def plot_results(ptgsk, q_obs=None):
h_obs = None
if ptgsk is not None:
plt.subplot(3, 1, 1)
discharge = ptgsk.region_model.statistics.discharge([0])
temp = ptgsk.region_model.statistics.temperature([0])
precip = ptgsk.region_model.statistics.precipitation([0])
# Results on same time axis, so we only need one
times = utc_to_greg([discharge.time(i) for i in range(discharge.size())])
plt.plot(times, np.array(discharge.v))
plt.gca().set_xlim(times[0], times[-1])
plt.ylabel(r"Discharge in $\mathbf{m^3s^{-1}}$")
set_calendar_formatter(Calendar())
if q_obs is not None:
obs_times = utc_to_greg([q_obs.time(i) for i in range(q_obs.size())])
ovs = [q_obs.value(i) for i in range(q_obs.size())]
h_obs, = plt.plot(obs_times, ovs, linewidth=2, color='k')
ax = plt.gca()
ax.set_xlim(obs_times[0], obs_times[-1])
if ptgsk is not None:
plt.subplot(3, 1, 2)
plt.plot(times, np.array(temp.v))
set_calendar_formatter(Calendar())
plt.gca().set_xlim(times[0], times[-1])
plt.ylabel(r"Temperature in C")
plt.subplot(3, 1, 3)
plt.plot(times, np.array(precip.v))
set_calendar_formatter(Calendar())
plt.gca().set_xlim(times[0], times[-1])
plt.ylabel(r"Precipitation in mm")
return h_obs
def plot_results(ptgsk, q_obs=None):
h_obs = None
if ptgsk is not None:
plt.subplot(3, 1, 1)
discharge = ptgsk.region_model.statistics.discharge([0])
temp = ptgsk.region_model.statistics.temperature([0])
precip = ptgsk.region_model.statistics.precipitation([0])
# Results on same time axis, so we only need one
times = utc_to_greg(
[discharge.time(i) for i in range(discharge.size())])
plt.plot(times, np.array(discharge.v))
plt.gca().set_xlim(times[0], times[-1])
plt.ylabel(r"Discharge in $\mathbf{m^3s^{-1}}$")
set_calendar_formatter(Calendar())
if q_obs is not None:
obs_times = utc_to_greg([q_obs.time(i) for i in range(q_obs.size())])
ovs = [q_obs.value(i) for i in range(q_obs.size())]
h_obs, = plt.plot(obs_times, ovs, linewidth=2, color='k')
ax = plt.gca()
ax.set_xlim(obs_times[0], obs_times[-1])
if ptgsk is not None:
plt.subplot(3, 1, 2)
plt.plot(times, np.array(temp.v))
set_calendar_formatter(Calendar())
plt.gca().set_xlim(times[0], times[-1])
plt.ylabel(r"Temperature in C")
plt.subplot(3, 1, 3)
plt.plot(times, np.array(precip.v))
set_calendar_formatter(Calendar())
plt.gca().set_xlim(times[0], times[-1])
plt.ylabel(r"Precipitation in mm")
return h_obs
def plot_percentiles(sim, percentiles, obs=None):
discharges = [s.region_model.statistics.discharge([0]) for s in sim]
times = utc_to_greg(np.array([discharges[0].time(i) for i in range(discharges[0].size())], dtype='d'))
all_discharges = np.array([d.v for d in discharges])
perc_arrs = [a for a in np.percentile(all_discharges, percentiles, 0)]
h, fill_handles = plot_np_percentiles(times, perc_arrs, base_color=(51/256, 102/256, 193/256))
percentile_texts = ["{} - {}".format(percentiles[i], percentiles[-(i + 1)]) for i in range(len(percentiles)//2)]
ax = plt.gca()
maj_loc = AutoDateLocator(tz=pytz.UTC, interval_multiples=True)
ax.xaxis.set_major_locator(maj_loc)
set_calendar_formatter(Calendar())
if len(percentiles) % 2:
fill_handles.append(h[0])
percentile_texts.append("{}".format(percentiles[len(percentiles)//2]))
if obs is not None:
h_obs = plot_results(None, obs)
fill_handles.append(h_obs)
percentile_texts.append("Observed")
ax.legend(fill_handles, percentile_texts)
ax.grid(b=True, color=(51/256, 102/256, 193/256), linewidth=0.1, linestyle='-', axis='y')
plt.xlabel("Time in UTC")
plt.ylabel(r"Discharge in $\mathbf{m^3s^{-1}}$", verticalalignment="top", rotation="horizontal")
ax.yaxis.set_label_coords(0, 1.1)
return h, ax
def plot_percentiles(sim, percentiles, obs=None):
discharges = [s.region_model.statistics.discharge([0]) for s in sim]
times = utc_to_greg(np.array([discharges[0].time(i) for i in range(discharges[0].size())], dtype='d'))
