From ca59a54d8fb09549740588165a3ece0213507f28 Mon Sep 17 00:00:00 2001
From: = <=>
Date: Mon, 28 Mar 2022 13:50:08 +0000
Subject: [PATCH] Get a sample rate from two sequential timestamps.

---
 .../empatica_electrodermal_activity/cf/main.py     | 13 +++++++++++--
 src/features/empatica_temperature/cf/main.py       | 14 +++++++++++---
 2 files changed, 22 insertions(+), 5 deletions(-)

diff --git a/src/features/empatica_electrodermal_activity/cf/main.py b/src/features/empatica_electrodermal_activity/cf/main.py
index c4dbfda9..997ff8a0 100644
--- a/src/features/empatica_electrodermal_activity/cf/main.py
+++ b/src/features/empatica_electrodermal_activity/cf/main.py
@@ -7,11 +7,20 @@ from CalculatingFeatures.calculate_features import calculateFeatures
 pd.set_option('display.max_columns', None)
 
 
+def getSampleRate(data):
+    try:
+        timestamps_diff = data['timestamp'].iloc[1] - data['timestamp'].iloc[0]
+    except:
+        raise Exception("Error occured while trying to get the sample rate from the first two sequential timestamps.")
+    
+    return 1000/timestamps_diff
 
 def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment, filter_data_by_segment):
     eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
 
-    if not eda_intraday_data.empty:        
+    if not eda_intraday_data.empty:   
+        sample_rate = getSampleRate(eda_intraday_data)   
+     
         eda_intraday_data = filter_data_by_segment(eda_intraday_data, time_segment)
 
         if not eda_intraday_data.empty:
@@ -21,7 +30,7 @@ def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment
             # apply methods from calculate features module 
             eda_intraday_features = \
                 eda_intraday_data.groupby('local_segment').apply(\
-                lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=4, featureNames=features))
+                lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=sample_rate, featureNames=features)) # To-Do: Hardcoded f/s ... 
 
             eda_intraday_features.reset_index(inplace=True)
 
diff --git a/src/features/empatica_temperature/cf/main.py b/src/features/empatica_temperature/cf/main.py
index 8c273a87..ec9a8f2a 100644
--- a/src/features/empatica_temperature/cf/main.py
+++ b/src/features/empatica_temperature/cf/main.py
@@ -4,13 +4,21 @@ from scipy.stats import entropy
 from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, genericFeatureNames
 from CalculatingFeatures.calculate_features import calculateFeatures
 
-import sys
 
+def getSampleRate(data):
+    try:
+        timestamps_diff = data['timestamp'].iloc[1] - data['timestamp'].iloc[0]
+    except:
+        raise Exception("Error occured while trying to get the sample rate from the first two sequential timestamps.")
+    
+    return 1000/timestamps_diff
 
 def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment):
     temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
 
-    if not temperature_intraday_data.empty:        
+    if not temperature_intraday_data.empty:
+        sample_rate = getSampleRate(temperature_intraday_data)   
+
         temperature_intraday_data = filter_data_by_segment(temperature_intraday_data, time_segment)
 
         if not temperature_intraday_data.empty:
@@ -20,7 +28,7 @@ def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, tim
             # apply methods from calculate features module  
             temperature_intraday_features = \
                 temperature_intraday_data.groupby('local_segment').apply(\
-                lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=4, featureNames=features))
+                lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=sample_rate, featureNames=features)) # To-Do: Hardcoded f/s ... 
 
             temperature_intraday_features.reset_index(inplace=True)