def get_projection(request):
"""
Return the projection string passed into the request.
Can be specified by \"SRS\" or \"CRS\" key (string).
If \"SRS\" or \"CRS\" is not available, default to mercator.
"""
projstr = request.GET.get("srs")
if not projstr:
projstr = request.GET.get("crs")
if not projstr:
projstr = "EPSG:3857"
logger.debug("SRS or CRS no available in requst, defaulting to EPSG:3857 (mercator)")
return pyproj.Proj(init=projstr)
def getmap(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]
# Calculate any vector padding if we need to
padding = None
vector_step = request.GET['vectorstep']
if request.GET['image_type'] == 'vectors':
padding_factor = calc_safety_factor(request.GET['vectorscale'])
padding = calc_lon_lat_padding(lon, lat, padding_factor) * vector_step
# Calculate the boolean spatial mask to slice with
bool_spatial_idx = data_handler.ugrid_lat_lon_subset_idx(lon, lat,
bbox=wgs84_bbox.bbox,
padding=padding)
# Randomize vectors to subset if we need to
if request.GET['image_type'] == 'vectors' and vector_step > 1:
num_vec = int(bool_spatial_idx.size / vector_step)
step = int(bool_spatial_idx.size / num_vec)
bool_spatial_idx[np.where(bool_spatial_idx==True)][0::step] = False # noqa: E225
# If no triangles intersect the field of view, return a transparent tile
if not np.any(bool_spatial_idx):
logger.info("No triangles in field of view, returning empty tile.")
return self.empty_response(layer, request)
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, :]
elif (len(data_obj.shape) == 2):
data = data_obj[time_index, :]
elif len(data_obj.shape) == 1:
data = data_obj[:]
else:
logger.debug("Dimension Mismatch: data_obj.shape == {0} and time = {1}".format(data_obj.shape, time_value))
return self.empty_response(layer, request)
if request.GET['image_type'] in ['pcolor', 'contours', 'filledcontours']:
# Avoid triangles with nan values
bool_spatial_idx[np.isnan(data)] = False
# Get the faces to plot
faces = ug.faces[:]
face_idx = data_handler.face_idx_from_node_idx(faces, bool_spatial_idx)
faces_subset = faces[face_idx]
tri_subset = Tri.Triangulation(lon, lat, triangles=faces_subset)
if request.GET['image_type'] == 'pcolor':
return mpl_handler.tripcolor_response(tri_subset, data, request, data_location=data_location)
else:
return mpl_handler.tricontouring_response(tri_subset, data, request)
elif request.GET['image_type'] in ['filledhatches', 'hatches']:
raise NotImplementedError('matplotlib does not support hatching on triangular grids... sorry!')
else:
raise NotImplementedError('Image type "{}" is not supported.'.format(request.GET['image_type']))
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, bool_spatial_idx])
elif (len(data_obj.shape) == 2):
data.append(data_obj[time_index, bool_spatial_idx])
elif len(data_obj.shape) == 1:
data.append(data_obj[bool_spatial_idx])
else:
logger.debug("Dimension Mismatch: data_obj.shape == {0} and time = {1}".format(data_obj.shape, time_value))
return self.empty_response(layer, request)
if request.GET['image_type'] == 'vectors':
return mpl_handler.quiver_response(lon[bool_spatial_idx],
lat[bool_spatial_idx],
data[0],
data[1],
request)
else:
raise NotImplementedError('Image type "{}" is not supported.'.format(request.GET['image_type']))
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.ugrid_lat_lon_subset_idx(lon, lat, bbox=wgs84_bbox.bbox)
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 getmap(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]
if request.GET['vectorscale'] is not None: # is not None if vectors are being plotted
vectorscale = request.GET['vectorscale']
padding_factor = calc_safety_factor(vectorscale)
vectorstep = request.GET['vectorstep'] # returns 1 by default if vectors are being plotted
spatial_idx_padding = calc_lon_lat_padding(lon, lat, padding_factor) * vectorstep
spatial_idx = data_handler.lat_lon_subset_idx(lon, lat,
wgs84_bbox.minx,
wgs84_bbox.miny,
wgs84_bbox.maxx,
wgs84_bbox.maxy,
padding=spatial_idx_padding
)
if vectorstep > 1:
np.random.shuffle(spatial_idx)
nvec = int(len(spatial_idx) / vectorstep)
spatial_idx = spatial_idx[:nvec]
else:
spatial_idx = data_handler.lat_lon_subset_idx(lon, lat,
wgs84_bbox.minx,
wgs84_bbox.miny,
wgs84_bbox.maxx,
wgs84_bbox.maxy
)
face_indicies = ug.faces[:]
face_indicies_spatial_idx = data_handler.faces_subset_idx(face_indicies, spatial_idx)
# If no triangles intersect the field of view, return a transparent tile
if (len(spatial_idx) == 0) or (len(face_indicies_spatial_idx) == 0):
logger.debug("No triangles in field of view, returning empty tile.")
