def test_save_modelS_amdl(self):
a1 = adipls.load_amdl('data/modelS.amdl')
a1.to_file(tmpfile)
a2 = adipls.load_amdl(tmpfile)
np.testing.assert_equal(a1.nmod, a2.nmod)
np.testing.assert_equal(a1.nn, a2.nn)
np.testing.assert_equal(a1.D, a1.D)
np.testing.assert_equal(a1.A, a1.A)
def test_save_modelS_amdl(self):
D1, A1, nmod1 = adipls.load_amdl('data/modelS.amdl', return_nmod=True)
adipls.save_amdl(tmpfile, D1, A1, nmod=nmod1)
D2, A2, nmod2 = adipls.load_amdl(tmpfile, return_nmod=True)
self.assertTrue(np.all(nmod1 == nmod2))
self.assertTrue(np.all(len(A1) == len(A2)))
self.assertTrue(np.all(D1 == D2))
self.assertTrue(np.all(A1 == A2))
def test_fgong_to_amdl_modelS(self):
a1 = adipls.load_amdl('data/modelS.amdl', G=6.67232e-8)
m2 = fgong.load_fgong('data/modelS.fgong', G=6.67232e-8)
a2 = m2.to_amdl()
np.testing.assert_allclose(a1.D, a2.D)
np.testing.assert_allclose(a1.A, a2.A)
def test_fgong_to_amdl_mesa(self):
a1 = adipls.load_amdl('data/mesa.amdl', G=6.67428e-8)
f = fgong.load_fgong('data/mesa.fgong', G=6.67428e-8)
a2 = f.to_amdl()
np.testing.assert_allclose(a1.D, a2.D)
np.testing.assert_allclose(a1.A, a2.A)
def test_amdl_to_fgong_mesa(self):
I = [0, 3, 4, 9,
14] # only these columns can be restored from AMDL format
m1 = fgong.load_fgong('data/mesa.fgong', G=6.67428e-8)
a2 = adipls.load_amdl('data/mesa.amdl', G=6.67428e-8)
m2 = a2.to_fgong()
np.testing.assert_allclose(m1.glob[:2], m2.glob[:2])
np.testing.assert_allclose(m1.var[:, I], m2.var[:, I])
def test_fgong_to_amdl_mesa(self):
D1, A1 = adipls.load_amdl('data/mesa.amdl')
glob, var = fgong.load_fgong('data/mesa.fgong')
D2, A2 = adipls.fgong_to_amdl(glob, var, G=6.67232e-8)
for (x, y) in zip(D1, D2):
self.assertAlmostEqual(x, y)
for i in range(len(A1)):
for (x, y) in zip(A1[i], A2[i]):
self.assertAlmostEqual(x, y)
def test_amdl_get(self):
D, A = adipls.load_amdl('data/mesa.amdl')
M, R, x, G1 = adipls.amdl_get(['M', 'R', 'x', 'G1'], D, A)
self.assertEqual(M, D[0])
self.assertEqual(R, D[1])
self.assertTrue(np.all(x == A[:, 0]))
self.assertTrue(np.all(G1 == A[:, 3]))
cs, = adipls.amdl_get(['cs'], D, A)
cs2 = adipls.amdl_get('cs2', D, A)
self.assertTrue(np.allclose(cs**2, cs2))
def test_amdl_get_cross_check(self):
D, A = adipls.load_amdl('data/mesa.amdl')
M, R, P_c, rho_c, r, x, m, q, g, rho, P, Hp, G1, cs2, cs, tau \
= adipls.amdl_get(['M','R', 'P_c', 'rho_c', 'r', 'x', 'm',
'q', 'g', 'rho', 'P', 'Hp', 'G1', 'cs2',
'cs', 'tau'], D, A)
self.assertTrue(np.allclose(q, m / M, rtol=4 * EPS))
self.assertTrue(np.allclose(x, r / R, rtol=4 * EPS))
self.assertTrue(
np.allclose(Hp, P / (rho * g), rtol=4 * EPS, equal_nan=True))
self.assertTrue(np.allclose(cs2, G1 * P / rho, rtol=4 * EPS))
def test_amdl_properties(self):
m = adipls.load_amdl('data/mesa.amdl')
self.assertEqual(m.M, m.D[0])
self.assertEqual(m.R, m.D[1])
np.testing.assert_equal(m.x, m.A[:, 0])
np.testing.assert_equal(m.Gamma_1, m.A[:, 3])
np.testing.assert_equal(m.G1, m.A[:, 3])
np.testing.assert_equal(m.G1, m.Gamma_1)
np.testing.assert_allclose(m.cs**2, m.cs2)
np.testing.assert_allclose(m.S2_1, m.S_1**2)
np.testing.assert_allclose(np.maximum(m.N2, 0), m.N**2)
def test_amdl_properties_cross_check(self):
m = adipls.load_amdl('data/mesa.amdl')
np.testing.assert_allclose(m.q, m.m / m.M, rtol=4 * EPS)
np.testing.assert_allclose(m.x, m.r / m.R, rtol=4 * EPS)
np.testing.assert_allclose(m.Hp,
m.P / (m.rho * m.g),
rtol=4 * EPS,
equal_nan=True)
np.testing.assert_allclose(m.cs2,
m.Gamma_1 * m.P / m.rho,
rtol=4 * EPS)
s = '%r' % m
def convert(args):
"""Convert function for `tomso` command-line script."""
