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():
mycm = cm.get_cmap('YlOrRd_r')
mycm.set_under('w')
mycm = truncate_colormap(mycm, 0.0, 0.8)
cset = brewer2mpl.get_map('YlOrRd', 'sequential', 9).mpl_colors
tab = Table.read('/data/lc585/QSOSED/Results/141031/sample1.fits')
fig = plt.figure(figsize=figsize(0.7, vscale=1.5))
ax1 = fig.add_subplot(2,1,1)
ax2 = fig.add_subplot(2,1,2)
fig.subplots_adjust(wspace=0.0)
fig.subplots_adjust(hspace=0.0)
fig.subplots_adjust(top=0.99, bottom=0.2)
#histogram definition
xyrange = [[0.5,1.5],[-0.4,1.2]] # data range
bins = [45,45] # number of bins
thresh = 4 #density threshold
#data definition
w1mag = tab['W1MPRO_ALLWISE'] + 2.699
w2mag = tab['W2MPRO_ALLWISE'] + 3.339
z = tab['Z_HEWETT']
xdat, ydat = z, w1mag - w2mag
# histogram the data
hh, locx, locy = histogram2d(xdat, ydat, range=xyrange, bins=bins)
posx = np.digitize(xdat, locx)
posy = np.digitize(ydat, locy)
#select points within the histogram
ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
hhsub = hh[posx[ind] - 1, posy[ind] - 1] # values of the histogram where the points are
xdat1 = xdat[ind][hhsub < thresh] # low density points
ydat1 = ydat[ind][hhsub < thresh]
hh[hh < thresh] = np.nan # fill the areas with low density by NaNs
im = ax1.imshow(np.flipud(hh.T),cmap=mycm,extent=np.array(xyrange).flatten(), interpolation='none',aspect='auto', vmin=thresh, vmax=45)
ax1.scatter(xdat1, ydat1,color=cset[-1])
ax1.set_ylabel(r'$W1-W2$',fontsize=14)
for tick in ax1.xaxis.get_major_ticks():
tick.label.set_fontsize(10)
for tick in ax1.yaxis.get_major_ticks():
tick.label.set_fontsize(10)
ax1.set_ylim(-0.4,1.2)
ax1.set_xlim(0.25,1.7)
im = ax2.imshow(np.flipud(hh.T),cmap=mycm,extent=np.array(xyrange).flatten(), interpolation='none',aspect='auto', vmin=thresh, vmax=45)
ax2.scatter(xdat1, ydat1,color=cset[-1])
axcb = fig.add_axes([0.13,0.1,0.75,0.02])
clb = fig.colorbar(im, cax=axcb,orientation='horizontal')
clb.set_label('Number of Objects',fontsize=12)
clb.ax.tick_params(labelsize=10)
ax2.set_xlabel(r'Redshift $z$',fontsize=14)
ax2.set_ylabel(r'$W1-W2$',fontsize=14)
for tick in ax2.xaxis.get_major_ticks():
tick.label.set_fontsize(10)
for tick in ax2.yaxis.get_major_ticks():
tick.label.set_fontsize(10)
ax2.set_ylim(ax1.get_ylim())
ax2.set_xlim(ax1.get_xlim())
tabtmp = tab
tabtmp.sort('Z')
#plt.tick_params(axis='both',which='major',labelsize=12)
ax1.get_xaxis().set_ticks([])
ax1.set_yticklabels(['',0.0,0.2,0.4,0.6,0.8,1.0,1.2])
ax2.set_yticklabels([-0.4,-0.2,0.0,0.2,0.4,0.6,0.8,1.0])
#plt.scatter(z,w1mag-w2mag,c='grey',alpha=0.3)
#xdat = z
#ydat = w1mag - w2mag
#zdat = tab['LOGLBOL']
#
#plt.hexbin(xdat,ydat,C=tab['LUM_UV'],gridsize=10,cmap=mycm)
#cb = plt.colorbar()
#cb.set_label('UV Luminosity')
#plt.ylim(-0.2,1.2)
#plt.xlim(0.4,1.6)
