interp-acf

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Extract the dominant period from a time series with missing data via autocorrelation in Python

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- - __init__.py
  - test_periods.py
- __init__.py
- interpacf.py
- paper.bib
- paper.md
.gitignore
.travis.yml
example.ipynb
LICENSE
README.md
requirements.txt
setup.cfg
setup.py

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Calculate the autocorrelation function for an evenly sampled time-series with 
missing data.

### Install
Clone this repository, change directories into it, and run this command to 
install the package: 
```
python setup.py install
```
You can run the tests locally with
```
python setup.py test
```

### Example
**Check out the a longer example with plots 
[here](https://github.com/bmorris3/interp-acf/blob/master/example.ipynb)**.

For time series (`times_incomplete`, `fluxes_incomplete`) with missing data, 
compute the autocorrelation function after interpolating over missing data, and
identify the dominant period in the smoothed autocorrelation function (after 
smoothing according to the recommendations of 
[McQuillan, Aigrain & Mazeh (2013)](http://adsabs.harvard.edu/abs/2013MNRAS.432.1203M)): 
```python
from interpacf import interpolated_acf, dominant_period

lag, acf = interpolated_acf(times_incomplete, fluxes_incomplete)
period = dominant_period(lag, acf, plot=True)
```
![](http://staff.washington.edu/bmmorris/images/acf.png)

### Detailed example:

For a full example code, see [`example.ipynb`](https://github.com/bmorris3/interp-acf/blob/master/example.ipynb)

#### Dependencies
* python 2/3
* numpy
* scipy
* matplotlib (optional)

interp-acf

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