def ensemble_demo():
utc = Calendar()
t_start = utc.time(YMDhms(2011, 9, 1))
t_fc_ens_start = utc.time(YMDhms(2015, 7, 26))
disp_start = utc.time(YMDhms(2015, 7, 20))
dt = deltahours(1)
n_obs = int(round((t_fc_ens_start - t_start) / dt))
n_fc_ens = 30
n_disp = int(round(t_fc_ens_start - disp_start) / dt) + n_fc_ens + 24 * 7
obs_time_axis = Timeaxis(t_start, dt, n_obs + 1)
fc_ens_time_axis = Timeaxis(t_fc_ens_start, dt, n_fc_ens)
display_time_axis = Timeaxis(disp_start, dt, n_disp)
q_obs_m3s_ts = observed_tistel_discharge(obs_time_axis.total_period())
ptgsk = create_tistel_simulator(
PTGSKOptModel, tistel.geo_ts_repository(tistel.grid_spec.epsg()))
initial_state = burn_in_state(ptgsk, t_start, utc.time(YMDhms(2012, 9, 1)),
q_obs_m3s_ts)
ptgsk.run(obs_time_axis, initial_state)
current_state = adjust_simulator_state(ptgsk, t_fc_ens_start, q_obs_m3s_ts)
q_obs_m3s_ts = observed_tistel_discharge(display_time_axis.total_period())
ens_repos = tistel.arome_ensemble_repository(tistel.grid_spec)
ptgsk_fc_ens = create_tistel_simulator(PTGSKModel, ens_repos)
sims = ptgsk_fc_ens.create_ensembles(fc_ens_time_axis, t_fc_ens_start,
current_state)
for sim in sims:
sim.simulate()
plt.hold(1)
percentiles = [10, 25, 50, 75, 90]
plot_percentiles(sims, percentiles, obs=q_obs_m3s_ts)
plt.interactive(1)
plt.show()
def ensemble_demo():
utc = Calendar()
t_start = utc.time(YMDhms(2011, 9, 1))
t_fc_ens_start = utc.time(YMDhms(2015, 7, 26))
disp_start = utc.time(YMDhms(2015, 7, 20))
dt = deltahours(1)
n_obs = int(round((t_fc_ens_start - t_start)/dt))
n_fc_ens = 30
n_disp = int(round(t_fc_ens_start - disp_start)/dt) + n_fc_ens + 24*7
obs_time_axis = Timeaxis(t_start, dt, n_obs + 1)
fc_ens_time_axis = Timeaxis(t_fc_ens_start, dt, n_fc_ens)
display_time_axis = Timeaxis(disp_start, dt, n_disp)
q_obs_m3s_ts = observed_tistel_discharge(obs_time_axis.total_period())
ptgsk = create_tistel_simulator(PTGSKOptModel, tistel.geo_ts_repository(tistel.grid_spec.epsg()))
initial_state = burn_in_state(ptgsk, t_start, utc.time(YMDhms(2012, 9, 1)), q_obs_m3s_ts)
ptgsk.run(obs_time_axis, initial_state)
current_state = adjust_simulator_state(ptgsk, t_fc_ens_start, q_obs_m3s_ts)
q_obs_m3s_ts = observed_tistel_discharge(display_time_axis.total_period())
ens_repos = tistel.arome_ensemble_repository(tistel.grid_spec)
ptgsk_fc_ens = create_tistel_simulator(PTGSKModel, ens_repos)
sims = ptgsk_fc_ens.create_ensembles(fc_ens_time_axis, t_fc_ens_start, current_state)
for sim in sims:
sim.simulate()
plt.hold(1)
percentiles = [10, 25, 50, 75, 90]
plot_percentiles(sims, percentiles, obs=q_obs_m3s_ts)
#plt.interactive(1)
plt.show()
def simple_run_demo():
"""Simple demo using HBV time-series and similar model-values
"""
# 1. Setup the time-axis for our simulation
normal_calendar = Calendar(3600) # we need UTC+1, since day-boundaries in day series in SmG is at UTC+1
t_start = normal_calendar.time(2010, 9, 1) # we start at
