def _embed_solo(t, x, ws, ss):
"""Embed the time series for each window"""
n = len(t)
nw = int(np.floor(float(n - ws) / float(ss)))
tm = np.empty(nw, dtype="object")
m, tau = [np.zeros(nw, dtype="int") for i in range(2)]
xw = np.zeros((nw, ws), dtype="float")
maxlag = 150
maxdim = 10
R = 0.025
pb = _progressbar_start(max_value=nw, pbar_on=args.verbose)
for i in range(nw):
start = i * ss
end = start + ws
x_ = x[start:end]
xw[i] = x_
# get mi
mi, mi_lags = rc.mi(x_, maxlag, pbar_on=False)
mi_filt, _ = utils.boxfilter(mi, filter_width=3, estimate="mean")
try:
tau[i] = rc.first_minimum(mi_filt)
except ValueError:
tau[i] = 1
# FNN
fnn, dims = rc.fnn(x_, tau[i], maxdim=maxdim, r=R, pbar_on=False)
m[i] = dims[rc.first_zero(fnn)]
tm[i] = t[start] + (t[end] - t[start]) / 2
_progressbar_update(pb, i)
_progressbar_finish(pb)
return tm, xw, m, tau
def fnn(x, tau, maxdim, r=0.10, pbar_on=True):
"""
Returns the number of false nearest neighbours up to max dimension.
"""
# initialize params
sd = x.std()
r = r * (x.max() - x.min())
e = sd / r
fnn = np.zeros(maxdim)
dims = np.arange(1, maxdim + 1, dtype="int")
# ensure that (m-1) tau is not greater than N = length(x)
N = len(x)
K = (maxdim + 1 - 1) * tau
if K >= N:
m_c = N / tau
i = np.where(dims >= m_c)
fnn[i] = np.nan
j = np.where(dims < m_c)
dims = dims[j]
# get first values of distances for m = 1
d_m, k_m = mindist(x, 1, tau)
# loop over dimensions and get FNN values
pb = _progressbar_start(max_value=maxdim + 1, pbar_on=pbar_on)
for m in dims:
# get minimum distances for one dimension higher
d_m1, k_m1 = mindist(x, m + 1, tau)
# remove those indices in the m-dimensional calculations which cannot
# occur in the m+1-dimensional arrays as the m+1-dimensional arrays are
# smaller
cond1 = k_m[1] > k_m1[0][-1]
cond2 = k_m[0] > k_m1[0][-1]
j = np.where(~(cond1 + cond2))[0]
k_m_ = (k_m[0][j], k_m[1][j])
d_k_m, d_k_m1 = d_m[k_m_], d_m1[k_m_]
n_m1 = d_k_m.shape[0]
# calculate quantities in Eq. 3.8 of Kantz, Schreiber (2004) 2nd Ed.
j = d_k_m > 0.
y = np.zeros(n_m1, dtype="float")
y[j] = (d_k_m1[j] / d_k_m[j] > e) # should be r instead of e = sd / r
w = (e > d_k_m)
num = float((y * w).sum())
den = float(w.sum())
# assign FNN value depending on whether denominator is zero
if den != 0.:
fnn[m - 1] = num / den
else:
fnn[m - 1] = np.nan
# assign higher dimensional values to current one before next iteration
d_m, k_m = d_m1, k_m1
_progressbar_update(pb, m)
_progressbar_finish(pb)
return fnn, dims
def mi(x, maxlag, binrule="fd", pbar_on=True):
"""
Returns the self mutual information of a time series up to max. lag.
