def test_update_until():
model = BmiHeat()
model.initialize()
model.update_until(10.1)
assert_almost_equal(model.get_current_time(), 10.1)
h.timestep = seconds_per_day
# run the model forward in time forced by the surface temperature.
while h.get_current_time() < duration_years * seconds_per_year:
# calculate the time to run until.
run_until = min([h.get_current_time() + seconds_per_year,
duration_years*seconds_per_year])
# determine the current surface temperature
current_time = h.get_current_time()/seconds_per_year
current_temperature_change = surface_temperature_change(current_time)
# set the surface temperature in the model.
h.set_value_at_indices("temperature",
[0],
T_init[0] + current_temperature_change)
# run forward in time.
h.update_until(run_until)
#########################################
# #
# Step 3: Write Output in format #
# Dakota expects #
# #
#########################################
# Each of the metrics listed in the Dakota .in file needs to be written to
# the specified output file given by sys.argv[2]. This is how information is
# sent back to Dakota.
# Calculate the root mean squared error (rmse)
interp_T = np.interp(df.Depth.values, model_z, h.get_value("temperature"))
rmse = (np.mean((interp_T - df.Temperature.values) ** 2)) ** 0.5