pyquibbler 0.2.5

Interactive, reproducible and efficient data analytics

Quibbler

Interactive, reproducible and efficient data analytics

https://user-images.githubusercontent.com/62783755/209929327-67766771-f271-40f0-8f3a-76deb88310b7.mp4

What is it?

Quibbler is a toolset for building highly interactive, yet reproducible, transparent and efficient data analysis pipelines. Quibbler allows using standard Python syntax to process data through any series of analysis steps, while automatically maintaining connectivity between downstream results and upstream raw data sources. Quibbler facilitates and embraces human interventions as an inherent part of the analysis pipeline: input parameters, as well as exceptions and overrides, can be specified and adjusted either programmatically, or by interacting with "live" graphics, and all such interventions are automatically recorded in well-documented human-machine readable files. Changes to such parameters propagate downstream, pinpointing which specific data items, or even specific elements thereof, are affected, thereby vastly saving unnecessary recalculations. Quibbler, therefore, facilitates hands-on interactions with data in ways that are not only flexible, fun and interactive, but also traceable, reproducible, and computationally efficient.

Check out our "Best Quibble" competition!

We just launched Quibbler in PyData Tel-Aviv. We are seeking engagement from users and developers and are also eager to learn of the range of applications for Quibbler. To get it fun and going, we are announcing a competition for the best "Quibble" - a short elegant quib-based code that demonstrates fun interactive graphics and/or hints to ideas of applications.

The competition is open to everyone. For details (and prizes!) see: Best Quibble Award

Main Features

Interactivity

• Creating interactive graphics is as simple as calling standard Matplotlib graphics functions with arguments that represent your parameter values.

• Any data presented graphically is automatically live and interactive (no need for the tedious programming of callback functions).

Traceability and Reproducibility

• Trace which specific data items and analysis parameters affect focal downstream results (see dependency graph).

• Inherent undo/redo functionality.

• Save/load parameter values as human-readable records (either as external text files, or inside Jupyter notebook).

Computational efficiency

• Upon parameter changes, Quibbler pinpoints and only recalculates the specifically affected array elements of downstream analysis steps (here).

All-of-the-above using completely standard functions and programming syntax - just "iquib" your inputs and your code comes to life!

Quibbler currently supports natural coding syntax with all Python operators, slicing, getitem, Numpy functions, Matplotlib graphics, Matplotlib widgets, and ipywidgets. It further provides an easy way to incorporate any user function or functions from any other (non-graphics) packages. Support for other graphics packages, besides Matplotlib, will be offered in future releases.

Minimal app

from pyquibbler import initialize_quibbler, iquib
initialize_quibbler()
import matplotlib.pyplot as plt

x = iquib(0.5)
plt.plot([0, 1], [1, 0], '-')
plt.plot([0, x, x], [1 - x, 1 - x, 0], 'D--')
plt.title(x, fontsize=20)

Documentation and Examples

For complete documentation and a getting-started tour, see readthedocs.

For simple demos and small apps, see our Examples.

Installation

We recommend installing Quibbler in a new virtual environment (see creating a new environment).

To install run:

pip install pyquibbler

If you are using Jupyter lab, you can also add the pyquibbler Jupyter Lab extensions:

pip install pyquibbler_labextension

To install for developers, see our guide here.

Credit

Quibbler was created by Roy Kishony, initially implemented as a Matlab toolbox.

The first release of Quibbler for Python, pyquibbler, was developed at the Kishony lab, Technion - Israel Institute of Technology, by Maor Kern, Maor Kleinberger and Roy Kishony.

We very much welcome any thoughts, suggestions and ideas and of course welcome PR contributions (for some proposed directions, see our pending issues).

Related packages

• Matplotlib
• Streamlit
• Plotly
• Shiny
• ipywidgets
• bokeh
• HoloViz
• Vega-Altair
• Datashader