"""
# initialize variables
n = len(x)
lags = np.arange(0, maxlag, dtype="int")
mi = np.zeros(len(lags))
# loop over lags and get MI
pb = _progressbar_start(max_value=maxlag, pbar_on=pbar_on)
for i, lag in enumerate(lags):
# extract lagged data
y1 = x[:n - lag].copy()
y2 = x[lag:].copy()
# use np.histogram to get individual entropies
H1, be1 = entropy1d(y1, binrule)
H2, be2 = entropy1d(y2, binrule)
H12, _, _ = entropy2d(y1, y2, [be1, be2])
# use the entropies to estimate MI
mi[i] = H1 + H2 - H12
_progressbar_update(pb, i)
_progressbar_finish(pb)
return mi, lags
def _embed_pair(t, x, y, ws, ss):
"""Determines common embedding parameters for both time series"""
n = len(t)
nw = int(np.floor(float(n - ws) / float(ss)))
tm = np.empty(nw, dtype="object")
m, tau = [np.zeros(nw, dtype="int") for i in range(2)]
xw, yw = [np.zeros((nw, ws), dtype="float") for i in range(2)]
maxlag = 150
maxdim = 10
R = 0.025
pb = _progressbar_start(max_value=nw, pbar_on=args.verbose)
for i in range(nw):
start = i * ss
end = start + ws
x_ = x[start:end]
y_ = y[start:end]
xw[i] = x_
yw[i] = y_
# get mi
mi1, mi_lags1 = rc.mi(x_, maxlag, pbar_on=False)
mi_filt1, _ = utils.boxfilter(mi1, filter_width=3, estimate="mean")
tau1 = rc.first_minimum(mi_filt1)
mi2, mi_lags2 = rc.mi(y_, maxlag, pbar_on=False)
mi_filt2, _ = utils.boxfilter(mi2, filter_width=3, estimate="mean")
tau2 = rc.first_minimum(mi_filt2)
tau[i] = int(max(tau1, tau2))
# FNN
fnn1, dims1 = rc.fnn(x_, tau[i], maxdim=maxdim, r=R, pbar_on=False)
m1 = dims1[rc.first_zero(fnn1)]
fnn2, dims2 = rc.fnn(y_, tau[i], maxdim=maxdim, r=R, pbar_on=False)
m2 = dims2[rc.first_zero(fnn2)]
m[i] = int(max(m1, m2))
tm[i] = t[start] + (t[end] - t[start]) / 2
_progressbar_update(pb, i)
_progressbar_finish(pb)
return tm, xw, yw, m, tau
if np.all((yR_[1:] - yR_[:-1]) < TOL):
yR_eq_.extend(yR_)
else:
d2y = np.diff(np.sign(np.diff(yR_)))
iipos = np.where(d2y == -2.)[0] + 1
if len(iipos) > 0:
yR_eq_.extend(yR_[iipos])
iineg = np.where(d2y == 2.)[0] + 1
if len(iineg) > 0:
yR_eq_.extend(yR_[iineg])
_progressbar_update(pb, count)
count += 1
np.random.shuffle(yR_eq_)
yR_eq.append(yR_eq_[-250:])
yR_eq = np.array(yR_eq)
_progressbar_finish(pb)
# Henon
# -----
print("Henon ...")
TH = np.arange(0, 5000, 1)
aH = np.arange(0.0, 1.40001, 0.005)
nH = len(aH)
mH = 100
xH_eq = []
kH = 10
pb = _progressbar_start(max_value=nH * kH, pbar_on=True)
count = 0
for i in range(nH):
params = (aH[i], 0.30)
xH_eq_ = []
def _get_data():
"""
Estimates Lyapunov, DET, and SPL for Henon map.
"""
# Henon map time series
print("Henon map time series ...")
t = np.arange(0, 10000, 1)
a = np.linspace(1.0, 1.4, na).reshape(na, 1)
b = 0.30
nt = len(t)
x, y = [np.zeros((nt, na, ns)) for i in range(2)]
x[0, :, :] = 1E-1 * np.random.rand(na, ns)
y[0, :, :] = 1E-1 * np.random.rand(na, ns)
pb = _progressbar_start(max_value=nt, pbar_on=True)
LPV = np.zeros((na, ns))
for i in range(1, nt):
x[i, :, :] = 1. - a * x[i - 1, :, :]**2 + y[i - 1, :, :]
y[i, :, :] = b * x[i - 1, :, :]
if i >= nt / 2:
LPV[:, :] += np.log(np.fabs(-2. * a * x[i - 1, :, :]))
_progressbar_update(pb, i)
_progressbar_finish(pb)
xH_eq = x[-neq:, :, :]
LPV /= float(nt)
# estimate embedding parameters
print("embedding parameters ...")
tau, m = np.ones(na, dtype="int"), 2 * np.ones(na, dtype="int")
# DET
print("DET ...")
