def BALnicity ( w, f, wline, norm_method = "SP", point=None): ''' calculate the BALnicity index of a line :INPUT nu_line float The frequency of the line at line centre nu_array float array of frequencies spec_array float array of intensities/fluxes :OUTPUT BALnicity index float ''' # we integrate over positive velocities as looking for blueshifted BALs # first we need to normalise to the continuum if norm_method == "fit": return -999 elif norm_method == "SP": if point == None: point = wline + 100.0 norm = util.get_flux_at_wavelength( w, f, point) elif norm_method == "mean": p1 = util.get_flux_at_wavelength( w, f, point[0]) p2 = util.get_flux_at_wavelength( w, f, point[0]) norm = np.mean([p1, p2]) fnorm = f / norm fv = 1 - (fnorm / 0.9) vel = C * (wline - w) / wline * 1e-5 # velocity in KM/S constant = np.zeros(len(vel)) for i, v in enumerate(vel): if v > 2000.0: if fnorm[i] < 0.9: select = (vel >= (v - 2000.0)) * (vel <= v) if fnorm[select].all() <= 0.9: constant[i] = 1.0 integration_limits = (vel < 25000.) * (vel > 3000.) if np.sum(constant) == 0: return 0.0, norm BI = trapz(constant*fv[integration_limits], vel[integration_limits]) return BI, norm
for iedd in range(len(edd_frac)):
mdot = mdot_from_edd(edd_frac[iedd], masses[im])
pretty.set_pretty()
for ispin in range(len(spin)):
f2000 = []
for ii in range(len(incs)):
fname = "sp%i_inc%2i_mbh%i_edd%itminus2" % (int(spin[ispin]+0.5),incs[ii], np.log10(masses[im]), edd_frac[iedd]/0.01)
w,f=read_agnspec(fname)
ff = util.get_flux_at_wavelength(w,f,flambda_look)
f2000.append(ff)
#plot(w,f,label=incs[ii])
if sys.argv[1] == "rel":
plot(incs, f2000, linestyle=lstyles[ispin], c=set2[iedd], label="$\epsilon=%.2f$" % edd_frac[iedd])
if spin[ispin] == 0 and edd_frac[iedd] == .2:
fnorm = np.array(f2000)
if masses[im] == 1e9 and flambda_look > 200 and flambda_look < 30000:
s = r.read_spectrum('disk_only')
f2000 = []
for ii in range(len(incs)):
f = s["A%iP0.50" % incs[ii]]
def make_spec_plot(s,
fname,
smooth_factor=10,
angles=True,
components=False,
with_composite=False):
'''
make a spectrum plot from astropy.table.table.Table object
Parameters
----------
s: astropy.table.table.Table
table containing spectrum data outputted from Python
fname: str
filename to save as e.g. sv
smooth_factor: int
factor you would like to smooth by, default 10
angles: Bool
Would you like to plot the viewing angle spectra?
components: Bool
would you like to plot the individual components e.g. Disk Wind
Returns
----------
Success returns 0
Failure returns 1
Saves output as "spectrum_%s.png" % (fname)
'''
if type(s) != astropy.table.table.Table:
raise TypeError(
"make_spec_plot takes astropy.table.table.Table object as first arg"
)
return 1
if s.colnames[8] != "Scattered":
print("Warning- colnames are not in expected order! {} != Scattered".
format(s.colnames[8]))
ncomponents = 9
if angles:
# first make viewing angle plot
p.figure(figsize=(8, 12))
nspecs = len(s.dtype.names) - ncomponents
nx = 1
ny = nspecs
if nspecs > 4:
nx = 2
ny = (1 + nspecs) / nx
print("Making a {} by {} plot, {} spectra".format(nx, ny, nspecs))
if with_composite:
lambda_composite, f_composite, errors = np.loadtxt(
"%s/examples/telfer_qso_composite_hst.asc" %
(os.environ["PYTHON"]),
unpack=True,
comments="#")
for i in range(nspecs):
p.subplot(ny, nx, i + 1)
if with_composite:
f_1100 = util.get_flux_at_wavelength(
s["Lambda"], s[s.dtype.names[ncomponents + i]], 1100.0)
p.plot(s["Lambda"],
util.smooth(s[s.dtype.names[ncomponents + i]] / f_1100,
window_len=smooth_factor),
label="Model")
p.plot(lambda_composite, f_composite, label="HST composite")
p.legend()
else:
p.plot(
s["Lambda"],
util.smooth(s[s.dtype.names[ncomponents + i]],
window_len=smooth_factor))
p.title(s.dtype.names[ncomponents + i])
p.xlabel("Wavelength")
p.ylabel("Flux")
p.savefig("spectrum_%s.png" % (fname), dpi=300)
p.clf()
if components:
p.figure(figsize=(8, 12))
p.subplot(211)
p.plot(s["Lambda"],
util.smooth(s["Created"], window_len=smooth_factor),
label="Created")
p.plot(s["Lambda"],
util.smooth(s["Emitted"], window_len=smooth_factor),
label="Emitted")
p.legend()
p.subplot(212)
for i in range(4, 9):
p.plot(s["Lambda"],
util.smooth(s[s.dtype.names[i]], window_len=smooth_factor),
label=s.dtype.names[i])
p.xlabel("Wavelength")
p.ylabel("Flux")
p.legend()
p.savefig("spec_components_%s.png" % (fname), dpi=300)
p.clf()
return 0
labels = ["LINER", "Quasar", "Sy 1", "Sy 2"]
fnames = ["liner_template.ascii",
"qso_template.ascii","seyfert1_template.ascii",
"seyfert2_template.ascii"]
figure(figsize=(10,14))
big_tick_labels(18)
for i, f in enumerate(fnames):
subplot(4,1,i+1)
w,flux = np.loadtxt(f, unpack=True)
fnorm = util.get_flux_at_wavelength(w,flux,2000)
plot(w,flux/fnorm, label=labels[i], linewidth=3, c='k')
xlim(1000,7000)
ylim(0.05,100.0)
semilogy()
#float_legend()
text(6000,10,labels[i], fontsize=24)
if i < 3:
gca().set_xticklabels([])
if i == 2:
text(400,100,"$F / F_{2000}$", fontsize=24, rotation="vertical")
