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.Timeaxis2(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_periodic_pattern_ts(self):
c = api.Calendar()
t0 = c.time(2016, 1, 1)
dt = api.deltahours(1)
n = 240
ta = api.Timeaxis2(t0, dt, n)
pattern_values = api.DoubleVector.from_numpy(np.arange(8))
pattern_dt = api.deltahours(3)
pattern_t0 = c.time(2015,6,1)
pattern_ts = api.create_periodic_pattern_ts(pattern_values, pattern_dt, pattern_t0, ta) # this is how to create a periodic pattern ts (used in gridpp/kalman bias handling)
self.assertAlmostEqual(pattern_ts.value(0), 0.0)
self.assertAlmostEqual(pattern_ts.value(1), 0.0)
self.assertAlmostEqual(pattern_ts.value(2), 0.0)
self.assertAlmostEqual(pattern_ts.value(3), 1.0) # next step in pattern starts here
self.assertAlmostEqual(pattern_ts.value(24), 0.0) # next day repeats the pattern
def test_periodic_pattern_ts(self):
c = api.Calendar()
t0 = c.time(2016, 1, 1)
dt = api.deltahours(1)
n = 240
ta = api.Timeaxis2(t0, dt, n)
pattern_values = api.DoubleVector.from_numpy(np.arange(8))
pattern_dt = api.deltahours(3)
pattern_t0 = c.time(2015,6,1)
pattern_ts = api.create_periodic_pattern_ts(pattern_values, pattern_dt, pattern_t0, ta) # this is how to create a periodic pattern ts (used in gridpp/kalman bias handling)
self.assertAlmostEqual(pattern_ts.value(0), 0.0)
self.assertAlmostEqual(pattern_ts.value(1), 0.0)
self.assertAlmostEqual(pattern_ts.value(2), 0.0)
self.assertAlmostEqual(pattern_ts.value(3), 1.0) # next step in pattern starts here
self.assertAlmostEqual(pattern_ts.value(24), 0.0) # next day repeats the pattern
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
