Metadata-Version: 1.1
Name: filterpy
Version: 0.1.3
Summary: Kalman filtering and optimal estimation library
Home-page: https://github.com/rlabbe/filterpy
Author: Roger Labbe
Author-email: rlabbejr@gmail.com
License: MIT
Description: FilterPy - Kalman filters and other optimal and non-optimal estimation filters in Python.
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        .. image:: https://img.shields.io/pypi/v/filterpy.svg
                :target: https://pypi.python.org/pypi/filterpy
        
        
        .. image:: https://anaconda.org/rlabbe/filterpy/badges/installer/conda.svg   
               :target: https://conda.anaconda.org/rlabbe
        
        
        .. image:: https://anaconda.org/rlabbe/filterpy/badges/version.svg   
               :target: https://anaconda.org/rlabbe/filterpy
        
        This library provides Kalman filtering and various related optimal and
        non-optimal filtering software written in Python. It contains Kalman
        filters, Extended Kalman filters, Unscented Kalman filters, Kalman
        smoothers, Least Squares filters, fading memory filters, g-h filters,
        discrete Bayes, and more.
        
        This is code I am developing in conjunction with my book Kalman Filters
        and Random Signals in Python, which you can read/download at
        https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/
        
        My aim is largely pedalogical - I opt for clear code that matches the
        equations in the relevant texts on a 1-to-1 basis, even when that has a
        performance cost. There are places where this tradeoff is unclear - for
        example, I find it somewhat clearer to write a small set of equations
        using linear algebra, but numpy's overhead on small matrices makes it
        run slower than writing each equation out by hand, and books such as
        Zarchan present the written out form, not the linear algebra form. It is
        hard for me to choose which presentation is 'clearer' - it depends on
        the audience. In that case I usually opt for the faster implementation.
        
        I use NumPy and SciPy for all of the computations. I have experimented
        with Numba, Continuum Analytics' just in time compiler, and it yields
        impressive speed ups with minimal costs, but I am not convinced that I
        want to add that requirement to my project. It is still on my list of
        things to figure out, however.
        
        As it evolves from alpha status I am adding documentation, tests, and
        examples, but at the moment the my book linked above serves as the best
        documentation. I am developing both in parallel, so one or the other has
        to suffer during the development phase. Reach out to me if you have
        questions or needs and I will either answer directly or shift my
        development to address your problem (assuming your question is a planned
        part of this library.
        
        Sphinx generated documentation lives at http://filterpy.readthedocs.org/.
        Generation is triggered by git when I do a check in, so this will always
        be bleeding edge development version - it will often be ahead of the
        released version. 
        
        You can also find the documentation at https://pythonhosted.org/filterpy/
        but that currently requires me to manually upload the documentation, so 
        it is possible that it will be out of date. It will never be of a development
        version, however.
        
        Installation
        ------------
        
        The most general installation is just to use pip, which should come with
        any modern Python distribution.
        
        .. image:: https://img.shields.io/pypi/v/filterpy.svg
                :target: https://pypi.python.org/pypi/filterpy
                
        ::
        
            pip install filterpy
        
        If you prefer to download the source yourself
        
        ::
        
            cd <directory you want to install to>
            git clone http://github.com/rlabbe/filterpy
            python setup.py install
        
        If you use Anaconda, you can install from a conda channel. I just (Feb 2016)
        added this capability, it's quite possible it is not working yet. Please
        let me know if you run into problems.
        
                  
        .. image:: https://anaconda.org/rlabbe/filterpy/badges/installer/conda.svg   
               :target: https://conda.anaconda.org/rlabbe
              
        ::
        
            conda install -c rlabbe filterpy
            
            
        And, if you want to install from the bleeding edge git version
        
        ::
        
            pip install git+https://github.com/rlabbe/filterpy.git
        
        Note: I make no guarantees that everything works if you install from here.
        I'm the only developer, and so I don't worry about dev/release branches and
        the like. Unless I fix a bug for you and tell you to get this version because
        I haven't made a new release yet, I strongly advise not installing from git.
        
        
            
        
        Basic use
        ---------
        
        First, import the filters and helper functions.
        
        .. code-block:: python
        
            import numpy as np
            from filterpy.kalman import KalmanFilter
            from filterpy.common import Q_discrete_white_noise
        
        Now, create the filter
        
        .. code-block:: python
        
            my_filter = KalmanFilter(dim_x=2, dim_z=1)
        
        
        Initialize the filter's matrices.
        
        .. code-block:: python
        
            f.x = np.array([[2.],
                            [0.]])       # initial state (location and velocity)
        
            f.F = np.array([[1.,1.],
                            [0.,1.]])    # state transition matrix
        
            f.H = np.array([[1.,0.]])    # Measurement function
            f.P *= 1000.                 # covariance matrix
            f.R = 5                      # state uncertainty
            f.Q = Q_discrete_white_noise(2, dt, .1) # process uncertainty
        
        
        Finally, run the filter.
        
