def from_lambda(name, lmd, flux, u_lmd="A"):
freq = rf.val_to_hz(np.asarray(lmd), inp=u_lmd) # Spectrum.wl2freq(lmd)
# flux = flux_freq * freq ** 2 / phys.c
flux_freq = np.asarray(flux)/freq**2 * phys.c # Flux_lambda (ergs/s/cm)
if u_lmd == "A":
flux_freq *= 1e8 # Flux_lambda (ergs/s/Angstrom)
return Spectrum(name, freq, flux_freq)
def test_spec_kcorr_zplot(self):
import matplotlib.pyplot as plt
nf, start, end = 100, 10., 1e5
wl = np.exp(np.linspace(np.log(end), np.log(start), nf))
freq = rf.val_to_hz(wl, inp="A")
Tcolor = 7.e3
sp = spectrum.SpectrumPlanck(freq, Tcolor, 'test')
bn_rest = 'B'
bn_obs = bn_rest
br = band_by_name(bn_rest)
bo = band_by_name(bn_obs)
z_arr = np.linspace(0, 0.3, 33)
kcoors = []
for z in z_arr:
k = kcorrection_spec(sp, z, br, bo)
kcoors.append(k)
fig, ax = plt.subplots(figsize=(4, 16))
ax.plot(z_arr, kcoors, marker='o', ls='', markersize=3)
# ax.set_ylim(-0., .3)
ax.grid()
ax.set_title("{} band, Tcol= {:.0f} K".format(bn_rest, Tcolor))
ax.set_xlabel('Redshift')
ax.set_ylabel('k-correction')
plt.show()
fig.savefig('k-correction.pdf')
def test_spec_kcorr_with_curve(self):
nf, start, end = 100, 10., 1e5
wl = np.exp(np.linspace(np.log(end), np.log(start), nf))
freq = rf.val_to_hz(wl, inp="A")
Tcolor = 7e3
sp = spectrum.SpectrumPlanck(freq, Tcolor, 'test')
z = 0.1
distance = 1e6 # phys.cosmology_D_by_z(z)
bn_rest = 'g'
# bn_obs = 'V'
bn_obs = bn_rest
br = band_by_name(bn_rest)
bo = band_by_name(bn_obs)
M_Q = sp.to_mag(br, z=0., d=phys.pc2cm(10.))
m_r = sp.to_mag(bo, z=z, d=phys.pc2cm(distance))
DM = phys.dist2MD(distance)
K_QR = m_r - M_Q - DM
kcorr = kcorrection_spec(sp, z, br, bo)
print("{}: z= {} K_QR= {:.5f} kcorr= {:.5f}".format(bn_rest, z, K_QR, kcorr))
self.assertAlmostEqual(K_QR, kcorr, 8, "{}: For z={} kcorr should be like K_QR. But kcorr={:.5f} K_QR= {:.5f}.".
format(bn_rest, z, kcorr, K_QR))
def setUp(self):
nf = 100
start, end = 10., 1e5
wl = np.exp(np.linspace(np.log(start), np.log(end), nf))
nu = rf.val_to_hz(wl, inp="A")
T = 5500
s = spectrum.SpectrumPlanck(nu, T)
self.sp = s
def setUp(self):
nf = 100
start, end = 10., 1e5
wl = np.exp(np.linspace(np.log(start), np.log(end), nf))
nu = rf.val_to_hz(wl, inp="A")
T = 5500
s = spectrum.SpectrumPlanck(nu, T)
self.sp = s
def setUp(self):
nf, start, end = 100, 10., 1e5
wl = np.exp(np.linspace(np.log(end), np.log(start), nf))
freq = rf.val_to_hz(wl, inp="A")
flux = np.ones(nf)
self.sp = spectrum.Spectrum('test_spectrum',
freq=freq,
flux=flux,
is_sort_wl=True)
def plot_bb(Trad, Tcol, W1, W2, wl_lim=(100., 1e5, 100)):
# freq
# nf, start, end = 100, 10., 1e5
wl = np.exp(np.linspace(np.log(wl_lim[1]), np.log(wl_lim[0]), wl_lim[2]))
freq = rad_func.val_to_hz(wl, inp="A")
sp_1 = spectrum.SpectrumDilutePlanck(freq, Trad, W1)
sp_2 = spectrum.SpectrumDilutePlanck(freq, Tcol, W2)
# setup figure
plt.matplotlib.rcParams.update({'font.size': 14})
fig = plt.figure(num=None,
figsize=(7, 11),
dpi=100,
facecolor='w',
edgecolor='k')
gs1 = gridspec.GridSpec(1, 1)
ax = fig.add_subplot(gs1[0, 0])
gs1.update(wspace=0.3, hspace=0.3, left=0.15, right=0.95)
# plot Trad
x = sp_1.Wl * phys.cm_to_angs
y = sp_1.FluxWl
ax.semilogy(x,
y,
color='red',
ls=":",
linewidth=2.5,
label='Diluted 1: Tcol=%6.1f W=%4.2f' % (sp_1.T, sp_1.W))
# plot Tcol & W
xx = sp_2.Wl * phys.cm_to_angs
yy = sp_2.FluxWl
ax.semilogy(xx,
yy,
color='blue',
ls="--",
linewidth=2.5,
label='Diluted 2: Tcol=%6.1f W=%4.2f' % (sp_2.T, sp_2.W))
ymax = np.max([y, yy])
ylim = [ymax * 1e-15, ymax * 1e1]
ax.set_ylim(ylim)
ax.set_ylabel(r'$F_\lambda, \, [erg\, s^{-1} cm^2]$')
ax.set_xscale('log')
# ax.set_yscale('log')
ax.set_xlabel(r'$\lambda, \, [\AA]$')
ax.legend(prop={'size': 9}, loc=4, borderaxespad=0.)
