def plot():
plslp1 = 0.71
plslp2 = 0.05
plbrk = 2679.92
bbt = 1158.99
bbflxnrm = 0.25
galfra = 0.89
elscal = 0.72
imod = 18.0
scahal = 1.0
with open('input.yml', 'r') as f:
parfile = yaml.load(f)
# Load stuff
fittingobj = load(parfile)
wavlen = fittingobj.get_wavlen()
flxcorr = np.array([1.0] * len(wavlen))
ik0, ik1, ik2 = [], [], []
zs = np.arange(2,4.01,0.01)
for z in zs:
print z
magtmp, wavlentmp1, fluxtmp1 = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
0.0,
imod,
z,
fittingobj,
flxcorr,
parfile)
ik0.append( magtmp[3] - magtmp[8] )
magtmp, wavlentmp1, fluxtmp1 = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
0.1,
imod,
z,
fittingobj,
flxcorr,
parfile)
ik1.append( magtmp[3] - magtmp[8] )
magtmp, wavlentmp1, fluxtmp1 = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
0.2,
imod,
z,
fittingobj,
flxcorr,
parfile)
ik2.append( magtmp[3] - magtmp[8] )
datmag, sigma, datz, name, snr = loaddatraw('DR10',
'/data/lc585/SDSS/DR10QSO_AllWISE_matched.v2.fits',
True,
0,
23.0,
15.0,
False,
0.1)
fig = plt.figure(figsize=figsize(0.7))
ax = fig.add_subplot(111)
ax.plot(zs,ik0,color='black',linewidth=2, zorder=1)
ax.plot(zs,ik1,color='black',linewidth=2, zorder=1)
ax.plot(zs,ik2,color='black',linewidth=2, zorder=1)
ax.plot(datz,
datmag[:,3] - datmag[:,8],
marker='o',
markersize=2,
alpha=0.5,
markeredgecolor='none',
markerfacecolor='gray',
linestyle='',
zorder=0)
textprops = dict(fontsize=10,ha='left',va='center', zorder=1, color='red')
ax.text(3.5,0.646,'E(B-V) = 0.0', **textprops)
ax.text(3.5,1.455,'E(B-V) = 0.1', **textprops)
ax.text(3.5,2.285,'E(B-V) = 0.2', **textprops)
ax.set_xlim(2,4)
ax.set_ylim(-2,4)
ax.set_xlabel(r'Redshift $z$')
ax.set_ylabel(r'$i - K$')
fig.tight_layout()
fig.savefig('/home/lc585/thesis/figures/chapter06/ik_versus_z_high_ext.pdf')
plt.show()
return None
def plot():
tab = Table.read('/data/lc585/QSOSED/Results/141203/sample1/out_add.fits')
tab = tab[tab['BBT_STDERR'] < 200.0]
tab = tab[tab['BBFLXNRM_STDERR'] < 0.05]
tab = tab[tab['CHI2_RED'] < 3.0]
tab = tab[tab['LUM_IR'] > 40.0] # just gets rid of one annoying point
mycm = cm.get_cmap('YlOrRd_r')
mycm.set_under('w')
cset = brewer2mpl.get_map('YlOrRd', 'sequential', 9).mpl_colors
mycm = truncate_colormap(mycm, 0.0, 0.8)
set_plot_properties() # change style
fig = plt.figure(figsize=figsize(1.5, 0.7))
gs = gridspec.GridSpec(9, 13)
ax1 = fig.add_subplot(gs[1:5, 0:4])
ax2 = fig.add_subplot(gs[5:, 0:4])
ax3 = fig.add_subplot(gs[0, 0:4])
ax4 = fig.add_subplot(gs[1:5, 4:8])
ax5 = fig.add_subplot(gs[5:, 4:8])
ax6 = fig.add_subplot(gs[0, 4:8])
ax7 = fig.add_subplot(gs[1:5, 8:12])
ax8 = fig.add_subplot(gs[5:, 8:12])
ax9 = fig.add_subplot(gs[0, 8:12])
ax10 = fig.add_subplot(gs[1:5, 12])
ax11 = fig.add_subplot(gs[5:, 12])
fig.subplots_adjust(wspace=0.0)
fig.subplots_adjust(hspace=0.0)
#histogram definition
xyrange = [[44, 48], [0, 2]] # data range
bins = [80, 40] # number of bins
thresh = 4 #density threshold
#data definition
xdat, ydat = tab['LUM_UV'], tab['RATIO_IR_UV']
# histogram the data
hh, locx, locy = histogram2d(xdat, ydat, range=xyrange, bins=bins)
posx = np.digitize(xdat, locx)
posy = np.digitize(ydat, locy)
#select points within the histogram
ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
hhsub = hh[posx[ind] - 1,
posy[ind] - 1] # values of the histogram where the points are
xdat1 = xdat[ind][hhsub < thresh] # low density points
ydat1 = ydat[ind][hhsub < thresh]
hh[hh < thresh] = np.nan # fill the areas with low density by NaNs
im = ax1.imshow(np.flipud(hh.T),
cmap=mycm,
extent=np.array(xyrange).flatten(),
interpolation='none',
aspect='auto',
vmin=thresh,
vmax=45)
ax1.scatter(xdat1, ydat1, color=cset[-1])
ax1.set_ylabel(r'$R_{{\rm NIR}/{\rm UV}}$', fontsize=14)
ax1.set_ylim(0., 2.)