##plt.savefig('/data/lc585/QSOSED/Results/141030/figure1.jpg')
#
#xyrange = [[0.5,1.5],[0,1.2]] # data range
#bins = [10,6] # number of bins
#thresh = 20
#hh, locx, locy = histogram2d(xdat,
# ydat,
# range=xyrange,
# bins=bins)
#
#
#print locy
#
#posx = np.digitize(xdat, locx)
#posy = np.digitize(ydat, locy)
#
#grid = []
#for i in range(10):
# row = []
# for j in range(6):
# row.append(np.median(zdat[(posx == i+1) & (posy ==j+1)]))
# grid.append(row)
#ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
#hhsub = hh[posx[ind] - 1, posy[ind] - 1] # values of the histogram where the points are
#xdat1 = xdat[ind][hhsub < thresh] # low density points
#ydat1 = ydat[ind][hhsub < thresh]
#hh[hh < thresh] = np.nan # fill the areas with low density by NaNs
#ax.scatter(z[ind],w1mag[ind]-w2mag[ind])
#im = ax.imshow(np.flipud(hh.T),
# cmap=mycm,
# extent=np.array(xyrange).flatten(),
# interpolation='none',
# aspect='auto',vmin=10)
#ax.scatter(xdat1,ydat1)
# loop over bins
#for i in range(len(locx)):
# for j in range(len(locy)):
# print np.median(tab['LUM_UV'][ (posx == i) & (posy == j)])
#ax.hist2d(z,
# w1mag-w2mag,
# range=xyrange,
# bins=bins,
# cmap=mycm,
# vmin=10.)
with open('/home/lc585/Dropbox/IoA/QSOSED/Model/qsofit/input.yml', 'r') as f:
parfile = yaml.load(f)
fittingobj = load(parfile)
wavlen = fittingobj.get_wavlen()
with open('/data/lc585/QSOSED/Results/140811/allsample_2/fluxcorr.array','rb') as f:
flxcorr = pickle.load(f)
plslp1 = 0.46
plslp2 = 0.03
plbrk = 2822.
bbt = 1216.
bbflxnrm = 0.24
elscal = 0.71
scahal = 0.86
galfra = 0.31
ebv = 0.0
imod = 18.0
zs = np.arange(0.5,1.525,0.025)
bbt = 1200.
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
print w1w2_model[0]
ax1.plot(zs,w1w2_model,linewidth=2.0,color='black')
bbt = 1100.
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
print w1w2_model[0]
ax1.plot(zs,w1w2_model,linewidth=2.0,color='black')
bbt = 1300.
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
print w1w2_model[0]
ax1.plot(zs,w1w2_model,linewidth=2.0,color='black')
bbt = 1400.
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
print w1w2_model[0]
ax1.plot(zs,w1w2_model,linewidth=2.0,color='black')
bbt = 1500.
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
print w1w2_model[0]
ax1.plot(zs,w1w2_model,linewidth=2.0,color='black')
bbt = 1000.
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
ax1.plot(zs,w1w2_model,linewidth=2.0,color='black')
ax1.text(0.33,0.67,'1000K',fontsize=12,color='black')
ax1.text(0.33,0.52,'1100K',fontsize=12,color='black')
ax1.text(0.33,0.40,'1200K',fontsize=12,color='black')
ax1.text(0.33,0.28,'1300K',fontsize=12,color='black')
ax1.text(0.33,0.20,'1400K',fontsize=12,color='black')
ax1.text(0.33,0.10,'1500K',fontsize=12)
#plt.savefig('/home/lc585/Dropbox/IoA/HotDustPaper/w1w2_temp.pdf')
bbt = 1216.
