Implementation of the second order features.

config.yaml

@@ -493,6 +493,7 @@ EMPATICA_ACCELEROMETER:
       WINDOWS:
         COMPUTE: True
         WINDOW_LENGTH: 10 # specify window length in seconds
+        SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min']
       SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py
 
 
@@ -510,11 +511,11 @@ EMPATICA_TEMPERATURE:
   CONTAINER: TEMP
   PROVIDERS:
     DBDP:
-      COMPUTE: False
+      COMPUTE: True
       FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"]
       SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py
     CR:
-      COMPUTE: False
+      COMPUTE: True
       FEATURES: ["autocorrelations", "countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", 
                   "longestStrikeBelowMean", "stdDev", "median", "meanChange", "numberOfZeroCrossings", "absEnergy", "linearTrendSlope", 
                   "ratioBeyondRSigma", "binnedEntropy", "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr",
@@ -523,6 +524,7 @@ EMPATICA_TEMPERATURE:
       WINDOWS:
         COMPUTE: True
         WINDOW_LENGTH: 90 # specify window length in seconds
+        SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min']
       SRC_SCRIPT: src/features/empatica_temperature/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-electrodermal-activity/
@@ -530,11 +532,11 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
   CONTAINER: EDA
   PROVIDERS:
     DBDP:
-      COMPUTE: False
+      COMPUTE: True
       FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
       SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
     CR:
-      COMPUTE: False
+      COMPUTE: True
       FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 
                   'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 
                   'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio',
@@ -543,6 +545,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
       WINDOWS:
         COMPUTE: True
         WINDOW_LENGTH: 80 # specify window length in seconds
+        SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min']
       SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/
@@ -550,16 +553,17 @@ EMPATICA_BLOOD_VOLUME_PULSE:
   CONTAINER: BVP
   PROVIDERS:
     DBDP:
-      COMPUTE: False
+      COMPUTE: True
       FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"]
       SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py
     CR:
-      COMPUTE: False
+      COMPUTE: True
       FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features
                   'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features
       WINDOWS:
         COMPUTE: True
         WINDOW_LENGTH: 10 # specify window length in seconds
+        SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min']
       SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-inter-beat-interval/
@@ -576,7 +580,8 @@ EMPATICA_INTER_BEAT_INTERVAL:
                   'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features
       WINDOWS:
         COMPUTE: True
-        WINDOW_LENGTH: 120 # specify window length in seconds
+        WINDOW_LENGTH: 2000 # specify window length in seconds
+        SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min']
       SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-tags/

environment.yml

@@ -110,12 +110,13 @@ dependencies:
     - bidict==0.22.0
     - biosppy==0.8.0
     - cached-property==1.5.2
-    - calculatingfeatures==0.1.1
     - configargparse==0.15.1
+    - cr-features==0.1.8
     - cycler==0.11.0
     - decorator==4.4.2
-    - fonttools==4.31.2
+    - fonttools==4.33.2
     - h5py==3.6.0
+    - hmmlearn==0.2.7
     - ipython-genutils==0.2.0
     - jupyter-core==4.6.3
     - kiwisolver==1.4.2
@@ -124,7 +125,7 @@ dependencies:
     - opencv-python==4.5.5.64
     - packaging==21.3
     - peakutils==1.3.3
-    - pillow==9.0.1
+    - pillow==9.1.0
     - pulp==2.4
     - pyparsing==2.4.7
     - pyrsistent==0.15.5
@@ -134,5 +135,5 @@ dependencies:
     - snakemake==5.30.2
     - toposort==1.5
     - traitlets==4.3.3
-    - typing-extensions==4.1.1
+    - typing-extensions==4.2.0
 prefix: /opt/conda/envs/rapids

rules/features.smk

@@ -771,7 +771,8 @@ rule empatica_accelerometer_python_features:
         sensor_key = "empatica_accelerometer"
     output:
         "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}.csv",
-        "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}_windows.csv"
+        "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}_windows.csv",
+        "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}_windows_SO_features.csv"
     script:
         "../src/features/entry.py"
 
@@ -798,7 +799,8 @@ rule empatica_heartrate_python_features:
         sensor_key = "empatica_heartrate"
     output:
         "data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}.csv",
-        "data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}_windows.csv"
+        "data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}_windows.csv",
+        "data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}_windows_SO_features.csv"
     script:
         "../src/features/entry.py"
 