ax1.set_xlim(45.25, 47)
ax3.hist(tab['LUM_UV'], color=cset[-1], bins=20)
ax3.set_xlim(ax1.get_xlim())
ax3.set_axis_off()
#histogram definition
xyrange = [[44, 48], [200, 2000]] # data range
bins = [90, 35] # number of bins
thresh = 4 #density threshold
#data definition
xdat, ydat = tab['LUM_UV'], tab['BBT']
# histogram the data
hh, locx, locy = histogram2d(xdat, ydat, range=xyrange, bins=bins)
posx = np.digitize(xdat, locx)
posy = np.digitize(ydat, locy)
#select points within the histogram
ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
hhsub = hh[posx[ind] - 1,
posy[ind] - 1] # values of the histogram where the points are
xdat1 = xdat[ind][hhsub < thresh] # low density points
ydat1 = ydat[ind][hhsub < thresh]
hh[hh < thresh] = np.nan # fill the areas with low density by NaNs
im = ax2.imshow(np.flipud(hh.T),
cmap=mycm,
extent=np.array(xyrange).flatten(),
interpolation='none',
aspect='auto',
vmin=thresh,
vmax=45)
ax2.scatter(xdat1, ydat1, color=cset[-1])
ax2.set_xlabel(r'Log$_{10} (L_{\rm UV} ({\rm erg/s}))$', fontsize=14)
ax2.set_ylabel(r'$T_{\mathrm{BB}}$', fontsize=14)
ax2.set_xlim(ax1.get_xlim())
ax2.set_ylim(500, 2100)
plt.tick_params(axis='both', which='major', labelsize=10)
ax1.get_xaxis().set_ticks([])
ax2.get_yaxis().set_ticks([600, 800, 1000, 1200, 1400, 1600, 1800, 2000])
ax2.get_xaxis().set_ticks([45.4, 45.6, 45.8, 46.0, 46.2, 46.4, 46.6, 46.8])
#############################################################################
#histogram definition
xyrange = [[7.5, 10.5], [0, 2]] # data range
bins = [30, 30] # number of bins
thresh = 4 #density threshold
#data definition
xdat, ydat = tab['LOGBH'], tab['RATIO_IR_UV']
# histogram the data
hh, locx, locy = histogram2d(xdat, ydat, range=xyrange, bins=bins)
posx = np.digitize(xdat, locx)
posy = np.digitize(ydat, locy)
#select points within the histogram
ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
hhsub = hh[posx[ind] - 1,
posy[ind] - 1] # values of the histogram where the points are
xdat1 = xdat[ind][hhsub < thresh] # low density points
ydat1 = ydat[ind][hhsub < thresh]
hh[hh < thresh] = np.nan # fill the areas with low density by NaNs
im = ax4.imshow(np.flipud(hh.T),
cmap=mycm,
extent=np.array(xyrange).flatten(),
interpolation='none',
aspect='auto',
vmin=thresh,
vmax=45)
ax4.scatter(xdat1, ydat1, color=cset[-1])
ax4.set_ylim(ax1.get_ylim())
ax4.set_xlim(7.5, 10.5)
ax4.set_xticklabels([])
ax4.set_yticklabels([])
ax6.hist(tab[tab['LOGBH'] > 6]['LOGBH'], color=cset[-1], bins=20)
ax6.set_xlim(ax4.get_xlim())
ax6.set_axis_off()
#histogram definition
xyrange = [[7.5, 10.5], [500, 2000]] # data range
bins = [50, 30] # number of bins
thresh = 4 #density threshold
#data definition
xdat, ydat = tab['LOGBH'], tab['BBT']
# histogram the data
hh, locx, locy = histogram2d(xdat, ydat, range=xyrange, bins=bins)
posx = np.digitize(xdat, locx)
posy = np.digitize(ydat, locy)
#select points within the histogram
ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
hhsub = hh[posx[ind] - 1,
posy[ind] - 1] # values of the histogram where the points are
xdat1 = xdat[ind][hhsub < thresh] # low density points
ydat1 = ydat[ind][hhsub < thresh]
hh[hh < thresh] = np.nan # fill the areas with low density by NaNs
im = ax5.imshow(np.flipud(hh.T),
cmap=mycm,
extent=np.array(xyrange).flatten(),
interpolation='none',
aspect='auto',
vmin=thresh,
vmax=45)
ax5.scatter(xdat1, ydat1, color=cset[-1])
ax5.set_xlabel(r'Log$_{10}$ (Black Hole Mass $M_{\rm BH}$)')
ax5.set_yticklabels([])
# ax5.hist(tab['BBT'], orientation='horizontal',color=cset[-1],bins=20)
# ax5.set_ylim(ax2.get_ylim())
# ax5.set_axis_off()
plt.tick_params(axis='both', which='major')
ax4.set_ylim(ax1.get_ylim())
ax5.set_ylim(ax2.get_ylim())
ax5.set_xlim(ax4.get_xlim())
ax6.hist(tab['LOGBH'][tab['LOGBH'] > 6], color=cset[-1], bins=20)
ax6.set_xlim(ax5.get_xlim())
ax6.set_axis_off()
######################################################################################
#histogram definition
xyrange = [[-2, 0.5], [0, 2]] # data range
bins = [30, 30] # number of bins
thresh = 4 #density threshold
#data definition
xdat, ydat = tab['LOGEDD_RATIO'], tab['RATIO_IR_UV']
# histogram the data
hh, locx, locy = histogram2d(xdat, ydat, range=xyrange, bins=bins)
posx = np.digitize(xdat, locx)
posy = np.digitize(ydat, locy)
#select points within the histogram
ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
hhsub = hh[posx[ind] - 1,
posy[ind] - 1] # values of the histogram where the points are
xdat1 = xdat[ind][hhsub < thresh] # low density points
ydat1 = ydat[ind][hhsub < thresh]
hh[hh < thresh] = np.nan # fill the areas with low density by NaNs
im = ax7.imshow(np.flipud(hh.T),
cmap=mycm,
extent=np.array(xyrange).flatten(),
interpolation='none',
aspect='auto',
vmin=thresh,
vmax=45)
ax7.scatter(xdat1, ydat1, color=cset[-1])
ax7.set_ylim(ax1.get_ylim())
ax7.set_xlim(-2, 0.5)
ax9.hist(tab[tab['LOGEDD_RATIO'] > -2.5]['LOGEDD_RATIO'],
color=cset[-1],
bins=20)
ax9.set_xlim(ax7.get_xlim())
ax9.set_axis_off()
