Exoplanets Analysis

With the current boom of exoplanets, it is interesting to analyse how the resolution of some properties has been increasing along time. Data has been taken of http://exoplanet.eu/catalog/



In [2]:

    
%pylab inline
import matplotlib.pylab as plt

"""Loading data
Format
1mass0, 2mass_error_min1, 3mass_error_max2, 4radius3, 5radius_error_min4, 6radius_error_max5, 7orbital_period6, 10semi_major_axis7,
13eccentricity8, 37discovered9, 50mag_v10, 55star_distance11, 56star_metallicity12, 57star_mass13, 58star_radius14, 60star_age15
"""
exoplanets = np.genfromtxt( "exoplanet.eu_catalog.csv", delimiter=",", usecols=(1,2,3,4,5,6,7,10,13,37,50,55,56,57,58,60) )
MJUP2EARTH = 317.828133









    



Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].
For more information, type 'help(pylab)'.



In [36]:

    
plt.figure(figsize=(10,8))
plt.subplots_adjust( hspace = 0.0 )
#Dispersion diagram of mass of discovered planets per year
plt.subplot(211)
plt.semilogy( exoplanets[:,9],exoplanets[:,0]*MJUP2EARTH, "o" )
minimum_mass=[]
for year in range(1985,2015):
    try:
        minimum_mass.append( [year, np.min( exoplanets[exoplanets[:,9]==year,0]*MJUP2EARTH )] )
    except:
        minimum_mass.append( [year, nan ] )
plt.ylabel("planetary mass [M$_{\odot}$]")
plt.title("Planetary mass vs Year of discovery")
plt.xticks( np.arange(1985,2015,5), "" )
plt.grid()
#Histogram of discovered planets per year
plt.subplot(212)
plt.hist( exoplanets[:,9], bins=30,range=(1985,2015), normed=False )
plt.ylabel("number of planets")
plt.xlabel("year of discovery")
plt.yticks( np.arange(0.0,800,200) )
plt.grid()



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