labpack 0.5

A Collection of Methods for Data Collection & Processing

labPack

A Collection of Methods for Data Collection & Processing

Downloads:

http://pypi.python.org/pypi/labPack

Source:

https://github.com/collectiveacuity/labPack

Classes

• labID: A class of methods for uniquely identifying records

• labDT: A class of methods for transforming datetime data

• labRegex: A class of methods for matching regex patterns in strings

• appdataClient: A class of methods for managing file storage in home dir

• localhostClient: A class of methods for negotiating OS specific configuration

Packages

• randomlab: A package of methods for generating random data

• performlab: A package of methods for running performance tests

• drep: A file storage protocol for encrypted record data

• cryptolab: A package for encrypting/decrypting data using AES256 sha512

• settings: A package of methods for handling local configuration settings

• flask: A package of methods for parsing request and response data

• classes: A package of methods for generating class attributes

Features

• Unique IDs which do not conflict nor leak record origin

• Transformations of datetime data between popular formats

• Randomization using best current algorithms

• drep compiler package for encrypted file storage protocol

• cryptolab package for encrypted data using AES 256bit sha512

• performlab package for running performance tests

• labRegex parsing class for mapping n-grams in strings

• appdataClient class for managing file storage on local host

• localhostClient class for negotiating os specific methods

• [FEATURE ADDED] classes compiler package for generating class attributes

• [FEATURE ADDED] flask parsing package for parsing request and response data

• [FEATURE ADDED] settings package for handling local configuration settings

Installation

>From PyPi:

$ pip install labpack

>From GitHub:

$ git clone https://github.com/collectiveacuity/labPack
$ cd labPack
$ python setup.py install

Getting Started

This module is designed to make the process of retrieving, managing and processing data more uniform across a variety of different sources and structures. A number of module methods are implementations of built-in python packages and standard python imports which have been optimized for data management and compensate for the messy state of real data. The methods in this module aggregate and curate python resources and online APIs to provide a set of best practices for handling data.

Create an unique ID for records:

from labpack.records.id import labID

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

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