t_end = normal_calendar.add(t_start,Calendar.YEAR,5) # 5 years period for simulation
time_axis = Timeaxis(t_start, model_dt, normal_calendar.diff_units(t_start,t_end,model_dt))
# 2. Create the shyft model from the HBV model-repository
shyft_model = create_kjela_model(PTHSKModel, kjela.geo_ts_repository)
# 3. establish the initial state
# using the *pattern* of distribution after one year (so hbv-zone 1..10 get approximate distribution of discharge)
# *and* the observed discharge at the start time t_start
#
t_burnin = normal_calendar.add(t_start,Calendar.YEAR,1) # use one year to get distribution between hbvzones
burnin_time_axis = Timeaxis(t_start, model_dt, normal_calendar.diff_units(t_start, t_burnin, model_dt))
q_obs_m3s_ts = observed_kjela_discharge(time_axis.total_period()) # get out the observation ts
q_obs_m3s_at_t_start= q_obs_m3s_ts(t_start) # get the m3/s at t_start
initial_state = burn_in_state(shyft_model,burnin_time_axis, q_obs_m3s_at_t_start)
# 4. now run the model with the established state
# that will start out with the burn in state
shyft_model.run(time_axis, initial_state)
# 5. display results etc. goes here
plot_results(shyft_model, q_obs_m3s_ts)
plt.show()
def continuous_calibration():
utc = Calendar()
t_start = utc.time(YMDhms(2011, 9, 1))
t_fc_start = utc.time(YMDhms(2015, 10, 1))
dt = deltahours(1)
n_obs = int(round((t_fc_start - t_start)/dt))
obs_time_axis = Timeaxis(t_start, dt, n_obs + 1)
q_obs_m3s_ts = observed_tistel_discharge(obs_time_axis.total_period())
ptgsk = create_tistel_simulator(PTGSKOptModel, tistel.geo_ts_repository(tistel.grid_spec.epsg()))
initial_state = burn_in_state(ptgsk, t_start, utc.time(YMDhms(2012, 9, 1)), q_obs_m3s_ts)
num_opt_days = 30
# Step forward num_opt_days days and store the state for each day:
recal_start = t_start + deltahours(num_opt_days*24)
t = t_start
state = initial_state
opt_states = {t: state}
while t < recal_start:
ptgsk.run(Timeaxis(t, dt, 24), state)
t += deltahours(24)
state = ptgsk.reg_model_state
opt_states[t] = state
recal_stop = utc.time(YMDhms(2011, 10, 30))
recal_stop = utc.time(YMDhms(2012, 5, 30))
curr_time = recal_start
q_obs_avg = TsTransform().to_average(t_start, dt, n_obs + 1, q_obs_m3s_ts)
target_spec = TargetSpecificationPts(q_obs_avg, IntVector([0]), 1.0, KLING_GUPTA)
target_spec_vec = TargetSpecificationVector([target_spec])
i = 0
times = []
values = []
p, p_min, p_max = construct_calibration_parameters(ptgsk)
while curr_time < recal_stop:
print(i)
i += 1
opt_start = curr_time - deltahours(24*num_opt_days)
opt_state = opt_states.pop(opt_start)
p = ptgsk.region_model.get_region_parameter()
p_opt = ptgsk.optimize(Timeaxis(opt_start, dt, 24*num_opt_days), opt_state, target_spec_vec,
p, p_min, p_max, tr_stop=1.0e-5)
ptgsk.region_model.set_region_parameter(p_opt)
corr_state = adjust_simulator_state(ptgsk, curr_time, q_obs_m3s_ts)
ptgsk.run(Timeaxis(curr_time, dt, 24), corr_state)
curr_time += deltahours(24)
