def get_average(
offset,
nregrid,
rrange=[0.0, 1.5e18],
phirange=[0.0, 6.283185307179586],
zrange=[-1.5e18, 1.5e18],
function=get,
filenameout="data",
type="pvtu",
):
# load the data:
pointdata = load(offset, filenameout=filenameout, type=type)
myaniso = function(pointdata)
points = ah.vtk2array(pointdata.GetPoints().GetData())
# Gathering data:
data = averageNumba(myaniso, points, nregrid, rrange=rrange, phirange=phirange, zrange=zrange)
return {
"data": data,
"nregrid": nregrid,
"rrange": rrange,
"phirange": phirange,
"zrange": zrange,
"offset": offset,
"filenameout": filenameout,
"type": type,
}
def get_average(offset,
nregrid,
rrange=[0., 1.5e18],
phirange=[0., 6.283185307179586],
zrange=[-1.5e18, 1.5e18],
function=get,
filenameout='data',
type='pvtu'):
# load the data:
pointdata = load(offset, filenameout=filenameout, type=type)
myaniso = function(pointdata)
points = ah.vtk2array(pointdata.GetPoints().GetData())
# Gathering data:
data = averageNumba(myaniso,
points,
nregrid,
rrange=rrange,
phirange=phirange,
zrange=zrange)
return {
'data': data,
'nregrid': nregrid,
'rrange': rrange,
'phirange': phirange,
'zrange': zrange,
'offset': offset,
'filenameout': filenameout,
'type': type
}
def plot(offset, nregrid, xrange=[-1.8e18, 1.8e18], yrange=[-1.8e18, 1.8e18], filenameout="data", type="pvtu"):
nlevels = 256
path = "movie"
timescale = 1.05772480719860e-1
time_start = 212.16
time = offset * timescale + time_start
pointdata = load(offset, filenameout=filenameout, type=type)
aniso = get(pointdata)
points = ah.vtk2array(pointdata.GetPoints().GetData())
x = points[:, 0]
y = points[:, 1]
z = points[:, 2]
# this is needed because griddata freaks out otherwise:
smalldouble = 1.0e-8
tmp = (xrange[1] - xrange[0]) * smalldouble
xi = np.linspace(xrange[0] - tmp, xrange[1] + tmp, nregrid[0])
tmp = (yrange[1] - yrange[0]) * smalldouble
yi = np.linspace(yrange[0] - tmp, yrange[1] + tmp, nregrid[1])
data = griddata((x, y), aniso[0], (xi[None, :], yi[:, None]), method="linear")
mask = griddata((x, y), aniso[1], (xi[None, :], yi[:, None]), method="linear")
data = np.ma.masked_array(data, mask)
plt.clf()
cs = plt.contourf(xi, yi, data, levels=np.arange(nlevels) / float(nlevels - 1))
cb = plt.colorbar()
cb.set_ticks(np.arange(11) / 10.0)
ax = plt.gca()
ax.set_aspect("equal")
plt.ylabel("z [cm]")
plt.xlabel("y [cm]")
plt.title("".join(["Departure from tor. field, t=", str(time)[0:5], " yrs"]))
plt.savefig("".join([path, "/aniso_", str(offset).zfill(4), ".png"]), format="png", transparent=False)
# plt.show()
plt.close()
def get_bcyl(pointdata):
bx = read.extract(pointdata, "b1", attribute_mode="point")
by = read.extract(pointdata, "b2", attribute_mode="point")
bz = read.extract(pointdata, "b3", attribute_mode="point")
points = ah.vtk2array(pointdata.GetPoints().GetData())
r = np.sqrt(np.square(points[:, 0]) + np.square(points[:, 1]))
m = r <= 0.0
cosphi = np.ma.array(points[:, 0] / r, mask=m, fill_value=0.0)
sinphi = np.ma.array(points[:, 1] / r, mask=m, fill_value=0.0)
br = bx * cosphi + by * sinphi
bphi = by * cosphi - bx * sinphi
return np.array([br, bphi, bz])
def get_bcyl(pointdata):
bx = read.extract(pointdata, 'b1', attribute_mode='point')
by = read.extract(pointdata, 'b2', attribute_mode='point')
bz = read.extract(pointdata, 'b3', attribute_mode='point')
points = ah.vtk2array(pointdata.GetPoints().GetData())
r = np.sqrt(np.square(points[:, 0]) + np.square(points[:, 1]))
m = r <= 0.
