def test_ungridded_ungridded_box_moments_no_missing_data_for_missing_sample(
self):
data = mock.make_regular_2d_ungridded_data()
sample = mock.make_dummy_sample_points(
latitude=[1.0, 3.0, -1.0],
longitude=[1.0, 3.0, -1.0],
altitude=[12.0, 7.0, 5.0],
time=[
dt.datetime(1984, 8, 29, 8, 34),
dt.datetime(1984, 8, 29, 8, 34),
dt.datetime(1984, 8, 29, 8, 34)
])
kernel = moments()
# Set a missing sample data-value
sample.data[1] = np.NaN
output = collocate(sample,
data,
kernel,
h_sep=500,
missing_data_for_missing_sample=False)
expected_result = np.array([28.0 / 3, 10.0, 20.0 / 3])
expected_stddev = np.array([1.52752523, 1.82574186, 1.52752523])
expected_n = np.array([3, 4, 3])
assert isinstance(output, xr.Dataset)
assert np.allclose(output['var'].data, expected_result)
assert np.allclose(output['var_std_dev'].data, expected_stddev)
assert np.allclose(output['var_num_points'].data, expected_n)
def test_averaging_basic_col_in_4d(self):
ug_data = mock.make_regular_4d_ungridded_data()
# Note - This isn't actually used for averaging
sample_points = mock.make_dummy_sample_points(
latitude=[1.0],
longitude=[1.0],
altitude=[12.0],
time=[dt.datetime(1984, 8, 29, 8, 34)])
new_data = collocate(sample_points, ug_data, moments())
means = new_data['var']
std_dev = new_data['var_std_dev']
no_points = new_data['var_num_points']
def test_basic_col_in_4d_with_pressure_not_altitude(self):
from collocate.kernels import moments
from collocate import collocate
import datetime as dt
data = mock.make_regular_4d_ungridded_data()
# Note - This isn't actually used for averaging
sample_points = mock.make_dummy_sample_points(
latitude=[1.0],
longitude=[1.0],
altitude=[12.0],
time=[dt.datetime(1984, 8, 29, 8, 34)])
new_data = collocate(sample_points, data, moments())
means = new_data['var']
std_dev = new_data['var_std_dev']
no_points = new_data['var_num_points']
assert_almost_equal(means.data[0], 25.5)
assert_almost_equal(std_dev.data[0], np.sqrt(212.5))
assert_almost_equal(no_points.data[0], 50)
def test_list_ungridded_ungridded_box_mean(self):
ug_data_1 = mock.make_regular_2d_ungridded_data()
ug_data_2 = mock.make_regular_2d_ungridded_data(data_offset=3)
ug_data_2.attrs['long_name'] = 'TOTAL SNOWFALL RATE: LS+CONV KG/M2/S'
ug_data_2.attrs['standard_name'] = 'snowfall_flux'
data_list = xr.Dataset({'precip': ug_data_1, 'snow': ug_data_2})
sample_points = mock.make_regular_2d_ungridded_data()
kernel = moments()
output = collocate(sample_points, data_list, kernel, h_sep=500)
expected_result = np.array(list(range(1, 16)))
expected_n = np.array(15 * [1])
assert isinstance(output, xr.Dataset)
assert output['snow'].name == 'snow'
assert output['snow_std_dev'].name == 'snow_std_dev'
assert output['snow_num_points'].name == 'snow_num_points'
assert np.allclose(output['precip'].data, expected_result)
assert np.isnan(output['precip_std_dev'].data).all()
assert np.allclose(output['precip_num_points'].data, expected_n)
assert np.allclose(output['snow'].data, expected_result + 3)
assert np.isnan(output['snow_std_dev'].data).all()
assert np.allclose(output['snow_num_points'].data, expected_n)