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():
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():
plslp1 = 0.71
plslp2 = 0.05
plbrk = 2679.92
bbt = 1158.99
bbflxnrm = 0.25
galfra = 0.89
elscal = 0.72
imod = 18.0
scahal = 1.0
with open('input.yml', 'r') as f:
parfile = yaml.load(f)
# Load stuff
fittingobj = load(parfile)
wavlen = fittingobj.get_wavlen()
flxcorr = np.array([1.0] * len(wavlen))
ik0, ik1, ik2 = [], [], []
zs = np.arange(2,4.01,0.01)
for z in zs:
print z
magtmp, wavlentmp1, fluxtmp1 = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
0.0,
imod,
z,
fittingobj,
flxcorr,
parfile)
ik0.append( magtmp[3] - magtmp[8] )
magtmp, wavlentmp1, fluxtmp1 = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
0.1,
imod,
z,
fittingobj,
flxcorr,
parfile)
ik1.append( magtmp[3] - magtmp[8] )
magtmp, wavlentmp1, fluxtmp1 = model(plslp1,
plslp2,
plbrk,
bbt,
bbflxnrm,
elscal,
scahal,
galfra,
0.2,
imod,
z,
fittingobj,
flxcorr,
parfile)
ik2.append( magtmp[3] - magtmp[8] )
datmag, sigma, datz, name, snr = loaddatraw('DR10',
'/data/lc585/SDSS/DR10QSO_AllWISE_matched.v2.fits',
True,
0,
23.0,
15.0,
False,
0.1)
fig = plt.figure(figsize=figsize(0.7))
ax = fig.add_subplot(111)
ax.plot(zs,ik0,color='black',linewidth=2, zorder=1)
ax.plot(zs,ik1,color='black',linewidth=2, zorder=1)
ax.plot(zs,ik2,color='black',linewidth=2, zorder=1)
ax.plot(datz,
datmag[:,3] - datmag[:,8],
marker='o',
markersize=2,
alpha=0.5,
markeredgecolor='none',
markerfacecolor='gray',
linestyle='',
zorder=0)
textprops = dict(fontsize=10,ha='left',va='center', zorder=1, color='red')
ax.text(3.5,0.646,'E(B-V) = 0.0', **textprops)
ax.text(3.5,1.455,'E(B-V) = 0.1', **textprops)
ax.text(3.5,2.285,'E(B-V) = 0.2', **textprops)
ax.set_xlim(2,4)
ax.set_ylim(-2,4)
ax.set_xlabel(r'Redshift $z$')
ax.set_ylabel(r'$i - K$')
fig.tight_layout()
fig.savefig('/home/lc585/thesis/figures/chapter06/ik_versus_z_high_ext.pdf')
plt.show()
return None
def plot():
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():
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 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
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