#histogram definition
xyrange = [[-2, 0.5], [500, 2000]] # data range
bins = [50, 30] # number of bins
thresh = 4 #density threshold
#data definition
xdat, ydat = tab['LOGEDD_RATIO'], tab['BBT']
# histogram the data
hh, locx, locy = histogram2d(xdat, ydat, range=xyrange, bins=bins)
posx = np.digitize(xdat, locx)
posy = np.digitize(ydat, locy)
#select points within the histogram
ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
hhsub = hh[posx[ind] - 1,
posy[ind] - 1] # values of the histogram where the points are
xdat1 = xdat[ind][hhsub < thresh] # low density points
ydat1 = ydat[ind][hhsub < thresh]
hh[hh < thresh] = np.nan # fill the areas with low density by NaNs
im = ax8.imshow(np.flipud(hh.T),
cmap=mycm,
extent=np.array(xyrange).flatten(),
interpolation='none',
aspect='auto',
vmin=thresh,
vmax=45)
ax8.scatter(xdat1, ydat1, color=cset[-1])
axcb = fig.add_axes([0.33, 0.05, 0.33, 0.02])
clb = fig.colorbar(im, cax=axcb, orientation='horizontal')
clb.set_label('Number of Objects')
ax8.set_xlabel(r'Log$_{10}$ (Eddington Ratio $\lambda$)')
ax8.set_ylim(ax2.get_ylim())
ax8.set_xlim(ax7.get_xlim())
plt.tick_params(axis='both', which='major')
ax8.set_yticklabels([])
ax8.set_xticklabels(['', '-1.5', '-1.0', '-0.5', '0.0', '0.5'])
ax7.set_yticklabels([])
ax7.set_xticklabels([])
ax9.hist(tab['LOGBH'][tab['LOGBH'] > 6], color=cset[-1], bins=20)
ax9.set_xlim(ax8.get_xlim())
ax9.set_axis_off()
ax10.hist(tab['RATIO_IR_UV'],
orientation='horizontal',
color=cset[-1],
bins=np.arange(0, 2, 0.1))
ax10.set_ylim(ax1.get_ylim())
ax10.set_axis_off()
ax11.hist(tab['BBT'], orientation='horizontal', color=cset[-1], bins=20)
ax11.set_ylim(ax2.get_ylim())
ax11.set_axis_off()
s1 = spearmanr(tab[tab['LOGEDD_RATIO'] > -2.5]['LOGEDD_RATIO'],
tab[tab['LOGEDD_RATIO'] > -2.5]['RATIO_IR_UV'])[0]
s2 = spearmanr(tab[tab['LOGBH'] > 6]['LOGBH'],
tab[tab['LOGBH'] > 6]['RATIO_IR_UV'])[0]
s3 = spearmanr(tab['LUM_UV'], tab['RATIO_IR_UV'])[0]
s4 = spearmanr(tab[tab['LOGEDD_RATIO'] > -2.5]['LOGEDD_RATIO'],
tab[tab['LOGEDD_RATIO'] > -2.5]['BBT'])[0]
s5 = spearmanr(tab[tab['LOGBH'] > 6]['LOGBH'],
tab[tab['LOGBH'] > 6]['BBT'])[0]
s6 = spearmanr(tab['LUM_UV'], tab['BBT'])[0]
ax1.text(46.5, 0.2, r'$\rho =$ {0:.2f}'.format(s3))
ax4.text(9.7, 0.2, r'$\rho =$ {0:.2f}'.format(s2))
ax7.text(-0.1, 0.2, r'$\rho =$ {0:.2f}'.format(s1))
ax2.text(46.5, 1900, r'$\rho =$ {0:.2f}'.format(s6))
ax5.text(9.7, 1900, r'$\rho =$ {0:.2f}'.format(s5))
ax8.text(-0.1, 1900, r'$\rho =$ {0:.2f}'.format(s4))
fig.savefig('/home/lc585/thesis/figures/chapter06/correlations.pdf')
plt.show()
return None
def plot():
mycm = cm.get_cmap('YlOrRd_r')
mycm.set_under('w')
mycm = truncate_colormap(mycm, 0.0, 0.8)
cset = brewer2mpl.get_map('YlOrRd', 'sequential', 9).mpl_colors
set_plot_properties() # change style
tab = Table.read('/data/lc585/QSOSED/Results/150217/sample2/out_add.fits')
tab = tab[~np.isnan(tab['BBT_STDERR'])]
tab = tab[tab['BBT_STDERR'] < 500.]
tab = tab[tab['BBT_STDERR'] > 5.0]
tab = tab[(tab['LUM_IR_SIGMA'] * tab['RATIO_IR_UV']) < 0.4]
fig = plt.figure(figsize=figsize(1, 0.6))
gs = gridspec.GridSpec(6, 13)
ax1 = fig.add_subplot(gs[1:5, 0:4])
ax3 = fig.add_subplot(gs[0, 0:4])
ax4 = fig.add_subplot(gs[1:5, 4:8])
ax6 = fig.add_subplot(gs[0, 4:8])
ax7 = fig.add_subplot(gs[1:5, 8:12])
ax9 = fig.add_subplot(gs[0, 8:12])
ax10 = fig.add_subplot(gs[1:5, 12])
fig.subplots_adjust(wspace=0.0)
fig.subplots_adjust(hspace=0.0)
#histogram definition
xyrange = [[43, 47], [0, 2]] # data range
bins = [60, 40] # number of bins
thresh = 4 #density threshold
#data definition
xdat, ydat = tab['LUM_UV'], tab['RATIO_IR_UV']
# histogram the data
hh, locx, locy = histogram2d(xdat, ydat, range=xyrange, bins=bins)
posx = np.digitize(xdat, locx)
posy = np.digitize(ydat, locy)
#select points within the histogram
ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
hhsub = hh[posx[ind] - 1,
posy[ind] - 1] # values of the histogram where the points are
xdat1 = xdat[ind][hhsub < thresh] # low density points
ydat1 = ydat[ind][hhsub < thresh]
hh[hh < thresh] = np.nan # fill the areas with low density by NaNs
im = ax1.imshow(np.flipud(hh.T),
cmap=mycm,
extent=np.array(xyrange).flatten(),
interpolation='none',
aspect='auto',
vmin=thresh)
ax1.scatter(xdat1, ydat1, color=cset[-1])
ax1.set_ylabel(r'$R_{1-3{\mu}m/UV}$')
ax1.set_ylim(-0.4, 1)
ax1.set_xlim(45, 47)
ax3.hist(tab['LUM_UV'], color=cset[-1], bins=20)
ax3.set_xlim(ax1.get_xlim())
ax3.set_axis_off()
#ax1.get_xaxis().set_ticks([46,46.5,47])
#ax1.get_yaxis().set_ticks([0.05,0.15,0.25,0.35,0.45])
############################################################################
#histogram definition
xyrange = [[7.5, 10.5], [0, 2]] # data range
bins = [40, 40] # number of bins
thresh = 4 #density threshold
#data definition
xdat, ydat = tab['LOGBH'], tab['RATIO_IR_UV']
# histogram the data
hh, locx, locy = histogram2d(xdat, ydat, range=xyrange, bins=bins)
posx = np.digitize(xdat, locx)
posy = np.digitize(ydat, locy)