RR = 0.30
DET = np.zeros((na, ns))
pb = _progressbar_start(max_value=ns * na, pbar_on=True)
k = 0
for j in range(ns):
for i in range(na):
R = rc.rp(xH_eq[:, i, j],
m=m[i],
tau=tau[i],
e=RR,
norm="euclidean",
threshold_by="frr")
DET[i, j] = rqa.det(R, lmin=2, hist=None, verb=False)
del R
_progressbar_update(pb, k)
k += 1
_progressbar_finish(pb)
# SPL
print("SPL ...")
SPL = np.zeros((na, ns))
pb = _progressbar_start(max_value=ns * na, pbar_on=True)
k = 0
for j in range(ns):
for i in range(na):
A = rc.rn(xH_eq[:, i, j],
m=m[i],
tau=tau[i],
e=RR,
norm="euclidean",
threshold_by="frr")
G = ig.Graph.Adjacency(A.tolist(), mode=ig.ADJ_UNDIRECTED)
pl_hist = G.path_length_hist(directed=False)
SPL[i, j] = pl_hist.mean
del A, G
_progressbar_update(pb, k)
k += 1
_progressbar_finish(pb)
# save output
FN = DATPATH + "det_spl_lpv_na%d_ns%s_neq%d" % (na, ns, neq)
np.savez(FN, DET=DET, SPL=SPL, LPV=LPV, t=t, a=a, b=b, x=x, y=y)
print("saved to %s.npz" % FN)
return None
def surrogates(x, ns, method, params=None, verbose=False):
"""
Returns m random surrogates using the specified method.
"""
nx = len(x)
xs = np.zeros((ns, nx))
if method == "iaaft": # iAAFT
# as per the steps given in Lancaster et al., Phys. Rep (2018)
fft, ifft = np.fft.fft, np.fft.ifft
TOL = 1E-6
MSE_0 = 100
MSE_K = 1000
MAX_ITER = 10000
ii = np.arange(nx)
x_amp = np.abs(fft(x))
x_srt = np.sort(x)
pb = _progressbar_start(max_value=ns, pbar_on=verbose)
for k in range(ns):
# 1) Generate random shuffle of the data
count = 0
ri = np.random.permutation(ii)
r_prev = x[ri]
MSE_prev = MSE_0
# while not np.all(rank_prev == rank_curr) and (count < MAX_ITER):
while (np.abs(MSE_K - MSE_prev) > TOL) * (count < MAX_ITER):
MSE_prev = MSE_K
# 2) FFT current iteration yk, and then invert it but while
# replacing the amplitudes with the original amplitudes but
# keeping the angles from the FFT-ed version of the random
phi_r_prev = np.angle(fft(r_prev))
r = ifft(x_amp * np.exp(phi_r_prev * 1j), nx)
# 3) rescale zk to the original distribution of x
# rank_prev = rank_curr
ind = np.argsort(r)
r[ind] = x_srt
MSE_K = (np.abs(x_amp - np.abs(fft(r)))).mean()
r_prev = r
# repeat until rank(z_k+1) = rank(z_k)
count += 1
if count >= MAX_ITER:
print("maximum number of iterations reached!")
xs[k] = np.real(r)
_progressbar_update(pb, k)
_progressbar_finish(pb)
elif method == "twins": # twin surrogates
# 1. Estimate RP according to given parameters
R = rp(x,
m=params["m"],
tau=params["tau"],
e=params["eps"],
norm=params["norm"],
threshold_by=params["thr_by"])
# import matplotlib.pyplot as pl
# pl.imshow(R, origin="lower", cmap=pl.cm.gray_r, interpolation="none")
# pl.show()
# 2. Get embedded vectors
xe = embed(x, params["m"], params["tau"])
ne = len(xe)
assert ne == len(R), "Something is wrong!"
