def test_covering_grid():
return
# We decompose in different ways
cs = np.mgrid[0.47:0.53:2j, 0.47:0.53:2j, 0.47:0.53:2j]
cs = np.array([a.ravel() for a in cs]).T
length = (1.0 / 128) * 16 # 16 half-widths of a cell
for nprocs in [1, 2, 4, 8]:
ds = fake_random_ds(64, nprocs=nprocs, fields=_fields)
streams = Streamlines(ds, cs, length=length)
streams.integrate_through_volume()
for path in (streams.path(i) for i in range(8)):
yield assert_rel_equal, path['dts'].sum(), 1.0, 14
yield assert_equal, np.all(path['t'] <= (1.0 + 1e-10)), True
path["density"]
def test_covering_grid():
return
# We decompose in different ways
cs = np.mgrid[0.47:0.53:2j,0.47:0.53:2j,0.47:0.53:2j]
cs = np.array([a.ravel() for a in cs]).T
length = (1.0/128) * 16 # 16 half-widths of a cell
for nprocs in [1, 2, 4, 8]:
ds = fake_random_ds(64, nprocs = nprocs, fields = _fields)
streams = Streamlines(ds, cs, length=length)
streams.integrate_through_volume()
for path in (streams.path(i) for i in range(8)):
yield assert_rel_equal, path['dts'].sum(), 1.0, 14
yield assert_equal, np.all(path['t'] <= (1.0 + 1e-10)), True
path["density"]
def get_streamlines(ds):
from yt.visualization.api import Streamlines
streamlines = Streamlines(ds, ds.domain_center)
streamlines.integrate_through_volume()
stream = streamlines.path(0)
matplotlib.pylab.semilogy(stream['t'], stream['Density'], '-x')
def get_streamlines(ds):
from yt.visualization.api import Streamlines
streamlines = Streamlines(ds, ds.domain_center)
streamlines.integrate_through_volume()
stream = streamlines.path(0)
matplotlib.pylab.semilogy(stream['t'], stream['Density'], '-x')
