def setUp(self):
nf = 100
start, end = 10, 1e5
wl = np.exp(np.linspace(np.log(start), np.log(end), nf))
freq = ps.rf.val_to_hz(wl, inp="A")
# freq = freq[np.argsort(freq)] # ascending order
flux = np.ones(nf)
self.sp = ps.Spectrum('uniform', freq=freq, flux=flux)
self.distance = 10 # pc
def test_series_redshift2obs(self):
import matplotlib.pyplot as plt
x = np.linspace(-10, 10, 10)
y = 10. - x**2
# norm
freq = x - np.min(x)
flux = y + np.min(y)
times = [10]
ss = ps.SeriesSpectrum("test_SeriesSpectrum")
for k, t in enumerate(times):
s = ps.Spectrum(freq=freq, flux=flux, name='test_spectrum')
ss.add(t, s)
z = 1.
series = ss
series_z = series.redshift2obs(z)
# plot
for t, sp in series:
plt.plot(sp.Freq,
sp.Flux,
marker='',
ls=':',
label='origin t={:f}'.format(t))
for t, sp in series_z:
plt.plot(sp.Freq,
sp.Flux,
marker='',
ls=':',
label=' z t={:f}'.format(t))
plt.xlabel('Freq')
plt.ylabel('Flux')
plt.legend(loc='best')
plt.show()
def plot_spec_wl(times, series, tt, wl_ab, **kwargs):
font_size = kwargs.get('font_size', 12)
nrow = np.math.ceil(len(times) / 2.)
ncol = 2
fig = plt.figure(figsize=(12, nrow * 4))
plt.matplotlib.rcParams.update({'font.size': font_size})
series_cut = series.copy(wl_ab=wl_ab)
# radiuses = np.interp(times, tt['time'], tt['Rph'], 0, 0)
radiuses = np.interp(times, tt['time'], tt['rbb'], 0, 0)
Tbbes = np.interp(times, tt['time'], tt['Tbb'], 0, 0)
marker_style = dict(linestyle=':', markersize=5)
for i, t in enumerate(times):
ax = fig.add_subplot(nrow, ncol, i + 1)
spec = series.get_spec_by_time(t)
spec.cut_flux(max(spec.Flux) * 1e-6) # cut flux
R = radiuses[i]
star_bb = ps.Star("bb", spec)
star_bb.set_distance(R)
spec_cut = series_cut.get_spec_by_time(t)
star_cut = ps.Star("bb_cut", spec_cut)
star_cut.set_distance(R)
# spectrum
ax.semilogy(star_bb.Wl * ps.phys.cm_to_angs,
star_bb.FluxWlObs,
label="Spec Ph")
# Tcolor
spec_obs = ps.Spectrum('wbb', star_cut.Freq, star_cut.FluxObs)
Tcol = spec_obs.T_color
T_wien = spec_obs.T_wien
zeta = (T_wien / Tcol)**4
wbb = ps.SpectrumDilutePlanck(spec.Freq, Tcol, zeta)
ax.semilogy(wbb.Wl * ps.phys.cm_to_angs,
wbb.FluxWl,
label="Tcol={:.0f} W={:.2f}".format(Tcol, zeta),
marker='<',
**marker_style)
# diluted
Tdil, W = spec_obs.T_color_zeta()
dil = ps.SpectrumDilutePlanck(spec.Freq, Tdil, W)
ax.semilogy(dil.Wl * ps.phys.cm_to_angs,
dil.FluxWl,
label="Tdil={:.0f} W={:.2f}".format(Tdil, W),
marker='>',
**marker_style)
# Tbb
Tbb = Tbbes[i]
bb = ps.SpectrumPlanck(spec.Freq, Tbb)
ax.semilogy(bb.Wl * ps.phys.cm_to_angs,
bb.FluxWl,
label="Tbb={:.0f}".format(Tbb),
marker='d',
**marker_style)
# wl range
if wl_ab is not None:
for xc in wl_ab:
plt.axvline(x=xc, color="grey", linestyle='--')
ax.legend(loc="best", prop={'size': 9})
ax.text(0.01,
0.05,
"$t_d: {:.1f}$".format(t),
horizontalalignment='left',
transform=ax.transAxes,
bbox=dict(facecolor='green', alpha=0.3))
xlim = ax.get_xlim()
ax.set_xlim(xlim[0], min(xlim[1], 2e4))
# fig.subplots_adjust(wspace=0, hspace=0)
# fig.subplots_adjust(wspace=0, hspace=0, left=0.07, right=0.96, top=0.97, bottom=0.06)
plt.subplots_adjust(left=0.07, right=0.96, top=0.97, bottom=0.06)
return fig
def plot_fit_wl(model, series, wl_ab, times=None, fsave=None):
tt = model.get_tt().load()
series_cut = series.copy(wl_ab=wl_ab)
time = series.Time
radius = np.interp(time, tt['time'], tt['rbb'])
if True:
Tcol, Twien, zeta = [], [], []
Tdil, Wdil = [], []
for i, t in enumerate(time):
sp = series_cut.get_spec(i)
# sp = series_cut.get_spec_by_time(t)
R = radius[i]
star_cut = ps.Star("bb_cut", sp)
star_cut.set_distance(R)
sp_obs = ps.Spectrum('cut', star_cut.Freq, star_cut.FluxObs)
Tc = sp_obs.T_color
Tw = sp_obs.T_wien
w = np.power(Tw / Tc, 4)
Td, W_d = sp_obs.T_color_zeta()
Tcol.append(Tc)
Twien.append(Tw)
zeta.append(w)
Tdil.append(Td)
Wdil.append(W_d)
else:
Tcol = series_cut.get_T_color()
Twien = series_cut.get_T_wien()
zeta = np.power(Twien / Tcol, 4)
res = series_cut.get_T_color_zeta()
Tdil, Wdil = res[:, 0], res[:, 1]
print("{:>8}".format("Time") + ' '.join("{:>12s}".format(s)
for s in ("T_dil", "W_dil")))
for t, *p in zip(time, Tdil, Wdil):
print("{:8.2f}".format(t) + ' '.join("{0:12.2f}".format(s) for s in p))
# print("%10.2f %12.6f %12.6f %12.6f " % p)
# save
if fsave is not None:
save_fit_wl(fsave, model.Name, time, Tdil, Wdil, wl_ab=wl_ab)
return
# plot
fig, ax = plt.subplots()
marker_style = dict(linestyle=':', color='cornflowerblue', markersize=10)
ax.semilogy(time,
Tcol,
'ks-',
markerfacecolor='white',
markersize=3,
label="Tcolor")
ax.semilogy(time, Twien, 'r:', label="Twien")
ax.semilogy(time, Tdil, 'ys-', markersize=3, label="T_dil")
ax.semilogy(tt['time'], tt['Tbb'], 'g:', label="tt Tbb")
# ax.semilogy(tt['time'], tt['Teff'], 'y:', label="tt Teff")
# ax.semilogy(results['time'], tt['T'], 'm:', label="tt Teff")
ax.legend()
# wl range
if times is not None:
for xc in times:
ax.axvline(x=xc, color="grey", linestyle='--')
fig = plot_spec_wl(times, series, tt, wl_ab)
else:
pass
return fig