def test_load_modelS_amde(self):
amde1 = adipls.load_amde('data/modelS_nfmode1.amde')
amde2 = adipls.load_amde('data/modelS_nfmode2.amde', nfmode=2)
amde3 = adipls.load_amde('data/modelS_nfmode3.amde', nfmode=3)
np.testing.assert_equal(amde1.eigs[:, :, 1:3], amde2.eigs)
np.testing.assert_equal(amde1.x, amde2.x)
np.testing.assert_equal(amde1.x, amde3.x)
np.testing.assert_equal(amde1.css, amde2.css)
np.testing.assert_equal(amde1.css, amde3.css)
np.testing.assert_equal(amde1.eigs[0],
amde1.eig_ln(amde1.l[0], amde1.n[0]))
np.testing.assert_equal(amde1.eigs[0],
amde1.eig_nl(amde1.n[0], amde1.l[0]))
s = '%s' % amde1
s = '%r' % amde2
def test_load_modelS_amde(self):
css1, eigs1 = adipls.load_amde('data/modelS_nfmode1.amde')
css2, eigs2, x2 = adipls.load_amde('data/modelS_nfmode2.amde',
nfmode=2)
css3, eigs3, x3 = adipls.load_amde('data/modelS_nfmode3.amde',
nfmode=3)
for cs1, cs2, cs3 in zip(css1, css2, css3):
self.assertTrue(np.all(cs1 == cs2))
self.assertTrue(np.all(cs1 == cs3))
self.assertAlmostEqual(cs1['M'], 1.989e33)
self.assertAlmostEqual(cs1['R'], 69599062580.0)
for eig1, eig2, eig3 in zip(eigs1, eigs2, eigs3):
self.assertTrue(np.all(eig1[:, 1:3] == eig2))
self.assertTrue(np.all(eig1[:, 5:] == eig3))
self.assertTrue(np.all(eig1[:, 0] == x2))
self.assertTrue(np.all(eig1[:, 0] == x3))
def myplot():
th = np.linspace(0.0, np.pi, 201)
ph = np.linspace(0.5*np.pi, 2.0*np.pi, 301)
Th, Ph = np.meshgrid(th, ph)
x = np.outer(np.cos(ph), np.sin(th))
y = np.outer(np.sin(ph), np.sin(th))
z = np.outer(np.ones(np.size(ph)), np.cos(th))
s = sph_harm(emm, ell, Ph, Th).real
mlab.mesh(x, y, z, scalars=s, colormap='seismic')
S = fgong.load_fgong('data/modelS.fgong', return_object=True)
css, eigs = adipls.load_amde('data/modelS.amde')
I = np.where(css['ell']==ell)[0]
if args.freq:
i = I[np.argmin((css['nu_Ri'][I]-args.freq)**2)]
elif args.enn:
i = I[css['enn'][I]==args.enn][0]
else:
i = I[css['enn'][I]==14][0]
print(' n = %i' % css['enn'][i])
print('freq = %.6f mHz' % css['nu_Ri'][i])
r = eigs[i][:,0]
y1 = eigs[i][:,1]
rho = np.interp(r, S.x[::-1], S.rho[::-1])
# r = np.linspace(0.,1.,51)
# s = np.outer(np.sin(1.5*np.pi*r), np.ones(len(th)))
s = np.outer(r*rho**0.5*y1, np.ones(len(th)))
s = s*sph_harm(emm, ell,
np.outer(np.ones(len(r)), np.ones(len(th))),
np.outer(np.ones(len(r)), th)).real
# s = 0.5+0.5*s/np.max(np.abs(s))
smax = np.max(np.abs(s))/2. # divide by two gives richer colour
smin = -smax # guarantees symmetry in colourmap
# first inner semicircle
x = np.outer(r, np.sin(th))
y = 0.*x
z = np.outer(r, np.cos(th))
mlab.mesh(x, y, z, scalars=-s, colormap='seismic',
vmin=smin, vmax=smax)
# second inner semicircle
y = np.outer(r, np.sin(th))
x = 0.*y
z = np.outer(r, np.cos(th))
mlab.mesh(x, y, z, scalars=-s, colormap='seismic',
vmin=smin, vmax=smax)
mlab.view(azimuth=args.view[0], elevation=args.view[1], distance=4.2)
psi = InterpolatedUnivariateSpline(result.x, result.y[0])(x)
K_psi = P_hat(x) * np.gradient(psi / P_hat(x), x)
#K_psi = P * np.gradient(psi / P, r)
pd.DataFrame({
'x': x,
'psi': psi,
'K_psi': K_psi
}).to_csv('psi_lambda.dat', sep='\t', index=False)
#exit()
## amde file holds the eigenfunctions
# ref: ibid., Section 8.4
# important note: TOMSO uses nfmode=1 !
amde = adipls.load_amde(amde_fname, nfmode=1, return_object=True)
## read equation of state derivatives from file, either FGONG or external
if os.path.exists(gamder_fname):
gamder = pd.read_table(gamder_fname,
sep='\\s+',
names=['x', 'dgam_P', 'dgam_rho', 'dgam_Y'])[::-1]
else:
gong = fgong.load_fgong(fgong_fname, return_object=True)
if gong.var.shape <= 25:
print("Error: FGONG does not contain EOS derivatives")
gamder = pd.DataFrame({
'x': gong.var[:, 0] / R,
'dgam_rho': gong.var[:, 25], # (dGamma_1/drho)_P,Y
'dgam_P': gong.var[:, 26], # (dGamma_1/dP)_rho,Y
--enn) or the cyclic frequency (-f, --freq)""")
amax = 0.55 # np.sqrt((2.*ell+1.)/(4.*np.pi)*factorial(l-m)/factorial(l+m)) ?
Ntheta = 201
def get_colour(theta, phi):
a = sph_harm(args.emm, args.ell,
np.outer(phi, np.ones(len(theta))),
np.outer(np.ones(len(phi)), theta)).real
a = 0.5+0.5*a/amax
return pl.cm.seismic(a)
S = fgong.load_fgong('data/modelS.fgong', return_object=True)
css, eigs = adipls.load_amde('data/modelS.amde')
I = np.where(css['ell']==args.ell)[0]
if args.freq:
i = I[np.argmin((css['nu_Ri'][I]-args.freq)**2)]
elif args.enn:
i = I[css['enn'][I]==args.enn][0]
else:
i = I[css['enn'][I]==14][0]
r = eigs[i][:,0]
y1 = eigs[i][:,1]
rho = np.interp(r, S.x[::-1], S.rho[::-1])
theta = np.linspace(0.0, 2.*np.pi, Ntheta)
a = np.outer(r*rho**0.5*y1, np.ones(len(theta)))
# use pi/2-theta instead of theta so that 0 degrees is at the top
a = a*sph_harm(args.emm, args.ell,
def test_cross_check_css(self):
agsm = adipls.load_agsm('data/modelS.agsm')
amde = adipls.load_amde('data/modelS_nfmode1.amde')
rkr = adipls.load_rkr('data/modelS.rkr')
np.testing.assert_equal(agsm.css, amde.css)
np.testing.assert_equal(agsm.css, rkr.css)
def test_cross_check_css(self):
css_agsm = adipls.load_agsm('data/modelS.agsm')
css_amde = adipls.load_amde('data/modelS_nfmode1.amde')[0]
css_rkr = adipls.load_rkr('data/modelS.rkr')[0]
self.assertTrue(np.all(css_agsm == css_amde))
self.assertTrue(np.all(css_agsm == css_rkr))
