Winglets

Implementation of Winglets: Visualizing Association with Uncertainty in Multi-class Scatterplots.

Project page | paper | video

We introduce Winglets, an enhancement to the classic scatterplot to better perceptually pronounce multiple classes by improving the perception of association and uncertainty of points to their related cluster. Designed as a pair of dual-sided strokes belonging to a data point, Winglets leverage the Gestalt principle of Closure to shape the perception of the form of the clusters, rather than use an explicit divisive encoding. Through a subtle design of two dominant attributes, length and orientation, Winglets enable viewers to perform a mental completion of the clusters.

Dependencies

• Python3: we used Python3.6
• Python Third-Party Libraries: tkinter, pandas, scipy, numpy, shapely, scikit-image, seaborn

Usage

• Run pip install Winglets to install Winglets. Note that you should install other python third-party libraries according to the tip.
• Use import Winglets to import Winglets libraires.
• You can run python testAPI.py to test related API. We prepare related test file(./testAPI.py) and data(./testFile.json) .

API

Winglets

drawWinglets(data, colorArray = ['red', 'blue', 'pink', 'orange', 'purple', 'indigo'], onlyWinglets=True)

Parameters

• data: the data form can be array or object
  • object: the key of the object is class of each group of data, e.g.,

    {
        "1" : [ 
            {
                "x" : 458.545253723722,
                "y" : 517.796113219558
            }, 
            ...
        ],
        "4" : [ 
            {
                "x" : 487.798180288922,
                "y" : 346.750755518256
            }, 
            {
                "x" : 458.787734845522,
                "y" : 316.371739750119
            }, 
            ...
        ]
    }
    

  • array：The array data does not identify which class it belongs to, so internally the program will set the data classes starting at 1, e.g.,

    [
        [ 
            [458.545253723722, 517.796113219558], 
            [487.798180288922, 346.750755518256]
            ...
        ],
        [ 
            [487.798180288922, 346.750755518256],
            [458.787734845522, 316.371739750119],
            ...
        ]
    ]
    

• colorArray：set the color for each group, e.g.,

  colorArray = ['red', 'blue', 'pink', 'orange']
  data = {
          "1" : [ 
              {
                  "x" : 458.545253723722,
                  "y" : 517.796113219558
              }, 
          ],
          "4" : [ 
              {
                  "x" : 487.798180288922,
                  "y" : 346.750755518256
              }, 
              
          ]
      }
  
  

  Note: An error will be reported if the length of the colorArray is less than the number of classes in the data
• onlyWinglets：the default is True. Buttons corresponding to proximity and commonFate will not appear in the program; if False, buttons corresponding to all operations will appear

CommonFate

drawCommonFate(data, colorArray = ['red', 'blue', 'pink', 'orange', 'purple', 'indigo'])

Parameters

• data：description same as API Winglets.
• colorArray：description same as API Winglets.

Proximity

drawProximity(data, colorArray = ['red', 'blue', 'pink', 'orange', 'purple', 'indigo'])

Parameters

• data：description same as API Winglets.
• colorArray：description same as API Winglets.

circle

drawCirlce(data, colorArray = ['red', 'blue', 'pink', 'orange', 'purple', 'indigo'], onlyCicle=True)

Parameters

• data：description same as API Winglets.
• colorArray：description same as API Winglets.
• onlyCircle: the default is True，there are no buttons for other actions. If False, there are buttons for all actions.

Citation

Please cite the paper in your publications if it helps your research:

@article{Winglets19,
title = {Winglets: Visualizing Association with Uncertainty in Multi-class Scatterplots},
author = {Min Lu, Shuaiqi Wang, Joel Lanir, Noa Fish, Yang Yue, Daniel Cohen-Or, Hui Huang},
journal = {IEEE Transactions on Visualization and Computer Graphics (Proceedings of InfoVis 2019)},
volume = {26},
number = {1}, 

pages = {770--779}, 

year = {2020},
}