Preparation for cleaning & imputation

commit 43ecc243cb62bb31eed85cb477ca4131555c7fe7
Author: Primoz <sisko.primoz@gmail.com>
Date:   Fri Jul 22 15:26:09 2022 +0000

    Adding TODO comments

commit 2df1ebf90c3a93812b112b8ed0ee4e23cd74533f
Author: Primoz <sisko.primoz@gmail.com>
Date:   Thu Jul 21 13:59:23 2022 +0000

    README update

commit 5182c2b16dff3537aad42984b8ea5214743cdb32
Author: Primoz <sisko.primoz@gmail.com>
Date:   Thu Jul 21 11:03:01 2022 +0000

    Few corrections for all_cleaning

commit 3d9254c1b3bed6e95e631d4e0402548830a19534
Author: Primoz <sisko.primoz@gmail.com>
Date:   Thu Jul 21 10:28:05 2022 +0000

    Adding the min overlap for corr threshold and preservation of esm cols.

commit e27c49cc8fa4c51f9fe8e593a8d25e9a032ab393
Author: Primoz <sisko.primoz@gmail.com>
Date:   Thu Jul 21 09:02:00 2022 +0000

    Commenting and cleaning.

commit 31a47a5ee4569264e39d7c445525a6e64bb7700a
Author: Primoz <sisko.primoz@gmail.com>
Date:   Wed Jul 20 13:49:22 2022 +0000

    Environment version change.

commit 5b274ed8993f58e783bda6d82fce936764209c28
Author: Primoz <sisko.primoz@gmail.com>
Date:   Tue Jul 19 16:10:07 2022 +0000

    Enabled cleaning for all participants + standardization files.

commit 203fdb31e0f3c647ef8c8a60cb9531831b7ab924
Author: Primoz <sisko.primoz@gmail.com>
Date:   Tue Jul 19 14:14:51 2022 +0000

    Features cleaning fixes after testing. Visualization script for phone features values.

commit 176178d73b154c30b9eb9eb4a67514f00d6a924e
Author: Primoz <sisko.primoz@gmail.com>
Date:   Tue Jul 19 09:05:14 2022 +0000

    Revert "Necessary config changes."

    This reverts commit 6ec1ef50430d2e1f5ce4670d505d5e84ac47f0a0.

commit 26ea6512c9d512f95837e7b047fe510c1d196403
Author: Primoz <sisko.primoz@gmail.com>
Date:   Mon Jul 18 13:19:47 2022 +0000

    Adding cleaning function condition and cleaning functionality.

commit 575c29eef9c21e6f2d7832871e73bc0941643734
Author: Primoz <sisko.primoz@gmail.com>
Date:   Mon Jul 18 12:51:56 2022 +0000

    Translation of the cleaning individual RAPIDS function from R to py.

commit 6ec1ef50430d2e1f5ce4670d505d5e84ac47f0a0
Author: Primoz <sisko.primoz@gmail.com>
Date:   Mon Jul 18 12:02:18 2022 +0000

    Necessary config changes.

commit b5669f51612fbd8378848615d639677851ab032f
Author: Primoz <sisko.primoz@gmail.com>
Date:   Fri Jul 15 15:26:00 2022 +0000

    Modified snakemake rule to dynamically choose script extention.

commit 66636be1e8ae4828228b37c59b9df1faf3fc3d3d
Author: Primoz <sisko.primoz@gmail.com>
Date:   Fri Jul 15 14:43:08 2022 +0000

    Trying to modify the snakefile rule to execute scripts in two languages depended on the provider.

commit 574778b00f3cbb368ef4bc74de15cf5070c65ea9
Author: Primoz <sisko.primoz@gmail.com>
Date:   Fri Jul 15 09:49:41 2022 +0000

    gitignore: adding required files so that RAPIDS can be run successfully.

commit 71018ab178256970535e78961602ab8c7f0ebb14
Author: Primoz <sisko.primoz@gmail.com>
Date:   Fri Jul 15 08:34:19 2022 +0000

    Standardization bug fixes

commit 6253c470a624e6bfbb02e0c453b652452eb2dbbc
Author: Primoz <sisko.primoz@gmail.com>
Date:   Thu Jul 14 15:28:02 2022 +0000

    Seperate rules for empatica vs. nonempatica standardization.
    Parameter in config that controls the creation of standardized merged files for individual and all participants..

commit 90f902778565e0896d3bae22ae8551be8b487e67
Author: Primoz <sisko.primoz@gmail.com>
Date:   Tue Jul 12 14:23:03 2022 +0000

    Preparing for final csvs' standardization.

commit d25dde3998786a9a582f5cda544ee104386778f9
Author: Primoz <sisko.primoz@gmail.com>
Date:   Mon Jul 11 12:08:47 2022 +0000

    Revert "Changes in config to be reverted."