# 2. Identify twins
_printmsg("identify twins ...", verbose)
is_twin = []
twins = []
TOL = np.floor((params["tol"] * float(nx)) / 100.).astype("int")
pb = _progressbar_start(max_value=ne, pbar_on=verbose)
R_ = R.T
for i in range(ne):
diff = R_ == R_[i]
j = np.sum(diff, axis=1) >= (ne - TOL)
j = np.where(j)[0].tolist()
j.remove(i)
if len(j) > 0:
is_twin.append(i)
twins.append(j)
_progressbar_update(pb, i)
_progressbar_finish(pb)
# 3. Generate surrogates
_printmsg("generate surrogates ...", verbose)
all_idx = range(ne)
start_idx = np.random.choice(np.arange(ne), size=ns)
xs[:, 0] = xe[start_idx, 0]
pb = _progressbar_start(max_value=ns, pbar_on=verbose)
for i in range(ns):
j = 1
k = start_idx[i]
while j < nx:
if k not in is_twin:
k += 1
else:
twins_k = twins[is_twin.index(k)]
others = list(set(all_idx).difference(set(twins_k)))
l = np.random.choice(others)
k = np.random.choice(np.r_[l, twins_k])
if k >= ne:
k = np.random.choice(np.arange(ne), size=1)
xs[i, j] = xe[k, 0]
j += 1
_progressbar_update(pb, i)
_progressbar_finish(pb)
elif method == "shuffle": # simple random shuffling
k = np.arange(nx)
for i in range(ns):
j = np.random.permutation(k)
xs[i] = x[j]
return xs
def _get_rmd():
"""Estimates the RMD between ENSO and PDO"""
# load data
utils._printmsg("load data ...", args.verbose)
t, x_enso, x_pdo = _load_indices()
x = {
"enso": x_enso,
"pdo": x_pdo,
}
names = ["enso", "pdo"]
# recurrence plot parameters
EPS = 0.30
thrby = "frr"
# embedding parameters
utils._printmsg("embedding parameters ...", args.verbose)
n = len(t)
m, tau = {}, {}
R = {}
maxlag = 150
maxdim = 20
r_fnn = 0.0010
for name in names:
if args.verbose: print("\t for %s" % name.upper())
# get embedding parameters
## get mi
mi, mi_lags = rc.mi(x[name], maxlag, pbar_on=False)
# mi, mi_lags = rc.acf(x[name], maxlag)
mi_filt, _ = utils.boxfilter(mi, filter_width=3, estimate="mean")
try:
tau[name] = rc.first_minimum(mi_filt)
except ValueError:
tau[name] = 1
## FNN
fnn, dims = rc.fnn(x[name],
tau[name],
maxdim=maxdim,
r=r_fnn,
pbar_on=False)
m[name] = dims[rc.first_zero(fnn)]
# take the maximum delay and the maximum embedding dimension
tau = np.max([tau["enso"], tau["pdo"]]).astype("int")
m = np.max([m["enso"], m["pdo"]]).astype("int")
# get surrogates
utils._printmsg("surrogates ...", args.verbose)
ns = args.nsurr
SURR = {}
params = {
"m": m,
"tau": tau,
"eps": EPS,
"norm": "euclidean",
"thr_by": thrby,
"tol": 2.
