rapids/tests/scripts/missing_vals.py

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt


participant = "p031"
all_sensors = ["eda", "bvp", "ibi", "temp", "acc"]

for sensor in all_sensors:

    if sensor == "eda":
        path = f"/rapids/data/interim/{participant}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv"
    elif sensor == "bvp":
        path = f"/rapids/data/interim/{participant}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_cr_windows.csv"
    elif sensor == "ibi":
        path = f"/rapids/data/interim/{participant}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_cr_windows.csv" 
    elif sensor == "acc":
        path = f"/rapids/data/interim/{participant}/empatica_accelerometer_features/empatica_accelerometer_python_cr_windows.csv" 
    elif sensor == "temp":
        path = f"/rapids/data/interim/{participant}/empatica_temperature_features/empatica_temperature_python_cr_windows.csv" 
    else:
        path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv" # all features all participants


    df = pd.read_csv(path)
    print(df)
    is_NaN = df.isnull()
    row_has_NaN = is_NaN.any(axis=1)
    rows_with_NaN = df[row_has_NaN]

    print("All rows:", len(df.index))
    print("\nCount NaN vals:", rows_with_NaN.size)
    print("\nDf mean:")
    print(df.mean())

    sns.heatmap(df.isna(), cbar=False)
    plt.savefig(f'{sensor}_{participant}_windows_NaN.png', bbox_inches='tight')