#select points within the histogram
ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
hhsub = hh[posx[ind] - 1,
posy[ind] - 1] # values of the histogram where the points are
xdat1 = xdat[ind][hhsub < thresh] # low density points
ydat1 = ydat[ind][hhsub < thresh]
hh[hh < thresh] = np.nan # fill the areas with low density by NaNs
im = ax4.imshow(np.flipud(hh.T),
cmap=mycm,
extent=np.array(xyrange).flatten(),
interpolation='none',
aspect='auto',
vmin=thresh)
ax4.scatter(xdat1, ydat1, color=cset[-1])
ax4.set_ylim(ax1.get_ylim())
ax4.set_xlim(7.9, 10.5)
ax4.set_xticklabels([7.5, 8, 8.5, 9, 9.5, 10])
#ax4.set_yticks([0.05,0.15,0.25,0.35,0.45])
ax4.set_yticklabels([])
ax6.hist(tab[tab['LOGBH'] > 6]['LOGBH'], color=cset[-1], bins=20)
ax6.set_xlim(ax4.get_xlim())
ax4.set_ylim(ax1.get_ylim())
ax6.hist(tab['LOGBH'][tab['LOGBH'] > 6], color=cset[-1], bins=20)
ax6.set_xlim(ax4.get_xlim())
ax6.set_axis_off()
######################################################################################
#histogram definition
xyrange = [[-2, 0.5], [0, 2]] # data range
bins = [40, 40] # number of bins
thresh = 4 #density threshold
#data definition
xdat, ydat = tab['LOGEDD_RATIO'], tab['RATIO_IR_UV']
# histogram the data
hh, locx, locy = histogram2d(xdat, ydat, range=xyrange, bins=bins)
posx = np.digitize(xdat, locx)
posy = np.digitize(ydat, locy)
#select points within the histogram
ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
hhsub = hh[posx[ind] - 1,
posy[ind] - 1] # values of the histogram where the points are
xdat1 = xdat[ind][hhsub < thresh] # low density points
ydat1 = ydat[ind][hhsub < thresh]
hh[hh < thresh] = np.nan # fill the areas with low density by NaNs
im = ax7.imshow(np.flipud(hh.T),
cmap=mycm,
extent=np.array(xyrange).flatten(),
interpolation='none',
aspect='auto',
vmin=thresh)
ax7.scatter(xdat1, ydat1, color=cset[-1])
ax7.set_ylim(ax1.get_ylim())
ax7.set_xlim(-1.8, 0.7)
ax9.hist(tab[tab['LOGEDD_RATIO'] > -2.5]['LOGEDD_RATIO'],
color=cset[-1],
bins=20)
ax9.set_xlim(ax7.get_xlim())
ax9.set_axis_off()
plt.tick_params(axis='both', which='major')
#ax7.set_yticks([0.05,0.15,0.25,0.35,0.45])
ax7.set_yticklabels([])
#ax7.set_xticklabels([])
ax9.hist(tab['LOGBH'][tab['LOGBH'] > 6], color=cset[-1], bins=20)
ax9.set_xlim(ax7.get_xlim())
ax9.set_axis_off()
ax10.hist(tab['RATIO_IR_UV'],
orientation='horizontal',
color=cset[-1],
bins=25)
ax10.set_ylim(ax1.get_ylim())
ax10.set_axis_off()
ax4.set_xlabel(r'Log$_{10}$ (BH Mass $M_{\rm BH}$)')
ax1.set_xlabel(r'Log$_{10} (L_{\rm UV} ({\rm erg/s}))$')
ax7.set_xlabel(r'Log$_{10}$ ($\lambda_{\rm Edd}$)')
axcb = fig.add_axes([0.33, 0.1, 0.33, 0.03])
clb = fig.colorbar(im, cax=axcb, orientation='horizontal')
clb.set_label('Number of Objects', fontsize=14)
s1 = spearmanr(tab[tab['LOGEDD_RATIO'] > -2.5]['LOGEDD_RATIO'],
tab[tab['LOGEDD_RATIO'] > -2.5]['RATIO_IR_UV'])[0]
s2 = spearmanr(tab[tab['LOGBH'] > 6]['LOGBH'],
tab[tab['LOGBH'] > 6]['RATIO_IR_UV'])[0]
s3 = spearmanr(tab['LUM_UV'], tab['RATIO_IR_UV'])[0]
p1 = spearmanr(tab[tab['LOGEDD_RATIO'] > -2.5]['LOGEDD_RATIO'],
tab[tab['LOGEDD_RATIO'] > -2.5]['RATIO_IR_UV'])[1]
p2 = spearmanr(tab[tab['LOGBH'] > 6]['LOGBH'],
tab[tab['LOGBH'] > 6]['RATIO_IR_UV'])[1]
p3 = spearmanr(tab['LUM_UV'], tab['RATIO_IR_UV'])[1]
ax1.text(45.6, -0.2, r'$\rho =$ {0:.2f} ({1:.2f})'.format(s3, p3))
ax4.text(8.7, -0.2, r'$\rho =$ {0:.2f} ({1:.2f})'.format(s2, p2))
ax7.text(-1.1, -0.2, r'$\rho =$ {0:.2f} ({1:.2f})'.format(s1, p1))
fig.savefig(
'/home/lc585/thesis/figures/chapter06/ratio_lowz_correlations.pdf')
plt.show()
return None
def plot():
from astropy.table import Table, join
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import spearmanr
import brewer2mpl
from PlottingTools.truncate_colormap import truncate_colormap
from matplotlib import cm
import matplotlib.gridspec as gridspec
from scipy import histogram2d
from PlottingTools.plot_setup import figsize, set_plot_properties
mycm = cm.get_cmap('YlOrRd_r')
mycm.set_under('w')
cset = brewer2mpl.get_map('YlOrRd', 'sequential', 9).mpl_colors
mycm = truncate_colormap(mycm, 0.0, 0.8)
set_plot_properties() # change style
tab = Table.read(
'/data/lc585/QSOSED/Results/141124/sample4/out_add_lumcalc.fits')
tab.sort('Z')
tab = tab[tab['BBPLSLP_STDERR'] < 0.25]
fig = plt.figure(figsize=figsize(1.2, 0.4))
gs = gridspec.GridSpec(6, 13)
ax1 = fig.add_subplot(gs[1:5, 0:4])
ax3 = fig.add_subplot(gs[0, 0:4])
ax4 = fig.add_subplot(gs[1:5, 4:8])
ax6 = fig.add_subplot(gs[0, 4:8])
ax7 = fig.add_subplot(gs[1:5, 8:12])
ax9 = fig.add_subplot(gs[0, 8:12])
ax10 = fig.add_subplot(gs[1:5, 12])
fig.subplots_adjust(wspace=0.0)
fig.subplots_adjust(hspace=0.0)
#histogram definition
xyrange = [[45.5, 48], [-0.5, 1.5]] # data range
bins = [50, 30] # number of bins
thresh = 4 #density threshold
#data definition
xdat, ydat = tab['LUM_UV'], tab['BBPLSLP']
# histogram the data
hh, locx, locy = histogram2d(xdat, ydat, range=xyrange, bins=bins)
posx = np.digitize(xdat, locx)
posy = np.digitize(ydat, locy)
