def _predict_bias(self, obs_set, fc_set):
# Return a set of bias_ts per observation geo_point
bias_set = api.TemperatureSourceVector()
kf = api.KalmanFilter()
kbp = api.KalmanBiasPredictor(kf)
kta = api.TimeAxis(self.t0, api.deltahours(3), 8)
for obs in obs_set:
kbp.update_with_forecast(fc_set, obs.ts, kta)
pattern = api.KalmanState.get_x(kbp.state)
# a_ts = api.TimeSeries(pattern, api.deltahours(3), self.ta) # can do using ct of TimeSeries, or:
b_ts = api.create_periodic_pattern_ts(pattern, api.deltahours(3),
self.ta.time(0),
self.ta) # function
bias_set.append(api.TemperatureSource(obs.mid_point(), b_ts))
return bias_set
def test_compute_running_bias(self):
"""
Verify that if we feed forecast[n] and observation into the bias-predictor
it will create the estimated bias offsets
"""
f = api.KalmanFilter()
bp = api.KalmanBiasPredictor(f)
self.assertIsNotNone(bp)
self.assertEqual(bp.filter.parameter.n_daily_observations, 8)
n_fc = 1
utc = api.Calendar()
t0 = utc.time(2016, 1, 1)
dt = api.deltahours(1)
n_fc_steps = 24 * 10 # 10 days history
fc_dt = api.deltahours(6)
fc_fx = lambda time_axis: self._create_fc_values(
time_axis, 2.0) # just return a constant 2.0 deg C for now
n_obs = n_fc_steps
obs_ta = api.TimeAxis(t0, dt, n_obs)
obs_ts = api.TimeSeries(obs_ta,
fill_value=0.0,
point_fx=api.POINT_INSTANT_VALUE)
kalman_dt = api.deltahours(
3) # suitable average for prediction temperature
kalman_ta = api.TimeAxis(t0, kalman_dt, n_obs // 3)
fc_ts = self._create_forecast_set(n_fc, t0, dt, n_fc_steps, fc_dt,
fc_fx)[0]
bias_ts = bp.compute_running_bias(
fc_ts, obs_ts,
kalman_ta) # also verify we can feed in a pure TsVector
bias_pattern = bp.state.x # the bp.state.x is now the best estimates fo the bias between fc and observation
self.assertEqual(len(bias_pattern), 8)
for i in range(len(bias_pattern)):
self.assertLess(abs(bias_pattern[i] - 2.0),
0.2) # bias should iterate to approx 2.0 degC now.
# and...:
for i in range(8):
self.assertAlmostEqual(bias_ts.value(i),
0.0) # expect 0.0 for the first day
for i in range(8):
self.assertLess(abs(bias_ts.value(bias_ts.size() - i - 1) - 2.0),
0.2) # last part should be 2.0 deg.C
def test_filter(self):
f = api.KalmanFilter()
self.assertEqual(f.parameter.n_daily_observations, 8)
s = f.create_initial_state()
self.assertEqual(s.size(), 8)
utc = api.Calendar()
t0 = utc.time(2015, 1, 1)
dt = api.deltahours(3)
n = 8
ta = api.Timeaxis2(t0, dt, n)
for i in range(ta.size()):
f.update(2.0, ta.time(i), s)
x = s.x
self.assertEqual(len(x), 8)
self.assertEqual(len(s.k), 8)
self.assertEqual(s.P.shape[0], 8)
self.assertEqual(s.P.shape[1], 8)
self.assertEqual(s.W.shape[0], 8)
self.assertEqual(s.W.shape[1], 8)
def test_bias_predictor(self):
"""
Verify that if we feed forecast[n] and observation into the bias-predictor
it will create the estimated bias offsets
"""
f = api.KalmanFilter()
bp = api.KalmanBiasPredictor(f)
self.assertIsNotNone(bp)
self.assertEqual(bp.filter.parameter.n_daily_observations, 8)
n_fc = 8
utc = api.Calendar()
t0 = utc.time(2016, 1, 1)
dt = api.deltahours(1)
n_fc_steps = 36 # e.g. like arome 36 hours
fc_dt = api.deltahours(6)
fc_fx = lambda time_axis: self._create_fc_values(
time_axis, 2.0) # just return a constant 2.0 deg C for now
fc_set = self._create_geo_forecast_set(n_fc, t0, dt, n_fc_steps, fc_dt,
fc_fx)
n_obs = 24
obs_ta = api.TimeAxis(t0, dt, n_obs)
obs_ts = api.TimeSeries(obs_ta,
fill_value=0.0,
point_fx=api.POINT_INSTANT_VALUE)
kalman_dt = api.deltahours(
3) # suitable average for prediction temperature
kalman_ta = api.TimeAxis(t0, kalman_dt, 8)
bp.update_with_forecast(
fc_set, obs_ts, kalman_ta
) # here we feed in forecast-set and observation into kalman
fc_setv = self._create_forecast_set(n_fc, t0, dt, n_fc_steps, fc_dt,
fc_fx)
bp.update_with_forecast(
fc_setv, obs_ts,
kalman_ta) # also verify we can feed in a pure TsVector
bias_pattern = bp.state.x # the bp.state.x is now the best estimates fo the bias between fc and observation
self.assertEqual(len(bias_pattern), 8)
for i in range(len(bias_pattern)):
self.assertLess(abs(bias_pattern[i] - 2.0),
0.2) # bias should iterate to approx 2.0 degC now.