def Calculate(lat, t0, tref, cons, typ=0):
const = t_getconsts(np.array([]))
Const = [con.decode('UTF-8') for con in const[0]['name']]
consindex = [Const.index(con.ljust(4)) for con in cons]
# Earth equilibrium at the beginning of the run
[v, u, _] = t_vuf('nodal', np.array(t0), consindex, lat)
tear = 360. * (v + u)
# Nodel factor at the middl of the run
[_, _, f] = t_vuf('nodal', np.array(tref), consindex, lat)
tnf = f
tfreq = (2 * np.pi) * const[0]['freq'][consindex] / 3600.
talpha = const[0]['name'][consindex]
if typ == 1:
tpspec = np.abs(const[0]['doodsonspecies'][consindex])
tpamp = t_equilib(const[0]['freq'][consindex],
const[0]['doodsonamp'][consindex],
const[0]['doodsonspecies'][consindex], lat)
return talpha, tpspec, tpamp, tfreq, tear, f
else:
return talpha, tfreq, tear, f
def extract_HC(consfile):
"""
Extract harmonic constituents 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
"""
###
# Read the filenames from the model file
pathfile = os.path.split(consfile)
path = pathfile[0]
f = Dataset(consfile, 'r')
ds = Dataset(consfile)
X = f.variables['lon_u'][:]
Y = f.variables['lat_u'][:]
rloni, rlati = np.meshgrid(X, Y)
###
# Check that the constituents are in the file
conList = []
conIDX = []
for ncon in range(0, len(f.variables['con'])):
x = ''
conList.append(''.join(
[x + n.decode('UTF-8') for n in f.variables['con'][ncon].data]))
conIDX.append(ncon)
const = t_getconsts(np.array([]))
Const = [con.decode('UTF-8') for con in const[0]['name']]
consindex = [Const.index(con.ljust(4).upper()) for con in conList]
tfreq = (2 * np.pi) * const[0]['freq'][consindex] / 3600.
var = {}
# interpolating to ROMS grid requires special care with phases!!
# this is subjected to parallelization via muitiprocessing.Process - do it!
# there is a sandbox in roms repo with a starting exercise
Vars = ['u', 'v']
for var0 in Vars:
var[var0] = np.ones(shape=(len(tfreq), 4,
rloni.shape[0] * rloni.shape[1])) * -1e-8
N = -1
for con, ncon in zip(const[0]['name'][consindex], conIDX):
N = N + 1
con = con.decode('UTF-8')
print("extracting %s for %s" % (var0, con))
Uim = f.variables[var0 + "Im"][ncon]
Ure = f.variables[var0 + "Re"][ncon]
h = Ure + Uim * 1j
amp = np.abs(h)
pha = np.arctan2(-np.imag(h), np.real(h))
var[var0][N, 0, :] = amp.flatten()
var[var0][N, 2, :] = pha.flatten() #*180./np.pi
return var, tfreq, consindex, rloni.flatten(), rlati.flatten()
def get_uv_cons(self, btypes):
self.ifcons = {}
for b in range(0, len(btypes)):
Lat = []
Lon = []
self.ifcons[b] = {}
if (btypes[b]['ifltype']['value']
== 3) or (btypes[b]['ifltype']['value'] == 5):
file = btypes[b]['ifltype']['filename']
var = btypes[b]['ifltype']['vars']
for node_i in self.nnode[b]:
Lat.append(self.hgrid.latitude[int(node_i)])
Lon.append(self.hgrid.longitude[int(node_i)])
HC, tfreq, constidx = extract_HC(file,
var,
Lon,
Lat,
logger=self.logger)
const = t_getconsts(np.array([]))
for i, n in enumerate(constidx):
self.ifcons[b][const[0]['name'][n].decode('UTF-8')] = {}
for k in var:
k = k.split('_')[0]
self.ifcons[b][const[0]['name'][n].decode('UTF-8')][
k + '_amp'] = HC[k][i, 0, :]
self.ifcons[b][const[0]['name'][n].decode('UTF-8')][
k + '_pha'] = np.mod(HC[k][i, 2, :], 360)
def Calculate(lat, t0, cons):
const = t_getconsts(np.array([]))
Const= [con.decode('UTF-8') for con in const[0]['name']]
consindex = [Const.index(con.ljust(4)) for con in cons]
# V: astronomical phase, U: nodal phase modulation, F: nodal amplitude correction
v,u,f = t_vuf('nodal', np.array(t0), consindex, lat)
tear = 360.*(v+u)
tfreq = (2*np.pi)*const[0]['freq'][consindex]/3600.
talpha = const[0]['name'][consindex]
return talpha, tfreq, tear, f
def Calculate(mean_lat, Tstart, Cons):
const = t_getconsts(np.array([]))
itemindex = []
for k in range(0, len(Cons)):
itemindex.append(np.where(const[0]['name'] == Cons[k].ljust(4))[0][0])
# Earth equilibrium at the beginning of the run
[v, u, f] = t_vuf('nodal', Tstart, itemindex, mean_lat)
tear = 360. * (v + u)
# Nodel factor at the middl of the run
[v, u, f] = t_vuf('nodal', Tstart, itemindex, mean_lat)
tnf = f
tfreq = 2. * np.pi * const[0]['freq'][itemindex] / 3600.
