def __init__(self, dataset, dry_depth=0.0):
"""
initilize a Mudflats object
:param dataset: a dataset with a depth Variable
:type dataset: gridded.Dataset
:param dry_depth=0.0: depth below which a tidal flat is considered dry
:type dry_depth: float
"""
if isinstance(dataset, gridded.Dataset):
self.dataset = dataset
else:
self.dataset = gridded.Dataset(dataset)
self.dry_depth = dry_depth
try:
self.depth = self.dataset.variables['depth']
except KeyError:
raise ValueError('Dataset must have a "depth" variable')
def __init__(self, dataset, dry_vel_u=-9999.0, grid_topology=None):
"""
initilize a Mudflats object for Matroos Files
:param dataset: a dataset with a VELU Variable
:type dataset: gridded.Dataset
:param dry_vel_u=-9999.0: vel_u == -9999.0 which is masked/undefined meaning a tidal flat is considered dry
:type dry_vel_u: float
"""
# check if given dataset is instance of gridded.Dataset
if isinstance(dataset, gridded.Dataset):
self.dataset = dataset
# add grid_topology for rgrid
else:
self.dataset = gridded.Dataset(dataset,
grid_topology=grid_topology)
self.dry_vel_u = dry_vel_u
try:
self.vel_u = self.dataset.variables['VELU']
except KeyError:
raise ValueError('Dataset must have a "VELU" variable')
def test_no_depth():
ds = gridded.Dataset()
ds.variables = {} # just so there is something
with pytest.raises(ValueError):
tf = Delft3D_Mudflats(ds)
def dataset():
ds = gridded.Dataset(infile)
return ds
units="meters",
data=depth,
data_file=nc,
grid_file=nc,
fill_value=0,
location='nodes',
attributes=None,
)
# global attributes
attrs = {key: nc.getncattr(key) for key in nc.ncattrs()}
# now make a dataset out of it all:
ds = gridded.Dataset(ncfile=None,
grid=grid,
variables={'Depth': depth_var},
attributes=attrs
)
## now learn a bit about it:
# What is its grid type?
print("The dataset Grid is:", type(ds.grid))
print("It has these variables:", list(ds.variables.keys()))
print('You can access the variable with indexing: ds["Depth"]')
Depth = ds["Depth"]
print(Depth)
"""checking a not-properly compliant dataset"""
from datetime import datetime
import gridded
filename = "../PACE_GNOME_01V2.nc"
# filename = "../PACE_GNOME_01V2_fixed.nc"
# grid_topology = {"node_lat": "latc",
# "node_lon": "lonc",
# "center_lon": "lon",
# "center_lat": "lat",
# }
ds = gridded.Dataset("../PACE_GNOME_01V2.nc",
# grid_topology=grid_topology
)
print ds
depth = ds.variables['depth']
print depth
print depth.grid
# see if we can interpolate
points = ((5.2, 53.3), (5.3, 53.3), (5.2, 53.25))
# should be middle of the timespan in the file
time = datetime(2009, 1, 15)
SELFE=
'http://comt.sura.org/thredds/dodsC/data/comt_1_archive/inundation_tropical/VIMS_SELFE/Hurricane_Rita_2D_final_run_without_waves'
)
bbox = [-95, -85, 27, 32] # Set the bounding box.
variable = 'sea_surface_height_above_geoid' # CF standard_name (or long_name, if no standard_name).
contour_levels = np.arange(-1, 5.0, 0.2) # Set the contour levels.
start_time = datetime(2005, 9, 24, 5, 0, 0) # UTC time
# ## Read the data
#
# In[3]:
# load up a gridded.dataset of the ADCIRC model:
ds = gridded.Dataset(models['SELFE'])
# ### Get some information about this dataset
# In[4]:
# Information about the grid:
print(ds.grid.info)
# In[5]:
# what variables are there"
print("Variables available and their standard names:\n")
for name, var in ds.variables.items():
try:
print(name, ":", var.attributes['standard_name'])