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    "info": {
        "author": "Nico Schl\u00f6mer",
        "author_email": "nico.schloemer@gmail.com",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering"
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        "description": "<p align=\"center\">\n  <a href=\"https://github.com/nschloe/cplot\"><img alt=\"cplot\" src=\"https://nschloe.github.io/cplot/cplot-logo.svg\" width=\"50%\"></a>\n  <p align=\"center\">Plot complex-valued functions with style.</p>\n</p>\n\n[![CircleCI](https://img.shields.io/circleci/project/github/nschloe/cplot/master.svg?style=flat-square)](https://circleci.com/gh/nschloe/cplot/tree/master)\n[![codecov](https://img.shields.io/codecov/c/github/nschloe/cplot.svg?style=flat-square)](https://codecov.io/gh/nschloe/cplot)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square)](https://github.com/ambv/black)\n[![PyPi Version](https://img.shields.io/pypi/v/cplot.svg?style=flat-square)](https://pypi.python.org/pypi/cplot)\n[![GitHub stars](https://img.shields.io/github/stars/nschloe/cplot.svg?style=flat-square&logo=github&label=Stars&logoColor=white)](https://github.com/nschloe/cplot)\n\ncplot helps plotting complex-valued functions in a visually appealing manner.  The\ngeneral idea is to map the absolute value to lightness and the complex argument (the\n\"angle\") to the chroma of the representing color. This follows the [domain\ncoloring](https://en.wikipedia.org/wiki/Domain_coloring) approach, also described by\n[John D. Cook](https://www.johndcook.com/blog/2017/11/09/visualizing-complex-functions/)\nand Elias Wegert in the book [Visual Complex\nFunctions](https://www.springer.com/gp/book/9783034801799) (with some tweaks).\n\nInstall with\n```\npip3 install cplot --user\n```\nand use as\n```python\nimport cplot\nimport numpy\n\ncplot.show(numpy.tan, -5, +5, -5, +5, 100, 100)\n\ncplot.save_fig(\"out.png\", numpy.tan, -5, +5, -5, +5, 100, 100)\ncplot.save_img(\"out.png\", numpy.tan, -5, +5, -5, +5, 100, 100)\n\n# There is a tripcolor function as well for triangulated 2D domains\n# cplot.tripcolor(triang, z)\n\n# The function get_srgb1 returns the SRGB1 triple for every complex input value.\n# (Accepts arrays, too.)\nz = 2 + 5j\nval = cplot.get_srgb1(z)\n```\nAll functions have the optional arguments (with their default values)\n```python\nalpha=1  # >= 0\ncolorspace=\"cam16\"  # \"cielab\", \"hsl\"\n```\n\n* `alpha` can be used to adjust the use of colors. A value less than 1 adds more color\n  which can help isolating the roots and poles (which are still black and white,\n  respectively). `alpha=0` ignores the magnitude of `f(z)` completely.\n\n* `colorspace` can be set to `hsl` to get the common fully saturated, vibrant\n  colors. This is usually a bad idea since it creates artifacts which are not related\n  with the underlying data. From [Wikipedia](https://en.wikipedia.org/wiki/Domain_coloring):\n\n  > Since the HSL color space is not perceptually uniform, one can see streaks of\n  > perceived brightness at yellow, cyan, and magenta (even though their absolute values\n  > are the same as red, green, and blue) and a halo around L = 1 / 2 .  Use of the Lab\n  > color space corrects this, making the images more accurate, but also makes them more\n  > drab/pastel.\n\n  Default is [`\"cam16\"`](http://onlinelibrary.wiley.com/doi/10.1002/col.22131/abstract);\n  very similar is `\"cielab\"` (not shown here).