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Toyplot version 0.19.0.
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docs/features.rst

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* Interaction types: hyperlinking, interactive mouse coordinates, export figure data to CSV.
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* Interactive output formats: Embeddable, self-contained HTML.
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* Static output formats: SVG, PDF, PNG, MP4.
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* Portability: single code base for Python 2.7 / Python 3.6.
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* Testing: greater-than-95% regression test coverage.
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docs/release-notes.rst

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Release Notes
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=============
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Toyplot 0.19.0 - April 21st, 2020
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---------------------------------
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* First Toyplot release that only supports Python 3.
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* Colormaps return the first color instead of the last when the domain is empty.
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* The `target` attribute is supported in rich text hyperlinks, useful for opening hyperlinks in separate tabs from Juypter notebooks.
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* Provide an explicit default style for rich text hyperlinks, for consistent default outputs regardless of render context.
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* Add rich text support for styling underlines.
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* `toyplot.locator.Timestamp` didn't produce correctly positioned labels in some cases with non-UTC timezones.
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* Warn callers who try to use with Python 2.
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* Markers and marks are formattable objects, for easier embedding in rich text.
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* Switched to an explicit scenegraph to manage relationships among internal objects.
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* Fixed a bug that affect the domain and range of shared axes.
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* Expanded the user guide section on rendering.
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* Include the LICENSE file in source distributions, for downstream maintainers.
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* Fixed code that used obsolete Pandas ix API.
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Toyplot 0.18.0 - December 24th, 2018
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------------------------------------
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setup.py

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"Intended Audience :: Science/Research",
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"License :: OSI Approved :: BSD License",
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"Natural Language :: English",
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"Programming Language :: Python :: 2",
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"Programming Language :: Python :: 3",
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"Topic :: Scientific/Engineering :: Visualization",
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],
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"reportlab",
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],
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long_description="""Toyplot is the kid-sized plotting toolkit for Python with grownup-sized goals:
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* Develop beautiful interactive, animated plots that embrace the unique capabilities of electronic publishing and support repoducibility.
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* Develop beautiful plots that embrace the unique capabilities of electronic publishing and support repoducibility.
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* Create the best possible data graphics "out-of-the-box", maximizing data ink and minimizing chartjunk.
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* Provide a clean, minimalist interface that scientists and engineers will love.
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toyplot/__init__.py

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from toyplot.canvas import Canvas
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__version__ = "0.19.0-dev"
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__version__ = "0.19.0"
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log = logging.getLogger(__name__)
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log.setLevel(logging.WARNING)

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