        .. code-block:: python
        
            while True:
                my_filter.predict()
                my_filter.update(get_some_measurement())
        
                # do something with the output
                x = my_filter.x
                do_something_amazing(x)
        
        Sorry, that is the extent of the documentation here. However, the library
        is broken up into subdirectories: gh, kalman, memory, leastsq, and so on.
        Each subdirectory contains python files relating to that form of filter.
        The functions and methods contain pretty good docstrings on use.
        
        My book https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/
        uses this library, and is the place to go if you are trying to learn
        about Kalman filtering and/or this library. These two are not exactly in 
        sync - my normal development cycle is to add files here, test them, figure 
        out how to present them pedalogically, then write the appropriate section
        or chapterin the book. So there is code here that is not discussed
        yet in the book.
        
        
        Requirements
        ------------
        
        This library uses NumPy, SciPy, Matplotlib, and Python. 
        
        I haven't extensively tested backwards compatibility - I use the
        Anaconda distribution, and so I am on Python 3.4 and 2.7.5, along with
        whatever version of numpy, scipy, and matplotlib they provide. But I am
        using pretty basic Python - numpy.array, maybe a list comprehension in
        my tests.
        
        I import from **__future__** to ensure the code works in Python 2 and 3.
        
        The matplotlib library is required because, *for now*, 'tests' are very
        visual. Meaning I generate some data, plot the data against the filtered
        results, and eyeball it. That is great for my personal development, and
        terrible as a foundation for regression testing. If you don't have
        matplotlib installed you won't be able to run the tests, but I'm not
        sure the tests will have a lot of meaning to you anyway.
        
        There is one import from the code from my book to plot ellipses. That
        dependency needs to be removed. This only affects the tests.
        
        Testing
        -------
        
        All tests are written to work with py.test. Just type ``py.test`` at the
        command line.
        
        As explained above, the tests are not robust. I'm still at the stage
        where visual plots are the best way to see how things are working.
        Apologies, but I think it is a sound choice for development. It is easy
        for a filter to perform within theoretical limits (which we can write a
        non-visual test for) yet be 'off' in some way. The code itself contains
        tests in the form of asserts and properties that ensure that arrays are
        of the proper dimension, etc.
        
        References
        ----------
        
        I use three main texts as my refererence, though I do own the majority
        of the Kalman filtering literature. First is Paul Zarchan's
        'Fundamentals of Kalman Filtering: A Practical Approach'. I think it by
        far the best Kalman filtering book out there if you are interested in
        practical applications more than writing a thesis. The second book I use
        is Eli Brookner's 'Tracking and Kalman Filtering Made Easy'. This is an
        astonishingly good book; its first chapter is actually readable by the
        layperson! Brookner starts from the g-h filter, and shows how all other
        filters - the Kalman filter, least squares, fading memory, etc., all
        derive from the g-h filter. It greatly simplifies many aspects of
        analysis and/or intuitive understanding of your problem. In contrast,
        Zarchan starts from least squares, and then moves on to Kalman
        filtering. I find that he downplays the predict-update aspect of the
        algorithms, but he has a wealth of worked examples and comparisons
        between different methods. I think both viewpoints are needed, and so I
        can't imagine discarding one book. Brookner also focuses on issues that
        are ignored in other books - track initialization, detecting and
        discarding noise, tracking multiple objects, an so on.
        
        I said three books. I also like and use Bar-Shalom's Estimation with
        Applications to Tracking and Navigation. Much more mathmatical than the
        previous two books, I would not recommend it as a first text unless you
        already have a background in control theory or optimal estimation. Once
        you have that experience, this book is a gem. Every sentence is crystal
        clear, his language is precise, but each abstract mathematical statement
        is followed with something like "and this means...".
        
        
        Last Changelog Entry
        --------------------
        
        Version 0.1.0
        
        Move to minor version numbering doesn't mean anything other than
        it got absurd to be using 3 digits for version numbers. We are
        far past alpha here. I will be moving to 1.0.0 soon, probably after
        I finish the book and flesh out a few points.
        
        * Implemented a fixed-point smoother, but it is not working all that well.
        
        Color on this: There are various recusive equations for the fixed point
        filter that I have found in various book - Simon, Crassidis, and Grewal.
        None seem to work very well. I have code that works pretty good when R 
        is < 0.5 or so, but then the filter diverges when R is larger. I'm not seeing
        much in the literature that explains this very well, nor any evidence of
        this smoother actually being used in practice. I will give this a bit
        more effort, and if I can't get something reliable I'll put it in a branch
        and remove from trunk. Someone will have to tackle this on a rainy day.
        
        * KalmanFilter.batch_filter() now accepts lists of all the KF matrices
        
        * lots of docstring corrections and additions
        
        License
        -------
        .. image:: https://anaconda.org/rlabbe/filterpy/badges/license.svg   :target: https://anaconda.org/rlabbe/filterpy
        
        The MIT License (MIT)
        
        Copyright (c) 2015 Roger R. Labbe Jr
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in
        all copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
        THE SOFTWARE.TION OF CONTRACT,
        TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
        SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
        
Keywords: Kalman filters filtering optimal estimation tracking
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Utilities
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
