In [15]:

    

import numpy as np
import scipy.optimize as spopt
import matplotlib.pyplot as plt


    

In [16]:

    

elvis = np.loadtxt('elvis.dat', skiprows = 1)
lbol = elvis[:, 0]
lx = elvis[:, 1]

fig = plt.figure(figsize = (10, 5), dpi = 400)
plt.scatter(np.log10(lbol), np.log10(lx), marker = 'x', c = 'r')
plt.title('Elvis SMBH Atlas', size = 18)
plt.xlabel('$\log L_{bol}\,[L_{\odot}]$', size = 14)
plt.ylabel('$\log L_{X}\,[L_{\odot}]$', size = 14)

p = np.polyfit(np.log10(lbol), np.log10(lx), 1)
plt.plot(np.linspace(11., 13.5, 50), np.polyval(p, np.linspace(11., 13.5, 50)), linestyle = '--', color = 'b')
plt.text(11., 68., '$\log L_{X}\,=\,$' + str(np.round(p[0], 3)) + '$\log L_{bol}\,+\,$' + str(np.round(p[1], 3)))

plt.show()


    

In [40]:

    

elvis_raw = np.loadtxt('elvis_raw.dat', skiprows = 1)
lbol_raw = 10.**(elvis_raw[:, 0]) # erg/s
lx_raw = 10.**(elvis_raw[:, 1]) # erg/s/cm^2
z = elvis_raw[:, 2]
c = 29979245800. # cm/s
h = 2.2685455e-18 # /s
d = z * c/h

#lbol_raw = fbol * 4*np.pi * d**2.
#lx_raw = fx * 4*np.pi * d**2. # erg/s

plt.title('Elvis SMBH Atlas', size = 18)
plt.xlabel('$\log L_{bol}\,[erg/s]$', size = 14)
plt.ylabel('$\log L_{X}\,[erg/s]$', size = 14)

plt.scatter(np.log10(lbol_raw), np.log10(lx_raw), marker = 'x', c = 'r')

p = np.polyfit(np.log10(lbol_raw), np.log10(lx_raw), 1)
plt.plot(np.linspace(np.min(np.log10(lbol_raw)), np.max(np.log10(lbol_raw)), 50), np.polyval(p, np.linspace(np.min(np.log10(lbol_raw)), 
    np.max(np.log10(lbol_raw)), 50)), linestyle = '--', color = 'b')
#plt.text(45., 45.5, '$\log L_{X}\,=\,$' + str(np.round(p[0], 3)) + '$\log L_{bol}\,+\,$' + str(np.round(p[1], 3)))

plt.show()


    

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