def test_subset(self):
fcst = utils.create_data()
query = {'hours': np.array([0., 2., 4., 6.]),
'domain': {'N': np.max(fcst['latitude'].values) - 1,
'S': np.min(fcst['latitude'].values) + 1,
'E': np.max(fcst['longitude'].values) - 1,
'W': np.min(fcst['longitude'].values) + 1},
'grid_delta': (1., 1.),
'variables': ['wind']}
ss = subset.subset_dataset(fcst, query)
np.testing.assert_array_equal(ss['longitude'].values,
np.arange(query['domain']['W'],
query['domain']['E'] + 1))
np.testing.assert_array_equal(np.sort(ss['latitude'].values),
np.arange(query['domain']['S'],
query['domain']['N'] + 1))
def test_gfs(self):
query = {'hours': [0., 3., 9., 24.],
'model': 'gfs',
'domain': {'N': 39.,
'S': 35,
'E': -120,
'W': -124},
'grid_delta': (1., 1.),
'variables': ['wind']}
model = grads.GFS()
gfs = model.fetch(self.temporary_fcst_path)
expected = subset.subset_dataset(gfs, query)
compressed = request.process_query(query, url=self.temporary_fcst_path)
actual = compress.decompress_dataset(compressed)
np.testing.assert_array_almost_equal(actual['x_wind'].values,
expected['x_wind'].values,
4)
np.testing.assert_array_almost_equal(actual['y_wind'].values,
expected['y_wind'].values,
4)
