Delete preprocessing_2dcnn.py

preprocessing_2dcnn.py

@@ -1,52 +0,0 @@
-import numpy as np
-import pandas as pd
-from scipy.signal import spectrogram
-
-#METHOD 1: STFT spectrogram
-def convert_epoch_to_spectrogram(epoch_row, channels, fs=250, nperseg=128, noverlap=64):
-    """
-    Given a pandas Series representing one epoch, where each channel column contains
-    a 1D numpy array of time series data, compute a spectrogram for each channel and
-    stack them into a 3D array of shape (n_channels, freq_bins, time_bins).
-    
-    Parameters:
-      epoch_row: pandas Series
-          One row of your preprocessed dataframe (one epoch).
-      channels: list of str
-          The list of channel names to process.
-      fs: int
-          Sampling frequency (default 250 Hz).
-      nperseg: int
-          Length of each segment for spectrogram calculation.
-      noverlap: int
-          Number of overlapping samples between segments.
-    
-    Returns:
-      spec_stack: numpy array
-          A 3D array with shape (n_channels, freq_bins, time_bins) containing the
-          spectrogram (in dB) for each channel.
-    """
-    spec_list = []
-    for ch in channels:
-        ts = epoch_row[ch]  # this is the 1D time series for the channel
-        # Compute the spectrogram
-        f, t, Sxx = spectrogram(ts, fs=fs, nperseg=nperseg, noverlap=noverlap)
-        # Convert the power spectrogram to dB scale
-        Sxx_db = 10 * np.log10(Sxx + 1e-10)
-        spec_list.append(Sxx_db)
-    spec_stack = np.stack(spec_list, axis=0)
-    return spec_stack
-
-
-def convert_preprocessed_df_to_2d(preprocessed_df, channels=["EEG FP1-REF", "EEG FP2-REF", "EEG F3-REF", "EEG F4-REF", "EEG C3-REF"], 
-                                  fs=250, nperseg=128, noverlap=64):
-
-    # Make a copy to avoid modifying the original dataframe
-    df = preprocessed_df.copy()
-    
-    # Compute the spectrogram for each row (epoch)
-    df["spectrogram"] = df.apply(lambda row: convert_epoch_to_spectrogram(row, channels, fs, nperseg, noverlap), axis=1)
-    
-    #drop channel columns and other useless ones
-    df_final = df[["spectrogram", "epilepsy", "age", "gender",'subject_id', 'edf_path']]
-    return df_final