talpha = const[0]['name'][itemindex]
return talpha, tfreq, tear, tnf
def extract_HC(consfile, Vars, lim, conlist=None, min_depth=1):
"""
Extract harmonic constituents 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
"""
###
# Read the filenames from the model file
pathfile = os.path.split(consfile)
path = pathfile[0]
f = Dataset(consfile, 'r')
###
# Read the grid file
X = f.variables['Easting'][:]
Y = f.variables['Northing'][:]
if 'Depth' in f.variables:
Z = f.variables['Depth'][:]
else:
Z = f.variables['dep'][:]
X, Y = transform_proj(X, Y, in_espg=2193, out_espg=WGS84)
gd = (X >= lim[0]) & (X <= lim[1]) & (Y >= lim[2]) & (Y <= lim[3]) & (
Z >= min_depth)
X = X[gd]
Y = Y[gd]
Z = Z[gd]
###
# Check that the constituents are in the file
conList = []
conIDX = []
if conlist is not None:
for ncon in range(0, len(f.variables['cons'])):
if ''.join([
y.decode('UTF-8') for y in f.variables['cons'][ncon]
if y != b' '
]) in conlist:
x = ''
conList.append(''.join([
x + n.decode('UTF-8') for n in f.variables['cons'][ncon]
]))
conIDX.append(ncon)
else:
for ncon in range(0, len(f.variables['cons'])):
x = ''
conList.append(''.join([
x + n.decode('UTF-8') for n in f.variables['cons'][ncon].data
]))
conIDX.append(ncon)
const = t_getconsts(np.array([]))
Const = [con.decode('UTF-8') for con in const[0]['name']]
consindex = [Const.index(con.ljust(4)) for con in conList]
tfreq = (2 * np.pi) * const[0]['freq'][consindex] / 3600.
if 'lev' in f.variables:
levs = f.variables['lev'][:]
Ilev = (levs == 0).nonzero()[0]
if len(Ilev) == 0:
print('Lev %f not found' % lev)
Ilev = 0
else:
Ilev = Ilev[0]
else:
Ilev = 0
var = {}
# interpolating to ROMS grid requires special care with phases!!
# this is subjected to parallelization via muitiprocessing.Process - do it!
# there is a sandbox in roms repo with a starting exercise
for var0 in Vars:
var[var0] = np.ones(shape=(len(tfreq), 4, len(X))) * -1e-8
N = -1
for con, ncon in zip(const[0]['name'][consindex], conIDX):
N = N + 1
con = con.decode('UTF-8')
print("extracting %s for %s" % (var0, con))
amp = f.variables[var0 + "_amp"][ncon]
pha = f.variables[var0 + "_pha"][ncon]
if len(amp.shape) > 1:
amp = amp[Ilev, gd]
pha = pha[Ilev, gd]
else:
amp = amp[gd]
pha = pha[gd]
var[var0][N, 0, :] = amp
var[var0][N, 2, :] = pha #*180./np.pi
return var, tfreq, consindex, X, Y, Z
def create_filetide_var(filetide, t0, vari, cons, X, Y, depth, nv, do_variance,
lev):
FillValue = -99999
ncons = len(cons)
nconstr = 4
ncout = netCDF4.Dataset(filetide, 'w')
# dimensions
dcons = ncout.createDimension('cons', ncons)
dconstr = ncout.createDimension('constr', nconstr)
dnode = ncout.createDimension('nSCHISM_hgrid_node', len(depth))
dnface = ncout.createDimension('nMaxSCHISM_hgrid_face_nodes', 4)
dface = ncout.createDimension('nSCHISM_hgrid_face', nv.shape[0])
if lev is not None:
dlev = ncout.createDimension('lev', len(lev))
# write variable
vcons = ncout.createVariable('cons', 'S1', dimensions=('cons', 'constr'))
vcons.long_name = 'Tide constituents'
vcons.standard_name = 'tide constituents'
vX = ncout.createVariable('Easting', np.float64, ('nSCHISM_hgrid_node', ))
vY = ncout.createVariable('Northing', np.float64, ('nSCHISM_hgrid_node', ))
vD = ncout.createVariable('Depth', np.float64, ('nSCHISM_hgrid_node', ))
vN = ncout.createVariable('ele', np.float64, (
'nSCHISM_hgrid_face',
'nMaxSCHISM_hgrid_face_nodes',
))
vfreq = ncout.createVariable('freq', np.float64, ('cons', ))
vfreq.long_name = 'freq'
if lev is not None:
vlev = ncout.createVariable('lev', np.float64, ('lev', ))
all_mean = {}
all_exp = {}
all_amp = {}
all_pha = {}
for nvar in vari:
if nvar is 'u' or nvar is 'v':
all_amp[nvar] = ncout.createVariable(
nvar + '_amp', np.float64,
('cons', 'lev', 'nSCHISM_hgrid_node'))
all_amp[nvar].fill_value = FillValue
