def getfeatureinfo(self, layer, request):
with self.dataset() as nc:
with self.topology() as topo:
data_obj = nc.variables[layer.access_name]
geo_index, closest_x, closest_y, start_time_index, end_time_index, return_dates = self.setup_getfeatureinfo(topo, data_obj, request)
return_arrays = []
z_value = None
if isinstance(layer, Layer):
if len(data_obj.shape) == 4:
z_index, z_value = self.nearest_z(layer, request.GET['elevation'])
data = data_obj[start_time_index:end_time_index, z_index, geo_index[0], geo_index[1]]
elif len(data_obj.shape) == 3:
data = data_obj[start_time_index:end_time_index, geo_index[0], geo_index[1]]
elif len(data_obj.shape) == 2:
data = data_obj[geo_index[0], geo_index[1]]
else:
raise ValueError("Dimension Mismatch: data_obj.shape == {0} and time indexes = {1} to {2}".format(data_obj.shape, start_time_index, end_time_index))
return_arrays.append((layer.var_name, data))
elif isinstance(layer, VirtualLayer):
# Data needs to be [var1,var2] where var are 1D (nodes only, elevation and time already handled)
for l in layer.layers:
if len(data_obj.shape) == 4:
z_index, z_value = self.nearest_z(layer, request.GET['elevation'])
data = data_obj[start_time_index:end_time_index, z_index, geo_index[0], geo_index[1]]
elif len(data_obj.shape) == 3:
data = data_obj[start_time_index:end_time_index, geo_index[0], geo_index[1]]
elif len(data_obj.shape) == 2:
data = data_obj[geo_index[0], geo_index[1]]
else:
raise ValueError("Dimension Mismatch: data_obj.shape == {0} and time indexes = {1} to {2}".format(data_obj.shape, start_time_index, end_time_index))
return_arrays.append((l.var_name, data))
# Data is now in the return_arrays list, as a list of numpy arrays. We need
# to add time and depth to them to create a single Pandas DataFrame
if len(data_obj.shape) == 4:
df = pd.DataFrame({'time': return_dates,
'x': closest_x,
'y': closest_y,
'z': z_value})
elif len(data_obj.shape) == 3:
df = pd.DataFrame({'time': return_dates,
'x': closest_x,
'y': closest_y})
elif len(data_obj.shape) == 2:
df = pd.DataFrame({'x': closest_x,
'y': closest_y})
else:
df = pd.DataFrame()
# Now add a column for each member of the return_arrays list
for (var_name, np_array) in return_arrays:
df.loc[:, var_name] = pd.Series(np_array, index=df.index)
return gfi_handler.from_dataframe(request, df)
def getfeatureinfo(self, layer, request):
with self.dataset() as nc:
with self.topology() as topo:
data_obj = nc.variables[layer.access_name]
data_location = data_obj.location
# mesh_name = data_obj.mesh
# Use local topology for pulling bounds data
# ug = UGrid.from_ncfile(self.topology_file, mesh_name=mesh_name)
geo_index, closest_x, closest_y, start_time_index, end_time_index, return_dates = self.setup_getfeatureinfo(
topo, data_obj, request, location=data_location)
logger.info("Start index: {}".format(start_time_index))
logger.info("End index: {}".format(end_time_index))
logger.info("Geo index: {}".format(geo_index))
return_arrays = []
z_value = 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[start_time_index:end_time_index,
z_index, geo_index]
elif len(data_obj.shape) == 2:
data = data_obj[start_time_index:end_time_index,
geo_index]
elif len(data_obj.shape) == 1:
data = data_obj[geo_index]
else:
raise ValueError(
"Dimension Mismatch: data_obj.shape == {0} and time indexes = {1} to {2}"
.format(data_obj.shape, start_time_index,
end_time_index))
return_arrays.append((layer.var_name, data))
elif isinstance(layer, VirtualLayer):
