def _prepare_AR_surrogates(a):
from var_model import VARModel
i, order_range, crit, ts = a
if not np.any(np.isnan(ts)):
v = VARModel()
v.estimate(ts, order_range, True, crit, None)
r = v.compute_residuals(ts)
else:
v = None
r = np.nan
return (i, v, r)
def _prepare_AR_surrogates(a):
from var_model import VARModel
i, order_range, crit, ts = a
if not np.any(np.isnan(ts)):
v = VARModel()
v.estimate(ts, order_range, True, crit, None)
r = v.compute_residuals(ts)
else:
v = None
r = np.nan
return (i, v, r)
def _get_MC_realizations(self, n=100, multivariate=False, residuals=True):
"""
Gets n surrogates for Monte Carlo testing.
If multivariate True, extimates AR(1) model for whole data, if False, treats as univariate
and estimates each channel separately.
If residuals True, generates AR model using actual residuals from fitting, if False,
only uses model matrix A.
"""
from var_model import VARModel
self.MCsurrs = np.zeros([n] + list(self.X.shape))
# multivariate model
if multivariate:
v = VARModel()
v.estimate(self.X, [1, 1], True, 'sbc', None)
if residuals:
r = v.compute_residuals(self.X)
# univariate model - estimating for each channel separately
else:
vs = {}
for d in range(self.X.shape[1]):
vs[d] = VARModel()
vs[d].estimate(self.X[:, d], [1, 1], True, 'sbc', None)
if residuals:
vs['res' + str(d)] = vs[d].compute_residuals(self.X[:, d])
for i in range(n):
if multivariate:
if not residuals:
self.MCsurrs[i, ...] = v.simulate(N=self.X.shape[0])
else:
self.MCsurrs[i, ...] = v.simulate_with_residuals(
r, orig_length=True)
else:
for d in range(self.X.shape[1]):
if not residuals:
self.MCsurrs[i, :, d] = np.squeeze(
vs[d].simulate(N=self.X.shape[0]))
else:
self.MCsurrs[i, :, d] = np.squeeze(
vs[d].simulate_with_residuals(vs['res' + str(d)],
orig_length=True))
def _get_MC_realizations(self, n = 100, multivariate = False, residuals = True):
"""
Gets n surrogates for Monte Carlo testing.
If multivariate True, extimates AR(1) model for whole data, if False, treats as univariate
and estimates each channel separately.
If residuals True, generates AR model using actual residuals from fitting, if False,
only uses model matrix A.
"""
from var_model import VARModel
self.MCsurrs = np.zeros([n] + list(self.X.shape))
# multivariate model
if multivariate:
v = VARModel()
v.estimate(self.X, [1,1], True, 'sbc', None)
if residuals:
r = v.compute_residuals(self.X)
# univariate model - estimating for each channel separately
else:
vs = {}
for d in range(self.X.shape[1]):
vs[d] = VARModel()
vs[d].estimate(self.X[:, d], [1,1], True, 'sbc', None)
if residuals:
vs['res' + str(d)] = vs[d].compute_residuals(self.X[:, d])
for i in range(n):
if multivariate:
if not residuals:
self.MCsurrs[i, ...] = v.simulate(N = self.X.shape[0])
else:
self.MCsurrs[i, ...] = v.simulate_with_residuals(r, orig_length = True)
else:
for d in range(self.X.shape[1]):
if not residuals:
self.MCsurrs[i, :, d] = np.squeeze(vs[d].simulate(N = self.X.shape[0]))
else:
self.MCsurrs[i, :, d] = np.squeeze(vs[d].simulate_with_residuals(vs['res' + str(d)], orig_length = True))
def run_parallel_sims():
ts = read_data2()
print("Fitting VAR model to data")
v = VARModel()
v.estimate(ts[:,0], [1, 30], True, 'sbc')
res = v.compute_residuals(ts[:, 0])
# cProfile.run('simulate_model((v, res))')
print("Running simulations")
t1 = datetime.now()
# simulate 10000 time series (one surrogate)
p = Pool(4)
# sim_ts_all = p.map(ident_model, [ts[:,0]] * 10000)
sim_ts_all = p.map(simulate_model, [(v, res)] * 100)
delta = datetime.now() - t1
print("DONE after %s" % (str(delta)))
def run_parallel_sims():
ts = read_data2()
print("Fitting VAR model to data")
v = VARModel()
v.estimate(ts[:, 0], [1, 30], True, 'sbc')
res = v.compute_residuals(ts[:, 0])
# cProfile.run('simulate_model((v, res))')
print("Running simulations")
t1 = datetime.now()
# simulate 10000 time series (one surrogate)
p = Pool(4)
# sim_ts_all = p.map(ident_model, [ts[:,0]] * 10000)
sim_ts_all = p.map(simulate_model, [(v, res)] * 100)
delta = datetime.now() - t1
print("DONE after %s" % (str(delta)))
def ident_model(ts):
v2 = VARModel()
v2.estimate(ts, [1, 30], True, 'sbc', None)
return v2.order()
def ident_model(ts):
v2 = VARModel()
v2.estimate(ts, [1, 30], True, 'sbc', None)
return v2.order()
def _prepare_surrogates(a):
i, j, order_range, crit, ts = a
v = VARModel()
v.estimate(ts, order_range, True, crit, None)
r = v.compute_residuals(ts)
return (i, j, v, r)
def _prepare_surrogates(a):
i, j, order_range, crit, ts = a
v = VARModel()
v.estimate(ts, order_range, True, crit, None)
r = v.compute_residuals(ts)
return (i, j, v, r)