Metadata-Version: 1.1
Name: filterpy
Version: 1.2.2
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-Content-Type: UNKNOWN
Description: FilterPy - Kalman filters and other optimal and non-optimal estimation filters in Python.

        -----------------------------------------------------------------------------------------

        

        .. image:: https://img.shields.io/pypi/v/filterpy.svg

                :target: https://pypi.python.org/pypi/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 and

        Bayesian Filter 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. Furthermore, books

        such 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 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.

        

        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. 

        

        

        Plan for dropping Python 2.7 support

        ------------------------------------

        

        I haven't finalized my decision on this, but NumPy is dropping

        Python 2.7 support in December 2018. I will certainly drop Python

        2.7 support by then; I will probably do it much sooner.

        

        At the moment FilterPy is on version 1.x. I plan to fork the project

        to version 2.0, and support only Python 3.5+. The 1.x version 

        will still be available, but I will not support it. If I add something

        amazing to 2.0 and someone really begs, I might backport it; more

        likely I would accept a pull request with the feature backported

        to 1.x. But to be honest I don't forsee this happening.

        

        Why 3.5+, and not 3.3+? 3.5 introduced the matrix multiply symbol,

        and I want my code to take advantage of it. Plus, to be honest,

        I'm being selfish. I don't want to spend my life supporting this

        package, and moving as far into the present as possible means

        a few extra years before the Python version I choose becomes

        hopelessly dated and a liability. I recognize this makes people

        running the default Python in their linux distribution more

        painful. All I can say is I did not decide to do the Python

        3 fork, and I don't have the time to support the bifurcation

        any longer.

        

        I am making edits to the package now in support of my book;

        once those are done I'll probably create the 2.0 branch. 

        I'm contemplating a SLAM addition to the book, and am not

        sure if I will do this in 3.5+ only or not.

        

        

        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 the conda-forge channel. You

        will need to add the conda-forge channel if you haven't already done so:

        

        ::

            conda config -add channels conda-forge

            

        and then install with:

        

        ::

            conda install 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

        

            my_filter.x = np.array([[2.],

                            [0.]])       # initial state (location and velocity)

        

            my_filter.F = np.array([[1.,1.],

                            [0.,1.]])    # state transition matrix

        

            my_filter.H = np.array([[1.,0.]])    # Measurement function

            my_filter.P *= 1000.                 # covariance matrix

            my_filter.R = 5                      # state uncertainty

            my_filter.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 3.6 and 2.7.14, 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.

        

        

        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...".

        

        

        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 :: 5 - Production/Stable
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
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