from_format = (guess_format(args.input_file)
if args.from_format == 'guess'
else args.from_format)
to_format = (guess_format(args.output_file)
if args.to_format == 'guess'
else args.to_format)
if from_format == to_format:
raise ValueError("input format and output format are both %s\n"
"did you mean to copy the file?" % from_format)
kwargs = {}
if args.G is not None:
kwargs['G'] = args.G
if from_format == 'fgong':
from tomso.fgong import load_fgong
m = load_fgong(args.input_file, **kwargs)
elif from_format == 'amdl':
from tomso.adipls import load_amdl
m = load_amdl(args.input_file, **kwargs)
elif from_format == 'gyre':
from tomso.gyre import load_gyre
m = load_gyre(args.input_file, **kwargs)
else:
raise ValueError("%s is not a valid input format" % from_format)
if to_format == 'fgong':
m.to_fgong(ivers=args.ivers).to_file(args.output_file)
elif to_format == 'amdl':
m.to_amdl().to_file(args.output_file)
elif to_format == 'gyre':
m.to_gyre().to_file(args.output_file)
else:
raise ValueError("%s is not a valid output format" % to_format)
def test_amdl_to_amdl(self):
a = load_amdl('data/modelS.amdl')
self.compare_models(a, a.to_fgong().to_amdl())
self.compare_models(a, a.to_gyre().to_amdl())
self.compare_models(a, a.to_fgong().to_gyre().to_amdl())
self.compare_models(a, a.to_gyre().to_fgong().to_amdl())
import numpy as np import pandas as pd from scipy.integrate import solve_bvp from scipy.interpolate import InterpolatedUnivariateSpline from tomso import fgong, adipls from tomso.utils import integrate, complement from tqdm import tqdm # we divide by 0 here, and we like it np.seterr(divide='ignore', invalid='ignore') ## load from amdl file because we redistribute the mesh # ref: Notes on adiabatic oscillation programme # Section 5: Equilibrium model variables # https://users-phys.au.dk/jcd/adipack.v0_3/ amdl = adipls.load_amdl(amdl_fname, return_object=True) G = amdl.G # gravitational constant M, R, P_c, rho_c = amdl.D[:4] # mass, radius, central density and pressure x, qv, Vg, G1, A, U = amdl.A.T # dimensionless structure r = x * R # distance from center m = qv * x**3 * M # mass coordinate g = G * m / r**2 # local gravitational acceleration rho = qv * U * M / 4. / np.pi / R**3 # density P = G * m * rho / G1 / r / Vg # pressure drho_dr = -(A + Vg) * rho / r # density gradient # fix irregularities at the centre rho[x == 0] = rho_c P[x == 0] = P_c g[x == 0] = 0 drho_dr[x == 0] = 0
def test_load_modelS_amdl(self):
a = adipls.load_amdl('data/modelS.amdl')
self.assertEqual(a.nmod, 1)
self.assertEqual(len(a.A), 2482)
self.assertAlmostEqual(a.D[0], 1.989e33)
self.assertAlmostEqual(a.D[1], 6.959894677e10)
def test_load_mesa_amdl(self):
a = adipls.load_amdl('data/mesa.amdl')
self.assertEqual(a.nmod, 1)
self.assertEqual(len(a.A), 601)
self.assertAlmostEqual(a.D[0], 1.988205400E+33)
self.assertAlmostEqual(a.D[1], 6.204550713E+10)
def test_load_mesa_amdl_fail(self):
np.savetxt(tmpfile, np.random.rand(100, 100))
with self.assertRaises(IOError):
a = adipls.load_amdl(tmpfile)
def test_load_modelS_amdl(self):
D, A, nmod = adipls.load_amdl('data/modelS.amdl', return_nmod=True)
self.assertEqual(nmod, 1)
self.assertEqual(len(A), 2482)
self.assertAlmostEqual(D[0], 1.989e33)
self.assertAlmostEqual(D[1], 69599062580.0)
def test_load_mesa_amdl(self):
D, A, nmod = adipls.load_amdl('data/mesa.amdl', return_nmod=True)
self.assertEqual(nmod, 1)
self.assertEqual(len(A), 601)
self.assertAlmostEqual(D[0], 1.988205400E+33)
self.assertAlmostEqual(D[1], 6.204550713E+10)