def _process_matrix_cond(self, jobq, resq):
while True:
a = jobq.get() # get queued input
if a is None: # if it is None, we are finished, put poison pill to resq
break # break infinity cycle
else:
i, j, ph1, ph2, cond_ts = a # compute stuff
resq.put((i, j, MI.cond_mutual_information(ph1, ph2, cond_ts, algorithm = 'EQQ2', bins = 4, log2 = False)))
def _process_TGsurrs(jobq, resq, nao, sg, pp, tau, mean, var):
while jobq.get() is not None:
sg.construct_fourier_surrogates(algorithm='FT')
sg.add_seasonality(mean, var, None)
caus_surrs_temp = np.zeros((2,5))
# no pressure = 3dim cond.
x, y, z = MI.get_time_series_condition([nao, sg.data], tau=tau, dim_of_condition=3, eta=3,
close_condition=True)
# MIGAU
caus_surrs_temp[0, 0] = MI.cond_mutual_information(x, y, z, algorithm='GCM', log2=False)
# MIEQQ8
caus_surrs_temp[0, 1] = MI.cond_mutual_information(x, y, z, algorithm='EQQ2', bins=8, log2=False)
# MIEQQ16
caus_surrs_temp[0, 2] = MI.cond_mutual_information(x, y, z, algorithm='EQQ2', bins=16, log2=False)
# MIKNN16
caus_surrs_temp[0, 3] = MI.knn_cond_mutual_information(x, y, z, k=16, standardize=True, dualtree=True)
# MIKNN64
caus_surrs_temp[0, 4] = MI.knn_cond_mutual_information(x, y, z, k=64, standardize=True, dualtree=True)
# with pressure = 4dim cond.
x, y, z = MI.get_time_series_condition([nao, sg.data], tau=tau, dim_of_condition=3, eta=3,
add_cond = pp, close_condition=True)
# MIGAU
caus_surrs_temp[1, 0] = MI.cond_mutual_information(x, y, z, algorithm='GCM', log2=False)
# MIEQQ8
caus_surrs_temp[1, 1] = MI.cond_mutual_information(x, y, z, algorithm='EQQ2', bins=8, log2=False)
# MIEQQ16
caus_surrs_temp[1, 2] = MI.cond_mutual_information(x, y, z, algorithm='EQQ2', bins=16, log2=False)
# MIKNN16
caus_surrs_temp[1, 3] = MI.knn_cond_mutual_information(x, y, z, k=16, standardize=True, dualtree=True)
# MIKNN64
caus_surrs_temp[1, 4] = MI.knn_cond_mutual_information(x, y, z, k=64, standardize=True, dualtree=True)
resq.put(caus_surrs_temp)
def _process_CMI_surrs(jobq, resq):
while True:
a = jobq.get()
if a is None:
break
else:
tau, la, lo = a
if not np.any(np.isnan(sg.data[:, la, lo])):
# 3d
x, y, z = MI.get_time_series_condition([nao.data, sg.data[:, la, lo]], tau=tau, dim_of_condition=3, eta=3,
close_condition=True)
cmi_3d = MI.cond_mutual_information(x, y, z, algorithm='GCM', log2=False)
# with pressure = 4dim cond.
if not np.any(np.isnan(pp.data[:, la, lo])):
x, y, z = MI.get_time_series_condition([nao.data, sg.data[:, la, lo]], tau=tau, dim_of_condition=3, eta=3,
add_cond=pp.data[:, la, lo], close_condition=True)
cmi_4d = MI.cond_mutual_information(x, y, z, algorithm='GCM', log2=False)
else:
cmi_4d = np.nan
resq.put([tau, la, lo, cmi_3d, cmi_4d])
else:
resq.put([tau, la, lo, np.nan, np.nan])
def _process_matrix_cond(self, jobq, resq):
while True:
a = jobq.get() # get queued input
if a is None: # if it is None, we are finished, put poison pill to resq
break # break infinity cycle
else:
i, j, ph1, ph2, cond_ts = a # compute stuff
resq.put((i, j,
MI.cond_mutual_information(ph1,
ph2,
cond_ts,
algorithm='EQQ2',
bins=4,
log2=False)))
# compute causality
NUM_SURRS = 100
WORKERS = 7
TAUS = np.arange(1,41,1)
data_caus = np.zeros((2, TAUS.shape[0], 5)) # pp yes or no x delays x (MIGAU, MIEQQ8, MIEQQ16, knn16, knn64)
surrs_NAOcaus = np.zeros((2, NUM_SURRS, TAUS.shape[0], 5))
surrs_TGcaus = np.zeros((2, NUM_SURRS, TAUS.shape[0], 5))
for tau, ii in zip(TAUS, range(TAUS.shape[0])):
print("computing for lag %d..." % (tau))
# no pressure = 3dim cond.
x, y, z = MI.get_time_series_condition([nao.data, prg_tg.data], tau=tau, dim_of_condition=3, eta=3,
close_condition=True)
# MIGAU
data_caus[0, ii, 0] = MI.cond_mutual_information(x, y, z, algorithm='GCM', log2=False)
# MIEQQ8
data_caus[0, ii, 1] = MI.cond_mutual_information(x, y, z, algorithm='EQQ2', bins=8, log2=False)
# MIEQQ16
data_caus[0, ii, 2] = MI.cond_mutual_information(x, y, z, algorithm='EQQ2', bins=16, log2=False)
# MIKNN16
data_caus[0, ii, 3] = MI.knn_cond_mutual_information(x, y, z, k=16, standardize=True, dualtree=True)
# MIKNN64
data_caus[0, ii, 4] = MI.knn_cond_mutual_information(x, y, z, k=64, standardize=True, dualtree=True)
# with pressure = 4dim cond.
x, y, z = MI.get_time_series_condition([nao.data, prg_tg.data], tau=tau, dim_of_condition=3, eta=3,
add_cond=prg_pp.data, close_condition=True)
# MIGAU
data_caus[1, ii, 0] = MI.cond_mutual_information(x, y, z, algorithm='GCM', log2=False)
# MIEQQ8