import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import spectrogram
def generate_random_signal_with_transforms(length, fs, freq_range):
    """
    Fungsi untuk membangkitkan sinyal analog acak dan melakukan Wavelet Transform serta FFT Spectrogram.
    Parameters:
    length (int): Panjang sinyal dalam detik.
    fs (int): Frekuensi sampling.
    freq_range (tuple): Rentang frekuensi minimum dan maksimum dalam Hz.
    Returns:
    Tuple(numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray): 
        Sinyal analog yang dibangkitkan, array waktu, koefisien wavelet, skala, frekuensi FFT, waktu FFT.
    """
    # Membangkitkan sinyal
    t = np.linspace(0, length, int(fs * length), endpoint=False)
    frequencies = np.random.uniform(freq_range[0], freq_range[1], size=(3,))
    signal = sum(np.sin(2 * np.pi * f * t) for f in frequencies)
    # Melakukan Continuous Wavelet Transform (CWT)
    scales = np.arange(1, 128)
    cwt_coefficients, _ = pywt.cwt(signal, scales, 'mexh')
    # Melakukan FFT Spectrogram
    fft_frequencies, fft_times, Sxx = spectrogram(signal, fs)
    return signal, t, cwt_coefficients, scales, fft_frequencies, fft_times, Sxx
# Contoh penggunaan fungsi
fs = 1000  # Frekuensi sampling 1000 Hz
length = 2  # Panjang sinyal 2 detik
freq_range = (10, 100)  # Rentang frekuensi antara 5 Hz hingga 50 Hz
# Contoh penggunaan fungsi dengan Wavelet Transform dan FFT Spectrogram
signal, t, cwt_coefficients, scales, fft_frequencies, fft_times, Sxx = generate_random_signal_with_transforms(length, fs, freq_range)
# Menambahkan visualisasi heatmap dan histogram
# Menghitung heatmap dan histogram
heatmap, xedges, yedges = np.histogram2d(t, signal, bins=50)
hist, bin_edges = np.histogram(signal, bins=50)
# Plot sinyal, Wavelet Transform, FFT Spectrogram, heatmap, dan histogram
fig, axs = plt.subplots(5, 1, figsize=(12, 20))
# Membuat koordinat 3D untuk visualisasi
X, Y = np.meshgrid(t, scales)
coefficients, frequencies = pywt.cwt(signal, scales, 'mexh')
Z = coefficients
# Plot sinyal asli
axs[0].plot(t, signal)
axs[0].set_title('Analog Signal')
axs[0].set_xlabel('Time [seconds]')
axs[0].set_ylabel('Amplitude')
# Plot Wavelet Transform
axs[1].pcolormesh(X, Y, cwt_coefficients, shading='gouraud', cmap='viridis')
axs[1].set_title('Visualisasi Transformasi Wavelet')
axs[1].set_xlabel('Time [seconds]')
axs[1].set_ylabel('Scale')
fig.colorbar(axs[1].pcolormesh(X, Y, cwt_coefficients, shading='gouraud', cmap='viridis'), ax=axs[1], label='Coefficient')
# Plot FFT Spectrogram
axs[2].pcolormesh(fft_times, fft_frequencies, 10 * np.log10(Sxx), shading='gouraud', cmap='plasma')
axs[2].set_ylabel('Frequency [Hz]')
axs[2].set_xlabel('Time [sec]')
axs[2].set_title('Visualisasi Spectrogram menggunakan FFT')
fig.colorbar(axs[2].pcolormesh(fft_times, fft_frequencies, 10 * np.log10(Sxx), shading='gouraud', cmap='plasma'), ax=axs[2], label='Intensity [dB]')
# Plot Heatmap
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
axs[3].imshow(heatmap.T, extent=extent, origin='lower', cmap='hot', aspect='auto')
axs[3].set_title('Heatmap dari sinyal')
axs[3].set_xlabel('Time [seconds]')
axs[3].set_ylabel('Amplitude')
# Plot Histogram
axs[4].bar(bin_edges[:-1], hist, width=np.diff(bin_edges), edgecolor='black')
axs[4].set_title('Histogram dari Sinyal')
axs[4].set_xlabel('Amplitude')
axs[4].set_ylabel('Frequency')
plt.tight_layout()
plt.show()

import mne
# Menentukan lokasi file .edf
file_path = 'chb01_01.edf'
# Membaca data dari file .edf
raw_data = mne.io.read_raw_edf(file_path, preload=True)
# Daftar semua nama channel
print("Available channels:", raw_data.ch_names)
scalings = {'eeg': 550e-6}
# Visualisasi data
plot = raw_data.plot(show=False)  # Set 'show' ke True jika menjalankan secara lokal
# Menampilkan informasi dasar tentang data
print(raw_data.info)

import mne
# Menentukan lokasi file .edf
file_path = 'chb01_01.edf'
# Membaca data dari file .edf
raw_data = mne.io.read_raw_edf(file_path, preload=True)
# Daftar semua nama channel
print("Available channels:", raw_data.ch_names)
scalings = {'eeg': 550e-6}
# Pilih channel yang ingin Anda visualisasikan, misalnya 'FP1'
selected_channel = 'FP1-F7'
# Memplot hanya channel tertentu
raw_data.plot(duration=60, n_channels=1, order=[1], scalings=scalings, show=True)
# Menampilkan informasi dasar tentang data
print(raw_data.info)

import mne
import matplotlib.pyplot as plt
# Path ke file .edf
file_path = 'chb01_01.edf'
# Membaca data dari file .edf
raw = mne.io.read_raw_edf(file_path, preload=True)
# Pilih channel yang ingin Anda visualisasikan
selected_channel = 'FP1-F7'
channel_index = raw.ch_names.index(selected_channel)
# Ekstrak data dari channel tertentu
data, times = raw[selected_channel]
# Membuat plot dengan ukuran figure yang diinginkan
plt.figure(figsize=(15, 5))  # Lebar 15 inci dan tinggi 5 inci
plt.plot(times, data.T)
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')
plt.title(f'EEG Data - Channel: {selected_channel}')
plt.show()

import mne
import matplotlib.pyplot as plt
# Path ke file .edf
file_path = 'chb01_01.edf'
# Membaca data dari file .edf
raw = mne.io.read_raw_edf(file_path, preload=True)
# Tentukan channel yang ingin Anda visualisasikan
selected_channels = ['FP1-F7', 'F7-T7', 'T7-P7']  # Contoh: Menampilkan channel 'FP1', 'FP2', dan 'T8'
# Membuat figure dan axes
fig, axes = plt.subplots(len(selected_channels), 1, figsize=(15, 5 * len(selected_channels)))
# Loop melalui setiap channel yang dipilih dan plot data
for i, ch in enumerate(selected_channels):
    data, times = raw[ch]
    axes[i].plot(times, data.T)
    axes[i].set_title(f'Channel: {ch}')
    axes[i].set_xlabel('Time (s)')
    axes[i].set_ylabel('Amplitude')
plt.tight_layout()
plt.show()