https://github.com/bmorris3/sacf
S-ACF: A selective estimator for the autocorrelation function of irregularly sampled time series
Science Score: 10.0%
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Low similarity (6.5%) to scientific vocabulary
Repository
S-ACF: A selective estimator for the autocorrelation function of irregularly sampled time series
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Metadata Files
readme.md
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)
The associated publication, which includes a detailed description of the S-ACF, can be found at arXiv:2304.10641.
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:
```python from sacf import SACF
lag_timeseries, correlations = SACF(timeseries, values, errors=None).autocorrelation() ```
with options:
python
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:
python
tox
Owner
- Name: Brett M. Morris
- Login: bmorris3
- Kind: user
- Location: Baltimore, MD
- Company: @SpaceTelescope
- Website: http://brettmorr.is
- Twitter: brettmor
- Repositories: 287
- Profile: https://github.com/bmorris3
Software engineer & astronomer.