all_pha[nvar] = ncout.createVariable(nvar + '_pha', np.float64, (
'cons',
'lev',
'nSCHISM_hgrid_node',
))
all_pha[nvar].fill_value = FillValue
all_pha[nvar].units = 'radians'
if do_variance is True:
all_mean[nvar] = ncout.createVariable(nvar + '_mean',
np.float64, (
'lev',
'nSCHISM_hgrid_node',
))
all_mean[nvar].fill_value = FillValue
all_exp[nvar] = ncout.createVariable(nvar + '_var', np.float64,
(
'lev',
'nSCHISM_hgrid_node',
))
all_exp[nvar].fill_value = FillValue
else:
all_amp[nvar] = ncout.createVariable(nvar + '_amp', np.float64, (
'cons',
'nSCHISM_hgrid_node',
))
all_amp[nvar].fill_value = FillValue
all_pha[nvar] = ncout.createVariable(nvar + '_pha', np.float64, (
'cons',
'nSCHISM_hgrid_node',
))
all_pha[nvar].fill_value = FillValue
all_pha[nvar].units = 'radians'
if do_variance is True:
all_mean[nvar] = ncout.createVariable(nvar + '_mean',
np.float64,
('nSCHISM_hgrid_node', ))
all_mean[nvar].fill_value = FillValue
all_exp[nvar] = ncout.createVariable(nvar + '_var', np.float64,
('nSCHISM_hgrid_node', ))
all_exp[nvar].fill_value = FillValue
for j in range(0, ncons):
for k in range(0, len(cons[j])):
vcons[j, k] = cons[j][k]
# write V0, freq
const, sat, cshallow = t_getconsts(np.array([t0]))
freq = np.zeros(shape=(len(cons), 1))
i = 0
for ic in const['name']:
if ic in cons:
IDX = np.nonzero(ic == cons)[0][0]
freq[IDX] = 2 * np.pi * 24 * const['freq'][i]
i = i + 1
vfreq[:] = freq
# for nvar in vari:
# all_amp[nvar][:]=np.zeros([len(depth),ncons])
# all_pha[nvar][:]=np.zeros([len(depth),ncons])
vX[:] = X
vY[:] = Y
vD[:] = depth
vN[:] = nv
if lev is not None:
vlev[:] = np.abs(lev)
return ncout, all_amp, all_pha, all_mean, all_exp
def extract_HC(modfile, Vars, lon, lat, conlist=None, logger=None):
"""
Extract harmonic constituents 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
"""
###
lon = np.asarray(lon)
lat = np.asarray(lat)
# 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 = Dataset(modfile, 'r')
###
# Read the grid file
X = np.mod(f.variables['lon'][:], 360)
Y = f.variables['lat'][:]
if len(X.shape) == 1:
X, Y = np.meshgrid(X, Y)
if 'dep' in f.variables:
depth = f.variables['dep'][:]
else:
depth = np.zeros((X.shape))
###
# Check that the constituents are in the file
conList = []
conIDX = []
if conlist is not None:
for ncon in range(0, len(f.variables['cons'])):
if f.variables['cons'][ncon][:] in conlist:
x = ''
conList.append(''.join(
[x + n for n in f.variables['cons'][ncon]]))
conIDX.append(ncon)
else:
for ncon in range(0, len(f.variables['cons'])):
x = ''
try:
conList.append(''.join([
x + n.decode('UTF-8')
for n in f.variables['cons'][ncon].data
]))
except:
conList.append(''.join(
[x + n for n in f.variables['cons'][ncon]]))
conIDX.append(ncon)
const = t_getconsts(np.array([]))
Const = [con.decode('UTF-8') for con in const[0]['name']]
consindex = [Const.index(con.ljust(4).upper()) for con in conList]
tfreq = (2 * np.pi) * const[0]['freq'][consindex] / 3600.
###
# Now go through and read the data for each
maskr = []
Vars = list(set([x.replace('_amp', '').replace('_pha', '') for x in Vars]))
var = {}
# interpolating to ROMS grid requires special care with phases!!
# this is subjected to parallelization via muitiprocessing.Process - do it!
# there is a sandbox in roms repo with a starting exercise
for var0 in Vars:
var[var0] = np.ones(shape=(len(tfreq), 4, len(lon))) * -1e-8
N = -1
for con, ncon in zip(const[0]['name'][consindex], conIDX):
N = N + 1
con = con.decode('UTF-8')
logger.info("Interpolating %s for %s" % (var0, con))
var[var0][N, 0, :] = extrapolate_amp(
X, Y, lon, lat, maskr, f.variables[var0 + "_amp"][ncon, :, :])
var[var0][N, 2, :] = extrapolate_pha(
X, Y, lon, lat, maskr,
f.variables[var0 + "_pha"][ncon, :, :]) * 180. / np.pi
return var, tfreq, consindex