opt_states[curr_time] = ptgsk.reg_model_state
discharge = ptgsk.region_model.statistics.discharge([0])
times.extend(discharge.time(i) for i in range(discharge.size()))
values.extend(list(np.array(discharge.v)))
plt.plot(utc_to_greg(times), values)
plot_results(None, q_obs=observed_tistel_discharge(UtcPeriod(recal_start, recal_stop)))
set_calendar_formatter(Calendar())
#plt.interactive(1)
plt.title("Continuously recalibrated discharge vs observed")
plt.xlabel("Time in UTC")
plt.ylabel(r"Discharge in $\mathbf{m^3s^{-1}}$", verticalalignment="top", rotation="horizontal")
plt.gca().yaxis.set_label_coords(0, 1.1)
def forecast_demo():
"""Simple forecast demo using arome data from met.no. Initial state
is bootstrapped by simulating one hydrological year (starting
Sept 1. 2011), and then calculating the state August 31. 2012. This
state is then used as initial state for simulating Sept 1, 2011,
after scaling with observed discharge. The validity of this approach
is limited by the temporal variation of the spatial distribution of
the discharge state, q, in the Kirchner method. The model is then
stepped forward until Oct 1, 2015, and then used to compute the
discharge for 65 hours using Arome data. At last, the results
are plotted as simple timeseries.
"""
utc = Calendar()
t_start = utc.time(YMDhms(2011, 9, 1))
t_fc_start = utc.time(YMDhms(2015, 10, 1))
dt = deltahours(1)
n_obs = int(round((t_fc_start - t_start) / dt))
n_fc = 65
obs_time_axis = Timeaxis(t_start, dt, n_obs)
fc_time_axis = Timeaxis(t_fc_start, dt, n_fc)
total_time_axis = Timeaxis(t_start, dt, n_obs + n_fc)
q_obs_m3s_ts = observed_tistel_discharge(total_time_axis.total_period())
ptgsk = create_tistel_simulator(
PTGSKOptModel, tistel.geo_ts_repository(tistel.grid_spec.epsg()))
initial_state = burn_in_state(ptgsk, t_start, utc.time(YMDhms(2012, 9, 1)),
q_obs_m3s_ts)
ptgsk.run(obs_time_axis, initial_state)
plot_results(ptgsk, q_obs_m3s_ts)
current_state = adjust_simulator_state(ptgsk, t_fc_start, q_obs_m3s_ts)
ptgsk_fc = create_tistel_simulator(
PTGSKModel, tistel.arome_repository(tistel.grid_spec, t_fc_start))
ptgsk_fc.run(fc_time_axis, current_state)
plt.figure()
q_obs_m3s_ts = observed_tistel_discharge(fc_time_axis.total_period())
plot_results(ptgsk_fc, q_obs_m3s_ts)
plt.interactive(1)
plt.show()
def forecast_demo():
"""Simple forecast demo using arome data from met.no. Initial state
is bootstrapped by simulating one hydrological year (starting
Sept 1. 2011), and then calculating the state August 31. 2012. This
state is then used as initial state for simulating Sept 1, 2011,
after scaling with observed discharge. The validity of this approach
is limited by the temporal variation of the spatial distribution of
the discharge state, q, in the Kirchner method. The model is then
stepped forward until Oct 1, 2015, and then used to compute the
discharge for 65 hours using Arome data. At last, the results
are plotted as simple timeseries.
