def test_halo_catalog_boundary_particles(self):
rs = np.random.RandomState(3670474)
n_halos = 100
fields = ["particle_mass"
] + [f"particle_position_{ax}" for ax in "xyz"]
units = ["g"] + ["cm"] * 3
data = {
field: YTArray(rs.random_sample(n_halos), unit)
for field, unit in zip(fields, units)
}
data["particle_position_x"][0] = 1.0
data["particle_position_x"][1] = 0.0
data["particle_position_y"][2] = 1.0
data["particle_position_y"][3] = 0.0
data["particle_position_z"][4] = 1.0
data["particle_position_z"][5] = 0.0
fn = fake_halo_catalog(data)
ds = yt_load(fn)
assert type(ds) is YTHaloCatalogDataset
for field in fields:
f1 = data[field].in_base()
f1.sort()
f2 = ds.r[("all", field)].in_base()
f2.sort()
assert_array_equal(f1, f2)
def get_datasets(fns):
pbar = get_pbar('Loading datasets', len(fns))
dss = []
for i, fn in enumerate(fns):
ds = yt_load(fn, minimal_fields=True)
dss.append(ds)
pbar.update(i + 1)
pbar.finish()
return dss
def test_halo_catalog(self):
rs = np.random.RandomState(3670474)
n_halos = 100
fields = [
f"particle_{name}"
for name in ["mass"] + [f"position_{ax}" for ax in "xyz"]
]
units = ["g"] + ["cm"] * 3
data = dict((field, YTArray(rs.random_sample(n_halos), unit))
for field, unit in zip(fields, units))
fn = fake_halo_catalog(data)
ds = yt_load(fn)
assert type(ds) is YTHaloCatalogDataset
for field in fields:
f1 = data[field].in_base()
f1.sort()
f2 = ds.r[field].in_base()
f2.sort()
assert_array_equal(f1, f2)