def minmax(self, layer, request):
_, time_value = self.nearest_time(layer, request.GET['time'])
wgs84_bbox = request.GET['wgs84_bbox']
us, vs, _, _ = self.get_tidal_vectors(layer, time=time_value, bbox=wgs84_bbox.bbox)
if us.size == 0 or vs.size == 0:
return gmd_handler.from_dict(dict(min=None, max=None))
magnitude = np.sqrt((us*us) + (vs*vs))
return gmd_handler.from_dict(dict(min=np.min(magnitude), max=np.max(magnitude)))
def minmax(self, layer, request):
_, time_value = self.nearest_time(layer, request.GET['time'])
wgs84_bbox = request.GET['wgs84_bbox']
us, vs, _, _ = self.get_tidal_vectors(layer,
time=time_value,
bbox=wgs84_bbox.bbox)
if us.size == 0 or vs.size == 0:
return gmd_handler.from_dict(dict(min=None, max=None))
magnitude = np.sqrt((us * us) + (vs * vs))
return gmd_handler.from_dict(
dict(min=np.min(magnitude), max=np.max(magnitude)))
def minmax(self, layer, request):
time_index, time_value = self.nearest_time(layer, request.GET['time'])
wgs84_bbox = request.GET['wgs84_bbox']
with self.dataset() as nc:
cached_sg = from_ncfile(self.topology_file)
lon_name, lat_name = cached_sg.face_coordinates
lon_obj = getattr(cached_sg, lon_name)
lat_obj = getattr(cached_sg, lat_name)
centers = cached_sg.centers
lon = centers[..., 0][lon_obj.center_slicing]
lat = centers[..., 1][lat_obj.center_slicing]
spatial_idx = data_handler.lat_lon_subset_idx(lon, lat,
lonmin=wgs84_bbox.minx,
latmin=wgs84_bbox.miny,
lonmax=wgs84_bbox.maxx,
latmax=wgs84_bbox.maxy)
subset_lon = np.unique(spatial_idx[0])
subset_lat = np.unique(spatial_idx[1])
grid_variables = cached_sg.grid_variables
vmin = None
vmax = None
raw_data = None
if isinstance(layer, Layer):
data_obj = getattr(cached_sg, layer.access_name)
raw_var = nc.variables[layer.access_name]
if len(raw_var.shape) == 4:
z_index, z_value = self.nearest_z(layer, request.GET['elevation'])
raw_data = raw_var[time_index, z_index, subset_lon, subset_lat]
elif len(raw_var.shape) == 3:
raw_data = raw_var[time_index, subset_lon, subset_lat]
elif len(raw_var.shape) == 2:
raw_data = raw_var[subset_lon, subset_lat]
else:
raise BaseException('Unable to trim variable {0} data.'.format(layer.access_name))
# handle grid variables
if set([layer.access_name]).issubset(grid_variables):
raw_data = avg_to_cell_center(raw_data, data_obj.center_axis)
vmin = np.nanmin(raw_data).item()
vmax = np.nanmax(raw_data).item()
elif isinstance(layer, VirtualLayer):
x_var = None
y_var = None
raw_vars = []
for l in layer.layers:
data_obj = getattr(cached_sg, l.access_name)
raw_var = nc.variables[l.access_name]
raw_vars.append(raw_var)
if len(raw_var.shape) == 4:
z_index, z_value = self.nearest_z(layer, request.GET['elevation'])
raw_data = raw_var[time_index, z_index, subset_lon, subset_lat]
elif len(raw_var.shape) == 3:
raw_data = raw_var[time_index, subset_lon, subset_lat]
elif len(raw_var.shape) == 2:
raw_data = raw_var[subset_lon, subset_lat]
else:
raise BaseException('Unable to trim variable {0} data.'.format(l.access_name))
if x_var is None:
if data_obj.vector_axis and data_obj.vector_axis.lower() == 'x':
x_var = raw_data
elif data_obj.center_axis == 1:
x_var = raw_data
if y_var is None:
if data_obj.vector_axis and data_obj.vector_axis.lower() == 'y':
y_var = raw_data
elif data_obj.center_axis == 0:
y_var = raw_data
if ',' in layer.var_name and raw_data is not None:
