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        "description": "# ddeint\n\n[![Build Status](https://travis-ci.org/Zulko/ddeint.svg?branch=master)](https://travis-ci.org/Zulko/ddeint)\n\nScipy-based delay differential equation (DDE) solver. See the docstrings and examples for more infos.\n\n## Examples\n\n\n```python\nfrom pylab import cos, linspace, subplots\nfrom ddeint import ddeint\n\n\ndef model(Y, t):\n    return -Y(t - 3 * cos(Y(t)) ** 2)\n\n\ndef values_before_zero(t):\n    return 1\n\n\ntt = linspace(0, 30, 2000)\nyy = ddeint(model, values_before_zero, tt)\n\nfig, ax = subplots(1, figsize=(4, 4))\nax.plot(tt, yy)\nax.figure.savefig(\"variable_delay.jpeg\")\n```\n\n![screenshot](https://github.com/Zulko/ddeint/raw/master/examples/variable_delay.jpeg)\n\n```python\nfrom pylab import array, linspace, subplots\nfrom ddeint import ddeint\n\n\ndef model(Y, t, d):\n    x, y = Y(t)\n    xd, yd = Y(t - d)\n    return array([0.5 * x * (1 - yd), -0.5 * y * (1 - xd)])\n\n\ng = lambda t: array([1, 2])\ntt = linspace(2, 30, 20000)\n\nfig, ax = subplots(1, figsize=(4, 4))\n\nfor d in [0, 0.2]:\n    print(\"Computing for d=%.02f\" % d)\n    yy = ddeint(model, g, tt, fargs=(d,))\n    # WE PLOT X AGAINST Y\n    ax.plot(yy[:, 0], yy[:, 1], lw=2, label=\"delay = %.01f\" % d)\n\nax.figure.savefig(\"lotka.jpeg\")\n```\n\n![screenshot](https://github.com/Zulko/ddeint/raw/master/examples/lotka.jpeg)\n\n## Licence\n\n\nPublic domain. Everyone is welcome to contribute !\n\n##Installation\n\nddeint can be installed by unzipping the source code in one directory and using this command: ::\n\n    (sudo) python setup.py install\n\nYou can also install it directly from the Python Package Index with this command: ::\n\n    (sudo) pip install ddeint",
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