bbflxnrm = 0.0
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
ax2.plot(zs,w1w2_model,linewidth=2.0,color='black')
bbflxnrm = 0.1
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
ax2.plot(zs,w1w2_model,linewidth=2.0,color='black')
bbflxnrm = 0.2
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
ax2.plot(zs,w1w2_model,linewidth=2.0,color='black')
bbflxnrm = 0.3
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
ax2.plot(zs,w1w2_model,linewidth=2.0,color='black')
bbflxnrm = 0.4
w1w2_model = []
for z in zs:
magtmp, wavlentmp, fluxtmp = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
ebv,
imod,
z,
fittingobj,
flxcorr,
parfile)
w1w2_model.append(magtmp[9] - magtmp[10])
ax2.plot(zs,w1w2_model,linewidth=2.0,color='black')
#plt.xlim(0.2,1.8)
ax2.text(1.55,-0.1,'0.17',fontsize=12,color='black')
ax2.text(1.55,0.38,'0.28',fontsize=12,color='black')
ax2.text(1.55,0.60,'0.40',fontsize=12,color='black')
ax2.text(1.55,0.75,'0.52',fontsize=12,color='black')
ax2.text(1.55,0.90,'0.64',fontsize=12,color='black')
fig.savefig('/home/lc585/thesis/figures/chapter06/w1w2_versus_redshift.pdf')
#plt.xlabel(r'$z$',fontsize=12)
#plt.ylabel('$W1$-$W2$',fontsize=12)
#plt.tight_layout()
#plt.tick_params(axis='both',which='major',labelsize=10)
#sns.set_style('ticks')
#xdat = np.arange(0.1,1.2,0.2)
#plt.savefig('/data/lc585/QSOSED/Results/141101/figure1.jpg')
#fig, ax = plt.subplots()
#for row in grid:
# ax.plot(xdat,row)
#ax.set_xlabel('W1-W2')
#ax.set_ylabel('LUM BOL')
#plt.savefig('/data/lc585/QSOSED/Results/141101/figure2.jpg')
#plt.show()
#plt.savefig('/home/lc585/Dropbox/IoA/HotDustPaper/w1w2_bbnorm.pdf')
plt.show()
return None
def plot():
plslp1 = 0.71
plslp2 = 0.05
plbrk = 2679.92
bbt = 1158.99
bbflxnrm = 0.25
galfra = 0.89
elscal = 0.72
imod = 18.0
scahal = 1.0
with open('input.yml', 'r') as f:
parfile = yaml.load(f)
# Load stuff
fittingobj = load(parfile)
wavlen = fittingobj.get_wavlen()
flxcorr = np.array([1.0] * len(wavlen))
ik0, ik1, ik2 = [], [], []
zs = np.arange(2,4.01,0.01)
for z in zs:
print z
magtmp, wavlentmp1, fluxtmp1 = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
0.0,
imod,
z,
fittingobj,
flxcorr,
parfile)
ik0.append( magtmp[3] - magtmp[8] )
magtmp, wavlentmp1, fluxtmp1 = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
0.1,
imod,
z,
fittingobj,
flxcorr,
parfile)
ik1.append( magtmp[3] - magtmp[8] )
magtmp, wavlentmp1, fluxtmp1 = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
0.2,
imod,
z,
fittingobj,
flxcorr,
parfile)
ik2.append( magtmp[3] - magtmp[8] )
datmag, sigma, datz, name, snr = loaddatraw('DR10',
'/data/lc585/SDSS/DR10QSO_AllWISE_matched.v2.fits',
True,
0,
23.0,
15.0,
False,
0.1)
fig = plt.figure(figsize=figsize(0.7))
ax = fig.add_subplot(111)
ax.plot(zs,ik0,color='black',linewidth=2, zorder=1)
ax.plot(zs,ik1,color='black',linewidth=2, zorder=1)
ax.plot(zs,ik2,color='black',linewidth=2, zorder=1)
ax.plot(datz,
datmag[:,3] - datmag[:,8],
marker='o',
markersize=2,
alpha=0.5,
markeredgecolor='none',
markerfacecolor='gray',
linestyle='',
zorder=0)
textprops = dict(fontsize=10,ha='left',va='center', zorder=1, color='red')
ax.text(3.5,0.646,'E(B-V) = 0.0', **textprops)
ax.text(3.5,1.455,'E(B-V) = 0.1', **textprops)
ax.text(3.5,2.285,'E(B-V) = 0.2', **textprops)
ax.set_xlim(2,4)
ax.set_ylim(-2,4)
ax.set_xlabel(r'Redshift $z$')
ax.set_ylabel(r'$i - K$')
fig.tight_layout()
fig.savefig('/home/lc585/thesis/figures/chapter06/ik_versus_z_high_ext.pdf')
plt.show()
return None
def 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