def shang_sed():
import matplotlib.pyplot as plt
import numpy as np
from scipy.constants import c
rltab = np.genfromtxt('/home/lc585/thesis/code/chapter01/rlmsedMR.txt')
fig = plt.figure(figsize=figsize(1, 0.8))
ax = fig.add_subplot(111)
ax.plot( 1.0e10 * c / (10**rltab[:,0]), 10**rltab[:,1], color='black', label='Radio-loud' )
ax.set_xlabel(r'Wavelength [${\rm \AA}$]')
ax.set_ylabel( r'log ${\lambda}f_{\lambda}$ [Arbitrary Units]')
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_ylim(1e-2,12)
ax.set_xlim(10,10**8)
plt.tight_layout()
plt.savefig('/home/lc585/thesis/figures/chapter05/shangsed.pdf')
plt.show()
return None
def zhist_elscal():
tab = Table.read('/data/lc585/QSOSED/Results/Red_Obj_Cat/RedObjCatExt_MCMC.fits')
fig = plt.figure(figsize=figsize(0.7))
ax = fig.add_subplot(1,1,1)
npbins = np.arange(2,4.1,0.1)
bins, edges = np.histogram( tab['Z'][ tab['ELSCAL_FIT'] < 0.01 ], bins=npbins)
bins_all, edges_all = np.histogram( tab['Z'], bins=npbins)
bins = np.array(bins,dtype='float') / np.array(bins_all,dtype='float')
left,right = edges[:-1],edges[1:]
X = np.array([left,right]).T.flatten()
Y = np.array([bins,bins]).T.flatten()
ax.plot(X,Y,color='black')
ax.set_xlabel(r'$z$')
ax.set_ylabel('Fraction of weak emission line objects')
ax.set_xlim(2,4)
ax.set_ylim(0,0.7)
plt.tick_params(axis='both',which='major')
plt.tight_layout()
plt.savefig('/home/lc585/thesis/figures/chapter06/zhist_elscal.pdf')
plt.show()
return None
def red_spectra():
set_plot_properties() # change style
from SpectraTools.get_spectra import read_boss_dr12_spec
fig, ax = plt.subplots(figsize=figsize(0.7))
wav, dw, flux, err = read_boss_dr12_spec('spec-4240-55455-0626.fits')
ax.plot(wav, flux, color=cs[-1], label='SDSSJ0240+0103')
wav, dw, flux, err = read_boss_dr12_spec('spec-5481-55983-0346.fits')
ax.plot(wav, flux + 10, color=cs[0], label='SDSSJ1500+0826')
plt.legend(frameon=False)
ax.set_xlim(4000, 9000)
ax.set_ylim(-5, 20)
ax.set_xlabel(r'Wavelength $\lambda$ [\AA]')
ax.set_ylabel('Flux F$_{\lambda}$ [Arbitary Units]')
fig.tight_layout()
fig.savefig('/home/lc585/thesis/figures/chapter06/red_spectra.pdf')
plt.show()
return None
def lum_z():
fig, ax = plt.subplots(figsize=figsize(1, 0.8))
from qsosed.get_data import get_data
df = get_data()
lims = (1, 3, 45.5, 48)
kde_contours(df.z_HW,
df.LOGLBOL,
ax,
lims=lims,
color='black',
filled=True)
ax.set_ylim(45.5, None)
ax.set_xlabel(r'Redshift $z$')
ax.set_ylabel(r'Log L$_{\rm Bol}$ [erg~$\rm{s}^{-1}$]')
plt.tight_layout()
plt.savefig('/home/lc585/thesis/figures/chapter05/lum_z.pdf')
plt.show()
def plot():
from PlottingTools.plot_setup_thesis import figsize, set_plot_properties
from astropy.table import Table, join
import numpy as np
import matplotlib.pyplot as plt
set_plot_properties() # change style
tab = Table.read('/data/lc585/QSOSED/Results/141209/sample1/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']) < 1.]
tab1 = Table.read('/data/lc585/QSOSED/Results/141124/sample2/out.fits'
) # 9000 - 23500 fit
tab1['BBPLSLP'] = tab1['BBPLSLP'] - 1.0
goodnames = Table()
goodnames['NAME'] = tab['NAME']
tab1 = join(tab1, goodnames, join_type='right', keys='NAME')
fig, ax = plt.subplots(figsize=figsize(1, 0.7))
im = ax.hexbin(
tab['BBT'],
tab['RATIO_IR_UV'],
C=tab1['BBPLSLP'],
gridsize=80,
)
cb = plt.colorbar(im)
cb.set_label(r'$\beta_{\rm NIR}$')
# Digitize beta
nbins = 20
bins = np.linspace(-1.5, 1.5, nbins + 1)
ind = np.digitize(tab1['BBPLSLP'], bins)
bbt_locus = [np.median(tab['BBT'][ind == j]) for j in range(1, nbins)]
ratio_locus = [
np.median(tab['RATIO_IR_UV'][ind == j]) for j in range(1, nbins)
]
ax.plot(bbt_locus[7:-2], ratio_locus[7:-2], color='black', linewidth=2.0)
ax.set_ylabel(r'$R_{{\rm NIR}/{\rm UV}}$')
ax.set_xlabel(r'$T_{\rm BB}$')
ax.set_ylim(0, 2)
plt.tight_layout()
fig.savefig('/home/lc585/thesis/figures/chapter06/ratio_tbb_beta.pdf')
plt.show()
return None
def ratio_tbb_contours():
tab = Table.read('/data/lc585/QSOSED/Results/150309/sample4/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']) < 1.]
tab = tab[ (tab['RATIO_IR_UV'] < 2.0) & (tab['RATIO_IR_UV'] > 0.0)]
tab = tab[ (tab['BBT'] > 600.0) & (tab['BBT'] < 2000.0)]
m1, m2 = tab['BBT'], tab['RATIO_IR_UV']
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)
# Sample from single (T,Norm) with gaussian errors on photometry.
# Mock magnitude file made in model.py and then fit in runsingleobjfit.py.
tab2 = Table.read('/data/lc585/QSOSED/Results/150309/sample5/out_add.fits')
fig, ax = plt.subplots(figsize=figsize(1.0))
CS = ax.contour(X,Y,Z, colors='grey', levels=[0.0015,0.003,0.0045,0.006,0.0075,0.009,0.0105])
ax.scatter(tab2['BBT'],
tab2['RATIO_IR_UV'],
edgecolor='None',
color='black',
s=8)
ax.set_ylim(0,0.8)
ax.set_xlim(600,2000)
ax.set_xlabel(r'$T_{BB}$')
ax.set_ylabel('$R_{NIR/UV}$')
fig.tight_layout()
fig.savefig('/home/lc585/thesis/figures/chapter05/ratio_tbb_contours.pdf')
plt.show()
return None
def civ_hot_dust_beta():
tab = Table.read('/data/lc585/QSOSED/Results/140905/fit2/out_add.fits')
tab = tab[ tab['W3SNR'] > 3.0]
tab = tab[ tab['BAL_FLAG'] == 0]
tab = tab[ tab['CIV_BLUESHIFT_PAUL'] < 10000.0]
tab = tab[ ~np.isnan(tab['CIV_EW_PAUL'])]
tab = tab[ tab['CIV_EW_PAUL'] > 10**1.2]
xdat = tab['CIV_BLUESHIFT_PAUL']
ydat = np.log10(tab['CIV_EW_PAUL'])
C = tab['BBPLSLP']
fig, ax = plt.subplots(figsize=figsize(1, vscale=0.9))
from LiamUtils import colormaps as cmaps
plt.register_cmap(name='inferno_r', cmap=cmaps.inferno_r)
plt.set_cmap(cmaps.inferno_r)
im = plt.hexbin(xdat,
ydat,
C=C,
gridsize=(50, 15),
mincnt=3,
reduce_C_function=np.median,
vmin=0.1,
vmax=0.7,
cmap='RdBu_r',
edgecolor='black',
linewidth=0.5)
cb = fig.colorbar(im,ax=ax)
cb.set_label(r'$R_{NIR/UV}$')
cb.set_ticks(np.linspace(0.1, 0.7, 5))
cb.set_ticklabels(np.linspace(0.1, 0.5, 5))
ax.set_xlabel(r'C\,{\sc iv} Blueshift [km~$\rm{s}^{-1}$]')
ax.set_ylabel(r'Log C\,{\sc iv} EQW [\AA]')
plt.xlim(-1000,4000)
plt.ylim(1, 2.2)
plt.tick_params(axis='both',which='major')
plt.tight_layout()
fig.savefig('/home/lc585/thesis/figures/chapter05/hot_dust_beta.pdf')
plt.show()
return None
def kishimoto_model():
set_plot_properties() # change style
fig, axs = plt.subplots(2, 2, figsize=figsize(0.9, 1.0))
plt.subplots_adjust(hspace=0.0, wspace=0.0)
for ax in axs.flatten():
ax.axes.get_xaxis().set_ticks([])
ax.axes.get_yaxis().set_ticks([])
axs[0, 0].set_title('Strong outflows', fontsize=10)
axs[0, 1].set_title('Weak outflows', fontsize=10)
axs[0, 0].set_ylabel('Face-on')
axs[1, 0].set_ylabel('Edge-on')
fig.savefig('../../figures/chapter05/kishimoto.jpg', dpi=1000)
plt.show()
def plot():
set_plot_properties() # change style
cs = palettable.colorbrewer.qualitative.Set1_8.mpl_colors
with open('/home/lc585/qsosed/input.yml', 'r') as f:
parfile = yaml.load(f)
fittingobj = load(parfile)
wavlen = fittingobj.get_wavlen()
lin = fittingobj.get_lin()
galspc = fittingobj.get_galspc()
ext = fittingobj.get_ext()
galcnt = fittingobj.get_galcnt()
ignmin = fittingobj.get_ignmin()
ignmax = fittingobj.get_ignmax()
ztran = fittingobj.get_ztran()
lyatmp = fittingobj.get_lyatmp()
lybtmp = fittingobj.get_lybtmp()
lyctmp = fittingobj.get_lyctmp()
whmin = fittingobj.get_whmin()
whmax = fittingobj.get_whmax()
qsomag = fittingobj.get_qsomag()
flxcorr = fittingobj.get_flxcorr()
cosmo = fittingobj.get_cosmo()
params = Parameters()
params.add('plslp1', value = -0.478)
params.add('plslp2', value = -0.199)
params.add('plbrk', value = 2.40250)
params.add('bbt', value = 1.30626)
params.add('bbflxnrm', value = 2.673)
params.add('elscal', value = 1.240)
params.add('scahal',value = 0.713)
params.add('galfra',value = 0.0)
params.add('bcnrm',value = 0.135)
params.add('ebv',value = 0.0)
params.add('imod',value = 18.0)
# Load median magnitudes
with open('/home/lc585/qsosed/sdss_ukidss_wise_medmag_ext.dat') as f:
datz = np.loadtxt(f, usecols=(0,))
datz = datz[:-5]
# Load filters
ftrlst = fittingobj.get_ftrlst()[2:-2]
lameff = fittingobj.get_lameff()[2:-2]
bp = fittingobj.get_bp()[2:-2] # these are in ab and data is in vega
dlam = fittingobj.get_bp()[2:-2]
zromag = fittingobj.get_zromag()[2:-2]
with open('ftgd_dr7.dat') as f:
ftgd = np.loadtxt(f, skiprows=1, usecols=(1,2,3,4,5,6,7,8,9))
modarr = residual(params,
parfile,
wavlen,
datz,
lin,
bp,
dlam,
zromag,
galspc,
ext,
galcnt,
ignmin,
ignmax,
ztran,
lyatmp,
lybtmp,
lyctmp,
ftrlst,
whmin,
whmax,
cosmo,
flxcorr,
qsomag,
ftgd)
fname = '/home/lc585/qsosed/sdss_ukidss_wise_medmag_ext.dat'
datarr = np.genfromtxt(fname, usecols=(5,7,9,11,13,15,17,19,21))
datarr[datarr < 0.0] = np.nan
datarr = datarr[:-5, :]
# remove less than lyman break
col1 = np.arange(8)
col2 = col1 + 1
col_label = ['$r$ - $i$',
'$i$ - $z$',
'$z$ - $Y$',
'$Y$ - $J$',
'$J$ - $H$',
'$H$ - $K$',
'$K$ - $W1$',
'$W1$ - $W2$']
df = get_data()
df = df[(df.z_HW > 1) & (df.z_HW < 3)]
colstr1 = ['rVEGA',
'iVEGA',
'zVEGA',
'YVEGA',
'JVEGA',
'HVEGA',
'KVEGA',
'W1VEGA']
colstr2 = ['iVEGA',
'zVEGA',
'YVEGA',
'JVEGA',
'HVEGA',
'KVEGA',
'W1VEGA',
'W2VEGA']
ylims = [[0, 0.6],
[-0.1, 0.5],
[-0.1, 0.5],
[-0.1, 0.5],
[0.2, 0.9],
[0.2, 0.9],
[0.5, 1.6],
[0.8, 1.5]]
fig, axs = plt.subplots(4, 2, figsize=figsize(1, vscale=2), sharex=True)
for i, ax in enumerate(axs.flatten()):
#data definition
ydat = datarr[:, col1[i]] - datarr[:, col2[i]]
ax.scatter(datz,
ydat,
color='black',
s=5,
label='Data')
ax.plot(datz,
modarr[:,col1[i]] - modarr[:, col2[i]],
color=cs[1],
label='Model')
# ax.scatter(df.z_HW, df[colstr1[i]] - df[colstr2[i]], s=1, alpha=0.1)
ax.set_title(col_label[i], size=10)
ax.set_ylim(ylims[i])
ax.set_xlim(0.75, 3.25)
axs[0, 0].legend(bbox_to_anchor=(0.7, 0.99),
bbox_transform=plt.gcf().transFigure,
fancybox=True,
shadow=True,
scatterpoints=1,
ncol=2)
axs[3, 0].set_xlabel(r'Redshift $z$')
axs[3, 1].set_xlabel(r'Redshift $z$')
fig.tight_layout()
fig.subplots_adjust(wspace=0.2, hspace=0.15, top=0.93)
fig.savefig('/home/lc585/thesis/figures/chapter05/sed_color_plot.pdf')
plt.show()
return None
def plot():
from PlottingTools.plot_setup_thesis import figsize, set_plot_properties
from astropy.table import Table
import matplotlib.pyplot as plt
from scipy import histogram2d
import numpy as np
from matplotlib import cm
from PlottingTools.truncate_colormap import truncate_colormap
import brewer2mpl
from PlottingTools import running
import collections
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/150309/sample3/out_add.fits')
# tab = tab[ tab['Z'] > 0.5 ]
tab = tab[ tab['BBT_STDERR'] < 1000.]
