Delete preprocessing.py

preprocessing.py

@@ -1,96 +0,0 @@
-import os
-import mne
-import numpy as np
-import pandas as pd
-
-
-def standardize_dataframe(df):
-    # Make a copy to avoid modifying the original dataframe
-    df_standardized = df.copy()
-    
-    # Only standardize numeric columns
-    numeric_columns = df.select_dtypes(include=np.number).columns
-    
-    for column in numeric_columns:
-        mean = df[column].mean()
-        std = df[column].std()
-        
-        df_standardized[column] = (df[column] - mean) / std
-    
-    return df_standardized
-
-def select_relevant_channels(raw):
-    # For relevant channel criteria check documentation
-    '''“EEG FP1-REF” for the left frontal pole
-
-        “EEG FP2-REF” for the right frontal pole
-
-        “EEG F3-REF” for the left frontal region
-
-        “EEG F4-REF” for the right frontal region
-
-        “EEG C3-REF” for the left central region'''
-        
-    desired = ["EEG FP1-REF", "EEG FP2-REF", "EEG F3-REF", "EEG F4-REF", "EEG C3-REF"]
-    #check if all desired channels are present; if not, skip this file
-    if not all(ch in raw.ch_names for ch in desired):
-        print("Skipping file because it doesn't have the full set of desired channels.")
-        return None
-    raw.pick_channels(desired, verbose=False)
-    return raw
-
-
-def collapse_epoch_df_by_channel(epoch_df):
-    # Identify channel columns (exclude time, epoch, condition)
-    channel_cols = [col for col in epoch_df.columns if col not in ['time', 'epoch']]
-    # Group by epoch
-    grouped = epoch_df.groupby('epoch')
-    rows = []
-    for epoch_num, group in grouped:
-        group_sorted = group.sort_values('time')
-        # For each channel, extract the 1D array for this epoch
-        row = {'epoch': epoch_num}
-        for ch in channel_cols:
-            row[ch] = group_sorted[ch].values  # 1D array of length = number of time samples in the epoch
-        rows.append(row)
-    return pd.DataFrame(rows)
-
-def preprocess_eeg_file(edf_path, fmin=1.0, fmax=45.0, segment_lenght=5, overlap=2):
-
-    # 1. Charger le fichier EDF avec MNE
-    raw = mne.io.read_raw_edf(edf_path, preload=True, verbose=False)
-    
-    # Resample (to 250 because it's the lowest sampling rate )
-    raw.resample(250, verbose=False)
-    
-    # Filtrage passe-bande (1-45 Hz)
-    raw.filter(fmin, fmax, fir_design='firwin', verbose=False)
-    
-    # Skip EEGs less than 5s
-    if raw.times[-1] < 5:
-        print(f"Skipping {edf_path}: duration ({raw.times[-1]:.2f} s) is less than required 5s.")
-        return None
-    
-    # Suppression des canaux non EEG
-    eeg_channels = mne.pick_types(raw.info, eeg=True, exclude=[])
-    raw.pick(eeg_channels, verbose=False)
-    
-    # Selectionner les channels pertinents (channel selection from EDA ?)
-    print(raw.ch_names)
-    raw = select_relevant_channels(raw)
-    if raw is None:
-        return None
-    
-    # Segmentation
-    epochs = mne.make_fixed_length_epochs(raw, duration=segment_lenght, preload=False, overlap=overlap, verbose=False)
-
-    # Transform to dataframe and standadize
-
-    df = epochs.to_data_frame() # epochs is returned by preprocess_eeg_file()
-    df_std = standardize_dataframe(df.drop(['time','epoch', 'condition'], axis=1))
-    result = pd.concat([df[['time','epoch']], df_std], axis=1)
-    
-    return collapse_epoch_df_by_channel(result)
-
-
-