def runstats(fname, opts):
V = cowpy.fromfile(fname,
"prim",
members=["vx", "vy", "vz"],
vec3d=True,
guard=2,
downsample=opts.downsample)
B = cowpy.fromfile(fname,
"prim",
members=["Bx", "By", "Bz"],
vec3d=True,
guard=2,
downsample=opts.downsample)
V.name = "V"
B.name = "B"
f = {}
f["divV"] = V.divergence()
f["divB"] = B.divergence()
f["curlV"] = V.curl()
f["curlB"] = B.curl()
f["vdotB"] = cowpy.dot_product(V, B)
f["vcrossB"] = cowpy.cross_product(V, B)
f["curlBdotvcrossB"] = cowpy.cross_product(f["curlB"], f["vcrossB"])
f["curlBdotB"] = cowpy.dot_product(f["curlB"], B)
f["vcrossBcrossB"] = cowpy.cross_product(f["vcrossB"], B)
f["divvcrossBcrossB"] = f["vcrossBcrossB"].divergence()
if opts.output:
for thing in f:
hist = maghist(f[thing])
hist.name = thing + "-hist"
hist.dump(opts.output)
def testfromfile():
field = cowpy.fromfile("test.h5", group="vel")
print field
sparsefield = cowpy.fromfile("test.h5", group="vel", downsample=2)
assert sparsefield.value.shape == (12 / 2, 12 / 2, 16 / 2, 3)
print sparsefield
# Test with a strange downsampling factor and with guard cells
sparsefield = cowpy.fromfile("test.h5", group="vel", downsample=3, guard=3)
print sparsefield
assert sparsefield.value.shape == (4 + 2 * 3, 4 + 2 * 3, 6 + 2 * 3, 3)
def testfromfile():
field = cowpy.fromfile("test.h5", group="vel")
print field
sparsefield = cowpy.fromfile("test.h5", group="vel", downsample=2)
assert sparsefield.value.shape == (12/2, 12/2, 16/2, 3)
print sparsefield
# Test with a strange downsampling factor and with guard cells
sparsefield = cowpy.fromfile("test.h5", group="vel", downsample=3, guard=3)
print sparsefield
assert sparsefield.value.shape == (4+2*3, 4+2*3, 6+2*3, 3)
def vrel(fname, opts):
V = cowpy.fromfile(fname,
"prim",
members=["vx", "vy", "vz"],
vec3d=True,
guard=2,
downsample=opts.downsample)
np.random.seed(V.domain.cart_rank)
nsamp = int(opts.pairs * 2)
npair = int(opts.pairs)
sampx = np.random.rand(nsamp, 3)
x, P = V.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
dP = np.dot(P[i1] - P[i0], P[i1] - P[i0])**0.5
histP.add_sample(dx, weight=dP)
histP.seal()
if V.domain.cart_rank == 0:
import matplotlib.pyplot as plt
plt.plot(histP.binloc, histP.binval, label="P")
plt.legend(loc='best')
plt.show()
def runstats(fname, opts):
V = cowpy.fromfile(fname, "prim", members=["vx","vy","vz"], vec3d=True,
guard=2, downsample=opts.downsample)
B = cowpy.fromfile(fname, "prim", members=["Bx","By","Bz"], vec3d=True,
guard=2, downsample=opts.downsample)
V.name = "V"
B.name = "B"
f = { }
f["divV"] = V.divergence()
f["divB"] = B.divergence()
f["curlV"] = V.curl()
f["curlB"] = B.curl()
f["vdotB"] = cowpy.dot_product(V, B)
f["vcrossB"] = cowpy.cross_product(V, B)
f["curlBdotvcrossB"] = cowpy.cross_product(f["curlB"], f["vcrossB"])
f["curlBdotB"] = cowpy.dot_product(f["curlB"], B)
f["vcrossBcrossB"] = cowpy.cross_product(f["vcrossB"], B)
f["divvcrossBcrossB"] = f["vcrossBcrossB"].divergence()
if opts.output:
for thing in f:
hist = maghist(f[thing])
hist.name = thing + "-hist"
hist.dump(opts.output)
def cversion(fname, opts):
V = cowpy.fromfile(fname, "prim", members=["vx","vy","vz"], vec3d=True,
guard=2, downsample=opts.downsample)
histpro, histlab = pairs(V, opts.pairs, bins=opts.bins)
histpro.name = "gamma-rel-drprop-hist"
histlab.name = "gamma-rel-drlab-hist"
if opts.output:
histpro.dump(opts.output)
histlab.dump(opts.output)
if opts.plot and V.domain.cart_rank == 0:
import matplotlib.pyplot as plt
plt.plot(histpro.binloc, histpro.binval, label=histpro.name)
plt.plot(histlab.binloc, histlab.binval, label=histlab.name)
plt.legend(loc='best')
#plt.ylim(1.0, 2.8)
plt.show()
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 cversion(fname, opts):
V = cowpy.fromfile(fname,
"prim",
members=["vx", "vy", "vz"],
vec3d=True,
guard=2,
downsample=opts.downsample)
histpro, histlab = pairs(V, opts.pairs, bins=opts.bins)
histpro.name = "gamma-rel-drprop-hist"
histlab.name = "gamma-rel-drlab-hist"
if opts.output:
histpro.dump(opts.output)
histlab.dump(opts.output)
if opts.plot and V.domain.cart_rank == 0:
import matplotlib.pyplot as plt
plt.plot(histpro.binloc, histpro.binval, label=histpro.name)
plt.plot(histlab.binloc, histlab.binval, label=histlab.name)
plt.legend(loc='best')
#plt.ylim(1.0, 2.8)
plt.show()
def vrel(fname, opts):
V = cowpy.fromfile(fname, "prim", members=["vx","vy","vz"], vec3d=True,
guard=2, downsample=opts.downsample)
np.random.seed(V.domain.cart_rank)
nsamp = int(opts.pairs * 2)
npair = int(opts.pairs)
sampx = np.random.rand(nsamp, 3)
x, P = V.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
dP = np.dot(P[i1] - P[i0], P[i1] - P[i0])**0.5
histP.add_sample(dx, weight=dP)
histP.seal()
if V.domain.cart_rank == 0:
import matplotlib.pyplot as plt
plt.plot(histP.binloc, histP.binval, label="P")
plt.legend(loc='best')
plt.show()
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()