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        "author": "Danny Price",
        "author_email": "dan@thetelegraphic.com",
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        "description": "[![Build Status](https://travis-ci.org/telegraphic/hickle.svg?branch=master)](https://travis-ci.org/telegraphic/hickle)\n[![Build status](https://ci.appveyor.com/api/projects/status/8cwrkjpwxet5jmgp?svg=true)](https://ci.appveyor.com/project/telegraphic/hickle)\n[![JOSS Status](http://joss.theoj.org/papers/0c6638f84a1a574913ed7c6dd1051847/status.svg)](http://joss.theoj.org/papers/0c6638f84a1a574913ed7c6dd1051847)\n\n\nHickle\n======\n\nHickle is a [HDF5](https://www.hdfgroup.org/solutions/hdf5/) based clone of `pickle`, with a twist: instead of serializing to a pickle file,\nHickle dumps to a HDF5 file (Hierarchical Data Format). It is designed to be a \"drop-in\" replacement for pickle (for common data objects), but is\nreally an amalgam of `h5py` and `dill`/`pickle` with extended functionality.\n\nThat is: `hickle` is a neat little way of dumping python variables to HDF5 files that can be read in most programming\nlanguages, not just Python. Hickle is fast, and allows for transparent compression of your data (LZF / GZIP).\n\nWhy use Hickle?\n---------------\n\nWhile `hickle` is designed to be a drop-in replacement for `pickle` (or something like `json`), it works very differently.\nInstead of serializing / json-izing, it instead stores the data using the excellent [h5py](https://www.h5py.org/) module.\n\nThe main reasons to use hickle are:\n\n  1. It's faster than pickle and cPickle.\n  2. It stores data in HDF5.\n  3. You can easily compress your data.\n\nThe main reasons not to use hickle are:\n\n  1. You don't want to store your data in HDF5. While hickle can serialize arbitrary python objects, this functionality is provided only for convenience, and you're probably better off just using the pickle module.\n  2. You want to convert your data in human-readable JSON/YAML, in which case, you should do that instead.\n\nSo, if you want your data in HDF5, or if your pickling is taking too long, give hickle a try.\nHickle is particularly good at storing large numpy arrays, thanks to `h5py` running under the hood.\n\nDocumentation\n-------------\n\nDocumentation for hickle can be found at [telegraphic.github.io/hickle/](http://telegraphic.github.io/hickle/).\n\n\nUsage example\n-------------\n\nHickle is nice and easy to use, and should look very familiar to those of you who have pickled before.\n\nIn short, `hickle` provides two methods: a [hickle.load](http://telegraphic.github.io/hickle/toc.html#hickle.load)\nmethod, for loading hickle files, and a [hickle.dump](http://telegraphic.github.io/hickle/toc.html#hickle.dump)\nmethod, for dumping data into HDF5. Here's a complete example:\n\n```python\nimport os\nimport hickle as hkl\nimport numpy as np\n\n# Create a numpy array of data\narray_obj = np.ones(32768, dtype='float32')\n\n# Dump to file\nhkl.dump(array_obj, 'test.hkl', mode='w')\n\n# Dump data, with compression\nhkl.dump(array_obj, 'test_gzip.hkl', mode='w', compression='gzip')\n\n# Compare filesizes\nprint('uncompressed: %i bytes' % os.path.getsize('test.hkl'))\nprint('compressed:   %i bytes' % os.path.getsize('test_gzip.hkl'))\n\n# Load data\narray_hkl = hkl.load('test_gzip.hkl')\n\n# Check the two are the same file\nassert array_hkl.dtype == array_obj.dtype\nassert np.all((array_hkl, array_obj))\n```\n\n### HDF5 compression options\n\nA major benefit of `hickle` over `pickle` is that it allows fancy HDF5 features to\nbe applied, by passing on keyword arguments on to `h5py`. So, you can do things like:\n  ```python\n  hkl.dump(array_obj, 'test_lzf.hkl', mode='w', compression='lzf', scaleoffset=0,\n           chunks=(100, 100), shuffle=True, fletcher32=True)\n  ```\nA detailed explanation of these keywords is given at http://docs.h5py.org/en/latest/high/dataset.html,\nbut we give a quick rundown below.