def _cmi_surrogates(a):
aa, aa_surr, aa_seas, temp, temp_surr, temp_seas, scales = a
aa_surr.construct_fourier_surrogates_spatial()
aa_surr.amplitude_adjust_surrogates(aa_seas[0], aa_seas[1], aa_seas[2])
# aa_surr.construct_surrogates_with_residuals()
# aa_surr.add_seasonality(aa_seas[0][:-1], aa_seas[1][:-1], aa_seas[2][:-1])
cmi1 = []
cmi2 = []
for sc in scales:
temp.wavelet(sc, period_unit = 'd')
phase_temp = temp.phase.copy()
phase_aa_surr = temp.wavelet(sc, period_unit = 'd', ts = aa_surr.surr_data)[0]
# cmi1
tmp = []
for tau in range(1,30):
x, y, z = mi.get_time_series_condition([phase_temp, phase_aa_surr], tau = tau, dim_of_condition = 1, eta = 1, phase_diff = True)
tmp.append(mi.cond_mutual_information(x, y, z, algorithm = 'EQQ2', bins = 8, log2 = False))
cmi1.append(np.mean(np.array(tmp)))
# cmi2
tmp = []
for tau in range(1,30):
x, y, z = mi.get_time_series_condition([phase_aa_surr, phase_temp], tau = tau, dim_of_condition = 1, eta = 1, phase_diff = True)
tmp.append(mi.cond_mutual_information(x, y, z, algorithm = 'EQQ2', bins = 8, log2 = False))
cmi2.append(np.mean(np.array(tmp)))
cmi1 = np.array(cmi1)
cmi2 = np.array(cmi2)
return cmi1, cmi2
def _cmi_surrogates(a):
aa, aa_surr, aa_seas, temp, temp_surr, temp_seas, scales = a
aa_surr.construct_fourier_surrogates_spatial()
aa_surr.amplitude_adjust_surrogates(aa_seas[0], aa_seas[1], aa_seas[2])
# aa_surr.construct_surrogates_with_residuals()
# aa_surr.add_seasonality(aa_seas[0][:-1], aa_seas[1][:-1], aa_seas[2][:-1])
cmi1 = []
cmi2 = []
for sc in scales:
temp.wavelet(sc, period_unit='d')
phase_temp = temp.phase.copy()
phase_aa_surr = temp.wavelet(sc, period_unit='d',
ts=aa_surr.surr_data)[0]
# cmi1
tmp = []
for tau in range(1, 30):
x, y, z = mi.get_time_series_condition([phase_temp, phase_aa_surr],
tau=tau,
dim_of_condition=1,
eta=1,
phase_diff=True)
tmp.append(
mi.cond_mutual_information(x,
y,
z,
algorithm='EQQ2',
bins=8,
log2=False))
cmi1.append(np.mean(np.array(tmp)))
# cmi2
tmp = []
for tau in range(1, 30):
x, y, z = mi.get_time_series_condition([phase_aa_surr, phase_temp],
tau=tau,
dim_of_condition=1,
eta=1,
phase_diff=True)
tmp.append(
mi.cond_mutual_information(x,
y,
z,
algorithm='EQQ2',
bins=8,
log2=False))
cmi2.append(np.mean(np.array(tmp)))
cmi1 = np.array(cmi1)
cmi2 = np.array(cmi2)
return cmi1, cmi2
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 _cmi_surrogates(a):
aa, aa_surr, aa_seas, temp, temp_surr, temp_seas, scales = a
aa_surr.construct_fourier_surrogates_spatial()
aa_surr.add_seasonality(aa_seas[0], aa_seas[1], aa_seas[2])
cmi1 = []
cmi2 = []
for sc in scales:
period = sc # frequency of interest in months
s0 = period / fourier_factor # get scale
wave_temp, _, _, _ = wvlt.continous_wavelet(temp.data, 1, False, wvlt.morlet, dj = 0, s0 = s0, j1 = 0, k0 = k0) # perform wavelet
phase_temp = np.arctan2(np.imag(wave_temp), np.real(wave_temp))[0, 12:-12] # get phases from oscillatory modes
wave_aa, _, _, _ = wvlt.continous_wavelet(aa_surr.surr_data, 1, False, wvlt.morlet, dj = 0, s0 = s0, j1 = 0, k0 = k0) # perform wavelet
phase_aa_surr = np.arctan2(np.imag(wave_aa), np.real(wave_aa))[0, 12:-12] # get phases from oscillatory modes
# cmi1
tmp = []
for tau in range(1,30):
x, y, z = mi.get_time_series_condition([phase_temp, phase_aa_surr], tau = tau, dim_of_condition = 1, eta = 1, phase_diff = True)
tmp.append(mi.cond_mutual_information(x, y, z, algorithm = 'EQQ2', bins = 4, log2 = False))
cmi1.append(np.mean(np.array(tmp)))
# cmi2
tmp = []
for tau in range(1,30):
x, y, z = mi.get_time_series_condition([phase_aa_surr, phase_temp], tau = tau, dim_of_condition = 1, eta = 1, phase_diff = True)
