def test_gridded_gridded_bin_when_sample_has_dimension_data_doesnt(self):
# JASCIS-204
from cis.data_io.gridded_data import make_from_cube
sample = make_from_cube(make_mock_cube(time_dim_length=7, dim_order=['lat', 'lon', 'time']))
data = make_from_cube(make_mock_cube(lat_dim_length=11, lon_dim_length=13,
time_dim_length=0, dim_order=['time', 'lon', 'lat']))
col = GeneralGriddedCollocator()
constraint = BinningCubeCellConstraint()
kernel = mean()
out_cube = col.collocate(points=sample, data=data, constraint=constraint, kernel=kernel)
assert out_cube[0].shape == (5, 3)
def test_basic_col_in_4d(self):
from cis.collocation.col_implementations import GeneralUngriddedCollocator, mean, SepConstraintKdtree
import datetime as dt
ug_data = mock.make_regular_4d_ungridded_data()
# Note - This isn't actually used for averaging
sample_points = UngriddedData.from_points_array(
[HyperPoint(lat=1.0, lon=1.0, alt=12.0, t=dt.datetime(1984, 8, 29, 8, 34))])
col = GeneralUngriddedCollocator()
new_data = col.collocate(sample_points, ug_data, SepConstraintKdtree(), mean())[0]
eq_(new_data.data[0], 25.5)
def test_basic_col_in_4d(self):
from cis.collocation.col_implementations import GeneralUngriddedCollocator, mean, SepConstraintKdtree
import datetime as dt
ug_data = mock.make_regular_4d_ungridded_data()
# Note - This isn't actually used for averaging
sample_points = UngriddedData.from_points_array([
HyperPoint(lat=1.0,
lon=1.0,
alt=12.0,
t=dt.datetime(1984, 8, 29, 8, 34))
])
col = GeneralUngriddedCollocator()
new_data = col.collocate(sample_points, ug_data, SepConstraintKdtree(),
mean())[0]
eq_(new_data.data[0], 25.5)
def test_gridded_ungridded_box_mean(self):
data = make_from_cube(mock.make_mock_cube())
data.name = lambda: 'Name'
data.var_name = 'var_name'
data._standard_name = 'y_wind'
sample = UngriddedData.from_points_array(
[HyperPoint(lat=1.0, lon=1.0, alt=12.0, t=dt.datetime(1984, 8, 29, 8, 34)),
HyperPoint(lat=3.0, lon=3.0, alt=7.0, t=dt.datetime(1984, 8, 29, 8, 34)),
HyperPoint(lat=-1.0, lon=-1.0, alt=5.0, t=dt.datetime(1984, 8, 29, 8, 34))])
constraint = SepConstraintKdtree('500km')
kernel = mean()
col = GeneralUngriddedCollocator()
output = col.collocate(sample, data, constraint, kernel)
expected_result = np.array([28.0/3, 10.0, 20.0/3])
assert len(output) == 1
assert isinstance(output, UngriddedDataList)
assert np.allclose(output[0].data, expected_result)
def __init__(self):
self.mean = mean()
def setUp(self):
self.kernel = mean()
def setUp(self):
self.kernel = mean()