\n\nIn HDF5, datasets are stored as B-trees, a tree data structure that has speed benefits over contiguous\nblocks of data. In the B-tree, data are split into [chunks](http://docs.h5py.org/en/latest/high/dataset.html#chunked-storage),\nwhich is leveraged to allow [dataset resizing](http://docs.h5py.org/en/latest/high/dataset.html#resizable-datasets) and\ncompression via [filter pipelines](http://docs.h5py.org/en/latest/high/dataset.html#filter-pipeline). Filters such as\n`shuffle` and `scaleoffset` move your data around to improve compression ratios, and `fletcher32` computes a checksum.\nThese file-level options are abstracted away from the data model.\n\nRecent changes\n--------------\n\n* December 2018: Accepted to Journal of Open-Source Software (JOSS).\n* June 2018: Major refactor and support for Python 3.\n* Aug 2016: Added support for scipy sparse matrices `bsr_matrix`, `csr_matrix` and `csc_matrix`.\n\nPerformance comparison\n----------------------\n\nHickle runs a lot faster than pickle with its default settings, and a little faster than pickle with `protocol=2` set:\n\n```Python\nIn [1]: import numpy as np\n\nIn [2]: x = np.random.random((2000, 2000))\n\nIn [3]: import pickle\n\nIn [4]: f = open('foo.pkl', 'w')\n\nIn [5]: %time pickle.dump(x, f)  # slow by default\nCPU times: user 2 s, sys: 274 ms, total: 2.27 s\nWall time: 2.74 s\n\nIn [6]: f = open('foo.pkl', 'w')\n\nIn [7]: %time pickle.dump(x, f, protocol=2)  # actually very fast\nCPU times: user 18.8 ms, sys: 36 ms, total: 54.8 ms\nWall time: 55.6 ms\n\nIn [8]: import hickle\n\nIn [9]: f = open('foo.hkl', 'w')\n\nIn [10]: %time hickle.dump(x, f)  # a bit faster\ndumping <type 'numpy.ndarray'> to file <HDF5 file \"foo.hkl\" (mode r+)>\nCPU times: user 764 us, sys: 35.6 ms, total: 36.4 ms\nWall time: 36.2 ms\n```\n\nSo if you do continue to use pickle, add the `protocol=2` keyword (thanks @mrocklin for pointing this out).  \n\nFor storing python dictionaries of lists, hickle beats the python json encoder, but is slower than uJson. For a dictionary with 64 entries, each containing a 4096 length list of random numbers, the times are:\n\n\n    json took 2633.263 ms\n    uJson took 138.482 ms\n    hickle took 232.181 ms\n\n\nIt should be noted that these comparisons are of course not fair: storing in HDF5 will not help you convert something into JSON, nor will it help you serialize a string. But for quick storage of the contents of a python variable, it's a pretty good option.\n\nInstallation guidelines\n-----------------------\n\n### Easy method\nInstall with `pip` by running `pip install hickle` from the command line.\n\n### Manual install\n\n1. You should have Python 2.7 and above installed\n\n2. Install h5py\n(Official page: http://docs.h5py.org/en/latest/build.html)\n\n3. Install hdf5\n(Official page: http://www.hdfgroup.org/ftp/HDF5/current/src/unpacked/release_docs/INSTALL)\n\n4. Download `hickle`:\nvia terminal: git clone https://github.com/telegraphic/hickle.git\nvia manual download: Go to https://github.com/telegraphic/hickle and on right hand side you will find `Download ZIP` file\n\n5. cd to your downloaded `hickle` directory\n\n6. Then run the following command in the `hickle` directory:\n     `python setup.py install`\n\n### Testing\n\nOnce installed from source, run `python setup.py test` to check it's all working.\n\n\nBugs & contributing\n--------------------\n\nContributions and bugfixes are very welcome. Please check out our [contribution guidelines](https://github.com/telegraphic/hickle/blob/master/CONTRIBUTING.md)\nfor more details on how to contribute to development.\n\n\nReferencing hickle\n------------------\n\nIf you use `hickle` in academic research, we would be grateful if you could reference [our paper](http://joss.theoj.org/papers/0c6638f84a1a574913ed7c6dd1051847) in the [Journal of Open-Source Software (JOSS)](http://joss.theoj.org/about).\n\n```\nPrice et al., (2018). Hickle: A HDF5-based python pickle replacement. Journal of Open Source Software, 3(32), 1115, https://doi.org/10.21105/joss.01115\n```\n\n\n",
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