tmp.append(mi.cond_mutual_information(x, y, z, algorithm = 'EQQ2', bins = 4, log2 = False))
cmi2.append(np.mean(np.array(tmp)))
cmi1 = np.array(cmi1)
cmi2 = np.array(cmi2)
return cmi1, cmi2
wave_temp, _, _, _ = wvlt.continous_wavelet(temp.data, 1, False, wvlt.morlet, dj = 0, s0 = s0, j1 = 0, k0 = k0) # perform wavelet
phase_temp = np.arctan2(np.imag(wave_temp), np.real(wave_temp))[0, 12:-12] # get phases from oscillatory modes
wave_aa, _, _, _ = wvlt.continous_wavelet(aa.data, 1, False, wvlt.morlet, dj = 0, s0 = s0, j1 = 0, k0 = k0) # perform wavelet
phase_aa = np.arctan2(np.imag(wave_aa), np.real(wave_aa))[0, 12:-12] # get phases from oscillatory modes
# mutual information coherence
coherence.append(mi.mutual_information(phase_aa, phase_temp, algorithm = 'EQQ2', bins = 8, log2 = False))
# cmi1
# plt.figure()
tmp = []
for tau in range(1,30):
x, y, z = mi.get_time_series_condition([phase_temp, phase_aa], tau = tau, dim_of_condition = 1, eta = 0, phase_diff = True)
tmp.append(mi.cond_mutual_information(x, y, z, algorithm = 'EQQ2', bins = 4, log2 = False))
cmi1.append(np.mean(np.array(tmp)))
# plt.plot(tmp, label = "1->2")
# cmi2
tmp = []
for tau in range(1,30):
x, y, z = mi.get_time_series_condition([phase_aa, phase_temp], tau = tau, dim_of_condition = 1, eta = 0, phase_diff = True)
tmp.append(mi.cond_mutual_information(x, y, z, algorithm = 'EQQ2', bins = 4, log2 = False))
cmi2.append(np.mean(np.array(tmp)))
# plt.plot(tmp, label = "2->1")
# plt.legend()
# plt.savefig("CMItesting%dmon.png" % sc)
# plt.close()
# wavelet coherence
def _cmi_surrogates(a):
aa, aa_surr, aa_seas, temp, temp_surr, temp_seas, scales = a
aa_surr.construct_fourier_surrogates_spatial()
aa_surr.add_seasonality(aa_seas[0], aa_seas[1], aa_seas[2])
cmi1 = []
cmi2 = []
for sc in scales:
period = sc # frequency of interest in months
s0 = period / fourier_factor # get scale
wave_temp, _, _, _ = wvlt.continous_wavelet(temp.data,
1,
False,
wvlt.morlet,
dj=0,
s0=s0,
j1=0,
k0=k0) # perform wavelet
phase_temp = np.arctan2(
np.imag(wave_temp),
np.real(wave_temp))[0, 12:-12] # get phases from oscillatory modes
wave_aa, _, _, _ = wvlt.continous_wavelet(aa_surr.surr_data,
1,
False,
wvlt.morlet,
dj=0,
s0=s0,
j1=0,
k0=k0) # perform wavelet
phase_aa_surr = np.arctan2(
np.imag(wave_aa),
np.real(wave_aa))[0, 12:-12] # get phases from oscillatory modes
# cmi1
tmp = []
for tau in range(1, 30):
x, y, z = mi.get_time_series_condition([phase_temp, phase_aa_surr],
tau=tau,
dim_of_condition=1,
eta=1,
phase_diff=True)
tmp.append(
mi.cond_mutual_information(x,
y,
z,
algorithm='EQQ2',
bins=4,
log2=False))
cmi1.append(np.mean(np.array(tmp)))
# cmi2
tmp = []
for tau in range(1, 30):
x, y, z = mi.get_time_series_condition([phase_aa_surr, phase_temp],
tau=tau,
dim_of_condition=1,
eta=1,
phase_diff=True)
tmp.append(
mi.cond_mutual_information(x,
y,
z,
algorithm='EQQ2',
bins=4,
log2=False))
cmi2.append(np.mean(np.array(tmp)))
cmi1 = np.array(cmi1)
cmi2 = np.array(cmi2)
return cmi1, cmi2
bins=8,
log2=False))
# cmi1
# plt.figure()
tmp = []
for tau in range(1, 30):
x, y, z = mi.get_time_series_condition([phase_temp, phase_aa],
tau=tau,
dim_of_condition=1,
eta=0,
phase_diff=True)
tmp.append(
mi.cond_mutual_information(x,
y,
z,
algorithm='EQQ2',
bins=4,
log2=False))
cmi1.append(np.mean(np.array(tmp)))
# plt.plot(tmp, label = "1->2")
# cmi2
tmp = []
for tau in range(1, 30):
x, y, z = mi.get_time_series_condition([phase_aa, phase_temp],
tau=tau,
dim_of_condition=1,
eta=0,
phase_diff=True)
tmp.append(
mi.cond_mutual_information(x,