    This reverts commit bea7608e7095021fb7c53a9afa07074448fe4313.

commit 6b23e70857e63deda98eb98d190af9090626c84b
Author: Primoz <sisko.primoz@gmail.com>
Date:   Mon Jul 11 12:08:26 2022 +0000

    Enabled standardization for rest (previously active)  phone features.
    Testing still needed.

commit 8ec58a6f34ba3d42e5cc71d26e6d91837472ca5f
Author: Primoz <sisko.primoz@gmail.com>
Date:   Mon Jul 11 09:07:55 2022 +0000

    Enabled standardization for phone calls.
    All steps completed and tested.

commit bea7608e7095021fb7c53a9afa07074448fe4313
Author: Primoz <sisko.primoz@gmail.com>
Date:   Mon Jul 11 07:47:51 2022 +0000

    Changes in config to be reverted.

commit 4e84ca0e51bf709bff56fd09437b95310ec6bedd
Author: Primoz <sisko.primoz@gmail.com>
Date:   Fri Jul 8 14:11:24 2022 +0000

    Standardization for the rest of the features.

commit cc581aa788e3d5c17131af8f3d5dd6b0c3b5aff7
Author: Primoz <sisko.primoz@gmail.com>
Date:   Fri Jul 8 14:11:08 2022 +0000

    README update again

.gitignore

@@ -93,10 +93,14 @@ packrat/*
 
 # exclude data from source control by default
 data/external/*
+!/data/external/empatica/empatica1/E4 Data.zip
 !/data/external/.gitkeep
 !/data/external/stachl_application_genre_catalogue.csv
 !/data/external/timesegments*.csv
 !/data/external/wiki_tz.csv
+!/data/external/main_study_usernames.csv
+!/data/external/timezone.csv
+
 data/raw/*
 !/data/raw/.gitkeep
 data/interim/*
@@ -114,3 +118,12 @@ settings.dcf
 tests/fakedata_generation/
 site/
 credentials.yaml
+
+# Docker container and other files
+.devcontainer
+
+# Calculating features module
+calculatingfeatures/
+
+# Temp folder for rapids data/external
+rapids_temp_data/

README.md

@@ -11,3 +11,71 @@
 For more information refer to our [documentation](http://www.rapids.science)
 
 By [MoSHI](https://www.moshi.pitt.edu/), [University of Pittsburgh](https://www.pitt.edu/)
+
+## Installation 
+
+For RAPIDS installation refer to to the [documentation](https://www.rapids.science/1.8/setup/installation/)
+
+## For the installation of the Docker version
+
+1. Follow the [instructions](https://www.rapids.science/1.8/setup/installation/) to setup RAPIDS via Docker (from scratch).
+
+2. Delete current contents in /rapids/ folder when in a container session.
+    ```
+    cd ..
+    rm -rf rapids/{*,.*}
+    cd rapids
+    ```
+
+3. Clone RAPIDS workspace from Git and checkout a specific branch.
+    ```
+    git clone "https://repo.ijs.si/junoslukan/rapids.git" .
+    git checkout <branch_name>
+    ```
+
+4. Install missing “libpq-dev” dependency with bash.
+    ```
+    apt-get update -y
+    apt-get install -y libpq-dev
+    ```
+
+5. Restore R venv.
+Type R to go to the interactive R session and then:
+    ```
+    renv::restore()
+    ```
+
+6. Install cr-features module 
+From: https://repo.ijs.si/matjazbostic/calculatingfeatures.git -> branch modifications_for_rapids. 
+Then follow the "cr-features module" section below.  
+
+7. Install all required packages from environment.yml, prune also deletes conda packages not present in environment file.
+    ```
+    conda env update --file environment.yml –prune
+    ```
+
+8. If you wish to update your R or Python venvs.
+    ```
+    R in interactive session:
+    renv::snapshot()
+    Python: 
+    conda env export --no-builds | sed 's/^.*libgfortran.*$/  - libgfortran/' | sed 's/^.*mkl=.*$/  - mkl/' >  environment.yml
+    ```
+
+## cr-features module 
+
+This RAPIDS extension uses cr-features library accessible [here](https://repo.ijs.si/matjazbostic/calculatingfeatures).
+
+To use cr-features library:
+
+- Follow the installation instructions in the [README.md](https://repo.ijs.si/matjazbostic/calculatingfeatures/-/blob/master/README.md).
+
+- Copy built calculatingfeatures folder into the RAPIDS workspace.
+
+- Install the cr-features package by:
+    ```
+    pip install path/to/the/calculatingfeatures/folder
+    e.g. pip install ./calculatingfeatures if the folder is copied to main parent directory
+    cr-features package has to be built and installed everytime to get the newest version. 
+    Or an the newest version of the docker image must be used.   
+    ```

