https://github.com/dfm/python-bls

Python bindings to BLS
https://github.com/dfm/python-bls

Last synced: 8 months ago
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Python bindings to BLS

• Host: GitHub
• URL: https://github.com/dfm/python-bls
• Owner: dfm
• License: mit
• Created: 2013-06-17T15:12:16.000Z (almost 13 years ago)
• Default Branch: main
• Last Pushed: 2020-06-12T18:24:59.000Z (almost 6 years ago)
• Last Synced: 2025-04-13T19:54:02.549Z (about 1 year ago)
• Language: Python
• Size: 19.5 KB
• Stars: 16
• Watchers: 8
• Forks: 8
• Open Issues: 4
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

          
Box Least Squares

=================

These are Python bindings for the original Fortran implementation of Box Least

Squares (BLS) algorithm from `Kovács et al. (2002)

`_.

Installation

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

Clone the source code from `the GitHub repository

`_ and install using the standard python

tools:

.. code-block:: bash

    git clone https://github.com/dfm/python-bls.git

    cd python-bls

    python setup.py install

For testing in development, you can use

.. code-block:: bash

    python setup.py build_ext --inplace

to build the bindings directly in the ``bls`` directory.

Authors

-------

These Python bindings were developed—building directly on the code released by

`Kovács `_—at the `SAMSI

`_ workshop `Modern Statistical and Computational Methods

for Analysis of Kepler

`_ by

* `Ruth Angus (Oxford) `_

* `Dan Foreman-Mackey (NYU) `_

License

-------

The Python bindings are licensed under the `MIT License

`_.

Basic Usage

-----------

TODO: describe Pythonic binding.

Advanced Usage

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

You can get direct access to `the Fortran subroutine provided by Kovács

`_ through the ``eebls()``

function:

.. code-block:: python

    import bls

    results = bls.eebls(time, flux, u, v, nf, fmin, df, nb, qmi, qma)

where

* ``time`` is an ``N``-dimensional array of timestamps for the light curve,

* ``flux`` is the ``N``-dimensional light curve array,

* ``u`` and ``v`` are ``N``-dimensional empty work arrays,

* ``nf`` is the number of frequency bins to test,

* ``fmin`` is the minimum frequency to test,

* ``df`` is the frequency grid spacing,

* ``nb`` is the number of bins to use in the folded light curve,

* ``qmi`` is the minimum transit duration to test, and

* ``qma`` is the maximum transit duration to test.

The returned values are

.. code-block:: python

    power, best_period, best_power, depth, q, in1, in2 = results

where

* ``power`` is the ``nf``-dimensional power spectrum array at frequencies ``f

  = fmin + arange(nf) * df``,

* ``best_period`` is the best-fit period in the same units as ``time``,

* ``best_power`` is the power at ``best_period``,

* ``depth`` is the depth of the transit at ``best_period``,

* ``q`` is the fractional transit duration,

* ``in1`` is the bin index at the start of transit, and

* ``in2`` is the bin index at the end of transit.