Metadata-Version: 1.1
Name: zarr
Version: 0.2.3
Summary: A minimal implementation of chunked, compressed, N-dimensional arrays for Python.
Home-page: https://github.com/alimanfoo/zarr
Author: Alistair Miles
Author-email: alimanfoo@googlemail.com
License: MIT
Description: # zarr
        
        A minimal implementation of chunked, compressed, N-dimensional arrays for 
        Python.
        
        ## Installation
        
        Install from GitHub (requires NumPy and Cython pre-installed):
        
        ```bash
        $ 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
        
        ```python
        >>> import numpy as np
        >>> import zarr
        
        ```
        
        Create an array:
        
        ```python
        >>> z = zarr.empty((10000, 1000), dtype='i4', chunks=(1000, 100))
        >>> z
        zarr.ext.Array((10000, 1000), int32, chunks=(1000, 100), nbytes=38.1M, cbytes=0, cname=blosclz, clevel=5, shuffle=1)
        
        ```
        
        Fill it with some data:
        
        ```python
        >>> z[:] = np.arange(10000000, dtype='i4').reshape(10000, 1000)
        >>> z
        zarr.ext.Array((10000, 1000), int32, chunks=(1000, 100), nbytes=38.1M, cbytes=2.0M, cratio=19.3, cname=blosclz, clevel=5, shuffle=1)
        
        ```
        
        Obtain a NumPy array:
        
        ```python
        >>> 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)
        
        ```
        
        Resize the array and add more data:
        
        ```python
        >>> z.resize(20000, 1000)
        >>> z
        zarr.ext.Array((20000, 1000), int32, chunks=(1000, 100), nbytes=76.3M, cbytes=2.0M, cratio=38.5, cname=blosclz, clevel=5, shuffle=1)
        >>> z[10000:, :] = np.arange(10000000, dtype='i4').reshape(10000, 1000)
        >>> z
        zarr.ext.Array((20000, 1000), int32, chunks=(1000, 100), nbytes=76.3M, cbytes=4.0M, cratio=19.3, cname=blosclz, clevel=5, shuffle=1)
        
        ```
        
        For convenience, an `append` method is also available, which can be used to 
        append data to any axis:
        
        ```python
        >>> 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), nbytes=38.1M, cbytes=2.0M, cratio=19.3, cname=blosclz, clevel=5, shuffle=1)
        >>> z.append(a+a)
        >>> z
        zarr.ext.Array((20000, 1000), int32, chunks=(1000, 100), nbytes=76.3M, cbytes=3.6M, cratio=21.2, cname=blosclz, clevel=5, shuffle=1)
        >>> z.append(np.vstack([a, a]), axis=1)
        >>> z
        zarr.ext.Array((20000, 2000), int32, chunks=(1000, 100), nbytes=152.6M, cbytes=7.6M, cratio=20.2, cname=blosclz, clevel=5, shuffle=1)
        
        ```
        
        ## Tuning
        
        ``zarr`` is designed for use in parallel computations working chunk-wise 
        over data. Try it with [dask.array](http://dask.pydata.org/en/latest/array.html).
        
        ``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](https://github.com/Blosc/c-blosc) internally for 
        compression and decompression and borrows code heavily from 
        [bcolz](http://bcolz.blosc.org/).
        
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
