def get_value(self, point, data):
"""
Co-location routine using nearest neighbour algorithm optimized for gridded data.
This calls out to iris to do the work.
"""
from iris.analysis.interpolate import extract_nearest_neighbour, nearest_neighbour_data_value
if not self.coord_names:
# Remove any tuples in the list that do not correspond to a dimension coordinate in the cube 'data'.
for coord_name, val in point.coord_tuple:
if len(data.coords(coord_name, dim_coords=True)) > 0:
self.coord_names.append(coord_name)
if len(data.coords('altitude', dim_coords=False)) > 0 and point.altitude is not None:
self.hybrid_coord = 'altitude'
elif len(data.coords('air_pressure', dim_coords=False)) > 0 and point.air_pressure is not None:
self.hybrid_coord = 'air_pressure'
new_coord_tuple_list = [(c, getattr(point, c)) for c in self.coord_names]
if self.hybrid_coord:
slice = extract_nearest_neighbour(data, new_coord_tuple_list)
val = nearest_neighbour_data_value(slice, [(self.hybrid_coord, getattr(point, self.hybrid_coord))])
else:
val = nearest_neighbour_data_value(data, new_coord_tuple_list)
return val
def processing(in_files, params):
"""
Main processing function to execute either on the main
thread or on IPython parallel engines.
"""
#print in_files
#print '------------'
#print params
# LOAD DATA
#global i_coord, j_coord, l_coord
station_region_indices = (params['iminmax'] +
params['jminmax'] +
params['lminmax'])
i_coord, j_coord, l_coord = iris_tools.gcgrid_2_coords(
params['grid_model_name'],
params['grid_model_resolution'],
region_box=station_region_indices
)
def assign_coord_nd49_or_subset_ctm(cube, field, filename):
"""
A callback for GEOS-Chem datafields loading with Iris.
If `cube` is loaded from a ND49 diagnostic file
(i.e., some undefined dimension coordinates), generate
the coordinates values from the grid indices of the 3D
region box.
(Else) If `cube` is loaded from a CTM file, extract
a subset that correspond to the region box.
"""
#global i_coord, j_coord, l_coord
cube_is_ctm = True
if not cube.coords('longitude'):
cube.add_dim_coord(i_coord, 0)
cube_is_ctm = False
if not cube.coords('latitude'):
cube.add_dim_coord(j_coord, 1)
cube_is_ctm = False
if not cube.coords('model_level_number'):
try:
cube.add_dim_coord(l_coord, 2)
except ValueError:
# as this callback applied before applying constraints,
# do nothing for, e.g., cubes related to emissions (2D)
pass
if cube_is_ctm:
lonlat_subset = iris.Constraint(longitude=i_coord.points,
latitude=j_coord.points)
cube = cube.extract(lonlat_subset)
tracers2load = iris.AttributeConstraint(
category=lambda category: category in params['categories'],
name=lambda name: name in params['tracers']
)
all_cubes = iris.load(in_files,
[tracers2load] + params['other_fields'],
callback=assign_coord_nd49_or_subset_ctm)
tracer_cubes = all_cubes.extract(tracers2load)
other_cubes = all_cubes.extract(params['other_fields'], strict=False)
# datafields required for columns and profiles calculation
#pedges_cube = other_cubes.extract_strict('PSURF_PEDGE-$')
box_height_cube = other_cubes.extract_strict('BXHEIGHT_BXHGHT-$')
try:
n_air_cube = other_cubes.extract_strict('N(AIR)_BXHGHT-$')
except iris.exceptions.ConstraintMismatchError:
# ND49: air density datafields have a different name
n_air_cube = other_cubes.extract_strict('AIRDEN_TIME-SER')
# ND49: fix missing pressure level
#pedges_cube = iris_tools.fix_nd49_pressure(pedges_cube)
# convert units for hydrocarbon tracers
for cube in tracer_cubes:
iris_tools.ppbC_2_ppbv(cube)
#print tracer_cubes
#print "------------------------"
#print tracer_cubes[0]
#print "------------------------"
#print other_cubes
# EXTRACT AND REGRID PROFILES
station_coords = [('latitude', params['station_lat']),
('longitude', params['station_lon'])]
tracer_profiles = iris.cube.CubeList(
[extract_nearest_neighbour(cube, station_coords)
for cube in tracer_cubes]
)
#pedges_profile, box_height_profile, n_air_profile = [
box_height_profile, n_air_profile = [
extract_nearest_neighbour(cube, station_coords)
for cube in [box_height_cube, n_air_cube] #, pedges_cube]
]
all_profiles = tracer_profiles + \
[box_height_profile, n_air_profile] #, pedges_profile]
#global_topography = iris.load_cube(params['global_topography_datafile'])
bh = box_height_profile.copy()
gt = global_topography.copy()
altitude_coord = iris_tools.get_altitude_coord(bh, gt)
#print altitude_coord
#return
for cube in tracer_profiles + [n_air_profile]:
if cube.coords('air_pressure'):
cube.remove_coord('air_pressure')
if cube.coords('altitude'):
cube.remove_coord('altitude')
cube.add_aux_coord(altitude_coord,
data_dims=range(0, box_height_profile.ndim))
station_profile = extract_nearest_neighbour(
#iris.load_cube(params['station_vertical_grid_file']),
station_vgrid,
station_coords
)
regridded_tracer_profiles = iris.cube.CubeList(
[regrid_profile(p, station_profile)
for p in tracer_profiles]
)
regridded_n_air_profile = regrid_profile(n_air_profile, station_profile)
regridded_profiles = regridded_tracer_profiles + \
[regridded_n_air_profile]
# CALCULATE COLUMNS
columns = [iris_tools.compute_tracer_columns(
p, regridded_n_air_profile, 'altitude'
)
for p in regridded_tracer_profiles]
tracer_columns = iris.cube.CubeList(columns)
return regridded_profiles, tracer_columns
def woa_profile(lon, lat, variable='temperature', clim_type='00', resolution='1.00', full=False): # noqa
"""
Return an iris.cube instance from a World Ocean Atlas 2013 variable at a
given lon, lat point.
