gistlib - the nmr sequence wavelet coefficients are input as cnn-resnet, and the corresponding nmr sequence wavelet coefficients are used as cnn-resnet output for denoising in python

To use wavelet coefficients as input to a CNN-ResNet for denoising an NMR sequence, you can follow the following steps:

Prepare your data: Load your NMR sequences into numpy arrays and apply the wavelet transform to them.


main.py
import pywt
import numpy as np

# Suppose `data` is your NMR sequence data
# and you want to use the 'db4' wavelet
wavelet = pywt.Wavelet('db4')
coeffs = pywt.wavedec(data, wavelet)
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Split the wavelet coefficients into smaller windows so that they can be used as individual inputs to the CNN-ResNet.


main.py
window_size = 128
X = []
for c in coeffs:
    # Split the coefficients into windows of length `window_size`
    n_windows = len(c) // window_size
    for i in range(n_windows):
        window = c[i*window_size:(i+1)*window_size]
        X.append(window)

X = np.array(X)
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Prepare your labels: If you have clean NMR sequences that correspond to your noisy NMR sequences, you can use them as labels. Otherwise, you can use the noisy NMR sequences as both input and labels, which will result in an autoencoder-like architecture.


main.py
# Suppose `labels` is your clean NMR sequence data
Y = []
for c in coeffs:
    # Split the coefficients into windows of length `window_size`
    n_windows = len(c) // window_size
    for i in range(n_windows):
        window = c[i*window_size:(i+1)*window_size]
        Y.append(window)

Y = np.array(Y)
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Define your CNN-ResNet model:


main.py
from tensorflow.keras.layers import Input, Conv1D, BatchNormalization, Activation
from tensorflow.keras.models import Model

def cnn_res_block(x, n_filters, kernel_size=3):
    y = Conv1D(n_filters, kernel_size, padding='same')(x)
    y = BatchNormalization()(y)
    y = Activation('relu')(y)
    y = Conv1D(n_filters, kernel_size, padding='same')(y)
    y = BatchNormalization()(y)
    y = Activation('relu')(y)

    # Add the input to the output (residual connection)
    y = x + y

    return y

def cnn_resnet(window_size):
    inputs = Input((window_size, 1))  # We use 1 channel since we only have one wavelet coefficient signal

    # First convolutional layer
    x = Conv1D(64, 3, padding='same')(inputs)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    # Multiple CNN-ResNet blocks
    n_filters = 64
    for i in range(3):
        x = cnn_res_block(x, n_filters)

    # Last convolutional layer
    x = Conv1D(1, 3, padding='same')(x)

    model = Model(inputs, x)
    return model
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Train your model:


main.py
model = cnn_resnet(window_size)
model.compile(loss='mse', optimizer='adam')

model.fit(X, Y, batch_size=32, epochs=50, validation_split=0.1)
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Note that this is a simple example and you may need to adjust the model architecture, hyperparameters, and data preprocessing depending on your specific problem.

the nmr sequence wavelet coefficients are input as cnn-resnet, and the corresponding nmr sequence wavelet coefficients are used as cnn-resnet output for denoising in python

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