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        "author": "Kenichi SHIRAKAWA",
        "author_email": "shirakawa.kenichi@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
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            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
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            "Topic :: Software Development :: Libraries"
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        "description": "**********************************************************************************\nminidemoKalmanFilter - Minimal Demo. of Kalman Filters (Linear/Extended/Unscented)\n**********************************************************************************\n\nProvides\n 1. Minimal Kalman Filter classes (Linear, Extended and Unscented).\n 2. Interactive demonstration and it's snapshot.\n \nThis package is very simple, and may suitable for educational use.\n | About 100 executable lines for LKF/EKF/UKF totally.\n | Demo program will show you the essence (assumption and limitation)\n | of Kalman Filter.\n\nFiles\n-----\n\n``filter.py``\n  An implementation of minimal Kalman Filter (LKF/EKF/UKF included).\n\nDemos\n-----\n\nInteractive demo\n^^^^^^^^^^^^^^^^\n\nInteractive style demo requires ``numpy`` and ``matplotlib``.\nTouch sliders to change the parameter of the filter,\nand you will find the estimated results updated on your screen.\nSome snapshots are included in the package directory (snapshot_*.png).\n\n::\n\n    python demo_ukf_gui.py\n\nBatch demo\n^^^^^^^^^^\n\nBatch style demo (console version) requires ``numpy``.\nThis demo estimates the position and velocity of 2-dimensinal \nlinear uniform motion, and output results to the console.\nYou can choose the filter class (LKF,EKF,UKF) by comman line.\n\nLKF, EFK and UKF gives almost same reseults for such a linear \nproblem here. Please extend significiant of output to confirm \nthe differences.\n\n::\n\n    python demo_ukf.py > out_ukf.txt\n\nRequirements\n------------\n\nUses NumPy and Matplotlib(for interactive demo).\n\nLicense\n-------\n\nCopyright (c) 2018 Kenich SHIRAKAWA\n\nThis is licensed under MIT license.\nSee Licence.txt for more information.\n\nThanks\n------\n\nThe basic design of unscented transformation class is based on \nthe Sam Burden's work (see https://github.com/sburden/uk ukf.py).",
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        "keywords": "Kalman UKF EKF LKF",
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