Parameters
----------
lon, lat: float
point positions to extract the profile.
Choose data `variable` from:
'temperature', 'silicate', 'salinity', 'phosphate',
'oxygen', 'o2sat', 'nitrate', and 'AOU'.
Choose `clim_type` averages from:
01-12 :: monthly
13-16 :: seasonal (North Hemisphere Winter, Spring, Summer,
and Autumn respectively)
00 :: annual
Choose `resolution` from:
1 (1 degree), or 4 (0.25 degrees)
Returns
-------
Iris.cube instance with the climatology.
Examples
--------
>>> import matplotlib.pyplot as plt
>>> from oceans.datasets import woa_profile
>>> cube = woa_profile(-143, 10, variable='temperature',
... clim_type='00', resolution='1.00', full=False)
>>> fig, ax = plt.subplots(figsize=(2.25, 5))
>>> z = cube.coord(axis='Z').points
>>> l = ax.plot(cube[0, :].data, z)
>>> ax.grid(True)
>>> ax.invert_yaxis()
"""
import iris
from iris.analysis.interpolate import extract_nearest_neighbour
if variable not in ['salinity', 'temperature']:
resolution = '1.00'
decav = 'all'
msg = '{} is only available at 1 degree resolution'.format
warnings.warn(msg(variable))
else:
decav = 'decav'
v = dict(temperature='t', silicate='i', salinity='s', phosphate='p',
oxygen='o', o2sat='O', nitrate='n', AOU='A')
r = dict({'1.00': '1', '0.25': '4'})
var = v[variable]
res = r[resolution]
uri = ('http://data.nodc.noaa.gov/thredds/dodsC/woa/WOA13/DATA/'
'{variable}/netcdf/{decav}/{resolution}/woa13_{decav}_{var}'
'{clim_type}_0{res}.nc').format
url = uri(**dict(variable=variable, decav=decav, resolution=resolution,
var=var, clim_type=clim_type, res=res))
cubes = iris.load_raw(url)
cubes = [extract_nearest_neighbour(cube, [('longitude', lon),
('latitude', lat)])
for cube in cubes]
cubes = iris.cube.CubeList(cubes)
if full:
return cubes
else:
cubes = [c for c in cubes if c.var_name == '{}_an'.format(var)]
return cubes[0]
def woa_profile(lon, lat, variable='temperature', clim_type='00',
resolution='1.00', full=False):
"""Return an iris.cube instance from a World Ocean Atlas 2013 variable at a
given lon, lat point.
Parameters
----------
lon, lat: float
point positions to extract the profile.
Choose data `variable` from:
'temperature', 'silicate', 'salinity', 'phosphate',
'oxygen', 'o2sat', 'nitrate', and 'AOU'.
Choose `clim_type` averages from:
01-12 :: monthly
13-16 :: seasonal (North Hemisphere Winter, Spring, Summer,
and Autumn respectively)
00 :: annual
Choose `resolution` from:
1 (1 degree), or 4 (0.25 degrees)
Returns
-------
Iris.cube instance with the climatology.
Examples
--------
>>> import matplotlib.pyplot as plt
>>> from oceans.datasets import woa_profile
>>> cube = woa_profile(-143, 10, variable='temperature',
... clim_type='00', resolution='1.00', full=False)
>>> fig, ax = plt.subplots(figsize=(2.25, 5))
>>> z = cube.coord(axis='Z').points
>>> l = ax.plot(cube[0, :].data, z)
>>> ax.grid(True)
>>> ax.invert_yaxis()
"""
if variable not in ['salinity', 'temperature']:
resolution = '1.00'
decav = 'all'
msg = '{} is only available at 1 degree resolution'.format
warnings.warn(msg(variable))
else:
decav = 'decav'
v = dict(temperature='t', silicate='i', salinity='s', phosphate='p',
oxygen='o', o2sat='O', nitrate='n', AOU='A')
r = dict({'1.00': '1', '0.25': '4'})
var = v[variable]
res = r[resolution]
uri = ("http://data.nodc.noaa.gov/thredds/dodsC/woa/WOA13/DATA/"
"{variable}/netcdf/{decav}/{resolution}/woa13_{decav}_{var}"
"{clim_type}_0{res}.nc").format
url = uri(**dict(variable=variable, decav=decav, resolution=resolution,
var=var, clim_type=clim_type, res=res))
cubes = iris.load_raw(url)
cubes = [extract_nearest_neighbour(cube, [('longitude', lon),
('latitude', lat)])
for cube in cubes]
cubes = iris.cube.CubeList(cubes)
if full:
return cubes
else:
cubes = [c for c in cubes if c.var_name == '{}_an'.format(var)]
return cubes[0]