#select points within the histogram
ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
hhsub = hh[posx[ind] - 1,
posy[ind] - 1] # values of the histogram where the points are
xdat1 = xdat[ind][hhsub < thresh] # low density points
ydat1 = ydat[ind][hhsub < thresh]
hh[hh < thresh] = np.nan # fill the areas with low density by NaNs
im = ax1.imshow(np.flipud(hh.T),
cmap=mycm,
extent=np.array(xyrange).flatten(),
interpolation='none',
aspect='auto',
vmin=thresh,
vmax=45)
ax1.scatter(xdat1, ydat1, color=cset[-1])
ax1.set_ylabel(r'$\beta_{\rm NIR}$')
ax1.set_ylim(-0.8, 1.5)
ax1.set_xlim(45.8, 47.5)
ax3.hist(tab['LUM_UV'], color=cset[-1], bins=20)
ax3.set_xlim(ax1.get_xlim())
ax3.set_axis_off()
ax1.get_xaxis().set_ticks([46, 46.5, 47])
#ax1.get_yaxis().set_ticks([0.05,0.15,0.25,0.35,0.45])
############################################################################
#histogram definition
xyrange = [[7.5, 10.5], [-0.5, 1.5]] # data range
bins = [30, 30] # number of bins
thresh = 4 #density threshold
#data definition
xdat, ydat = tab['LOGBH'], tab['BBPLSLP']
# histogram the data
hh, locx, locy = histogram2d(xdat, ydat, range=xyrange, bins=bins)
posx = np.digitize(xdat, locx)
posy = np.digitize(ydat, locy)
#select points within the histogram
ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
hhsub = hh[posx[ind] - 1,
posy[ind] - 1] # values of the histogram where the points are
xdat1 = xdat[ind][hhsub < thresh] # low density points
ydat1 = ydat[ind][hhsub < thresh]
hh[hh < thresh] = np.nan # fill the areas with low density by NaNs
im = ax4.imshow(np.flipud(hh.T),
cmap=mycm,
extent=np.array(xyrange).flatten(),
interpolation='none',
aspect='auto',
vmin=thresh,
vmax=45)
ax4.scatter(xdat1, ydat1, color=cset[-1])
ax4.set_ylim(ax1.get_ylim())
ax4.set_xlim(7.9, 10.5)
ax4.set_xticklabels([7.5, 8, 8.5, 9, 9.5, 10])
#ax4.set_yticks([0.05,0.15,0.25,0.35,0.45])
ax4.set_yticklabels([])
ax6.hist(tab[tab['LOGBH'] > 6]['LOGBH'], color=cset[-1], bins=20)
ax6.set_xlim(ax4.get_xlim())
plt.tick_params(axis='both', which='major')
ax4.set_ylim(ax1.get_ylim())
ax6.hist(tab['LOGBH'][tab['LOGBH'] > 6], color=cset[-1], bins=20)
ax6.set_xlim(ax4.get_xlim())
ax6.set_axis_off()
######################################################################################
#histogram definition
xyrange = [[-2, 0.5], [-0.5, 1.5]] # data range
bins = [30, 30] # number of bins
thresh = 4 #density threshold
#data definition
xdat, ydat = tab['LOGEDD_RATIO'], tab['BBPLSLP']
# histogram the data
hh, locx, locy = histogram2d(xdat, ydat, range=xyrange, bins=bins)
posx = np.digitize(xdat, locx)
posy = np.digitize(ydat, locy)
#select points within the histogram
ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
hhsub = hh[posx[ind] - 1,
posy[ind] - 1] # values of the histogram where the points are
xdat1 = xdat[ind][hhsub < thresh] # low density points
ydat1 = ydat[ind][hhsub < thresh]
hh[hh < thresh] = np.nan # fill the areas with low density by NaNs
im = ax7.imshow(np.flipud(hh.T),
cmap=mycm,
extent=np.array(xyrange).flatten(),
interpolation='none',
aspect='auto',
vmin=thresh,
vmax=45)
ax7.scatter(xdat1, ydat1, color=cset[-1])
ax7.set_ylim(ax1.get_ylim())
ax7.set_xlim(-1.8, 0.7)
ax9.hist(tab[tab['LOGEDD_RATIO'] > -2.5]['LOGEDD_RATIO'],
color=cset[-1],
bins=20)
ax9.set_xlim(ax7.get_xlim())
ax9.set_axis_off()
plt.tick_params(axis='both', which='major')
#ax7.set_yticks([0.05,0.15,0.25,0.35,0.45])
ax7.set_yticklabels([])
#ax7.set_xticklabels([])
ax9.hist(tab['LOGBH'][tab['LOGBH'] > 6], color=cset[-1], bins=20)
ax9.set_xlim(ax7.get_xlim())
ax9.set_axis_off()
ax10.hist(tab['BBPLSLP'],
orientation='horizontal',
color=cset[-1],
bins=25)
ax10.set_ylim(ax1.get_ylim())
ax10.set_axis_off()
ax4.set_xlabel(r'Log$_{10}$ (Black Hole Mass $M_{\rm BH}$)')
ax1.set_xlabel(r'Log$_{10} (L_{\rm UV} ({\rm erg/s}))$')
ax7.set_xlabel(r'Log$_{10}$ (Eddington Ratio $\lambda$)')
axcb = fig.add_axes([0.33, 0.1, 0.33, 0.03])
clb = fig.colorbar(im, cax=axcb, orientation='horizontal')
clb.set_label('Number of Objects')
s1 = spearmanr(tab[tab['LOGEDD_RATIO'] > -2.5]['LOGEDD_RATIO'],
tab[tab['LOGEDD_RATIO'] > -2.5]['BBPLSLP'])[0]
s2 = spearmanr(tab[tab['LOGBH'] > 6]['LOGBH'],
tab[tab['LOGBH'] > 6]['BBPLSLP'])[0]
s3 = spearmanr(tab['LUM_UV'], tab['BBPLSLP'])[0]
ax1.text(46.95, 1.2, r'$\rho =$ {0:.2f}'.format(s3))
ax4.text(9.7, 1.2, r'$\rho =$ {0:.2f}'.format(s2))
ax7.text(-0.1, 1.2, r'$\rho =$ {0:.2f}'.format(s1))
fig.savefig('/home/lc585/thesis/figures/chapter06/correlations_highz.pdf')
plt.show()
return None
def plot():
set_plot_properties() # change style
tab1 = Table.read('/data/lc585/QSOSED/Results/141209/sample1/out_add.fits')
tab1 = tab1[~np.isnan(tab1['BBT_STDERR'])]
tab1 = tab1[tab1['BBT_STDERR'] < 500.]
tab1 = tab1[tab1['BBT_STDERR'] > 5.0]
tab1 = tab1[(tab1['LUM_IR_SIGMA'] * tab1['RATIO_IR_UV']) < 0.4]
tab2 = Table.read('/data/lc585/QSOSED/Results/150211/sample2/out_add.fits')
tab2 = tab2[~np.isnan(tab2['BBT_STDERR'])]
tab2 = tab2[tab2['BBT_STDERR'] < 500.]
