How to plot connectivity circular graph for specific frequency band measured with 'cwt_morlet'

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The objective is to plot the connectivity for specific bands which has been calculated using the ‘cwt_morlet’.

However, I’m not so clear how to extract information for specific band-range (e.g., delta, theta, alpha,beta,gamma) from the con array of the spectral_connectivity.

The snippet below is to plot circular graph for each band for each connectivity measure method

for con ,measure_type in zip(epochs.ch_names,set_data['connectivity_methods']):
    all_fig = [plot_conn ( con [:, :, idx], all_ch, idx, band) for idx, band in
                   enumerate ( ["delta", "theta", "alpha", "beta", "gamma"] )]

However, I’m not sure what is the proper setting to select specific freq band (e.g., alpha) in the con [:, :, idx].

Appreciate for any insight about this.

The full code together with the explanation is as below.

import mne
from mne.connectivity import spectral_connectivity
from mne.viz import circular_layout, plot_connectivity_circle
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas

# Formating setting
Freq_Bands = {"delta": [1.25, 4.0],"theta": [4.0, 8.0],
                  "alpha": [8.0, 13.0],"beta": [13.0, 30.0],"gamma": [30.0, 49.0]}
nband=len(Freq_Bands)
# Setting for number of channels, connectivity ploting, and frequency range for the 'cwt_morlet' calclation
set_data = {'label_names': ['FP1', 'FP2', 'F3', 'F4', 'F7', 'F8', 'C3', 'C4','T3', 'T4', 'O1', 'O2'],
            'lh_labels': ['FP1', 'F7', 'F3', 'C3', 'T3', 'O1'],
                'rh_labels': ['FP2', 'F8', 'F4', 'C4', 'T4', 'O2'], 'connectivity_methods': ["coh",'wpli2_debiased'],
            'cwt_freqs' :np.arange(1, 49, 2),'cwt_n_cycles': np.arange(1, 49, 2) / 5.}

# Setting for the epochs
sig_setting=dict(n_epochs = 5,n_channels = len ( set_data ['label_names'] ),n_times = 1000,
                 sfreq = 256,wave_freq = 10 )

def create_epochs ():

    np.random.seed ( 42 )

    data = np.random.rand ( sig_setting['n_epochs'], sig_setting['n_channels'], sig_setting['n_times'] )
    epoch_len = sig_setting['n_times'] / sig_setting['sfreq']
    for i in range (  sig_setting['n_epochs']):
        for c in range ( sig_setting['n_channels'] ):
            phase = np.random.rand ( 1 ) * 10
            data [i, c] = np.squeeze ( np.sin ( np.linspace ( -sig_setting['wave_freq'] * epoch_len * np.pi + phase,
                              sig_setting['wave_freq'] * epoch_len * np.pi + phase, sig_setting['n_times'] ) ) )

    return mne.EpochsArray ( data, mne.create_info ( ch_names=set_data ['label_names'],
                                                     ch_types=['eeg'] * len ( set_data ['label_names'] ),
                                                     sfreq=sig_setting['sfreq']) )

def plot_conn (conmat, all_ch, idx, bands, set_data):
    # Generate circular graph
    node_order = set_data ['lh_labels'] + set_data ['rh_labels']
    node_angles = circular_layout ( all_ch, node_order, start_pos=90,
                                    group_boundaries=[0, len ( all_ch ) // 2] )

    fig = plt.figure ( num=None, figsize=(8, 8), facecolor='black' )

    canvas = FigureCanvas ( fig )
    plot_connectivity_circle ( conmat, all_ch, n_lines=300,
                               node_angles=node_angles,
                               title=f'All-to-All Connectivity_ band_{bands}', fig=fig )

    canvas.draw ()
    s, (width, height) = canvas.print_to_buffer ()
    im0 = np.frombuffer ( s, np.uint8 ).reshape ( (height, width, 4) )
    return im0

# Create the epochs
epochs=epochs=create_epochs ( )

# Get the connectivity measure
con_all, freqs, times, _, _ = spectral_connectivity(epochs,method=set_data['connectivity_methods'],
                                            mode='cwt_morlet', sfreq=epochs.info['sfreq']  ,
        cwt_freqs=set_data['cwt_freqs'], cwt_n_cycles=set_data['cwt_n_cycles'], n_jobs=1)

# Plot the connectivity
for con ,measure_type in zip(epochs.ch_names,set_data['connectivity_methods']):
    all_fig = [plot_conn ( con [:, :, idx], all_ch, idx, band) for idx, band in
                   enumerate ( ["delta", "theta", "alpha", "beta", "gamma"] )]

    plt.imsave ( f'connectivity_{measure_type}.png', np.hstack ( all_fig ) )

Due to complain received in last post, I had trimmed the code-structure to a reasonable mean possible without compromising the readability level.