def runtest_BrokenPowerlawSED(sed, plot=False):
nu_912 = sed.lambdanu(912.)
ratio_1500_L_num = (sed(sed.lambdanu(1500.))/
sed(sed.lambdanu(912. - 1e-10)))[0]
# Test the magnitude of the break.
assert numpy.abs(ratio_1500_L_num - sed.break_factor) < 1e-9
# Test the normalization of the integral.
numintegral = utils.logquad(sed, sed.lambdanu(3000.), sed.lambdanu(1e-6))[0]
assert numpy.abs(numintegral - 1.) < 1e-6
if plot:
import pylab
wav = numpy.arange(100.,2000.,1.0)
nu = sed.lambdanu(wav)
pylab.subplot(121)
#norm = sed(nu_912 + 1e10)
norm = 1.0
pylab.plot(wav, sed(nu)/norm)
#pylab.axhline(y=6, ls=':')
#pylab.axvline(x=1500, ls=':')
pylab.subplot(122)
pylab.plot(nu, sed(nu)/norm)
def test_plot_schechter():
phiStar = 1.8e-3
MStar = -20.04
alpha = -1.71
LStar = magnitudes.L_nu_from_magAB(MStar)
mags = numpy.arange(-22, -11.0, 0.5)
lums = magnitudes.L_nu_from_magAB(mags)
phi_L = schechterL(lums, phiStar, alpha, LStar)
phi_M = schechterM(mags, phiStar, alpha, MStar)
L_L = schechterCumuLL(lums, phiStar, alpha, LStar)
L_M = schechterCumuLM(mags, phiStar, alpha, MStar)
phi_L_func = lambda l: l * schechterL(l, phiStar, alpha, LStar)
L_L_num = utils.logquad(phi_L_func, lums, 1e35)[0]
L_L_num2 = utils.vecquad(phi_L_func, lums, 1e29)[0]
phi_M_func = lambda m: (magnitudes.L_nu_from_magAB(m) * schechterM(
m, phiStar, alpha, MStar))
L_M_num2 = utils.vecquad(phi_M_func, -25, mags)[0]
Ltot_L = schechterTotLL(phiStar, alpha, LStar)
Ltot_M = schechterTotLM(phiStar, alpha, MStar)
pylab.figure()
pylab.subplot(221)
pylab.plot(lums, lums * lums * phi_L)
pylab.xscale('log')
pylab.yscale('log')
pylab.ylabel(r'$ L^2 \Phi_L$')
pylab.subplot(222)
pylab.plot(mags, -mags * lums * phi_M)
pylab.yscale('log')
pylab.ylabel(r'$ -M L \Phi_M$')
pylab.subplot(223)
pylab.plot(lums, Ltot_L - L_L)
pylab.plot(lums, L_M)
pylab.plot(lums, L_L_num, '--')
pylab.plot(lums, L_L_num2, ':')
pylab.plot(lums, L_M_num2, 'x')
pylab.axhline(y=Ltot_L)
pylab.axhline(y=Ltot_M)
pylab.xscale('log')
pylab.yscale('log')
pylab.subplot(224)
pylab.plot(mags, Ltot_M - L_M)
pylab.plot(mags, L_L)
pylab.plot(mags, L_L_num, '--')
pylab.plot(mags, L_L_num2, ':')
pylab.plot(mags, L_M_num2, 'x')
pylab.axhline(y=Ltot_L)
pylab.axhline(y=Ltot_M)
pylab.yscale('log')
def test_plot_schechter():
phiStar = 1.8e-3
MStar = -20.04
alpha = -1.71
LStar = magnitudes.L_nu_from_magAB(MStar)
mags = numpy.arange(-22, -11.0, 0.5)
lums = magnitudes.L_nu_from_magAB(mags)
phi_L = schechterL(lums, phiStar, alpha, LStar)
phi_M = schechterM(mags, phiStar, alpha, MStar)
L_L = schechterCumuLL(lums, phiStar, alpha, LStar)
L_M = schechterCumuLM(mags, phiStar, alpha, MStar)
phi_L_func = lambda l: l * schechterL(l, phiStar, alpha, LStar)
