def make_rtree(self):
with self.dataset() as nc:
sg = load_grid(nc)
def rtree_generator_function():
c = 0
centers = pair_arrays(sg.center_lon, sg.center_lat)
for i, axis in enumerate(centers):
for j, (x, y) in enumerate(axis):
c += 1
yield (c, (x, y, x, y), (i, j))
logger.info("Building Faces (centers) Rtree Topology Cache for {0}".format(self.name))
_, temp_file = tempfile.mkstemp(suffix='.face')
start = time.time()
p = index.Property()
p.filename = str(temp_file)
p.overwrite = True
p.storage = index.RT_Disk
p.dimension = 2
index.Index(p.filename.decode('utf-8'),
rtree_generator_function(),
properties=p,
overwrite=True,
interleaved=True)
logger.info("Built Faces (centers) Rtree Topology Cache in {0} seconds.".format(time.time() - start))
shutil.move('{}.dat'.format(temp_file), self.face_tree_data_file)
shutil.move('{}.idx'.format(temp_file), self.face_tree_index_file)
def make_rtree(self):
with self.dataset() as nc:
sg = load_grid(nc)
def rtree_generator_function():
c = 0
centers = np.dstack((sg.center_lon, sg.center_lat))
for i, axis in enumerate(centers):
for j, (x, y) in enumerate(axis):
c += 1
yield (c, (x, y, x, y), (i, j))
logger.info(
"Building Faces (centers) Rtree Topology Cache for {0}".format(
self.name))
_, temp_file = tempfile.mkstemp(suffix='.face')
start = time.time()
p = index.Property()
p.filename = str(temp_file)
p.overwrite = True
p.storage = index.RT_Disk
p.dimension = 2
idx = index.Index(p.filename,
rtree_generator_function(),
properties=p,
overwrite=True,
interleaved=True)
idx.close()
logger.info(
"Built Faces (centers) Rtree Topology Cache in {0} seconds.".
format(time.time() - start))
shutil.move('{}.dat'.format(temp_file), self.face_tree_data_file)
shutil.move('{}.idx'.format(temp_file), self.face_tree_index_file)
def wgs84_bounds(self, layer):
try:
cached_sg = load_grid(self.topology_file)
except:
pass
else:
#centers = cached_sg.centers
centers = pair_arrays(cached_sg.center_lon, cached_sg.center_lat)
longitudes = centers[..., 0]
latitudes = centers[..., 1]
lon_name, lat_name = cached_sg.face_coordinates
lon_var_obj = getattr(cached_sg, lon_name)
lat_var_obj = getattr(cached_sg, lat_name)
lon_trimmed = longitudes[lon_var_obj.center_slicing]
lat_trimmed = latitudes[lat_var_obj.center_slicing]
lon_max = lon_trimmed.max()
lon_min = lon_trimmed.min()
lat_max = lat_trimmed.max()
lat_min = lat_trimmed.min()
return DotDict(minx=lon_min,
miny=lat_min,
maxx=lon_max,
maxy=lat_max,
bbox=(lon_min, lat_min, lon_max, lat_max)
)
def update_grid_cache(self, force=False):
with self.dataset() as nc:
if nc is None:
logger.error(
"Failed update_grid_cache, could not load dataset "
"as a netCDF4 object")
return
sg = load_grid(nc)
# Atomic write
tmphandle, tmpsave = tempfile.mkstemp()
try:
sg.save_as_netcdf(tmpsave)
finally:
os.close(tmphandle)
if os.path.isfile(tmpsave):
shutil.move(tmpsave, self.topology_file)
else:
logger.error(
"Failed to create topology_file cache for Dataset '{}'"
.format(self.dataset.name))
return
# Now do the RTree index
self.make_rtree()
def roms_field(filename=None, dataset=None):
if dataset is None:
dataset = nc4.Dataset(filename)
grid = pysgrid.load_grid(dataset)
time = Time(dataset['ocean_time'])
u = grid.u
v = grid.v
u_mask = grid.mask_u
v_mask = grid.mask_v
r_mask = grid.mask_rho
land_mask = grid.mask_psi
variables = {'u': u,
'v': v,
'u_mask': u_mask,
'v_mask': v_mask,
