Welcome to ProgressiVis

ProgressiVis is a Python toolkit and scientific workflow system that implements a new programming paradigm that we call Progressive Analytics or Progressive Data Analysis and Visualization aimed at performing analytics in a progressive way. It allows analysts to see the progress of their analysis and to steer it interactively while the computation is being done.

Contents:

• Introduction
  • Why?
• Installation of Progressivis
  • Installation for users
  • Installation for developers
• User Guide
  • Key concepts
  • Main Components
  • Running a Progressive Program
  • Communication between ProgressiVis and the Notebook
• Dealing with Noisy Data
• Progressive Notebooks
  • Creating a progressive analysis
  • Chaining widgets
  • Navigation with the DAG Widget
  • Working with chaining widgets (user guide)
  • Chaining widgets list
  • Recording a scenario
  • Chaining widgets persistent settings
  • How to create a chaining widget ?
• Library Reference
  • First level variables
  • Dataflow Management
  • Progressive Data Structures
  • Decorators
• Module Library
  • I/O Modules
  • Data Filtering Modules
  • Indexing Modules
  • Data Grouping/Joining/Aggregation Modules
  • Set and Flow Control Operations
  • Statistical Modules
  • Clustering Modules
  • Linear Algebra Modules
  • Visualisation
  • Utility
  • Format Adaptors
• Unit tests
• Custom Modules
  • The run_step() method
  • Cooperative Scheduling
• Dependencies
  • Datashape

Indices and tables

• Index

• Module Index

• Search Page