# In[ ]:
import os.path
from scipy.io import loadmat
mat = os.path.join('..', 't_tide_v1.3beta', 't_constituents.mat')
con_info = loadmat(mat, squeeze_me=True)
con_info = con_info['const'] # I am ignore shallow water and sat constants!
# In[ ]:
from utide import _ut_constants_fname
from utide.utilities import loadmatbunch
con_info = loadmatbunch(_ut_constants_fname)['const']
# In[ ]:
# Find the indices of the tidal constituents.
k = 0
ind_nc, ind_ttide = [], []
const_name = [e.strip() for e in con_info['name'].tolist()]
for name in consts:
try:
if name == 'STEADY':
indx = const_name.index('Z0')
print('Size of boundary: ', len(bnd))
seg_types = [0] * len(bnd)
adcirc.order_boundary(bnd, seg_types)
adcirc.update(
os.path.join(data_files_dir, 'vdatum', 'vdatum_fl_sab_adcirc54.nc'))
names = []
const = adcirc.Dataset.variables['tidenames'][:]
for name in const:
names.append(''.join(name.tolist()).strip())
from utide import _ut_constants_fname
from utide.utilities import loadmatbunch
con_info = loadmatbunch(_ut_constants_fname)['const']
k = 0
ind_nc, ind_ttide = [], []
const_name = [e.strip() for e in con_info['name'].tolist()]
consts = ['STEADY', 'M2', 'S2', 'N2', 'K1', 'O1', 'P1', 'M4', 'M6']
for name in consts:
try:
if name == 'STEADY':
indx = const_name.index('Z0')
else:
indx = const_name.index(name)
k += 1
ind_ttide.append(indx)
def get_tidal_vectors(self,
layer,
time,
bbox,
vector_scale=None,
vector_step=None):
vector_scale = vector_scale or 1
vector_step = vector_step or 1
with netCDF4.Dataset(self.topology_file) as nc:
data_obj = nc.variables[layer.access_name]
data_location = getattr(data_obj, 'location', 'node')
mesh_name = data_obj.mesh
ug = UGrid.from_nc_dataset(nc, mesh_name=mesh_name)
coords = np.empty(0)
if data_location == 'node':
coords = ug.nodes
elif data_location == 'face':
coords = ug.face_coordinates
elif data_location == 'edge':
coords = ug.edge_coordinates
lon = coords[:, 0]
lat = coords[:, 1]
padding_factor = calc_safety_factor(vector_scale)
padding = calc_lon_lat_padding(lon, lat,
padding_factor) * vector_step
spatial_idx = data_handler.ugrid_lat_lon_subset_idx(
lon, lat, bbox=bbox, padding=padding)
tnames = nc.get_variables_by_attributes(
standard_name='tide_constituent')[0]
tfreqs = nc.get_variables_by_attributes(
standard_name='tide_frequency')[0]
from utide import _ut_constants_fname
from utide.utilities import loadmatbunch
con_info = loadmatbunch(_ut_constants_fname)['const']
# Get names from the utide constant file
utide_const_names = [e.strip() for e in con_info['name'].tolist()]
# netCDF4-python is returning ugly arrays of bytes...
names = []
for n in tnames[:]:
z = ''.join([x.decode('utf-8') for x in n.tolist()
if x]).strip()
names.append(z)
if 'STEADY' in names:
names[names.index('STEADY')] = 'Z0'
extract_names = list(
set(utide_const_names).intersection(set(names)))
ntides = data_obj.shape[data_obj.dimensions.index('ntides')]
extract_mask = np.zeros(shape=(ntides, ), dtype=bool)
for n in extract_names:
extract_mask[names.index(n)] = True
if not spatial_idx.any() or not extract_mask.any():
e = np.ma.empty(0)
return e, e, e, e
ua = nc.variables['u'][extract_mask, spatial_idx]
va = nc.variables['v'][extract_mask, spatial_idx]
up = nc.variables['u_phase'][extract_mask, spatial_idx]
vp = nc.variables['v_phase'][extract_mask, spatial_idx]
freqs = tfreqs[extract_mask]
omega = freqs * 3600 # Convert from radians/s to radians/hour.
from utide.harmonics import FUV
from matplotlib.dates import date2num
v, u, f = FUV(
t=np.array([date2num(time) + 366.1667]),
tref=np.array([0]),
lind=np.array(
[utide_const_names.index(x) for x in extract_names]),
lat=
55, # Reference latitude for 3rd order satellites (degrees) (55 is fine always)
ngflgs=[0, 0, 0,
0]) # [NodsatLint NodsatNone GwchLint GwchNone]
s = calendar.timegm(time.timetuple()) / 60 / 60.
v, u, f = map(np.squeeze, (v, u, f))
v = v * 2 * np.pi # Convert phase in radians.
u = u * 2 * np.pi # Convert phase in radians.