'land_mask': land_mask,
'time': time}
return SField(grid, time=time, variables=variables)
def wgs84_bounds(self, layer):
try:
cached_sg = load_grid(self.topology_file)
except BaseException:
pass
else:
lon_name, lat_name = cached_sg.face_coordinates
lon_var_obj = getattr(cached_sg, lon_name)
lat_var_obj = getattr(cached_sg, lat_name)
lon_trimmed = cached_sg.center_lon[lon_var_obj.center_slicing]
lat_trimmed = cached_sg.center_lat[lat_var_obj.center_slicing]
lon_max = lon_trimmed.max()
lon_min = lon_trimmed.min()
lat_max = lat_trimmed.max()
lat_min = lat_trimmed.min()
return DotDict(minx=lon_min,
miny=lat_min,
maxx=lon_max,
maxy=lat_max,
bbox=(lon_min, lat_min, lon_max, lat_max))
def update_cache(self, force=False):
with self.dataset() as nc:
sg = load_grid(nc)
sg.save_as_netcdf(self.topology_file)
if not os.path.exists(self.topology_file):
logger.error("Failed to create topology_file cache for Dataset '{}'".format(self.dataset))
return
# add time to the cached topology
time_vars = nc.get_variables_by_attributes(standard_name='time')
time_dims = list(itertools.chain.from_iterable([time_var.dimensions for time_var in time_vars]))
unique_time_dims = list(set(time_dims))
with EnhancedDataset(self.topology_file, mode='a') as cached_nc:
# create pertinent time dimensions if they aren't already present
for unique_time_dim in unique_time_dims:
dim_size = len(nc.dimensions[unique_time_dim])
try:
cached_nc.createDimension(unique_time_dim, size=dim_size)
except RuntimeError:
continue
# support cases where there may be more than one variable with standard_name='time' in a dataset
for time_var in time_vars:
try:
time_var_obj = cached_nc.createVariable(time_var.name,
time_var.dtype,
time_var.dimensions)
except RuntimeError:
time_var_obj = cached_nc.variables[time_var.name]
finally:
time_var_obj[:] = time_var[:]
time_var_obj.units = time_var.units
time_var_obj.standard_name = 'time'
# Now do the RTree index
self.make_rtree()
self.cache_last_updated = datetime.utcnow().replace(tzinfo=pytz.utc)
self.save()
def load_grid(nc):
"""
Takes a `nc` (netCDF4-python object), a file/URL path, or an
`iris.cube.Cube` instance.
Returns a tuple with the `grid` object, `mesh` type, and grid `polygons`.
"""
grid = None
mesh = 'non-compliant'
if nc.__class__.__name__ == 'Cube':
polygons = _parse_cube(nc)
return grid, polygons, mesh
elif isinstance(nc, Dataset):
pass
else:
nc = Dataset(nc)
try:
grid = pysgrid.load_grid(nc)
polygons = _parse_sgrid(grid)
mesh = 'sgrid'
return grid, polygons, mesh
except (ValueError, KeyError):
pass
try:
grid = pyugrid.UGrid.from_nc_dataset(nc)
polygons = _parse_ugrid(grid)
mesh = 'ugrid'
return grid, polygons, mesh
except ValueError:
pass
# When all fails try non-compliant `grid` type.
polygons = _parse_grid(nc)
return grid, polygons, mesh
import pysgrid
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
# from cartopy.io import shapereader
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
# rotation is still ugly...
from pysgrid.processing_2d import rotate_vectors, vector_sum
# url = ('http://geoport.whoi.edu/thredds/dodsC/clay/usgs/users/jcwarner/Projects/Sandy/triple_nest/00_dir_NYB05.ncml') # noqa
url2 = ('http://geoport-dev.whoi.edu/thredds/dodsC/clay/usgs/users/zdefne/run076/his/00_dir_roms_display.ncml') # noqa
nc = Dataset(url2)
sgrid = pysgrid.load_grid(nc)
sgrid # We need a better __repr__ and __str__ !!!
