#!/usr/bin/env python
# coding: utf-8

# # What is the Periodogram class?

# *lightkurve* has a class specifically for dealing with periodograms of time series data. This can be useful for finding the periods of variable stars. Below is a quick example of how to find the period of an eclipsing binary star using *lightkurve*.
# 
# Firstly lets grab a light curve file from mast. We'll use KIC 10030943, which is an eclipsing binary observed by the original Kepler mission. We're just going to use one quarter for this demo.

# In[1]:


import astropy.units as u  # We'll need this later.


# In[2]:


# Obtain the data
from lightkurve import search_lightcurvefile
lc = search_lightcurvefile('10264202', quarter=10).download().PDCSAP_FLUX.remove_nans()


# Let's plot the light curve to see what we're working with.

# In[3]:


lc.scatter();


# This light curve looks like it has some structure in it! Let's use the periodogram class to find the rotation period. You can create a periodogram from the `KeplerLightCurve` object by using the `periodogram` method.

# In[4]:


pg = lc.to_periodogram()


# Now we can plot the periodogram in the same way that we plot the original light curve.

# In[5]:


pg.plot();


# This looks like there is a huge signal at a certain frequency! Let's plot it in period space, so that we can see what period the oscillation is occurring at.

# In[6]:


pg.plot(format='period', scale='log');


# This looks like a very fast period. Let's find the period with the highest power and fold out light curve.

# In[7]:


period = pg.period_at_max_power
print('Best period: {}'.format(period))
lc.fold(period.value).scatter();


# As is quite common for eclipsing binaries with deep secondary eclipses, we have found a harmonic of the period of the eclipsing binary. Let's plot it again with quadruple the period.

# In[8]:


period = pg.period_at_max_power * 4
print('Best period: {}'.format(period))
lc.fold(period.value).scatter();


# It looks like we could probably get a better fit than this. Let's try increasing the number of points in our periodogram.

# In[9]:


# I've set min_period and max_period to slightly reduce the computational time
pg = lc.to_periodogram(min_period=0.9*u.day, max_period=1.2*u.day, oversample_factor=10)


# In[10]:


period = pg.period_at_max_power
print('Best period: {}'.format(period))
lc.fold(period.value).scatter();


# That's improved our fit! It looks like this eclipsing binary has a period of approximately 1 day.