print 'acorr lagf is %s' % lagf
# determine significance levels
(signif, fft_theory) = wave_sig.wave_signif(x, dt, scales, lag1=lagf)
sig95 = np.ones_like(power) * np.array([signif] * len(t)).transpose()
sig95 = power / sig95 # where ratio > 1, power is significant
# Global wavelet spectrum & significance levels:
global_int = variance * (np.sum(
power, axis=0)) / x.shape[0] # time-average over all times
gs = ((np.sum(power, axis=1)) / x.shape[0]) / variance #assume var=1
gswa = wa.global_wavelet_spectrum
# Global wavelet significance
(signif_g, fft_theory_g) = wave_sig.global_wave_signif(x,
dt,
scales,
lag1=lagf,
sigtest=1,
dof=len(x))
"""----------------------------- plot setup ------------------------------------------"""
T, S = np.meshgrid(t, scales)
"""----------- plotting WaveTransform Power with confidence interval contour ----------"""
plt, fig = wavelet_analy_plot.plot_wavetransf(wa,
T,
S,
sig95,
time_base,
plot_percentile=True)
plt.savefig((fig_name_base + '_wave_' +
str(depth[level]).replace('.0', 'm') + '.png'),
scalemax = 32
scale_ind = ((wa1c.scales >= scalemin) & (wa1c.scales <= scalemax))
# determine significance levels for stream 1
(signif_1, fft_theory_1) = wave_sig.wave_signif(data_1c['anom'],wa1c.dt,wa1c.scales[scale_ind],lag1=lagf_1)
sig95_1 = np.ones_like(wa1c.wavelet_power[scale_ind,:]) * np.array([signif_1] * len(wa1c.time)).transpose()
sig95_1 = wa1c.wavelet_power[scale_ind,:] / sig95_1 # where ratio > 1, power is significant
# Global wavelet spectrum & significance levels stream 1:
global_int_1 = data_1['variance']*(np.sum(wa1c.wavelet_power, axis=0) ) / data_1c['anom'].shape[0] # time-average over all times
gs_1 = ((np.sum(wa1c.wavelet_power, axis=1) ) / data_1c['anom'].shape[0]) / data_1['variance'] #assume var=1
gswa_1 = wa1c.global_wavelet_spectrum
# Global wavelet significance
(signif_g_1, fft_theory_g_1) = wave_sig.global_wave_signif(data_1c['anom'],wa1c.dt,wa1c.scales,lag1=lagf_1,sigtest=1, dof=len(data_1c['anom']))
"""----------------------------- plot setup ------------------------------------------"""
"""----------- plotting WaveTransform Power with confidence interval contour ----------"""
fig_name_base = 'images/' + file1.split('/')[-1].split('.')[0] + '_' + "_".join(file2.split('/')[-1].split('_')[1:3]).split('.')[0] + '_'
"""----------------- zoom in to specified scales ----------"""
T1, S1 = np.meshgrid(data_1c['time'], wa1c.scales[scale_ind])
plt, fig = wavelet_analy_plot_goa.plot_wavetransf_time_zoom(data_1c['anom'], wa1c, T1, S1, sig95_1,\
gs_1, signif_g_1, data_1['time_base'], data_ind=scale_ind, scalemin=scalemin, scalemax=scalemax, ylabel=par_1[1], plot_percentile=True)
# determine significance levels for stream 1
(signif_1, fft_theory_1) = wave_sig.wave_signif(data_1c['anom'],wa1c.dt,wa1c.scales,lag1=lagf_1)
sig95_1 = np.ones_like(wa1c.wavelet_power) * np.array([signif_1] * len(wa1c.time)).transpose()
sig95_1 = wa1c.wavelet_power / sig95_1 # where ratio > 1, power is significant
# determine significance levels for stream 1
(signif_2, fft_theory_2) = wave_sig.wave_signif(data_2c['anom'],wa2c.dt,wa2c.scales,lag1=lagf_2)
sig95_2 = np.ones_like(wa2c.wavelet_power) * np.array([signif_2] * len(wa2c.time)).transpose()
sig95_2 = wa2c.wavelet_power / sig95_2 # where ratio > 1, power is significant
# Global wavelet spectrum & significance levels stream 1:
global_int_1 = data_1['variance']*(np.sum(wa1c.wavelet_power, axis=0) ) / data_1c['anom'].shape[0] # time-average over all times