}
for name in names:
utils._printmsg("\t for %s" % name.upper(), args.verbose)
# SURR[name] = rc.surrogates(x[name], ns, "iaaft", verbose=args.verbose)
SURR[name] = rc.surrogates(x[name],
ns,
"twins",
params,
verbose=args.verbose)
# get RMD for original data
utils._printmsg("RMD for original data ...", args.verbose)
ws, ss = args.window_size, args.step_size
nw = int(np.floor(float(n - ws) / float(ss)))
tm = np.empty(nw, dtype="object")
for name in names:
R[name] = rc.rp(
x[name],
m=m,
tau=tau,
e=EPS,
norm="euclidean",
threshold_by=thrby,
)
rmd = np.zeros(nw)
pb = _progressbar_start(max_value=nw, pbar_on=args.verbose)
for i in range(nw):
start = i * ss
end = start + ws
Rw_enso = R["enso"][start:end, start:end]
Rw_pdo = R["pdo"][start:end, start:end]
rmd[i] = rqa.rmd(Rw_enso, Rw_pdo)
tm[i] = t[start] + (t[end] - t[start]) / 2
_progressbar_update(pb, i)
_progressbar_finish(pb)
# get RMD for surrogate data
utils._printmsg("RMD for surrogates ...", args.verbose)
Rs = {}
rmdsurr = np.zeros((ns, nw), dtype="float")
pb = _progressbar_start(max_value=ns, pbar_on=args.verbose)
for k in range(ns):
for name in names:
xs = SURR[name][k]
Rs[name] = rc.rp(
xs,
m=m,
tau=tau,
e=EPS,
norm="euclidean",
threshold_by=thrby,
)
for i in range(nw):
start = i * ss
end = start + ws
Rsw_enso = Rs["enso"][start:end, start:end]
Rsw_pdo = Rs["pdo"][start:end, start:end]
rmdsurr[k, i] = rqa.rmd(Rsw_enso, Rsw_pdo)
_progressbar_update(pb, k)
_progressbar_finish(pb)
# get each individual array out of dict to avoid NumPy import error
SURR_enso = SURR["enso"]
SURR_pdo = SURR["pdo"]
tm = np.array([date.toordinal() for date in tm])
# save output
EPS = int(EPS * 100)
FN = DATPATH + "rmd_WS%d_SS%d_EPS%dpc_NSURR%d" \
% (ws, ss, EPS, ns)
np.savez(
FN,
rmd=rmd,
tm=tm,
rmdsurr=rmdsurr,
SURR_enso=SURR_enso,
SURR_pdo=SURR_pdo,
)
if args.verbose: print("output saved to: %s.npz" % FN)
return None
def _get_spl():
"""
Estimates the average shortest path length SPL for the indices.
"""
# load data
utils._printmsg("load data ...", args.verbose)
t, x_enso, x_pdo = _load_indices()
x = {
"enso": x_enso,
"pdo": x_pdo,
}
names = ["enso", "pdo"]
# get surrogates
utils._printmsg("iAAFT surrogates ...", args.verbose)
ns = args.nsurr
SURR = {}
for name in names:
utils._printmsg("\t for %s" % name.upper(), args.verbose)
SURR[name] = rc.surrogates(x[name], ns, "iaaft", verbose=args.verbose)
# recurrence plot parameters
EPS, LMIN = 0.30, 3
thrby = "frr"
# get SPL for original data
utils._printmsg("SPL for original data ...", args.verbose)
n = len(t)
ws, ss = args.window_size, args.step_size