U = (f * ua.T *
np.cos(v + s * omega + u - up.T * np.pi / 180)).sum(axis=1)
V = (f * va.T *
np.cos(v + s * omega + u - vp.T * np.pi / 180)).sum(axis=1)
return U, V, lon[spatial_idx], lat[spatial_idx]
def get_tidal_vectors(self, layer, time, bbox, vector_scale=None, vector_step=None):
vector_scale = vector_scale or 1
vector_step = vector_step or 1
with netCDF4.Dataset(self.topology_file) as nc:
data_obj = nc.variables[layer.access_name]
data_location = getattr(data_obj, 'location', 'node')
mesh_name = data_obj.mesh
ug = UGrid.from_nc_dataset(nc, mesh_name=mesh_name)
coords = np.empty(0)
if data_location == 'node':
coords = ug.nodes
elif data_location == 'face':
coords = ug.face_coordinates
elif data_location == 'edge':
coords = ug.edge_coordinates
lon = coords[:, 0]
lat = coords[:, 1]
padding_factor = calc_safety_factor(vector_scale)
padding = calc_lon_lat_padding(lon, lat, padding_factor) * vector_step
spatial_idx = data_handler.ugrid_lat_lon_subset_idx(lon, lat,
bbox=bbox,
padding=padding)
tnames = nc.get_variables_by_attributes(standard_name='tide_constituent')[0]
tfreqs = nc.get_variables_by_attributes(standard_name='tide_frequency')[0]
from utide import _ut_constants_fname
from utide.utilities import loadmatbunch
con_info = loadmatbunch(_ut_constants_fname)['const']
# Get names from the utide constant file
utide_const_names = [ e.strip() for e in con_info['name'].tolist() ]
# netCDF4-python is returning ugly arrays of bytes...
names = []
for n in tnames[:]:
z = ''.join([ x.decode('utf-8') for x in n.tolist() if x ]).strip()
names.append(z)
if 'STEADY' in names:
names[names.index('STEADY')] = 'Z0'
extract_names = list(set(utide_const_names).intersection(set(names)))
ntides = data_obj.shape[data_obj.dimensions.index('ntides')]
extract_mask = np.zeros(shape=(ntides,), dtype=bool)
for n in extract_names:
extract_mask[names.index(n)] = True
if not spatial_idx.any() or not extract_mask.any():
e = np.ma.empty(0)
return e, e, e, e
ua = nc.variables['u'][extract_mask, spatial_idx]
va = nc.variables['v'][extract_mask, spatial_idx]
up = nc.variables['u_phase'][extract_mask, spatial_idx]
vp = nc.variables['v_phase'][extract_mask, spatial_idx]
freqs = tfreqs[extract_mask]
omega = freqs * 3600 # Convert from radians/s to radians/hour.
from utide.harmonics import FUV
from matplotlib.dates import date2num
v, u, f = FUV(t=np.array([date2num(time) + 366.1667]),
tref=np.array([0]),
lind=np.array([ utide_const_names.index(x) for x in extract_names ]),
lat=55, # Reference latitude for 3rd order satellites (degrees) (55 is fine always)
ngflgs=[0, 0, 0, 0]) # [NodsatLint NodsatNone GwchLint GwchNone]
s = calendar.timegm(time.timetuple()) / 60 / 60.
v, u, f = map(np.squeeze, (v, u, f))
v = v * 2 * np.pi # Convert phase in radians.
u = u * 2 * np.pi # Convert phase in radians.
U = (f * ua.T * np.cos(v + s * omega + u - up.T * np.pi / 180)).sum(axis=1)
V = (f * va.T * np.cos(v + s * omega + u - vp.T * np.pi / 180)).sum(axis=1)
return U, V, lon[spatial_idx], lat[spatial_idx]
