pre-processing new module update

R2PY.py

@@ -0,0 +1,97 @@
+from datetime import datetime
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+
+def base_line_analysis(sub_id, date_of, st):
+
+    # Inputs to Function:
+    # ID of participant
+    # Date of experiment (**Issue- would like to update so it pulls this from file)
+    # Participant skin tone (**Issue- would like to update so it pulls this from file)
+
+    # Outputs to Function:
+    # .csv file with all timestamps for every participant in the study, which has all devices used in the study, skin tone, activity condition, and participant ID
+
+    # Set filepath of folder with all subject data
+    filepath = (Path('filepath') / sub_id).absolute()
+
+    df = pd.read_csv('filepath\\skintonestudyTIMES.csv', header=True)
+    time = df.loc[df['ï..Subject.ID'] == sub_id]
+    f = lambda t: round(datetime.strptime('%m/%d/%Y %H:%M:%S', t).timestamp())
+    for dst, src in (('R', 'Baseline'), ('A', 'Activity'), ('B', 'DB'), ('T', 'Type')):
+        for i in range(1, 4):
+            src_key, dst_key = f'{src}{i}', f'{dst}{i}'
+            time[dst_key] = time[src_key].apply(f)
+
+    date_num = datetime.strptime(f'%Y-%m_%d', date_of).timestamp()
+    df_hr = pd.read_csv(filepath / 'HR.csv', header=False)
+    df_hrt = pd.read_csv(filepath / 'HRT.csv', header=False)
+    df_hrt = np.round_(df_hrt + date_num)
+    df_ecg = pd.concat([df_hrt, df_hr], axis=1)
+    df_ecg.rename(columns=dict(zip(df_ecg.columns, ('Time', 'ECG'))), inplace=True)
+
+    df_e4: pd.DataFrame = pd.read_csv(filepath / 'Empatica/HR.csv', header=False)
+    e4_start_time = df_e4.loc[:, 1]
+    df_e4 = df_e4.loc[:, [-1, -2]]
+    seconds_passed_e4 = df_e4.shape[0]
+    e4_end_time = e4_start_time + seconds_passed_e4
+    e4_time = [i for i in range(e4_start_time, e4_end_time)]
+    df_e4 = pd.concat([e4_time, df_e4])
+    df_e4.rename(columns=dict(zip(df_ecg.columns, ('Time', 'Empatica'))), inplace=True)
+
+    df_aw = pd.read_csv(filepath / 'Apple Watch.csv', header=False)
+    aw_start_time = df_aw.loc[1, 2]
+    apple_hr = df_aw.loc[1:7, 2]
+    apple_sec = df_aw.loc[1:7, 1] + aw_start_time
+    df_apple_watch = pd.DataFrame([apple_sec, apple_hr], columns=['Time', 'AppleWatch'])
+
+    df_fb = pd.read_csv(filepath / 'Fitbit.csv', header=False)
+    fb_hr = df_fb.loc[-1, 2]
+    fb_time = df_fb[-1, 1]
+    df_fitbit = pd.DataFrame([fb_time, fb_hr], columns=["Time", "Fitbit"])
+
+    if (filepath / 'garmin.tcx').exists():
+        df_gm = pd.read_xml(filepath / 'garmin.tcx')
+        df_ecg.rename(columns=dict(zip(df_gm.columns, ('Time', 'Garmin'))), inplace=True)
+    else:
+        df_gm = pd.DataFrame([], columns=['Time', 'Garmin'])
+
+    if (filepath / 'Miband.xls').exists():
+        df_miband = pd.read_xml(filepath / 'Miband.xls')
+        df_miband.rename(columns=dict(zip(df_gm.columns, ('Time', 'Miband'))), inplace=True)
+    else:
+        df_miband = pd.DataFrame([], columns=['Time', 'Miband'])
+
+    if (filepath / 'Biovotion\\BHR.csv').exists():
+        df_biovotion = pd.read_xml(filepath / 'Biovotion\\BHR.csv')
+        df_biovotion.rename(columns=dict(zip(df_gm.columns, ('Time', 'Biovotion'))), inplace=True)
+    else:
+        df_biovotion = pd.DataFrame([], columns=['Time', 'Biovotion'])
+
+    df = pd.merge(df_ecg, df_apple_watch, on='Time')
+    df = pd.merge(df, df_e4, on='Time')
+    df = pd.merge(df, df_gm, on='Time')
+    df = pd.merge(df, df_fitbit, on='Time')
+    df = pd.merge(df, df_miband, on='Time')
+    df = pd.merge(df, df_biovotion, on='Time')
+    df['ID'] = sub_id
+    df['ST'] = st
+    df['Condition'] = ''
+
+    for condition, count in (
+            ('Activity', 300),
+            ('Rest', 240),
+            ('Breathe', 60),
+            ('Type', 60),
+    ):
+        for i in range(1, 4):
+            key = f'{condition[0]}{i}'
+            df.loc[(df['Time'] > time[key]) & (df['Time'] < time[key] + count)] = condition
+    df.to_csv('filename.csv', index=False, header=False)
+
+
+if __name__ == '__main__':
+    base_line_analysis('19-###', '2019-##-##', '#')

README.md

@@ -183,4 +183,8 @@ various health monitoring sensors |
 
 ### Continued Development
 
+### Data type conversion functions:
+    data_conversion: with funciton used to convert the time feature in the csv file
+
+
 We are frequently updating this package with new devices and insights from the DBDP (Digital Biomarker Discovery Pipeline).

data_conversion.py

@@ -0,0 +1,50 @@
+"""
+Created on 6/9/22
+
+@author: qinyuzhu
+"""
+
+import pandas as pd
+from datetime import datetime, timedelta
+
+def type_conversion():
+     """Function that do data type conversion for time features
+    Parameters
+    ----------
+    column name of the time feature in the file as the parameter of the funtion named type_conversion
+
+    Returns
+    -------
+    a new csv file with one new column of the converted start time
+
+    Raises
+    ------
+    Exception
+        raise exception when the initial time (ini_time) is not set or the format of initial time is not '%b %d %Y %I:%M%p'
+        raise exception when the output csv file name is not written
+    """
+    df = pd.read_csv("")
+
+
+    lst = []
+    for date in df:
+        new = datetime.strptime(date, '')
+    df["new_date"] = datetime.strftime()
+
+    ini_time =  ""
+    ini_time_for_now = datetime.strptime(ini_time, '%b %d %Y %I:%M%p')
+
+    lst=[]
+    new_final_time = ini_time_for_now + timedelta(days=1)
+    difference = new_final_time - ini_time_for_now
+    lst.append(difference)
+
+    lst.to_csv("")
+
+
+
+
+
+
+if __name__ == '__main__':
+    pass