tab2 = tab2[tab2['BBT_STDERR'] > 5.0]
tab2 = tab2[(tab2['LUM_IR_SIGMA'] * tab2['RATIO_IR_UV']) < 0.4]
fig, axs = plt.subplots(1, 3, figsize=figsize(1, vscale=0.4), sharey=True)
m1, m2 = tab1['LUM_UV'], tab1['BBT']
xmin = m1.min()
xmax = m1.max()
ymin = m2.min()
ymax = m2.max()
X, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions = np.vstack([X.ravel(), Y.ravel()])
values = np.vstack([m1, m2])
kernel = stats.gaussian_kde(values)
Z = np.reshape(kernel(positions).T, X.shape)
CS = axs[0].contour(X, Y, Z, colors='black')
threshold = CS.levels[0]
z = kernel(values)
# mask points above density threshold
x = np.ma.masked_where(z > threshold, m1)
y = np.ma.masked_where(z > threshold, m2)
# plot unmasked points
axs[0].scatter(x, y, c='black', edgecolor='None', s=3)
m1, m2 = tab2['LUM_UV'], tab2['BBT']
xmin = m1.min()
xmax = m1.max()
ymin = m2.min()
ymax = m2.max()
X, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions = np.vstack([X.ravel(), Y.ravel()])
values = np.vstack([m1, m2])
kernel = stats.gaussian_kde(values)
Z = np.reshape(kernel(positions).T, X.shape)
CS = axs[0].contour(X, Y, Z, colors='red')
threshold = CS.levels[0]
z = kernel(values)
# mask points above density threshold
x = np.ma.masked_where(z > threshold, m1)
y = np.ma.masked_where(z > threshold, m2)
# plot unmasked points
axs[0].scatter(x, y, c='red', edgecolor='None', s=3)
m1, m2 = tab1['LOGBH'], tab1['BBT']
xmin = m1.min()
xmax = m1.max()
ymin = m2.min()
ymax = m2.max()
X, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions = np.vstack([X.ravel(), Y.ravel()])
values = np.vstack([m1, m2])
kernel = stats.gaussian_kde(values)
Z = np.reshape(kernel(positions).T, X.shape)
CS = axs[1].contour(X, Y, Z, colors='black')
threshold = CS.levels[0]
z = kernel(values)
# mask points above density threshold
x = np.ma.masked_where(z > threshold, m1)
y = np.ma.masked_where(z > threshold, m2)
# plot unmasked points
axs[1].scatter(x, y, c='black', edgecolor='None', s=3)
m1, m2 = tab2['LOGBH'], tab2['BBT']
xmin = m1.min()
xmax = m1.max()
ymin = m2.min()
ymax = m2.max()
X, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions = np.vstack([X.ravel(), Y.ravel()])
values = np.vstack([m1, m2])
kernel = stats.gaussian_kde(values)
Z = np.reshape(kernel(positions).T, X.shape)
CS = axs[1].contour(X, Y, Z, colors='red')
threshold = CS.levels[0]
z = kernel(values)
# mask points above density threshold
x = np.ma.masked_where(z > threshold, m1)
y = np.ma.masked_where(z > threshold, m2)
# plot unmasked points
axs[1].scatter(x, y, c='red', edgecolor='None', s=3)
m1, m2 = tab1['LOGEDD_RATIO'][tab1['LOGEDD_RATIO'] > -2.0], tab1['BBT'][
tab1['LOGEDD_RATIO'] > -2.0]
xmin = m1.min()
xmax = m1.max()
ymin = m2.min()
ymax = m2.max()
X, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions = np.vstack([X.ravel(), Y.ravel()])
values = np.vstack([m1, m2])
kernel = stats.gaussian_kde(values)
Z = np.reshape(kernel(positions).T, X.shape)
CS = axs[2].contour(X, Y, Z, colors='black')
threshold = CS.levels[0]
z = kernel(values)
# mask points above density threshold
x = np.ma.masked_where(z > threshold, m1)
y = np.ma.masked_where(z > threshold, m2)
# plot unmasked points
axs[2].scatter(x, y, c='black', edgecolor='None', s=3)
m1, m2 = tab2['LOGEDD_RATIO'][tab2['LOGEDD_RATIO'] > -2.0], tab2['BBT'][
tab2['LOGEDD_RATIO'] > -2.0]
xmin = m1.min()
xmax = m1.max()
ymin = m2.min()
ymax = m2.max()
X, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions = np.vstack([X.ravel(), Y.ravel()])
values = np.vstack([m1, m2])
kernel = stats.gaussian_kde(values)
Z = np.reshape(kernel(positions).T, X.shape)
CS = axs[2].contour(X, Y, Z, colors='red')
threshold = CS.levels[0]
z = kernel(values)
# mask points above density threshold
x = np.ma.masked_where(z > threshold, m1)
y = np.ma.masked_where(z > threshold, m2)
# plot unmasked points
axs[2].scatter(x, y, c='red', edgecolor='None', s=3)
axs[0].set_ylim(800, 1800)
axs[0].set_xlim(45.2, 47)
axs[1].set_xlim(8, 10.5)
axs[2].set_xlim(-1.6, 0.5)
axs[0].set_xticks([45.4, 45.8, 46.2, 46.6])
axs[0].set_ylabel('Blackbody Temperature')
axs[0].set_xlabel('UV Luminosity')
axs[1].set_xlabel('Black Hole Mass')
axs[2].set_xlabel('Eddington Ratio')
fig.tight_layout()
fig.savefig(
'/home/lc585/thesis/figures/chapter06/bbt_correlations_contour.pdf')
plt.show()
return None
def plot():
mycm = cm.get_cmap('YlOrRd_r')
mycm.set_under('w')
mycm = truncate_colormap(mycm, 0.0, 0.8)
cset = brewer2mpl.get_map('YlOrRd', 'sequential', 9).mpl_colors
tab = Table.read('/data/lc585/QSOSED/Results/141031/sample1.fits')
fig = plt.figure(figsize=figsize(0.7, vscale=1.5))
ax1 = fig.add_subplot(2,1,1)
ax2 = fig.add_subplot(2,1,2)
fig.subplots_adjust(wspace=0.0)
fig.subplots_adjust(hspace=0.0)
fig.subplots_adjust(top=0.99, bottom=0.2)
#histogram definition
xyrange = [[0.5,1.5],[-0.4,1.2]] # data range
bins = [45,45] # number of bins
thresh = 4 #density threshold
#data definition
w1mag = tab['W1MPRO_ALLWISE'] + 2.699
