Added components for the temperature features extraction.

config.yaml

@@ -495,9 +495,17 @@ 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
+    CF:
+      COMPUTE: True
+      FEATURES: ["autocorrelations", "countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", 
+                      "longestStrikeBelowMean", "stdDev", "median", "meanChange", "numberOfZeroCrossings", "absEnergy", "linearTrendSlope", 
+                      "ratioBeyondRSigma", "binnedEntropy", "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr",
+                       "calcMeanCrossingRateAutocorr", "countAboveMeanAutocorr", "sumPer", "sumSquared", "squareSumOfComponent", 
+                       "sumOfSquareComponents"]
+      SRC_SCRIPT: src/features/empatica_temperature/cf/main.py
 
 # See https://www.rapids.science/latest/features/empatica-electrodermal-activity/
 EMPATICA_ELECTRODERMAL_ACTIVITY:
@@ -509,14 +517,11 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
       SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
     CF:
       COMPUTE: True
-      FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks',
+      FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 
-                   'sumPosDeriv', 'propPosDeriv', 'derivTonic', 'sigTonicDifference', 'freqFeats',
+                   'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 
-                   'maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter',
+                   'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio',
-                   'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio',
+                   'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', 'maxPeakResponseSlopeAfter', 'signalOverallChange',
-                   'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio',
+                   'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease']
-                   'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore',
-                   'maxPeakResponseSlopeAfter', 'signalOverallChange', 'changeDuration', 'changeRate',
-                   'significantIncrease', 'significantDecrease']
       SRC_SCRIPT: src/features/empatica_electrodermal_activity/cf/main.py
 
