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        "description": "|Github CI Status| |Appveyor Status| |PyPi version| |Conda Forge|\n|Codecov Badge| |Docs Status| |Zenodo Badge| |PyUp Badge|\n\nDarr is a Python library for disk-based NumPy arrays that persist in a format\nthat is simple, self-documented and tool-independent. It keeps your data \neasily accessible from a wide range of computing environments. Every\narray is automatically documented, and includes code to read itself in\nlanguages such as R, Julia, IDL, Matlab, Maple, and Mathematica, or in\nPython/Numpy without Darr (see `example\n<https://github.com/gbeckers/Darr/tree/master/examplearrays/arrays\n/array_int32_2D.darr>`__). Keeping data universally readable and documented is \na pillar of good scientific practice. More rationale for this approach is \nprovided `here \n<https://darr.readthedocs.io/en/latest/rationale.html>`__.\n\nUnder the hood, Darr uses NumPy memory-mapped arrays, which is a widely\nestablished, tested and trusted way of working with disk-based numerical\narrays, and which makes Darr fully NumPy compatible. It enables efficient\nout-of-core read/write access to potentially very large arrays. What Darr adds\nis that it automatically keeps your arrays fully documented, open, and thus\nwidely readable. Further, Darr adds functionality to make your life easier\nin other ways, such as the support for ragged arrays, the ability to create\narrays from iterators, append and truncate functionality, and the easy use\nof metadata.\n\nFlat binary files and (JSON) text files are accompanied by a README text file\nthat explains how the array and metadata are stored. It is trivially easy to\nshare your data with others or with yourself when working in different\ncomputing environments because it always contains clear documentation,\nincluding code to read it. Does your colleague want to try out an interesting\nalgorithm in R or Matlab on your array data?  No need to export anything or to\nprovide elaborate explanation. A copy-paste of a few lines of code from the\ndocumentation stored with the data is sufficient. No dependence on complicated\nformats or specialized libraries. Self-documentation and code examples are\nautomatically updated as you change your arrays when working with them.\n\n\nSee this `tutorial <https://darr.readthedocs.io/en/latest/tutorial.html>`__\nfor a brief introduction, or the\n`documentation <http://darr.readthedocs.io/>`__ for more info.\n\nDarr is currently pre-1.0, still undergoing significant development. 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