def testglobind():
domain = cowpy.DistributedDomain([10, 10, 10], guard=3)
dfield = cowpy.DataField(domain, ["vx", "vy", "vz"])
dfield.interior[:, :, :, :] = np.mgrid[0:10, 0:10,
0:10].transpose([1, 2, 3, 0])
x, y, z = dfield.index_global([2, 3, 4])
assert abs(x - 2) < 1e-16
assert abs(y - 3) < 1e-16
assert abs(z - 4) < 1e-16
def testio():
domain = cowpy.DistributedDomain([12, 12, 16], guard=3)
dfield = cowpy.DataField(domain, ["vx", "vy", "vz"], name="vel")
dfield["vx"] = 2.1
dfield["vy"] = 2.2
dfield["vz"] = 2.3
dfield.dump("test.h5")
dfield.value[:] = 0.0
dfield.read("test.h5")
assert abs(dfield["vx"] - 2.1).all() < 1e-16
assert abs(dfield["vy"] - 2.2).all() < 1e-16
assert abs(dfield["vz"] - 2.3).all() < 1e-16
def testverify():
domain = cowpy.DistributedDomain([4, 4], guard=1)
dfield = cowpy.DataField(domain, ["vx", "vy", "vz"], name="vel")
dfield["vx"] = 2.1
dfield["vy"] = 2.2
dfield["vz"] = 2.3
dfield["vx"][3, 3] = np.nan
dfield["vx"][3, 4] = np.inf
dfield.flags = 0
dfield.setflags_infnan()
assert (dfield.flags[3, 3] == 4)
assert (dfield.flags[3, 4] == 8)
def testsamp():
domain = cowpy.DistributedDomain([10, 10, 10], guard=3)
dfield = cowpy.DataField(domain, ["vx", "vy", "vz"])
np.random.seed(domain.cart_rank)
sampx = np.random.rand(10, 3)
dfield["vx"] = 2.1
dfield["vy"] = 2.2
dfield["vz"] = 2.3
x, P = dfield.sample_global(sampx)
assert abs(P[:, 0] - 2.1).all() < 1e-16
assert abs(P[:, 1] - 2.2).all() < 1e-16
assert abs(P[:, 2] - 2.3).all() < 1e-16
assert len(x) == len(sampx)
def gammarel(fname, opts):
V = cowpy.fromfile(fname,
"prim",
members=["vx", "vy", "vz"],
vec3d=True,
guard=2,
downsample=opts.downsample)
U = cowpy.DataField(V.domain, members=["u0", "u1", "u2", "u3"])
np.random.seed(V.domain.cart_rank)
nsamp = int(opts.pairs * 2)
npair = int(opts.pairs)
sampx = np.random.rand(nsamp, 3)
vx, vy, vz = V.value[..., 0], V.value[..., 1], V.value[..., 2]
U.value[..., 0] = 1.0 / (1.0 - (vx**2 + vy**2 + vz**2))**0.5
U.value[..., 1] = vx * U.value[..., 0]
U.value[..., 2] = vy * U.value[..., 0]
U.value[..., 3] = vz * U.value[..., 0]
U.sync_guard()
x, P = U.sample_global(sampx)
histP = cowpy.Histogram1d(0.0, 1.5, bins=opts.bins, binmode="average")
for n in range(npair):
i0 = np.random.randint(0, len(P))
i1 = np.random.randint(0, len(P))
dx = np.dot(x[i1] - x[i0], x[i1] - x[i0])**0.5
dg = P[i0][0] * P[i1][0] - np.dot(P[i0][1:], P[i1][1:])
histP.add_sample(dx, weight=dg)
histP.seal()
histP.name = "gamma-rel-drlab-hist"
if opts.output:
histP.dump(opts.output)
if opts.plot and V.domain.cart_rank == 0:
import matplotlib.pyplot as plt
plt.plot(histP.binloc, histP.binval, label="P")
plt.legend(loc='best')
plt.ylim(1.0, 2.8)
plt.show()
def test1():
domain = cowpy.DistributedDomain([N, N, N], guard=3)
dfield = cowpy.DataField(domain, ["vx"], name="vel1")
dfield.dump(fname)
def testuserbuffer():
buffer = np.zeros([10, 10, 10, 1], order='C')
domain = cowpy.DistributedDomain([6, 6, 6], guard=2)
dfield = cowpy.DataField(domain, members=['f'], buffer=buffer)