 # See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/

environment.yml

@@ -13,34 +13,22 @@ dependencies:
   - attrs=20.3.0
   - binaryornot=0.4.4
   - blas=1.0
-  - brotli=1.0.9
   - brotlipy=0.7.0
   - bzip2=1.0.8
-  - ca-certificates=2022.2.1
+  - ca-certificates=2021.7.5
-  - certifi=2021.10.8
+  - certifi=2021.5.30
   - cffi=1.14.4
   - chardet=3.0.4
   - click=7.1.2
   - colorama=0.4.4
   - cookiecutter=1.6.0
   - cryptography=3.3.1
-  - cycler=0.11.0
   - datrie=0.8.2
-  - dbus=1.13.18
   - docutils=0.16
-  - expat=2.4.4
-  - fontconfig=2.13.1
-  - fonttools=4.25.0
-  - freetype=2.11.0
   - future=0.18.2
-  - giflib=5.2.1
   - gitdb=4.0.5
   - gitdb2=4.0.2
   - gitpython=3.1.11
-  - glib=2.69.1
-  - gst-plugins-base=1.14.0
-  - gstreamer=1.14.0
-  - icu=58.2
   - idna=2.10
   - imbalanced-learn=0.6.2
   - importlib-metadata=2.0.0
@@ -49,10 +37,7 @@ dependencies:
   - jinja2=2.11.2
   - jinja2-time=0.2.0
   - joblib=1.0.0
-  - jpeg=9d
   - jsonschema=3.2.0
-  - kiwisolver=1.3.2
-  - lcms2=2.12
   - ld_impl_linux-64=2.36.1
   - libblas=3.8.0
   - libcblas=3.8.0
@@ -66,38 +51,24 @@ dependencies:
   - libgfortran
   - liblapack=3.8.0
   - libopenblas=0.3.10
-  - libpng=1.6.37
   - libstdcxx-ng=11.2.0
-  - libtiff=4.2.0
-  - libuuid=1.0.3
-  - libwebp=1.2.2
-  - libwebp-base=1.2.2
-  - libxcb=1.14
   - libxgboost=0.90
-  - libxml2=2.9.12
   - libzlib=1.2.11
   - lightgbm=3.1.1
   - llvm-openmp=10.0.0
-  - lz4-c=1.9.3
   - markupsafe=1.1.1
-  - matplotlib=3.5.1
-  - matplotlib-base=3.5.1
   - mkl
   - mkl-service=2.3.0
   - mkl_fft=1.2.0
   - mkl_random=1.1.1
   - more-itertools=8.6.0
-  - munkres=1.1.4
   - ncurses=6.2
   - numpy=1.19.2
   - numpy-base=1.19.2
   - openblas=0.3.4
-  - openssl=1.1.1m
+  - openssl=1.1.1k
-  - packaging=21.3
   - pandas=1.1.5
   - pbr=5.5.1
-  - pcre=8.45
-  - pillow=9.0.1
   - pip=20.3.3
   - plotly=4.14.1
   - poyo=0.5.0
@@ -106,28 +77,24 @@ dependencies:
   - pycparser=2.20
   - pyerfa=1.7.1.1
   - pyopenssl=20.0.1
-  - pyqt=5.9.2
   - pysocks=1.7.1
   - python=3.7.9
   - python-dateutil=2.8.1
   - python_abi=3.7
   - pytz=2020.4
   - pyyaml=5.3.1
-  - qt=5.9.7
   - readline=8.0
   - requests=2.25.0
   - retrying=1.3.3
   - scikit-learn=0.23.2
   - scipy=1.5.2
   - setuptools=51.0.0
-  - sip=4.19.8
   - six=1.15.0
   - smmap=3.0.4
   - smmap2=3.0.1
   - sqlite=3.33.0
   - threadpoolctl=2.1.0
   - tk=8.6.10
-  - tornado=6.1
   - tqdm=4.62.0
   - urllib3=1.25.11
   - wheel=0.36.2
@@ -138,23 +105,26 @@ dependencies:
   - yaml=0.2.5
   - zipp=3.4.0
   - zlib=1.2.11
-  - zstd=1.4.9
   - pip:
     - amply==0.1.4
-    - bidict==0.21.4
+    - bidict==0.22.0
     - biosppy==0.8.0
-    - build==0.7.0
     - cached-property==1.5.2
     - calculatingfeatures==0.1.1
     - configargparse==0.15.1
+    - cycler==0.11.0
     - decorator==4.4.2
+    - fonttools==4.31.2
     - h5py==3.6.0
     - ipython-genutils==0.2.0
     - jupyter-core==4.6.3
+    - kiwisolver==1.4.1
+    - matplotlib==3.5.1
     - nbformat==5.0.7
     - opencv-python==4.5.5.64
+    - packaging==21.3
     - peakutils==1.3.3
-    - pep517==0.12.0
+    - pillow==9.0.1
     - pulp==2.4
     - pyparsing==2.4.7
     - pyrsistent==0.15.5
@@ -162,7 +132,7 @@ dependencies:
     - ratelimiter==1.2.0.post0
     - shortuuid==1.0.8
     - snakemake==5.30.2
-    - tomli==2.0.1
     - toposort==1.5
     - traitlets==4.3.3
+    - typing-extensions==4.1.1
 prefix: /opt/conda/envs/rapids

src/features/empatica_electrodermal_activity/cf/main.py

@@ -18,7 +18,7 @@ def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment
 
             eda_intraday_features = pd.DataFrame()
 
-            # apply a method from calculate features module 
+            # 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))

src/features/empatica_temperature/cf/main.py

@@ -0,0 +1,44 @@
+import pandas as pd
+from scipy.stats import entropy
+
+from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, genericFeatureNames
+from CalculatingFeatures.calculate_features import calculateFeatures
+
+import sys
+
+
+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:        
+        temperature_intraday_data = filter_data_by_segment(temperature_intraday_data, time_segment)
+
+        if not temperature_intraday_data.empty:
+
+            temperature_intraday_features = pd.DataFrame()
+
+            # 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))
+
+            temperature_intraday_features.reset_index(inplace=True)
+
+    return temperature_intraday_features
+
+
+def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
+    temperature_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
+
+    requested_intraday_features = provider["FEATURES"]
+    # name of the features this function can compute
+    base_intraday_features_names = genericFeatureNames
+    # the subset of requested features this function can compute
+    intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
+
+    # extract features from intraday data
+    temperature_intraday_features = extractTempFeaturesFromIntradayData(temperature_intraday_data,
+                                                                    intraday_features_to_compute, time_segment,
+                                                                    filter_data_by_segment)
+
+    return temperature_intraday_features