Kalman filtering and optimal estimation library

## Project Description

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

.. 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 mathematical 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

.. 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 mathematical 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

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