Snakefile

@@ -33,6 +33,12 @@ for provider in config["PHONE_DATA_YIELD"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_data_yield.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
+        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
+            and config["PHONE_DATA_YIELD"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
+            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_data_yield.csv", pid=config["PIDS"]))
+            if config["STANDARDIZATION"]["MERGE_ALL"]:
+                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
+                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_MESSAGES"]["PROVIDERS"].keys():
     if config["PHONE_MESSAGES"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -42,6 +48,12 @@ for provider in config["PHONE_MESSAGES"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_messages.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
+        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
+            and config["PHONE_MESSAGES"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
+            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_messages.csv", pid=config["PIDS"]))
+            if config["STANDARDIZATION"]["MERGE_ALL"]:
+                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
+                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_CALLS"]["PROVIDERS"].keys():
     if config["PHONE_CALLS"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -56,6 +68,12 @@ for provider in config["PHONE_CALLS"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_calls.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
+        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
+            and config["PHONE_CALLS"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
+            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_calls.csv", pid=config["PIDS"]))
+            if config["STANDARDIZATION"]["MERGE_ALL"]:
+                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
+                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_BLUETOOTH"]["PROVIDERS"].keys():
     if config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -65,6 +83,12 @@ for provider in config["PHONE_BLUETOOTH"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_bluetooth.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
+        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
+            and config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
+            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_bluetooth.csv", pid=config["PIDS"]))
+            if config["STANDARDIZATION"]["MERGE_ALL"]:
+                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
+                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"].keys():
     if config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -77,6 +101,12 @@ for provider in config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_activity_recognition.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
+        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
+            and config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
+            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_activity_recognition.csv", pid=config["PIDS"]))
+            if config["STANDARDIZATION"]["MERGE_ALL"]:
+                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
+                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_BATTERY"]["PROVIDERS"].keys():
     if config["PHONE_BATTERY"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -88,6 +118,12 @@ for provider in config["PHONE_BATTERY"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_battery.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
+        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
+            and config["PHONE_BATTERY"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
+            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_battery.csv", pid=config["PIDS"]))
+            if config["STANDARDIZATION"]["MERGE_ALL"]:
+                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
+                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_SCREEN"]["PROVIDERS"].keys():
     if config["PHONE_SCREEN"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -104,6 +140,12 @@ for provider in config["PHONE_SCREEN"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_screen.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
+        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
+            and config["PHONE_SCREEN"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
+            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_screen.csv", pid=config["PIDS"]))
+            if config["STANDARDIZATION"]["MERGE_ALL"]:
+                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
+                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_LIGHT"]["PROVIDERS"].keys():
     if config["PHONE_LIGHT"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -113,6 +155,12 @@ for provider in config["PHONE_LIGHT"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_light.csv", pid=config["PIDS"],))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
+        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
+            and config["PHONE_LIGHT"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
+            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_light.csv", pid=config["PIDS"]))
+            if config["STANDARDIZATION"]["MERGE_ALL"]:
+                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
+                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_ACCELEROMETER"]["PROVIDERS"].keys():
     if config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -136,6 +184,12 @@ for provider in config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_applications_foreground.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
+        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
+            and config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
+            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_applications_foreground.csv", pid=config["PIDS"]))
+            if config["STANDARDIZATION"]["MERGE_ALL"]:
+                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
+                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_WIFI_VISIBLE"]["PROVIDERS"].keys():
     if config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -145,6 +199,12 @@ for provider in config["PHONE_WIFI_VISIBLE"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_visible.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
+        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
+            and config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
+            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_wifi_visible.csv", pid=config["PIDS"]))
+            if config["STANDARDIZATION"]["MERGE_ALL"]:
+                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
+                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_WIFI_CONNECTED"]["PROVIDERS"].keys():
     if config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -171,8 +231,14 @@ for provider in config["PHONE_ESM"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/interim/{pid}/phone_esm_clean.csv",pid=config["PIDS"]))
         files_to_compute.extend(expand("data/interim/{pid}/phone_esm_features/phone_esm_{language}_{provider_key}.csv",pid=config["PIDS"],language=get_script_language(config["PHONE_ESM"]["PROVIDERS"][provider]["SRC_SCRIPT"]),provider_key=provider.lower()))
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_esm.csv", pid=config["PIDS"]))
-        #files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv",pid=config["PIDS"]))
+        # files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv",pid=config["PIDS"]))
-        #files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
+        # files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
+        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
+            and config["PHONE_ESM"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
+            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_esm.csv", pid=config["PIDS"]))
+            if config["STANDARDIZATION"]["MERGE_ALL"]:
+                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
+                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 # We can delete these if's as soon as we add feature PROVIDERS to any of these sensors
 if isinstance(config["PHONE_APPLICATIONS_CRASHES"]["PROVIDERS"], dict):
@@ -238,6 +304,12 @@ for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_locations.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
+        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
+            and config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
+            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_locations.csv", pid=config["PIDS"]))
+            if config["STANDARDIZATION"]["MERGE_ALL"]:
+                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
+                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["FITBIT_CALORIES_INTRADAY"]["PROVIDERS"].keys():
     if config["FITBIT_CALORIES_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -328,6 +400,13 @@ for provider in config["EMPATICA_ACCELEROMETER"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/empatica_accelerometer.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
+        if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"] \
+            and config["EMPATICA_ACCELEROMETER"]["PROVIDERS"][provider]["WINDOWS"]["STANDARDIZE_FEATURES"]:
+            files_to_compute.extend(expand("data/interim/{pid}/empatica_accelerometer_features/z_empatica_accelerometer_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower()))
+            files_to_compute.extend(expand("data/processed/features/{pid}/z_empatica_accelerometer.csv", pid=config["PIDS"]))
+            if config["STANDARDIZATION"]["MERGE_ALL"]:
+                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
+                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
      