cosphi = np.ma.array(points[:, 0] / r, mask=m, fill_value=0.)
sinphi = np.ma.array(points[:, 1] / r, mask=m, fill_value=0.)
br = bx * cosphi + by * sinphi
bphi = by * cosphi - bx * sinphi
return np.array([br, bphi, bz])
def get_bsphere(pointdata):
bx = read.extract(pointdata, "b1", attribute_mode="point")
by = read.extract(pointdata, "b2", attribute_mode="point")
bz = read.extract(pointdata, "b3", attribute_mode="point")
points = ah.vtk2array(pointdata.GetPoints().GetData())
x = points[:, 0]
y = points[:, 1]
z = points[:, 2]
rcyl = np.sqrt(np.square(points[:, 0]) + np.square(points[:, 1]))
r = np.sqrt(np.square(points[:, 0]) + np.square(points[:, 1]) + np.square(points[:, 2]))
br = 1.0 / r * (x * bx + y * by + z * bz)
bphi = 1.0 / rcyl * (-y * bx + x * by)
btheta = 1.0 / r * (1.0 / rcyl * z * x * bx + 1.0 / rcyl * z * y * by - rcyl * bz)
return {"br": br, "bphi": bphi, "btheta": btheta}
def get_correlation(
offset,
nregrid,
rrange=[0.0, 1.5e18],
phirange=[0.0, 6.283185307179586],
zrange=[-1.5e18, 1.5e18],
functiona=get,
functionb=fieldstrength,
filenameout="data",
type="pvtu",
):
# load the data:
pointdata = load(offset, filenameout=filenameout, type=type)
a = functiona(pointdata)
b = functionb(pointdata)
points = ah.vtk2array(pointdata.GetPoints().GetData())
# Gathering data:
meanA = averageNumba(a, points, nregrid, rrange=rrange, phirange=phirange, zrange=zrange)
meanB = averageNumba(b, points, nregrid, rrange=rrange, phirange=phirange, zrange=zrange)
stddevA = stddevNumba(a, points, nregrid, rrange=rrange, phirange=phirange, zrange=zrange)
stddevB = stddevNumba(b, points, nregrid, rrange=rrange, phirange=phirange, zrange=zrange)
data = correlationNumba(
a, b, meanA, meanB, stddevA, stddevB, points, nregrid, rrange=rrange, phirange=phirange, zrange=zrange
)
return {
"data": data,
"nregrid": nregrid,
"rrange": rrange,
"phirange": phirange,
"zrange": zrange,
"offset": offset,
"filenameout": filenameout,
"type": type,
}
def get_bsphere(pointdata):
bx = read.extract(pointdata, 'b1', attribute_mode='point')
by = read.extract(pointdata, 'b2', attribute_mode='point')
bz = read.extract(pointdata, 'b3', attribute_mode='point')
points = ah.vtk2array(pointdata.GetPoints().GetData())
x = points[:, 0]
y = points[:, 1]
z = points[:, 2]
rcyl = np.sqrt(np.square(points[:, 0]) + np.square(points[:, 1]))
r = np.sqrt(
np.square(points[:, 0]) + np.square(points[:, 1]) +
np.square(points[:, 2]))
br = 1. / r * (x * bx + y * by + z * bz)
bphi = 1. / rcyl * (-y * bx + x * by)
btheta = 1. / r * (1. / rcyl * z * x * bx + 1. / rcyl * z * y * by -
rcyl * bz)
return {'br': br, 'bphi': bphi, 'btheta': btheta}
def get1D(
offset,
nregrid,
rrange=[0.0, 1.5e18],
phirange=[0.0, 6.283185307179586],
zrange=[-1.5e18, 1.5e18],
filenameout="data",
type="pvtu",
):
