Calculating HRV features with IBI.csv.

config.yaml

@@ -576,7 +576,7 @@ EMPATICA_INTER_BEAT_INTERVAL:
                   'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features
       WINDOWS:
         COMPUTE: True
-        WINDOW_LENGTH: 4 # specify window length in seconds
+        WINDOW_LENGTH: 120 # specify window length in seconds
       SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-tags/

src/data/streams/empatica_zip/container.py

@@ -62,13 +62,15 @@ def extract_empatica_data(data,  sensor):
         df.index.name = 'timestamp'
 
     elif sensor == 'EMPATICA_INTER_BEAT_INTERVAL':
+
         df = pd.read_csv(sensor_data_file, names=['timestamp', column], header=None)
+        df['timings'] = df['timestamp']
         timestampstart = float(df['timestamp'][0])
-        df['timestamp'] = (df['timestamp'][1:len(df)]).astype(float) + timestampstart
+        df['timestamp'] = (df['timestamp'][1:len(df)]).astype(float) + timestampstart        
         df = df.drop([0])
         df[column] = df[column].astype(float)
         df = df.set_index('timestamp')
-
+        
     else:
         raise ValueError(
             "sensor has an invalid name: {}".format(sensor))

src/data/streams/empatica_zip/format.yaml

@@ -50,6 +50,7 @@ EMPATICA_INTER_BEAT_INTERVAL:
     TIMESTAMP: timestamp
     DEVICE_ID: device_id
     INTER_BEAT_INTERVAL: inter_beat_interval
+    TIMINGS: timings
   MUTATION:
     COLUMN_MAPPINGS:
     SCRIPTS: # List any python or r scripts that mutate your raw data

src/data/streams/rapids_columns.yaml

@@ -217,6 +217,7 @@ EMPATICA_INTER_BEAT_INTERVAL:
   - TIMESTAMP
   - DEVICE_ID
   - INTER_BEAT_INTERVAL
+  - TIMINGS
 
 EMPATICA_TAGS:
   - TIMESTAMP

src/features/empatica_inter_beat_interval/cr/main.py

@@ -1,12 +1,14 @@
 import pandas as pd
-from scipy.stats import entropy
+import numpy as np
 
 from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features, hrv_freq_features
-from cr_features.calculate_features import calculate_features
+from cr_features.hrv import extract_hrv_features_2d_wrapper
 
+import math
 import sys
 
 pd.set_option('display.max_rows', 1000)
+pd.set_option('display.max_columns', None)
 
 
 def get_sample_rate(data):
@@ -28,23 +30,32 @@ 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])
 
-            print(ibi_intraday_data.head(100))
-            sys.exit()
+            # sys.exit()
 
             # apply methods from calculate features module
             if window_length is None:
                 ibi_intraday_features = \
                     ibi_intraday_data.groupby('local_segment').apply(\
+                    lambda x: 
                         extract_hrv_features_2d_wrapper(
-                            convert_to2d(x['inter_beat_interval'], window_length*sample_rate), 
-                            sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features)) 
+                            signal_2D = \
+                                convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[0], 
+                            ibi_timings = \
+                            convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[1], 
+                            sampling=None, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features)) 
             else:
                 ibi_intraday_features = \
                     ibi_intraday_data.groupby('local_segment').apply(\
+                    lambda x: 
                         extract_hrv_features_2d_wrapper(
-                            convert_to2d(x['blood_volume_pulse'], window_length*sample_rate), 
-                            sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features)) 
+                            signal_2D = convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[0],
+                            ibi_timings = convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[1],
+                            sampling=None, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features)) 
 
             ibi_intraday_features.reset_index(inplace=True)