 for provider in config["EMPATICA_HEARTRATE"]["PROVIDERS"].keys():
     if config["EMPATICA_HEARTRATE"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -347,6 +426,13 @@ for provider in config["EMPATICA_TEMPERATURE"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/empatica_temperature.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
+        if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"] \
+            and config["EMPATICA_TEMPERATURE"]["PROVIDERS"][provider]["WINDOWS"]["STANDARDIZE_FEATURES"]:
+            files_to_compute.extend(expand("data/interim/{pid}/empatica_temperature_features/z_empatica_temperature_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower()))
+            files_to_compute.extend(expand("data/processed/features/{pid}/z_empatica_temperature.csv", pid=config["PIDS"]))
+            if config["STANDARDIZATION"]["MERGE_ALL"]:
+                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
+                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["EMPATICA_ELECTRODERMAL_ACTIVITY"]["PROVIDERS"].keys():
     if config["EMPATICA_ELECTRODERMAL_ACTIVITY"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -356,6 +442,13 @@ for provider in config["EMPATICA_ELECTRODERMAL_ACTIVITY"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/empatica_electrodermal_activity.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
+        if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"] \
+            and config["EMPATICA_ELECTRODERMAL_ACTIVITY"]["PROVIDERS"][provider]["WINDOWS"]["STANDARDIZE_FEATURES"]:
+            files_to_compute.extend(expand("data/interim/{pid}/empatica_electrodermal_activity_features/z_empatica_electrodermal_activity_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower()))
+            files_to_compute.extend(expand("data/processed/features/{pid}/z_empatica_electrodermal_activity.csv", pid=config["PIDS"]))
+            if config["STANDARDIZATION"]["MERGE_ALL"]:
+                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
+                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"].keys():
     if config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -365,6 +458,13 @@ for provider in config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/empatica_blood_volume_pulse.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
+        if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"] \
+            and config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"][provider]["WINDOWS"]["STANDARDIZE_FEATURES"]:
+            files_to_compute.extend(expand("data/interim/{pid}/empatica_blood_volume_pulse_features/z_empatica_blood_volume_pulse_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower()))
+            files_to_compute.extend(expand("data/processed/features/{pid}/z_empatica_blood_volume_pulse.csv", pid=config["PIDS"]))
+            if config["STANDARDIZATION"]["MERGE_ALL"]:
+                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
+                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"].keys():
     if config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -374,6 +474,13 @@ for provider in config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/empatica_inter_beat_interval.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
+        if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"] \
+            and config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][provider]["WINDOWS"]["STANDARDIZE_FEATURES"]:
+            files_to_compute.extend(expand("data/interim/{pid}/empatica_inter_beat_interval_features/z_empatica_inter_beat_interval_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower()))
+            files_to_compute.extend(expand("data/processed/features/{pid}/z_empatica_inter_beat_interval.csv", pid=config["PIDS"]))
+            if config["STANDARDIZATION"]["MERGE_ALL"]:
+                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
+                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
      
 if isinstance(config["EMPATICA_TAGS"]["PROVIDERS"], dict):
     for provider in config["EMPATICA_TAGS"]["PROVIDERS"].keys():
@@ -408,10 +515,26 @@ if config["HEATMAP_FEATURE_CORRELATION_MATRIX"]["PLOT"]:
 # Data Cleaning
 for provider in config["ALL_CLEANING_INDIVIDUAL"]["PROVIDERS"].keys():
     if config["ALL_CLEANING_INDIVIDUAL"]["PROVIDERS"][provider]["COMPUTE"]:
-        files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features_cleaned_" + provider.lower() +".csv", pid=config["PIDS"]))
+        if provider == "STRAW":
+            files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features_cleaned_" + provider.lower() + "_py.csv", pid=config["PIDS"]))
+            if config["ALL_CLEANING_INDIVIDUAL"]["CLEAN_STANDARDIZED"]:
+                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features_cleaned_" + provider.lower() + "_py.csv", pid=config["PIDS"]))
+        else:
+            files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features_cleaned_" + provider.lower() + "_R.csv", pid=config["PIDS"]))
+            if config["ALL_CLEANING_INDIVIDUAL"]["CLEAN_STANDARDIZED"]:
+                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features_cleaned_" + provider.lower() + "_R.csv", pid=config["PIDS"]))
+
 for provider in config["ALL_CLEANING_OVERALL"]["PROVIDERS"].keys():
     if config["ALL_CLEANING_OVERALL"]["PROVIDERS"][provider]["COMPUTE"]:
-        files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +".csv"))
+        if provider == "STRAW":
+            files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +"_py.csv"))
+            if config["ALL_CLEANING_OVERALL"]["CLEAN_STANDARDIZED"]:
+                files_to_compute.extend(expand("data/processed/features/all_participants/z_all_sensor_features_cleaned_" + provider.lower() +"_py.csv"))
+        else:
+            files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +"_R.csv"))
+            if config["ALL_CLEANING_OVERALL"]["CLEAN_STANDARDIZED"]:
+                files_to_compute.extend(expand("data/processed/features/all_participants/z_all_sensor_features_cleaned_" + provider.lower() +"_R.csv"))
+        
 
 # Baseline features
 if config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["COMPUTE"]:
@@ -422,8 +545,10 @@ if config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["COMPUTE"]:
 
 # Targets (labels)
 if config["PARAMS_FOR_ANALYSIS"]["TARGET"]["COMPUTE"]:
-    files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/input.csv", pid=config["PIDS"]))
+    # files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/input.csv", pid=config["PIDS"]))
-    files_to_compute.extend(expand("data/processed/models/population_model/input.csv"))
+    # files_to_compute.extend(expand("data/processed/models/population_model/input.csv"))
+    files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/z_input.csv", pid=config["PIDS"]))
+    files_to_compute.extend(expand("data/processed/models/population_model/z_input.csv"))
 
 #files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/output_{cv_method}/baselines.csv", pid=config["PIDS"], cv_method=config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"]))
 

config.yaml

@@ -16,7 +16,7 @@ CREATE_PARTICIPANT_FILES:
     ADD: False
     IGNORED_DEVICE_IDS: []
   EMPATICA_SECTION:
-    ADD: False
+    ADD: True
     IGNORED_DEVICE_IDS: []
 