tab = tab[ tab['BBFLXNRM_STDERR'] < 1.]
tab = tab[ tab['CHI2_RED'] < 2.0]
fig, ax = plt.subplots(figsize=figsize(1, vscale=0.9))
#histogram definition
xyrange = [[1,4],[0,3]] # data range
bins = [120,80] # number of bins
thresh = 3 #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 = ax.imshow(np.flipud(hh.T),
cmap=mycm,
extent=np.array(xyrange).flatten(),
interpolation='none',
aspect='auto',
)
ax.scatter(xdat1, ydat1,color=cset[-1],s=8)
#axcb = fig.add_axes([0.13,0.01,0.6,0.02])
clb = fig.colorbar(im,orientation='horizontal')
clb.set_label('Number of Objects')
ax.set_xlabel(r'Redshift $z$')
ax.set_ylabel(r'$R_{\mathrm{NIR/UV}}$')
ax.set_ylim(0,1.5)
ax.set_xlim(1.5,3.2)
tabtmp = tab
tabtmp.sort('Z')
xdat = running.RunningMedian(np.array(tabtmp['Z']),101)
ydat = running.RunningMedian(np.array(tabtmp['RATIO_IR_UV']),101)
ax.plot(xdat[::100],ydat[::100],color='black',linewidth=2.0)
plt.tick_params(axis='both',which='major')
# Generate magnitude file in model.py
# Fit in runsingleobjfit.py (comment out load dat section)
modtab = Table.read('/data/lc585/QSOSED/Results/150107/sample2/out_lumcalc.fits')
ax.plot(modtab['Z'],modtab['RATIO_IR_UV'],color='black',linestyle='--', linewidth=2.0)
ax.grid(which='major',c='k')
#histogram definition
x_range = [0.5,3.1] # data range
bins = [13] # number of bins
dat = np.vstack([tab['Z'],tab['LUM_IR_SIGMA']*tab['RATIO_IR_UV']])
# histogram the data
h, locx = np.histogram(dat[0,:], range=x_range, bins=bins[0])
posx = np.digitize(dat[0,:], locx)
xindices = collections.defaultdict(set)
for index, bin in enumerate(posx):
xindices[bin].add(index)
ys = []
for i in range(bins[0]):
ys.append(np.mean(dat[1,list(xindices[i+1])]))
ax.errorbar(locx[:-1] + 0.1,np.repeat(1,13),yerr=ys,linestyle='',color='black')
ax.axvline(2.0, color='black')
ax.axvline(2.7, color='black')
fig.tight_layout()
fig.savefig('/home/lc585/thesis/figures/chapter06/ratio_z_errors_highz.pdf')
plt.show()
return None
def shang_sed():
import matplotlib.pyplot as plt
import numpy as np
from scipy.constants import c
from PlottingTools.plot_setup_thesis import figsize, set_plot_properties
set_plot_properties() # change style
rltab = np.genfromtxt('rlmsedMR.txt')
fig, ax = plt.subplots(figsize=figsize(1.0, vscale=0.8))
ax.plot(1.0e10 * c / (10**rltab[:, 0]),
10**rltab[:, 1],
color='black',
lw=1)
ax.set_xlabel(r'Wavelength [\AA]')
ax.set_ylabel(r'${\lambda}f_{\lambda}$ [Arbitrary Units]')
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_ylim(1e-2, 10)
ax.set_xlim(10, 10**8)
ax.text(0.22,
0.3,
'Accretion \n disc',
transform=ax.transAxes,
multialignment='center')
ax.text(0.52,
0.8,
'Torus',
transform=ax.transAxes,
multialignment='center')
ax.text(0.1,
0.85,
'BLR \& \n NLR',
transform=ax.transAxes,
multialignment='center')
ax.arrow(0.30,
0.4,
0.0,
0.2,
color='black',
transform=ax.transAxes,
head_width=0.015)
ax.arrow(0.22,
0.88,
0.04,
0.0,
color='black',
transform=ax.transAxes,
head_width=0.015)
ax.arrow(0.57,
0.78,
0.0,
-0.06,
color='black',
transform=ax.transAxes,
head_width=0.015)
plt.tight_layout()
plt.savefig('../../figures/chapter01/shangsed.pdf')
plt.show()
return None
def plot():
import yaml
from qsosed.load import load
import numpy as np
from qsosed.sedmodel import model
import matplotlib.pyplot as plt
from qsosed.pl import pl
from qsosed.bb import bb
import cosmolopy.distance as cd
from PlottingTools.plot_setup_thesis import figsize, set_plot_properties
import palettable
from lmfit import Parameters
from qsosed.qsrmod import qsrmod
from qsosed.flx2mag import flx2mag
set_plot_properties() # change style
cs = palettable.colorbrewer.qualitative.Set1_9.mpl_colors
plslp1 = 0.508
plslp2 = 0.068
plbrk = 2944.99
bbt = 1174.0
bbflxnrm = 0.208
galfra = 0.313
elscal = 0.624
imod = 18.0
ebv = 0.0
redshift = 2.0
scahal = 0.8
with open('/home/lc585/qsosed/input.yml', 'r') as f:
parfile = yaml.load(f)
fittingobj = load(parfile)
wavlen = fittingobj.get_wavlen()
flxcorr = np.array([1.0] * len(wavlen))
params = Parameters()
params.add('plslp1', value=-0.478)
params.add('plslp2', value=-0.199)
params.add('plbrk', value=2.40250)
params.add('bbt', value=1.30626)
params.add('bbflxnrm', value=2.673)
params.add('elscal', value=1.240)
params.add('scahal', value=0.713)
params.add('galfra', value=0.0)
params.add('bcnrm', value=0.135)
params.add('ebv', value=0.0)
params.add('imod', value=18.0)
lin = fittingobj.get_lin()
galspc = fittingobj.get_galspc()
ext = fittingobj.get_ext()
galcnt = fittingobj.get_galcnt()
ignmin = fittingobj.get_ignmin()
ignmax = fittingobj.get_ignmax()
bp = fittingobj.get_bp()
dlam = fittingobj.get_dlam()
zromag = fittingobj.get_zromag()
ftrlst = fittingobj.get_ftrlst()
ztran = fittingobj.get_ztran()
lyatmp = fittingobj.get_lyatmp()
lybtmp = fittingobj.get_lybtmp()
lyctmp = fittingobj.get_lyctmp()
whmin = fittingobj.get_whmin()
whmax = fittingobj.get_whmax()
qsomag = fittingobj.get_qsomag()
cosmo = fittingobj.get_cosmo()
nftr = len(bp)
redshift = 0.5
wavlentmp, fluxtmp = qsrmod(params, parfile, wavlen, redshift, lin, galspc,
ext, galcnt, ignmin, ignmax, ztran, lyatmp,
lybtmp, lyctmp, whmin, whmax, cosmo, flxcorr,
qsomag)
magtmp = flx2mag(params, wavlentmp, fluxtmp, bp, dlam, zromag, ftrlst)
fig = plt.figure(figsize=figsize(1, vscale=0.8))
ax1 = fig.add_subplot(111)
ax1.loglog(wavlentmp, 0.5 * fluxtmp, color='black', label=r'$z=0.5$')
redshift = 2.0
wavlentmp, fluxtmp = qsrmod(params, parfile, wavlen, redshift, lin, galspc,
ext, galcnt, ignmin, ignmax, ztran, lyatmp,
lybtmp, lyctmp, whmin, whmax, cosmo, flxcorr,
qsomag)
magtmp = flx2mag(params, wavlentmp, fluxtmp, bp, dlam, zromag, ftrlst)
ax1.loglog(wavlentmp, 5 * fluxtmp, color='black', label=r'$z=2.0$')
redshift = 3.5
wavlentmp, fluxtmp = qsrmod(params, parfile, wavlen, redshift, lin, galspc,
ext, galcnt, ignmin, ignmax, ztran, lyatmp,
lybtmp, lyctmp, whmin, whmax, cosmo, flxcorr,
qsomag)
magtmp = flx2mag(params, wavlentmp, fluxtmp, bp, dlam, zromag, ftrlst)
ax1.loglog(wavlentmp, 50 * fluxtmp, color='black', label=r'$z=3.5$')
ax1.set_xlabel(r'log $\lambda$ [${\rm \AA}$]')
ax1.set_ylabel(r'log $F_{\lambda}$ [Arbitary Units]')
# ax1.loglog(wavlentmp,1.e10/wavlentmp**2,linestyle='--')
ax2 = ax1.twinx()
ax2.set_yticks([])
labs = ['u', 'g', 'r', 'i', 'z', 'Y', 'J', 'H', 'K', 'W1', 'W2', 'W3']
xpos = fittingobj.get_lameff()
xpos[:5] = xpos[:5] - 200.0
xpos[5] = 10405
xpos[6] = 12505
xpos[7] = 16411
xpos[8] = 21942
xpos[9] = 33500
xpos[10] = 46027
xpos[11] = 112684
ax2.text(19225, 3.923, r'$z=3.5$', ha='right')
ax2.text(11674, 3.099, r'$z=2.0$', ha='right')
ax2.text(6000, 2.135, r'$z=0.5$', ha='right')
color_idx = np.linspace(0, 1, 12)
from palettable.colorbrewer.diverging import Spectral_11_r
for i in range(len(bp[:-1])):
wavtmp = (bp[i][0, :])
flxtmp = bp[i][1, :] / np.max(bp[i][1, :])
ax2.plot(wavtmp,
flxtmp,
color=Spectral_11_r.mpl_colormap(color_idx[i]))
# ax2.fill_between(wavtmp, flxtmp, alpha=0.2, facecolor=Spectral_11.mpl_colormap(color_idx[i]))
ax2.text(xpos[i], 0.2, r'${}$'.format(labs[i]), ha='center')
ax2.set_ylim(0, 5)
ax1.set_ylim(1e-3, 200)
ax1.set_xlim(2800, 190000)
ax2.set_xlim(ax1.get_xlim())
plt.tight_layout()
fig.savefig('/home/lc585/thesis/figures/chapter05/throughput.pdf')
plt.show()
return None
def normalise_to_sdss():
import sys
sys.path.insert(0, '/home/lc585/Dropbox/IoA/nirspec/python_code')
from get_nir_spec import get_nir_spec
from get_sdss_spec import get_sdss_spec
sys.path.insert(0, '/home/lc585/Dropbox/IoA/QSOSED/Model/qsofit')
from qsrmod import qsrmod
from load import load
import cosmolopy.distance as cd
from get_mono_lum import resid_mag_fit
fig, axs = plt.subplots(2, 1, figsize=figsize(1, 1.4))
cs = palettable.colorbrewer.qualitative.Set1_8.mpl_colors
cs_light = palettable.colorbrewer.qualitative.Pastel1_6.mpl_colors
df = pd.read_csv('/home/lc585/Dropbox/IoA/nirspec/tables/masterlist_liam.csv', index_col=0)
row = df.ix['QSO125']
wav_nir, dw_nir, flux_nir, err_nir = get_nir_spec(row.NIR_PATH, row.INSTR)
wav_nir = wav_nir / (1.0 + row.z_IR)
axs[0].plot(wav_nir, flux_nir*1e15, color=cs_light[0], label='Near-IR')