nw = int(np.floor(float(n - ws) / float(ss)))
tm = np.empty(nw, dtype="object")
m, tau = {}, {}
A = {}
maxlag = 150
maxdim = 20
r_fnn = 0.0010
SPL = {}
for name in names:
if args.verbose: print("\t for %s" % name.upper())
# get embedding parameters
## get mi
mi, mi_lags = rc.mi(x[name], maxlag, pbar_on=False)
# mi, mi_lags = rc.acf(x[name], maxlag)
mi_filt, _ = utils.boxfilter(mi, filter_width=3, estimate="mean")
try:
tau[name] = rc.first_minimum(mi_filt)
except ValueError:
tau[name] = 1
## FNN
fnn, dims = rc.fnn(x[name],
tau[name],
maxdim=maxdim,
r=r_fnn,
pbar_on=False)
m[name] = dims[rc.first_zero(fnn)]
A[name] = rc.rn(
x[name],
m=m[name],
tau=tau[name],
e=EPS,
norm="euclidean",
threshold_by=thrby,
)
A_ = A[name]
G_ = ig.Graph.Adjacency(A_.tolist(), mode=ig.ADJ_UNDIRECTED)
nw = len(tm)
spl = np.zeros(nw)
pb = _progressbar_start(max_value=nw, pbar_on=args.verbose)
for i in range(nw):
start = i * ss
end = start + ws
Gw = G_.subgraph(vertices=G_.vs[start:end])
pl_hist = Gw.path_length_hist(directed=False)
spl[i] = pl_hist.mean
tm[i] = t[start] + (t[end] - t[start]) / 2
_progressbar_update(pb, i)
_progressbar_finish(pb)
SPL[name] = spl
# get SPL for surrogate data
utils._printmsg("SPL for surrogates ...", args.verbose)
SPLSURR = {}
for name in names:
utils._printmsg("\tfor %s" % name.upper(), args.verbose)
xs = SURR[name]
y = np.diff(xs, axis=0)
splsurr = np.zeros((ns, nw), dtype="float")
pb = _progressbar_start(max_value=ns, pbar_on=args.verbose)
for k in range(ns):
As = rc.rp(
xs[k],
m=m[name],
tau=tau[name],
e=EPS,
norm="euclidean",
threshold_by=thrby,
)
Gs = ig.Graph.Adjacency(As.tolist(), mode=ig.ADJ_UNDIRECTED)
for i in range(nw):
start = i * ss
end = start + ws
Gw = Gs.subgraph(vertices=Gs.vs[start:end])
pl_hist = Gw.path_length_hist(directed=False)
splsurr[k, i] = pl_hist.mean
_progressbar_update(pb, k)
_progressbar_finish(pb)
SPLSURR[name] = splsurr
# get each individual array out of dict to avoid NumPy import error
SPL_enso = SPL["enso"]
SPL_pdo = SPL["pdo"]
SPLSURR_enso = SPLSURR["enso"]
SPLSURR_pdo = SPLSURR["pdo"]
SURR_enso = SURR["enso"]
SURR_pdo = SURR["pdo"]
tm = np.array([date.toordinal() for date in tm])
# save output
EPS = int(EPS * 100)
FN = DATPATH + "spl_WS%d_SS%d_EPS%dpc_LMIN%d_NSURR%d" \
% (ws, ss, EPS, LMIN, ns)
np.savez(FN,
SPL_enso=SPL_enso,
SPL_pdo=SPL_pdo,
SPLSURR_enso=SPLSURR_enso,
SPLSURR_pdo=SPLSURR_pdo,
SURR_enso=SURR_enso,
SURR_pdo=SURR_pdo,
tm=tm)
if args.verbose: print("output saved to: %s.npz" % FN)
return None
def _get_det():
"""
Estimates the determinism DET for the indices.
