def read_fixed_points(datadir = 'data/', fileName = 'fixed_points.dat', hm = 1):
"""
Reads the fixed points files.
call signature::
fixed = read_fixed_points(datadir = 'data/', fileName = 'fixed_points.dat', hm = 1)
Reads from the fixed points files. Returns the fixed points positions.
Keyword arguments:
*datadir*:
Data directory.
*fileName*:
Name of the fixed points file.
*hm*:
Header multiplication factor in case Fortran's binary data writes extra large
header. For most cases hm = 1 is sufficient. For the cluster in St Andrews use hm = 2.
"""
# read the cpu structure
dim = pc.read_dim(datadir = datadir)
if (dim.nprocz > 1):
print("error: number of cores in z-direction > 1")
data = []
# read the data
fixed_file = open(datadir+fileName, 'rb')
tmp = fixed_file.read(4*hm)
data = pc.fixed_struct()
eof = 0
# length of longest array of fixed points
fixedMax = 0
if tmp == '':
eof = 1
while (eof == 0):
data.t.append(struct.unpack("<"+str(hm+1)+"f", fixed_file.read(4*(hm+1)))[0])
n_fixed = int(struct.unpack("<"+str(2*hm+1)+"f", fixed_file.read(4*(2*hm+1)))[1+int(hm/2)])
x = list(np.zeros(n_fixed))
y = list(np.zeros(n_fixed))
q = list(np.zeros(n_fixed))
for j in range(n_fixed):
x[j] = struct.unpack("<"+str(hm+1)+"f", fixed_file.read(4*(hm+1)))[-1]
y[j] = struct.unpack("<f", fixed_file.read(4))[0]
q[j] = struct.unpack("<"+str(hm+1)+"f", fixed_file.read(4*(hm+1)))[0]
data.x.append(x)
data.y.append(y)
data.q.append(q)
data.fidx.append(n_fixed)
tmp = fixed_file.read(4*hm)
if tmp == '':
eof = 1
if fixedMax < len(x):
fixedMax = len(x)
fixed_file.close()
# add NaN to fill up the times with smaller number of fixed points
fixed = pc.fixed_struct()
for i in range(len(data.t)):
annex = list(np.zeros(fixedMax - len(data.x[i])) + np.nan)
fixed.t.append(data.t[i])
fixed.x.append(data.x[i] + annex)
fixed.y.append(data.y[i] + annex)
fixed.q.append(data.q[i] + annex)
fixed.fidx.append(data.fidx[i])
fixed.t = np.array(fixed.t)
fixed.x = np.array(fixed.x)
fixed.y = np.array(fixed.y)
fixed.q = np.array(fixed.q)
fixed.fidx = np.array(fixed.fidx)
return fixed
def read_fixed_points(datadir='data/', fileName='fixed_points.dat', hm=1):
"""
Reads the fixed points files.
call signature::
fixed = read_fixed_points(datadir = 'data/', fileName = 'fixed_points.dat', hm = 1)
Reads from the fixed points files. Returns the fixed points positions.
Keyword arguments:
*datadir*:
Data directory.
*fileName*:
Name of the fixed points file.
*hm*:
Header multiplication factor in case Fortran's binary data writes extra large
header. For most cases hm = 1 is sufficient. For the cluster in St Andrews use hm = 2.