gs_1 = ((np.sum(wa1c.wavelet_power, axis=1) ) / data_1c['anom'].shape[0]) / data_1['variance'] #assume var=1
gswa_1 = wa1c.global_wavelet_spectrum
# Global wavelet significance
(signif_g_1, fft_theory_g_1) = wave_sig.global_wave_signif(data_1c['anom'],wa1c.dt,wa1c.scales,lag1=lagf_1,sigtest=1, dof=len(data_1c['anom']))
# Global wavelet spectrum & significance levels stream 2:
global_int_2 = data_2['variance']*(np.sum(wa2c.wavelet_power, axis=0) ) / data_2c['anom'].shape[0] # time-average over all times
gs_2 = ((np.sum(wa2c.wavelet_power, axis=1) ) / data_2c['anom'].shape[0]) / data_2['variance'] #assume var=1
gswa_2 = wa2c.global_wavelet_spectrum
# Global wavelet significance
(signif_g_2, fft_theory_g_2) = wave_sig.global_wave_signif(data_2c['anom'],wa2c.dt,wa2c.scales,lag1=lagf_2,sigtest=1, dof=len(data_2c['anom']))
"""----------------------------- plot setup ------------------------------------------"""
T1, S1 = np.meshgrid(data_1c['time'], wa1c.scales)
T2, S2 = np.meshgrid(data_2c['time'], wa2c.scales)
"""----------- plotting WaveTransform Power with confidence interval contour ----------"""
# determine acor factor for red noise
acorr = acf(x)
lagf = (acorr[1]+np.sqrt(acorr[2]))/2
print 'acorr lagf is %s' % lagf
# determine significance levels
(signif, fft_theory) = wave_sig.wave_signif(x,dt,scales,lag1=lagf)
sig95 = np.ones_like(power) * np.array([signif] * len(t)).transpose()
sig95 = power / sig95 # where ratio > 1, power is significant
# Global wavelet spectrum & significance levels:
global_int = variance*(np.sum(power, axis=0) ) / x.shape[0] # time-average over all times
gs = ((np.sum(power, axis=1) ) / x.shape[0]) / variance #assume var=1
gswa = wa.global_wavelet_spectrum
# Global wavelet significance
(signif_g, fft_theory_g) = wave_sig.global_wave_signif(x,dt,scales,lag1=lagf,sigtest=1, dof=len(x))
"""----------------------------- plot setup ------------------------------------------"""
T, S = np.meshgrid(t, scales)
"""----------- plotting WaveTransform Power with confidence interval contour ----------"""
plt, fig = wavelet_analy_plot.plot_wavetransf(wa, T, S, sig95, time_base, plot_percentile=True)
plt.savefig((fig_name_base + '_wave' + str(depth[level]).replace('.0','m') + '.png'), bbox_inches='tight', dpi = (100))
plt.close()
# determine acor factor for red noise
acorr = acf(data_1['anom'])
lagf = (acorr[1]+np.sqrt(acorr[2]))/2
print 'acorr lagf is %s' % lagf
# determine significance levels
(signif, fft_theory) = wave_sig.wave_signif(data_1['anom'],data_1['dt'],scales,lag1=lagf)
sig95 = np.ones_like(power) * np.array([signif] * len(t)).transpose()
sig95 = power / sig95 # where ratio > 1, power is significant
# Global wavelet spectrum & significance levels:
global_int = data_1['variance']*(np.sum(power, axis=0) ) / data_1['anom'].shape[0] # time-average over all times
gs = ((np.sum(power, axis=1) ) / data_1['anom'].shape[0]) / data_1['variance'] #assume var=1
gswa = wa.global_wavelet_spectrum
# Global wavelet significance
(signif_g, fft_theory_g) = wave_sig.global_wave_signif(data_1['anom'],data_1['dt'],scales,lag1=lagf,sigtest=1, dof=len(data_1['anom']))
"""----------------------------- plot setup ------------------------------------------"""
T, S = np.meshgrid(t, scales)
"""----------- plotting WaveTransform Power with confidence interval contour ----------"""
"""----------------- plotting contours w/global and timeseries ----------"""
"""----------------- zoom in to specified scales ----------"""
