Interactive Exploration of Fits

This notebook contains some interactive explorations of multiband Lomb-Scargle fits. Note that the interaction requires running the notebook in IPython 2.0+, and that the interactive elements will not appear in static views on, e.g. nbviewer.



In [4]:

    
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn; seaborn.set()

import sys; import os; sys.path.append(os.path.abspath('..'))
from multiband_LS.interactive import interact_data, interact_multifit

Just the data

Let's start by showing the data, allowing it to be folded or not:



In [5]:

    
interact_data();

Data and fits

Here we define a function to show the data and the multi-band Lomb-Scargle fits to the data:

Note that object_index controls the index of the RR Lyrae object we're looking at, and the model complexity is controlled by Nterms_base and Nterms_band.

Nterms_base is the number of terms in the truncated Fourier model of the global variation: you can think of this as approximating the bolometric luminosity: it's the model which best accounts for the variation in all bands.
Nterms_band is the number of Fourier terms used to fit the residuals of each band from the base model. A light regularization is uses to drive the bulk of the variation into the base model.



In [6]:

    
interact_multifit();