"""
utc = Calendar()
t_start = utc.time(YMDhms(2011, 9, 1))
t_fc_start = utc.time(YMDhms(2015, 10, 1))
dt = deltahours(1)
n_obs = int(round((t_fc_start - t_start)/dt))
n_fc = 65
obs_time_axis = Timeaxis(t_start, dt, n_obs)
fc_time_axis = Timeaxis(t_fc_start, dt, n_fc)
total_time_axis = Timeaxis(t_start, dt, n_obs + n_fc)
q_obs_m3s_ts = observed_tistel_discharge(total_time_axis.total_period())
ptgsk = create_tistel_simulator(PTGSKOptModel, tistel.geo_ts_repository(tistel.grid_spec.epsg()))
initial_state = burn_in_state(ptgsk, t_start, utc.time(YMDhms(2012, 9, 1)), q_obs_m3s_ts)
ptgsk.run(obs_time_axis, initial_state)
plot_results(ptgsk, q_obs_m3s_ts)
current_state = adjust_simulator_state(ptgsk, t_fc_start, q_obs_m3s_ts)
ptgsk_fc = create_tistel_simulator(PTGSKModel, tistel.arome_repository(tistel.grid_spec, t_fc_start))
ptgsk_fc.run(fc_time_axis, current_state)
plt.figure()
q_obs_m3s_ts = observed_tistel_discharge(fc_time_axis.total_period())
plot_results(ptgsk_fc, q_obs_m3s_ts)
#plt.interactive(1)
plt.show()
def continuous_calibration():
utc = Calendar()
t_start = utc.time(YMDhms(2011, 9, 1))
t_fc_start = utc.time(YMDhms(2015, 10, 1))
dt = deltahours(1)
n_obs = int(round((t_fc_start - t_start) / dt))
obs_time_axis = Timeaxis(t_start, dt, n_obs + 1)
q_obs_m3s_ts = observed_tistel_discharge(obs_time_axis.total_period())
ptgsk = create_tistel_simulator(
PTGSKOptModel, tistel.geo_ts_repository(tistel.grid_spec.epsg()))
initial_state = burn_in_state(ptgsk, t_start, utc.time(YMDhms(2012, 9, 1)),
q_obs_m3s_ts)
num_opt_days = 30
# Step forward num_opt_days days and store the state for each day:
recal_start = t_start + deltahours(num_opt_days * 24)
t = t_start
state = initial_state
opt_states = {t: state}
while t < recal_start:
ptgsk.run(Timeaxis(t, dt, 24), state)
t += deltahours(24)
state = ptgsk.reg_model_state
opt_states[t] = state
recal_stop = utc.time(YMDhms(2011, 10, 30))
recal_stop = utc.time(YMDhms(2012, 5, 30))
curr_time = recal_start
q_obs_avg = TsTransform().to_average(t_start, dt, n_obs + 1, q_obs_m3s_ts)
target_spec = TargetSpecificationPts(q_obs_avg, IntVector([0]), 1.0,
KLING_GUPTA)
target_spec_vec = TargetSpecificationVector([target_spec])
i = 0
times = []
values = []
p, p_min, p_max = construct_calibration_parameters(ptgsk)
while curr_time < recal_stop:
print(i)
i += 1
opt_start = curr_time - deltahours(24 * num_opt_days)
opt_state = opt_states.pop(opt_start)
p = ptgsk.region_model.get_region_parameter()
p_opt = ptgsk.optimize(Timeaxis(opt_start, dt, 24 * num_opt_days),
opt_state,
target_spec_vec,
p,
p_min,
p_max,
tr_stop=1.0e-5)
ptgsk.region_model.set_region_parameter(p_opt)
corr_state = adjust_simulator_state(ptgsk, curr_time, q_obs_m3s_ts)
ptgsk.run(Timeaxis(curr_time, dt, 24), corr_state)
curr_time += deltahours(24)
opt_states[curr_time] = ptgsk.reg_model_state
discharge = ptgsk.region_model.statistics.discharge([0])
times.extend(discharge.time(i) for i in range(discharge.size()))
values.extend(list(np.array(discharge.v)))
plt.plot(utc_to_greg(times), values)
plot_results(None,
q_obs=observed_tistel_discharge(
UtcPeriod(recal_start, recal_stop)))
set_calendar_formatter(Calendar())
plt.interactive(1)
plt.title("Continuously recalibrated discharge vs observed")
plt.xlabel("Time in UTC")
plt.ylabel(r"Discharge in $\mathbf{m^3s^{-1}}$",
verticalalignment="top",
rotation="horizontal")
plt.gca().yaxis.set_label_coords(0, 1.1)