"""
# read the cpu structure
dim = pc.read_dim(datadir=datadir)
if (dim.nprocz > 1):
print "error: number of cores in z-direction > 1"
data = []
# read the data
fixed_file = open(datadir + fileName, 'rb')
tmp = fixed_file.read(4 * hm)
data = pc.fixed_struct()
eof = 0
# length of longest array of fixed points
fixedMax = 0
if tmp == '':
eof = 1
while (eof == 0):
data.t.append(
struct.unpack("<" + str(hm + 1) + "f",
fixed_file.read(4 * (hm + 1)))[0])
n_fixed = int(
struct.unpack("<" + str(2 * hm + 1) + "f",
fixed_file.read(4 * (2 * hm + 1)))[1 + hm / 2])
x = list(np.zeros(n_fixed))
y = list(np.zeros(n_fixed))
q = list(np.zeros(n_fixed))
for j in range(n_fixed):
x[j] = struct.unpack("<" + str(hm + 1) + "f",
fixed_file.read(4 * (hm + 1)))[-1]
y[j] = struct.unpack("<f", fixed_file.read(4))[0]
q[j] = struct.unpack("<" + str(hm + 1) + "f",
fixed_file.read(4 * (hm + 1)))[0]
data.x.append(x)
data.y.append(y)
data.q.append(q)
data.fidx.append(n_fixed)
tmp = fixed_file.read(4 * hm)
if tmp == '':
eof = 1
if fixedMax < len(x):
fixedMax = len(x)
fixed_file.close()
# add NaN to fill up the times with smaller number of fixed points
fixed = pc.fixed_struct()
for i in range(len(data.t)):
annex = list(np.zeros(fixedMax - len(data.x[i])) + np.nan)
fixed.t.append(data.t[i])
fixed.x.append(data.x[i] + annex)
fixed.y.append(data.y[i] + annex)
fixed.q.append(data.q[i] + annex)
fixed.fidx.append(data.fidx[i])
fixed.t = np.array(fixed.t)
fixed.x = np.array(fixed.x)
fixed.y = np.array(fixed.y)
fixed.q = np.array(fixed.q)
fixed.fidx = np.array(fixed.fidx)
return fixed
def fixed_points(datadir = 'data/', fileName = 'fixed_points_post.dat', varfile = 'VAR0', ti = -1, tf = -1,
traceField = 'bb', hMin = 2e-3, hMax = 2e4, lMax = 500, tol = 1e-2,
interpolation = 'weighted', trace_sub = 1, integration = 'simple', nproc = 1):
"""
Find the fixed points.
call signature::
fixed = fixed_points(datadir = 'data/', fileName = 'fixed_points_post.dat', varfile = 'VAR0', ti = -1, tf = -1,
traceField = 'bb', hMin = 2e-3, hMax = 2e4, lMax = 500, tol = 1e-2,
interpolation = 'weighted', trace_sub = 1, integration = 'simple', nproc = 1)
Finds the fixed points. Returns the fixed points positions.
Keyword arguments:
*datadir*:
Data directory.
*fileName*:
Name of the fixed points file.
*varfile*:
Varfile to be read.
*ti*:
Initial VAR file index for tracer time sequences. Overrides 'varfile'.
*tf*:
Final VAR file index for tracer time sequences. Overrides 'varfile'.
*traceField*:
Vector field used for the streamline tracing.
*hMin*:
Minimum step length for and underflow to occur.
*hMax*:
Parameter for the initial step length.
*lMax*:
Maximum length of the streamline. Integration will stop if l >= lMax.
*tol*:
Tolerance for each integration step. Reduces the step length if error >= tol.
*interpolation*:
Interpolation of the vector field.
'mean': takes the mean of the adjacent grid point.
'weighted': weights the adjacent grid points according to their distance.
*trace_sub*:
Number of sub-grid cells for the seeds for the initial mapping.
*intQ*:
Quantities to be integrated along the streamlines.
*integration*:
Integration method.
'simple': low order method.
'RK6': Runge-Kutta 6th order.
*nproc*:
Number of cores for multi core computation.
"""
class data_struct:
def __init__(self):
self.t = []
self.fidx = [] # number of fixed points at this time
self.x = []
self.y = []
self.q = []
# Computes rotation along one edge.
def edge(vv, p, sx, sy, diff1, diff2, phiMin, rec, hMin = hMin, hMax = hMax,
lMax = lMax, tol = tol, interpolation = interpolation, integration = integration):
dtot = m.atan2(diff1[0]*diff2[1] - diff2[0]*diff1[1], diff1[0]*diff2[0] + diff1[1]*diff2[1])
if ((abs(dtot) > phiMin) and (rec < 4)):
xm = 0.5*(sx[0]+sx[1])
ym = 0.5*(sy[0]+sy[1])
# trace intermediate field line
s = pc.stream(vv, p, hMin = hMin, hMax = hMax, lMax = lMax, tol = tol,
interpolation = interpolation, integration = integration, xx = np.array([xm, ym, p.Oz]))
tracer = np.concatenate((s.tracers[0,0:2], s.tracers[s.sl-1,:], np.reshape(s.l,(1))))
# discard any streamline which does not converge or hits the boundary
if ((tracer[5] >= lMax) or (tracer[4] < p.Oz+p.Lz-p.dz)):
dtot = 0.