# Vectors, so return magnitude
data = [ sqrt((u*u) + (v*v)) for (u, v,) in zip(x_var.flatten(), y_var.flatten()) if u != np.nan and v != np.nan]
vmin = min(data)
vmax = max(data)
return gmd_handler.from_dict(dict(min=vmin, max=vmax))
def minmax(self, layer, request):
time_index, time_value = self.nearest_time(layer, request.GET['time'])
wgs84_bbox = request.GET['wgs84_bbox']
with self.dataset() as nc:
data_obj = nc.variables[layer.access_name]
data_location = data_obj.location
mesh_name = data_obj.mesh
ug = UGrid.from_ncfile(self.topology_file, 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]
spatial_idx = data_handler.lat_lon_subset_idx(
lon, lat, wgs84_bbox.minx, wgs84_bbox.miny, wgs84_bbox.maxx,
wgs84_bbox.maxy)
vmin = None
vmax = None
data = None
if isinstance(layer, Layer):
if (len(data_obj.shape) == 3):
z_index, z_value = self.nearest_z(layer,
request.GET['elevation'])
data = data_obj[time_index, z_index, spatial_idx]
elif (len(data_obj.shape) == 2):
data = data_obj[time_index, spatial_idx]
elif len(data_obj.shape) == 1:
data = data_obj[spatial_idx]
else:
logger.debug(
"Dimension Mismatch: data_obj.shape == {0} and time = {1}"
.format(data_obj.shape, time_value))
if data is not None:
vmin = np.nanmin(data).item()
vmax = np.nanmax(data).item()
elif isinstance(layer, VirtualLayer):
# Data needs to be [var1,var2] where var are 1D (nodes only, elevation and time already handled)
data = []
for l in layer.layers:
data_obj = nc.variables[l.var_name]
if (len(data_obj.shape) == 3):
z_index, z_value = self.nearest_z(
layer, request.GET['elevation'])
data.append(data_obj[time_index, z_index, spatial_idx])
elif (len(data_obj.shape) == 2):
data.append(data_obj[time_index, spatial_idx])
elif len(data_obj.shape) == 1:
data.append(data_obj[spatial_idx])
else:
logger.debug(
"Dimension Mismatch: data_obj.shape == {0} and time = {1}"
.format(data_obj.shape, time_value))
if ',' in layer.var_name and data:
# Vectors, so return magnitude
data = [
sqrt((u * u) + (v * v)) for (
u,
v,
) in data.T if u != np.nan and v != np.nan
]
vmin = min(data)
vmax = max(data)
return gmd_handler.from_dict(dict(min=vmin, max=vmax))
def minmax(self, layer, request):
time_index, time_value = self.nearest_time(layer, request.GET['time'])
wgs84_bbox = request.GET['wgs84_bbox']
with self.dataset() as nc:
cached_sg = from_ncfile(self.topology_file)
lon_name, lat_name = cached_sg.face_coordinates
lon_obj = getattr(cached_sg, lon_name)
lat_obj = getattr(cached_sg, lat_name)
centers = cached_sg.centers
lon = centers[..., 0][lon_obj.center_slicing]
lat = centers[..., 1][lat_obj.center_slicing]
spatial_idx = data_handler.lat_lon_subset_idx(
lon,
lat,
lonmin=wgs84_bbox.minx,
latmin=wgs84_bbox.miny,
lonmax=wgs84_bbox.maxx,
latmax=wgs84_bbox.maxy)
subset_lon = np.unique(spatial_idx[0])
subset_lat = np.unique(spatial_idx[1])
grid_variables = cached_sg.grid_variables
vmin = None
vmax = None
raw_data = None
if isinstance(layer, Layer):
data_obj = getattr(cached_sg, layer.access_name)
raw_var = nc.variables[layer.access_name]
if len(raw_var.shape) == 4:
z_index, z_value = self.nearest_z(layer,
request.GET['elevation'])
raw_data = raw_var[time_index, z_index, subset_lon,
subset_lat]
elif len(raw_var.shape) == 3:
raw_data = raw_var[time_index, subset_lon, subset_lat]
elif len(raw_var.shape) == 2:
raw_data = raw_var[subset_lon, subset_lat]
else:
raise BaseException(
