def run_main_prog(params):
#start = time.clock()
(m,numSig) = params # m is index for noise level, numSig is idex for realization
numSurr = 99
SignalAndSurr = numpy.zeros((numSurr+1,N),dtype='float32')
noise = NL[m]*numpy.std(SignalX[numSig,:].copy())*numpy.random.normal(0,1,N)
SignalAndSurr[0,:] = SignalX[numSig,:].copy()+noise
for j in range(1,numSurr+1,1):
SignalAndSurr[j,:] = UnivariateSurrogates(SignalAndSurr[0,:].copy(),120)
T = numpy.zeros((numSurr+1,1),dtype='float32')
for k in range(0,numSurr+1,1):
ts = SignalAndSurr[k,:]
ts.shape = (-1,1)
psv = RecurrencePlot.embed_time_series(ts,dim=3,tau=3)
randomVertices = random.sample(xrange(psv.shape[0]), int(sys.argv[1]))
R = RecurrenceNetwork(psv[randomVertices,:],recurrence_rate=float(sys.argv[2]),silence_level=2)
T[k] = R.transitivity() # compute network measure for hypothesis testing
if T[0] > max(T[1:]): #non-parametric testing
H0 = 1 #null-hypothesis rejected
else:
H0 = 0
#elapsed = (time.clock() - start)
#print elapsed
return (H0)
# Calculate and print the recurrence rate again to check if it worked...
RR = rp.recurrence_rate()
print("Recurrence rate:", RR)
# Calculate some standard RQA measures
DET = rp.determinism(l_min=2)
LAM = rp.laminarity(v_min=2)
print("Determinism:", DET)
print("Laminarity:", LAM)
# Generate a recurrence network at fixed recurrence rate
rn = RecurrenceNetwork(time_series,
dim=DIM,
tau=TAU,
metric=METRIC,
normalize=False,
recurrence_rate=RR)
# Calculate average path length, transitivity and assortativity
L = rn.average_path_length()
T = rn.transitivity()
C = rn.global_clustering()
R = rn.assortativity()
print("Average path length:", L)
print("Transitivity:", T)
print("Global clustering:", C)
print("Assortativity:", R)
local_result[measure] = np.empty(t_steps)
# Initialize progress bar
progress = progressbar.ProgressBar().start()
# Loop over moving windows
for j in xrange(t_steps):
# Get time series section for current window
time_series = data[j * delta:j * delta + T_embedded]
local_step_sequence[j] = j * delta + T_embedded / 2
# Prepare recurrence network from original data
rec_net = RecurrenceNetwork(time_series.flatten(),
dim=DIM,
tau=TAU,
metric=METRIC,
normalize=False,
silence_level=2,
recurrence_rate=RR)
# Calculations for original recurrence network
local_result["Average path length"][j] = rec_net.average_path_length()
local_result["Transitivity"][j] = rec_net.transitivity()
#local_result["Assortativity"][j] = rec_net.assortativity()
#local_result["Diameter"][j] = rec_net.diameter()
# Calculate RQA measures
#local_result["Determinism"][j] = rec_net.determinism()
#local_result["Laminarity"][j] = rec_net.laminarity()
#local_result["Mean diagonal line length"][j] = rec_net.average_diaglength()
#https://www.researchgate.net/post/How_can_we_find_out_which_value_of_embedding_dimensions_is_more_accurate #when choosing emb_dim for Takens, each dimension should have at least 10 dp ==> 10^1 == 1D, 10^2 == 2D, ..., 10^6 == 6D #FALSE NEAREST NEIGHBOR FOR DETERMINING MINIMAL EMBEDDING DIMENSION #MEASURES OF COMPLEXITY # https://hackaday.io/project/707-complexity-of-a-time-series # general entropy with discrete pdf - [H = sum_i - p_i * log( p_i)] , we cannot use because we have not well defined states # Approximate entropy # Recurrency plot from pyunicorn.timeseries import RecurrenceNetwork x = np.sin(np.linspace(0, 10*np.pi, 1000)) net = RecurrenceNetwork(x, recurrence_rate = 0.05) # NOTE WE CAN ALWAYS MAKE ALL THE ROLLING ITEMS IN PY AND ANALYSE IN R! ######## # Parameters window_size = 100 emb_dim = 4 rolling = rolling_window(df.logR_ask.dropna(), window_size, 10) #dropped NaN from logR #rolling_ns = rolling_window(df.ask, window_size, 10) #rolling_ts = rolling_window(df.index, window_size, 10) df_ = pd.DataFrame(rolling)
import numpy as np from pyunicorn.timeseries import RecurrenceNetwork x = np.sin(np.linspace(0, 10 * np.pi, 1000)) net = RecurrenceNetwork(x, recurrence_rate=0.05) print net.transitivity()
# Generate a recurrence plot object with fixed recurrence rate RR
rp = RecurrencePlot(time_series, dim=DIM, tau=TAU, metric=METRIC,
normalize=False, recurrence_rate=RR)
# Calculate and print the recurrence rate again to check if it worked...
RR = rp.recurrence_rate()
print "Recurrence rate:", RR
# Calculate some standard RQA measures
DET = rp.determinism(l_min=2)
LAM = rp.laminarity(v_min=2)
print "Determinism:", DET
print "Laminarity:", LAM
# Generate a recurrence network at fixed recurrence rate
rn = RecurrenceNetwork(time_series, dim=DIM, tau=TAU, metric=METRIC,
normalize=False, recurrence_rate=RR)
# Calculate average path length, transitivity and assortativity
L = rn.average_path_length()
T = rn.transitivity()
C = rn.global_clustering()
R = rn.assortativity()
print "Average path length:", L
print "Transitivity:", T
print "Global clustering:", C
print "Assortativity:", R
# -*- coding: utf-8 -*- """ Created on Wed Aug 19 11:25:43 2015 @author: George """ from __future__ import absolute_import, division, print_function, unicode_literals import numpy as np from pyunicorn.timeseries import RecurrenceNetwork x = np.sin(np.linspace(0, 10 * np.pi, 1000)) net = RecurrenceNetwork(x, recurrence_rate=0.05) print(net.transitivity())
