"""----------- 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()