else:
diffm = np.array([tracer[2] - tracer[0], tracer[3] - tracer[1]])
if (sum(diffm**2) != 0):
diffm = diffm / np.sqrt(sum(diffm**2))
dtot = edge(vv, p, [sx[0], xm], [sy[0], ym], diff1, diffm, phiMin, rec+1,
hMin = hMin, hMax = hMax, lMax = lMax, tol = tol, interpolation = interpolation, integration = integration)+ \
edge(vv, p, [xm, sx[1]], [ym, sy[1]], diffm, diff2, phiMin, rec+1,
hMin = hMin, hMax = hMax, lMax = lMax, tol = tol, interpolation = interpolation, integration = integration)
return dtot
# Finds the Poincare index of this grid cell.
def pIndex(vv, p, sx, sy, diff, phiMin, hMin = hMin, hMax = hMax,
lMax = lMax, tol = tol, interpolation = interpolation, integration = integration):
poincare = 0
poincare += edge(vv, p, [sx[0], sx[1]], [sy[0], sy[0]], diff[0,:], diff[1,:], phiMin, 0,
hMin = hMin, hMax = hMax, lMax = lMax, tol = tol, interpolation = interpolation, integration = integration)
poincare += edge(vv, p, [sx[1], sx[1]], [sy[0], sy[1]], diff[1,:], diff[2,:], phiMin, 0,
hMin = hMin, hMax = hMax, lMax = lMax, tol = tol, interpolation = interpolation, integration = integration)
poincare += edge(vv, p, [sx[1], sx[0]], [sy[1], sy[1]], diff[2,:], diff[3,:], phiMin, 0,
hMin = hMin, hMax = hMax, lMax = lMax, tol = tol, interpolation = interpolation, integration = integration)
poincare += edge(vv, p, [sx[0], sx[0]], [sy[1], sy[0]], diff[3,:], diff[0,:], phiMin, 0,
hMin = hMin, hMax = hMax, lMax = lMax, tol = tol, interpolation = interpolation, integration = integration)
return poincare
# fixed point finder for a subset of the domain
def subFixed(queue, ix0, iy0, vv, p, tracers, iproc, hMin = 2e-3, hMax = 2e4, lMax = 500,
tol = 1e-2, interpolation = 'weighted', integration = 'simple'):
diff = np.zeros((4,2))
phiMin = np.pi/8.
x = []
y = []
q = []
fidx = 0
for ix in ix0:
for iy in iy0:
# compute Poincare index around this cell (!= 0 for potential fixed point)
diff[0,:] = tracers[iy, ix, 0, 2:4] - tracers[iy, ix, 0, 0:2]
diff[1,:] = tracers[iy, ix+1, 0, 2:4] - tracers[iy, ix+1, 0, 0:2]
diff[2,:] = tracers[iy+1, ix+1, 0, 2:4] - tracers[iy+1, ix+1, 0, 0:2]
diff[3,:] = tracers[iy+1, ix, 0, 2:4] - tracers[iy+1, ix, 0, 0:2]
if (sum(np.sum(diff**2, axis = 1) != 0) == True):
diff = np.swapaxes(np.swapaxes(diff, 0, 1) / np.sqrt(np.sum(diff**2, axis = 1)), 0, 1)
poincare = pIndex(vv, p, tracers[iy, ix:ix+2, 0, 0], tracers[iy:iy+2, ix, 0, 1], diff, phiMin,
hMin = hMin, hMax = hMax, lMax = lMax, tol = tol, interpolation = interpolation, integration = integration)
if (abs(poincare) > 5): # use 5 instead of 2pi to account for rounding errors
# subsample to get starting point for iteration
nt = 4
xmin = tracers[iy, ix, 0, 0]
ymin = tracers[iy, ix, 0, 1]
xmax = tracers[iy, ix+1, 0, 0]
ymax = tracers[iy+1, ix, 0, 1]
xx = np.zeros((nt**2,3))
tracersSub = np.zeros((nt**2,5))
i1 = 0
for j1 in range(nt):
for k1 in range(nt):
xx[i1,0] = xmin + j1/(nt-1.)*(xmax - xmin)
xx[i1,1] = ymin + k1/(nt-1.)*(ymax - ymin)
xx[i1,2] = p.Oz
i1 += 1
for it1 in range(nt**2):
s = pc.stream(vv, p, hMin = hMin, hMax = hMax, lMax = lMax, tol = tol,
interpolation = interpolation, integration = integration, xx = xx[it1,:])
tracersSub[it1,0:2] = xx[it1,0:2]
tracersSub[it1,2:] = s.tracers[s.sl-1,:]
min2 = 1e6
minx = xmin
miny = ymin
i1 = 0
for j1 in range(nt):
for k1 in range(nt):
