Source code for Protomix.apodization

import pandas as pd
import numpy as np



def apodization(fid_df: pd.DataFrame, LB: float = 1, apodization_type: str = 'gaussian') -> pd.DataFrame:
    """
    Apply an apodization function to each Free Induction Decay (FID) row in a DataFrame.

    Apodization, in the context of NMR (Nuclear Magnetic Resonance) spectroscopy, is a windowing technique applied 
    to FID signals to enhance the signal-to-noise ratio and improve line shape in the frequency domain. 
    This function supports two types of apodization: Gaussian and Exponential.

    :param fid_df: A DataFrame where each row represents an FID signal, and columns represent time points.
    :type fid_df: pd.DataFrame
    :param LB: Line broadening parameter, which determines the width of the apodization function. 
               A higher value results in more broadening. Defaults to 1.
    :type LB: float, optional
    :param apodization_type: Type of apodization function to apply. Options are 'gaussian' for Gaussian apodization, 
                             and 'exponential' for Exponential apodization. Defaults to 'gaussian'.
    :type apodization_type: str, optional
    
    :return: A DataFrame of the same shape as `fid_df`, containing the apodized FID values. Each FID row in the input 
             DataFrame is multiplied by the apodization function, transforming the data in the time domain.
    :rtype: pd.DataFrame
    
    :raises AssertionError: If `fid_df` is not a pandas DataFrame, `LB` is negative, or `apodization_type` is not 
                            'gaussian' or 'exponential'.
    """

    # Assertive lines
    assert isinstance(fid_df, pd.DataFrame), "fid_df should be a pandas DataFrame."
    assert LB >= 0, "LB should be non-negative."
    assert apodization_type in ['gaussian', 'exponential'], "apodization_type should be either 'gaussian' or 'exponential'."
    
    time = fid_df.columns.astype(float)
    
    if apodization_type == 'gaussian':
        apod_func = np.exp(-0.5 * (LB * time)**2)
    else:
        apod_func = np.exp(-LB * time)
    
    # Vectorized operation
    apodized_df = fid_df.multiply(apod_func, axis=1)
    
    return apodized_df