"""
# load data
utils._printmsg("load data ...", args.verbose)
t, x_enso, x_pdo = _load_indices()
x = {
"enso": x_enso,
"pdo": x_pdo,
}
names = ["enso", "pdo"]
# get surrogates
utils._printmsg("iAAFT surrogates ...", args.verbose)
ns = args.nsurr
SURR = {}
for name in names:
utils._printmsg("\t for %s" % name.upper(), args.verbose)
SURR[name] = rc.surrogates(x[name], ns, "iaaft", verbose=args.verbose)
# recurrence plot parameters
EPS, LMIN = 0.30, 3
thrby = "frr"
# get DET for original data
utils._printmsg("DET for original data ...", args.verbose)
n = len(t)
ws, ss = args.window_size, args.step_size
nw = int(np.floor(float(n - ws) / float(ss)))
tm = np.empty(nw, dtype="object")
m, tau = {}, {}
R = {}
maxlag = 150
maxdim = 20
r_fnn = 0.0010
DET = {}
for name in names:
if args.verbose: print("\t for %s" % name.upper())
# get embedding parameters
## get mi
mi, mi_lags = rc.mi(x[name], maxlag, pbar_on=False)
# mi, mi_lags = rc.acf(x[name], maxlag)
mi_filt, _ = utils.boxfilter(mi, filter_width=3, estimate="mean")
try:
tau[name] = rc.first_minimum(mi_filt)
except ValueError:
tau[name] = 1
## FNN
fnn, dims = rc.fnn(x[name],
tau[name],
maxdim=maxdim,
r=r_fnn,
pbar_on=False)
m[name] = dims[rc.first_zero(fnn)]
R[name] = rc.rp(
x[name],
m=m[name],
tau=tau[name],
e=EPS,
norm="euclidean",
threshold_by=thrby,
)
R_ = R[name]
nw = len(tm)
det = np.zeros(nw)
pb = _progressbar_start(max_value=nw, pbar_on=args.verbose)
for i in range(nw):
start = i * ss
end = start + ws
Rw = R_[start:end, start:end]
det[i] = rqa.det(Rw, lmin=LMIN, hist=None, verb=False)
tm[i] = t[start] + (t[end] - t[start]) / 2
_progressbar_update(pb, i)
_progressbar_finish(pb)
DET[name] = det
# get DET for surrogate data
utils._printmsg("DET for surrogates ...", args.verbose)
DETSURR = {}
for name in names:
utils._printmsg("\tfor %s" % name.upper(), args.verbose)
xs = SURR[name]
y = np.diff(xs, axis=0)
detsurr = np.zeros((ns, nw), dtype="float")
pb = _progressbar_start(max_value=ns, pbar_on=args.verbose)
for k in range(ns):
Rs = rc.rp(
xs[k],
m=m[name],
tau=tau[name],
e=EPS,
norm="euclidean",
threshold_by=thrby,
)
for i in range(nw):
start = i * ss
end = start + ws
Rw = Rs[start:end, start:end]
detsurr[k, i] = rqa.det(Rw, lmin=LMIN, hist=None, verb=False)
_progressbar_update(pb, k)
_progressbar_finish(pb)
DETSURR[name] = detsurr
# get each individual array out of dict to avoid NumPy import error
DET_enso = DET["enso"]
DET_pdo = DET["pdo"]
DETSURR_enso = DETSURR["enso"]
DETSURR_pdo = DETSURR["pdo"]
SURR_enso = SURR["enso"]
SURR_pdo = SURR["pdo"]
tm = np.array([date.toordinal() for date in tm])
# save output
EPS = int(EPS * 100)
FN = DATPATH + "det_WS%d_SS%d_EPS%dpc_LMIN%d_NSURR%d" \
% (ws, ss, EPS, LMIN, ns)
np.savez(FN,
DET_enso=DET_enso,
DET_pdo=DET_pdo,
DETSURR_enso=DETSURR_enso,
DETSURR_pdo=DETSURR_pdo,
SURR_enso=SURR_enso,
SURR_pdo=SURR_pdo,
tm=tm)
if args.verbose: print("output saved to: %s.npz" % FN)
return None
def _get_data():
"""
Estimates Lyapunov, DET, and SPL for Henon map.