diff2 = (tracersSub[i1, 2] - tracersSub[i1, 0])**2 + (tracersSub[i1, 3] - tracersSub[i1, 1])**2
if (diff2 < min2):
min2 = diff2
minx = xmin + j1/(nt-1.)*(xmax - xmin)
miny = ymin + k1/(nt-1.)*(ymax - ymin)
it1 += 1
# get fixed point from this starting position using Newton's method
#TODO:
dl = np.min(var.dx, var.dy)/100. # step-size for calculating the Jacobian by finite differences
it = 0
# tracers used to find the fixed point
tracersNull = np.zeros((5,4))
point = np.array([minx, miny])
while True:
# trace field lines at original point and for Jacobian:
# (second order seems to be enough)
xx = np.zeros((5,3))
xx[0,:] = np.array([point[0], point[1], p.Oz])
xx[1,:] = np.array([point[0]-dl, point[1], p.Oz])
xx[2,:] = np.array([point[0]+dl, point[1], p.Oz])
xx[3,:] = np.array([point[0], point[1]-dl, p.Oz])
xx[4,:] = np.array([point[0], point[1]+dl, p.Oz])
for it1 in range(5):
s = pc.stream(vv, p, hMin = hMin, hMax = hMax, lMax = lMax, tol = tol,
interpolation = interpolation, integration = integration, xx = xx[it1,:])
tracersNull[it1,:2] = xx[it1,:2]
tracersNull[it1,2:] = s.tracers[s.sl-1,0:2]
# check function convergence
ff = np.zeros(2)
ff[0] = tracersNull[0,2] - tracersNull[0,0]
ff[1] = tracersNull[0,3] - tracersNull[0,1]
#TODO:
if (sum(abs(ff)) <= 1e-4):
fixedPoint = np.array([point[0], point[1]])
break
# compute the Jacobian
fjac = np.zeros((2,2))
fjac[0,0] = ((tracersNull[2,2] - tracersNull[2,0]) - (tracersNull[1,2] - tracersNull[1,0]))/2./dl
fjac[0,1] = ((tracersNull[4,2] - tracersNull[4,0]) - (tracersNull[3,2] - tracersNull[3,0]))/2./dl
fjac[1,0] = ((tracersNull[2,3] - tracersNull[2,1]) - (tracersNull[1,3] - tracersNull[1,1]))/2./dl
fjac[1,1] = ((tracersNull[4,3] - tracersNull[4,1]) - (tracersNull[3,3] - tracersNull[3,1]))/2./dl
# invert the Jacobian
fjin = np.zeros((2,2))
det = fjac[0,0]*fjac[1,1] - fjac[0,1]*fjac[1,0]
#TODO:
if (abs(det) < dl):
fixedPoint = point
break
fjin[0,0] = fjac[1,1]
fjin[1,1] = fjac[0,0]
fjin[0,1] = -fjac[0,1]
fjin[1,0] = -fjac[1,0]
fjin = fjin/det
dpoint = np.zeros(2)
dpoint[0] = -fjin[0,0]*ff[0] - fjin[0,1]*ff[1]
dpoint[1] = -fjin[1,0]*ff[0] - fjin[1,1]*ff[1]
point += dpoint
# check root convergence
#TODO:
if (sum(abs(dpoint)) < 1e-4):
fixedPoint = point
break
if (it > 20):
fixedPoint = point
print("warning: Newton did not converged")
break
it += 1
# check if fixed point lies inside the cell
if ((fixedPoint[0] < tracers[iy, ix, 0, 0]) or (fixedPoint[0] > tracers[iy, ix+1, 0, 0]) or
(fixedPoint[1] < tracers[iy, ix, 0, 1]) or (fixedPoint[1] > tracers[iy+1, ix, 0, 1])):
print("warning: fixed point lies outside the cell")
else:
x.append(fixedPoint[0])
y.append(fixedPoint[1])
#q.append()
fidx += 1
queue.put((x, y, q, fidx, iproc))
# multi core setup
if (np.isscalar(nproc) == False) or (nproc%1 != 0):
print("error: invalid processor number")
return -1
queue = mp.Queue()
proc = []
# make sure to read the var files with the correct magic
if (traceField == 'bb'):
magic = 'bb'
if (traceField == 'jj'):
magic = 'jj'
if (traceField == 'vort'):
magic = 'vort'
# read the cpu structure
dim = pc.read_dim(datadir = datadir)
if (dim.nprocz > 1):
print("error: number of cores in z-direction > 1")
var = pc.read_var(varfile = varfile, datadir = datadir, magic = magic, quiet = True, trimall = True)
grid = pc.read_grid(datadir = datadir, quiet = True, trim = True)