all_discharges = np.array([d.v for d in discharges])
perc_arrs = [a for a in np.percentile(all_discharges, percentiles, 0)]
h, fill_handles = plot_np_percentiles(times, perc_arrs, base_color=(51/256, 102/256, 193/256))
percentile_texts = ["{} - {}".format(percentiles[i], percentiles[-(i + 1)]) for i in range(len(percentiles)//2)]
ax = plt.gca()
maj_loc = AutoDateLocator(tz=pytz.UTC, interval_multiples=True)
ax.xaxis.set_major_locator(maj_loc)
set_calendar_formatter(Calendar())
if len(percentiles) % 2:
fill_handles.append(h[0])
percentile_texts.append("{}".format(percentiles[len(percentiles)//2]))
if obs is not None:
h_obs = plot_results(None, obs)
fill_handles.append(h_obs)
percentile_texts.append("Observed")
ax.legend(fill_handles, percentile_texts)
ax.grid(b=True, color=(51/256, 102/256, 193/256), linewidth=0.1, linestyle='-', axis='y')
plt.xlabel("Time in UTC")
plt.ylabel(r"Discharge in $\mathbf{m^3s^{-1}}$", verticalalignment="top", rotation="horizontal")
ax.yaxis.set_label_coords(0, 1.1)
return h, ax
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 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 = TimeAxisFixedDeltaT(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(TimeAxisFixedDeltaT(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(TimeAxisFixedDeltaT(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(TimeAxisFixedDeltaT(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 plot_results(ptxsk, q_obs=None):
h_obs = None
n_temp = 1
temp=[]
precip = None
discharge = None
plt.figure() # to start a new plot window
if ptxsk is not None:
plt.subplot(8, 1, 1) # dimension 8 rows of plots
discharge = ptxsk.region_model.statistics.discharge([]) # get the sum discharge all areas
n_temp=ptxsk.region_model.size()
temp = [ptxsk.region_model.statistics.temperature([i]) for i in range(n_temp)]
precip = [ptxsk.region_model.statistics.precipitation([i]) for i in range(n_temp)]
radiation = [ptxsk.region_model.statistics.radiation([i]) for i in range(n_temp)]
rel_hum = [ptxsk.region_model.statistics.rel_hum([i]) for i in range(n_temp)]
wind_speed = [ptxsk.region_model.statistics.wind_speed([i]) for i in range(n_temp)]
snow_sca = [ptxsk.region_model.hbv_snow_state.sca([i]) for i in range(n_temp)]
snow_swe = [ptxsk.region_model.hbv_snow_state.swe([i]) for i in range(n_temp)]
# Results on same time axis, so we only need one
times = utc_to_greg([discharge.time(i) for i in range(discharge.size())])
plt.plot(times, np.array(discharge.v),color='blue')
ax=plt.gca()
ax.set_xlim(times[0], times[-1])
plt.ylabel(r"Discharge in $\mathbf{m^3/s}$")
set_calendar_formatter(Calendar())
if q_obs is not None:
obs_times = utc_to_greg([q_obs.time(i) for i in range(q_obs.size())])
ovs = [q_obs.value(i) for i in range(q_obs.size())]
h_obs, = plt.plot(obs_times, ovs, linewidth=2,color='green')
if ptxsk is not None:
plt.subplot(8, 1, 2,sharex=ax)
for i in range(n_temp):
plt.plot(times, np.array(temp[i].v))
#set_calendar_formatter(Calendar())
plt.ylabel(r"Temperature in C")
plt.subplot(8, 1, 3,sharex=ax)
for i in range(n_temp):
plt.plot(times, np.array(precip[i].v))
plt.ylabel(r"Precipitation in mm")
plt.subplot(8,1,4,sharex=ax)
for i in range(n_temp):
plt.plot(times,np.array(radiation[i].v))
plt.ylabel(r"Radiation w/m2")
plt.subplot(8, 1, 5,sharex=ax)
for i in range(n_temp):
plt.plot(times, np.array(rel_hum[i].v))
plt.ylabel(r"Rel.hum %")
plt.subplot(8, 1, 6,sharex=ax)
for i in range(n_temp):
plt.plot(times, np.array(wind_speed[i].v))
plt.ylabel(r"Wind speed m/s")
plt.subplot(8, 1, 7,sharex=ax)
for i in range(n_temp):
plt.plot(times, np.asarray(snow_swe[i].v)[:-1])
plt.ylabel(r"SWE mm")
plt.subplot(8, 1, 8,sharex=ax)
for i in range(n_temp):
plt.plot(times, np.asarray(snow_sca[i].v)[:-1])
plt.ylabel(r"SCA %")
return h_obs
