51 lines
2.3 KiB
Python
51 lines
2.3 KiB
Python
import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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from itertools import compress
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# path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv" # all features all participants
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# path = "/rapids/data/interim/p03/empatica_accelerometer_features/empatica_accelerometer_python_cr_windows.csv"
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path = "/rapids/data/interim/p01/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv"
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# path = "/rapids/data/interim/p02/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_cr_windows.csv"
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# path = "/rapids/data/interim/p02/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_cr_windows.csv"
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# path = "/rapids/data/interim/p02/empatica_temperature_features/empatica_temperature_python_cr_windows.csv"
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df = pd.read_csv(path)
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df_num_peaks_zero = df[df["empatica_electrodermal_activity_cr_numPeaks"] == 0]
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columns_num_peaks_zero = df_num_peaks_zero.columns[df_num_peaks_zero.isna().any()].tolist()
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df_num_peaks_non_zero_t = df[df["empatica_electrodermal_activity_cr_numPeaks"] != 0]
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df_num_peaks_non_zero = df_num_peaks_non_zero_t[columns_num_peaks_zero]
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# row_has_NaN = is_NaN. any(axis=1)
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# rows_with_NaN = df[row_has_NaN]
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# print(rows_with_NaN.size)
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pd.set_option('display.max_columns', None)
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# # pd.set_option('display.max_rows', None)
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# print(df_num_peaks_non_zero)
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df_q = pd.DataFrame()
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for col in df_num_peaks_non_zero:
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df_q[col] = pd.to_numeric(pd.cut(df_num_peaks_non_zero[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
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sns.heatmap(df_q)
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plt.savefig('eda_windows_p01_window_non_zero.png', bbox_inches='tight')
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plt.close()
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# Filter columns that do not contain 0
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non_zero_cols = list(compress(columns_num_peaks_zero, df_num_peaks_non_zero.all().tolist()))
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zero_cols = list(set(columns_num_peaks_zero) - set(non_zero_cols))
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print(non_zero_cols, "\n")
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print(zero_cols)
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# maxPeakAmplitudeChangeBefore
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mpacb = df_num_peaks_non_zero_t\
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[(df_num_peaks_non_zero_t['empatica_electrodermal_activity_cr_avgPeakAmplitudeChangeBefore'] != 0) \
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& (df_num_peaks_non_zero_t['empatica_electrodermal_activity_cr_numPeaks'] != 0)]
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print(mpacb['empatica_electrodermal_activity_cr_numPeaks'])
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sns.heatmap(mpacb['empatica_electrodermal_activity_cr_numPeaks'])
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plt.savefig('maxPeakAmplitudeChangeBefore.png', bbox_inches='tight')
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