vv = getattr(var, traceField)
# initialize the parameters
p = pc.pClass()
p.dx = var.dx; p.dy = var.dy; p.dz = var.dz
p.Ox = var.x[0]; p.Oy = var.y[0]; p.Oz = var.z[0]
p.Lx = grid.Lx; p.Ly = grid.Ly; p.Lz = grid.Lz
p.nx = dim.nx; p.ny = dim.ny; p.nz = dim.nz
# create the initial mapping
tracers, mapping, t = pc.tracers(traceField = 'bb', hMin = hMin, hMax = hMax, lMax = lMax, tol = tol,
interpolation = interpolation, trace_sub = trace_sub, varfile = varfile,
integration = integration, datadir = datadir, destination = '', nproc = nproc)
# find fixed points
fixed = pc.fixed_struct()
xyq = [] # list of return values from subFixed
ix0 = range(0,p.nx*trace_sub-1) # set of grid indices for the cores
iy0 = range(0,p.ny*trace_sub-1) # set of grid indices for the cores
subFixedLambda = lambda queue, ix0, iy0, vv, p, tracers, iproc: \
subFixed(queue, ix0, iy0, vv, p, tracers, iproc, hMin = hMin, hMax = hMax, lMax = lMax, tol = tol,
interpolation = interpolation, integration = integration)
for iproc in range(nproc):
proc.append(mp.Process(target = subFixedLambda, args = (queue, ix0[iproc::nproc], iy0, vv, p, tracers, iproc)))
for iproc in range(nproc):
proc[iproc].start()
for iproc in range(nproc):
xyq.append(queue.get())
for iproc in range(nproc):
proc[iproc].join()
# put together return values from subFixed
fixed.fidx = 0
fixed.t = var.t
for iproc in range(nproc):
fixed.x.append(xyq[xyq[iproc][4]][0])
fixed.y.append(xyq[xyq[iproc][4]][1])
fixed.q.append(xyq[xyq[iproc][4]][2])
fixed.fidx += xyq[xyq[iproc][4]][3]
fixed.t = np.array(fixed.t)
fixed.x = np.array(fixed.x)
fixed.y = np.array(fixed.y)
fixed.q = np.array(fixed.q)
fixed.fidx = np.array(fixed.fidx)
return fixed
def fixed_points(datadir='data/',
fileName='fixed_points_post.dat',
varfile='VAR0',
ti=-1,
tf=-1,
traceField='bb',
hMin=2e-3,
hMax=2e4,
lMax=500,
tol=1e-2,
interpolation='weighted',
trace_sub=1,
integration='simple',
nproc=1):
"""
Find the fixed points.
call signature::
fixed = fixed_points(datadir = 'data/', fileName = 'fixed_points_post.dat', varfile = 'VAR0', ti = -1, tf = -1,
traceField = 'bb', hMin = 2e-3, hMax = 2e4, lMax = 500, tol = 1e-2,
interpolation = 'weighted', trace_sub = 1, integration = 'simple', nproc = 1)
Finds the fixed points. Returns the fixed points positions.
Keyword arguments:
*datadir*:
Data directory.
*fileName*:
Name of the fixed points file.
*varfile*:
Varfile to be read.
*ti*:
Initial VAR file index for tracer time sequences. Overrides 'varfile'.
*tf*:
Final VAR file index for tracer time sequences. Overrides 'varfile'.
*traceField*:
Vector field used for the streamline tracing.
*hMin*:
Minimum step length for and underflow to occur.
*hMax*:
Parameter for the initial step length.
*lMax*:
Maximum length of the streamline. Integration will stop if l >= lMax.
*tol*:
Tolerance for each integration step. Reduces the step length if error >= tol.
*interpolation*:
Interpolation of the vector field.
'mean': takes the mean of the adjacent grid point.
'weighted': weights the adjacent grid points according to their distance.
*trace_sub*:
Number of sub-grid cells for the seeds for the initial mapping.
*intQ*:
Quantities to be integrated along the streamlines.
*integration*:
Integration method.
'simple': low order method.
'RK6': Runge-Kutta 6th order.
*nproc*:
Number of cores for multi core computation.
