<jupyter_start><jupyter_text>Resonant Angle Analysis for Kepler-36In this example notebook, we'll integrate some posterior samples in rebound to investigate the behavior of various resonant arguments in the Kepler-36 system. First, we'll fetch the results of our retrieval:<jupyter_code>%matplotlib inline
import ttvnest
import numpy as np

kepler36 = ttvnest.io_utils.load_results('kepler36.p')<jupyter_output><empty_output><jupyter_text>Now, let's plot an angle that we know circulates, so we can see what it looks like:<jupyter_code>resonance_ratio_string = '9:8'
sim_length = 100 #yrs
ttvnest.plot_utils.plot_resonant_angle(kepler36, resonance_ratio_string, sim_length, 'k36_9:8')<jupyter_output><empty_output><jupyter_text>Great, that's exactly how circulation should look. Now, we know the system is close to the 7:6 mean-motion resonance, so let's plot that resonant argument:<jupyter_code>resonance_ratio_string = '7:6'
sim_length = 10000
ttvnest.plot_utils.plot_resonant_angle(kepler36, resonance_ratio_string, sim_length,
                                       downsample = 1000, outname = 'k36_7:6')<jupyter_output><empty_output><jupyter_text>Though there are some superimposed oscillations, the overall average behavior for the resonant angle here is circulation. The system is also near the 34:29 resonance. Let's see what's going on there:<jupyter_code>resonance_ratio_string = '34:29'
sim_length = 10000
ttvnest.plot_utils.plot_resonant_angle(kepler36, resonance_ratio_string, sim_length,
                                       downsample = 1000, outname = 'k36_34:29')<jupyter_output><empty_output>
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