#--------------------------------------------------
#
#Description:
#   This script plots processed exoplanet data tables produced
#   by ProcessExoplanets.py.  The 2010 version of the data
#   is already located in the dataset directory, and this script
#   is set up to use that. (The figures in Chapter 1 of Principles
#   of Planetary Climate were done using 2006 data). 
#   

#A note on units in the data file:
#     -- Masses are given in Jupiter masses
#     -- Distances are given in AU


from ClimateUtilities import *

#Path to Workbook datasets
datapath = '/Users/rtp1/Havsornen/PlanetaryClimateBook/WorkbookDatasets/'

#Tack on the directory path
#Edit this according to where you put the processed data produced
#by ProcessExoplanets.py, if you're not using the ready-made
#2010 version of the processed data
exoplanetFile = datapath+'Chapter1Data/exoplanets/'+'exoplanets2010Processed.txt'
#Note that this file does not contain the names of the planets or their stars.
#To get that, you should look at the original files stars2010.txt and planets2010.txt


#Read in the data
c = readTable(exoplanetFile)

#Take a look at what's there
print c.listVariables()
#
#The usual parameters are:
#   mPlanet           Mass of planet in Jupiter masses
#   rsm               semi-major axis of the orbit, in AU
#   ecc               Orbital eccentricity
#   PeriApRat         Ratio of Periastron distance to Apastron distance
#   yearLength        Length of year (in Earth days)
#   Luminosity        Stellar luminosity (relative to the present Sun)
#   PeriSolarConstant Stellar constant at periastron (relative to Earth)
#   ApSolarConstant   Stellar constant at apastron  (relative to Earth)
#   SMSolarConstant   Stellar constant at distance equal to semi-major axis
#                         (approximately equal to annual mean), relative to Earth
#   TStar             Photospheric temperature of the star (Kelvins)
#

#Make a scatter plot.  Scatter plots like those
#in the exoplanets section of Chapter 1 can be done
#similarly

#Here's an example of a scatter plot of the perihelion
#stellar constant vs the planet mass (in Jupiter masses)
#You'll note an outlier in there with a ridiculous stellar
#constant (about 10**9 the Sun). That's an error in the exoplanets.org
#table. I left it in because some of the other data for that star is OK.
#Hopefully the error will be corrected in some future version of the table
c1 = c.extract(['mPlanet','PeriSolarConstant'])
c1.scatter['PeriSolarConstant'] = True #Don't draw lines
c1.YlogAxis = True #Use a log axis
c1.XlogAxis = True #Use a log axis
c1.Ylabel = 'Peri Stellar Constant, W/m**2'
c1.Xlabel = 'Mass (in Jupiter masses)'
plot(c1)

#This shows an example of using the data to do a calculation. Here
#we compute the no-atmosphere isothermal blackbody temperature of the
#planet. The physics needed to do this
#is explained in Chapter 3.
TbbEarth = 255.
Tbb = c['SMSolarConstant']**.25 * TbbEarth
c2 = Curve()
c2.addCurve(c['TStar'],'TStar')
c2.addCurve(Tbb,'BlackbodyTemp')
c2.scatter['BlackbodyTemp'] = True
c2.Xlabel = 'Stellar Temperature'
c2.Ylabel = 'Blackbody Temperature of Planet'
plot(c2)

#You'll see that because of the bad solar constant in one entry
#(it's one of the OGLE stars), the good data is all squished up near
#the bottom.  Let's do the plot again, putting in a zero for any
#temperature that exceeds 10000K. That at least makes the plot readable

c2['BlackbodyTemp'] = numpy.where(Tbb > 10000., 0.,Tbb)
plot(c2)