"""
class data_struct:
def __init__(self):
self.t = []
self.fidx = [] # number of fixed points at this time
self.x = []
self.y = []
self.q = []
# Computes rotation along one edge.
def edge(vv,
p,
sx,
sy,
diff1,
diff2,
phiMin,
rec,
hMin=hMin,
hMax=hMax,
lMax=lMax,
tol=tol,
interpolation=interpolation,
integration=integration):
dtot = m.atan2(diff1[0] * diff2[1] - diff2[0] * diff1[1],
diff1[0] * diff2[0] + diff1[1] * diff2[1])
if ((abs(dtot) > phiMin) and (rec < 4)):
xm = 0.5 * (sx[0] + sx[1])
ym = 0.5 * (sy[0] + sy[1])
# trace intermediate field line
s = pc.stream(vv,
p,
hMin=hMin,
hMax=hMax,
lMax=lMax,
tol=tol,
interpolation=interpolation,
integration=integration,
xx=np.array([xm, ym, p.Oz]))
tracer = np.concatenate(
(s.tracers[0,
0:2], s.tracers[s.sl - 1, :], np.reshape(s.l, (1))))
# discard any streamline which does not converge or hits the boundary
if ((tracer[5] >= lMax) or (tracer[4] < p.Oz + p.Lz - p.dz)):
dtot = 0.
else:
diffm = np.array(
[tracer[2] - tracer[0], tracer[3] - tracer[1]])
if (sum(diffm**2) != 0):
diffm = diffm / np.sqrt(sum(diffm**2))
dtot = edge(vv, p, [sx[0], xm], [sy[0], ym], diff1, diffm, phiMin, rec+1,
hMin = hMin, hMax = hMax, lMax = lMax, tol = tol, interpolation = interpolation, integration = integration)+ \
edge(vv, p, [xm, sx[1]], [ym, sy[1]], diffm, diff2, phiMin, rec+1,
hMin = hMin, hMax = hMax, lMax = lMax, tol = tol, interpolation = interpolation, integration = integration)
return dtot
# Finds the Poincare index of this grid cell.
def pIndex(vv,
p,
sx,
sy,
diff,
phiMin,
hMin=hMin,
hMax=hMax,
lMax=lMax,
tol=tol,
interpolation=interpolation,
integration=integration):
poincare = 0
poincare += edge(vv,
p, [sx[0], sx[1]], [sy[0], sy[0]],
diff[0, :],
diff[1, :],
phiMin,
0,
hMin=hMin,
hMax=hMax,
lMax=lMax,
tol=tol,
interpolation=interpolation,
integration=integration)
poincare += edge(vv,
p, [sx[1], sx[1]], [sy[0], sy[1]],
diff[1, :],
diff[2, :],
phiMin,
0,
hMin=hMin,
hMax=hMax,
lMax=lMax,
tol=tol,
interpolation=interpolation,
integration=integration)
poincare += edge(vv,
p, [sx[1], sx[0]], [sy[1], sy[1]],
diff[2, :],
diff[3, :],
phiMin,
0,
hMin=hMin,
hMax=hMax,
lMax=lMax,
tol=tol,
interpolation=interpolation,
integration=integration)
poincare += edge(vv,
p, [sx[0], sx[0]], [sy[1], sy[0]],
diff[3, :],
diff[0, :],
phiMin,
0,
hMin=hMin,
hMax=hMax,
lMax=lMax,
tol=tol,
interpolation=interpolation,
integration=integration)
return poincare
# fixed point finder for a subset of the domain
def subFixed(queue,
ix0,
iy0,
vv,
p,
tracers,
iproc,
hMin=2e-3,
hMax=2e4,
lMax=500,
tol=1e-2,
interpolation='weighted',
integration='simple'):
diff = np.zeros((4, 2))
phiMin = np.pi / 8.
