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        "author": "Yuki Koyama",
        "author_email": "yuki@koyama.xyz",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
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            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
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        "description": "# VisOpt Slider\n\n![GitHub](https://img.shields.io/github/license/yuki-koyama/visoptslider.svg)\n![PyPI](https://img.shields.io/pypi/v/visoptslider.svg)\n\nQt-based implementation of VisOpt Slider widget [UIST 2014]\n\nIf your applications are based on Qt (PySide2), it is quite easy to integrate a VisOpt Slider widget into your applications.\n\n## Features\n\nVisOpt 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.\n\n- __Visualization__: VisOpt Slider visualizes the values of the target function along with the sliders in the interface using a colormap.\n- __Optimization__: _Not available yet_. Please refer to the original paper [(Koyama et al. 2014)](https://koyama.xyz/project/CrowdPoweredAnalysis/) and its extended version [(Koyama et al. 2016)](https://koyama.xyz/project/SelPh/).\n\n## Install\n\nThis package can be install via `pip`:\n```\npip install visoptslider\n```\nBy this, the dependencies (`matplotlib`, `numpy`, `PySide2`, and their dependencies) will be automatically installed together.\n\n## Example\n\n```python\nfrom PySide2.QtWidgets import QApplication\nimport numpy as np\nimport visoptslider\n\nif __name__ == \"__main__\":\n    app = QApplication()\n\n    # Define a target function\n    num_dimensions = 3\n    def target_function(x):\n        return 1.0 - np.linalg.norm(x)\n\n    # Define a target bound\n    upper_bound = np.array([+1.0, +1.0, +1.0])\n    lower_bound = np.array([-1.0, -1.0, -1.0])\n    maximum_value = 1.0\n    minimum_value = 0.0\n\n    # Instantiate and initialize VisOpt Slider\n    sliders_widget = visoptslider.SlidersWidget()\n    sliders_widget.initialize(num_dimensions=num_dimensions,\n                              target_function=target_function,\n                              upper_bound=upper_bound,\n                              lower_bound=lower_bound,\n                              maximum_value=maximum_value,\n                              minimum_value=minimum_value)\n\n    # Show VisOpt Sliders\n    sliders_widget.show()\n\n    app.exec_()\n```\nSee <https://github.com/yuki-koyama/visoptslider/tree/master/python_tests> for more detailed examples.\n\n## References\n\n- 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>\n  - Project page: <https://koyama.xyz/project/CrowdPoweredAnalysis/>\n- 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>\n  - Project page: <https://koyama.xyz/project/SelPh/>",
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