 # See https://www.rapids.science/latest/setup/configuration/#time-segments
@@ -93,6 +93,7 @@ PHONE_ACTIVITY_RECOGNITION:
         STATIONARY: ["still", "tilting"]
         MOBILE: ["on_foot", "walking", "running", "on_bicycle"]
         VEHICLE: ["in_vehicle"]
+      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_activity_recognition/rapids/main.py
 
 # See https://www.rapids.science/latest/features/phone-applications-crashes/
@@ -133,6 +134,7 @@ PHONE_APPLICATIONS_FOREGROUND:
         APP_EPISODES: ["countepisode", "minduration", "maxduration", "meanduration", "sumduration"]
       IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable
       IGNORE_EPISODES_LONGER_THAN: 300 # in minutes, set to 0 to disable
+      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_applications_foreground/rapids/main.py
 
 # See https://www.rapids.science/latest/features/phone-applications-notifications/
@@ -153,6 +155,7 @@ PHONE_BATTERY:
     RAPIDS:
       COMPUTE: True
       FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"]
+      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_battery/rapids/main.py
 
 # See https://www.rapids.science/latest/features/phone-bluetooth/
@@ -162,6 +165,7 @@ PHONE_BLUETOOTH:
     RAPIDS:
       COMPUTE: True
       FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
+      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_bluetooth/rapids/main.R
 
     DORYAB:
@@ -179,6 +183,7 @@ PHONE_BLUETOOTH:
             DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"]
             SCANS_MOST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]
             SCANS_LEAST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]
+      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_bluetooth/doryab/main.py
 
 # See https://www.rapids.science/latest/features/phone-calls/
@@ -193,6 +198,7 @@ PHONE_CALLS:
         missed:  [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact]
         incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact]
         outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact]
+      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_calls/rapids/main.R
 
 # See https://www.rapids.science/latest/features/phone-conversation/
@@ -232,6 +238,7 @@ PHONE_DATA_YIELD:
       COMPUTE: True
       FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours]
       MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1, minimum percentage of valid minutes in an hour to be considered valid.
+      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_data_yield/rapids/main.R
 
 PHONE_ESM:
@@ -241,6 +248,7 @@ PHONE_ESM:
       COMPUTE: True
       SCALES: ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support"]
       FEATURES: [mean]
+      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_esm/straw/main.py
 
 # See https://www.rapids.science/latest/features/phone-keyboard/
@@ -259,6 +267,7 @@ PHONE_LIGHT:
     RAPIDS:
       COMPUTE: True
       FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]
+      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_light/rapids/main.py
 
 # See https://www.rapids.science/latest/features/phone-locations/
@@ -283,6 +292,7 @@ PHONE_LOCATIONS:
       MINIMUM_DAYS_TO_DETECT_HOME_CHANGES: 3
       CLUSTERING_ALGORITHM: DBSCAN # DBSCAN, OPTICS
       RADIUS_FOR_HOME: 100
+      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_locations/doryab/main.py
 
     BARNETT:
@@ -290,6 +300,7 @@ PHONE_LOCATIONS:
       FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"]
       IF_MULTIPLE_TIMEZONES: USE_MOST_COMMON
       MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features
+      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_locations/barnett/main.R
 
 # See https://www.rapids.science/latest/features/phone-log/
@@ -309,6 +320,7 @@ PHONE_MESSAGES:
       FEATURES: 
         received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
         sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
+      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_messages/rapids/main.R
 
 # See https://www.rapids.science/latest/features/phone-screen/
@@ -322,6 +334,7 @@ PHONE_SCREEN:
       IGNORE_EPISODES_LONGER_THAN: 360 # in minutes, set to 0 to disable
       FEATURES: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] # "episodepersensedminutes" needs to be added later
       EPISODE_TYPES: ["unlock"]
+      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_screen/rapids/main.py
 
 # See https://www.rapids.science/latest/features/phone-wifi-connected/
@@ -340,6 +353,7 @@ PHONE_WIFI_VISIBLE:
     RAPIDS:
       COMPUTE: True
       FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
+      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_wifi_visible/rapids/main.R
 
 
@@ -485,6 +499,7 @@ FITBIT_STEPS_INTRADAY:
       INCLUDE_ZERO_STEP_ROWS: False
       SRC_SCRIPT: src/features/fitbit_steps_intraday/rapids/main.py
 
+
 ########################################################################################################################
 #                                                 EMPATICA                                                             #
 ########################################################################################################################
@@ -506,6 +521,16 @@ EMPATICA_ACCELEROMETER:
       COMPUTE: False
       FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
       SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
+    CR:
+      COMPUTE: True
+      FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features
+      WINDOWS:
+        COMPUTE: True
+        WINDOW_LENGTH: 15 # specify window length in seconds
+        SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows']
+        STANDARDIZE_FEATURES: True
+      SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py
+
 