w2mag = tab['W2MPRO_ALLWISE'] + 3.339
z = tab['Z_HEWETT']
xdat, ydat = z, w1mag - w2mag
# histogram the data
hh, locx, locy = histogram2d(xdat, ydat, range=xyrange, bins=bins)
posx = np.digitize(xdat, locx)
posy = np.digitize(ydat, locy)
#select points within the histogram
ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
hhsub = hh[posx[ind] - 1, posy[ind] - 1] # values of the histogram where the points are
xdat1 = xdat[ind][hhsub < thresh] # low density points
ydat1 = ydat[ind][hhsub < thresh]
hh[hh < thresh] = np.nan # fill the areas with low density by NaNs
im = ax1.imshow(np.flipud(hh.T),cmap=mycm,extent=np.array(xyrange).flatten(), interpolation='none',aspect='auto', vmin=thresh, vmax=45)
ax1.scatter(xdat1, ydat1,color=cset[-1])
ax1.set_ylabel(r'$W1-W2$',fontsize=14)
for tick in ax1.xaxis.get_major_ticks():
tick.label.set_fontsize(10)
for tick in ax1.yaxis.get_major_ticks():
tick.label.set_fontsize(10)
ax1.set_ylim(-0.4,1.2)
ax1.set_xlim(0.25,1.7)
im = ax2.imshow(np.flipud(hh.T),cmap=mycm,extent=np.array(xyrange).flatten(), interpolation='none',aspect='auto', vmin=thresh, vmax=45)
ax2.scatter(xdat1, ydat1,color=cset[-1])
axcb = fig.add_axes([0.13,0.1,0.75,0.02])
clb = fig.colorbar(im, cax=axcb,orientation='horizontal')
clb.set_label('Number of Objects',fontsize=12)
clb.ax.tick_params(labelsize=10)
ax2.set_xlabel(r'Redshift $z$',fontsize=14)
ax2.set_ylabel(r'$W1-W2$',fontsize=14)
for tick in ax2.xaxis.get_major_ticks():
tick.label.set_fontsize(10)
for tick in ax2.yaxis.get_major_ticks():
tick.label.set_fontsize(10)
ax2.set_ylim(ax1.get_ylim())
ax2.set_xlim(ax1.get_xlim())
tabtmp = tab
tabtmp.sort('Z')
#plt.tick_params(axis='both',which='major',labelsize=12)
ax1.get_xaxis().set_ticks([])
ax1.set_yticklabels(['',0.0,0.2,0.4,0.6,0.8,1.0,1.2])
ax2.set_yticklabels([-0.4,-0.2,0.0,0.2,0.4,0.6,0.8,1.0])
#plt.scatter(z,w1mag-w2mag,c='grey',alpha=0.3)
#xdat = z
#ydat = w1mag - w2mag
#zdat = tab['LOGLBOL']
#
#plt.hexbin(xdat,ydat,C=tab['LUM_UV'],gridsize=10,cmap=mycm)
#cb = plt.colorbar()
#cb.set_label('UV Luminosity')
#plt.ylim(-0.2,1.2)
#plt.xlim(0.4,1.6)
##plt.savefig('/data/lc585/QSOSED/Results/141030/figure1.jpg')
#
#xyrange = [[0.5,1.5],[0,1.2]] # data range
#bins = [10,6] # number of bins
#thresh = 20
#hh, locx, locy = histogram2d(xdat,
# ydat,
# range=xyrange,
# bins=bins)
#
#
#print locy
#
#posx = np.digitize(xdat, locx)
#posy = np.digitize(ydat, locy)
#
#grid = []
#for i in range(10):
# row = []
# for j in range(6):
# row.append(np.median(zdat[(posx == i+1) & (posy ==j+1)]))
# grid.append(row)
#ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
#hhsub = hh[posx[ind] - 1, posy[ind] - 1] # values of the histogram where the points are
#xdat1 = xdat[ind][hhsub < thresh] # low density points
#ydat1 = ydat[ind][hhsub < thresh]
#hh[hh < thresh] = np.nan # fill the areas with low density by NaNs
#ax.scatter(z[ind],w1mag[ind]-w2mag[ind])
#im = ax.imshow(np.flipud(hh.T),
# cmap=mycm,
# extent=np.array(xyrange).flatten(),
# interpolation='none',
# aspect='auto',vmin=10)
#ax.scatter(xdat1,ydat1)
# loop over bins
#for i in range(len(locx)):
# for j in range(len(locy)):
# print np.median(tab['LUM_UV'][ (posx == i) & (posy == j)])
#ax.hist2d(z,
# w1mag-w2mag,
# range=xyrange,
# bins=bins,
# cmap=mycm,
# vmin=10.)
with open('/home/lc585/Dropbox/IoA/QSOSED/Model/qsofit/input.yml', 'r') as f:
parfile = yaml.load(f)
fittingobj = load(parfile)
wavlen = fittingobj.get_wavlen()
with open('/data/lc585/QSOSED/Results/140811/allsample_2/fluxcorr.array','rb') as f:
flxcorr = pickle.load(f)
plslp1 = 0.46
plslp2 = 0.03
plbrk = 2822.
bbt = 1216.
bbflxnrm = 0.24
elscal = 0.71
scahal = 0.86
galfra = 0.31
ebv = 0.0
imod = 18.0
zs = np.arange(0.5,1.525,0.025)
bbt = 1200.
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
print w1w2_model[0]
ax1.plot(zs,w1w2_model,linewidth=2.0,color='black')
bbt = 1100.
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
print w1w2_model[0]
ax1.plot(zs,w1w2_model,linewidth=2.0,color='black')
bbt = 1300.
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
print w1w2_model[0]
ax1.plot(zs,w1w2_model,linewidth=2.0,color='black')
bbt = 1400.
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
print w1w2_model[0]
ax1.plot(zs,w1w2_model,linewidth=2.0,color='black')
bbt = 1500.
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
print w1w2_model[0]
ax1.plot(zs,w1w2_model,linewidth=2.0,color='black')
bbt = 1000.
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
ax1.plot(zs,w1w2_model,linewidth=2.0,color='black')
ax1.text(0.33,0.67,'1000K',fontsize=12,color='black')
ax1.text(0.33,0.52,'1100K',fontsize=12,color='black')
ax1.text(0.33,0.40,'1200K',fontsize=12,color='black')
ax1.text(0.33,0.28,'1300K',fontsize=12,color='black')
ax1.text(0.33,0.20,'1400K',fontsize=12,color='black')
ax1.text(0.33,0.10,'1500K',fontsize=12)
#plt.savefig('/home/lc585/Dropbox/IoA/HotDustPaper/w1w2_temp.pdf')
bbt = 1216.