L_L_num = utils.logquad(phi_L_func, lums, 1e35)[0]
L_L_num2 = utils.vecquad(phi_L_func, lums, 1e29)[0]
phi_M_func = lambda m: (magnitudes.L_nu_from_magAB(m) *
schechterM(m, phiStar, alpha, MStar))
L_M_num2 = utils.vecquad(phi_M_func, -25, mags)[0]
Ltot_L = schechterTotLL(phiStar, alpha, LStar)
Ltot_M = schechterTotLM(phiStar, alpha, MStar)
pylab.figure()
pylab.subplot(221)
pylab.plot(lums, lums * lums * phi_L)
pylab.xscale('log')
pylab.yscale('log')
pylab.ylabel(r'$ L^2 \Phi_L$')
pylab.subplot(222)
pylab.plot(mags, -mags * lums * phi_M)
pylab.yscale('log')
pylab.ylabel(r'$ -M L \Phi_M$')
pylab.subplot(223)
pylab.plot(lums, Ltot_L - L_L)
pylab.plot(lums, L_M)
pylab.plot(lums, L_L_num, '--')
pylab.plot(lums, L_L_num2, ':')
pylab.plot(lums, L_M_num2, 'x')
pylab.axhline(y=Ltot_L)
pylab.axhline(y=Ltot_M)
pylab.xscale('log')
pylab.yscale('log')
pylab.subplot(224)
pylab.plot(mags, Ltot_M - L_M)
pylab.plot(mags, L_L)
pylab.plot(mags, L_L_num, '--')
pylab.plot(mags, L_L_num2, ':')
pylab.plot(mags, L_M_num2, 'x')
pylab.axhline(y=Ltot_L)
pylab.axhline(y=Ltot_M)
pylab.yscale('log')
def runtest_BrokenPowerlawSED(sed, plot=False):
nu_912 = sed.lambdanu(912.)
ratio_1500_L_num = (sed(sed.lambdanu(1500.)) /
sed(sed.lambdanu(912. - 1e-10)))[0]
# Test the magnitude of the break.
assert numpy.abs(ratio_1500_L_num - sed.break_factor) < 1e-9
# Test the normalization of the integral.
numintegral = utils.logquad(sed, sed.lambdanu(3000.),
sed.lambdanu(1e-6))[0]
assert numpy.abs(numintegral - 1.) < 1e-6
if plot:
import pylab
wav = numpy.arange(100., 2000., 1.0)
nu = sed.lambdanu(wav)
pylab.subplot(121)
#norm = sed(nu_912 + 1e10)
norm = 1.0
pylab.plot(wav, sed(nu) / norm)
#pylab.axhline(y=6, ls=':')
#pylab.axvline(x=1500, ls=':')
pylab.subplot(122)
pylab.plot(nu, sed(nu) / norm)
def test_plot_norm_imf(imf_function):
"""Plot and test the normalization of imf_function."""
label = imf_function.__name__.replace('imf_', '')
label = label.replace('_', ' ')
print label
dm = 0.005
mmin = 0.1
mmax = 100.
mass = numpy.arange(mmin, mmax + 1.1 * dm, dm)
imf = imf_function(mass)
# Test that the normalization is correct.
liimf = utils.logquad(imf_function, mmin, mmax)
print liimf[0], ", ",
print 1.-liimf[0]
assert abs(liimf[0] - 1) < 1e-10
# Test normalization using a different integration method.
cimf = scipy.integrate.cumtrapz(imf * dm)
print cimf[-1], ", ",
print 1.-cimf[-1]
assert abs(cimf[-1] - 1) < 1e-3
pylab.subplot(121)
l = pylab.plot(mass, imf, label=label)
pylab.plot(mass[1:], cimf, l[0].get_c())
pylab.gca().set_yscale('log')
pylab.legend(loc='best')
pylab.subplot(122)
pylab.plot(mass, imf, l[0].get_c(), label=label)
pylab.plot(mass[1:], cimf, l[0].get_c())
pylab.gca().set_yscale('log')
pylab.gca().set_xscale('log')