return self.empty_response(layer, request)
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, :]
elif (len(data_obj.shape) == 2):
data = data_obj[time_index, :]
elif len(data_obj.shape) == 1:
data = data_obj[:]
else:
logger.debug("Dimension Mismatch: data_obj.shape == {0} and time = {1}".format(data_obj.shape, time_value))
return self.empty_response(layer, request)
if request.GET['image_type'] == 'filledcontours':
mask = np.isnan(data) # array with NaNs appearing as True
if mask.any():
data_mask = ~mask # negate the NaN boolean array; mask for non-NaN data elements
# slice the data, lon, and lat to get elements that correspond to non-NaN values
data = data_mask[data_mask]
lon = lon[data_mask]
lat = lat[data_mask]
# recalculate the spatial index using the subsetted lat/lon
spatial_idx = data_handler.lat_lon_subset_idx(lon,
lat,
wgs84_bbox.minx,
wgs84_bbox.miny,
wgs84_bbox.maxx,
wgs84_bbox.maxy
)
face_indicies_spatial_idx = data_handler.faces_subset_idx(face_indicies, spatial_idx)
tri_subset = Tri.Triangulation(lon,
lat,
triangles=face_indicies[face_indicies_spatial_idx]
)
return mpl_handler.tricontourf_response(tri_subset,
data,
request
)
else:
raise NotImplementedError('Image type "{}" is not supported.'.format(request.GET['image_type']))
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, :])
elif (len(data_obj.shape) == 2):
data.append(data_obj[time_index, :])
elif len(data_obj.shape) == 1:
data.append(data_obj[:])
else:
logger.debug("Dimension Mismatch: data_obj.shape == {0} and time = {1}".format(data_obj.shape, time_value))
return self.empty_response(layer, request)
if request.GET['image_type'] == 'vectors':
return mpl_handler.quiver_response(lon[spatial_idx],
lat[spatial_idx],
data[0][spatial_idx],
data[1][spatial_idx],
request,
vectorscale
)
else:
raise NotImplementedError('Image type "{}" is not supported.'.format(request.GET['image_type']))
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 getmap(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]
if request.GET[
'vectorscale'] is not None: # is not None if vectors are being plotted
vectorscale = request.GET['vectorscale']
padding_factor = calc_safety_factor(vectorscale)
vectorstep = request.GET[
'vectorstep'] # returns 1 by default if vectors are being plotted
spatial_idx_padding = calc_lon_lat_padding(
lon, lat, padding_factor) * vectorstep
spatial_idx = data_handler.lat_lon_subset_idx(
lon,
lat,
wgs84_bbox.minx,
wgs84_bbox.miny,
wgs84_bbox.maxx,
wgs84_bbox.maxy,
padding=spatial_idx_padding)
if vectorstep > 1:
np.random.shuffle(spatial_idx)
nvec = int(len(spatial_idx) / vectorstep)
spatial_idx = spatial_idx[:nvec]
else:
spatial_idx = data_handler.lat_lon_subset_idx(
lon, lat, wgs84_bbox.minx, wgs84_bbox.miny,
wgs84_bbox.maxx, wgs84_bbox.maxy)
face_indicies = ug.faces[:]
face_indicies_spatial_idx = data_handler.faces_subset_idx(
face_indicies, spatial_idx)
# If no triangles intersect the field of view, return a transparent tile
if (len(spatial_idx) == 0) or (len(face_indicies_spatial_idx)
== 0):
logger.debug(
"No triangles in field of view, returning empty tile.")