@@ -825,7 +827,8 @@ rule empatica_temperature_python_features:
         sensor_key = "empatica_temperature"
     output:
         "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}.csv",
-        "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}_windows.csv"
+        "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}_windows.csv",
+        "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}_windows_SO_features.csv"
     script:
         "../src/features/entry.py"
 
@@ -852,7 +855,8 @@ rule empatica_electrodermal_activity_python_features:
         sensor_key = "empatica_electrodermal_activity"
     output:
         "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}.csv",
-        "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}_windows.csv"
+        "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}_windows.csv",
+        "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}_windows_SO_features.csv"
     script:
         "../src/features/entry.py"
 
@@ -879,7 +883,8 @@ rule empatica_blood_volume_pulse_python_features:
         sensor_key = "empatica_blood_volume_pulse"
     output:
         "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}.csv",
-        "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows.csv"
+        "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows.csv",
+        "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows_SO_features.csv"
     script:
         "../src/features/entry.py"
 
@@ -906,7 +911,8 @@ rule empatica_inter_beat_interval_python_features:
         sensor_key = "empatica_inter_beat_interval"
     output:
         "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}.csv",
-        "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}_windows.csv"
+        "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}_windows.csv",
+        "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}_windows_SO_features.csv"
     script:
         "../src/features/entry.py"
 

src/features/cr_features_helper_methods.py

@@ -0,0 +1,30 @@
+import pandas as pd
+
+def extract_second_order_features(intraday_features, so_features_names):
+    if not intraday_features.empty:
+        so_features = pd.DataFrame()
+        if "mean" in so_features_names:
+            so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).mean().add_suffix("_SO_mean")], axis=1)
+        if "median" in so_features_names:
+            so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).mean().add_suffix("_SO_median")], axis=1)
+        if "sd" in so_features_names:
+            so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).std().add_suffix("_SO_sd")], axis=1)
+        if "max" in so_features_names:
+            so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).max().add_suffix("_SO_max")], axis=1)
+        if "min" in so_features_names:
+            so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).min().add_suffix("_SO_min")], axis=1)
+        
+        so_features.reset_index(inplace=True)
+
+    else:
+        so_features = pd.DataFrame(columns=["local_segment"])
+
+    return so_features
+
+def get_sample_rate(data):
+    try:
+        timestamps_diff = data['timestamp'].diff().dropna().mean()
+    except:
+        raise Exception("Error occured while trying to get the mean sample rate from the data.")
+
+    return int(1000/timestamps_diff)

src/features/empatica_accelerometer/cr/main.py

@@ -3,6 +3,7 @@ from scipy.stats import entropy
 
 from cr_features.helper_functions import convert_to2d, accelerometer_features, frequency_features
 from cr_features.calculate_features import calculate_features
+from cr_features_helper_methods import get_sample_rate, extract_second_order_features
 
 import sys
 
@@ -69,4 +70,9 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
     acc_intraday_features = extract_acc_features_from_intraday_data(acc_intraday_data, intraday_features_to_compute, 
                                                                 requested_window_length, time_segment, filter_data_by_segment)
 
+    if calc_windows:
+        so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
+        acc_second_order_features = extract_second_order_features(acc_intraday_features, so_features_names)
+        return acc_intraday_features, acc_second_order_features
+
     return acc_intraday_features

src/features/empatica_blood_volume_pulse/cr/main.py

@@ -3,6 +3,7 @@ from scipy.stats import entropy
 
 from cr_features.helper_functions import convert_to2d, hrv_features, hrv_freq_features
 from cr_features.hrv import extract_hrv_features_2d_wrapper
+from cr_features_helper_methods import get_sample_rate, extract_second_order_features
 
 import sys
 
@@ -74,4 +75,9 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
     bvp_intraday_features = extract_bvp_features_from_intraday_data(bvp_intraday_data, intraday_features_to_compute, 
                                                                 requested_window_length, time_segment, filter_data_by_segment)
 