# load the data:
pointdata = load(offset, filenameout=filenameout, type=type)
aniso = get(pointdata)
points = ah.vtk2array(pointdata.GetPoints().GetData())
# Gathering data:
datarz = average(aniso, points, nregrid, rrange=rrange, phirange=phirange, zrange=zrange)
rsrange = [0.0, np.sqrt(rrange[1] ** 2 + np.max(zrange) ** 2)]
# create r and z arrays:
r = np.linspace(rrange[0], rrange[1], nregrid[0])
z = np.linspace(zrange[0], zrange[1], nregrid[2])
rr, zz = np.meshgrid(r, z)
rr = np.reshape(rr, -1)
zz = np.reshape(zz, -1)
# create rs_i and theta_j arrays:
rsi = np.linspace(rsrange[0], rsrange[1], nregrid[0])
thetaj = np.linspace(phirange[0], phirange[1] / 2.0, nregrid[1])
# allocate xij arrays:
nelements = nregrid[0] * nregrid[1]
rij = np.empty(nelements)
zij = np.empty(nelements)
# create single indexed points:
for i in range(nregrid[0]):
for j in range(nregrid[1]):
zij[j + i * nregrid[1]] = rsi[i] * np.cos(thetaj[j])
rij[j + i * nregrid[1]] = rsi[i] * np.sin(thetaj[j])
# griddata to points:
data = griddata((rr, zz), np.reshape(datarz, -1), (rij, zij), method="linear")
data = np.ma.masked_array(data, np.isnan(data))
# Now create the theta average:
aniso_av = np.zeros(nregrid[0])
for i in range(nregrid[0]):
index = np.arange(nregrid[1]) + i * nregrid[1]
aniso_av[i] = data[index].mean()
aniso_av = np.ma.masked_array(aniso_av, np.isnan(aniso_av))
return {
"aniso_av": aniso_av,
"nregrid": nregrid,
"rrange": rrange,
"phirange": phirange,
"zrange": zrange,
"rsph": rsi,
"offset": offset,
"filenameout": filenameout,
"type": type,
}
def get1D(offset,
nregrid,
rrange=[0., 1.5e18],
phirange=[0., 6.283185307179586],
zrange=[-1.5e18, 1.5e18],
filenameout='data',
type='pvtu'):
# load the data:
pointdata = load(offset, filenameout=filenameout, type=type)
aniso = get(pointdata)
points = ah.vtk2array(pointdata.GetPoints().GetData())
# Gathering data:
datarz = average(aniso,
points,
nregrid,
rrange=rrange,
phirange=phirange,
zrange=zrange)
rsrange = [0., np.sqrt(rrange[1]**2 + np.max(zrange)**2)]
# create r and z arrays:
r = np.linspace(rrange[0], rrange[1], nregrid[0])
z = np.linspace(zrange[0], zrange[1], nregrid[2])
rr, zz = np.meshgrid(r, z)
rr = np.reshape(rr, -1)
zz = np.reshape(zz, -1)
# create rs_i and theta_j arrays:
rsi = np.linspace(rsrange[0], rsrange[1], nregrid[0])
thetaj = np.linspace(phirange[0], phirange[1] / 2., nregrid[1])
# allocate xij arrays:
nelements = nregrid[0] * nregrid[1]
rij = np.empty(nelements)
zij = np.empty(nelements)
# create single indexed points:
for i in range(nregrid[0]):
for j in range(nregrid[1]):
zij[j + i * nregrid[1]] = rsi[i] * np.cos(thetaj[j])
rij[j + i * nregrid[1]] = rsi[i] * np.sin(thetaj[j])
# griddata to points:
data = griddata((rr, zz),
np.reshape(datarz, -1), (rij, zij),
method='linear')
data = np.ma.masked_array(data, np.isnan(data))
