def run_model(self, dt=1., t_end=1000., t_spinup=48.):
""" Run the current model with a simple Euler marching algorithm
Parameters
----------
dt : float
Timestep, in hours
t_end : float
Cut-off time in hours to end integration/marching
t_spinup : float
Time in hours after which productivity will be scaled by
daily averages of nutrient availability
Returns
-------
result : DataFrame
A DataFrame with the columns V, S, N, O corresponding to the
components of the model state vector, indexed along time in hours.
S, N, O are in kg/m3 and mmol/m3, and V is % of initial volume
"""
# Initialize output as an array
out_y = vstack([self.y0, ])
ts = [0., ]
# Main integration loop
i, t = 1, 0.
while t < t_end:
# Pop last state off of stack
y = out_y[-1].T
# If we're past spin-up, then average the N concentration over
# the last 24 hours to scale productivity
if t > t_spinup:
n_24hrs = int(ceil(24./dt))
P_scale = \
mean(out_y[-n_24hrs:, 2]/out_y[-n_24hrs:, 0])/self.N_ocean
else:
P_scale = 1.
# Euler step
t += dt
new_y = y + dt*self.estuary_ode(y, t, P_scale)
# Correct non-physical V, S, N, or O (where they're < 0)
new_y[new_y < 0] = 0.
# Save output onto stack
out_y = vstack([out_y, new_y])
ts.append(t)
i += 1
# Shape output into DataFrame
out = out_y[:]
ts = array(ts)
result = DataFrame(data=out, columns=['V', 'S', 'N', 'O'],
dtype=float,
index=Index(ts, name='time'))
# Convert to molar concentrations
result.S /= result.V
result.N /= result.V
result.O /= result.V
# Add tidal height (meters) to output
result['Z'] = result.V/self.estuary_area
# Convert volume to percentage relative to initial
result.V = 100*(result.V - self.V)/self.V
return result
