def iterate_runs(V, initial_aerosols, T, P, dt=0.01, dt_iters=2, max_steps=500):
"""
Iterate through several different strategies for integrating the parcel model.
"""
aerosols = initial_aerosols
if V <= 0:
return 0., 0., 0.
## Check that there are actually aerosols to deal with
aerosol_N = [a.distribution.N for a in initial_aerosols]
if len(aerosol_N) == 1:
if aerosol_N[0] < 0.01: return (-9999., -9999., -9999.)
else:
new_aerosols = []
for i in xrange(len(aerosol_N)):
if aerosol_N[i] > 0.01:
new_aerosols.append(initial_aerosols[i])
aerosols = new_aerosols[:]
S_max_arg, _ = arg2000(V, T, P, aerosols)
S_max_fn, _ = fn2005(V, T, P, aerosols)
dt_orig = dt*1.
finished = False
S_max = None
## Strategy 1: Try CVODE with modest tolerances.
print " Trying CVODE with default tolerance"
S_max = run_model(V, aerosols, T, P, dt, max_steps=2000, solver='cvode')
## Strategy 2: Iterate over some increasingly relaxed tolerances for LSODA.
if not S_max:
while dt > dt_orig/(2**dt_iters):
print " Trying LSODA, dt = %1.3e, max_steps = %d" % (dt, max_steps)
S_max = run_model(V, aerosols, T, P, dt, max_steps, solver='lsoda')
if not S_max:
dt = dt/2.
print " Retrying..."
else:
finished = True
break
## Strategy 3: Last ditch numerical integration with LSODE. This will likely take a
## a very long time.
if not finished and not S_max:
print " Trying LSODE"
S_max = run_model(V, aerosols, T, P, dt_orig, max_steps=1000, solver='lsode')
## Strategy 4: If all else fails return -9999.
if not S_max:
S_max = -9999.
print " failed", V, dt
return S_max, S_max_arg, S_max_fn
from lognorm import Lognorm
from parcel import AerosolSpecies
from pce_params import pce_deg1, pce_deg2, pce_deg3, pce_deg4
from activation import arg2000, fn2005
P0 = 80000. # Pressure, Pa
T0 = 283.15 # Temperature, K
S0 = -0.00 # Supersaturation. 1-RH from wv term
V = 0.7076312079 # m/s
aerosol1 = AerosolSpecies('(NH4)2SO4', Lognorm(mu=0.02494149518, sigma=1.301854178, N=2299.298741 ),
bins=200, kappa=0.4983795899)
pce1_Smax, pce1_ratio = pce_deg1(V, T0, P0, aerosol1)
pce2_Smax, pce2_ratio = pce_deg2(V, T0, P0, aerosol1)
pce3_Smax, pce3_ratio = pce_deg3(V, T0, P0, aerosol1)
pce4_Smax, pce4_ratio = pce_deg4(V, T0, P0, aerosol1)
ghan_Smax, ghan_ratio = arg2000(V, T0, P0, [aerosol1, ])
nenes_Smax, nenes_ratio = fn2005(V, T0, P0, [aerosol1, ])
def iterate_runs(V, initial_aerosols, T, P, S0=-0.0, dt=0.01, dt_iters=2,
t_end=500., max_steps=500, output='smax',
fail_easy=True):
""" Iterate through several different strategies for integrating the parcel model.
As long as `fail_easy` is set to `False`, the strategies this method implements are:
1. **CVODE** with a 10 second time limit and 2000 step limit.
2. **LSODA** with up to `dt_iters` iterations, where the timestep `dt` is
halved each time.
3. **LSODE** with coarse tolerance and the original timestep.
If these strategies all fail, the model will print a statement indicating such
and return either -9999 if `output` was 'smax', or an empty array or DataFrame
accordingly.
Parameters
----------
V, T, P : float
Updraft speed and parcel initial temperature and pressure.
S0 : float, optional, default 0.0
Initial supersaturation, as a percent. Defaults to 100% relative humidity.
initial_aerosols : array_like of :class:`AerosolSpecies`
Set of aerosol populations contained in the parcel.
dt : float
Solver timestep, in seconds.
dt_iters : int, optional, default 2
Number of times to halve `dt` when attempting **LSODA** solver.
max_steps : int, optional, default 1000
Maximum number of steps per solver iteration. Defaults to 1000; setting
excessively high could produce extremely long computation times.
t_end : float, optional, default 500.0
Model time in seconds after which the integration will stop.
solver : string, optional, default 'lsoda'
Alias of which solver to use; see :class:`Integrator` for all options.
output : string, optional, default 'smax'
Alias indicating which output format to use; see :class:`ParcelModel` for
all options.
fail_easy : boolean, optional, default `True`
If `True`, then stop after the first strategy (**CVODE**)
Returns
-------
Smax : (user-defined)
Output from parcel model simulation based on user-specified `output` argument. See
:class:`ParcelModel` for details.
"""
aerosols = initial_aerosols
if V <= 0:
return 0., 0., 0.
## Check that there are actually aerosols to deal with
aerosol_N = [a.distribution.N for a in initial_aerosols]
if len(aerosol_N) == 1:
if aerosol_N[0] < 0.01: return (-9999., -9999., -9999.)
else:
new_aerosols = []
for i in xrange(len(aerosol_N)):
if aerosol_N[i] > 0.01:
new_aerosols.append(initial_aerosols[i])
aerosols = new_aerosols[:]
S_max_arg, _, _ = arg2000(V, T, P, aerosols)
S_max_fn, _, _ = mbn2014(V, T, P, aerosols)
dt_orig = dt*1.
finished = False
S_max = None
## Strategy 1: Try CVODE with modest tolerances.
print " Trying CVODE with default tolerance"
S_max = run_model(V, aerosols, T, P, dt, S0=S0, max_steps=2000, solver='cvode',
t_end=t_end, output=output,
solver_args={'iter': 'Newton', 'time_limit': 10.0,
'linear_solver': "DENSE"})
## Strategy 2: Iterate over some increasingly relaxed tolerances for LSODA.
if not S_max and not fail_easy:
while dt > dt_orig/(2**dt_iters):
print " Trying LSODA, dt = %1.3e, max_steps = %d" % (dt, max_steps)
S_max = run_model(V, aerosols, T, P, dt, S0, max_steps, solver='lsoda',
t_end=t_end)
if not S_max:
dt /= 2.
print " Retrying..."
else:
finished = True
break
## Strategy 3: Last ditch numerical integration with LSODE. This will likely take a
## a very long time.
if not finished and not S_max and not fail_easy:
print " Trying LSODE"
S_max = run_model(V, aerosols, T, P, dt_orig, max_steps=1000, solver='lsode',
t_end=t_end, S0=S0)
## Strategy 4: If all else fails return -9999.
if not S_max:
if output == "smax":
S_max = -9999.
elif output == "arrays":
S_max = empty([0]), empty([0])
elif output == "dataframes":
S_max = DataFrame(data={"S": [nan]}), DataFrame(data={"aerosol1": [nan]})
else:
S_max = nan
print " failed", V, dt
return S_max, S_max_arg, S_max_fn