+    if calc_windows:
+        so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
+        bvp_second_order_features = extract_second_order_features(bvp_intraday_features, so_features_names)
+        return bvp_intraday_features, bvp_second_order_features
+
     return bvp_intraday_features

src/features/empatica_electrodermal_activity/cr/main.py

@@ -3,6 +3,7 @@ from scipy.stats import entropy
 
 from cr_features.helper_functions import convert_to2d, gsr_features
 from cr_features.calculate_features import calculate_features
+from cr_features_helper_methods import get_sample_rate, extract_second_order_features
 
 
 def getSampleRate(data):
@@ -62,4 +63,9 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
     eda_intraday_features = extract_eda_features_from_intraday_data(eda_intraday_data, intraday_features_to_compute, 
                                                                 requested_window_length, time_segment, filter_data_by_segment)
 
+    if calc_windows:
+        so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
+        eda_second_order_features = extract_second_order_features(eda_intraday_features, so_features_names)
+        return eda_intraday_features, eda_second_order_features
+
     return eda_intraday_features

src/features/empatica_inter_beat_interval/cr/main.py

@@ -3,6 +3,7 @@ import numpy as np
 
 from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features, hrv_freq_features
 from cr_features.hrv import extract_hrv_features_2d_wrapper
+from cr_features_helper_methods import get_sample_rate, extract_second_order_features
 
 import math
 import sys
@@ -11,14 +12,6 @@ pd.set_option('display.max_rows', 1000)
 pd.set_option('display.max_columns', None)
 
 
-def get_sample_rate(data):
-    try:
-        timestamps_diff = data['timestamp'].diff().dropna().mean()
-    except:
-        raise Exception("Error occured while trying to get the mean sample rate from the data.")
-
-    return int(1000/timestamps_diff)
-
 def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment):
     ibi_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
 
@@ -30,12 +23,6 @@ def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_
         if not ibi_intraday_data.empty:
 
             ibi_intraday_features = pd.DataFrame()
-            # np.set_printoptions(threshold=sys.maxsize)
-            # print(ibi_intraday_data.groupby('local_segment').apply(lambda x: math.ceil(x['timings'].iloc[-1])))
-            # nekaj = ibi_intraday_data.groupby('local_segment').apply(lambda x: \
-            #     convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[1])
-
-            # sys.exit()
             
             # apply methods from calculate features module
             if window_length is None:
@@ -82,5 +69,10 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
     # extract features from intraday data
     ibi_intraday_features = extract_ibi_features_from_intraday_data(ibi_intraday_data, intraday_features_to_compute, 
                                                                 requested_window_length, time_segment, filter_data_by_segment)
+    if calc_windows:
+        so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
+        ibi_second_order_features = extract_second_order_features(ibi_intraday_features, so_features_names)
+        return ibi_intraday_features, ibi_second_order_features
+
 
     return ibi_intraday_features

src/features/empatica_temperature/cr/main.py

@@ -3,6 +3,7 @@ from scipy.stats import entropy
 
 from cr_features.helper_functions import convert_to2d, generic_features
 from cr_features.calculate_features import calculate_features
+from cr_features_helper_methods import get_sample_rate, extract_second_order_features
 
 import sys
 
@@ -61,4 +62,10 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
     # extract features from intraday data
     temperature_intraday_features = extract_temp_features_from_intraday_data(temperature_intraday_data, intraday_features_to_compute, 
                                                                         requested_window_length, time_segment, filter_data_by_segment)
+
+    if calc_windows:
+        so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
+        temperature_second_order_features = extract_second_order_features(temperature_intraday_features, so_features_names)
+        return temperature_intraday_features, temperature_second_order_features
+
     return temperature_intraday_features

src/features/entry.py

@@ -22,13 +22,17 @@ else:
     del sensor_data_files["time_segments_labels"]
     time_segments_file = snakemake.input["time_segments_labels"]
 
-    sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=calc_windows)
-    # Calculation over multiple windows in case of Empatica's CR-features
     if calc_windows:
-        sensor_features.to_csv(snakemake.output[1], index=False)
+        first_order_features, second_order_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=calc_windows)
-        sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=False)
+        first_order_features.to_csv(snakemake.output[1], index=False)
+        second_order_features.to_csv(snakemake.output[2], index=False)
+        calc_windows = False
     elif "empatica" in sensor_key: 
         pd.DataFrame().to_csv(snakemake.output[1], index=False)
+        pd.DataFrame().to_csv(snakemake.output[2], index=False)
+
+
+    sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=calc_windows)
 
 
 sensor_features.to_csv(snakemake.output[0], index=False)

