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Description:

darr 0.5.5

Darr is a Python science library that allows you to work efficiently with
potentially very large, disk-based Numpy arrays that are 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, 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.

Drawbacks

No compression, although compression for archiving purposes is supported.
Array storages uses multiple files as binary data is separated from text
documentation and metadata.

See the documentation for more information.