def smoothDepths(self):
"""
Smooth the data by running an interpolant over the model grid points
"""
print 'Smoothing the data...'
if self.smoothmethod=='gaussian':
print '\tUsing Gaussian filter...'
Fsmooth = ufilter(self.xy, self.smoothrange)
else:
Fsmooth =interpXYZ(self.xy,self.xy,\
method=self.smoothmethod,NNear=self.smoothnear,vrange=self.smoothrange)
self.grd.dv = Fsmooth(self.grd.dv)
def interp_depths(self):
# Initialise the Interpolation class
if not self.isDEM:
print 'Building interpolant class...'
self.F = interpXYZ(self.indata.XY,self.xy,method=self.interpmethod,NNear=self.NNear,\
p=self.p,varmodel=self.varmodel,nugget=self.nugget,sill=self.sill,vrange=self.vrange)
# Interpolate the data
print 'Interpolating data...'
dv = self.F(self.indata.Zin)*self.scalefac
else:
print 'Interpolating DEM data...'
dv = self.indata.interp(self.xy[:,0],self.xy[:,1])*self.scalefac
if self.interpnodes:
self.grd.dv = np.zeros_like(self.grd.xv)
for nn in range(self.grd.Nc):
self.grd.dv[nn] = np.max(dv[self.grd.cells[nn,0:self.grd.nfaces[nn] ] ])
#self.grd.dv[nn] = np.mean(dv[self.grd.cells[nn,0:self.grd.nfaces[nn] ] ])
else:
self.grd.dv = dv
def extract_HC(modfile,lon,lat,z=None,conlist=None):
"""
Extract harmonic constituents from OTIS binary output and interpolate onto points in lon,lat
set "z" to specifiy depth for transport to velocity conversion
set "constituents" in conlist
Returns:
u_re, u_im, v_re, v_im, h_re, h_im, omega, conlist
"""
###
# Make sure the longitude is between 0 and 360
lon = np.mod(lon,360.0)
###
# Read the filenames from the model file
pathfile = os.path.split(modfile)
path = pathfile[0]
f = open(modfile,'r')
hfile = path+'/' + f.readline().strip()
uvfile = path+'/' + f.readline().strip()
grdfile = path+'/' + f.readline().strip()
f.close()
###
# Read the grid file
X,Y,depth, mask = read_OTPS_grd(grdfile)
#X[X>180.0] = 180.0 - X[X>180.0]
mask = mask == 1
# Create an interpolation object
sz = lon.shape
lon = lon.ravel()
lat = lat.ravel()
nx = lon.size
F= interpXYZ(np.vstack((X[mask],Y[mask])).T,np.vstack((lon,lat)).T,method='idw',NNear=3,p=1.0)
# Interpolate the model depths onto the points if z is none
if z == None:
z = F(depth[mask])
else:
z = np.abs(z) # make sure they are positive
###
# Check that the constituents are in the file
conOTIS = get_OTPS_constits(hfile)
if conlist == None:
conlist = conOTIS
for vv in conlist:
if not vv in conOTIS:
print 'Warning: constituent name: %s not present in OTIS file.'%vv
conlist.remove(vv)
###
# Now go through and read the data for each
# Initialse the arrays
ncon = len(conlist)
u_re = np.zeros((ncon,nx))
u_im = np.zeros((ncon,nx))
v_re = np.zeros((ncon,nx))
v_im = np.zeros((ncon,nx))
h_re = np.zeros((ncon,nx))
h_im = np.zeros((ncon,nx))
omega = np.zeros((ncon,))
for ii, vv in enumerate(conlist):
idx = otis_constits[vv]['index']
omega[ii] = otis_constits[vv]['omega']
print 'Interpolating consituent: %s...'%vv
# Read and interpolate h
X ,Y, tmp_h_re, tmp_h_im = read_OTPS_h(hfile,idx)
h_re[ii,:] = F(tmp_h_re[mask])
