empty((10000, 1000), dtype='i4', chunks=(1000, 100))
>>> z
zarr.ext.Array((10000, 1000), int32, chunks=(1000, 100), cname='blosclz', clevel=5, shuffle=1)
  nbytes: 38.1M; cbytes: 0

Fill it with some data:

>>> z[:] = np.arange(10000000, dtype='i4').reshape(10000, 1000)
>>> z
zarr.ext.Array((10000, 1000), int32, chunks=(1000, 100), cname='blosclz', clevel=5, shuffle=1)
  nbytes: 38.1M; cbytes: 2.0M; ratio: 19.3

Obtain a NumPy array by slicing:

>>> z[:]
array([[      0,       1,       2, ...,     997,     998,     999],
       [   1000,    1001,    1002, ...,    1997,    1998,    1999],
       [   2000,    2001,    2002, ...,    2997,    2998,    2999],
       ...,
       [9997000, 9997001, 9997002, ..., 9997997, 9997998, 9997999],
       [9998000, 9998001, 9998002, ..., 9998997, 9998998, 9998999],
       [9999000, 9999001, 9999002, ..., 9999997, 9999998, 9999999]], dtype=int32)
>>> z[:100]
array([[    0,     1,     2, ...,   997,   998,   999],
       [ 1000,  1001,  1002, ...,  1997,  1998,  1999],
       [ 2000,  2001,  2002, ...,  2997,  2998,  2999],
       ...,
       [97000, 97001, 97002, ..., 97997, 97998, 97999],
       [98000, 98001, 98002, ..., 98997, 98998, 98999],
       [99000, 99001, 99002, ..., 99997, 99998, 99999]], dtype=int32)
>>> z[:, :100]
array([[      0,       1,       2, ...,      97,      98,      99],
       [   1000,    1001,    1002, ...,    1097,    1098,    1099],
       [   2000,    2001,    2002, ...,    2097,    2098,    2099],
       ...,
       [9997000, 9997001, 9997002, ..., 9997097, 9997098, 9997099],
       [9998000, 9998001, 9998002, ..., 9998097, 9998098, 9998099],
       [9999000, 9999001, 9999002, ..., 9999097, 9999098, 9999099]], dtype=int32)

Resize the array and add more data:

>>> z.resize(20000, 1000)
>>> z
zarr.ext.Array((20000, 1000), int32, chunks=(1000, 100), cname='blosclz', clevel=5, shuffle=1)
  nbytes: 76.3M; cbytes: 2.0M; ratio: 38.5
>>> z[10000:, :] = np.arange(10000000, dtype='i4').reshape(10000, 1000)
>>> z
zarr.ext.Array((20000, 1000), int32, chunks=(1000, 100), cname='blosclz', clevel=5, shuffle=1)
  nbytes: 76.3M; cbytes: 4.0M; ratio: 19.3

For convenience, an append() method is also available, which can be used to append data to any axis:

>>> a = np.arange(10000000, dtype='i4').reshape(10000, 1000)
>>> z = zarr.array(a, chunks=(1000, 100))
>>> z
zarr.ext.Array((10000, 1000), int32, chunks=(1000, 100), cname='blosclz', clevel=5, shuffle=1)
  nbytes: 38.1M; cbytes: 2.0M; ratio: 19.3
>>> z.append(a+a)
>>> z
zarr.ext.Array((20000, 1000), int32, chunks=(1000, 100), cname='blosclz', clevel=5, shuffle=1)
  nbytes: 76.3M; cbytes: 3.6M; ratio: 21.2
>>> z.append(np.vstack([a, a]), axis=1)
>>> z
zarr.ext.Array((20000, 2000), int32, chunks=(1000, 100), cname='blosclz', clevel=5, shuffle=1)
  nbytes: 152.6M; cbytes: 7.6M; ratio: 20.2

Tuning

zarr is designed for use in parallel computations working chunk-wise over data. Try it with dask.array.

zarr is optimised for accessing and storing data in contiguous slices, of the same size or larger than chunks. It is not and will never be optimised for single item access.

Chunks sizes >= 1M are generally good. Optimal chunk shape will depend on the correlation structure in your data.

Acknowledgments

zarr uses c-blosc internally for compression and decompression and borrows code heavily from bcolz.