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import numpy as np
import pywt
import os
from scipy.signal import butter, filtfilt, iirnotch, medfilt, find_peaks
import matplotlib.pyplot as plt
# 1. Load the Data

print("Loading datasets...")
data_train = np.load("X_train.npy")
data_valid = np.load("X_val.npy")
data_test = np.load("X_test.npy")

print("\n--- Dataset Shapes ---")
print(f"Train data shape: {data_train.shape}")
print(f"Validation data shape: {data_valid.shape}")
print(f"Test data shape: {data_test.shape}")
print("----------------------\n")

os.mkdir("filtered_data")

# 2. Define the Processing Functions
def _remove_baseline_wander(ecg_signal, wavelet='sym8'):
    desired  = int(np.ceil(np.log2(100/0.5)))
    max_level = pywt.dwt_max_level(len(ecg_signal), pywt.Wavelet(wavelet).dec_len)
    level =  min(desired, max_level)
    
    coeffs = pywt.wavedec(ecg_signal, wavelet, level=level)
    coeffs_filt = [coeffs[0]] + [np.zeros_like(c) for c in coeffs[1:]]
    
    baseline = pywt.waverec(coeffs_filt, wavelet)
    baseline = baseline[:len(ecg_signal)]
    
    return ecg_signal - baseline

def _denoise(data):
    # Wavelet transform
    coeffs = pywt.wavedec(data=data, wavelet='db5', level=9)
    cA9, cD9, cD8, cD7, cD6, cD5, cD4, cD3, cD2, cD1 = coeffs

    # Threshold denoising
    threshold = (np.median(np.abs(cD1)) / 0.6745) * (np.sqrt(2 * np.log(len(cD1))))
    cD1.fill(0)
    cD2.fill(0)
    
    for i in range(1, len(coeffs) - 2):
        coeffs[i] = pywt.threshold(coeffs[i], threshold)

    # Inverse wavelet transform to obtain the denoised signal
    rdata = pywt.waverec(coeffs=coeffs, wavelet='db5')
    return rdata

def remove_noise_and_wander(data):
    baseline_wander = _remove_baseline_wander(data)
    cleaned = _denoise(baseline_wander)
    return cleaned

# 3. Apply the Filter
# Assuming your data shape is (num_samples, signal_length). 
# If it's a 1D array of a single continuous signal, remove the list comprehension loop.
def process_batch(dataset_array):
    print(f"Processing {len(dataset_array)} samples...")
    # Applies the cleaning function to each row/signal in the array
    return [remove_noise_and_wander(signal) for signal in dataset_array]

print("\nFiltering Train Data...")
train_filtered = process_batch(data_train)

print("Filtering Validation Data...")
valid_filtered = process_batch(data_valid)

print("Filtering Test Data...")
test_filtered = process_batch(data_test)

np.save("filtered_data/X_train_filtered.npy", train_filtered)
np.save("filtered_data/X_val_filtered.npy", valid_filtered)
np.save("filtered_data/X_test_filtered.npy", test_filtered)


print("Loading data...")
data_train_new = np.load("filtered_data/X_train_filtered.npy")
data_valid_new = np.load("filtered_data/X_val_filtered.npy")
data_test_new = np.load("filtered_data/X_test_filtered.npy")

print("\n--- Dataset Shapes ---")
print(f"Train data shape: {data_train_new.shape}")
print(f"Validation data shape: {data_valid_new.shape}")
print(f"Test data shape: {data_test_new.shape}")
print("----------------------\n")


# --- 3. Plot 5 Samples Before and After ---
# We assume data_train is a 2D array of shape (num_samples, signal_length)
num_samples_to_plot = 5

# Create a figure with 5 rows and 2 columns
fig, axes = plt.subplots(nrows=num_samples_to_plot, ncols=2, figsize=(16, 12))
fig.suptitle('ECG Signal Processing: Before vs. After', fontsize=18, fontweight='bold')

for i in range(num_samples_to_plot):
    # Get the original 1D signal for the i-th sample
    original_signal = data_train_new[i]
    
    # Process the signal
    filtered_signal = remove_noise_and_wander(original_signal)
    
    # Plot Original (Before) - Left Column
    axes[i, 0].plot(original_signal, color='crimson', linewidth=1)
    axes[i, 0].set_title(f"Sample {i+1}: Original (Raw)")
    axes[i, 0].set_ylabel("Amplitude")
    axes[i, 0].grid(True, linestyle='--', alpha=0.6)
    
    # Plot Filtered (After) - Right Column
    axes[i, 1].plot(filtered_signal, color='navy', linewidth=1)
    axes[i, 1].set_title(f"Sample {i+1}: Filtered (Denoised & Baseline Removed)")
    axes[i, 1].grid(True, linestyle='--', alpha=0.6)
    
    # Only show x-axis label for the bottom-most plots to keep it clean
    if i == num_samples_to_plot - 1:
        axes[i, 0].set_xlabel("Time steps")
        axes[i, 1].set_xlabel("Time steps")

