"""----------- plotting WaveTransform Power with confidence interval contour ----------"""
fig_name_base = 'images/' + dfile_1.split('/')[-1].split('.')[0] + '_'
"""----------------- plotting contours w/global and timeseries ----------"""
plt, fig = wavelet_analy_plot.timeseries_comp(data_1['anom'], data_1c['anom'], data_1['time'], par_1[1])
plt.savefig(fig_name_base + par_1[0] + 'datavfilt.png', bbox_inches='tight', dpi = (100))
plt.close()
"""----------------- zoom in to specified scales ----------"""
plt, fig = wavelet_analy_plot.plot_wavetransf_time_zoom(data_1c['anom'], wa1c, T1, S1, sig95_1,\
gs_1, signif_g_1, data_1['time_base'], scalemin=.1, scalemax=64, ylabel=par_1[1], plot_percentile=True)
plt.savefig(fig_name_base + par_1[0] + '.png', bbox_inches='tight', dpi = (100))
plt.close()
plt, fig = wavelet_analy_plot.plot_wavetransf_time_zoom(data_2c['anom'], wa2c, T2, S2, sig95_2,\
gs_2, signif_g_2, data_2['time_base'], scalemin=.1, scalemax=64, ylabel=par_2[1], plot_percentile=True)
plt.savefig(fig_name_base + par_2[0] + '.png', bbox_inches='tight', dpi = (100))
plt.close()
xwt_plot = False
if not xwt_plot: #switch to turn on/off remaining xwt analysis
continue
"""-----------------------------cross wavelet analysis ---------------------------"""
### since the time scales are matched and the dt is the same, the wavelet analysis for
# dataset 1 or two can be used and they should be equivalent for coi, scales and fourier
plot_percentile=True)
plt.savefig(fig_name_base + '_wave2' +
str(depth[level]).replace('.0', 'm') + '.png',
bbox_inches='tight',
dpi=(100))
plt.close()
"""----------------- plotting contours w/global and timeseries ----------"""
"""----------------- zoom in to specified scales ----------"""
plt, fig = wavelet_analy_plot.plot_wavetransf_time_zoom(
x,
wa,
T,
S,
sig95,
gs,
signif_g,
time_base,
scalemin=.1,
scalemax=128,
ylabel='Echo Intens.',
plot_percentile=True)
plt.savefig(fig_name_base + '_wave3' +
str(depth[level]).replace('.0', 'm') + '.png',
bbox_inches='tight',
dpi=(100))
plt.close()
"""----------------------- plotting power spectrum FFT --------------------------------"""
(plt, fig) = wavelet_analy_plot.fft_power_spec(x, time_base, Fs=24)
plt.savefig(fig_name_base + '_FFTspec' +
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()
"""----------------- plotting contours w/global and timeseries ----------"""
plt, fig = wavelet_analy_plot.plot_wavetransf_time(x, wa, T, S, sig95, gs, signif_g,
time_base, ylabel='Echo Intens.', plot_percentile=True)
plt.savefig(fig_name_base + '_wave2' + str(depth[level]).replace('.0','m') + '.png', bbox_inches='tight', dpi = (100))
plt.close()
"""----------------- plotting contours w/global and timeseries ----------"""
"""----------------- zoom in to specified scales ----------"""
plt, fig = wavelet_analy_plot.plot_wavetransf_time_zoom(x, wa, T, S, sig95, gs, signif_g,
time_base, scalemin=.1, scalemax=128, ylabel='Echo Intens.', plot_percentile=True)
plt.savefig(fig_name_base + '_wave3' + str(depth[level]).replace('.0','m') + '.png', bbox_inches='tight', dpi = (100))
plt.close()
"""----------------------- plotting power spectrum FFT --------------------------------"""
(plt, fig) = wavelet_analy_plot.fft_power_spec(x, time_base, Fs=24)
plt.savefig(fig_name_base + '_FFTspec' + str(depth[level]).replace('.0','m') + '.png', bbox_inches='tight', dpi = (100))
plt.close()
"""
# Do FFT analysis of array
sp = np.fft.fft(x)
# Getting the related frequencies
freq = np.fft.fftfreq(t.shape[-1], d=.25)
pyy = sp*np.conj(sp)
"""----------------------------- 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 ----------"""
plt, fig = wavelet_analy_plot.plot_wavetransf_time_zoom(data_1['anom'], wa, T, S, sig95, gs, signif_g,
data_1['time_base'], scalemin=.1, scalemax=64, ylabel=par_1[1], plot_percentile=True)
plt.savefig(fig_name_base + '_wave3' + depth + '.png', bbox_inches='tight', dpi = (100))
plt.close()
"""----------------------- plotting power spectrum FFT --------------------------------"""
(plt, fig) = wavelet_analy_plot.fft_power_spec(data_1['anom'], data_1['time_base'], Fs=24)
plt.savefig(fig_name_base + '_FFTspec' + depth + '.png', bbox_inches='tight', dpi = (100))
plt.close()