x = []
y = []
q = []
fidx = 0
for ix in ix0:
for iy in iy0:
# compute Poincare index around this cell (!= 0 for potential fixed point)
diff[0, :] = tracers[iy, ix, 0, 2:4] - tracers[iy, ix, 0, 0:2]
diff[1, :] = tracers[iy, ix + 1, 0, 2:4] - tracers[iy, ix + 1,
0, 0:2]
diff[2, :] = tracers[iy + 1, ix + 1, 0,
2:4] - tracers[iy + 1, ix + 1, 0, 0:2]
diff[3, :] = tracers[iy + 1, ix, 0, 2:4] - tracers[iy + 1, ix,
0, 0:2]
if (sum(np.sum(diff**2, axis=1) != 0) == True):
diff = np.swapaxes(
np.swapaxes(diff, 0, 1) /
np.sqrt(np.sum(diff**2, axis=1)), 0, 1)
poincare = pIndex(vv,
p,
tracers[iy, ix:ix + 2, 0, 0],
tracers[iy:iy + 2, ix, 0, 1],
diff,
phiMin,
hMin=hMin,
hMax=hMax,
lMax=lMax,
tol=tol,
interpolation=interpolation,
integration=integration)
if (abs(poincare) > 5
): # use 5 instead of 2pi to account for rounding errors
# subsample to get starting point for iteration
nt = 4
xmin = tracers[iy, ix, 0, 0]
ymin = tracers[iy, ix, 0, 1]
xmax = tracers[iy, ix + 1, 0, 0]
ymax = tracers[iy + 1, ix, 0, 1]
xx = np.zeros((nt**2, 3))
tracersSub = np.zeros((nt**2, 5))
i1 = 0
for j1 in range(nt):
for k1 in range(nt):
xx[i1, 0] = xmin + j1 / (nt - 1.) * (xmax - xmin)
xx[i1, 1] = ymin + k1 / (nt - 1.) * (ymax - ymin)
xx[i1, 2] = p.Oz
i1 += 1
for it1 in range(nt**2):
s = pc.stream(vv,
p,
hMin=hMin,
hMax=hMax,
lMax=lMax,
tol=tol,
interpolation=interpolation,
integration=integration,
xx=xx[it1, :])
tracersSub[it1, 0:2] = xx[it1, 0:2]
tracersSub[it1, 2:] = s.tracers[s.sl - 1, :]
min2 = 1e6
minx = xmin
miny = ymin
i1 = 0
for j1 in range(nt):
for k1 in range(nt):
diff2 = (tracersSub[i1, 2] - tracersSub[i1, 0]
)**2 + (tracersSub[i1, 3] -
tracersSub[i1, 1])**2
if (diff2 < min2):
min2 = diff2
minx = xmin + j1 / (nt - 1.) * (xmax - xmin)
miny = ymin + k1 / (nt - 1.) * (ymax - ymin)
it1 += 1
# get fixed point from this starting position using Newton's method
#TODO:
dl = np.min(
var.dx, var.dy
) / 100. # step-size for calculating the Jacobian by finite differences
it = 0
# tracers used to find the fixed point
tracersNull = np.zeros((5, 4))
point = np.array([minx, miny])
while True:
# trace field lines at original point and for Jacobian:
# (second order seems to be enough)
xx = np.zeros((5, 3))
xx[0, :] = np.array([point[0], point[1], p.Oz])
xx[1, :] = np.array([point[0] - dl, point[1], p.Oz])
xx[2, :] = np.array([point[0] + dl, point[1], p.Oz])
xx[3, :] = np.array([point[0], point[1] - dl, p.Oz])
xx[4, :] = np.array([point[0], point[1] + dl, p.Oz])
for it1 in range(5):
s = pc.stream(vv,
p,
hMin=hMin,
hMax=hMax,
lMax=lMax,
tol=tol,
interpolation=interpolation,
integration=integration,
xx=xx[it1, :])
tracersNull[it1, :2] = xx[it1, :2]
tracersNull[it1, 2:] = s.tracers[s.sl - 1, 0:2]
# check function convergence
ff = np.zeros(2)
ff[0] = tracersNull[0, 2] - tracersNull[0, 0]
ff[1] = tracersNull[0, 3] - tracersNull[0, 1]
#TODO:
if (sum(abs(ff)) <= 1e-4):
fixedPoint = np.array([point[0], point[1]])
break
# compute the Jacobian
fjac = np.zeros((2, 2))
fjac[0, 0] = (
(tracersNull[2, 2] - tracersNull[2, 0]) -
(tracersNull[1, 2] - tracersNull[1, 0])) / 2. / dl
fjac[0, 1] = (
(tracersNull[4, 2] - tracersNull[4, 0]) -
(tracersNull[3, 2] - tracersNull[3, 0])) / 2. / dl
fjac[1, 0] = (
(tracersNull[2, 3] - tracersNull[2, 1]) -
(tracersNull[1, 3] - tracersNull[1, 1])) / 2. / dl
fjac[1, 1] = (
