def least_sq(p, fjac):
T = p[0]
w = p[1]
if is_Fj:
wbb = w * rf.planck(self.Freq, T, inp="Hz", out="freq")
else:
wbb = np.pi * w * rf.planck(self.Freq, T, inp="Hz", out="freq")
# model interpolation
res = np.abs(self.Flux - wbb)
return 0, res
def test_fit_bb(self):
def func(nu, T):
return np.pi * rf.planck(nu, T, inp="Hz", out="freq")
self.sp.cut_flux(max(self.sp.Flux) * 1e-5)
freq = self.sp.Freq
flux = self.sp.Flux
Tinit = 1.e4
popt, pcov = curve_fit(func, freq, flux, p0=Tinit)
Tbest = popt
# bestT, pcov = curve_fit(rf.fit_planck(nu, inp='Hz'), nu, flux, p0=Tinit, sigma=sigma)
sigmaT = np.sqrt(np.diag(pcov))
print 'True model values'
print ' Tbb = %.2f K' % self.sp.T
print 'Parameters of best-fitting model:'
print ' T = %.2f +/- %.2f K' % (Tbest, sigmaT)
# Tcol = self.sp.temp_color
ybest = np.pi * rf.planck(freq, Tbest, inp="Hz", out="freq")
# plot the solution
plt.plot(freq, flux, 'b*', label='Spectral T: %f' % self.sp.T)
plt.plot(freq, ybest, 'r-', label='Best Tcol: %f' % Tbest)
plt.xscale('log')
plt.yscale('log')
plt.legend(loc=3)
plt.show()
self.assertAlmostEqual(Tbest, self.sp.T,
msg="For planck Tcolor [%f] should be equal sp.T [%f]." % (Tbest, self.sp.T),
delta=Tbest*0.01)
def test_flux_wl_with_ReddeningLaw(self):
import matplotlib.pyplot as plt
from pystella.rf.reddening import ReddeningLaw
import pystella.rf.rad_func as rf
from pystella.util.phys_var import phys
wl = np.logspace(3.0, np.log10(5e4), num=100)
freq = phys.c / (wl * phys.angs_to_cm)
T = 15500.
flux = rf.planck(freq, temperature=T)
flux_wl = rf.Fnu2Fwl(freq, flux)
# flux_wl = flux * freq**2 / phys.c
ebv = 0.074
plt.plot(wl, flux_wl, color='black', label='Origin planck T={}'.format(T))
for mode in ReddeningLaw.Modes:
x = wl
y = flux_reddening_wl(wl, flux_wl, ebv, mode=mode)
plt.plot(x, y, label=ReddeningLaw.Names[mode])
plt.legend()
plt.xscale("log", nonposx='clip')
plt.yscale("log", nonposy='clip')
plt.xlabel(r'$\lambda\; [A]$')
plt.ylabel(r'$F_\lambda\; [ergs s^{-1} cm^{-2} A^{-1}]$')
plt.grid(linestyle=':', linewidth=1)
plt.show()
def plot(self):
# plot the input model and synthetic data
nu = self.sp._freq
flux = self.sp.flux_q
Tcol = self.sp.T_color
ybest = rf.planck(nu, Tcol)
# plot the solution
plt.plot(nu, flux, 'b*', nu, ybest, 'r-', label='Tinit=%f, Tcol=%f'%(self.sp.Tbb, Tcol))
plt.xscale('log')
plt.yscale('log')
plt.show()
def plot(self):
# plot the input model and synthetic data
nu = self.sp.Freq
flux = self.sp.Flux
Tcol = self.sp.T_color
ybest = rf.planck(nu, Tcol)
# plot the solution
plt.plot(nu,
flux,
'b*',
nu,
ybest,
'r-',
label='Tinit=%f, Tcol=%f' % (self.sp.T, Tcol))
plt.xscale('log')
plt.yscale('log')
plt.show()
def test_fit_bb(self):
def func(nu, T):
return np.pi * rf.planck(nu, T, inp="Hz", out="freq")
self.sp.cut_flux(max(self.sp.Flux) * 1e-5)
freq = self.sp.Freq
flux = self.sp.Flux
Tinit = 1.e4
popt, pcov = curve_fit(func, freq, flux, p0=Tinit)
Tbest = float(popt)
# bestT, pcov = curve_fit(rf.fit_planck(nu, inp='Hz'), nu, flux, p0=Tinit, sigma=sigma)
sigmaT = np.sqrt(np.diag(pcov))
print('True model values')
print(' Tbb = %.2f K' % self.sp.T)
print('Parameters of best-fitting model:')
print(" T = {} +/- {} K".format(Tbest, sigmaT))
# Tcol = self.sp.temp_color
ybest = np.pi * rf.planck(freq, Tbest, inp="Hz", out="freq")
# plot the solution
plt.plot(freq, flux, 'b*', label='Spectral T: %f' % self.sp.T)
plt.plot(freq, ybest, 'r-', label='Best Tcol: %f' % Tbest)
plt.xscale('log')
plt.yscale('log')
plt.legend(loc=3)
plt.show()
self.assertAlmostEqual(
Tbest,
self.sp.T,
msg="For planck Tcolor [{:,f}] should be equal sp.T [{:,f}].".
format(Tbest, self.sp.T),
delta=Tbest * 0.01)
def func(nu, T):
return np.pi * rf.planck(nu, T, inp="Hz", out="freq")
def __init__(self, freq, temperature, W, name='wbb'):
freq = np.array(freq)
flux = math.pi * W * rf.planck(freq, temperature=temperature)
Spectrum.__init__(self, name, freq=freq, flux=flux)
self._W = W
self._T = temperature
def __init__(self, freq, temperature, name='bb'):
flux = math.pi * rf.planck(freq, temperature=temperature)
Spectrum.__init__(self, name, freq=freq, flux=flux)
self._T = temperature
def func(nu, T):
return np.pi * rf.planck(nu, T, inp="Hz", out="freq")
def __init__(self, freq, temperature, W, name='wbb'):
flux = math.pi * W * rf.planck(freq, temperature=temperature)
Spectrum.__init__(self, name, freq=freq, flux=flux)
self._W = W
self._T = temperature