def test_random_cube(self):
numpy.random.seed(1232367)
gen = stellar_wind.PositionGenerator('random')
cube = gen.cube_generator(1000)
self.assertEqual(cube.shape, (1000, 3))
self.assertAlmostEqual(cube.min(), -0.99970440813)
self.assertAlmostEqual(cube.max(), 0.997978975085)
def test_regular_hollow_sphere(self):
numpy.random.seed(1232367)
gen = stellar_wind.PositionGenerator(grid_type="regular")
points = gen.uniform_hollow_sphere(2, 0.6)
self.assertEqual(len(points), 2)
points = gen.uniform_hollow_sphere(1000, 0.1)
self.assertEqual(len(points), 1000)
def test_regular_cube(self):
numpy.random.seed(1232367)
gen = stellar_wind.PositionGenerator(grid_type="regular")
cube = gen.cube_generator(25)
print cube
self.assertEqual(cube.shape, (25, 3))
self.assertAlmostEqual(cube.min(), -2. / 3.)
self.assertAlmostEqual(cube.max(), 2. / 3.)
def test_rotate_positions(self):
gen = stellar_wind.PositionGenerator()
axis = [0, 1, 0]
angle = numpy.pi
positions = [[0.5, 0.5, 0.5], [0.2, -0.5, 1.3]]
rotated = gen.rotate_positions(positions, axis, angle)
print rotated
expected = [[-0.5, 0.5, -0.5], [-0.2, -0.5, -1.3]]
self.assertAlmostEqual(rotated, expected)
def test_rotation_matrix(self):
gen = stellar_wind.PositionGenerator()
axis = [0, 0, 1]
angle = numpy.pi
matrix = gen.rotation_matrix(axis, angle)
print numpy.rint(matrix)
expected = [[-1, 0, 0], [0, -1, 0], [0, 0, 1]]
self.assertAlmostEqual(matrix, expected)
def test_random_rotation(self):
numpy.random.seed(1232367)
gen = stellar_wind.PositionGenerator()
axis, angle = gen.random_rotation()
print axis
print angle
self.assertEqual(len(axis), 3)
self.assertAlmostEqual((axis**2).sum(), 1.)
self.assertGreaterEqual(angle, 0.)
self.assertLessEqual(angle, 2. * numpy.pi)
def test_cutout_sphere(self):
points = [
[0., 0., 0.],
[1., 1., 1.],
[-1., -1., -1.],
[-0., 0., -.99],
[0., .5, 0.],
]
points = numpy.array(points)
gen = stellar_wind.PositionGenerator()
remaining = gen.cutout_sphere(points, 0.1)
self.assertEqual(len(remaining), 2)
self.assertEqual(remaining, points[-2:])
def test_density_distribution(self):
numpy.random.seed(1234567)
N = 100000
rmin = 1 | units.RSun
rmax = 10 | units.RSun
gen = stellar_wind.PositionGenerator()
p, _ = gen.generate_positions(N, rmin, rmax)
r = p.lengths()
print 'rmin', r.min()
print 'rmax', r.max()
print r.mean()
self.assertEquals(len(r), N)
self.assertGreaterEqual(r.min(), rmin)
self.assertLessEqual(r.max(), rmax)
self.assertAlmostEquals(r.mean(), 5.49955427602 | units.RSun)
return
r = r.value_in(units.RSun)
n_bins = 50
n, bins = numpy.histogram(r, n_bins, range=(1, 10))
bin_volume = 4. / 3. * numpy.pi * (bins[1:]**3 - bins[:-1]**3)
dens = n / bin_volume
bin_means = (bins[1:] + bins[:-1]) / 2.
s = p[abs(p[:, 2]) < (0.1 | units.RSun)]
from matplotlib import pyplot
from amuse import plot as aplot
aplot.plot(s[:, 0], s[:, 1], '.')
pyplot.axis('equal')
pyplot.savefig("scatter.pdf")
pyplot.clf()
x = numpy.linspace(1, 10, num=200)
y = 300. / x**2
# y = 0. * x + dens.mean()
from matplotlib import pyplot
# pyplot.plot(x, y)
pyplot.loglog(x, y)
pyplot.plot(bin_means, dens, '*')
pyplot.savefig("dens.pdf")
def test_alternate_radius_function(self):
numpy.random.seed(123)
gen = stellar_wind.PositionGenerator()
# func = stellar_wind.LogisticVelocityAcceleration().radius_from_number
func = stellar_wind.ConstantVelocityAcceleration().radius_from_number
N = 100000
rmin = 2 | units.RSun
rmax = 6 | units.RSun
star = Particle()
star.radius = rmin
star.acc_cutoff = 10 | units.RSun
star.initial_wind_velocity = 4 | units.kms
star.terminal_wind_velocity = 12 | units.kms
p, _ = gen.generate_positions(N, rmin, rmax, func, star)
r = p.lengths()
self.assertEquals(len(r), N)
self.assertGreaterEqual(r.min(), rmin)
self.assertLessEqual(r.max(), rmax)
print r.mean()
self.assertAlmostEquals(r.mean(), 4.00196447056 | units.RSun)
return
r = r.value_in(units.RSun)
n_bins = 50
n, bins = numpy.histogram(r, n_bins)
bin_volume = 4. / 3. * numpy.pi * (bins[1:]**3 - bins[:-1]**3)
dens = n / bin_volume
bin_means = (bins[1:] + bins[:-1]) / 2.
from matplotlib import pyplot
pyplot.plot(bin_means, dens, '*')
pyplot.savefig("dens.pdf")