Selective Estimator for the Autocorrelation Function
S-ACF: A selective estimator for the autocorrelation function of irregularly sampled time series (credit Lars Kreutzer, c++ implementation by Josh Briegal jtb34@cam.ac.uk)
Installation
Requirements:
- CMAKE (https://cmake.org) > 3.8.
- C++14
From above top level directory run
pip install ./sacf
in python:
SACF follows Astropy LombScargle implementation:
from sacf import SACF
lag_timeseries, correlations = SACF(timeseries, values, errors=None).autocorrelation()
with options:
sacf.autocorrelation(max_lag=None, lag_resolution=None, selection_function='natural', weight_function='fast', alpha=None)
NOTE: If users specify selection_function="fast", weight_function="fractional_squared" or weight_function="gaussian", a python implementation of the SACF will be invoked which is considerably slower than the default C++ option.
Tests
From root directory run:
tox
1 maintainer
Metadata
1 maintainer
Release files for sacf 2.0
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| File | Size | Uploaded | Provenance | Actions |
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Total release size: 850.4 kB
Release files / sacf-2.0.tar.gz
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| Download URL | sacf-2.0-cp37-cp37m-macosx_11_0_x86_64.whl |
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