def test_particle_regions():
np.random.seed(int(0x4d3d3d3))
# We are going to test having 31, 127, 128 and 257 data files.
for nfiles in [2, 31, 127, 128, 129]:
# Now we create particles
# Note: we set N to nfiles here for testing purposes. Inside the code
# we set it to min(N, 256)
N = nfiles
reg = ParticleRegions([0.0, 0.0, 0.0, 0.0], [nfiles, nfiles, nfiles],
[N, N, N], nfiles)
Y, Z = np.mgrid[0.1:nfiles - 0.1:nfiles * 1j,
0.1:nfiles - 0.1:nfiles * 1j]
X = 0.5 * np.ones(Y.shape, dtype="float64")
pos = np.array([X.ravel(), Y.ravel(), Z.ravel()],
dtype="float64").transpose()
for i in range(nfiles):
reg.add_data_file(pos, i)
pos[:, 0] += 1.0
pos[:, 0] = 0.5
fr = FakeRegion(nfiles)
for i in range(nfiles):
fr.set_edges(i)
selector = RegionSelector(fr)
df = reg.identify_data_files(selector)
yield assert_equal, len(df), 1
yield assert_equal, df[0], i
pos[:, 0] += 1.0
for mask in reg.masks:
maxs = np.unique(mask.max(axis=-1).max(axis=-1))
mins = np.unique(mask.min(axis=-1).min(axis=-1))
yield assert_equal, maxs, mins
yield assert_equal, maxs, np.unique(mask)
def test_particle_regions():
np.random.seed(int(0x4d3d3d3))
# We are going to test having 31, 127, 128 and 257 data files.
for nfiles in [2, 31, 127, 128, 129]:
# Now we create particles
# Note: we set N to nfiles here for testing purposes. Inside the code
# we set it to min(N, 256)
N = nfiles
reg = ParticleRegions([0.0, 0.0, 0.0, 0.0],
[nfiles, nfiles, nfiles],
[N, N, N], nfiles)
Y, Z = np.mgrid[0.1 : nfiles - 0.1 : nfiles * 1j,
0.1 : nfiles - 0.1 : nfiles * 1j]
X = 0.5 * np.ones(Y.shape, dtype="float64")
pos = np.array([X.ravel(),Y.ravel(),Z.ravel()],
dtype="float64").transpose()
for i in range(nfiles):
reg.add_data_file(pos, i)
pos[:,0] += 1.0
pos[:,0] = 0.5
fr = FakeRegion(nfiles)
for i in range(nfiles):
fr.set_edges(i)
selector = RegionSelector(fr)
df = reg.identify_data_files(selector)
yield assert_equal, len(df), 1
yield assert_equal, df[0], i
pos[:,0] += 1.0
for mask in reg.masks:
maxs = np.unique(mask.max(axis=-1).max(axis=-1))
mins = np.unique(mask.min(axis=-1).min(axis=-1))
yield assert_equal, maxs, mins
yield assert_equal, maxs, np.unique(mask)
