SIMD Kalman

Fast Kalman filters in Python leveraging single-instruction multiple-data vectorization. That is, running n similar Kalman filters on n independent series of observations. Not to be confused with SIMD processor instructions.

import simdkalman

kf = simdkalman.KalmanFilter(
    state_transition = np.array([[1,1],[0,1]]),
    process_noise = np.diag([0.1, 0.01]),
    observation_model = np.array([[1,0]]),
    observation_noise = 1.0)

data = numpy.random.normal(size=(200, 1000))

# smooth and explain existing data
smoothed = kf.smooth(data)
# predict new data
pred = kf.predict(data, 15)

See examples/example.py for a more comprehensive example and ReadTheDocs for the full documentation. For the changelog, see releases page

According to examples/benchmark.py. This can be up to 100x faster than pykalman and 70x faster than filterpy when can be vectorized over many independent timeseries. Also in the non-vectorized case, it can be 2x faster.

Installation

pip install simdkalman

Development

1. Create virtualenv
   • Python 2: virtualenv venvs/python2
   • Python 3: python3 -m venv venvs/python3
2. Activate virtualenv: source venvs/pythonNNN/bin/activate
3. Install locally pip install -e .[dev,test]
4. ./run-tests.sh
5. deactivate virtualenv

Distribution

(personal howto)

Once:

1. create an account in https://testpypi.python.org/pypi and https://pypi.python.org/pypi
2. create ~/.pypirc as described here
3. sudo pip install twine
4. create testing virutalenvs:
   • virtualenv venvs/test-python2
   • python3 -m venv venvs/test-python3

Each distribution:

# first, set version in setup.py
# then create distributable package
python setup.py bdist_wheel
# test PyPI site
twine upload --repository testpypi dist/simdkalman-VERSION*
# the real thing
twine upload dist/simdkalman-VERSION*
# update git tags
git tag VERSION -m "released VERSION"
git push --tags

Test installation from the test site with

source venvs/test-pythonNNN/bin/activate
pip install \
    --index-url https://test.pypi.org/simple/ \
    --extra-index-url https://pypi.org/simple \
    simdkalman --upgrade
# or the real thing with just
# pip install simdkalman
pip install matplotlib
python examples/example.py
deactivate

TODO

• [ ] EM algorithm documentation and options