def test_can_run_bayesian_kriging_from_observation_sites_to_1km_grid(self):
"""
Somewhat more complex test, first do kriging of 1 timeseries out to grid (expect same values flat)
then do kriging of 3 time-series out to the grid (expect different values, no real verification here since this is done elsewhere
"""
# arrange the test with a btk_parameter, a source grid and a destination grid
btk_parameter = api.BTKParameter(temperature_gradient=-0.6, temperature_gradient_sd=0.25, sill=25.0, nugget=0.5, range=20000.0, zscale=20.0)
fx = lambda z: api.DoubleVector.from_numpy(np.zeros(self.n))
grid_1km_1 = self._create_geo_point_grid(self.mnx, self.mny, self.dx_model)
grid_1km_3 = self._create_geo_point_grid(self.mnx, self.mny, self.dx_model)
observation_sites = api.TemperatureSourceVector()
ta_obs = api.TimeAxisFixedDeltaT(self.t, self.d * 3, int(self.n / 3))
ta_grid = api.TimeAxisFixedDeltaT(self.t, self.d, self.n)
point_fx = api.point_interpretation_policy.POINT_AVERAGE_VALUE
ts_site_1 = api.TimeSeries(ta_obs,
values=api.DoubleVector.from_numpy(
(20.0 - 0.6 * 5.0 / 100) + 3.0 * np.sin(np.arange(start=0, stop=ta_obs.size(), step=1) * 2 * np.pi / 8.0 - np.pi / 2.0)
),
point_fx=point_fx)
ts_site_2 = api.TimeSeries(ta_obs, values=api.DoubleVector.from_numpy(
(20.0 - 0.6 * 500.0 / 100) + 3.0 * np.sin(np.arange(start=0, stop=ta_obs.size(), step=1) * 2 * np.pi / 8.0 - np.pi / 2.0)),
point_fx=point_fx)
ts_site_3 = api.TimeSeries(ta_obs, values=api.DoubleVector.from_numpy(
(20.0 - 0.6 * 1050.0 / 100) + 3.0 * np.sin(np.arange(start=0, stop=ta_obs.size(), step=1) * 2 * np.pi / 8.0 - np.pi / 2.0)),
point_fx=point_fx)
observation_sites.append(api.TemperatureSource(api.GeoPoint(50.0, 50.0, 5.0), ts_site_1))
# act 1: just one time-series put into the system, should give same ts (true-averaged) in all the grid-1km_ts (which can be improved using std.gradient..)
grid_1km_1ts = api.bayesian_kriging_temperature(observation_sites, grid_1km_1, ta_grid, btk_parameter)
# assert 1:
self.assertEqual(len(grid_1km_1ts), self.mnx * self.mny)
expected_grid_1ts_values = ts_site_1.average(api.TimeAxis(ta_grid)).values.to_numpy()
for gts in grid_1km_1ts:
self.assertEqual(gts.ts.size(), ta_grid.size())
self.assertTrue(np.allclose(expected_grid_1ts_values, gts.ts.values.to_numpy()))
observation_sites.append(api.TemperatureSource(api.GeoPoint(9000.0, 500.0, 500), ts_site_2))
observation_sites.append(api.TemperatureSource(api.GeoPoint(9000.0, 12000.0, 1050.0), ts_site_3))
grid_1km_3ts = api.bayesian_kriging_temperature(observation_sites, grid_1km_3, ta_grid, btk_parameter)
self.assertEqual(len(grid_1km_3ts), self.mnx * self.mny)
for gts in grid_1km_3ts:
self.assertEqual(gts.ts.size(), ta_grid.size())
self.assertFalse(np.allclose(expected_grid_1ts_values, gts.ts.values.to_numpy()))
def test_can_run_bayesian_kriging_from_arome25_to_1km(self):
"""
Verify that if we run btk interpolation, we do get updated time-series according to time-axis and range
specified.
"""
# arrange the test with a btk_parameter, a source grid and a destination grid
btk_parameter = api.BTKParameter(temperature_gradient=-0.6,
temperature_gradient_sd=0.25,
sill=25.0,
nugget=0.5,
range=20000.0,
zscale=20.0)
fx = lambda z: api.DoubleVector.from_numpy(
(20.0 - 0.6 * z / 100) + 3.0 * np.sin(
np.arange(start=0, stop=self.n, step=1) * 2 * np.pi / 24.0 - np
.pi / 2.0))
arome_grid = self._create_geo_temperature_grid(self.nx, self.ny,
self.dx_arome, fx)
destination_grid = self._create_geo_point_grid(self.mnx, self.mny,
self.dx_model)
ta = api.TimeAxisFixedDeltaT(self.t, self.d * 3, int(self.n / 3))