return self.empty_response(layer, request)
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, :]
elif (len(data_obj.shape) == 2):
data = data_obj[time_index, :]
elif len(data_obj.shape) == 1:
data = data_obj[:]
else:
logger.debug(
"Dimension Mismatch: data_obj.shape == {0} and time = {1}"
.format(data_obj.shape, time_value))
return self.empty_response(layer, request)
if request.GET['image_type'] in [
'pcolor', 'contours', 'filledcontours'
]:
mask = np.isnan(data) # array with NaNs appearing as True
if mask.any():
data_mask = ~mask # negate the NaN boolean array; mask for non-NaN data elements
# slice the data, lon, and lat to get elements that correspond to non-NaN values
data = data_mask[data_mask]
lon = lon[data_mask]
lat = lat[data_mask]
# recalculate the spatial index using the subsetted lat/lon
spatial_idx = data_handler.lat_lon_subset_idx(
lon, lat, wgs84_bbox.minx, wgs84_bbox.miny,
wgs84_bbox.maxx, wgs84_bbox.maxy)
face_indicies_spatial_idx = data_handler.faces_subset_idx(
face_indicies, spatial_idx)
tri_subset = Tri.Triangulation(
lon,
lat,
triangles=face_indicies[face_indicies_spatial_idx])
if request.GET['image_type'] == 'pcolor':
return mpl_handler.tripcolor_response(
tri_subset,
data,
request,
data_location=data_location)
else:
return mpl_handler.tricontouring_response(
tri_subset, data, request)
elif request.GET['image_type'] in ['filledhatches', 'hatches']:
raise NotImplementedError(
'matplotlib does not support hatching on triangular grids... sorry!'
)
else:
raise NotImplementedError(
'Image type "{}" is not supported.'.format(
request.GET['image_type']))
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, :])
elif (len(data_obj.shape) == 2):
data.append(data_obj[time_index, :])
elif len(data_obj.shape) == 1:
data.append(data_obj[:])
else:
logger.debug(
"Dimension Mismatch: data_obj.shape == {0} and time = {1}"
.format(data_obj.shape, time_value))
return self.empty_response(layer, request)
if request.GET['image_type'] == 'vectors':
return mpl_handler.quiver_response(lon[spatial_idx],
lat[spatial_idx],
data[0][spatial_idx],
data[1][spatial_idx],
request, vectorscale)
else:
raise NotImplementedError(
'Image type "{}" is not supported.'.format(
request.GET['image_type']))
def getmap(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]
# Calculate any vector padding if we need to
padding = None
vector_step = request.GET['vectorstep']
if request.GET['image_type'] == 'vectors':
padding_factor = calc_safety_factor(request.GET['vectorscale'])
padding = calc_lon_lat_padding(lon, lat, padding_factor) * vector_step
# Calculate the boolean spatial mask to slice with
bool_spatial_idx = data_handler.ugrid_lat_lon_subset_idx(lon, lat,
bbox=wgs84_bbox.bbox,
padding=padding)
# Randomize vectors to subset if we need to
if request.GET['image_type'] == 'vectors' and vector_step > 1:
num_vec = int(bool_spatial_idx.size / vector_step)
step = int(bool_spatial_idx.size / num_vec)
bool_spatial_idx[np.where(bool_spatial_idx==True)][0::step] = False
# If no triangles intersect the field of view, return a transparent tile
if not np.any(bool_spatial_idx):
logger.warning("No triangles in field of view, returning empty tile.")
return self.empty_response(layer, request)
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, :]
elif (len(data_obj.shape) == 2):
data = data_obj[time_index, :]
elif len(data_obj.shape) == 1:
data = data_obj[:]
else:
logger.debug("Dimension Mismatch: data_obj.shape == {0} and time = {1}".format(data_obj.shape, time_value))
return self.empty_response(layer, request)
if request.GET['image_type'] in ['pcolor', 'contours', 'filledcontours']:
# Avoid triangles with nan values
bool_spatial_idx[np.isnan(data)] = False
# Get the faces to plot
faces = ug.faces[:]
face_idx = data_handler.face_idx_from_node_idx(faces, bool_spatial_idx)
faces_subset = faces[face_idx]
tri_subset = Tri.Triangulation(lon, lat, triangles=faces_subset)
if request.GET['image_type'] == 'pcolor':
return mpl_handler.tripcolor_response(tri_subset, data, request, data_location=data_location)
else:
return mpl_handler.tricontouring_response(tri_subset, data, request)
elif request.GET['image_type'] in ['filledhatches', 'hatches']:
raise NotImplementedError('matplotlib does not support hatching on triangular grids... sorry!')
else:
raise NotImplementedError('Image type "{}" is not supported.'.format(request.GET['image_type']))
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, bool_spatial_idx])
elif (len(data_obj.shape) == 2):
data.append(data_obj[time_index, bool_spatial_idx])
elif len(data_obj.shape) == 1:
data.append(data_obj[bool_spatial_idx])
else:
logger.debug("Dimension Mismatch: data_obj.shape == {0} and time = {1}".format(data_obj.shape, time_value))
return self.empty_response(layer, request)
if request.GET['image_type'] == 'vectors':
return mpl_handler.quiver_response(lon[bool_spatial_idx],
lat[bool_spatial_idx],
data[0],
data[1],
request)
else:
raise NotImplementedError('Image type "{}" is not supported.'.format(request.GET['image_type']))
