FeatureExtraction.py

import pandas as pd
import numpy as np
from dataCleaning import read_run, column_clean, preprocessing
import pdb

def overall_cleaning():
    df_p3_exo = read_run("P3_Exo_1_0.csv") # second run, male
    df_p3_noexo = read_run("P3_NoExo_1_0.csv") # first run, male
    df_p4_exo = read_run("P4_Exo_1_0.csv") # 1st run female
    df_p4_noexo = read_run("P4_NoExo_1_0.csv") # 2nd female

    df_p3_exo = column_clean(df_p3_exo, run_num = 2, gender = 'male')
    df_p3_noexo = column_clean(df_p3_noexo, run_num = 1, gender = 'male')
    df_p4_exo = column_clean(df_p4_exo, run_num = 2, gender = 'female')
    df_p4_noexo = column_clean(df_p4_noexo, run_num = 1, gender = 'female')
    combined_df = pd.concat([df_p3_exo, df_p3_noexo, df_p4_exo, df_p4_noexo], ignore_index=True)
    dfs = [df_p3_exo, df_p3_noexo, df_p4_exo, df_p4_noexo] #jack's list for the data cleaning he does later. 
    # # Show the head of the data
    # df_p3_exo.describe()
    df_p3_noexo.head()
    # df_p4_exo.head()
    # df_p4_noexo.head()
    # # Choose inputs
    # features = df_p3_exo[['EMG 1 (mV)', 'ACC X (G)', 'ACC Y (G)', 'ACC Z (G)', 'GYRO X (deg/s)', 'GYRO Y (deg/s)', 'GYRO Z (deg/s)']].dropna()
    # features.head()
    feature_sets = []
    # Run functions to extract features for each dataframe
    #CP: does this make sure to remove the redundant time series columns?
    #can keep  ACC X Time Series (s) in each sensor group, and remove any other column with 'Time Series (s)' in its name 
    for df in dfs:
        emg_features = compute_emg_features(df['EMG 1 (mV)'])
        accel_features = compute_accel_features(df['ACC X (G)'], df['ACC Y (G)'], df['ACC Z (G)'])
        gyro_features = compute_gyro_features(df['GYRO X (deg/s)'], df['GYRO Y (deg/s)'], df['GYRO Z (deg/s)'])
        features = {
            'emg': emg_features,
            'accel': accel_features,
            'gyro': gyro_features
        }
        feature_sets.append(features)

    # feature_sets now contains extracted features for each df
    p3exo_feats, p3noexo_feats, p4exo_feats, p4noexo_feats = feature_sets
    return p3exo_feats, p3noexo_feats, p4exo_feats, p4noexo_feats

# Calculations for Feature Extraction from Project_Guide
def compute_emg_features(signal):
    return {
        'mean': np.mean(signal),
        'max': np.max(signal),
        'min': np.min(signal),
        'std': np.std(signal),
        'rms': np.sqrt(np.mean(signal**2))
    }

def compute_accel_features(a_x, a_y, a_z):
    a_mag = np.sqrt(a_x**2 + a_y**2 + a_z**2)
    
    features = {
        'peak_accel': np.max(a_mag),
        'mean_accel': np.mean(a_mag),
        'total_accel': np.sqrt(np.mean(a_x**2) + np.mean(a_y**2) + np.mean(a_z**2)),
        'accel_range': np.max(a_mag) - np.min(a_mag)
    }
    return features

def compute_gyro_features(w_x, w_y, w_z):
    w_mag = np.sqrt(w_x**2 + w_y**2 + w_z**2)
    
    features = {
        'peak_angular_vel': np.max(w_mag),
        'mean_angular_vel': np.mean(w_mag),
        'total_angular_vel': np.sqrt(np.mean(w_x**2) + np.mean(w_y**2) + np.mean(w_z**2)),
        'angular_vel_range': np.max(w_mag) - np.min(w_mag)
    }
    return features    


if __name__ == '__main__':
    p3exo_feats, p3noexo_feats, p4exo_feats, p4noexo_feats = overall_cleaning()