bbflxnrm = 0.0
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
ax2.plot(zs,w1w2_model,linewidth=2.0,color='black')
bbflxnrm = 0.1
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
ax2.plot(zs,w1w2_model,linewidth=2.0,color='black')
bbflxnrm = 0.2
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
ax2.plot(zs,w1w2_model,linewidth=2.0,color='black')
bbflxnrm = 0.3
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
ax2.plot(zs,w1w2_model,linewidth=2.0,color='black')
bbflxnrm = 0.4
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
ax2.plot(zs,w1w2_model,linewidth=2.0,color='black')
#plt.xlim(0.2,1.8)
ax2.text(1.55,-0.1,'0.17',fontsize=12,color='black')
ax2.text(1.55,0.38,'0.28',fontsize=12,color='black')
ax2.text(1.55,0.60,'0.40',fontsize=12,color='black')
ax2.text(1.55,0.75,'0.52',fontsize=12,color='black')
ax2.text(1.55,0.90,'0.64',fontsize=12,color='black')
fig.savefig('/home/lc585/thesis/figures/chapter06/w1w2_versus_redshift.pdf')
#plt.xlabel(r'$z$',fontsize=12)
#plt.ylabel('$W1$-$W2$',fontsize=12)
#plt.tight_layout()
#plt.tick_params(axis='both',which='major',labelsize=10)
#sns.set_style('ticks')
#xdat = np.arange(0.1,1.2,0.2)
#plt.savefig('/data/lc585/QSOSED/Results/141101/figure1.jpg')
#fig, ax = plt.subplots()
#for row in grid:
# ax.plot(xdat,row)
#ax.set_xlabel('W1-W2')
#ax.set_ylabel('LUM BOL')
#plt.savefig('/data/lc585/QSOSED/Results/141101/figure2.jpg')
#plt.show()
#plt.savefig('/home/lc585/Dropbox/IoA/HotDustPaper/w1w2_bbnorm.pdf')
plt.show()
return None
def plot():
set_plot_properties() # change style
tab = Table.read('/data/lc585/QSOSED/Results/141203/sample1/out_add.fits')
tab = tab[ tab['BBT_STDERR'] < 200.0 ]
tab = tab[ tab['BBFLXNRM_STDERR'] < 0.05]
tab = tab[ tab['CHI2_RED'] < 3.0]
tab = tab[ tab['LUM_IR'] > 40.0] # just gets rid of one annoying point
mycm = cm.get_cmap('YlOrRd_r')
mycm.set_under('w')
cset = brewer2mpl.get_map('YlOrRd', 'sequential', 9).mpl_colors
mycm = truncate_colormap(mycm, 0.0, 0.8)
fig = plt.figure(figsize=figsize(0.8, 1.8))
gs = gridspec.GridSpec(9, 5)
ax1 = fig.add_subplot(gs[1:5, 0:4])
ax2 = fig.add_subplot(gs[5:, 0:4])
ax3 = fig.add_subplot(gs[0,0:4])
ax4 = fig.add_subplot(gs[1:5,4])
ax5 = fig.add_subplot(gs[5:,4])
fig.subplots_adjust(wspace=0.0)
fig.subplots_adjust(hspace=0.0)
#histogram definition
xyrange = [[-0.4,1.6],[0,2]] # data range
bins = [120,30] # number of bins
thresh = 4 #density threshold
#data definition
xdat, ydat = tab['Z'],tab['RATIO_IR_UV']
# histogram the data
hh, locx, locy = histogram2d(xdat, ydat, range=xyrange, bins=bins)
posx = np.digitize(xdat, locx)
posy = np.digitize(ydat, locy)
#select points within the histogram
ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
hhsub = hh[posx[ind] - 1, posy[ind] - 1] # values of the histogram where the points are
xdat1 = xdat[ind][hhsub < thresh] # low density points
ydat1 = ydat[ind][hhsub < thresh]
hh[hh < thresh] = np.nan # fill the areas with low density by NaNs
im = ax1.imshow(np.flipud(hh.T),
cmap=mycm,
extent=np.array(xyrange).flatten(),
interpolation='none',
aspect='auto',
vmin=thresh,
vmax=45)
ax1.scatter(xdat1, ydat1,color=cset[-1])
ax1.set_ylabel(r'$R_{{\rm NIR}/{\rm UV}}$')
ax1.set_ylim(0.,2.0)
ax1.set_xlim(1,1.5)
tabtmp = tab
tabtmp.sort('Z')
xdat = running.RunningMedian(np.array(tabtmp['Z']),101)
ydat = running.RunningMedian(np.array(tabtmp['RATIO_IR_UV']),101)
ax1.plot(xdat[::100],ydat[::100],color='black',linewidth=2.0)
ax1.axhline(np.median(tab['RATIO_IR_UV']),color='black',linestyle='--')
ax1.axhline(np.percentile(tab['RATIO_IR_UV'],70),color='black',linestyle='--')
ax1.axhline(np.percentile(tab['RATIO_IR_UV'],30),color='black',linestyle='--')
ax1.get_xaxis().set_visible(False)
ax3.hist(tab['Z'],color=cset[-1],bins=20)
ax3.set_xlim(ax1.get_xlim())
ax3.set_axis_off()
ax4.hist(tab['RATIO_IR_UV'], orientation='horizontal',color=cset[-1],bins=np.arange(0,2,0.1))
ax4.set_ylim(ax1.get_ylim())
ax4.set_axis_off()
#histogram definition
xyrange = [[-0.4,1.6],[500,2000]] # data range
bins = [100,50] # number of bins
thresh = 4 #density threshold
#data definition
xdat, ydat = tab['Z'],tab['BBT']
# histogram the data
hh, locx, locy = histogram2d(xdat, ydat, range=xyrange, bins=bins)
posx = np.digitize(xdat, locx)
posy = np.digitize(ydat, locy)
#select points within the histogram
ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
hhsub = hh[posx[ind] - 1, posy[ind] - 1] # values of the histogram where the points are
xdat1 = xdat[ind][hhsub < thresh] # low density points
ydat1 = ydat[ind][hhsub < thresh]
hh[hh < thresh] = np.nan # fill the areas with low density by NaNs
im = ax2.imshow(np.flipud(hh.T),
cmap=mycm,
extent=np.array(xyrange).flatten(),
interpolation='none',
aspect='auto',
vmin=thresh,
vmax=45)
ax2.scatter(xdat1, ydat1,color=cset[-1])
axcb = fig.add_axes([0.13,0.05,0.6,0.02])
clb = fig.colorbar(im, cax=axcb,orientation='horizontal')
clb.set_label('Number of Objects')
ax2.set_xlabel(r'Redshift $z$')
ax2.set_ylabel(r'$T_{\mathrm{BB}}$')
ax2.set_ylim(500,1900)
ax2.set_xlim(1,1.5)
tabtmp = tab
tabtmp.sort('Z')
xdat = running.RunningMedian(np.array(tabtmp['Z']),101)
ydat = running.RunningMedian(np.array(tabtmp['BBT']),101)
ax2.plot(xdat[::100],ydat[::100],color='black',linewidth=2.0)
ax2.axhline(1200,color='black',linestyle='--')
ax2.axhline(1100,color='black',linestyle='--')
ax2.axhline(1300,color='black',linestyle='--')
ax5.hist(tab['BBT'], orientation='horizontal',color=cset[-1],bins=20)
ax5.set_ylim(ax2.get_ylim())
ax5.set_axis_off()
fig.savefig('/home/lc585/thesis/figures/chapter06/ratio_bbt_z.pdf')
plt.show()
return None