您可以使用imshow或pcolormesh，正如已经注解的那样（差异解释为here）。
这里有一个例子，声谱图是用scipy.signal.stft制作的，它为我们创建了dft数组。为了缩短代码，我没有详细说明帮助函数，如果你需要的话，可以随时询问细节。

红色区域的STFT谱图+ FFT

import numpy as np
from scipy.io import wavfile
from scipy.signal import stft, hamming
from scipy.fftpack import fft, fftfreq, fftshift
from matplotlib import pyplot as plt

#def init_figure(titles, x_labels, grid=False): ...
#def freq_idx(freqs, fs, n, center=0): ...
#def sample_idx(samples, fs): ...

# Parameters
window = 'hamming' # Type of window
nperseg = 180 # Sample per segment
noverlap = int(nperseg * 0.7) # Overlapping samples
nfft = 256 # Padding length
return_onesided = False # Negative + Positive 
scaling = 'spectrum' # Amplitude

freq_low, freq_high = 600, 1780
time_low, time_high = 0.103, 0.1145

# Read data
fs, data = wavfile.read(filepath)
if len(data.shape) > 1: data = data[:,0] # select first channel

# Prepare plot
fig, (ax1, ax2) = init_figure([f'STFT padding={nfft}', 'DFT of selected samples'], ['time (s)', 'amplitude'])

# STFT (f=freqs, t=times, Zxx=STFT of input)
f, t, Zxx = stft(data, fs, window=window, nperseg=nperseg, noverlap=noverlap,
                 nfft=nfft, return_onesided=return_onesided, scaling=scaling)
f_shifted = fftshift(f)
Z_shifted = fftshift(Zxx, axes=0)

# Plot STFT for selected frequencies
freq_slice = slice(*freq_idx([freq_low, freq_high], fs, nfft, center=len(Zxx)//2))
ax1.pcolormesh(t, f_shifted[freq_slice], np.abs(Z_shifted[freq_slice]), shading='gouraud')
ax1.grid()

# FFT on selected samples
sample_slice = slice(*sample_idx([time_low, time_high], fs))
selected_samples = data[sample_slice]
selected_n = len(selected_samples)
X_shifted = fftshift(fft(selected_samples * hamming(selected_n)) / selected_n)
freqs_shifted = fftshift(fftfreq(selected_n, 1/fs))
ax1.axvspan(time_low, time_high, color = 'r', alpha=0.4)

# Plot FFT
freq_slice = slice(*freq_idx([freq_low, freq_high], fs, len(freqs_shifted), center=len(freqs_shifted)//2))
ax2.plot(abs(X_shifted[freq_slice]), freqs_shifted[freq_slice])
ax2.margins(0, tight=True)
ax2.grid()
fig.tight_layout()