# Adjust layout so titles and labels don't overlap
plt.tight_layout(rect=[0, 0.03, 1, 0.96])

# Save the figure as a high-resolution PNG
plt.savefig("ecg_filtering_results.png", dpi=300, bbox_inches='tight')
print("Image saved successfully as 'ecg_filtering_results.png'")
plt.show()

	import numpy as np
	import pywt
	import os
	from scipy.signal import butter, filtfilt, iirnotch, medfilt, find_peaks
	import matplotlib.pyplot as plt
	# 1. Load the Data

	print("Loading datasets...")
	data_train = np.load("X_train.npy")
	data_valid = np.load("X_val.npy")
	data_test = np.load("X_test.npy")

	print("\n--- Dataset Shapes ---")
	print(f"Train data shape: {data_train.shape}")
	print(f"Validation data shape: {data_valid.shape}")
	print(f"Test data shape: {data_test.shape}")
	print("----------------------\n")

	os.mkdir("filtered_data")

	# 2. Define the Processing Functions
	def _remove_baseline_wander(ecg_signal, wavelet='sym8'):
	desired = int(np.ceil(np.log2(100/0.5)))
	max_level = pywt.dwt_max_level(len(ecg_signal), pywt.Wavelet(wavelet).dec_len)
	level = min(desired, max_level)

	coeffs = pywt.wavedec(ecg_signal, wavelet, level=level)
	coeffs_filt = [coeffs[0]] + [np.zeros_like(c) for c in coeffs[1:]]

	baseline = pywt.waverec(coeffs_filt, wavelet)
	baseline = baseline[:len(ecg_signal)]

	return ecg_signal - baseline

	def _denoise(data):
	# Wavelet transform
	coeffs = pywt.wavedec(data=data, wavelet='db5', level=9)
	cA9, cD9, cD8, cD7, cD6, cD5, cD4, cD3, cD2, cD1 = coeffs

	# Threshold denoising
	threshold = (np.median(np.abs(cD1)) / 0.6745) * (np.sqrt(2 * np.log(len(cD1))))
	cD1.fill(0)
	cD2.fill(0)

	for i in range(1, len(coeffs) - 2):
	coeffs[i] = pywt.threshold(coeffs[i], threshold)

	# Inverse wavelet transform to obtain the denoised signal
	rdata = pywt.waverec(coeffs=coeffs, wavelet='db5')
	return rdata

	def remove_noise_and_wander(data):
	baseline_wander = _remove_baseline_wander(data)
	cleaned = _denoise(baseline_wander)
	return cleaned

	# 3. Apply the Filter
	# Assuming your data shape is (num_samples, signal_length).
	# If it's a 1D array of a single continuous signal, remove the list comprehension loop.
	def process_batch(dataset_array):
	print(f"Processing {len(dataset_array)} samples...")
	# Applies the cleaning function to each row/signal in the array
	return [remove_noise_and_wander(signal) for signal in dataset_array]

	print("\nFiltering Train Data...")
	train_filtered = process_batch(data_train)

	print("Filtering Validation Data...")
	valid_filtered = process_batch(data_valid)

	print("Filtering Test Data...")
	test_filtered = process_batch(data_test)

	np.save("filtered_data/X_train_filtered.npy", train_filtered)
	np.save("filtered_data/X_val_filtered.npy", valid_filtered)
	np.save("filtered_data/X_test_filtered.npy", test_filtered)


	print("Loading data...")
	data_train_new = np.load("filtered_data/X_train_filtered.npy")
	data_valid_new = np.load("filtered_data/X_val_filtered.npy")
	data_test_new = np.load("filtered_data/X_test_filtered.npy")

	print("\n--- Dataset Shapes ---")
	print(f"Train data shape: {data_train_new.shape}")
	print(f"Validation data shape: {data_valid_new.shape}")
	print(f"Test data shape: {data_test_new.shape}")
	print("----------------------\n")


	# --- 3. Plot 5 Samples Before and After ---
	# We assume data_train is a 2D array of shape (num_samples, signal_length)
	num_samples_to_plot = 5

	# Create a figure with 5 rows and 2 columns
	fig, axes = plt.subplots(nrows=num_samples_to_plot, ncols=2, figsize=(16, 12))
	fig.suptitle('ECG Signal Processing: Before vs. After', fontsize=18, fontweight='bold')

	for i in range(num_samples_to_plot):
	# Get the original 1D signal for the i-th sample
	original_signal = data_train_new[i]

	# Process the signal
	filtered_signal = remove_noise_and_wander(original_signal)

	# Plot Original (Before) - Left Column
	axes[i, 0].plot(original_signal, color='crimson', linewidth=1)
	axes[i, 0].set_title(f"Sample {i+1}: Original (Raw)")
	axes[i, 0].set_ylabel("Amplitude")
	axes[i, 0].grid(True, linestyle='--', alpha=0.6)

	# Plot Filtered (After) - Right Column
	axes[i, 1].plot(filtered_signal, color='navy', linewidth=1)
	axes[i, 1].set_title(f"Sample {i+1}: Filtered (Denoised & Baseline Removed)")
	axes[i, 1].grid(True, linestyle='--', alpha=0.6)

	# Only show x-axis label for the bottom-most plots to keep it clean
	if i == num_samples_to_plot - 1:
	axes[i, 0].set_xlabel("Time steps")
	axes[i, 1].set_xlabel("Time steps")

	# Adjust layout so titles and labels don't overlap
	plt.tight_layout(rect=[0, 0.03, 1, 0.96])

	# Save the figure as a high-resolution PNG
	plt.savefig("ecg_filtering_results.png", dpi=300, bbox_inches='tight')
	print("Image saved successfully as 'ecg_filtering_results.png'")
	plt.show()