import librosa.display
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.style as ms
from PIL import Image

import matplotlib.ticker as ticker
from librosa.feature import melspectrogram
from python.util.time_util import int_to_min_sec

import librosa
import librosa.display
import numpy as np
from io import BytesIO

# https://stackoverflow.com/questions/69924881/userwarning-starting-a-matplotlib-gui-outside-of-the-main-thread-will-likely-fa
matplotlib.use('agg')
ms.use('seaborn-muted')


def update_ticks(x, pos):
    which_second = (x / 16000)
    return int_to_min_sec(which_second)


def plt_line(y_points, sample_rate=16000):
    # plt line
    fig, ax = plt.subplots()
    ax.xaxis.set_major_formatter(ticker.FuncFormatter(update_ticks))
    plt.plot(y_points)

    # plot to image
    buffer = BytesIO()
    plt.savefig(buffer, format='png')
    image = Image.open(buffer)

    return image


# plt mfcc, https://www.cnblogs.com/LXP-Never/p/10918590.html
def plt_mfcc(single_channel, sample_rate):
    mel_spec = melspectrogram(y=single_channel, sr=sample_rate, n_fft=1024, hop_length=512, n_mels=128)
    log_mel_spec = librosa.power_to_db(mel_spec)
    plt.figure()
    librosa.display.specshow(log_mel_spec, sr=sample_rate, x_axis='time', y_axis='mel')
    plt.colorbar(format='%+2.0f dB')  # 右边的色度条
    plt.title('mfcc waveform')

    # plot to image
    buffer = BytesIO()
    plt.savefig(buffer, format='png')
    image = Image.open(buffer)

    return image


# https://gist.github.com/stevemclaugh/80f192130852353ad53e6d8b6b275983
def plt_mfcc2(wav_pathname, sample_rate):
    y, sr = librosa.load(wav_pathname)
    # Let's make and display a mel-scaled power (energy-squared) spectrogram
    S = librosa.feature.melspectrogram(y=y, sr=sample_rate, n_mels=128)

    # Convert to log scale (dB). We'll use the peak power as reference.
    log_S = librosa.amplitude_to_db(S)

    # Make a new figure
    plt.figure(figsize=(12, 4))

    # Display the spectrogram on a mel scale
    # sample rate and hop length parameters are used to render the time axis
    librosa.display.specshow(log_S, sr=sample_rate, x_axis='time', y_axis='mel')

    # Put a descriptive title on the plot
    plt.title('mel power spectrogram')

    # draw a color bar
    plt.colorbar(format='%+02.0f dB')

    # Make the figure layout compact
    plt.tight_layout()

    S_rot = np.rot90(S, 3)

    # Next, we'll extract the first 13 Mel-frequency cepstral coefficients (MFCCs)
    mfcc = librosa.feature.mfcc(S=log_S, n_mfcc=13)

    # Padding first and second deltas
    delta_mfcc = librosa.feature.delta(mfcc)
    delta2_mfcc = librosa.feature.delta(mfcc, order=2)

    # We'll show each in its own subplot
    # plt.figure(figsize=(12, 6))
    plt.figure()

    # plt.subplot(3, 1, 1)
    # librosa.display.specshow(mfcc)
    # plt.ylabel('MFCC')
    # plt.colorbar()

    # plt.subplot(1, 1, 1)
    # librosa.display.specshow(delta_mfcc)
    # plt.ylabel('MFCC-$\Delta$')
    # plt.colorbar()

    plt.subplot()
    librosa.display.specshow(delta2_mfcc, sr=sample_rate, x_axis='time')
    plt.ylabel('MFCC-$\Delta^2$')
    plt.colorbar()

    plt.tight_layout()

    # plot to image
    buffer = BytesIO()
    plt.savefig(buffer, format='png')
    image = Image.open(buffer)

    return image