def update_typical_vals(networks, int_times, rtol = 1e-9, fraction=1.0,
cutoff=1e-14):
"""
Update the typical var values for a group of networks.
Find the maximum of each variable over the integrations. In each network
the typical value is set to fraction of that maximum. If that maximum value
is less than cutoff, the typical value is set to 1.
networks List of networks to work with
int_times List of corresponding integration endpoints
(ie. [(0, 100), (0, 50)])
fraction Relative size of typical value, compared to max value over
the integrations.
rtol Relative tolerance for the integrations.
cutoff Values below this are considered to be zero
"""
max_vals = {}
for net, times in zip(networks, int_times):
traj = Dynamics.integrate(net, times, rtol=rtol, fill_traj=True)
for var_id in list(net.variables.keys()):
curr_max = max(traj.get_var_traj(var_id))
max_vals[var_id] = max(curr_max, max_vals.get(var_id, 0))
for var_id, val in list(max_vals.items()):
for net in networks:
if var_id in net.variables:
if val > cutoff:
net.set_var_typical_val(var_id, val*fraction)
else:
net.set_var_typical_val(var_id, 1.0)
return max_vals
def get_s_integration(net,
p=None,
Tmin=None,
Tmax=None,
k=None,
tol=None,
to_ser=False):
"""
"""
if p is not None:
net.update_optimizable_vars(p)
if Tmin is None:
Tmin = TMIN
if Tmax is None:
Tmax = TMAX
if k is None:
k = K
if tol is None:
tol = TOL_SS
nsp = len(net.dynamicVars)
tmin, tmax = 0, Tmin
x0 = net.x0.copy()
constants = net.constantVarValues
while tmax <= Tmax:
# yp0 helps integration stability
yp0 = Dynamics.find_ics(net.x0, net.x0, tmin,
net._dynamic_var_algebraic, [1e-6] * nsp,
[1e-6] * nsp, constants, net)[1]
# using daskr to save computational overhead
out = daskr.daeint(res=net.res_function,
t=[tmin, tmax],
y0=x0,
yp0=yp0,
atol=[1e-6] * nsp,
rtol=[1e-6] * nsp,
intermediate_output=False,
rpar=constants,
max_steps=100000.0,
max_timepoints=100000.0,
jac=net.ddaskr_jac)
xt = out[0][-1]
dxdt = net.res_function(tmax, xt, [0] * nsp, constants)
if np.max(np.abs(dxdt)) < tol:
net.updateVariablesFromDynamicVars(xt, tmax)
net.t = tmax
if to_ser:
return butil.Series(xt, net.xids)
else:
return xt
else:
tmin, tmax = tmax, tmax * k
x0 = xt
raise Exception("Cannot reach steady state for p=%s" % p)
def discrete_data(net, params, pts, interval, vars=None, random=False,
uncert_func=typ_val_uncert(0.1, 1e-14)):
"""
Return a set of data points for the given network generated at the given
parameters.
net Network to generate data for
params Parameters for this evaluation of the network
pts Number of data points to output
interval Integration interval
vars Variables to output data for, defaults to all species in net
random If False data points are distributed evenly over interval
If True they are spread randomly and uniformly over each
variable
uncert_func Function that takes in a trajectory and a variable id and
returns what uncertainty should be assumed for that variable,
either as a scalar or a list the same length as the trajectory.
"""
# Default for vars
if vars is None:
vars = list(net.species.keys())
# Assign observed times to each variable
var_times = {}
for var in vars:
if random:
var_times[var] = np.random.rand(pts) * (interval[1]-interval[0]) + interval[0]
else:
var_times[var] = np.linspace(interval[0], interval[1], pts)
# Create a sorted list of the unique times in the var_times dict
int_times = set(np.ravel(list(var_times.values())))
int_times.add(0)
int_times = list(int_times)
int_times.sort()
# Get the trajectory
traj = Dynamics.integrate(net, int_times, params=params, fill_traj=False)
# Build up our data dictionary
data = {}
for var, times in list(var_times.items()):
var_data = {}
data[var] = var_data
# Calculate our uncertainties
var_uncerts = uncert_func(traj, var)
for time in times:
val = traj.get_var_val(var, time)
if np.isscalar(var_uncerts):
uncert = var_uncerts
else:
index = traj._get_time_index(time)
uncert = var_uncerts[index]
var_data[time] = (val, uncert)
return data
def integrate_sensitivity(net, times, p=None, x0=None, rtol=None, varids=None):
"""
:param net:
:param times: list or tuple
:param atol:
:param rtol:
:param varids:
"""
if isinstance(times, tuple):
intermediate_output = True
else:
intermediate_output = False
t0 = times[0]
if t0 == 0:
_times = times
else:
_times = [0] + list(times)
if p is not None:
net.p = p
if x0 is None:
x0 = net.x0
if rtol is None:
rtol = RTOL
if not hasattr(net, 'res_function'):
net.compile()
out = Dynamics.integrate_sensitivity(net,
_times,
rtol=[rtol] * net.xdim,
fill_traj=intermediate_output)
if varids is not None:
if set(varids) < set(net.varids):
varids = itertools.product(varids, net.pids)
out = out.copy_subset(varids)
traj = out.values
times = out.timepoints
varids = out.key_column.keys()
if t0 != 0:
idx_t0 = list(times).index(t0)
times = times[idx_t0:]
traj = traj[idx_t0:]
traj = Trajectory(traj, index=pd.Index(times, name='time'), columns=varids)
return traj
def make_sens_traj(calcobject,params,times,senstrajfilename):
""" Make the sensitivity trajectory for the calculation
calcoject (same as in setup(...) above).