lons, lats = np.mgrid[-74.38:-74.26:600j, 39.45:39.56:600j]
points = np.stack((lons, lats), axis=-1)
print(points.shape)
time_idx = 0
v_idx = 0
interp_u = sgrid.interpolate_var_to_points(
points, sgrid.u[time_idx, v_idx], slices=None)
interp_v = sgrid.interpolate_var_to_points(
points, sgrid.v, slices=[time_idx, v_idx])
ind = sgrid.locate_faces(points)
VelocityTS.deserialize(vel.serialize()))
velfromweb.name = 'velfromweb'
pp.pprint(vel.serialize(json_='save'))
pp.pprint(velfromweb.serialize(json_='save'))
velfromsave = VelocityTS.new_from_dict(
VelocityTS.deserialize(velfromweb.serialize(json_='save')))
pp.pprint(velfromsave)
velfromsave.at(np.array([(0, 0)]), datetime(2000, 1, 1, 0, 0))
url = (
'http://geoport.whoi.edu/thredds/dodsC/clay/usgs/users/jcwarner/Projects/Sandy/triple_nest/00_dir_NYB05.ncml'
)
test_grid = pysgrid.load_grid(url)
grid_u = test_grid.u
grid_v = test_grid.v
grid_time = test_grid.ocean_time._data
u2 = GriddedProp('u',
'm/s',
time=grid_time,
data=grid_u,
grid=test_grid,
data_file=url,
grid_file=url)
v2 = GriddedProp('v',
'm/s',
time=grid_time,
data=grid_v,
from netCDF4 import Dataset
import numpy as np
import pysgrid
#url = ('http://geoport.whoi.edu/thredds/dodsC/clay/usgs/users/jcwarner/Projects/Sandy/triple_nest/00_dir_NYB05.ncml')
url2 = (
'http://geoport-dev.whoi.edu/thredds/dodsC/clay/usgs/users/zdefne/run076/his/00_dir_roms_display.ncml'
)
nc = Dataset(url2)
sgrid = pysgrid.load_grid(nc)
sgrid # We need a better __repr__ and __str__ !!!
lons, lats = np.mgrid[-74.38:-74.26:600j, 39.45:39.56:600j]
points = np.stack((lons, lats), axis=-1)
print points.shape
time_idx = 0
v_idx = 0
# interp_u = sgrid.interpolate_var_to_points(
# points, sgrid.u, slice=[time_idx, v_idx])
# interp_v = sgrid.interpolate_var_to_points(
# points, sgrid.v, slice=[time_idx, v_idx])
# sgrid.interpolate_var_to_points(
# points[19:21, 241:243], sgrid.u, slices=[time_idx, v_idx])
# interp_u = sgrid.interpolate_var_to_points(
# points, sgrid.u, slices=[time_idx, v_idx])
interp_u = sgrid.interpolate_var_to_points(points,
sgrid.u[time_idx, v_idx],
slices=None)
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:
cached_sg = load_grid(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
centers = pair_arrays(cached_sg.center_lon, cached_sg.center_lat)
lon = centers[..., 0][lon_obj.center_slicing]
lat = centers[..., 1][lat_obj.center_slicing]
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, data_obj.center_slicing[-2], data_obj.center_slicing[-1]]
elif len(raw_var.shape) == 3:
raw_data = raw_var[time_index, data_obj.center_slicing[-2], data_obj.center_slicing[-1]]
elif len(raw_var.shape) == 2:
raw_data = raw_var[data_obj.center_slicing]
else:
raise BaseException('Unable to trim variable {0} data.'.format(layer.access_name))
# handle edge variables
if data_obj.location is not None and 'edge' in data_obj.location:
raw_data = avg_to_cell_center(raw_data, data_obj.center_axis)
if request.GET['image_type'] == 'pcolor':
return mpl_handler.pcolormesh_response(lon, lat, data=raw_data, request=request)
elif request.GET['image_type'] in ['filledhatches', 'hatches', 'filledcontours', 'contours']:
return mpl_handler.contouring_response(lon, lat, data=raw_data, request=request)
else:
raise NotImplementedError('Image type "{}" is not supported.'.format(request.GET['image_type']))
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, data_obj.center_slicing[-2], data_obj.center_slicing[-1]]
elif len(raw_var.shape) == 3:
raw_data = raw_var[time_index, data_obj.center_slicing[-2], data_obj.center_slicing[-1]]
elif len(raw_var.shape) == 2:
raw_data = raw_var[data_obj.center_slicing]
else:
raise BaseException('Unable to trim variable {0} data.'.format(l.access_name))
raw_data = avg_to_cell_center(raw_data, data_obj.center_axis)
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 x_var is None or y_var is None:
raise BaseException('Unable to determine x and y variables.')
dim_lengths = [ len(v.dimensions) for v in raw_vars ]
if len(list(set(dim_lengths))) != 1:
raise AttributeError('One or both of the specified variables has screwed up dimensions.')