h_im[ii,:] = F(tmp_h_im[mask])
# Read and interpolate u and v - Note the conversion from transport to velocity
X ,Y, tmp_u_re, tmp_u_im, tmp_v_re, tmp_v_im = read_OTPS_UV(uvfile,idx)
u_re[ii,:] = F(tmp_u_re[mask]) / z
u_im[ii,:] = F(tmp_u_im[mask]) / z
v_re[ii,:] = F(tmp_v_re[mask]) / z
v_im[ii,:] = F(tmp_v_im[mask]) / z
# Return the arrays in their original shape
szout = (ncon,) + sz
return u_re.reshape(szout), u_im.reshape(szout), v_re.reshape(szout), \
v_im.reshape(szout), h_re.reshape(szout), h_im.reshape(szout), omega, conlist
def extract_HC(modfile, lon, lat, z=None, conlist=None):
"""
Extract harmonic constituents from OTIS binary output and interpolate onto points in lon,lat
set "z" to specifiy depth for transport to velocity conversion
set "constituents" in conlist
Returns:
u_re, u_im, v_re, v_im, h_re, h_im, omega, conlist
"""
###
# Make sure the longitude is between 0 and 360
lon = np.mod(lon, 360.0)
###
# Read the filenames from the model file
pathfile = os.path.split(modfile)
path = pathfile[0]
f = open(modfile, 'r')
hfile = path + '/' + f.readline().strip()
uvfile = path + '/' + f.readline().strip()
grdfile = path + '/' + f.readline().strip()
f.close()
###
# Read the grid file
X, Y, depth, mask = read_OTPS_grd(grdfile)
#X[X>180.0] = 180.0 - X[X>180.0]
mask = mask == 1
# Create an interpolation object
sz = lon.shape
lon = lon.ravel()
lat = lat.ravel()
nx = lon.size
F = interpXYZ(np.vstack((X[mask], Y[mask])).T,
np.vstack((lon, lat)).T,
method='idw',
NNear=3,
p=1.0)
# Interpolate the model depths onto the points if z is none
if z == None:
z = F(depth[mask])
else:
z = np.abs(z) # make sure they are positive
###
# Check that the constituents are in the file
conOTIS = get_OTPS_constits(hfile)
if conlist == None:
conlist = conOTIS
for vv in conlist:
if not vv in conOTIS:
print('Warning: constituent name: %s not present in OTIS file.' %
vv)
conlist.remove(vv)
###
# Now go through and read the data for each
# Initialse the arrays
ncon = len(conlist)
u_re = np.zeros((ncon, nx))
u_im = np.zeros((ncon, nx))
v_re = np.zeros((ncon, nx))
v_im = np.zeros((ncon, nx))
h_re = np.zeros((ncon, nx))
h_im = np.zeros((ncon, nx))
omega = np.zeros((ncon, ))
for ii, vv in enumerate(conlist):
idx = otis_constits[vv]['index']
omega[ii] = otis_constits[vv]['omega']
print('Interpolating consituent: %s...' % vv)
# Read and interpolate h
X, Y, tmp_h_re, tmp_h_im = read_OTPS_h(hfile, idx)
h_re[ii, :] = F(tmp_h_re[mask])
h_im[ii, :] = F(tmp_h_im[mask])
# Read and interpolate u and v - Note the conversion from transport to velocity
X, Y, tmp_u_re, tmp_u_im, tmp_v_re, tmp_v_im = read_OTPS_UV(
uvfile, idx)
u_re[ii, :] = F(tmp_u_re[mask]) / z
u_im[ii, :] = F(tmp_u_im[mask]) / z
v_re[ii, :] = F(tmp_v_re[mask]) / z
v_im[ii, :] = F(tmp_v_im[mask]) / z
# Return the arrays in their original shape
szout = (ncon, ) + sz
return u_re.reshape(szout), u_im.reshape(szout), v_re.reshape(szout), \
v_im.reshape(szout), h_re.reshape(szout), h_im.reshape(szout), omega, conlist