params: parameters as a KeyedList, sensitivity traj is
calculated at these parameters (should be same as in paramfile
in setup(...) above)
times: the timepoints in the sensitivity trajectory (1-d array)
senstrajfilename: the file to save the sensitivity trajectory to
Note that if times is very finely spaced, the
sensitivity trajectory will need a lot of storage space """
senstraj = Dynamics.integrate_sensitivity(calcobject, times, params, 1.0e-6)
save(senstraj,senstrajfilename)
def few_ensemble_trajs(net, times, elements):
import SloppyCell.ReactionNetworks.Dynamics as Dynamics
traj_set = []
for params in elements:
try:
traj = Dynamics.integrate(net, times, params=params,
fill_traj=False)
if not scipy.any(scipy.isnan(traj.values)):
traj_set.append(traj)
except Utility.SloppyCellException:
logger.warn('Exception in network integration on node %i.'
% my_rank)
return traj_set
def make_sens_traj(calcobject, params, times, senstrajfilename):
""" Make the sensitivity trajectory for the calculation
calcoject (same as in setup(...) above).
params: parameters as a KeyedList, sensitivity traj is
calculated at these parameters (should be same as in paramfile
in setup(...) above)
times: the timepoints in the sensitivity trajectory (1-d array)
senstrajfilename: the file to save the sensitivity trajectory to
Note that if times is very finely spaced, the
sensitivity trajectory will need a lot of storage space """
senstraj = Dynamics.integrate_sensitivity(calcobject, times, params,
1.0e-6)
save(senstraj, senstrajfilename)
def few_ensemble_trajs(net, times, elements):
import SloppyCell.ReactionNetworks.Dynamics as Dynamics
traj_set = []
for params in elements:
try:
traj = Dynamics.integrate(net, times, params=params,
fill_traj=False)
if not scipy.any(scipy.isnan(traj.values)):
traj_set.append(traj)
except Utility.SloppyCellException:
logger.warning('Exception in network integration on node %i.'
% my_rank)
return traj_set
def get_s(net,
p=None,
method=None,
Tmin=None,
Tmax=None,
k=None,
x0=None,
tol=None,
to_ser=False,
**kwargs_rootfinding):
"""
Input:
method:
Tmin, Tmax, k:
x0:
"""
if p is not None:
net.update_optimizable_vars(p)
if method is None:
method = METHOD
if Tmin is None:
Tmin = TMIN
if method == 'integration':
return get_s_integration(net,
Tmin=Tmin,
Tmax=Tmax,
k=k,
tol=tol,
to_ser=to_ser)
elif method == 'rootfinding':
return get_s_rootfinding(net,
x0=x0,
tol=tol,
to_ser=to_ser,
**kwargs_rootfinding)
elif method == 'mixed':
nsp = len(net.dynamicVars)
constants = net.constantVarValues
yp0 = Dynamics.find_ics(net.x0, net.x0, 0, net._dynamic_var_algebraic,
[1e-6] * nsp, [1e-6] * nsp, constants, net)[1]
out = daskr.daeint(res=net.res_function,
t=[0, Tmin],
y0=net.x0.copy(),
yp0=yp0,
atol=[1e-6] * nsp,
rtol=[1e-6] * nsp,
intermediate_output=False,
rpar=constants,
max_steps=100000.0,
max_timepoints=100000.0,
jac=net.ddaskr_jac)
xt = out[0][-1]
return get_s_rootfinding(net,
x0=xt,
tol=tol,
to_ser=to_ser,
**kwargs_rootfinding)
else:
raise ValueError("Unrecognized value for method: %s" % method)
def integrate(net, times, p=None, x0=None, atol=None, rtol=None, varids=None):
"""A wrapper of SloppyCell.ReactionNetworks.Dynamics.integrate, where
two previously nonintuitive behaviors are fixed here:
1) x(t0) always falsely takes the value of x(t1), where t0 and t1 are
the first and second time points in the return traj;
2) when the argument times does not start from 0, the integration goes
from 0 to tmax-tmin.
:param net:
:param times: list or tuple
:param atol:
:param rtol:
:param varids:
"""
if isinstance(times, tuple):
intermediate_output = True
else:
intermediate_output = False
t0 = times[0]
if t0 == 0:
_times = times
else:
_times = [0] + list(times)
if p is not None:
net.p = p
if x0 is None:
x0 = net.x0
if atol is None:
atol = ATOL
if rtol is None:
rtol = RTOL
if not hasattr(net, 'res_function'):
net.compile()
"""
out = daskr.daeint(res=net.res_function, t=_times,
y0=x0.copy(), yp0=[0]*net.xdim,
atol=[atol]*net.xdim, rtol=[rtol]*net.xdim,
intermediate_output=intermediate_output,
rpar=net.constants)
traj = out[0]
traj[0] = x0
times = out[1]
"""
# Use SloppyCell.ReactionNetworks.Dynamics.integrate for now as it wraps
# around SloppyCell.daskr and is somehow more stable than daskr itself
# but not much slower.
# It automatically updates net.x hence no need to manually update x.
out = Dynamics.integrate(net,
_times,
atol=[atol] * net.xdim,
rtol=[rtol] * net.xdim,
fill_traj=intermediate_output)
if varids is not None:
out = out.copy_subset(varids)
traj = out.values
times = out.timepoints
varids = out.key_column.keys()
if t0 != 0:
idx_t0 = list(times).index(t0)
times = times[idx_t0:]
traj = traj[idx_t0:]
return Trajectory(traj, index=pd.Index(times, name='time'), columns=varids)