Make your data more resilient, portable and easy to work with by packaging files & metadata into self contained datasets.
Documentation: http://dtool.readthedocs.io
Free software: MIT License
Overview
dtool is a suite of software for managing scientific data and making it accessible programatically. It consists of a command line interface dtool and a Python API: dtoolcore.
The dtool command line interface allows one to organise files into datasets and to move datasets between different storage solutions, for example from local disk to remote object storage. Importantly it also provides methods to verify that the transfer has been successful.
The Python API gives complete access to the data and metadata in a dataset. It makes it easy to create scripts for processing the items, or a subset of items, in a dataset. The Python API also allows datasets to be constructed programatically.
dtool is extensible, meaning that it is possible to create plugins both for adding functionality to the command line interface and for creating interfaces to custom storage backends.
The dtool Python package is a meta package that installs the packages:
dtoolcore - core API
dtool-cli - CLI plugin scaffold
dtool-annotation - CLI commands for working with dataset annotations
dtool-config - CLI commands for configuring dtool
dtool-create - CLI commands for creating datasets
dtool-info - CLI commands for getting information about datasets
dtool-overlay - CLI commands for working with per item metadata stored as overlays
dtool-symlink - storage broker interface allowing symlinking to data
dtool-http - storage broker interface allowing read only access to datasets over HTTP
Installation:
$ pip install dtool
There are support packages for several object storage solutions:
dtool-s3 - storage broker interface to S3 object storage
dtool-azure - storage broker interface to Azure Storage
dtool-ecs - storage broker interface to ECS S3 object storage
dtool-irods - storage broker interface to iRODS
If you have access to Amazon S3, Microsoft Azure, ECS S3 or iRODS storage you may also want to install support for these:
$ pip install dtool-s3 dtool-azure dtool-ecs dtool-irods
Usage:
$ dtool create my-awesome-dataset Created proto dataset file:///Users/olssont/my-awesome-dataset Next steps: 1. Add raw data, eg: dtool add item my_file.txt file:///Users/olssont/my-awesome-dataset Or use your system commands, e.g: mv my_data_directory /Users/olssont/my-awesome-dataset/data/ 2. Add descriptive metadata, e.g: dtool readme interactive file:///Users/olssont/my-awesome-dataset 3. Convert the proto dataset into a dataset: dtool freeze file:///Users/olssont/my-awesome-dataset
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