plt.grid()
plt.show()
def setUp(self):
nf, start, end = 100, 10., 1e5
wl = np.exp(np.linspace(np.log(end), np.log(start), nf))
freq = rf.val_to_hz(wl, inp="A")
times = np.linspace(0., 200., 20)
ss = spectrum.SeriesSpectrum("test_SeriesSpectrum")
for k, t in enumerate(times):
flux = np.ones(nf) # * np.sin(t)
s = spectrum.Spectrum('test_spectrum', freq=freq, flux=flux)
ss.add(t, s)
self.series = ss
def test_spectrum_T_color_zeta(self):
nf, start, end = 100, 10., 1e5
wl = np.exp(np.linspace(np.log(end), np.log(start), nf))
freq = rf.val_to_hz(wl, inp="A")
W = 0.5
Tcolor = 7e3
sp = spectrum.SpectrumDilutePlanck(freq, Tcolor, W, 'test')
Tbb, zeta = sp.T_color_zeta()
self.assertAlmostEqual(
Tcolor, Tbb, 5,
"Init Tcolor={:.2f} should be as Tbb={:.2f}.".format(Tcolor, Tbb))
self.assertAlmostEqual(
W, zeta, 5,
"Init W={:.2f} should be as zeta={:.2f}.".format(W, zeta))
def test_spec_kcorr_positive(self):
nf = 300
start, end = 100., 1e5
wl = np.exp(np.linspace(np.log(end), np.log(start), nf))
freq = rf.val_to_hz(wl, inp="A")
Tcolor = 7e3
sp = spectrum.SpectrumPlanck(freq, Tcolor, 'test')
z = 0.1
for bn_rest in ['U', 'B', 'V', 'R', 'u', 'g', 'r']:
bn_obs = bn_rest
br = band_by_name(bn_rest)
bo = band_by_name(bn_obs)
kcorr = kcorrection_spec(sp, z, br, bo)
print('{}: z= {} kcorr= {}'.format(bn_rest, z, kcorr))
self.assertGreater(kcorr, 0., "{}: For z={} kcorr should be > 0. But kcorr={:.5f}.".
format(bn_rest, z, kcorr))
def test_spec_kcorr_z0(self):
nf, start, end = 100, 10., 1e5
wl = np.exp(np.linspace(np.log(end), np.log(start), nf))
freq = rf.val_to_hz(wl, inp="A")
Tcolor = 7e3
sp = spectrum.SpectrumPlanck(freq, Tcolor, 'test')
z = 0.
bn_rest = 'V'
# bn_obs = 'V'
for bn_rest in ['U', 'B', 'V', 'R', 'g']:
bn_obs = bn_rest
br = band_by_name(bn_rest)
bo = band_by_name(bn_obs)
kcorr = kcorrection_spec(sp, z, br, bo)
self.assertAlmostEqual(kcorr, 0., 8, "{}: For z={} kcorr should be 0. But kcorr={:.5f}.".
format(bn_rest, z, kcorr))
def plot_bb(Trad, Tcol, W1, W2, wl_lim=[100, 1e5, 100.]):
# freq
# nf, start, end = 100, 10., 1e5
wl = np.exp(np.linspace(np.log(wl_lim[1]), np.log(wl_lim[0]), wl_lim[2]))
freq = rad_func.val_to_hz(wl, inp="A")
sp_1 = spectrum.SpectrumDilutePlanck(freq, Trad, W1)
sp_2 = spectrum.SpectrumDilutePlanck(freq, Tcol, W2)
# setup figure
plt.matplotlib.rcParams.update({'font.size': 14})
fig = plt.figure(num=None, figsize=(7, 11), dpi=100, facecolor='w', edgecolor='k')
gs1 = gridspec.GridSpec(1, 1)
ax = fig.add_subplot(gs1[0, 0])
gs1.update(wspace=0.3, hspace=0.3, left=0.15, right=0.95)
# plot Trad
x = sp_1.Wl * phys.cm_to_angs
y = sp_1.Flux_wl
ax.plot(x, y, color='red', ls=":", linewidth=2.5, label='Diluted 1: Tcol=%6.1f W=%4.2f' % (sp_1.T, sp_1.W))
# plot Tcol & W
xx = sp_2.Wl * phys.cm_to_angs
yy = sp_2.Flux_wl
ax.plot(xx, yy, color='blue', ls="--", linewidth=2.5, label='Diluted 2: Tcol=%6.1f W=%4.2f' % (sp_2.T, sp_2.W))
ymax = np.max([y, yy])
ylim = [ymax*1e-12, ymax*1e1]
ax.set_ylim(ylim)
ax.set_ylabel(r'$F_\lambda, \, [erg\, s^{-1} cm^2]$')
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlabel(r'$\lambda, \, [\AA]$')
ax.legend(prop={'size': 9}, loc=4, borderaxespad=0.)
plt.grid()
plt.show()
def setUp(self):
nf, start, end = 100, 10., 1e5
wl = np.exp(np.linspace(np.log(end), np.log(start), nf))
freq = rf.val_to_hz(wl, inp="A")
flux = np.ones(nf)
self.sp = spectrum.Spectrum('test_spectrum', freq=freq, flux=flux, is_sort_wl=True)