# Now create the theta average:
aniso_av = np.zeros(nregrid[0])
for i in range(nregrid[0]):
index = np.arange(nregrid[1]) + i * nregrid[1]
aniso_av[i] = data[index].mean()
aniso_av = np.ma.masked_array(aniso_av, np.isnan(aniso_av))
return {
'aniso_av': aniso_av,
'nregrid': nregrid,
'rrange': rrange,
'phirange': phirange,
'zrange': zrange,
'rsph': rsi,
'offset': offset,
'filenameout': filenameout,
'type': type
}
def get_correlation(offset,
nregrid,
rrange=[0., 1.5e18],
phirange=[0., 6.283185307179586],
zrange=[-1.5e18, 1.5e18],
functiona=get,
functionb=fieldstrength,
filenameout='data',
type='pvtu'):
# load the data:
pointdata = load(offset, filenameout=filenameout, type=type)
a = functiona(pointdata)
b = functionb(pointdata)
points = ah.vtk2array(pointdata.GetPoints().GetData())
# Gathering data:
meanA = averageNumba(a,
points,
nregrid,
rrange=rrange,
phirange=phirange,
zrange=zrange)
meanB = averageNumba(b,
points,
nregrid,
rrange=rrange,
phirange=phirange,
zrange=zrange)
stddevA = stddevNumba(a,
points,
nregrid,
rrange=rrange,
phirange=phirange,
zrange=zrange)
stddevB = stddevNumba(b,
points,
nregrid,
rrange=rrange,
phirange=phirange,
zrange=zrange)
data = correlationNumba(a,
b,
meanA,
meanB,
stddevA,
stddevB,
points,
nregrid,
rrange=rrange,
phirange=phirange,
zrange=zrange)
return {
'data': data,
'nregrid': nregrid,
'rrange': rrange,
'phirange': phirange,
'zrange': zrange,
'offset': offset,
'filenameout': filenameout,
'type': type
}
def plot(offset,
nregrid,
xrange=[-1.8e18, 1.8e18],
yrange=[-1.8e18, 1.8e18],
filenameout='data',
type='pvtu'):
nlevels = 256
path = 'movie'
timescale = 1.05772480719860e-1
time_start = 212.16
time = offset * timescale + time_start
pointdata = load(offset, filenameout=filenameout, type=type)
aniso = get(pointdata)
points = ah.vtk2array(pointdata.GetPoints().GetData())
x = points[:, 0]
y = points[:, 1]
z = points[:, 2]
# this is needed because griddata freaks out otherwise:
smalldouble = 1.e-8
tmp = (xrange[1] - xrange[0]) * smalldouble
xi = np.linspace(xrange[0] - tmp, xrange[1] + tmp, nregrid[0])
tmp = (yrange[1] - yrange[0]) * smalldouble
yi = np.linspace(yrange[0] - tmp, yrange[1] + tmp, nregrid[1])
data = griddata((x, y),
aniso[0], (xi[None, :], yi[:, None]),
method='linear')
mask = griddata((x, y),
aniso[1], (xi[None, :], yi[:, None]),
method='linear')
data = np.ma.masked_array(data, mask)
plt.clf()
cs = plt.contourf(xi,
yi,
data,
levels=np.arange(nlevels) / float(nlevels - 1))
cb = plt.colorbar()
cb.set_ticks(np.arange(11) / 10.)
ax = plt.gca()
ax.set_aspect('equal')
plt.ylabel('z [cm]')
plt.xlabel('y [cm]')
plt.title(''.join(
['Departure from tor. field, t=',
str(time)[0:5], ' yrs']))
plt.savefig(''.join([path, '/aniso_',
str(offset).zfill(4), '.png']),
format="png",
transparent=False)
# plt.show()
plt.close()