 # See https://www.rapids.science/latest/features/empatica-heartrate/
 EMPATICA_HEARTRATE:
@@ -524,6 +549,16 @@ EMPATICA_TEMPERATURE:
       COMPUTE: False
       FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"]
       SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py
+    CR:
+      COMPUTE: True
+      FEATURES: ["maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", 
+                  "stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"]
+      WINDOWS:
+        COMPUTE: True
+        WINDOW_LENGTH: 300 # specify window length in seconds
+        SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows']
+        STANDARDIZE_FEATURES: True
+      SRC_SCRIPT: src/features/empatica_temperature/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-electrodermal-activity/
 EMPATICA_ELECTRODERMAL_ACTIVITY:
@@ -533,6 +568,20 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
       COMPUTE: False
       FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
       SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
+    CR:
+      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',
+                  'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 
+                  'significantDecrease']
+      WINDOWS:
+        COMPUTE: True
+        WINDOW_LENGTH: 60 # specify window length in seconds
+        SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', count_windows, eda_num_peaks_non_zero]
+        STANDARDIZE_FEATURES: True
+        IMPUTE_NANS: True
+      SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/
 EMPATICA_BLOOD_VOLUME_PULSE:
@@ -542,6 +591,16 @@ EMPATICA_BLOOD_VOLUME_PULSE:
       COMPUTE: False
       FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"]
       SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py
+    CR:
+      COMPUTE: False
+      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: 300 # specify window length in seconds
+        SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows', 'hrv_num_windows_non_nan']
+        STANDARDIZE_FEATURES: True
+      SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-inter-beat-interval/
 EMPATICA_INTER_BEAT_INTERVAL:
@@ -551,6 +610,17 @@ EMPATICA_INTER_BEAT_INTERVAL:
       COMPUTE: False
       FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"]
       SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py
+    CR:
+      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            
+      PATCH_WITH_BVP: True
+      WINDOWS:
+        COMPUTE: True
+        WINDOW_LENGTH: 300 # specify window length in seconds
+        SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows', 'hrv_num_windows_non_nan']
+        STANDARDIZE_FEATURES: True
+      SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-tags/
 EMPATICA_TAGS:
@@ -566,7 +636,7 @@ EMPATICA_TAGS:
 
 # See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#1-histograms-of-phone-data-yield
 HISTOGRAM_PHONE_DATA_YIELD:
-  PLOT: True
+  PLOT: False
 
 # See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#2-heatmaps-of-overall-data-yield
 HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT:
@@ -575,7 +645,7 @@ HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT:
 
 # See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#3-heatmap-of-recorded-phone-sensors
 HEATMAP_SENSORS_PER_MINUTE_PER_TIME_SEGMENT:
-  PLOT: True
+  PLOT: False
 
 # See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#4-heatmap-of-sensor-row-count
 HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT:
@@ -586,7 +656,7 @@ HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT:
 
 # See https://www.rapids.science/latest/visualizations/feature-visualizations/#1-heatmap-correlation-matrix
 HEATMAP_FEATURE_CORRELATION_MATRIX:
-  PLOT: True
+  PLOT: False
   MIN_ROWS_RATIO: 0.5
   CORR_THRESHOLD: 0.1
   CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"}
@@ -597,43 +667,94 @@ HEATMAP_FEATURE_CORRELATION_MATRIX:
 ########################################################################################################################
 
 ALL_CLEANING_INDIVIDUAL:
+  CLEAN_STANDARDIZED: True
   PROVIDERS:
     RAPIDS:
       COMPUTE: True
       IMPUTE_SELECTED_EVENT_FEATURES:
-        COMPUTE: True
+        COMPUTE: False
         MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
-      COLS_NAN_THRESHOLD: 0.3 # set to 1 to disable
+      COLS_NAN_THRESHOLD: 1 # set to 1 to disable
       COLS_VAR_THRESHOLD: True
       ROWS_NAN_THRESHOLD: 1 # set to 1 to disable
       DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
-      DATA_YIELD_RATIO_THRESHOLD: 0.3 # set to 0 to disable
+      DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
       DROP_HIGHLY_CORRELATED_FEATURES:
-        COMPUTE: False
+        COMPUTE: True
         MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
         CORR_THRESHOLD: 0.95
       SRC_SCRIPT: src/features/all_cleaning_individual/rapids/main.R
+    STRAW: # currently the same as RAPIDS provider with a change in selecting the imputation type
+      COMPUTE: True
+      IMPUTE_PHONE_SELECTED_EVENT_FEATURES:
+        COMPUTE: False
+        TYPE: median # options: zero, mean, median or k-nearest
+        MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
+      COLS_NAN_THRESHOLD: 1 # set to 1 to disable
+      COLS_VAR_THRESHOLD: True
+      ROWS_NAN_THRESHOLD: 1 # set to 1 to disable
+      DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
+      DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
+      DROP_HIGHLY_CORRELATED_FEATURES:
+        COMPUTE: True
+        MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
+        CORR_THRESHOLD: 0.95
+      SRC_SCRIPT: src/features/all_cleaning_individual/straw/main.py
 
 ALL_CLEANING_OVERALL:
+  CLEAN_STANDARDIZED: True
   PROVIDERS:
     RAPIDS:
       COMPUTE: True
       IMPUTE_SELECTED_EVENT_FEATURES:
-        COMPUTE: True
+        COMPUTE: False
         MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
-      COLS_NAN_THRESHOLD: 0.3 # set to 1 to disable
+      COLS_NAN_THRESHOLD: 1 # set to 1 to disable
       COLS_VAR_THRESHOLD: True
       ROWS_NAN_THRESHOLD: 1 # set to 1 to disable
       DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
-      DATA_YIELD_RATIO_THRESHOLD: 0.3 # set to 0 to disable
+      DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
       DROP_HIGHLY_CORRELATED_FEATURES:
-        COMPUTE: False
+        COMPUTE: True
         MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
         CORR_THRESHOLD: 0.95
       SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R
+    STRAW: # currently the same as RAPIDS provider with a change in selecting the imputation type
+      COMPUTE: True
+      IMPUTE_PHONE_SELECTED_EVENT_FEATURES:
+        COMPUTE: False
+        TYPE: median # options: zero, mean, median or k-nearest
+        MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
+      COLS_NAN_THRESHOLD: 1 # set to 1 to disable
+      COLS_VAR_THRESHOLD: True
+      ROWS_NAN_THRESHOLD: 1 # set to 1 to disable
+      DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
+      DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
+      DROP_HIGHLY_CORRELATED_FEATURES:
+        COMPUTE: True
+        MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
+        CORR_THRESHOLD: 0.95
+      SRC_SCRIPT: src/features/all_cleaning_overall/straw/main.py
+
 
