lightkurve.LightCurve.interact_bls#
- LightCurve.interact_bls(notebook_url=None, minimum_period=None, maximum_period=None, resolution=2000)[source]#
Display an interactive Jupyter Notebook widget to find planets.
The Box Least Squares (BLS) periodogram is a statistical tool used for detecting transiting exoplanets and eclipsing binaries in light curves. This method will display a Jupyter Notebook Widget which enables the BLS algorithm to be used interactively. Behind the scenes, the widget uses the AstroPy implementation of BLS [1].
This feature only works inside an active Jupyter Notebook. It requires Bokeh v1.0 (or later). An error message will be shown if these dependencies are not available.
- Parameters
- notebook_url: str
Location of the Jupyter notebook page (default: “localhost:8888”) When showing Bokeh applications, the Bokeh server must be explicitly configured to allow connections originating from different URLs. This parameter defaults to the standard notebook host and port. If you are running on a different location, you will need to supply this value for the application to display properly. If no protocol is supplied in the URL, e.g. if it is of the form “localhost:8888”, then “http” will be used. For use with JupyterHub, set the environment variable LK_JUPYTERHUB_EXTERNAL_URL to the public hostname of your JupyterHub and notebook_url will be defined appropriately automatically.
- minimum_periodfloat or None
Minimum period to assess the BLS to. If None, default value of 0.3 days will be used.
- maximum_periodfloat or None
Maximum period to evaluate the BLS to. If None, the time coverage of the lightcurve / 2 will be used.
- resolutionint
Number of points to use in the BLS panel. Lower this value for faster but less accurate performance. You can also vary this value using the widget’s Resolution Slider.
References
Examples
Load the light curve for Kepler-10, remove long-term trends, and display the BLS tool as follows:
>>> import lightkurve as lk >>> lc = lk.search_lightcurve('kepler-10', quarter=3).download() >>> lc = lc.normalize().flatten() >>> lc.interact_bls()