def _load_netcdf4(self, path, variable, lon0, lat0, pressure=None):
with netCDF4.Dataset(path) as dataset:
try:
var = dataset.variables[variable]
except KeyError:
return {"x": [], "y": []}
lons = geo.to_180(self._longitudes(dataset, var))
lats = self._latitudes(dataset, var)
i = np.argmin(np.abs(lons - lon0))
j = np.argmin(np.abs(lats - lat0))
times = self._times(dataset, var)
values = var[..., j, i]
if (("pressure" in var.coordinates)
or ("pressure" in var.dimensions)):
pressures = self._pressures(dataset, var)
if len(var.dimensions) == 3:
pts = self.search(pressures, pressure)
values = values[pts]
if isinstance(times, dt.datetime):
times = [times]
else:
times = times[pts]
else:
mask = self.search(pressures, pressure)
values = values[:, mask][:, 0]
return {"x": times, "y": values}
def _load_cube(self, path, variable, lon0, lat0, pressure=None):
import iris
cube = iris.load_cube(path, iris.Constraint(variable))
coord = cube.coord('time')
times = netCDF4.num2date(coord.points, units=str(coord.units))
lons = cube.coord('longitude').points
lats = cube.coord('latitude').points
if lons.ndim == 2:
lons = lons[0, :]
if lats.ndim == 2:
lats = lats[:, 0]
lons = geo.to_180(lons)
i = np.argmin(np.abs(lons - lon0))
j = np.argmin(np.abs(lats - lat0))
values = cube.data
# Index array based on coordinate ordering
pts = []
for c in cube.coords():
if c.name() == 'longitude':
pts.append(i)
elif c.name() == 'latitude':
pts.append(j)
else:
pts.append(slice(None))
pts = tuple(pts)
values = values[pts]
# Filter time/pressure axes to select correct pressure
if self._has_dim(cube, 'pressure'):
pressures = cube.coord('pressure').points
if len(cube.ndim) == 3:
pts = self.search(pressures, pressure)
values = values[pts]
if isinstance(times, dt.datetime):
times = [times]
else:
times = times[pts]
else:
mask = self.search(pressures, pressure)
values = values[:, mask][:, 0]
return times, values