How to combine multiple spectrogram subplots to produce single plot? - python-3.x

Question

I am visualizing four classes of the spectrogram. For a single class, My spectrogram code looks like this
Considering this as one image.
And the code to produce this, is
def spec(filename):
time_period = 0.5 # FFT time period (in seconds). Can comfortably process time frames from 0.05 seconds - 10 seconds
# ==============================================
fs_rate, signal_original = wavfile.read(filename)
total_time = int(np.floor(len(signal_original)/fs_rate))
sample_range = np.arange(0,total_time,time_period)
total_samples = len(sample_range)
print ("Frequency sampling", fs_rate)
print ("total time: ", total_time)
print ("sample time period: ", time_period)
print ("total samples: ", total_samples)
output_array = []
for i in sample_range:
# print ("Processing: %d / %d (%d%%)" % (i/time_period + 1, total_samples, (i/time_period + 1)*100/total_samples))
sample_start = int(i*fs_rate)
sample_end = int((i+time_period)*fs_rate)
signal = signal_original[sample_start:sample_end]
l_audio = len(signal.shape)
#print ("Channels", l_audio)
if l_audio == 2:
signal = signal.sum(axis=1) / 2
N = signal.shape[0]
#print ("Complete Samplings N", N)
secs = N / float(fs_rate)
# print ("secs", secs)
Ts = 1.0/fs_rate # sampling interval in time
#print ("Timestep between samples Ts", Ts)
t = scipy.arange(0, secs, Ts) # time vector as scipy arange field / numpy.ndarray
FFT = abs(scipy.fft(signal))
FFT_side = FFT[range(int(N/2))] # one side FFT range
freqs = scipy.fftpack.fftfreq(signal.size, t[1]-t[0])
fft_freqs = np.array(freqs)
freqs_side = freqs[range(int(N/2))] # one side frequency range
fft_freqs_side = np.array(freqs_side)
# Reduce to 0-5000 Hz
bucket_size = 5
buckets = 16
FFT_side = FFT_side[0:bucket_size*buckets]
fft_freqs_side = fft_freqs_side[0:bucket_size*buckets]
# Combine frequencies into buckets
FFT_side = np.array([int(sum(FFT_side[current: current+bucket_size])) for current in range(0, len(FFT_side), bucket_size)])
fft_freqs_side = np.array([int(sum(fft_freqs_side[current: current+bucket_size])) for current in range(0, len(fft_freqs_side), bucket_size)])
# FFT_side: Normalize (0-1)
max_value = max(FFT_side)
if (max_value != 0):
FFT_side_norm = FFT_side / max_value
# Append to output array
output_array.append(FFT_side_norm)
# ============================================
# Plotting
plt.figure(figsize=(4,7))
plt.subplot(411)
plt.subplots_adjust(hspace = 0.5)
plt.plot(t, signal, "g") # plotting the signal
plt.xlabel('Time')
plt.ylabel('Amplitude')
plt.subplot(412)
diff = np.diff(fft_freqs_side)
widths = np.hstack([diff, diff[-1]])
plt.bar(fft_freqs_side, abs(FFT_side_norm), width=widths) # plotting the positive fft spectrum
plt.xticks(fft_freqs_side, fft_freqs_side, rotation='vertical')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Count single-sided')
FFT_side_norm_line = FFT_side_norm.copy()
FFT_side_norm_line.resize( (1,buckets) )
plt.subplot(413)
plt.imshow(FFT_side_norm_line)
plt.xlabel('Image Representation 1D')
plt.show()
print("\n\n\n\n\n\n")
How can I combine four images plot like this, and make a single output image. Something like this

Answer 1

I'd suggest using fig.subfigures and plt.subplot_mosaic.
The plot above was obtained using this simple script:
import matplotlib.pyplot as plt
fig = plt.figure(figsize = (8, 10), layout='constrained')
# next two lines make the trick
sfigs = fig.subfigures(2,2)
mosaics = [f.subplot_mosaic('t;t;t;f;f;f;i;.') for f in sfigs.flat]
# next, "how to" reference the subplots in subfigures
mosaics[0]['t'].plot(range(5), color='b')
mosaics[1]['t'].plot(range(5), color='k')
mosaics[2]['t'].plot(range(5), color='r')
mosaics[3]['t'].plot(range(5), color='g')
mosaics[0]['f'].plot(range(3), color='b')
mosaics[1]['f'].plot(range(3), color='k')
mosaics[2]['f'].plot(range(3), color='r')
mosaics[3]['f'].plot(range(3), color='g')
mosaics[0]['i'].imshow([range(10)]*2)
plt.show()

Answer 2

You can do it this way:
fig, axs = plt.subplots(2, 2)
axs[0, 0].plot(x, y)
axs[0, 0].set_title('Axis [0, 0]')
axs[0, 1].plot(x, y, 'tab:orange')
axs[0, 1].set_title('Axis [0, 1]')
axs[1, 0].plot(x, -y, 'tab:green')
axs[1, 0].set_title('Axis [1, 0]')
axs[1, 1].plot(x, -y, 'tab:red')
axs[1, 1].set_title('Axis [1, 1]')
for ax in axs.flat:
ax.set(xlabel='x-label', ylabel='y-label')
# Hide x labels and tick labels for top plots and y ticks for right plots.
for ax in axs.flat:
ax.label_outer()
The result will be like this:
Taken from https://matplotlib.org/stable/gallery/subplots_axes_and_figures/subplots_demo.html