src/features/utils/utils.py

@@ -93,6 +93,8 @@ def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_file
     from importlib import import_module, util
 
     sensor_features = pd.DataFrame(columns=["local_segment"])
+    sensor_fo_features = pd.DataFrame(columns=["local_segment"])
+    sensor_so_features = pd.DataFrame(columns=["local_segment"])
     time_segments_labels = pd.read_csv(time_segments_file, header=0)
     if "FEATURES" not in provider:
         raise ValueError("Provider config[{}][PROVIDERS][{}] is missing a FEATURES attribute in config.yaml".format(sensor_key.upper(), provider_key.upper()))
@@ -106,7 +108,20 @@ def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_file
             time_segments_labels["label"] = [""]
         for time_segment in time_segments_labels["label"]:
             print("{} Processing {} {} {}".format(rapids_log_tag, sensor_key, provider_key, time_segment))
+
             features = feature_function(sensor_data_files, time_segment, provider, filter_data_by_segment=filter_data_by_segment, chunk_episodes=chunk_episodes, calc_windows=calc_windows)
+            
+            # In case of calc_window = True
+            if isinstance(features, tuple):
+                if not "local_segment" in features[0].columns or not "local_segment" in features[1].columns:
+                        raise ValueError("The dataframe returned by the " + sensor_key + " provider '" + provider_key + "' is missing the 'local_segment' column added by the 'filter_data_by_segment()' function. Check the provider script is using such function and is not removing 'local_segment' by accident (" + provider["SRC_SCRIPT"] + ")\n  The 'local_segment' column is used to index a provider's features (each row corresponds to a different time segment instance (e.g. 2020-01-01, 2020-01-02, 2020-01-03, etc.)")
+                features[0].columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features[0].columns]
+                features[1].columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features[1].columns]
+                if not features[0].empty:
+                    sensor_fo_features = pd.concat([sensor_features, features[0]], axis=0, sort=False)                
+                if not features[1].empty:
+                    sensor_so_features = pd.concat([sensor_features, features[1]], axis=0, sort=False)
+            else: 
                 if not "local_segment" in features.columns:
                     raise ValueError("The dataframe returned by the " + sensor_key + " provider '" + provider_key + "' is missing the 'local_segment' column added by the 'filter_data_by_segment()' function. Check the provider script is using such function and is not removing 'local_segment' by accident (" + provider["SRC_SCRIPT"] + ")\n  The 'local_segment' column is used to index a provider's features (each row corresponds to a different time segment instance (e.g. 2020-01-01, 2020-01-02, 2020-01-03, etc.)")
                 features.columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features.columns]
@@ -114,6 +129,27 @@ def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_file
     else:
         for feature in provider["FEATURES"]:
             sensor_features[feature] = None
+    
+    if calc_windows:
+        segment_colums = pd.DataFrame()
+        sensor_fo_features['local_segment'] = sensor_fo_features['local_segment'].str.replace(r'_RR\d+SS', '')
+        split_segemnt_columns = sensor_fo_features["local_segment"].str.split(pat="(.*)#(.*),(.*)", expand=True)
+        new_segment_columns = split_segemnt_columns.iloc[:,1:4] if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_segment_start_datetime","local_segment_end_datetime"])
+        segment_colums[["local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]] = new_segment_columns
+        for i in range(segment_colums.shape[1]):
+                sensor_fo_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]])
+        
+        segment_colums = pd.DataFrame()
+        sensor_so_features['local_segment'] = sensor_so_features['local_segment'].str.replace(r'_RR\d+SS', '')
+        split_segemnt_columns = sensor_so_features["local_segment"].str.split(pat="(.*)#(.*),(.*)", expand=True)
+        new_segment_columns = split_segemnt_columns.iloc[:,1:4] if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_segment_start_datetime","local_segment_end_datetime"])
+        segment_colums[["local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]] = new_segment_columns
+        for i in range(segment_colums.shape[1]):
+                sensor_so_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]])
+
+        return sensor_fo_features, sensor_so_features
+
+    else: 
         segment_colums = pd.DataFrame()
         sensor_features['local_segment'] = sensor_features['local_segment'].str.replace(r'_RR\d+SS', '')
         split_segemnt_columns = sensor_features["local_segment"].str.split(pat="(.*)#(.*),(.*)", expand=True)