# Data needs to be [var1,var2] where var are 1D (nodes only, elevation and time already handled)
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 = data_obj[start_time_index:end_time_index,
z_index, geo_index]
elif len(data_obj.shape) == 2:
data = data_obj[start_time_index:end_time_index,
geo_index]
elif len(data_obj.shape) == 1:
data = data_obj[geo_index]
else:
raise ValueError(
"Dimension Mismatch: data_obj.shape == {0} and time indexes = {1} to {2}"
.format(data_obj.shape, start_time_index,
end_time_index))
return_arrays.append((l.var_name, data))
# Data is now in the return_arrays list, as a list of numpy arrays. We need
# to add time and depth to them to create a single Pandas DataFrame
if (len(data_obj.shape) == 3):
df = pd.DataFrame({
'time': return_dates,
'x': closest_x,
'y': closest_y,
'z': z_value
})
elif (len(data_obj.shape) == 2):
df = pd.DataFrame({
'time': return_dates,
'x': closest_x,
'y': closest_y
})
elif (len(data_obj.shape) == 1):
df = pd.DataFrame({'x': closest_x, 'y': closest_y})
else:
df = pd.DataFrame()
# Now add a column for each member of the return_arrays list
for (var_name, np_array) in return_arrays:
df.loc[:, var_name] = pd.Series(np_array, index=df.index)
return gfi_handler.from_dataframe(request, df)
def getfeatureinfo(self, layer, request):
with self.dataset() as nc:
data_obj = nc.variables[layer.access_name]
geo_index, closest_x, closest_y, start_time_index, end_time_index, return_dates = self.setup_getfeatureinfo(
layer, request)
return_arrays = []
z_value = None
if isinstance(layer, Layer):
if len(data_obj.shape) == 4:
z_index, z_value = self.nearest_z(layer,
request.GET['elevation'])
data = data_obj[start_time_index:end_time_index, z_index,
geo_index[0], geo_index[1]]
elif len(data_obj.shape) == 3:
data = data_obj[start_time_index:end_time_index,
geo_index[0], geo_index[1]]
elif len(data_obj.shape) == 2:
data = data_obj[geo_index[0], geo_index[1]]
else:
raise ValueError(
"Dimension Mismatch: data_obj.shape == {0} and time indexes = {1} to {2}"
.format(data_obj.shape, start_time_index,
end_time_index))
return_arrays.append((layer.var_name, data))
elif isinstance(layer, VirtualLayer):
# Data needs to be [var1,var2] where var are 1D (nodes only, elevation and time already handled)
for l in layer.layers:
if len(data_obj.shape) == 4:
z_index, z_value = self.nearest_z(
layer, request.GET['elevation'])
data = data_obj[start_time_index:end_time_index,
z_index, geo_index[0], geo_index[1]]
elif len(data_obj.shape) == 3:
data = data_obj[start_time_index:end_time_index,
geo_index[0], geo_index[1]]
elif len(data_obj.shape) == 2:
data = data_obj[geo_index[0], geo_index[1]]
else:
raise ValueError(
"Dimension Mismatch: data_obj.shape == {0} and time indexes = {1} to {2}"
.format(data_obj.shape, start_time_index,
end_time_index))
return_arrays.append((l.var_name, data))
# Data is now in the return_arrays list, as a list of numpy arrays. We need
# to add time and depth to them to create a single Pandas DataFrame
if len(data_obj.shape) == 4:
df = pd.DataFrame({
'time': return_dates,
'x': closest_x,
'y': closest_y,
'z': z_value
})
elif len(data_obj.shape) == 3:
df = pd.DataFrame({
'time': return_dates,
'x': closest_x,
'y': closest_y
})
elif len(data_obj.shape) == 2:
df = pd.DataFrame({'x': closest_x, 'y': closest_y})
else:
df = pd.DataFrame()
# Now add a column for each member of the return_arrays list
for (var_name, np_array) in return_arrays:
df.loc[:, var_name] = pd.Series(np_array, index=df.index)
return gfi_handler.from_dataframe(request, df)