Qt-based implementation of VisOpt Slider widget
Qt-based implementation of VisOpt Slider widget [UIST 2014]
If your applications are based on Qt (PySide2), it is quite easy to integrate a VisOpt Slider widget into your applications.
VisOpt Slider is a GUI widget consisting of multiple sliders. It is specifically designed for interactive exploration of a high-dimensional scalar-valued function. It has the following special features.
- Visualization: VisOpt Slider visualizes the values of the target function along with the sliders in the interface using a colormap.
- Optimization: Not available yet. Please refer to the original paper (Koyama et al. 2014) and its extended version (Koyama et al. 2016).
This package can be install via
pip install visoptslider
By this, the dependencies (
PySide2, and their dependencies) will be automatically installed together.
from PySide2.QtWidgets import QApplication import numpy as np import visoptslider if __name__ == "__main__": app = QApplication() # Define a target function num_dimensions = 3 def target_function(x): return 1.0 - np.linalg.norm(x) # Define a target bound upper_bound = np.array([+1.0, +1.0, +1.0]) lower_bound = np.array([-1.0, -1.0, -1.0]) maximum_value = 1.0 minimum_value = 0.0 # Instantiate and initialize VisOpt Slider sliders_widget = visoptslider.SlidersWidget() sliders_widget.initialize(num_dimensions=num_dimensions, target_function=target_function, upper_bound=upper_bound, lower_bound=lower_bound, maximum_value=maximum_value, minimum_value=minimum_value) # Show VisOpt Sliders sliders_widget.show() app.exec_()
See https://github.com/yuki-koyama/visoptslider/tree/master/python_tests for more detailed examples.
- Yuki Koyama, Daisuke Sakamoto, and Takeo Igarashi. 2014. Crowd-Powered Parameter Analysis for Visual Design Exploration. In Proceedings of the 27th annual ACM symposium on User interface software and technology (UIST '14), pp.65-74. DOI: https://doi.org/10.1145/2642918.2647386
- Project page: https://koyama.xyz/project/CrowdPoweredAnalysis/
- Yuki Koyama, Daisuke Sakamoto, and Takeo Igarashi. 2016. SelPh: Progressive Learning and Support of Manual Photo Color Enhancement. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16), pp.2520--2532. DOI: https://doi.org/10.1145/2858036.2858111
- Project page: https://koyama.xyz/project/SelPh/
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