def test_get_time_series_for_point_out_of_bounds(self):
ctx = new_test_service_context()
actual_result = get_time_series(
ctx, 'demo', 'conc_tsm',
dict(type="Point", coordinates=[-150.0, -30.0]))
expected_result = []
self.assertEqual(expected_result, actual_result)
def test_get_time_series_for_polygon_only_valids(self):
ctx = new_test_service_context()
actual_result = get_time_series(ctx,
'demo',
'conc_tsm',
dict(type="Polygon",
coordinates=[[[1.,
51.], [2., 51.],
[2.,
52.], [1., 52.],
[1., 51.]]]),
agg_methods=['mean', 'count'],
max_valids=-1)
expected_result = [{
'count': 122392,
'count_tot': 159600,
'mean': 56.12519223634024,
'time': '2017-01-16T10:09:22Z'
}, {
'count': 132066,
'count_tot': 159600,
'mean': 49.70755256053988,
'time': '2017-01-28T09:58:11Z'
}]
self.assertAlmostEqualDeep(expected_result, actual_result)
def test_get_time_series_for_point_collection(self):
ctx = new_test_service_context()
actual_result = get_time_series(
ctx,
'demo',
'conc_tsm',
dict(type="GeometryCollection",
geometries=[dict(type="Point", coordinates=[2.1, 51.4])]),
agg_methods=['mean', 'count'],
start_date=np.datetime64('2017-01-15'),
end_date=np.datetime64('2017-01-29'))
expected_result = [[{
'mean': 3.534773588180542,
'time': '2017-01-16T10:09:22Z'
}, {
'mean': None,
'time': '2017-01-25T09:35:51Z'
}, {
'mean': None,
'time': '2017-01-26T10:50:17Z'
}, {
'mean': 20.12085723876953,
'time': '2017-01-28T09:58:11Z'
}]]
self.assertAlmostEqualDeep(expected_result, actual_result)
def test_get_time_series_for_polygon_collection(self):
ctx = new_test_service_context()
actual_result = get_time_series(ctx,
'demo', 'conc_tsm',
dict(type="GeometryCollection",
geometries=[dict(type="Polygon", coordinates=[[
[1., 51.], [2., 51.], [2., 52.], [1., 52.],
[1., 51.]
]])]),
agg_methods=['mean', 'count'])
expected_result = [[{'count': 122392,
'count_tot': 159600,
'mean': 56.12519223634024,
'time': '2017-01-16T10:09:22Z'},
{'count': 0,
'count_tot': 159600,
'mean': None,
'time': '2017-01-25T09:35:51Z'},
{'count': 0,
'count_tot': 159600,
'mean': None,
'time': '2017-01-26T10:50:17Z'},
{'count': 132066,
'count_tot': 159600,
'mean': 49.70755256053988,
'time': '2017-01-28T09:58:11Z'},
{'count': 0,
'count_tot': 159600,
'mean': None,
'time': '2017-01-30T10:46:34Z'}]]
self.assertAlmostEqualDeep(expected_result, actual_result)
def test_get_time_series_for_polygon_with_all_agg_methods(self):
ctx = new_test_service_context()
actual_result = get_time_series(
ctx,
'demo',
'conc_tsm',
dict(type="Polygon",
coordinates=[[[1., 51.], [2., 51.], [2., 52.], [1., 52.],
[1., 51.]]]),
agg_methods=['mean', 'median', 'std', 'min', 'max', 'count'])
expected_result = [{
'count': 122392,
'count_tot': 159600,
'max': 166.57278442382812,
'mean': 56.12519223634024,
'median': 48.009796142578125,
'min': 0.02251400426030159,
'std': 40.78859862094861,
'time': '2017-01-16T10:09:22Z'
}, {
'count': 0,
'count_tot': 159600,
'max': None,
'mean': None,
'median': None,
'min': None,
'std': None,
'time': '2017-01-25T09:35:51Z'
}, {
'count': 0,
'count_tot': 159600,
'max': None,
'mean': None,
'median': None,
'min': None,
'std': None,
'time': '2017-01-26T10:50:17Z'
}, {
'count': 132066,
'count_tot': 159600,
'max': 158.7908477783203,
'mean': 49.70755256053988,
'median': 39.326446533203125,
'min': 0.02607600949704647,
'std': 34.98868194514787,
'time': '2017-01-28T09:58:11Z'
}, {
'count': 0,
'count_tot': 159600,
'max': None,
'mean': None,
'median': None,
'min': None,
'std': None,
'time': '2017-01-30T10:46:34Z'
}]
self.assertAlmostEqualDeep(expected_result, actual_result)
def test_get_time_series_for_point_only_valids(self):
ctx = new_test_service_context()
actual_result = get_time_series(ctx, 'demo', 'conc_tsm',
dict(type="Point", coordinates=[2.1, 51.4]),
start_date=np.datetime64('2017-01-15'),
end_date=np.datetime64('2017-01-29'),
max_valids=-1)
expected_result = [{'mean': 3.534773588180542, 'time': '2017-01-16T10:09:22Z'},
{'mean': 20.12085723876953, 'time': '2017-01-28T09:58:11Z'}]
self.assertAlmostEqualDeep(expected_result, actual_result)
def test_get_time_series_for_point_one_valid(self):
ctx = new_test_service_context()
actual_result = get_time_series(ctx, 'demo', 'conc_tsm',
dict(type="Point", coordinates=[2.1, 51.4]),
agg_methods=['mean', 'count'],
start_date=np.datetime64('2017-01-15'),
end_date=np.datetime64('2017-01-29'),
max_valids=1)
expected_result = [{'mean': 20.12085723876953, 'time': '2017-01-28T09:58:11Z'}]
self.assertAlmostEqualDeep(expected_result, actual_result)
def test_point_ts_perf(self):
TEST_CUBE = 'ts_test.zarr'
if not os.path.isdir(TEST_CUBE):
from xcube.core.new import new_cube
cube = new_cube(time_periods=2000, variables=dict(analysed_sst=280.4))
cube = cube.chunk(dict(time=1, lon=90, lat=90))
cube.to_zarr(TEST_CUBE)
ctx = ServiceContext(
base_dir='.',
config=dict(
Datasets=[
dict(Identifier='ts_test',
FileSystem='file',
Path=TEST_CUBE,
Format='zarr')
]
))
N = 5
import random
import time
time_sum = 0.0
for i in range(N):
lon = -180 + 360 * random.random()
lat = -90 + 180 * random.random()
t1 = time.perf_counter()
result = get_time_series(ctx, 'ts_test', 'analysed_sst', dict(type='Point', coordinates=[lon, lat]))
t2 = time.perf_counter()
self.assertIsInstance(result, list)
self.assertEqual(2000, len(result))
time_delta = t2 - t1
time_sum += time_delta
print(f'test {i + 1} took {time_delta} seconds')
print(f'all tests took {time_sum / N} seconds in average')