if request.GET['image_type'] == 'vectors':
angles = cached_sg.angles[lon_obj.center_slicing]
vectorstep = request.GET['vectorstep']
# don't do this if the vectorstep is 1; let's save a microsecond or two
# it's identical to getting all the data
if vectorstep > 1:
data_dim = len(lon.shape)
step_slice = (np.s_[::vectorstep],) * data_dim # make sure the vector step is used for all applicable dimensions
lon = lon[step_slice]
lat = lat[step_slice]
x_var = x_var[step_slice]
y_var = y_var[step_slice]
angles = angles[step_slice]
vectorscale = request.GET['vectorscale']
padding_factor = calc_safety_factor(vectorscale)
# figure out the average distance between lat/lon points
# do the math after taking into the vectorstep if specified
spatial_idx_padding = calc_lon_lat_padding(lon, lat, padding_factor)
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,
padding=spatial_idx_padding
)
subset_lon = self._spatial_data_subset(lon, spatial_idx)
subset_lat = self._spatial_data_subset(lat, spatial_idx)
# rotate vectors
x_rot, y_rot = rotate_vectors(x_var, y_var, angles)
spatial_subset_x_rot = self._spatial_data_subset(x_rot, spatial_idx)
spatial_subset_y_rot = self._spatial_data_subset(y_rot, spatial_idx)
return mpl_handler.quiver_response(subset_lon,
subset_lat,
spatial_subset_x_rot,
spatial_subset_y_rot,
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:
cached_sg = load_grid(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
centers = pair_arrays(cached_sg.center_lon, cached_sg.center_lat)
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 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:
cached_sg = load_grid(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)
lon = cached_sg.center_lon[lon_obj.center_slicing]
lat = cached_sg.center_lat[lat_obj.center_slicing]
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,
data_obj.center_slicing[-2],
data_obj.center_slicing[-1]]
elif len(raw_var.shape) == 3:
raw_data = raw_var[time_index, data_obj.center_slicing[-2],
data_obj.center_slicing[-1]]
elif len(raw_var.shape) == 2:
raw_data = raw_var[data_obj.center_slicing]
else:
raise BaseException(
'Unable to trim variable {0} data.'.format(
layer.access_name))
# handle edge variables
if data_obj.location is not None and 'edge' in data_obj.location:
raw_data = avg_to_cell_center(raw_data,
data_obj.center_axis)
if request.GET['image_type'] == 'pcolor':
return mpl_handler.pcolormesh_response(lon,
lat,
data=raw_data,
request=request)
elif request.GET['image_type'] in [
'filledhatches', 'hatches', 'filledcontours',
'contours'
]:
return mpl_handler.contouring_response(lon,
lat,
data=raw_data,
request=request)
else:
raise NotImplementedError(
'Image type "{}" is not supported.'.format(
request.GET['image_type']))
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,
data_obj.center_slicing[-2],
data_obj.center_slicing[-1]]
elif len(raw_var.shape) == 3:
raw_data = raw_var[time_index,
data_obj.center_slicing[-2],
data_obj.center_slicing[-1]]
elif len(raw_var.shape) == 2:
raw_data = raw_var[data_obj.center_slicing]
else:
raise BaseException(
'Unable to trim variable {0} data.'.format(
l.access_name))
raw_data = avg_to_cell_center(raw_data,
data_obj.center_axis)
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 x_var is None or y_var is None:
raise BaseException(
'Unable to determine x and y variables.')
dim_lengths = [len(v.dimensions) for v in raw_vars]
if len(list(set(dim_lengths))) != 1:
raise AttributeError(
'One or both of the specified variables has incorrect dimensions.'
)
if request.GET['image_type'] == 'vectors':
angles = cached_sg.angles[lon_obj.center_slicing]
vectorstep = request.GET['vectorstep']
# don't do this if the vectorstep is 1; let's save a microsecond or two
# it's identical to getting all the data
if vectorstep > 1:
data_dim = len(lon.shape)
step_slice = (
np.s_[::vectorstep],
) * data_dim # make sure the vector step is used for all applicable dimensions
lon = lon[step_slice]
lat = lat[step_slice]
x_var = x_var[step_slice]
y_var = y_var[step_slice]
angles = angles[step_slice]
vectorscale = request.GET['vectorscale']
padding_factor = calc_safety_factor(vectorscale)
# figure out the average distance between lat/lon points
# do the math after taking into the vectorstep if specified
spatial_idx_padding = calc_lon_lat_padding(
lon, lat, padding_factor)
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,
padding=spatial_idx_padding)
subset_lon = self._spatial_data_subset(lon, spatial_idx)
subset_lat = self._spatial_data_subset(lat, spatial_idx)
# rotate vectors
x_rot, y_rot = rotate_vectors(x_var, y_var, angles)
spatial_subset_x_rot = self._spatial_data_subset(
x_rot, spatial_idx)
spatial_subset_y_rot = self._spatial_data_subset(
y_rot, spatial_idx)
return mpl_handler.quiver_response(subset_lon, subset_lat,
spatial_subset_x_rot,
spatial_subset_y_rot,
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:
cached_sg = load_grid(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)
lon = cached_sg.center_lon[lon_obj.center_slicing]
lat = cached_sg.center_lat[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))