"""
# Henon map time series
print("Henon map time series ...")
t = np.arange(0, 10000, 1)
a = np.linspace(1.28, 1.32, na).reshape(na, 1)
j, k = (1 * na) / 8, ns / 2
print "a = ", a[j]
# sys.exit()
b = 0.30
nt = len(t)
x, y = [np.zeros((nt, na, ns)) for i in range(2)]
x[0, :, :] = 1E-2 * np.random.rand(na, ns)
y[0, :, :] = 1E-2 * np.random.rand(na, ns)
pb = _progressbar_start(max_value=nt, pbar_on=True)
LPV = np.zeros((na, ns))
for i in range(1, nt):
x[i, :, :] = 1. - a * x[i - 1, :, :]**2 + y[i - 1, :, :]
y[i, :, :] = b * x[i - 1, :, :]
if i >= nt / 2:
LPV[:, :] += np.log(np.fabs(-2. * a * x[i - 1, :, :]))
_progressbar_update(pb, i)
_progressbar_finish(pb)
xH_eq = x[-neq:, :, :]
LPV /= float(nt)
print("RP ...")
RR = 0.30
y = xH_eq[:, j, k].flatten()
R = rc.rp(y, m=2, tau=1, e=RR, norm="euclidean", threshold_by="frr")
DET = rqa.det(R, lmin=2, hist=None, verb=False)
print DET
print("plot...")
pl.subplot(211)
pl.plot(y, alpha=0.5)
pl.subplot(212)
pl.imshow(R, cmap=pl.cm.gray_r, origin="lower", interpolation="none")
pl.show()
sys.exit()
print("plot data ...")
xplot = np.zeros((na, neq * ns))
for i in range(na):
xplot[i] = xH_eq[:, i, :].flatten()
print("plot ...")
fig = pl.figure(figsize=[21., 12.], facecolor="none")
ax = fig.add_axes([0.10, 0.10, 0.80, 0.80])
ax.plot(a,
xplot,
"o",
ms=1.00,
alpha=0.25,
rasterized=True,
mfc="k",
mec="none")
print("prettify ...")
ax.tick_params(labelsize=14, size=8)
ax.tick_params(size=5, which="minor")
# ax.set_xticks(np.arange(1.0, 1.401, 0.05), minor=False)
# ax.set_xticks(np.arange(1.0, 1.401, 0.01), minor=True)
ax.grid(which="both")
# ax.set_xlim(1.0, 1.4)
print("save figure ...")
FN = "../plots/" + __file__[2:-3] + ".png"
fig.savefig(FN, rasterized=True, dpi=100)
print("figure saved to: %s" % FN)
sys.exit()
# estimate embedding parameters
print("embedding parameters ...")
tau, m = np.ones(na, dtype="int"), 2 * np.ones(na, dtype="int")
# DET
print("DET ...")
RR = 0.25
DET = np.zeros((na, ns))
pb = _progressbar_start(max_value=ns * na, pbar_on=True)
k = 0
for j in range(ns):
for i in range(na):
R = rc.rp(xH_eq[:, i, j],
m=m[i],
tau=tau[i],
e=RR,
norm="euclidean",
threshold_by="frr")
DET[i, j] = rqa.det(R, lmin=2, hist=None, verb=False)
del R
_progressbar_update(pb, k)
k += 1
_progressbar_finish(pb)
# SPL
print("SPL ...")
SPL = np.zeros((na, ns))
pb = _progressbar_start(max_value=ns * na, pbar_on=True)
k = 0
for j in range(ns):
for i in range(na):
A = rc.rn(xH_eq[:, i, j],
m=m[i],
tau=tau[i],
e=RR,
norm="euclidean",
threshold_by="frr")
G = ig.Graph.Adjacency(A.tolist(), mode=ig.ADJ_UNDIRECTED)
pl_hist = G.path_length_hist(directed=False)
SPL[i, j] = pl_hist.mean
del A, G
_progressbar_update(pb, k)
k += 1
_progressbar_finish(pb)
# save output
FN = DATPATH + "det_spl_lpv_na%d_ns%s_neq%d" % (na, ns, neq)
np.savez(FN, DET=DET, SPL=SPL, LPV=LPV, t=t, a=a, b=b, x=x, y=y)
print("saved to %s.npz" % FN)
return None