def _initInterp(self):
"""
Initialise the interpolant
Finds the horizontal indices of the slice points and
constructs the 3D mask array
"""
# Find the cell index of each point along the slice
self.Tri = GridSearch(self.xp, self.yp, self.cells)
self.cellind = self.Tri(self.xslice, self.yslice)
klayer, Nkmax = self.get_klayer()
# Construct the 3D coordinate arrays
self.xslice = np.repeat(self.xslice.reshape((1, self.Npt)),
self.Nkmax,
axis=0)
self.yslice = np.repeat(self.yslice.reshape((1, self.Npt)),
self.Nkmax,
axis=0)
self.distslice = np.repeat(self.distslice.reshape((1, self.Npt)),
self.Nkmax,
axis=0)
self.zslice = np.repeat(-self.z_r[klayer].reshape((self.Nkmax, 1)),
self.Npt,
axis=1)
# Construct the mask array
self.calc_mask()
# Get the bathymetry along the slice
self.hslice = -self.dv[self.cellind]
def __init__(self,
ncfile,
xpt=None,
ypt=None,
Npt=100,
klayer=[-99],
**kwargs):
self.Npt = Npt
Spatial.__init__(self, ncfile, klayer=klayer, **kwargs)
# Load the grid as a hybridgrid
self.grd = GridSearch(self.xp,self.yp,self.cells,nfaces=self.nfaces,\
edges=self.edges,mark=self.mark,grad=self.grad,neigh=self.neigh,\
xv=self.xv,yv=self.yv)
# Find the edge indices along the line
self.update_xy(xpt, ypt)
def GridParticles(grdfile,dx,dy,nz,xypoly=None,splitvec=1):
"""
Returns the locations of particles on a regular grid inside of suntans grid
Inputs:
grdfile - netcdf filename containing the suntans grid
dx,dy - resolution in x and y component respectively.
nz - number of particles in the vertical. Particles are arranged in sigma layers.
xypoly - [optional] coordinates of an additional bounding polygon [Nx2 array]
"""
# Load the suntans grid
sun = Grid(grdfile)
# Load a trisearch object
tri = GridSearch(sun.xp,sun.yp,sun.cells,nfaces=sun.nfaces,verbose=False)
if xypoly == None:
xlims = [sun.xlims[0],sun.xlims[1]]
ylims = [sun.ylims[0],sun.ylims[1]]
else:
xlims = [xypoly[:,0].min(),xypoly[:,0].max()]
ylims = [xypoly[:,1].min(),xypoly[:,1].max()]
# Construct a 2D mesh of particles
x = np.arange(xlims[0],xlims[1],dx)
y = np.arange(ylims[0],ylims[1],dy)
X,Y = np.meshgrid(x,y)
X=X.ravel()
Y=Y.ravel()
# Check which particles are inside the grid
cellind = tri(X,Y)
mask = cellind!=-1
# Check which particles are also inside of the polygon
if not xypoly == None:
inpoly = inpolygon(np.vstack((X,Y)).T,xypoly)
mask = operator.and_(mask,inpoly)
xout = X[mask]
yout = Y[mask]
nx = xout.shape[0]
# Construct the 3D mesh
xout = np.repeat(xout.reshape((nx,1)),nz,axis=1)
yout = np.repeat(yout.reshape((nx,1)),nz,axis=1)
zout = np.linspace(0.05,0.95,nz)
zout = np.repeat(zout.reshape((nz,1)),nx,axis=1)
zout *= -sun.dv[cellind[mask]]
zout = zout.T
xout = xout.ravel()
yout = yout.ravel()
zout = zout.ravel()
# Rearrange the vectors to avoid clustering (this helps even the MPI workload)
#xout_split=[]
#yout_split=[]
#zout_split=[]
#for start in range(splitvec):
# xout_split.append(xout[start::splitvec])
# yout_split.append(yout[start::splitvec])
# zout_split.append(zout[start::splitvec])
#xout = np.hstack(xout_split)
#yout = np.hstack(yout_split)
#zout = np.hstack(zout_split)
return xout, yout, zout