\n\nConsider the test function `(z ** 2 - 1) * (z - 2 - 1j) ** 2 / (z ** 2 + 2 + 2j)`:\n\n| `alpha = 1`          |  `alpha = 0.5`       |  `alpha = 0.0`    |\n| :----------:         |  :---------:         |  :--------:       |\n| <img src=\"https://nschloe.github.io/cplot/cam16-10.png\" width=\"70%\"> | <img src=\"https://nschloe.github.io/cplot/cam16-05.png\" width=\"70%\"> | <img src=\"https://nschloe.github.io/cplot/cam16-00.png\" width=\"70%\"> |\n| <img src=\"https://nschloe.github.io/cplot/hsl-10.png\" width=\"70%\"> | <img src=\"https://nschloe.github.io/cplot/hsl-05.png\" width=\"70%\"> | <img src=\"https://nschloe.github.io/cplot/hsl-00.png\" width=\"70%\"> |\n\nThe representation is chosen such that\n\n  * values around **0** are **black**,\n  * values around **infinity** are **white**,\n  * values around **+1** are **green**,\n  * values around **-1** are [**deep purple**](https://youtu.be/zUwEIt9ez7M),\n  * values around **+i** are **blue**,\n  * values around **-i** are **orange**.\n\n(Compare to the z<sup>1</sup> reference plot below.)\n\nWith this, it is easy to see where a function has very small and very large values, and\nthe multiplicty of zeros and poles is instantly identified by counting the color wheel\npasses around a black or white point.\n\n#### Gallery\n\nAll plots are created with default settings.\n\n<img src=\"https://nschloe.github.io/cplot/z1.png\" width=\"70%\"> |\n<img src=\"https://nschloe.github.io/cplot/z2.png\" width=\"70%\"> |\n<img src=\"https://nschloe.github.io/cplot/z3.png\" width=\"70%\">\n:-------------------:|:------------------:|:----------:|\n`z**1`               |  `z**2`            |  `z**3`    |\n\n<img src=\"https://nschloe.github.io/cplot/1z.png\" width=\"70%\"> |\n<img src=\"https://nschloe.github.io/cplot/z-absz.png\" width=\"70%\"> |\n<img src=\"https://nschloe.github.io/cplot/z+1-z-1.png\" width=\"70%\"> |\n:-------------------:|:------------------:|:----------:|\n`1/z`               |  `z / abs(z)`            |  `(z+1) / (z-1)`    |\n\n<img src=\"https://nschloe.github.io/cplot/root2.png\" width=\"70%\"> |\n<img src=\"https://nschloe.github.io/cplot/root3.png\" width=\"70%\"> |\n<img src=\"https://nschloe.github.io/cplot/root4.png\" width=\"70%\">\n:-------------------:|:------------------:|:-------------------------:|\n`numpy.sqrt`          |  `x**(1/3)`       |  `x**(1/4)`    |\n\n<img src=\"https://nschloe.github.io/cplot/log.png\" width=\"70%\"> |\n<img src=\"https://nschloe.github.io/cplot/exp.png\" width=\"70%\">\n<img src=\"https://nschloe.github.io/cplot/exp1z.png\" width=\"70%\">\n:-------------------:|:------------------:|:-------------------------:|\n`numpy.log`          |  `numpy.exp`       |  `exp(1/x)`    |\n\n<img src=\"https://nschloe.github.io/cplot/sin.png\" width=\"70%\"> |\n<img src=\"https://nschloe.github.io/cplot/cos.png\" width=\"70%\"> |\n<img src=\"https://nschloe.github.io/cplot/tan.png\" width=\"70%\">\n:-------------------:|:------------------:|:-------------------------:|\n`numpy.sin`          |  `numpy.cos`       |  `numpy.tan`    |\n\n<img src=\"https://nschloe.github.io/cplot/sinh.png\" width=\"70%\"> |\n<img src=\"https://nschloe.github.io/cplot/cosh.png\" width=\"70%\"> |\n<img src=\"https://nschloe.github.io/cplot/tanh.png\" width=\"70%\">\n:-------------------:|:------------------:|:-------------------------:|\n`numpy.sinh`          |  `numpy.cosh`       |  `numpy.tanh`    |\n\n<img src=\"https://nschloe.github.io/cplot/arcsin.png\" width=\"70%\"> |\n<img src=\"https://nschloe.github.io/cplot/arccos.png\" width=\"70%\"> |\n<img src=\"https://nschloe.github.io/cplot/arctan.png\" width=\"70%\">\n:-------------------:|:------------------:|:-------------------------:|\n`numpy.arcsin`          |  `numpy.arccos`       |  `numpy.arctan`    |\n\n<img src=\"https://nschloe.github.io/cplot/gamma.png\" width=\"70%\"> |\n<img src=\"https://nschloe.github.io/cplot/digamma.png\" width=\"70%\"> |\n<img src=\"https://nschloe.github.io/cplot/zeta.png\" width=\"70%\">\n:-------------------:|:------------------:|:-------------------------:|\n`scipy.special.gamma`          |  `scipy.special.digamma`       |  `mpmath.zeta`    |\n\n\n### Testing\n\nTo run the cplot unit tests, check out this repository and type\n```\npytest\n```\n\n### License\n\ncplot is published under the [MIT license](https://en.wikipedia.org/wiki/MIT_License).\n\n\n",
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