 ########################################################################################################################
-#                                              Analysis Workflow Example                                               #
+#                                                 Z-score standardization                                              #
+########################################################################################################################
+
+STANDARDIZATION: # Standardization for both providers is executed if only one of two providers is marked COMPUTE: TRUE
+  MERGE_ALL: True # Creates the joint standardized file for each participant and all participants. Similar to merge_sensor_features_for_all_participants rule  
+  PROVIDERS:
+    CR:
+      COMPUTE: True
+      SRC_SCRIPT: src/features/standardization/main.py
+    OTHER:
+      COMPUTE: True
+      LIST: [RAPIDS, DORYAB, BARNETT, STRAW]
+      SRC_SCRIPT: src/features/standardization/main.py
+
+
+########################################################################################################################
+#                                                      Baseline                                                        #
 ########################################################################################################################
 
 PARAMS_FOR_ANALYSIS:

data/external/aware_csv/calls.csv

@@ -0,0 +1,9 @@
+"_id","timestamp","device_id","call_type","call_duration","trace"
+1,1587663260695,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",2,14,"d5e84f8af01b2728021d4f43f53a163c0c90000c"
+2,1587739118007,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",3,0,"47c125dc7bd163b8612cdea13724a814917b6e93"
+5,1587746544891,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",2,95,"9cc793ffd6e88b1d850ce540b5d7e000ef5650d4"
+6,1587911379859,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",2,63,"51fb9344e988049a3fec774c7ca622358bf80264"
+7,1587992647361,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",3,0,"2a862a7730cfdfaf103a9487afe3e02935fd6e02"
+8,1588020039448,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",1,11,"a2c53f6a086d98622c06107780980cf1bb4e37bd"
+11,1588176189024,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",2,65,"56589df8c830c70e330b644921ed38e08d8fd1f3"
+12,1588197745079,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",3,0,"cab458018a8ed3b626515e794c70b6f415318adc"

data/external/main_study_usernames.csv

@@ -0,0 +1,57 @@
+label,empatica_id
+uploader_79170,A0245B
+uploader_89788,A02731
+uploader_68294,A02705
+uploader_92856,A024AF
+uploader_23726,A0231C
+uploader_66620,A02305
+uploader_58435,A026B5
+uploader_87801,A022A8
+uploader_96055,A027BA
+uploader_69549,A0226C
+uploader_26363,A0263D
+uploader_72010,A023FA
+uploader_13997,A024AF
+uploader_31156,A02305
+uploader_63187,A027BA
+uploader_94821,A022A8
+uploader_65413,A023F1;A023FA
+uploader_36488,A02713
+uploader_91087,A0231C
+uploader_35174,A025D1
+uploader_73880,A02705
+uploader_78650,A02731
+uploader_70578,A0245B
+uploader_88313,A02736
+uploader_58482,A0261A
+uploader_80601,A027BA
+uploader_93729,A0226C
+uploader_61663,A0245B
+uploader_80848,A025D1
+uploader_57312,A023F9;A02361;A027A0
+uploader_52087,A02666
+uploader_98770,A02953
+uploader_51327,A0245F
+uploader_11737,A02732
+uploader_77440,A0264E
+uploader_57277,A02422
+uploader_13098,A026E5
+uploader_80719,A023C8
+uploader_54698,A02953
+uploader_95571,A02853
+uploader_21880,A024DC
+uploader_92905,A02920
+uploader_12108,A023F4
+uploader_17436,A026E5
+uploader_58440,A0273F
+uploader_22172,A0245F
+uploader_39250,A02422
+uploader_15311,A023F9
+uploader_45766,A02920
+uploader_23096,A02361
+uploader_78243,A02422
+uploader_58777,A0245F
+uploader_82941,A02666
+uploader_89606,A023F4
+uploader_82969,A023C8
+uploader_53573,A024DC;A02361

data/external/participant_files/p01.yaml

@@ -0,0 +1,11 @@
+PHONE:
+  DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524] # the participant's AWARE device id
+  PLATFORMS: [android] # or ios
+  LABEL: MyTestP01 # any string
+  START_DATE: 2020-01-01 # this can also be empty
+  END_DATE: 2021-01-01 # this can also be empty
+EMPATICA:
+  DEVICE_IDS: [empatica1]
+  LABEL: test01
+  START_DATE: 
+  END_DATE: 

data/external/timesegments_frequency.csv

@@ -1,2 +1,2 @@
 label,length
-thirtyminutes,30
+fiveminutes,5

data/external/timesegments_periodic.csv

@@ -1,9 +1,2 @@
 label,start_time,length,repeats_on,repeats_value
-threeday,00:00:00,2D 23H 59M 59S,every_day,0
 daily,00:00:00,23H 59M 59S,every_day,0
-morning,06:00:00,5H 59M 59S,every_day,0
-afternoon,12:00:00,5H 59M 59S,every_day,0
-evening,18:00:00,5H 59M 59S,every_day,0
-night,00:00:00,5H 59M 59S,every_day,0
-two_weeks_overlapping,00:00:00,13D 23H 59M 59S,every_day,0
-weekends,00:00:00,2D 23H 59M 59S,wday,5

