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Set of Python tools for the RATOM project

Project description

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libratom

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Python library and supporting utilities to parse and process PST and MBOX email sources.

This project is under development

Installation

Libratom requires Python 3.6 or newer, and can be installed via the Python Package Index (PyPI). Installing via pip will automatically install all required dependencies.

To install and test this software in a new Python virtual environment in Ubuntu 16.04LTS or newer:

Make sure Python 3.6 or newer, python3-pip, and python3-venv are installed:

sudo apt install python3 python3-pip python3-venv

Create and activate a Python virtual environment:

python3 -m venv venv
source venv/bin/activate

Make sure pip is upgraded to the latest version:

pip install --upgrade pip

Install libratom:

pip install libratom

Entity extraction

Libratom provides a CLI with planned support for a range of email processing tasks. Currently, the CLI supports entity extraction from individual PST files and directories of PST files.

To see available commands, type:

(venv) user@host:~$ ratom -h

To see detailed help for the entity extraction command, type:

(venv) user@host:~$ ratom entities -h

To run the extractor with default settings over a PST file or directory of PST files, type the following:

(venv) user@host:~$ ratom entities -p /path/to/PST-file-or-directory

Progress is displayed in a bar at the bottom of the window. To terminate a job early and shut down all workers, type Ctrl-C.

By default, the tool will use the spaCy en_core_web_sm model, and will start as many concurrent jobs as there are virtual cores available. Entities are written to a sqlite3 file automatically named using the existing file or directory name and current datetime stamp, and with the following schema:

sqlite> .schema
CREATE TABLE file_report (
	id INTEGER NOT NULL,
	path VARCHAR,
	name VARCHAR,
	size INTEGER,
	md5 VARCHAR,
	sha256 VARCHAR,
	PRIMARY KEY (id)
);
CREATE TABLE message (
	id INTEGER NOT NULL,
	pff_identifier INTEGER,
	processing_start_time DATETIME,
	processing_end_time DATETIME,
	file_report_id INTEGER,
	PRIMARY KEY (id),
	FOREIGN KEY(file_report_id) REFERENCES file_report (id)
);
CREATE TABLE entity (
	id INTEGER NOT NULL,
	text VARCHAR,
	label_ VARCHAR,
	filepath VARCHAR,
	message_id INTEGER,
	file_report_id INTEGER,
	PRIMARY KEY (id),
	FOREIGN KEY(message_id) REFERENCES message (id),
	FOREIGN KEY(file_report_id) REFERENCES file_report (id)
);

The schema contains 3 tables representing file information, message information and entity information.

In the entity table, text is the entity instance, label_ is the entity type, filepath is the PST file associated with this entity. Full message and file information for each entity are also available through message_id and file_report_id respectively.

The notebooks linked below contain examples of how to query the information in these tables.

Additional libratom use cases

More usage documentation will appear here as the project matures. For now, you can try out some of the functionality in Jupyter notebooks we've prepared at:

https://github.com/libratom/ratom-notebooks

License(s)

Logos, documentation, and other non-software products of the RATOM team are distributed under the terms of Creative Commons 4.0 Attribution. Software items in RATOM repositories are distributed under the terms of the MIT License. See the LICENSE file for additional details.

© 2019, The University of North Carolina at Chapel Hill.

Development Team and Support

Developed by the RATOM team at the University of North Carolina at Chapel Hill.

See https://ratom.web.unc.edu for additional project details, staff bios, and news.

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