A Collection of Methods for Data Collection & Processing
Project description
labPack
A Collection of Methods for Data Collection & Processing
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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
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