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130 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoMinorLocator
import matplotlib.gridspec as gridspec
def custom_round(freq):
if freq < 1:
return round(freq, 1)
else:
return round(freq)
# Setting global parameters
plt.rcParams.update({
'font.size': 12, # Global font size
'axes.titlesize': 11, # Title font size
'axes.labelsize': 11, # Axis label font size
'xtick.labelsize': 10, # X-axis tick label font size
'ytick.labelsize': 10, # Y-axis tick label font size
'legend.fontsize': 12, # Legend font size
'figure.titlesize': 12.5, # Figure title font size
'axes.grid': False, # Turn on grid by default
'grid.alpha': 0.5, # Grid transparency
'grid.linestyle': '--', # Grid line style
})
significance_threshold=0.05
plt.rc('axes', linewidth=1.0)
plt.rc('lines', linewidth=1.3)
# Color cycle for consistency across plots
colors = plt.cm.tab10(np.linspace(0, 1, len(imfs)))
# Create a multi-panel plot with custom grid layout
fig = plt.figure(figsize=(8., 9.2), constrained_layout=True)
gs = gridspec.GridSpec(len(imfs) + 2, 2, height_ratios=[1]*len(imfs) + [0.3, 2], figure=fig)
# Plot each IMF and its instantaneous frequency side by side
for i, (imf, freq) in enumerate(zip(imfs, instantaneous_frequencies)):
ax_imf = fig.add_subplot(gs[i, 0])
ax_if = fig.add_subplot(gs[i, 1])
if IMF_significance_levels[i] > significance_threshold:
ax_imf.set_facecolor('lightgray')
ax_imf.plot(time, imf, label=f'IMF {i+1}', color=colors[i])
ax_imf.set_ylabel(f'IMF {i+1}')
ax_imf.yaxis.set_label_coords(-0.16, 0.5) # Align y-axis labels
ax_imf.xaxis.set_minor_locator(AutoMinorLocator(5))
ax_imf.yaxis.set_minor_locator(AutoMinorLocator(3))
ax_imf.tick_params(axis='both', which='major', direction='in', length=6, width=1.0)
ax_imf.tick_params(axis='both', which='minor', direction='in', length=3, width=1.0)
if i < len(imfs) - 1:
ax_imf.set_xticklabels([]) # Hide x labels for all but the last IMF plot
if i == 0:
ax_imf.set_title('(a) IMFs')
ax_imf.tick_params(axis='x', which='both', top=False) # Turn off upper x-axis ticks
ax_imf.set_xlim(0, 10)
if i == len(imfs) - 1:
ax_imf.set_xlabel('Time (s)')
if IMF_significance_levels[i] > significance_threshold:
ax_if.set_facecolor('lightgray')
if len(freq) < len(time):
freq = np.append(freq, freq[-1]) # Extend freq to match the length of time
ax_if.plot(time, freq, label=f'IF {i+1}', color=colors[i])
ax_if.set_ylabel(f'IF {i+1} (Hz)')
ax_if.yaxis.set_label_coords(-0.1, 0.5) # Align y-axis labels
ax_if.xaxis.set_minor_locator(AutoMinorLocator(5))
ax_if.yaxis.set_minor_locator(AutoMinorLocator(5))
ax_if.tick_params(axis='both', which='major', direction='in', length=6, width=1.0)
ax_if.tick_params(axis='both', which='minor', direction='in', length=3, width=1.0)
if i < len(imfs) - 1:
ax_if.set_xticklabels([]) # Hide x labels for all but the last IF plot
if i == 0:
ax_if.set_title('(b) Instantaneous Frequencies')
ax_if.tick_params(axis='x', which='both', top=False) # Turn off upper x-axis ticks
ax_if.set_xlim(0, 10)
if i == len(imfs) - 1:
ax_if.set_xlabel('Time (s)')
# Plot the HHT marginal spectrum and FFT spectra in the last row
# Panel (c): HHT Marginal Spectrum
ax_hht = fig.add_subplot(gs[-1, 0])
# freq_bins = np.linspace(0, 50, len(HHT_power_spectrum)) # Assuming a maximum frequency of 50 Hz for illustration
ax_hht.plot(HHT_freq_bins, HHT_power_spectrum, color='black')
ax_hht.plot(HHT_freq_bins, HHT_significance_level, linestyle='--', color='green')
ax_hht.set_title('(c) HHT Marginal Spectrum')
ax_hht.set_xlabel('Frequency (Hz)')
ax_hht.set_ylabel('Power')
ax_hht.xaxis.set_minor_locator(AutoMinorLocator(5))
ax_hht.yaxis.set_minor_locator(AutoMinorLocator(5))
ax_hht.tick_params(axis='both', which='major', direction='out', length=6, width=1.0)
ax_hht.tick_params(axis='both', which='minor', direction='out', length=3, width=1.0)
ax_hht.tick_params(axis='x', which='both', top=False) # Turn off upper x-axis ticks
ax_hht.set_xlim(0,36)
ax_hht.set_ylim(bottom=0)
# Mark pre-defined frequencies with vertical lines
pre_defined_freq = [2,5,10,12,15,18,25,33]
for freq in pre_defined_freq:
rounded_freq = custom_round(freq)
plt.axvline(x=freq, color='gray', linestyle=':')
# Panel (d): FFT Spectra of IMFs
ax_fft = fig.add_subplot(gs[-1, 1])
for i, (xf, psd) in enumerate(psd_spectra_fft):
ax_fft.plot(xf, psd, label=f'IMF {i+1}', color=colors[i])
for i, confidence_level in enumerate(confidence_levels_fft):
ax_fft.plot(xf, confidence_level, linestyle='--', color=colors[i])
ax_fft.set_title('(d) FFT Spectra of IMFs')
ax_fft.set_xlabel('Frequency (Hz)')
ax_fft.set_ylabel('Power')
ax_fft.xaxis.set_minor_locator(AutoMinorLocator(5))
ax_fft.yaxis.set_minor_locator(AutoMinorLocator(5))
ax_fft.tick_params(axis='both', which='major', direction='out', length=6, width=1.0)
ax_fft.tick_params(axis='both', which='minor', direction='out', length=3, width=1.0)
ax_fft.tick_params(axis='x', which='both', top=False) # Turn off upper x-axis ticks
ax_fft.set_xlim(0,36)
ax_fft.set_ylim(0,2.5)
# Mark pre-defined frequencies with vertical lines
pre_defined_freq = [2,5,10,12,15,18,25,33]
for freq in pre_defined_freq:
rounded_freq = custom_round(freq)
plt.axvline(x=freq, color='gray', linestyle=':')
# Save the figure as a PDF
pdf_path = 'Figures/FigS2_EMD_analysis.pdf'
WaLSA_save_pdf(fig, pdf_path, color_mode='CMYK', dpi=300, bbox_inches='tight', pad_inches=0)
plt.show()
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