def setUp(self):
lats = np.array(range(-60, 61, 1))
lons = np.array(range(-170, 171, 1))
times = np.array([
datetime.datetime(year, month, 1) for year in range(2000, 2010)
for month in range(1, 13)
])
values = np.ones([len(times), len(lats), len(lons)])
self.target_dataset = ds.Dataset(lats,
lons,
times,
values,
variable="test variable name",
units='test variable units',
name='foo')
self.spatial_out_of_bounds = ds.Bounds(-165, 165, -180, 180,
datetime.datetime(2001, 1, 1),
datetime.datetime(2004, 1, 1))
self.temporal_out_of_bounds = ds.Bounds(-40, 40, -160.25, 160.5,
datetime.datetime(1999, 1, 15),
datetime.datetime(2222, 2, 15))
self.everything_out_of_bounds = ds.Bounds(
-165, 165, -180, 180, datetime.datetime(1999, 1, 15),
datetime.datetime(2222, 2, 15))
def setUp(self):
self.target_dataset = ten_year_monthly_dataset()
self.name = 'foo'
self.subregion = ds.Bounds(-81, 81, -161, 161,
datetime.datetime(2001, 1, 1),
datetime.datetime(2004, 1, 1))
self.non_exact_spatial_subregion = ds.Bounds(
-80.25, 80.5, -160.25, 160.5, datetime.datetime(2001, 1, 1),
datetime.datetime(2004, 1, 1))
self.non_exact_temporal_subregion = ds.Bounds(
-80.25, 80.5, -160.25, 160.5, datetime.datetime(2001, 1, 15),
datetime.datetime(2004, 2, 15))
def setUp(self):
self.target_dataset = ten_year_monthly_dataset()
self.target_dataset.lats = np.array(range(-89, 88, 2))
self.target_dataset.lons = np.array(range(-179, 178, 2))
self.subregion = ds.Bounds(-81, 81, -161, 161,
datetime.datetime(2001, 1, 1),
datetime.datetime(2004, 1, 1))
def setUp(self):
self.target_dataset = ten_year_monthly_dataset()
self.target_dataset.lats = np.array(range(-89, 88, 2))
self.target_dataset.lons = np.array(range(-179, 178, 2))
self.subregion = ds.Bounds(
lat_min=-81, lat_max=81,
lon_min=-161, lon_max=161,
start=datetime.datetime(2001, 1, 1),
end=datetime.datetime(2004, 1, 1)
)
def setUp(self):
self.target_dataset = ten_year_monthly_dataset()
self.name = 'foo'
self.subregion = ds.Bounds(
lat_min=-81, lat_max=81,
lon_min=-161, lon_max=161,
start=datetime.datetime(2001, 1, 1),
end=datetime.datetime(2004, 1, 1)
)
self.non_exact_spatial_subregion = ds.Bounds(
lat_min=-80.25, lat_max=80.5,
lon_min=-160.25, lon_max=160.5,
start=datetime.datetime(2001, 1, 1),
end=datetime.datetime(2004, 1, 1)
)
self.non_exact_temporal_subregion = ds.Bounds(
lat_min=-80.25, lat_max=80.5,
lon_min=-160.25, lon_max=160.5,
start=datetime.datetime(2001, 1, 15),
end=datetime.datetime(2004, 2, 15)
)
def test_subset_without_start_index(self):
self.subregion = ds.Bounds(
lat_min=-81, lat_max=81,
lon_min=-161, lon_max=161,
)
subset = dp.subset(self.target_dataset, self.subregion)
times = np.array([datetime.datetime(year, month, 1)
for year in range(2000, 2010)
for month in range(1, 13)])
self.assertEqual(subset.lats.shape[0], 82)
self.assertSequenceEqual(list(np.array(range(-81, 82, 2))),
list(subset.lats))
self.assertEqual(subset.lons.shape[0], 162)
self.assertEqual(subset.values.shape, (120, 82, 162))
self.assertEqual(subset.times.shape[0], 120)
np.testing.assert_array_equal(subset.times, times)
# specific language governing permissions and limitations
# under the License.
import ocw.dataset as ds
import ocw.data_source.local as local
import ocw.dataset_processor as dsp
import ocw.plotter as plotter
import numpy as np
import numpy.ma as ma
''' data source: https://dx.doi.org/10.6084/m9.figshare.3753321.v1
AOD_monthly_2000-Mar_2016-FEB_from_MISR_L3_JOINT.nc is publicly available.'''
dataset = local.load_file(
'AOD_monthly_2000-MAR_2016-FEB_from_MISR_L3_JOINT.nc', 'nonabsorbing_ave')
''' Subset the data for East Asia'''
Bounds = ds.Bounds(lat_min=20, lat_max=57.7, lon_min=90, lon_max=150)
dataset = dsp.subset(dataset, Bounds)
'''The original dataset includes nonabsorbing AOD values between March 2000 and February 2015.
dsp.temporal_subset will extract data in September-October-November.'''
dataset_SON = dsp.temporal_subset(dataset,
month_start=9,
month_end=11,
average_each_year=True)
ny, nx = dataset_SON.values.shape[1:]
# multi-year mean aod
clim_aod = ma.zeros([3, ny, nx])
clim_aod[0, :] = ma.mean(dataset_SON.values, axis=0) # 16-year mean
clim_aod[1, :] = ma.mean(dataset_SON.values[-5:, :],