(tracersNull[4, 3] - tracersNull[4, 1]) -
(tracersNull[3, 3] - tracersNull[3, 1])) / 2. / dl
# invert the Jacobian
fjin = np.zeros((2, 2))
det = fjac[0, 0] * fjac[1, 1] - fjac[0, 1] * fjac[1, 0]
#TODO:
if (abs(det) < dl):
fixedPoint = point
break
fjin[0, 0] = fjac[1, 1]
fjin[1, 1] = fjac[0, 0]
fjin[0, 1] = -fjac[0, 1]
fjin[1, 0] = -fjac[1, 0]
fjin = fjin / det
dpoint = np.zeros(2)
dpoint[0] = -fjin[0, 0] * ff[0] - fjin[0, 1] * ff[1]
dpoint[1] = -fjin[1, 0] * ff[0] - fjin[1, 1] * ff[1]
point += dpoint
# check root convergence
#TODO:
if (sum(abs(dpoint)) < 1e-4):
fixedPoint = point
break
if (it > 20):
fixedPoint = point
print "warning: Newton did not converged"
break
it += 1
# check if fixed point lies inside the cell
if ((fixedPoint[0] < tracers[iy, ix, 0, 0])
or (fixedPoint[0] > tracers[iy, ix + 1, 0, 0])
or (fixedPoint[1] < tracers[iy, ix, 0, 1])
or (fixedPoint[1] > tracers[iy + 1, ix, 0, 1])):
print "warning: fixed point lies outside the cell"
else:
x.append(fixedPoint[0])
y.append(fixedPoint[1])
#q.append()
fidx += 1
queue.put((x, y, q, fidx, iproc))
# multi core setup
if (np.isscalar(nproc) == False) or (nproc % 1 != 0):
print("error: invalid processor number")
return -1
queue = mp.Queue()
proc = []
# make sure to read the var files with the correct magic
if (traceField == 'bb'):
magic = 'bb'
if (traceField == 'jj'):
magic = 'jj'
if (traceField == 'vort'):
magic = 'vort'
# read the cpu structure
dim = pc.read_dim(datadir=datadir)
if (dim.nprocz > 1):
print "error: number of cores in z-direction > 1"
var = pc.read_var(varfile=varfile,
datadir=datadir,
magic=magic,
quiet=True,
trimall=True)
grid = pc.read_grid(datadir=datadir, quiet=True, trim=True)
vv = getattr(var, traceField)
# initialize the parameters
p = pc.pClass()
p.dx = var.dx
p.dy = var.dy
p.dz = var.dz
p.Ox = var.x[0]
p.Oy = var.y[0]
p.Oz = var.z[0]
p.Lx = grid.Lx
p.Ly = grid.Ly
p.Lz = grid.Lz
p.nx = dim.nx
p.ny = dim.ny
p.nz = dim.nz
# create the initial mapping
tracers, mapping, t = pc.tracers(traceField='bb',
hMin=hMin,
hMax=hMax,
lMax=lMax,
tol=tol,
interpolation=interpolation,
trace_sub=trace_sub,
varfile=varfile,
integration=integration,
datadir=datadir,
destination='',
nproc=nproc)
# find fixed points
fixed = pc.fixed_struct()
xyq = [] # list of return values from subFixed
ix0 = range(0, p.nx * trace_sub - 1) # set of grid indices for the cores
iy0 = range(0, p.ny * trace_sub - 1) # set of grid indices for the cores
subFixedLambda = lambda queue, ix0, iy0, vv, p, tracers, iproc: \
subFixed(queue, ix0, iy0, vv, p, tracers, iproc, hMin = hMin, hMax = hMax, lMax = lMax, tol = tol,
interpolation = interpolation, integration = integration)
for iproc in range(nproc):
proc.append(
mp.Process(target=subFixedLambda,
args=(queue, ix0[iproc::nproc], iy0, vv, p, tracers,
iproc)))
for iproc in range(nproc):
proc[iproc].start()
for iproc in range(nproc):
xyq.append(queue.get())
for iproc in range(nproc):
proc[iproc].join()
# put together return values from subFixed
fixed.fidx = 0
fixed.t = var.t
for iproc in range(nproc):
fixed.x.append(xyq[xyq[iproc][4]][0])
fixed.y.append(xyq[xyq[iproc][4]][1])
fixed.q.append(xyq[xyq[iproc][4]][2])
fixed.fidx += xyq[xyq[iproc][4]][3]
fixed.t = np.array(fixed.t)
fixed.x = np.array(fixed.x)
fixed.y = np.array(fixed.y)
fixed.q = np.array(fixed.q)
fixed.fidx = np.array(fixed.fidx)
return fixed
