metrics/visualizations.py

import numpy as np
from matplotlib import pyplot as plt
from scipy.fft import fft
from scipy.signal import savgol_filter
from tools import rms_normalize

colors = [
    # (0, 0, 0),       # Black
    # (86, 180, 233),  # Sky blue
    # (240, 228, 66),  # Yellow
    # (204, 121, 167),  # Reddish purple
    (213, 94, 0),  # Vermilion
    (0, 114, 178),  # Blue
    (230, 159, 0),  # Orange
    (0, 158, 115),  # Bluish green
]


def plot_psd_multiple_signals(signals_list, labels_list, sample_rate=16000, window_size=500,

                              figsize=(10, 6), save_path=None, normalize=False):
    """

    在同一张图上绘制多组音频信号的功率谱密度比较图，使用对数刻度的响度轴（以2为底），并应用平滑处理。



    参数:

    signals_list: 包含多组音频信号的列表，每组信号形状为 [sample_number, sample_length] 的numpy array

    labels_list: 每组音频信号对应的标签字符串列表

    sample_rate: 音频的采样率

    """

    # 确保传入的signals_list和labels_list长度相同
    assert len(signals_list) == len(labels_list), "每组信号必须有一个对应的标签。"

    signals_list = [np.array([rms_normalize(signal) for signal in signals]) for signals in signals_list]

    # 绘图准备
    plt.figure(figsize=figsize)

    # 遍历所有的音频信号
    i = 0
    for signal, label in zip(signals_list, labels_list):
        # 计算FFT
        fft_signal = fft(signal, axis=1)

        # 计算平均功率谱密度
        psd_signal = np.mean(np.abs(fft_signal)**2, axis=0)

        # 计算频率轴
        freqs = np.fft.fftfreq(signal.shape[1], 1/sample_rate)

        # 应用Savitzky-Golay滤波器进行平滑
        psd_smoothed = savgol_filter(np.log2(psd_signal[:signal.shape[1] // 2] + 1), window_size, 3)  # 窗口大小51, 多项式阶数3

        # Normalize each curve if normalize is True
        if normalize:
            psd_smoothed /= np.mean(psd_smoothed)

        # 绘制每组信号的功率谱密度
        plt.plot(freqs[:signal.shape[1] // 2], psd_smoothed, label=label, color=[x/255.0 for x in colors[i % len(colors)]], linewidth=1)
        i += 1

    # 设置图表元素
    plt.xlabel('Frequency (Hz)')
    plt.ylabel('Mean Log-Amplitude')
    plt.legend()

    # 根据save_path参数决定保存图像还是直接显示
    if save_path:
        plt.savefig(save_path)
    else:
        plt.show()


def plot_amplitude_over_time(signals_list, labels_list, sample_rate=16000, window_size=500,

                             figsize=(10, 6), save_path=None, normalize=False, start_time=0):
    """

    Plot the loudness of multiple sets of audio signals over time on the same graph,

    using a logarithmic scale for the loudness axis (base 2), with smoothing applied.



    Parameters:

    signals_list: List of sets of audio signals, each set is a numpy array with shape [sample_number, sample_length]

    labels_list: List of labels corresponding to each set of audio signals

    sample_rate: Sampling rate of the audio

    window_size: Window size for the Savitzky-Golay filter

    figsize: Figure size

    save_path: Path to save the figure, if None, the figure will be displayed

    normalize: Whether to normalize each curve so that the sum of each curve is the same

    start_time: Time (in seconds) to start plotting, only data after this time will be retained

    """
    assert len(signals_list) == len(labels_list), f"len(signals_list) != len(labels_list) for " \
                                                  f"len(signals_list) = {len(signals_list)} and len(labels_list) = {len(labels_list)}"

    # Compute starting sample index
    start_sample = int(start_time * sample_rate)
    
    # Normalize signals and truncate data
    signals_list = [np.array([rms_normalize(signal)[start_sample:] for signal in signals]) for signals in signals_list]
    time_axis = np.arange(start_sample, start_sample + signals_list[0].shape[1]) / sample_rate

    plt.figure(figsize=figsize)

    i = 0
    for signal, label in zip(signals_list, labels_list):
        amplitude_mean = np.mean(np.abs(signal), axis=0)

        amplitude_smoothed = savgol_filter(np.log2(amplitude_mean + 1), window_size, 3)

        # Normalize each curve if normalize is True
        if normalize:
            amplitude_smoothed /= np.mean(amplitude_smoothed)

        plt.plot(time_axis, amplitude_smoothed, label=label, color=[x/255.0 for x in colors[i % len(colors)]], linewidth=1)
        i += 1

    plt.xlabel('Time (seconds)')
    plt.ylabel('Mean Log-Amplitude')
    plt.legend()

    # Save or show the figure based on save_path parameter
    if save_path:
        plt.savefig(save_path)
    else:
        plt.show()