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A Collection of Methods for Data Collection & Processing



Lab Pack is designed to make the process of retrieving, managing and processing data more uniform across a variety of different sources and structures. The classes and methods in this module aggregate and curate python resources and online APIs to provide a set of best practices for handling data across laboratory projects.


From PyPi:

$ pip install labpack

From GitHub:

$ git clone
$ cd labPack
$ python install

Getting Started

This module contains a variety of classes, clients and packages for use in laboratory projects. For example to store records in an indexed file store on the local device, you can use the following methods:

Create an unique ID for records:

from import labID

id = labID()
url_safe_id_string = id.id48
id_datetime = id.epoch
id_mac_address = id.mac

Save record data in local user data:

from import appdataClient

msg_key = '%s/%s.yaml' % (id_mac_address, id_datetime)
msg_details = { 'dt': id_datetime, 'mac': id_mac_address, 'msg': 'Text me back soon' }
msg_client = appdataClient('Outgoing', 'My Team', 'My App')
mgs_client.create(msg_key, msg_details)

Further Reading

For more details about how to use labPack, refer to the Reference Documentation on GitHub

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