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https://github.com/gbeckers/darr

A Python library for numpy arrays that persist on disk in a format that is simple, self-documented and tool-independent, and maximizes universal readability.
https://github.com/gbeckers/darr

array bsd-3-clause data-science data-sharing data-storage idl interoperability jagged-array julia-language maple mathematica matlab numeric octave python r ragged-array science scilab

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A Python library for numpy arrays that persist on disk in a format that is simple, self-documented and tool-independent, and maximizes universal readability.

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README 

        
Darr

====



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Darr is a Python science library that allows you to work efficiently with

potentially very large, disk-based Numpy arrays that are widely readable and

self-documented. Documentation includes copy-paste ready code to read arrays

in many popular data science languages such as R, Julia, Scilab, IDL,

Matlab, Maple, and Mathematica, or in Python/Numpy without Darr. Without

exporting them and with minimal effort.



Universal readability of data is a pillar of good scientific practice. It is

also generally a good idea for anyone who wants to flexibly move between

analysis environments, who wants to save data for the longer term, or who

wants to share data with others without spending much time on figuring out

and/or explaining how the receiver can read it. No idea how to read your

7-dimensional uint32 numpy array in Matlab to quickly try out an algorithm

your colleague wrote? No worries, a quick copy-paste of code from the array

documentation is all that is needed to read your data in, e.g. R or Matlab

(see `example

`__).

As you work with your array, its documentation is automatically kept up to

date. No need to export anything, make notes, or to provide elaborate

explanation. No looking up things. No dependence on complicated formats or

specialized libraries for reading you data elsewhere later.



In essence, Darr makes it trivially easy to share your numerical arrays with

others or with yourself when working in different computing environments,

and stores them in a future-proof way.



More rationale for a tool-independent approach to numeric array storage is

provided `here `__.



Under the hood, Darr uses NumPy memory-mapped arrays, which is a widely

established and trusted way of working with disk-based numerical data, and

which makes Darr fully NumPy compatible. This enables efficient out-of-core

read/write access to potentially very large arrays. In addition to automatic

documentation, Darr adds other functionality to NumPy's memmap, such as easy

the appending and truncating of data, support for ragged arrays, the ability

to create arrays from iterators, and easy use of metadata. Flat binary files

and (JSON) text files are accompanied by a README text file that explains how

the array and metadata are stored (`see example arrays

`__).



See this `tutorial `__

for a brief introduction, or the

`documentation `__ for more info.



Darr is currently pre-1.0, and still undergoing development. It is open source

and freely available under the `New BSD License

`__ terms.



Features

--------

-  Data is stored purely based on flat binary and text files, maximizing

   universal readability.

-  Automatic self-documention, including copy-paste ready code snippets for

   reading the array in a number of popular data analysis environments, such as

   Python (without Darr), R, Julia, Scilab, Octave/Matlab, GDL/IDL, and

   Mathematica

   (see `example array

   `__).

-  Disk-persistent array data is directly accessible through `NumPy

   indexing `__

   and may be larger than RAM

-  Easy and efficient appending of data (`see example `__).

-  Supports ragged arrays.

-  Easy use of metadata, stored in a widely readable separate

   JSON text file (`see example

   `__).

-  Many numeric types are supported: (u)int8-(u)int64, float16-float64,

   complex64, complex128.

-  Integrates easily with the `Dask `__

   library for out-of-core computation on very large arrays.

-  Minimal dependencies, only `NumPy `__.



Limitations

-----------

- No `structured (record) arrays `__

  supported yet, just

  `ndarrays `__

- No string data, just numeric.

- No compression, although compression for archiving purposes is supported.

- Uses multiple files per array, as binary data is separated from text

  documentation and metadata. This can be a disadvantage in terms of storage

  space if you have very many very small arrays.



Installation

------------



Darr officially depends on Python 3.9 or higher. Older versions may work

(probably >= 3.6) but are not tested.



Install Darr from PyPI::



    $ pip install darr



Or, install Darr via conda::



    $ conda install -c conda-forge darr



To install the latest development version, use pip with the latest GitHub

master::



    $ pip install git+https://github.com/gbeckers/darr@master



Documentation

-------------

See the `documentation `_ for more information.



Contributing

------------

Any help / suggestions / ideas / contributions are welcome and very much

appreciated. For any comment, question, or error, please open an issue or

propose a pull request.



Other interesting projects

--------------------------

If Darr is not exactly what you are looking for, have a look at these projects:



-  `asdf `__

-  `exdir `__

-  `h5py `__

-  `pyfbf `__

-  `pytables `__

-  `zarr `__



Darr is BSD licensed (BSD 3-Clause License). (c) 2017-2024, Gabriël

Beckers



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