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 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 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()
from qsosed.loaddatraw import loaddatraw
import numpy as np
import numpy.ma as ma
from qsosed.sedmodel import model
from scipy.interpolate import interp1d
from matplotlib import cm
import brewer2mpl
from PlottingTools.truncate_colormap import truncate_colormap
import matplotlib.colors as colors
import numpy as np
import palettable
import matplotlib.pyplot as plt
from scipy import histogram2d
from qsosed.get_data import get_data, extcut
from PlottingTools.plot_setup_thesis import figsize, set_plot_properties
set_plot_properties() # change style
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
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
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 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 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():
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 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():
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 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():
"""
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