if (row.SPEC_OPT == 'SDSS') | (row.SPEC_OPT == 'BOSS+SDSS'):
wav_opt, dw_opt, flux_opt, err_opt = get_sdss_spec('SDSS', row.DR7_PATH)
elif (row.SPEC_OPT == 'BOSS') :
wav_opt, dw_opt, flux_opt, err_opt = get_sdss_spec('BOSS', row.DR12_PATH)
wav_opt = wav_opt / (1.0 + row.z_IR)
axs[0].plot(wav_opt, flux_opt*1e15, color=cs_light[1], label='SDSS')
# Normalise SED model to SDSS spectra ----------------------------------------------------
"""
SDSS spectra in Shen & Liu emission line free windows
"""
fit_region = [[1350,1360], [1445,1465], [1700,1705], [2155,2400], [2480,2675], [2925,3500], [4200,4230], [4435,4700], [5100,5535], [6000,6250], [6800,7000]]
fit_mask = np.zeros(len(wav_opt), dtype=bool)
for r in fit_region:
fit_mask[(wav_opt > r[0]) & (wav_opt < r[1])] = True
tmp = ma.array(flux_opt)
tmp[~fit_mask] = ma.masked
for item in ma.extras.flatnotmasked_contiguous(tmp):
axs[0].plot(wav_opt[item], flux_opt[item]*1e15, color=cs[1])
# ax.plot(wav_opt[fit_mask], flux_opt[fit_mask], color=cs[0])
plslp1 = 0.46
plslp2 = 0.03
plbrk = 2822.0
bbt = 1216.0
bbflxnrm = 0.24
elscal = 0.71
scahal = 0.86
galfra = 0.31
ebv = 0.0
imod = 18.0
with open('/home/lc585/Dropbox/IoA/QSOSED/Model/qsofit/input.yml', 'r') as f:
parfile = yaml.load(f)
fittingobj = load(parfile)
lin = fittingobj.get_lin()
galspc = fittingobj.get_galspc()
ext = fittingobj.get_ext()
galcnt = fittingobj.get_galcnt()
ignmin = fittingobj.get_ignmin()
ignmax = fittingobj.get_ignmax()
wavlen_rest = fittingobj.get_wavlen()
ztran = fittingobj.get_ztran()
lyatmp = fittingobj.get_lyatmp()
lybtmp = fittingobj.get_lybtmp()
lyctmp = fittingobj.get_lyctmp()
whmin = fittingobj.get_whmin()
whmax = fittingobj.get_whmax()
cosmo = {'omega_M_0':0.3, 'omega_lambda_0':0.7, 'h':0.7}
cosmo = cd.set_omega_k_0(cosmo)
flxcorr = np.array( [1.0] * len(wavlen_rest) )
params = Parameters()
params.add('plslp1', value = plslp1, vary=False)
params.add('plslp2', value = plslp2, vary=False)
params.add('plbrk', value = plbrk, vary=False)
params.add('bbt', value = bbt, vary=False)
params.add('bbflxnrm', value = bbflxnrm, vary=False)
params.add('elscal', value = elscal, vary=False)
params.add('scahal', value = scahal, vary=False)
params.add('galfra', value = galfra, vary=False)
params.add('ebv', value = ebv, vary=True)
params.add('imod', value = imod, vary=False)
params.add('norm', value = 1e-17, vary=True)
def resid(params,
wav_opt,
flux_opt):
wav_sed, flux_sed = qsrmod(params,
parfile,
wavlen_rest,
row.z_IR,
lin,
galspc,
ext,
galcnt,
ignmin,
ignmax,
ztran,
lyatmp,
lybtmp,
lyctmp,
whmin,
whmax,
cosmo,
flxcorr)
wav_sed = wav_sed / (1.0 + row.z_IR)
spc = interp1d(wav_sed, flux_sed, bounds_error=True, fill_value=0.0)
flux_sed_fit = spc(wav_opt)
return flux_opt - params['norm'].value * flux_sed_fit
resid_p = partial(resid,
wav_opt = wav_opt[fit_mask],
flux_opt = flux_opt[fit_mask])
result = minimize(resid_p, params, method='leastsq')
# ---------------------------------------------------------------------------------------
xs = np.arange(np.nanmin(wav_opt), np.nanmax(wav_nir), 1)
wav_sed, flux_sed = qsrmod(result.params,
parfile,
wavlen_rest,
row.z_IR,
lin,
galspc,
ext,
galcnt,
ignmin,
ignmax,
ztran,
lyatmp,
lybtmp,
lyctmp,
whmin,
whmax,
cosmo,
flxcorr)
wav_sed = wav_sed / (1.0 + row.z_IR)
spc = interp1d(wav_sed, flux_sed * result.params['norm'].value, bounds_error=True, fill_value=0.0)
# do error weighted fit of spectra to SED model
# Hewett et al. 1985
# mask out regions between bandpasses
wav_nir_obs = wav_nir * (1.0 + row.z_IR)
goodinds = ((wav_nir_obs > 11800.0) & (wav_nir_obs < 13100.0))\
| ((wav_nir_obs > 15000.0) & (wav_nir_obs < 17500.0))\
| ((wav_nir_obs > 19500.0) & (wav_nir_obs < 23500.0))
wav_nir = wav_nir[goodinds]
flux_nir = flux_nir[goodinds]
err_nir = err_nir[goodinds]
goodinds = err_nir > 0.0
wav_nir = wav_nir[goodinds]
flux_nir = flux_nir[goodinds]
err_nir = err_nir[goodinds]
k = np.nansum((flux_nir * spc(wav_nir)) / err_nir**2) / np.nansum((spc(wav_nir) / err_nir)**2)
inds = np.argsort(np.diff(wav_nir))[-2:]
wav_nir[inds] = np.nan
flux_nir[inds] = np.nan
axs[0].plot(wav_nir, flux_nir*1e15 / k, color=cs[0], alpha=1.0)
axs[0].plot(xs, spc(xs)*1e15, color='black', lw=1, label='Model')
axs[0].legend(loc='upper right')
axs[0].set_xlim(1300,7300)
axs[0].set_ylim(0, 1)
# axs[0].set_xlabel(r'Rest-frame wavelength [${\mathrm \AA}$]')
axs[0].set_ylabel(r'F$_{\lambda}$ [Arbitary units]')
# --------------------------------------------------------------------------------
df = pd.read_csv('/home/lc585/Dropbox/IoA/nirspec/tables/masterlist_liam.csv', index_col=0)
row = df.ix['QSO010']
wav_nir, dw_nir, flux_nir, err_nir = get_nir_spec(row.NIR_PATH, row.INSTR)
wav_nir = wav_nir / (1.0 + row.z_IR)
ftrlst, maglst, errlst, lameff = [], [], [], []
# 1250 condition so we don't go near the lyman break
if (~np.isnan(row.psfMag_u)) & ((3546.0 / (1.0 + row.z_IR)) > 1250.0) & (~np.isnan(row.psfMagErr_u)):
ftrlst.append('u.response')
maglst.append(row.psfMag_u - 0.91)
errlst.append(row.psfMagErr_u)
lameff.append(3546.0)
if (~np.isnan(row.psfMag_g)) & ((4670.0 / (1.0 + row.z_IR)) > 1250.0) & (~np.isnan(row.psfMagErr_g)):
ftrlst.append('g.response')
maglst.append(row.psfMag_g + 0.08)
errlst.append(row.psfMagErr_g)
lameff.append(4670.0)
if (~np.isnan(row.psfMag_r)) & ((6156.0 / (1.0 + row.z_IR)) > 1250.0) & (~np.isnan(row.psfMagErr_r)):
ftrlst.append('r.response')
maglst.append(row.psfMag_r - 0.16)
errlst.append(row.psfMagErr_r)
lameff.append(6156.0)
if (~np.isnan(row.psfMag_i)) & ((7471.0 / (1.0 + row.z_IR)) > 1250.0) & (~np.isnan(row.psfMagErr_i)):
ftrlst.append('i.response')
maglst.append(row.psfMag_i - 0.37)
errlst.append(row.psfMagErr_i)
lameff.append(7471.0)
if (~np.isnan(row.psfMag_z)) & ((8918.0 / (1.0 + row.z_IR)) > 1250.0) & (~np.isnan(row.psfMagErr_z)):
ftrlst.append('z.response')
maglst.append(row.psfMag_z - 0.54)
errlst.append(row.psfMagErr_z)
lameff.append(8918.0)
if (~np.isnan(row.VHS_YAperMag3)) & (~np.isnan(row.VHS_YAperMag3Err)):
ftrlst.append('VISTA_Filters_at80K_forETC_Y2.txt')
maglst.append(row.VHS_YAperMag3)
errlst.append(row.VHS_YAperMag3Err)
lameff.append(10210.0)
elif (~np.isnan(row.Viking_YAperMag3)) & (~np.isnan(row.Viking_YAperMag3Err)):
ftrlst.append('VISTA_Filters_at80K_forETC_Y2.txt')
maglst.append(row.Viking_YAperMag3)
errlst.append(row.Viking_YAperMag3Err)
lameff.append(10210.0)
elif (~np.isnan(row.UKIDSS_YAperMag3)) & (~np.isnan(row.UKIDSS_YAperMag3Err)):
ftrlst.append('Y.response')
maglst.append(row.UKIDSS_YAperMag3)
errlst.append(row.UKIDSS_YAperMag3Err)
lameff.append(10305.0)
if (~np.isnan(row.VHS_JAperMag3)) & (~np.isnan(row.VHS_JAperMag3Err)):
ftrlst.append('VISTA_Filters_at80K_forETC_J2.txt')
maglst.append(row.VHS_JAperMag3)
errlst.append(row.VHS_JAperMag3Err)
lameff.append(12540.0)
elif (~np.isnan(row.Viking_JAperMag3)) & (~np.isnan(row.Viking_JAperMag3Err)):
ftrlst.append('VISTA_Filters_at80K_forETC_J2.txt')
maglst.append(row.Viking_JAperMag3)
errlst.append(row.Viking_JAperMag3Err)
lameff.append(12540.0)
elif (~np.isnan(row.UKIDSS_J_1AperMag3)) & (~np.isnan(row.UKIDSS_J_1AperMag3Err)):
ftrlst.append('J.response')
maglst.append(row.UKIDSS_J_1AperMag3)
errlst.append(row.UKIDSS_J_1AperMag3Err)
lameff.append(12483.0)
elif (~np.isnan(row['2massMag_j'])) & (~np.isnan(row['2massMagErr_j'])):
ftrlst.append('J2MASS.response')
maglst.append(row['2massMag_j'])
errlst.append(row['2massMagErr_j'])
lameff.append(12350.0)
if (~np.isnan(row.VHS_HAperMag3)) & (~np.isnan(row.VHS_HAperMag3Err)):
ftrlst.append('VISTA_Filters_at80K_forETC_H2.txt')
maglst.append(row.VHS_HAperMag3)
errlst.append(row.VHS_HAperMag3Err)
lameff.append(16460.0)
elif (~np.isnan(row.Viking_HAperMag3)) & (~np.isnan(row.Viking_HAperMag3Err)):
ftrlst.append('VISTA_Filters_at80K_forETC_H2.txt')
maglst.append(row.Viking_HAperMag3)
errlst.append(row.Viking_HAperMag3Err)
lameff.append(16460.0)
elif (~np.isnan(row.UKIDSS_HAperMag3)) & (~np.isnan(row.UKIDSS_HAperMag3Err)):
ftrlst.append('H.response')
maglst.append(row.UKIDSS_HAperMag3)
errlst.append(row.UKIDSS_HAperMag3Err)
lameff.append(16313.0)
elif (~np.isnan(row['2massMag_h'])) & (~np.isnan(row['2massMagErr_h'])):
ftrlst.append('H2MASS.response')
maglst.append(row['2massMag_h'])
errlst.append(row['2massMagErr_h'])
lameff.append(16620.0)
if (~np.isnan(row.VHS_KAperMag3)) & (~np.isnan(row.VHS_KAperMag3Err)):
ftrlst.append('VISTA_Filters_at80K_forETC_Ks2.txt')
maglst.append(row.VHS_KAperMag3)
errlst.append(row.VHS_KAperMag3Err)
lameff.append(21490.0)
elif (~np.isnan(row.Viking_KsAperMag3)) & (~np.isnan(row.Viking_KsAperMag3Err)):
ftrlst.append('VISTA_Filters_at80K_forETC_Ks2.txt')
maglst.append(row.Viking_KsAperMag3)
errlst.append(row.Viking_KsAperMag3Err)
lameff.append(21490.0)
elif (~np.isnan(row.UKIDSS_KAperMag3)) & (~np.isnan(row.UKIDSS_KAperMag3Err)):
ftrlst.append('K.response')
maglst.append(row.UKIDSS_KAperMag3)
errlst.append(row.UKIDSS_KAperMag3Err)
lameff.append(22010.0)
elif (~np.isnan(row['2massMag_k'])) & (~np.isnan(row['2massMagErr_k'])):
ftrlst.append('K2MASS.response')
maglst.append(row['2massMag_k'])
errlst.append(row['2massMagErr_k'])
lameff.append(21590.0)
ftrlst, maglst, errlst, lameff = np.array(ftrlst), np.array(maglst), np.array(errlst), np.array(lameff)
#-------Filters---------------------------------------------
nftr = len(ftrlst)
bp = np.empty(nftr,dtype='object')
dlam = np.zeros(nftr)
for nf in range(nftr):
with open(os.path.join('/home/lc585/Dropbox/IoA/QSOSED/Model/Filter_Response/', ftrlst[nf]), 'r') as f:
wavtmp, rsptmp = np.loadtxt(f,unpack=True)
dlam[nf] = (wavtmp[1] - wavtmp[0])
bptmp = np.ndarray(shape=(2,len(wavtmp)), dtype=float)
bptmp[0,:], bptmp[1,:] = wavtmp, rsptmp
bp[nf] = bptmp
#--------------------------------------------------------------------------------
f_0 = np.zeros(nftr) # flux zero points
fvega = '/data/vault/phewett/vista_work/vega_2007.lis'
vspec = np.loadtxt(fvega)
vf = interp1d(vspec[:,0], vspec[:,1])
for nf in range(nftr):
sum1 = np.sum( bp[nf][1] * vf(bp[nf][0]) * bp[nf][0] * dlam[nf])
sum2 = np.sum( bp[nf][1] * bp[nf][0] * dlam[nf])
f_0[nf] = sum1 / sum2
flxlst = f_0 * 10.0**(-0.4 * maglst) # data fluxes in erg/cm^2/s/A
flxerrlst = flxlst * (-0.4) * np.log(10) * errlst
axs[1].scatter(lameff / (1.0 + row.z_IR), flxlst*1e16, s=50, facecolor=cs[5], edgecolor='black', zorder=10, label='Photometry')
plslp1 = 0.46
plslp2 = 0.03
plbrk = 2822.0
bbt = 1216.0
bbflxnrm = 0.24
elscal = 0.71
scahal = 0.86
galfra = 0.31
ebv = 0.0
imod = 18.0
with open('/home/lc585/Dropbox/IoA/QSOSED/Model/qsofit/input.yml', 'r') as f:
parfile = yaml.load(f)
fittingobj = load(parfile)
lin = fittingobj.get_lin()
galspc = fittingobj.get_galspc()
ext = fittingobj.get_ext()
galcnt = fittingobj.get_galcnt()
ignmin = fittingobj.get_ignmin()
ignmax = fittingobj.get_ignmax()
wavlen_rest = fittingobj.get_wavlen()
ztran = fittingobj.get_ztran()
lyatmp = fittingobj.get_lyatmp()
lybtmp = fittingobj.get_lybtmp()
lyctmp = fittingobj.get_lyctmp()
whmin = fittingobj.get_whmin()
whmax = fittingobj.get_whmax()
cosmo = {'omega_M_0':0.3, 'omega_lambda_0':0.7, 'h':0.7}
cosmo = cd.set_omega_k_0(cosmo)
flxcorr = np.array( [1.0] * len(wavlen_rest) )
params = Parameters()
params.add('plslp1', value = plslp1, vary=False)
params.add('plslp2', value = plslp2, vary=False)
params.add('plbrk', value = plbrk, vary=False)
params.add('bbt', value = bbt, vary=False)
params.add('bbflxnrm', value = bbflxnrm, vary=False)
params.add('elscal', value = elscal, vary=False)
params.add('scahal', value = scahal, vary=False)
params.add('galfra', value = galfra, vary=False)
params.add('ebv', value = ebv, vary=True)
params.add('imod', value = imod, vary=False)
params.add('norm', value = 1e-17, vary=True)
resid_p = partial(resid_mag_fit,
flx=flxlst,
err=flxerrlst,
parfile=parfile,
wavlen_rest=wavlen_rest,
z=row.z_IR,
lin=lin,
galspc=galspc,
ext=ext,
galcnt=galcnt,
ignmin=ignmin,
ignmax=ignmax,
ztran=ztran,
lyatmp=lyatmp,
lybtmp=lybtmp,
lyctmp=lyctmp,
whmin=whmin,
whmax=whmax,
cosmo=cosmo,
flxcorr=flxcorr,
bp=bp,
dlam=dlam)
result = minimize(resid_p, params, method='leastsq')
# ---------------------------------------------------------------------------------------
wav_sed, flux_sed = qsrmod(result.params,
parfile,
wavlen_rest,
row.z_IR,
lin,
galspc,
ext,
galcnt,
ignmin,
ignmax,
ztran,
lyatmp,
lybtmp,
lyctmp,
whmin,
whmax,
cosmo,
flxcorr)
spc = interp1d(wavlen_rest, flux_sed * result.params['norm'].value, bounds_error=True, fill_value=0.0)
xs = np.arange(1000, 10000, 10)
axs[1].plot(xs, spc(xs)*1e16, color='black', lw=1, label='Model', zorder=2)
# do error weighted fit of spectra to SED model
# Hewett et al. 1985
# mask out regions between bandpasses
wav_nir_obs = wav_nir * (1.0 + row.z_IR)
goodinds = ((wav_nir_obs > 11800.0) & (wav_nir_obs < 13100.0))\
| ((wav_nir_obs > 15000.0) & (wav_nir_obs < 17500.0))\
| ((wav_nir_obs > 19500.0) & (wav_nir_obs < 23500.0))
wav_nir = wav_nir[goodinds]
flux_nir = flux_nir[goodinds]
err_nir = err_nir[goodinds]
goodinds = err_nir > 0.0
wav_nir = wav_nir[goodinds]
flux_nir = flux_nir[goodinds]
err_nir = err_nir[goodinds]
k = np.nansum((flux_nir * spc(wav_nir)) / err_nir**2) / np.nansum((spc(wav_nir) / err_nir)**2)
inds = np.argsort(np.diff(wav_nir))[-2:]
wav_nir[inds] = np.nan
flux_nir[inds] = np.nan
axs[1].plot(wav_nir, flux_nir*1e16 / k, color=cs[0], zorder=1)
# modified this so need to reload
wav_nir, dw_nir, flux_nir, err_nir = get_nir_spec(row.NIR_PATH, row.INSTR)
wav_nir = wav_nir / (1.0 + row.z_IR)
axs[1].plot(wav_nir[wav_nir > 2800.0], flux_nir[wav_nir > 2800.0]*1e16 / k, color=cs_light[0], label='Near-IR', zorder=0)
axs[1].set_xlim(1250, 9000)
axs[1].set_ylim(0, 5)
axs[1].set_xlabel(r'Rest-frame wavelength [${\mathrm \AA}$]')
axs[1].set_ylabel(r'F$_{\lambda}$ [Arbitary units]')
# -------------------------------------------------
axs[1].legend(scatterpoints=1)
axs[0].text(0.1, 0.93, '(a) J092952+355450',
horizontalalignment='left',
verticalalignment='center',
transform = axs[0].transAxes)
axs[1].text(0.1, 0.93, '(b) J100247+002104',
horizontalalignment='left',
verticalalignment='center',
transform = axs[1].transAxes)
fig.tight_layout()
fig.savefig('/home/lc585/thesis/figures/chapter02/normalise_to_sdss.pdf')
plt.show()
return None
def luminosity_z():
cs = palettable.colorbrewer.qualitative.Set1_9.mpl_colors
# definitions for the axes
left, width = 0.13, 0.65
bottom, height = 0.13, 0.65
bottom_h = left_h = left + width + 0.0
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
# no labels
nullfmt = NullFormatter()
fig = plt.figure(figsize=figsize(1, vscale=1.0))
axScatter = plt.axes(rect_scatter)
axHistx = plt.axes(rect_histx)
axHisty = plt.axes(rect_histy)
# no labels
axHistx.xaxis.set_major_formatter(nullfmt)
axHistx.yaxis.set_major_formatter(nullfmt)
axHisty.xaxis.set_major_formatter(nullfmt)
axHisty.yaxis.set_major_formatter(nullfmt)
axHistx.set_xticks([])
axHistx.set_yticks([])
axHisty.set_xticks([])
axHisty.set_yticks([])
t = Table.read('/data/lc585/SDSS/dr7_bh_Nov19_2013.fits')
t = t[t['LOGLBOL'] > 0.0]
t = t[t['Z_HW'] > 1.0]
kde_contours(t['Z_HW'],
t['LOGLBOL'],
axScatter,
{'levels':[0.02, 0.05, 0.13, 0.2, 0.4, 0.8, 1.2]},
{'label':'SDSS DR7'},
color=cs[-1],
lims=(1.0, 5.0, 44.0, 48.0))
df = pd.read_csv('/home/lc585/Dropbox/IoA/nirspec/tables/masterlist_liam.csv', index_col=0)
df.drop_duplicates(subset=['ID'], inplace=True)
df = df[df.SPEC_NIR != 'None']
df.dropna(subset=['LogL5100'], inplace=True)
axScatter.scatter(df.z_IR,
np.log10(9.26) + df.LogL5100,
c=cs[1],
edgecolor='None',
label='This work',
zorder=10,
s=10)
axScatter.set_xlabel(r'Redshift $z$')
axScatter.set_ylabel(r'log $L_{\mathrm{Bol}}$ [erg/s]')
legend = axScatter.legend(frameon=True,
scatterpoints=1,
numpoints=1,
loc='lower right')
axHistx.hist(t['Z_HW'],
bins=np.arange(0.0, 5.0, 0.25),
facecolor=cs[-1],
edgecolor='None',
alpha=0.4,
normed=True)
axHisty.hist(t['LOGLBOL'],
bins=np.arange(44, 49, 0.25),
facecolor=cs[-1],
edgecolor='None',
orientation='horizontal',
alpha=0.4,
normed=True)
axHistx.hist(df.z_IR,
bins=np.arange(1, 5.0, 0.25),
histtype='step',
edgecolor=cs[1],
normed=True,
lw=2)
axHisty.hist(np.log10(9.26) + np.array(df.LogL5100),
bins=np.arange(44, 49, 0.25),
histtype='step',
edgecolor=cs[1],
normed=True,
lw=2,
orientation='horizontal')
axScatter.set_xlim(1.0, 4.5)
axScatter.set_ylim(44, 48.5)
axHistx.set_xlim(axScatter.get_xlim())
axHisty.set_ylim(axScatter.get_ylim())
axHistx.set_ylim(0, 1.5)
axHisty.set_xlim(0, 1.2)
fig.savefig('/home/lc585/thesis/figures/chapter02/luminosity_z.pdf')
plt.show()
return None
def plot():
redshift = 1
with open('/home/lc585/qsosed/input.yml', 'r') as f:
parfile = yaml.load(f)
# Load stuff
fittingobj = load(parfile)
lin = fittingobj.get_lin()
qsomag = fittingobj.get_qsomag()
whmin = fittingobj.get_whmin()
whmax = fittingobj.get_whmax()
ignmin = fittingobj.get_ignmin()
ignmax = fittingobj.get_ignmax()
galcnt = fittingobj.get_galcnt()
galspc = fittingobj.get_galspc()
wavlen = fittingobj.get_wavlen()
fig, ax = plt.subplots(figsize=figsize(1, vscale=0.9))
# ax.plot(wavlen,wavlen*fluxtmp,color='black')
# import lineid_plot
# line_wave = [1216,
# 1549,
# 1909,
# 2798,
# 4861,
# 6563,
# 18744]
# line_names = [r'Ly$\alpha$',
# r'C\,{\sc iv}',
# r'C\,{\sc iii}]',
# r'Mg\,{\sc ii}',
# r'H$\beta$',
# r'H$\alpha$',
# r'Pa$\alpha$']
# lineid_plot.plot_line_ids(wavlen, wavlen*fluxtmp, line_wave, line_names, ax=ax, arrow_tip=10000)
plslp1 = -0.478
plslp2 = -0.199
plbrk = 2402
bbt = 1306
bbflxnrm = 3.673
elscal = 1.240
scahal = 0.713
galfra = 0.4
bcnrm = 0.135
ebv = 0.0
flux = np.zeros(len(wavlen), dtype=np.float)
flxnrm = parfile['quasar']['flxnrm']
wavnrm = parfile['quasar']['wavnrm']
# Define normalisation constant to ensure continuity at wavbrk
const2 = flxnrm / (wavnrm**(-plslp2))
const1 = const2 * ((plbrk**(-plslp2)) / (plbrk**(-plslp1)))
wavnumbrk = wav2num(wavlen, plbrk)
flux[:wavnumbrk] = flux[:wavnumbrk] + pl(wavlen[:wavnumbrk], plslp1,
const1)
flux[wavnumbrk:] = flux[wavnumbrk:] + pl(wavlen[wavnumbrk:], plslp2,
const2)
# Now add steeper power-law component for sub-Lyman-alpha wavelengths
# Define normalisation constant to ensure continuity at wavbrk
plbrk_tmp = parfile['quasar']['pl_steep']['brk']
plslp_tmp = plslp1 + parfile['quasar']['pl_steep']['step']
plbrknum_tmp = wav2num(wavlen, plbrk_tmp)
const_tmp = flux[plbrknum_tmp] / (plbrk_tmp**-plslp_tmp)
flux[:plbrknum_tmp] = pl(wavlen[:plbrknum_tmp], plslp_tmp, const_tmp)
flux_pl = flux * 1.0
# Hot blackbody ---------------------------------------------------
bbwavnrm = parfile['quasar']['bb']['wavnrm']
flux = flux + bb(
wavlen * u.AA, bbt * u.K, bbflxnrm, bbwavnrm * u.AA, units='freq')
flux_bb = bb(wavlen * u.AA,
bbt * u.K,
bbflxnrm,
bbwavnrm * u.AA,
units='freq')
# Balmer continuum --------------------------------------------------
# Add Balmer Continuum and blur to simulate effect of bulk-velocity
# shifts comparable to those present in emission lines
# Determine height of power-law continuum at wavelength wbcnrm to allow
# correct scaling of Balmer continuum contribution
wbcnrm = parfile['quasar']['balmercont']['wbcnrm']
wbedge = parfile['quasar']['balmercont']['wbedge']
tbc = parfile['quasar']['balmercont']['tbc']
taube = parfile['quasar']['balmercont']['taube']
vfwhm = parfile['quasar']['balmercont']['vfwhm']
# mean wavelength increment
winc = np.diff(wavlen).mean()
cfact = flux[wav2num(wavlen, wbcnrm)]
flux = flux / cfact
flux_bc = bc(wavlen=wavlen * u.AA,
tbb=tbc * u.K,
fnorm=bcnrm,
taube=taube,
wavbe=wbedge * u.AA,
wnorm=wbcnrm * u.AA)
vsigma = vfwhm * (u.km / u.s) / 2.35
wsigma = wbedge * u.AA * vsigma / const.c
wsigma = wsigma.to(u.AA)
psigma = wsigma / (winc * u.AA)
# Performs a simple Gaussian smooth with dispersion psigma pixels
gauss = Gaussian1DKernel(stddev=psigma)
flux_bc = convolve(flux_bc, gauss)
flux = flux + flux_bc
#-----------------------------------------------------------------
# Now convert to flux per unit wavelength
# Presumably the emission line spectrum and galaxy spectrum
# are already in flux per unit wavelength.
# c / lambda^2 conversion
flux = flux * (u.erg / u.s / u.cm**2 / u.Hz)
flux = flux.to(u.erg / u.s / u.cm**2 / u.AA,
equivalencies=u.spectral_density(wavlen * u.AA))
scale = flux[wav2num(wavlen, wavnrm)]
flux = flxnrm * flux / scale
flux_pl = flux_pl * (u.erg / u.s / u.cm**2 / u.Hz)
flux_pl = flux_pl.to(u.erg / u.s / u.cm**2 / u.AA,
equivalencies=u.spectral_density(wavlen * u.AA))
flux_pl = flxnrm * flux_pl / scale
flux_bb = flux_bb * (u.erg / u.s / u.cm**2 / u.Hz)
flux_bb = flux_bb.to(u.erg / u.s / u.cm**2 / u.AA,
equivalencies=u.spectral_density(wavlen * u.AA))
flux_bb = flxnrm * flux_bb / scale
flux_bc = flux_bc * (u.erg / u.s / u.cm**2 / u.Hz)
flux_bc = flux_bc.to(u.erg / u.s / u.cm**2 / u.AA,
equivalencies=u.spectral_density(wavlen * u.AA))
flux_bc = flxnrm * flux_bc / scale
# Emission lines -------------------------------------------------
linwav, linval, conval = lin[:, 0], lin[:, 1], lin[:, 2]
# Normalise such that continuum flux at wavnrm equal to that
# of the reference continuum at wavnrm
inorm = wav2num(wavlen, wavnrm)
scale = flux[inorm]
flux = conval[inorm] * flux / scale
flux_pl = conval[inorm] * flux_pl / scale
flux_bb = conval[inorm] * flux_bb / scale
flux_bc = conval[inorm] * flux_bc / scale
# Calculate Baldwin Effect Scaling for Halpha
zbenrm = parfile['quasar']['el']['zbenrm']
beslp = parfile['quasar']['el']['beslp']
# Line added to stop enormous BE evolution at low z
zval = np.max([redshift, zbenrm])
# I think this is the absolute magnitude of the SDSS sample as
# a function of redshift, which is not the same as how the
# absolute magnitude of a object of a given flux changes
# as a function of redshift
qsomag_itp = interp1d(qsomag[:, 0], qsomag[:, 1])
# Absolute magnitude at redshift z minus
# normalisation absolute magnitude
vallum = qsomag_itp(zval) - qsomag_itp(zbenrm)
# Convert to luminosity
vallum = 10.0**(-0.4 * vallum)
scabe = vallum**(-beslp)
flux_el = np.zeros_like(flux)
flux_el[:whmin] = linval[:whmin] * np.abs(
elscal) * flux[:whmin] / conval[:whmin]
flux_el[whmax:] = linval[whmax:] * np.abs(
elscal) * flux[whmax:] / conval[whmax:]
flux[:whmin] = flux[:whmin] + linval[:whmin] * np.abs(
elscal) * flux[:whmin] / conval[:whmin]
flux[whmax:] = flux[whmax:] + linval[whmax:] * np.abs(
elscal) * flux[whmax:] / conval[whmax:]
# Scaling for Ha with Baldwin effect
scatmp = elscal * scahal / scabe
flux_el[whmin:whmax] = linval[whmin:whmax] * np.abs(
scatmp) * flux[whmin:whmax] / conval[whmin:whmax]
flux[whmin:whmax] = flux[whmin:whmax] + \
linval[whmin:whmax] * np.abs(scatmp) * flux[whmin:whmax] / conval[whmin:whmax]
gznorm = parfile['gal']['znrm']
gplind = parfile['gal']['plind']
# Determine fraction of galaxy sed to add to unreddened quasar SED
qsocnt = np.sum(flux[ignmin:ignmax])
# Factor cscale just to bring input galaxy and quasar flux zero-points equal
cscale = qsocnt / galcnt
# Find absolute magnitude of quasar at redshift z
vallum = qsomag_itp(redshift)
# Find absolute magnitude of quasar at redshift gznorm
galnrm = qsomag_itp(gznorm)
# Subtract supplied normalisation absolute magnitude
vallum = vallum - galnrm
# Convert to a luminosity
vallum = 10.0**(-0.4 * vallum)
# Luminosity scaling
scaval = vallum**(gplind - 1.0)
scagal = (galfra / (1.0 - galfra)) * scaval
flux_gal = cscale * scagal * galspc
# flux = flux + cscale * scagal * galspc
ax.plot(wavlen[:wavnumbrk],
wavlen[:wavnumbrk] * flux_pl[:wavnumbrk],
color=cs[1],
label='Accretion Disc')
# ax.fill_between(wavlen[:wavnumbrk], wavlen[:wavnumbrk]*flux_pl[:wavnumbrk], facecolor=cs[1], alpha=0.2)
ax.plot(wavlen[wavnumbrk:],
wavlen[wavnumbrk:] * flux_pl[wavnumbrk:],
color=cs[0],
label='Accretion Disc')
# ax.fill_between(wavlen[wavnumbrk:], wavlen[wavnumbrk:]*flux_pl[wavnumbrk:], facecolor=cs[0], alpha=0.2)
ax.plot(wavlen, wavlen * (flux_bc), color=cs[2], label='Balmer Continuum')
# ax.fill_between(wavlen, wavlen*(flux_bc), facecolor=cs[2], alpha=0.2)
ax.plot(wavlen, wavlen * (flux_bb), color=cs[4], label='Hot Dust')
# ax.fill_between(wavlen, wavlen*(flux_bb), facecolor=cs[4], alpha=0.2)
# ax.plot(wavlen, wavlen*(flux_gal), color=cs[3], label='Galaxy')
# ax.fill_between(wavlen, wavlen*(flux_gal), facecolor=cs[3], alpha=0.2)
ax.plot(wavlen, wavlen * flux, color='black', label='Total')
ax.legend()
ax.set_xlim(1216, 20000)
ax.set_ylim(0, 10000)
ax.set_ylabel(r'${\lambda}F_{\lambda}$ [Arbitary Units]')
ax.set_xlabel(r'Wavelength $\lambda$ [${\rm \AA}$]')
plt.tight_layout()
plt.subplots_adjust(top=0.85)
fig.savefig('/home/lc585/thesis/figures/chapter05/sed_model.pdf')
plt.show()
return None
def plot():
set_plot_properties() # change style
fig, axs = plt.subplots(2,
3,
figsize=figsize(1, vscale=0.8),
sharex='col',
sharey='row')
tab1 = Table.read('/data/lc585/QSOSED/Results/150224/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/150224/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]
kde_contours(tab1['LUM_UV'], tab1['RATIO_IR_UV'], axs[0, 0])
kde_contours(tab2['LUM_UV'], tab2['RATIO_IR_UV'], axs[0, 0], color='red')
kde_contours(tab1['LOGBH'], tab1['RATIO_IR_UV'], axs[0, 1])
kde_contours(tab2['LOGBH'], tab2['RATIO_IR_UV'], axs[0, 1], color='red')
kde_contours(tab1['LOGEDD_RATIO'][tab1['LOGEDD_RATIO'] > -2.0],
tab1['RATIO_IR_UV'][tab1['LOGEDD_RATIO'] > -2.0], axs[0, 2])
kde_contours(tab2['LOGEDD_RATIO'][tab2['LOGEDD_RATIO'] > -2.0],
tab2['RATIO_IR_UV'][tab2['LOGEDD_RATIO'] > -2.0],
axs[0, 2],
color='red')
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]
kde_contours(tab1['LUM_UV'], tab1['BBT'], axs[1, 0])
kde_contours(tab2['LUM_UV'], tab2['BBT'], axs[1, 0], color='red')
kde_contours(tab1['LOGBH'], tab1['BBT'], axs[1, 1])
kde_contours(tab2['LOGBH'], tab2['BBT'], axs[1, 1], color='red')
kde_contours(tab1['LOGEDD_RATIO'][tab1['LOGEDD_RATIO'] > -2.0],
tab1['BBT'][tab1['LOGEDD_RATIO'] > -2.0], axs[1, 2])
kde_contours(tab2['LOGEDD_RATIO'][tab2['LOGEDD_RATIO'] > -2.0],
tab2['BBT'][tab2['LOGEDD_RATIO'] > -2.0],
axs[1, 2],
color='red')
axs[0, 0].set_xlim(45, 47)
axs[0, 1].set_xlim(8, 10.5)
axs[0, 2].set_xlim(-1.5, 0.5)
axs[1, 0].set_ylim(800, 1800)
axs[0, 0].set_ylim(0, 0.8)
axs[0, 0].set_ylabel(r'$R_{NIR/UV}$')
axs[1, 0].set_ylabel(r'$T_{BB}$')
axs[1, 0].set_xlabel('UV Luminosity')
axs[1, 1].set_xlabel('Black Hole Mass')
axs[1, 2].set_xlabel('Eddington Ratio')
fig.tight_layout()
plt.subplots_adjust(wspace=0.2, hspace=0.1)
fig.savefig(
'/home/lc585/thesis/figures/chapter06/correlations_contour.pdf')
plt.show()
return None
def dr7_completeness():
# Manda matched DR7Q to ALLWISE
wisedat = Table.read('/data/sdss/DR7/AllWISE/DR7QSO_AllWISE_matched.fits')
# Main DR7Q
dr7dat = Table.read('/data/sdss/DR7/dr7qso.fit')
imin = np.arange(18.0,21.0,0.1)
w1frac, w2frac, w3frac, w4frac = [], [], [], []
for i in range(len(imin)):
objsall = wisedat[ (wisedat['IMAG'] < imin[i]) ]
objs = wisedat[ (wisedat['IMAG'] < imin[i]) & (wisedat['W1SNR_ALLWISE'] > 5.0) ]
w1frac.append(float(len(objs)) / float(len(objsall)) )
objs = wisedat[ (wisedat['IMAG'] < imin[i]) & (wisedat['W2SNR_ALLWISE'] > 5.0) ]
w2frac.append(float(len(objs)) / float(len(objsall)))
objs = wisedat[ (wisedat['IMAG'] < imin[i]) & (wisedat['W3SNR_ALLWISE'] > 5.0) ]
w3frac.append(float(len(objs)) / float(len(objsall)))
objs = wisedat[ (wisedat['IMAG'] < imin[i]) & (wisedat['W4SNR_ALLWISE'] > 5.0) ]
w4frac.append(float(len(objs)) / float(len(objsall)))
ulasdat = np.genfromtxt('/data/mbanerji/Projects/QSO/DR7QSO/DR7QSO_ULASDR9_ABmags.cat')
YSNR = 1.0 / (ulasdat[:,18] * 0.4 * np.log(10) )
JSNR = 1.0 / (ulasdat[:,19] * 0.4 * np.log(10) )
HSNR = 1.0 / (ulasdat[:,20] * 0.4 * np.log(10) )
KSNR = 1.0 / (ulasdat[:,21] * 0.4 * np.log(10) )
Yfrac, Jfrac, Hfrac, Kfrac, totalnum = [], [], [], [], []
for i in range(len(imin)):
objsall = ulasdat[ (ulasdat[:,7] < imin[i]) ]
objs = ulasdat[ (ulasdat[:,7] < imin[i]) & (YSNR > 5.0) ]
Yfrac.append(float(len(objs)) / float(len(objsall)) )
objs = ulasdat[ (ulasdat[:,7] < imin[i]) & (JSNR > 5.0)]
Jfrac.append(float(len(objs)) / float(len(objsall)) )
objs = ulasdat[ (ulasdat[:,7] < imin[i]) & (HSNR > 5.0) ]
Hfrac.append(float(len(objs)) / float(len(objsall)) )
objs = ulasdat[ (ulasdat[:,7] < imin[i]) & (KSNR > 5.0) ]
Kfrac.append(float(len(objs)) / float(len(objsall)) )
totalnum = []
for i in range(len(imin)):
objsall = dr7dat[ (dr7dat['IMAG'] < imin[i]) ]
totalnum.append(len(objsall))
fig = plt.figure(figsize=figsize(0.7))
ax = fig.add_subplot(111)
set2 = brewer2mpl.get_map('Set2', 'qualitative', 7).mpl_colors
ax.plot(imin,Yfrac,label='Y',color=set2[0])
ax.plot(imin,Jfrac,label='J',color=set2[1])
ax.plot(imin,Hfrac,label='H',color=set2[2])
ax.plot(imin,Kfrac,label='K',color=set2[3])
ax.plot(imin,w1frac,label='W1',color=set2[4])
ax.plot(imin,w2frac,label='W2',color=set2[5])
ax.plot(imin,w3frac,label='W3',color=set2[6])
plt.legend(loc='lower left',prop={'size':10})
ax2 = ax.twinx()
ax2.plot(imin,totalnum,color='black',linestyle='--')
ax2.axvline(19.1,color='grey')
ax.set_ylim(0.6,1.05)
ax2.set_ylim(0,110000)
ax.set_xlabel(r'$i_{\rm min}$')
ax.set_ylabel('Completeness')
ax2.set_ylabel('Number of Objects')
plt.tight_layout()
fig.savefig('/home/lc585/thesis/figures/chapter06/dr7completeness.pdf')
plt.show()
return None
def dr10_completeness():
wisedat = Table.read('/data/lc585/SDSS/DR10QSO_AllWISE_matched.fits')
dat = Table.read('/data/sdss/DR10/DR10Q_v2.fits')
gmag_all = dat['PSFMAG'][:,1] - dat['EXTINCTION_RECAL'][:,1] + 0.03
gmag_wise = wisedat['PSFMAG'][:,1] - wisedat['EXTINCTION_RECAL'][:,1] + 0.03
ysnr = dat['YFLUX'] / dat['YFLUX_ERR']
jsnr = dat['JFLUX'] / dat['JFLUX_ERR']
hsnr = dat['HFLUX'] / dat['HFLUX_ERR']
ksnr = dat['KFLUX'] / dat['KFLUX_ERR']
gmin = np.arange(18.0,22.5,0.1)
yfrac, jfrac, hfrac, kfrac, w1frac, w2frac, w3frac, w4frac, totalnum = [], [], [], [], [], [], [], [], []
for i in range(len(gmin)):
objsall = wisedat[ (gmag_wise < gmin[i]) ]
objs = wisedat[ (gmag_wise < gmin[i]) & (wisedat['W1SNR_ALLWISE'] > 5.0) ]
w1frac.append(float(len(objs)) / float(len(objsall)) )
objs = wisedat[ (gmag_wise < gmin[i]) & (wisedat['W2SNR_ALLWISE'] > 5.0) ]
w2frac.append(float(len(objs)) / float(len(objsall)))
objs = wisedat[ (gmag_wise < gmin[i]) & (wisedat['W3SNR_ALLWISE'] > 5.0) ]
w3frac.append(float(len(objs)) / float(len(objsall)))
objs = wisedat[ (gmag_wise < gmin[i]) & (wisedat['W4SNR_ALLWISE'] > 5.0) ]
w4frac.append(float(len(objs)) / float(len(objsall)))
objsall = dat[ (dat['UKIDSS_MATCHED'] == 1) & (gmag_all < gmin[i]) ]
objs = dat[ (dat['UKIDSS_MATCHED'] == 1) & (gmag_all < gmin[i]) & (ysnr > 5.0) ]
yfrac.append(float(len(objs)) / float(len(objsall)) )
objs = dat[(dat['UKIDSS_MATCHED'] == 1) & (gmag_all < gmin[i]) & (jsnr > 5.0) ]
jfrac.append(float(len(objs)) / float(len(objsall)) )
objs = dat[(dat['UKIDSS_MATCHED'] == 1) & (gmag_all < gmin[i]) & (hsnr > 5.0) ]
hfrac.append(float(len(objs)) / float(len(objsall)) )
objs = dat[(dat['UKIDSS_MATCHED'] == 1) & (gmag_all < gmin[i]) & (ksnr > 5.0) ]
kfrac.append(float(len(objs)) / float(len(objsall)) )
for i in range(len(gmin)):
objsall = dat[ (gmag_all < gmin[i]) ]
totalnum.append(len(objsall))
fig = plt.figure(figsize=figsize(0.7))
ax = fig.add_subplot(111)
set2 = brewer2mpl.get_map('Set2', 'qualitative', 7).mpl_colors
ax.plot(gmin,yfrac,label='Y',color=set2[0])
ax.plot(gmin,jfrac,label='J',color=set2[1])
ax.plot(gmin,hfrac,label='H',color=set2[2])
ax.plot(gmin,kfrac,label='K',color=set2[3])
ax.plot(gmin,w1frac,label='W1',color=set2[4])
ax.plot(gmin,w2frac,label='W2',color=set2[5])
ax.plot(gmin,w3frac,label='W3',color=set2[6])
# ax.plot(gmin,w4frac,label='W4')
ax.axvline(22.0,color='grey')
plt.legend(loc='lower left', prop={'size':10})
ax2 = ax.twinx()
ax2.plot(gmin,totalnum,color='black',linestyle='--')
ax2.set_ylim(0,170000)
ax.set_xlabel(r'$g_{\rm min}$')
ax.set_ylabel('Completeness')
ax2.set_ylabel('Number of Objects')
plt.tight_layout()
plt.savefig('/home/lc585/thesis/figures/chapter06/dr10completeness.pdf')
plt.show()
return None
def plot():
set_plot_properties() # change style
fig, axs = plt.subplots(3, 2, figsize=figsize(1, vscale=1.5), sharey='col', sharex='row')
t = Table.read('/home/lc585/qsosed/out_bbt_fixed.fits')
t = t[['NAME', 'BBT', 'IR_UV_RATIO']]
t.rename_column('NAME', 'SDSS_NAME')
df_fit = t.to_pandas()
df_bhm = pd.read_csv('/data/lc585/SDSS/civ_bs_basesub.liam', delimiter='|')
df_bhm.drop('Unnamed: 0', inplace=True, axis=1)
df_bhm.drop('Unnamed: 18', inplace=True, axis=1)
df = pd.merge(df_fit, df_bhm, how='inner', on='SDSS_NAME')
Lbol = 3.81 * 10**df['LOGL1350_SDSS']
Ledd = 3.2e4 * 10**df['CIV_LOGBH_SDSS']
Lbol = Lbol / (3.846e33*(u.erg/u.s)) # in units of solar luminosity
EddRatio = Lbol / Ledd
df['EddRatio_Bias'] = EddRatio
Lbol = 3.81 * 10**df['LOGL1350_SDSS']
Ledd = 3.2e4 * 10**df['CIV_LOGBH_CORR_HW10']
Lbol = Lbol / (3.846e33*(u.erg/u.s)) # in units of solar luminosity
EddRatio = Lbol / Ledd
df['EddRatio'] = EddRatio
df = df[~np.isinf(df.EddRatio_Bias)]
df = df[(df.LOGL1350_SDSS > 45) & (df.LOGL1350_SDSS < 47)]
df = df[(df.CIV_LOGBH_SDSS > 8) & (df.CIV_LOGBH_SDSS < 10.5)]
df = df[(np.log10(df.EddRatio_Bias) > -1.5) & (np.log10(df.EddRatio_Bias) < 0.5)]
kde_contours(df.LOGL1350_SDSS, df.IR_UV_RATIO, axs[0, 0], color=cs[1])
kde_contours(df.CIV_LOGBH_SDSS, df.IR_UV_RATIO, axs[1, 0], color=cs[1])
kde_contours(np.log10(df.EddRatio_Bias), df.IR_UV_RATIO, axs[2, 0], color=cs[1])
kde_contours(df.LOGL1350_SDSS, df.IR_UV_RATIO, axs[0, 1], color=cs[1])
kde_contours(df.CIV_LOGBH_CORR_HW10, df.IR_UV_RATIO, axs[1, 1], color=cs[1])
kde_contours(np.log10(df.EddRatio), df.IR_UV_RATIO, axs[2, 1], color=cs[1])
axs[0, 0].set_xlim(45.8, 47)
axs[0, 0].xaxis.set_major_locator(MaxNLocator(4))
axs[1, 0].set_xlim(8.25, 10.25)
axs[2, 0].set_xlim(-1.5, 0.5)
axs[0, 1].set_xlim(45.8, 47)
axs[1, 1].set_xlim(8.25, 10.25)
axs[2, 1].set_xlim(-1.5, 0.5)
axs[0, 0].set_ylim(0, 0.8)
axs[0, 1].set_ylim(0, 0.8)
axs[0, 0].yaxis.set_major_locator(MaxNLocator(4))
axs[0, 1].yaxis.set_major_locator(MaxNLocator(4))
axs[0, 0].set_ylabel(r'R$_{\rm NIR/UV}$')
axs[1, 0].set_ylabel(r'R$_{\rm NIR/UV}$')
axs[2, 0].set_ylabel(r'R$_{\rm NIR/UV}$')
axs[0, 0].set_xlabel(r'Log L$_{\rm UV}$ [erg~$\rm{s}^{-1}$]')
axs[1, 0].set_xlabel(r'Log M$_{\rm BH}$ [M$\odot$]')
axs[2, 0].set_xlabel(r'Log $\lambda_{\rm Edd}$')
axs[0, 1].set_xlabel(r'Log L$_{\rm UV}$ [erg~$\rm{s}^{-1}$]')
axs[1, 1].set_xlabel(r'Log M$_{\rm BH}$ [M$\odot$]')
axs[2, 1].set_xlabel(r'Log $\lambda_{\rm Edd}$')
labels = ['(a)', '(b)', '(b)', '(d)', '(c)', '(e)']
print len(axs.flatten())
for i, ax in enumerate(axs.flatten()):
ax.text(0.1, 0.93, labels[i],
horizontalalignment='center',
verticalalignment='center',
transform = ax.transAxes)
fig.delaxes(axs[0, 1])
fig.tight_layout()
plt.subplots_adjust(wspace=0.25, hspace=0.25)
fig.savefig('/home/lc585/thesis/figures/chapter05/correlations_contour.pdf')
plt.show()
return None
def ratio_tbb_beta(density=False):
from astropy.table import join
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]
tab1 = Table.read('/data/lc585/QSOSED/Results/141124/sample2/out.fits') # 9000 - 23500 fit
tab2 = Table.read('/data/lc585/QSOSED/Results/141124/sample1/out.fits') # 10000 - 23500 fit
tab1['BBPLSLP'] = tab1['BBPLSLP'] - 1.0
tab2['BBPLSLP'] = tab2['BBPLSLP'] - 1.0
goodnames = Table()
goodnames['NAME'] = tab['NAME']
tab1 = join( tab1, goodnames, join_type='right', keys='NAME')
tab2 = join( tab2, goodnames, join_type='right', keys='NAME')
fig, ax = plt.subplots(figsize=figsize(1, vscale=0.8))
if not density:
im = ax.hexbin(tab['BBT'],
tab['RATIO_IR_UV'],
C = tab1['BBPLSLP'],
gridsize=(80,80))
cb = plt.colorbar(im)
cb.set_label(r'$\beta_{\rm NIR}$')
# Digitize beta
nbins = 20
bins = np.linspace(-1.5,1.5,nbins+1)
ind = np.digitize(tab1['BBPLSLP'],bins)
bbt_locus = [np.median(tab['BBT'][ind == j]) for j in range(1, nbins)]
ratio_locus = [np.median(tab['RATIO_IR_UV'][ind == j]) for j in range(1, nbins)]
ax.plot(bbt_locus[7:-2],ratio_locus[7:-2],color='black',linewidth=2.0)
ax.set_ylabel(r'$R_{{\rm NIR}/{\rm UV}}$')
ax.set_xlabel(r'$T_{\rm BB}$')
ax.set_ylim(0,2)
ax.set_xlim(700,1900)
fig.tight_layout()
fig.savefig('/home/lc585/thesis/figures/chapter06/ratio_tbb_beta.pdf')
else:
from matplotlib import cm
from PlottingTools.truncate_colormap import truncate_colormap
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)
im = ax.hexbin(tab['BBT'],
tab['RATIO_IR_UV'],
gridsize=(80,80),
cmap = mycm,
mincnt=1)
cb = plt.colorbar(im)
cb.set_label(r'Number of Objects')
ax.set_ylabel(r'$R_{{\rm NIR}/{\rm UV}}$')
ax.set_xlabel(r'$T_{\rm BB}$')
ax.set_ylim(0,2)
ax.set_xlim(700,1900)
fig.tight_layout()
fig.savefig('/home/lc585/thesis/figures/chapter06/ratio_tbb_beta_density.pdf')
plt.show()
return None
def civ_hot_dust_ratio():
"""
df = get_data()
df = extcut(df)
df = df[(df.z_HW >= 2.0) & (df.z_HW < 2.7)]
df.to_csv('/home/lc585/qsosed/temp.csv')
to make sample, and then runsingleobjfit
temp is fixed
"""
t = Table.read('/home/lc585/qsosed/out_bbt_fixed.fits')
t_ica = Table.read('/data/vault/phewett/LiamC/liam_civpar_zica_160115.dat', format='ascii') # new ICA
t_ica.rename_column('col1', 'NAME')
t_ica.rename_column('col2', 'CIV_BLUESHIFT_ICA')
t_ica.rename_column('col3', 'CIV_EQW_ICA')
print len(t)
t = join(t, t_ica, join_type='inner', keys='NAME')
print len(t)
t = t[np.log10(t['CIV_EQW_ICA']) > 1.2]
xdat = t['CIV_BLUESHIFT_ICA']
ydat = np.log10(t['CIV_EQW_ICA'])
C = t['IR_UV_RATIO']
fig, ax = plt.subplots(figsize=figsize(1, vscale=1.0))
im = ax.hexbin(xdat,
ydat,
C=C,
gridsize=(25,18),
mincnt=1,
reduce_C_function=np.median,
cmap='RdBu_r',
edgecolor='black',
linewidth=0.5,
vmax=0.55,
vmin=0.15)
cb = fig.colorbar(im,ax=ax)
cb.set_label(r'$R_{NIR/UV}$')
ax.set_xlabel(r'C\,{\sc iv} Blueshift [km~$\rm{s}^{-1}$]')
ax.set_ylabel(r'log(C\,{\sc iv} EW) [\AA]')
ax.set_xlim(-750,4000)
ax.set_ylim(1.1 ,2)
fig.tight_layout()
fig.savefig('/home/lc585/thesis/figures/chapter05/hot_dust_ratio.pdf')
plt.show()
return None
def plot():
from PlottingTools.plot_setup_thesis import figsize, set_plot_properties
from astropy.table import Table
import matplotlib.pyplot as plt
from scipy import histogram2d
import numpy as np
from matplotlib import cm
from PlottingTools.truncate_colormap import truncate_colormap
import brewer2mpl
from PlottingTools import running
import collections
from PlottingTools.kde_contours import kde_contours
from PlottingTools.scatter_hist import scatter_hist
import palettable
cs = palettable.colorbrewer.qualitative.Set1_9.mpl_colors
fig, axScatter, axHistx, axHisty = scatter_hist(figsize=figsize(1, 1),
left=0.12,
bottom=0.12)
set_plot_properties() # change style
tab = Table.read('/home/lc585/qsosed/out.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']) < 1.]
#data definition
xdat, ydat = tab['BBT'] * 1e3, tab['IR_UV_RATIO']
bad = np.isnan(xdat) | np.isnan(ydat)
xdat = xdat[~bad]
ydat = ydat[~bad]
bad = (xdat > 2000) | (xdat < 500) | (ydat < 0) | (ydat > 1)
xdat = xdat[~bad]
ydat = ydat[~bad]
kde_contours(xdat, ydat, axScatter, filled=True, lims=(600, 1800, 0, 0.8))
axScatter.set_xlabel(r'$T_{\rm BB}$')
axScatter.set_ylabel(r'$R_{{\rm NIR}/{\rm UV}}$')
axScatter.set_ylim(0, 0.8)
axScatter.set_xlim(800, 1600)
axHisty.hist(ydat,
bins=np.arange(0, 0.8, 0.05),
facecolor=cs[1],
histtype='stepfilled',
edgecolor='black',
orientation='horizontal',
normed=True)
axHistx.hist(xdat,
bins=np.arange(800, 1600, 50),
histtype='stepfilled',
edgecolor='black',
facecolor=cs[1],
normed=True)
# Sample from single (T,Norm) with gaussian errors on photometry.
# Mock magnitude file made in model.py and then fit in runsingleobjfit.py.
# tab2 = Table.read('/data/lc585/QSOSED/Results/150309/sample5/out_add.fits')
# axScatter.scatter(tab2['BBT'],
# tab2['RATIO_IR_UV'],
# edgecolor='None',
# color=cs[0],
# s=8,
# zorder=10)
axHistx.set_xlim(axScatter.get_xlim())
axHisty.set_ylim(axScatter.get_ylim())
axHistx.set_ylim(0, 5e-3)
axHisty.set_xlim(0, 4)
fig.savefig('/home/lc585/thesis/figures/chapter05/ratio_tbb_density.pdf')
plt.show()
return None
def plot():
set_plot_properties() # change style
with open('/home/lc585/qsosed/input.yml', 'r') as f:
parfile = yaml.load(f)
fittingobj = load(parfile)
df = get_data()
zs = np.linspace(1, 3.0, 100)
wavlen = fittingobj.get_wavlen()
uvintmin = np.argmin(np.abs(wavlen - 2000.))
uvintmax = np.argmin(np.abs(wavlen - 9000.))
irintmin = np.argmin(np.abs(wavlen - 10000.))
irintmax = np.argmin(np.abs(wavlen - 30000.))
def ratiogoal(rg):
ratio = 0.0
bbflxnrm = 0.0
z = 2.0
bbt = 1306
plslp1 = -0.478
plslp2 = -0.199
plbrk = 2402.0
flxnrm = 1.0
wavnrm = 5500.0
bbwavnrm = 20000.0
# while ratio < rg:
# flux = np.zeros(len(wavlen), dtype=np.float)
# # Define normalisation constant to ensure continuity at wavbrk
# const2 = flxnrm / (wavnrm**(-plslp2))
# const1 = const2 * ((plbrk**(-plslp2)) / (plbrk**(-plslp1)))
# wavnumbrk = wav2num(wavlen, plbrk)
# flux[:wavnumbrk] = flux[:wavnumbrk] + pl(wavlen[:wavnumbrk], plslp1, const1)
# flux[wavnumbrk:] = flux[wavnumbrk:] + pl(wavlen[wavnumbrk:], plslp2, const2)
# # Hot blackbody ---------------------------------------------------
# bbflux = bb(wavlen*u.AA,
# bbt*u.K,
# bbflxnrm,
# bbwavnrm*u.AA,
# units='freq')
# flux = flux*(u.erg / u.s / u.cm**2 / u.Hz)
# flux = flux.to(u.erg / u.s / u.cm**2 / u.AA,
# equivalencies=u.spectral_density(wavlen * u.AA))
# bbflux = bbflux*(u.erg / u.s / u.cm**2 / u.Hz)
# bbflux = bbflux.to(u.erg / u.s / u.cm**2 / u.AA,
# equivalencies=u.spectral_density(wavlen * u.AA))
# ratio = np.sum(bbflux[irintmin:irintmax]) / np.sum(flux[uvintmin:uvintmax])
# bbflxnrm = bbflxnrm + 0.01
# print ratio, bbflxnrm
# parfile['quasar']['bb']['flxnrm'] = bbflxnrm
print rg
if rg == 0.5: parfile['quasar']['bb']['flxnrm'] = 4.47
elif rg == 0.4: parfile['quasar']['bb']['flxnrm'] = 3.58
elif rg == 0.3: parfile['quasar']['bb']['flxnrm'] = 2.69
elif rg == 0.2: parfile['quasar']['bb']['flxnrm'] = 1.8
elif rg == 0.1: parfile['quasar']['bb']['flxnrm'] = 0.91
elif rg == 0.0: parfile['quasar']['bb']['flxnrm'] = 0.0
cols = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(redshift=z, parfile=parfile)
cols.append(magtmp[9] - magtmp[10])
return cols
fig, ax = plt.subplots(figsize=figsize(1.0, vscale=0.7))
mycm = cm.get_cmap('Blues_r')
mycm.set_under('w')
mycm = truncate_colormap(mycm, 0.0, 0.8)
cset = brewer2mpl.get_map('Blues', 'sequential', 9).mpl_colors
#histogram definition
xyrange = [[1, 3], [0, 2]] # data range
bins = [50, 50] # number of bins
thresh = 4 #density threshold
#data definition
xdat, ydat = df.z_HW, df.W1VEGA - df.W2VEGA
# 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 = ax.imshow(np.flipud(hh.T),
cmap=mycm,
extent=np.array(xyrange).flatten(),
interpolation='none',
aspect='auto',
vmin=thresh,
vmax=45)
cb = plt.colorbar(im)
cb.set_label('Number of Objects')
ax.scatter(xdat1, ydat1, color=cset[-1], s=3)
cset = brewer2mpl.get_map('YlOrRd', 'sequential', 8).mpl_colors
ax.plot(zs, ratiogoal(0.5), c=cset[7], linewidth=2.0, label=r'0.5')
ax.plot(zs, ratiogoal(0.4), c=cset[6], linewidth=2.0, label=r'0.4')
ax.plot(zs, ratiogoal(0.3), c=cset[5], linewidth=2.0, label=r'0.3')
ax.plot(zs, ratiogoal(0.2), c=cset[4], linewidth=2.0, label=r'0.2')
ax.plot(zs, ratiogoal(0.1), c=cset[3], linewidth=2.0, label=r'0.1')
ax.plot(zs, ratiogoal(0.0), c=cset[2], linewidth=2.0, label=r'0.0')
ax.set_xlabel(r'Redshift $z$')
ax.set_ylabel(r'$W1-W2$')
ax.set_xlim(1, 4)
ax.set_ylim(0.2, 2)
ax.text(0.72,
0.7,
r'${\mathrm R_{\mathrm NIR/UV}}$',
transform=ax.transAxes)
plt.legend(frameon=False, loc=(0.7, 0.15))
plt.tight_layout()
fig.savefig(
'/home/lc585/thesis/figures/chapter05/w1w2_versus_redshift_ratio.pdf')
plt.show()
return None
def plot():
cs = palettable.colorbrewer.qualitative.Set1_3.mpl_colors
# Load parameter file
with open('/home/lc585/qsosed/input.yml', 'r') as f:
parfile = yaml.load(f)
ebvmin = -0.075
ebvmax = 0.075
zmin = 1
zmax = 3.0
# Load stuff
fittingobj = load(parfile)
wavlen = fittingobj.get_wavlen()
# Load data
df = get_data()
df = df[(df.z_HW >= zmin) & (df.z_HW <= zmax)]
colmin, colmax = [], []
zs = np.arange(zmin, zmax + 0.025, 0.025)
mycm = cm.get_cmap('Blues_r')
mycm.set_under('w')
cset = brewer2mpl.get_map('Blues', 'sequential', 9).mpl_colors
mycm = truncate_colormap(mycm, 0.0, 0.8)
fig = plt.figure(figsize=(figsize(1, vscale=0.8)))
ax = fig.add_subplot(1, 1, 1)
plt.tight_layout()
#histogram definition
xyrange = [[0., 3.0], [0, 3]] # data range
bins = [100, 60] # number of bins
thresh = 7 #density threshold
#data definition
xdat, ydat = df.z_HW, df.iVEGA - df.KVEGA
# 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 = ax.imshow(np.flipud(hh.T),
cmap=mycm,
extent=np.array(xyrange).flatten(),
interpolation='none',
aspect='auto',
vmin=thresh,
vmax=45)
clb = fig.colorbar(im)
clb.set_label('Number of Objects')
ax.scatter(xdat1, ydat1, color=cset[-1], s=2)
plt.ylim(0, 3.5)
col = []
for z in zs:
parfile['ext']['EBV'] = ebvmax
magtmp, wavlentmp, fluxtmp = model(redshift=z, parfile=parfile)
col.append(magtmp[3] - magtmp[8])
plt.plot(zs, col, label='E(B-V) = 0.075', color=cs[0], linewidth=1.0)
upper_bound_1 = col[np.argmin(np.abs(zs - 1.0)):np.argmin(np.abs(zs -
1.5))]
upper_bound_2 = col[np.argmin(np.abs(zs - 2.0)):np.argmin(np.abs(zs -
2.7))]
col = []
for z in zs:
parfile['ext']['EBV'] = ebvmin
magtmp, wavlentmp, fluxtmp = model(redshift=z, parfile=parfile)
col.append(magtmp[3] - magtmp[8])
plt.plot(zs, col, label='E(B-V) = -0.075', color=cs[0], linewidth=1.0)
lower_bound_1 = col[np.argmin(np.abs(zs - 1.0)):np.argmin(np.abs(zs -
1.5))]
lower_bound_2 = col[np.argmin(np.abs(zs - 2.0)):np.argmin(np.abs(zs -
2.7))]
plt.fill_between(zs[np.argmin(np.abs(zs - 2.0)):np.argmin(np.abs(zs -
2.7))],
lower_bound_2,
upper_bound_2,
facecolor='None',
edgecolor=cs[0],
linewidth=3.0)
col = []
for z in zs:
parfile['ext']['EBV'] = 0.0
magtmp, wavlentmp, fluxtmp = model(redshift=z, parfile=parfile)
col.append(magtmp[3] - magtmp[8])
plt.plot(zs, col, label='E(B-V) = 0.0', color=cs[0], linewidth=1.0)
plt.xlim(0.75, 3.5)
plt.text(3.33821,
2.5,
'E(B-V)=',
horizontalalignment='right',
verticalalignment='center',
color='black')
plt.text(3.09608,
2.2,
'0.075',
horizontalalignment='left',
verticalalignment='center',
color='black')
plt.text(3.09608,
1.2,
'-0.075',
horizontalalignment='left',
verticalalignment='center',
color='black')
plt.text(3.09608,
1.7,
'0.0',
horizontalalignment='left',
verticalalignment='center',
color='black')
plt.xlabel(r'Redshift $z$')
plt.ylabel(r'$i$-$K$')
plt.tight_layout()
fig.savefig('/home/lc585/thesis/figures/chapter05/ik_versus_z_low_ext.pdf')
plt.show()
return None
def plot():
"""
Generates residual plot using model parameters in input.yml
"""
set_plot_properties() # change style
with open('/home/lc585/qsosed/input.yml', 'r') as f:
parfile = yaml.load(f)
fittingobj = load(parfile)
wavlen = fittingobj.get_wavlen()
lin = fittingobj.get_lin()
galspc = fittingobj.get_galspc()
ext = fittingobj.get_ext()
galcnt = fittingobj.get_galcnt()
ignmin = fittingobj.get_ignmin()
ignmax = fittingobj.get_ignmax()
ztran = fittingobj.get_ztran()
lyatmp = fittingobj.get_lyatmp()
lybtmp = fittingobj.get_lybtmp()
lyctmp = fittingobj.get_lyctmp()
whmin = fittingobj.get_whmin()
whmax = fittingobj.get_whmax()
qsomag = fittingobj.get_qsomag()
flxcorr = fittingobj.get_flxcorr()
cosmo = fittingobj.get_cosmo()
# Load median magnitudes
with open('/home/lc585/qsosed/sdss_ukidss_wise_medmag_ext.dat') as f:
datz = np.loadtxt(f, usecols=(0,))
datz = datz[:-5]
# Load filters
ftrlst = fittingobj.get_ftrlst()[2:-2]
lameff = fittingobj.get_lameff()[2:-2]
bp = fittingobj.get_bp()[2:-2] # these are in ab and data is in vega
dlam = fittingobj.get_bp()[2:-2]
zromag = fittingobj.get_zromag()[2:-2]
with open('ftgd_dr7.dat') as f:
ftgd = np.loadtxt(f, skiprows=1, usecols=(1,2,3,4,5,6,7,8,9))
fname = '/home/lc585/qsosed/sdss_ukidss_wise_medmag_ext.dat'
datarr = np.genfromtxt(fname, usecols=(5,7,9,11,13,15,17,19,21))
datarr[datarr < 0.0] = np.nan
datarr = datarr[:-5, :]
params = Parameters()
params.add('plslp1', value = -0.478)
params.add('plslp2', value = -0.199)
params.add('plbrk', value = 2.40250)
params.add('bbt', value = 1.30626)
params.add('bbflxnrm', value = 2.673)
params.add('elscal', value = 1.240)
params.add('scahal',value = 0.713)
params.add('galfra',value = 0.0)
params.add('bcnrm',value = 0.135)
params.add('ebv',value = 0.0)
params.add('imod',value = 18.0)
modarr = residual(params,
parfile,
wavlen,
datz,
lin,
bp,
dlam,
zromag,
galspc,
ext,
galcnt,
ignmin,
ignmax,
ztran,
lyatmp,
lybtmp,
lyctmp,
ftrlst,
whmin,
whmax,
cosmo,
flxcorr,
qsomag,
ftgd)
lameff = lameff.reshape( len(lameff), 1)
lameff = np.repeat(lameff,len(datz),axis=1)
datz = datz.reshape(1, len(datz) )
datz = np.repeat(datz,len(lameff),axis=0)
lam = lameff / (1.0 + datz)
res = np.ndarray.transpose(modarr - datarr)
fig = plt.figure(figsize=figsize(1, 0.8))
ax = fig.add_subplot(1,1,1)
colormap = plt.cm.Paired
plt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 0.8, 9)])
labels = ['r','i','z','Y','J','H','K','W1','W2']
for i in range(9):
ax.plot(lam[i,:], res[i,:], label=labels[i])
ax.grid()
ax.set_xlim(1000,30000)
ax.set_ylim(-0.3,0.3)
ax.set_xlabel(r'Rest Frame Wavelength [${\rm \AA}$]')
ax.set_ylabel(r'$m_{\rm mod} - m_{\rm dat}$')
plt.legend(prop={'size':10})
plt.tick_params(axis='both',which='major')
plt.tight_layout()
plt.savefig('/home/lc585/thesis/figures/chapter05/model_residuals.pdf')
plt.show()
return None