# act, - run the bayesian_kriging_temperature algoritm.
r = api.bayesian_kriging_temperature(arome_grid, destination_grid, ta,
btk_parameter)
# assert
self.assertIsNotNone(r)
self.assertEqual(len(r), self.mnx * self.mny)
for gts in r: # do some sanity checks for the btk. Note that full-range checking is already done elsewhere
self.assertEqual(gts.ts.size(), ta.size())
self.assertLess(np.max(gts.ts.values.to_numpy()),
23.0) # all values less than ~max
self.assertGreater(np.min(gts.ts.values.to_numpy()),
7.0) # all values larger than ~ min
def test_can_run_bayesian_kriging_from_observation_sites_to_1km_grid(self):
"""
Somewhat more complex test, first do kriging of 1 timeseries out to grid (expect same values flat)
then do kriging of 3 time-series out to the grid (expect different values, no real verification here since this is done elsewhere
"""
# arrange the test with a btk_parameter, a source grid and a destination grid
btk_parameter = api.BTKParameter(temperature_gradient=-0.6, temperature_gradient_sd=0.25, sill=25.0, nugget=0.5, range=20000.0, zscale=20.0)
fx = lambda z: api.DoubleVector.from_numpy(np.zeros(self.n))
grid_1km_1 = self._create_geo_point_grid(self.mnx, self.mny, self.dx_model)
grid_1km_3 = self._create_geo_point_grid(self.mnx, self.mny, self.dx_model)
observation_sites = api.TemperatureSourceVector()
ta_obs = api.Timeaxis(self.t, self.d * 3, int(self.n / 3))
ta_grid = api.Timeaxis(self.t, self.d, self.n)
ts_site_1 = api.Timeseries(ta_obs, values=api.DoubleVector.from_numpy(
(20.0 - 0.6 * 5.0 / 100) + 3.0 * np.sin(np.arange(start=0, stop=ta_obs.size(), step=1) * 2 * np.pi / 8.0 - np.pi / 2.0)))
ts_site_2 = api.Timeseries(ta_obs, values=api.DoubleVector.from_numpy(
(20.0 - 0.6 * 500.0 / 100) + 3.0 * np.sin(np.arange(start=0, stop=ta_obs.size(), step=1) * 2 * np.pi / 8.0 - np.pi / 2.0)))
ts_site_3 = api.Timeseries(ta_obs, values=api.DoubleVector.from_numpy(
(20.0 - 0.6 * 1050.0 / 100) + 3.0 * np.sin(np.arange(start=0, stop=ta_obs.size(), step=1) * 2 * np.pi / 8.0 - np.pi / 2.0)))
observation_sites.append(api.TemperatureSource(api.GeoPoint(50.0, 50.0, 5.0), ts_site_1))
# act 1: just one time-series put into the system, should give same ts (true-averaged) in all the grid-1km_ts (which can be improved using std.gradient..)
grid_1km_1ts = api.bayesian_kriging_temperature(observation_sites, grid_1km_1, ta_grid, btk_parameter)
# assert 1:
self.assertEqual(len(grid_1km_1ts), self.mnx * self.mny)
expected_grid_1ts_values = ts_site_1.average(api.Timeaxis2(ta_grid)).values.to_numpy()
for gts in grid_1km_1ts:
self.assertEqual(gts.ts.size(), ta_grid.size())
self.assertTrue(np.allclose(expected_grid_1ts_values, gts.ts.values.to_numpy()))
observation_sites.append(api.TemperatureSource(api.GeoPoint(9000.0, 500.0, 500), ts_site_2))
observation_sites.append(api.TemperatureSource(api.GeoPoint(9000.0, 12000.0, 1050.0), ts_site_3))
grid_1km_3ts = api.bayesian_kriging_temperature(observation_sites, grid_1km_3, ta_grid, btk_parameter)
self.assertEqual(len(grid_1km_3ts), self.mnx * self.mny)
for gts in grid_1km_3ts:
self.assertEqual(gts.ts.size(), ta_grid.size())
self.assertFalse(np.allclose(expected_grid_1ts_values, gts.ts.values.to_numpy()))
def test_can_run_bayesian_kriging_from_arome25_to_1km(self):
"""
Verify that if we run btk interpolation, we do get updated time-series according to time-axis and range
specified.
"""
# arrange the test with a btk_parameter, a source grid and a destination grid
btk_parameter = api.BTKParameter(temperature_gradient=-0.6, temperature_gradient_sd=0.25, sill=25.0, nugget=0.5, range=20000.0, zscale=20.0)
fx = lambda z: api.DoubleVector.from_numpy((20.0 - 0.6 * z / 100) + 3.0 * np.sin(np.arange(start=0, stop=self.n, step=1) * 2 * np.pi / 24.0 - np.pi / 2.0))
arome_grid = self._create_geo_ts_grid(self.nx, self.ny, self.dx_arome, fx)
destination_grid = self._create_geo_point_grid(self.mnx, self.mny, self.dx_model)
ta = api.Timeaxis(self.t, self.d * 3, int(self.n / 3))
# act, - run the bayesian_kriging_temperature algoritm.
r = api.bayesian_kriging_temperature(arome_grid, destination_grid, ta, btk_parameter)
# assert
self.assertIsNotNone(r)
self.assertEqual(len(r), self.mnx * self.mny)
for gts in r: # do some sanity checks for the btk. Note that full-range checking is already done elsewhere
self.assertEqual(gts.ts.size(), ta.size())
self.assertLess(np.max(gts.ts.values.to_numpy()), 23.0) # all values less than ~max
self.assertGreater(np.min(gts.ts.values.to_numpy()), 7.0) # all values larger than ~ min
