#!/usr/bin/env python # coding: utf-8 # In[1]: # Predict RV jitter median +/- one sigma, and MC simulation. Note that # the RV jitter is firstly predicted with sole contribution from stellar # oscillations, and then multiplied by a recommended factor, which differs slightly in # different models, to include an additional source, granulation. The correction # factor can be reset via "CorFact", such as CorFact=2.0. import RVJitter target = RVJitter.rvjitter(lumi=12.006, lumierr=1.131, mass=1.304, masserr=0.064, teff=4963.00, tefferr=80.000) sigmarv, sigmarvperr, sigmarvmerr, mcsigmarv = target.rv() # In[6]: # Visualize the Monte Carlo simulation. # Predict RV jitter from luminosity, mass, and effectice temperature. import RVJitter target = RVJitter.rvjitter(lumi=12.006, lumierr=1.131, mass=1.304, masserr=0.064, teff=4963.00, tefferr=80.000) sigmarv, sigmarvperr, sigmarvmerr, mcsigmarv = target.plot(figshow=True, figsave=True, figname='jitter.png') # In[7]: # Predict RV jitter from luminosity, effectice temperature, and surface gravity. import RVJitter target = RVJitter.rvjitter(lumi=12.006, lumierr=1.131, teff=4963.00, tefferr=80.000, logg=3.210, loggerr=0.006) sigmarv, sigmarvperr, sigmarvmerr, mcsigmarv = target.plot(figshow=True, figsave=True, figname='jitter.png') # In[5]: import RVJitter target = RVJitter.rvjitter(teff=4963.00, tefferr=80.000, logg=3.210, loggerr=0.006) sigmarv, sigmarvperr, sigmarvmerr, mcsigmarv = target.plot(figshow=True, figsave=True, figname='jitter.png') # In[8]: # Predict RV jitter from luminosity and effective temperature, in which case # the evolutionary stage need to be specified, either giant (Lgiant=True) or # dwarf/subgiant (False). A default dividing point, log(g)=3.5, is adopted. import RVJitter target = RVJitter.rvjitter(lumi=12.006, lumierr=1.131, teff=4963.00, tefferr=80.000, Lgiant=False) sigmarv, sigmarvperr, sigmarvmerr, mcsigmarv = target.plot(figshow=True, figsave=True, figname='jitter.png') # In[ ]: