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
import matplotlib.pyplot as plt
import os
import scipy
from matplotlib import cm
import matplotlib.colors as colors
import brewer2mpl
from qsosed.load import load
import yaml
from lmfit import Parameters
import cPickle as pickle
from qsosed.loaddat import loaddat
from PlottingTools.plot_setup import figsize, set_plot_properties
from qsosed.sedmodel import model
from qsosed.loaddatraw import loaddatraw
set_plot_properties() # change style
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)
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():
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