'Unable to trim variable {0} data.'.format(
layer.access_name))
# handle grid variables
if set([layer.access_name]).issubset(grid_variables):
raw_data = avg_to_cell_center(raw_data,
data_obj.center_axis)
vmin = np.nanmin(raw_data).item()
vmax = np.nanmax(raw_data).item()
elif isinstance(layer, VirtualLayer):
x_var = None
y_var = None
raw_vars = []
for l in layer.layers:
data_obj = getattr(cached_sg, l.access_name)
raw_var = nc.variables[l.access_name]
raw_vars.append(raw_var)
if len(raw_var.shape) == 4:
z_index, z_value = self.nearest_z(
layer, request.GET['elevation'])
raw_data = raw_var[time_index, z_index, subset_lon,
subset_lat]
elif len(raw_var.shape) == 3:
raw_data = raw_var[time_index, subset_lon, subset_lat]
elif len(raw_var.shape) == 2:
raw_data = raw_var[subset_lon, subset_lat]
else:
raise BaseException(
'Unable to trim variable {0} data.'.format(
l.access_name))
if x_var is None:
if data_obj.vector_axis and data_obj.vector_axis.lower(
) == 'x':
x_var = raw_data
elif data_obj.center_axis == 1:
x_var = raw_data
if y_var is None:
if data_obj.vector_axis and data_obj.vector_axis.lower(
) == 'y':
y_var = raw_data
elif data_obj.center_axis == 0:
y_var = raw_data
if ',' in layer.var_name and raw_data is not None:
# Vectors, so return magnitude
data = [
sqrt((u * u) + (v * v)) for (
u,
v,
) in zip(x_var.flatten(), y_var.flatten())
if u != np.nan and v != np.nan
]
vmin = min(data)
vmax = max(data)
return gmd_handler.from_dict(dict(min=vmin, max=vmax))
def minmax(self, layer, request):
time_index, time_value = self.nearest_time(layer, request.GET['time'])
wgs84_bbox = request.GET['wgs84_bbox']
with self.dataset() as nc:
data_obj = nc.variables[layer.access_name]
data_location = data_obj.location
mesh_name = data_obj.mesh
ug = UGrid.from_ncfile(self.topology_file, 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]
spatial_idx = data_handler.lat_lon_subset_idx(lon, lat, wgs84_bbox.minx, wgs84_bbox.miny, wgs84_bbox.maxx, wgs84_bbox.maxy)
vmin = None
vmax = None
data = None
if isinstance(layer, Layer):
if (len(data_obj.shape) == 3):
z_index, z_value = self.nearest_z(layer, request.GET['elevation'])
data = data_obj[time_index, z_index, spatial_idx]
elif (len(data_obj.shape) == 2):
data = data_obj[time_index, spatial_idx]
elif len(data_obj.shape) == 1:
data = data_obj[spatial_idx]
else:
logger.debug("Dimension Mismatch: data_obj.shape == {0} and time = {1}".format(data_obj.shape, time_value))
if data is not None:
vmin = np.nanmin(data).item()
vmax = np.nanmax(data).item()
elif isinstance(layer, VirtualLayer):
# Data needs to be [var1,var2] where var are 1D (nodes only, elevation and time already handled)
data = []
for l in layer.layers:
data_obj = nc.variables[l.var_name]
if (len(data_obj.shape) == 3):
z_index, z_value = self.nearest_z(layer, request.GET['elevation'])
data.append(data_obj[time_index, z_index, spatial_idx])
elif (len(data_obj.shape) == 2):
data.append(data_obj[time_index, spatial_idx])
elif len(data_obj.shape) == 1:
data.append(data_obj[spatial_idx])
else:
logger.debug("Dimension Mismatch: data_obj.shape == {0} and time = {1}".format(data_obj.shape, time_value))
if ',' in layer.var_name and data:
# Vectors, so return magnitude
data = [ sqrt((u*u) + (v*v)) for (u, v,) in data.T if u != np.nan and v != np.nan]
vmin = min(data)
vmax = max(data)
return gmd_handler.from_dict(dict(min=vmin, max=vmax))