environment.yml

@@ -3,114 +3,138 @@ channels:
   - conda-forge
   - defaults
 dependencies:
+  - _libgcc_mutex=0.1
+  - _openmp_mutex=4.5
   - _py-xgboost-mutex=2.0
-  - appdirs=1.4.*
+  - appdirs=1.4.4
   - arrow=0.16.0
-  - asn1crypto=1.4.*
+  - asn1crypto=1.4.0
-  - astropy=4.2.*
+  - astropy=4.2.1
-  - attrs=20.3.*
+  - attrs=20.3.0
-  - binaryornot=0.4.*
+  - binaryornot=0.4.4
   - blas=1.0
-  - brotlipy=0.7.*
+  - brotlipy=0.7.0
-  - bzip2=1.0.*
+  - bzip2=1.0.8
-  - ca-certificates
+  - ca-certificates=2021.7.5
-  - certifi
+  - certifi=2021.5.30
   - cffi=1.14.4
-  - chardet=3.0.*
+  - chardet=3.0.4
-  - click=7.1.*
+  - click=7.1.2
-  - cookiecutter=1.6.*
+  - colorama=0.4.4
-  - cryptography=3.3.*
+  - cookiecutter=1.6.0
-  - datrie=0.8.*
+  - cryptography=3.3.1
+  - datrie=0.8.2
   - docutils=0.16
   - future=0.18.2
-  - gitdb=4.0.*
+  - gitdb=4.0.5
-  - gitdb2=4.0.*
+  - gitdb2=4.0.2
-  - gitpython=3.1.*
+  - gitpython=3.1.11
   - idna=2.10
-  - imbalanced-learn=0.6.*
+  - imbalanced-learn=0.6.2
-  - importlib-metadata=2.0.*
+  - importlib-metadata=2.0.0
-  - importlib_metadata=2.0.*
+  - importlib_metadata=2.0.0
   - intel-openmp=2019.4
   - jinja2=2.11.2
-  - jinja2-time=0.2.*
+  - jinja2-time=0.2.0
-  - joblib=1.0.*
+  - joblib=1.0.0
-  - jsonschema=3.2.*
+  - jsonschema=3.2.0
-  - libblas=3.8.*
+  - ld_impl_linux-64=2.36.1
-  - libcblas=3.8.*
+  - libblas=3.8.0
-  - libcxx=10.0.*
+  - libcblas=3.8.0
-  - libedit=3.1.*
+  - libcxx=10.0.0
+  - libcxxabi=10.0.0
+  - libedit=3.1.20191231
   - libffi=3.3
+  - libgcc-ng=11.2.0
   - libgfortran
-  - liblapack=3.8.*
+  - libgfortran
-  - libopenblas=0.3.*
+  - libgfortran
+  - liblapack=3.8.0
+  - libopenblas=0.3.10
+  - libstdcxx-ng=11.2.0
   - libxgboost=0.90
-  - lightgbm=3.1.*
+  - libzlib=1.2.11
-  - llvm-openmp=10.0.*
+  - lightgbm=3.1.1
-  - markupsafe=1.1.*
+  - llvm-openmp=10.0.0
+  - markupsafe=1.1.1
   - mkl
-  - mkl-service=2.3.*
+  - mkl-service=2.3.0
-  - mkl_fft=1.2.*
+  - mkl_fft=1.2.0
-  - mkl_random=1.1.*
+  - mkl_random=1.1.1
-  - more-itertools=8.6.*
+  - more-itertools=8.6.0
   - ncurses=6.2
   - numpy=1.19.2
   - numpy-base=1.19.2
-  - openblas=0.3.*
+  - openblas=0.3.4
-  - openssl
+  - openssl=1.1.1k
-  - pandas=1.1.*
+  - pandas=1.1.5
-  - pbr=5.5.*
+  - pbr=5.5.1
-  - pip=20.3.*
+  - pip=20.3.3
   - plotly=4.14.1
-  - poyo=0.5.*
+  - poyo=0.5.0
-  - psutil=5.7.*
+  - psutil=5.7.2
-  - psycopg2
   - py-xgboost=0.90
   - pycparser=2.20
-  - pyerfa=1.7.*
+  - pyerfa=1.7.1.1
-  - pyopenssl=20.0.*
+  - pyopenssl=20.0.1
-  - pyprojroot
+  - pysocks=1.7.1
-  - pysocks=1.7.*
+  - python=3.7.9
-  - python=3.7.*
+  - python-dateutil=2.8.1
-  - python-dateutil=2.8.*
-  - python-dotenv
   - python_abi=3.7
   - pytz=2020.4
-  - pyyaml=5.3.*
+  - pyyaml=5.3.1
   - readline=8.0
   - requests=2.25.0
-  - retrying=1.3.*
+  - retrying=1.3.3
   - scikit-learn=0.23.2
-  - scipy=1.5.*
+  - scipy=1.5.2
-  - setuptools=51.0.*
+  - setuptools=51.0.0
   - six=1.15.0
-  - smmap=3.0.*
+  - smmap=3.0.4
-  - smmap2=3.0.*
+  - smmap2=3.0.1
-  - sqlalchemy
   - sqlite=3.33.0
-  - threadpoolctl=2.1.*
+  - threadpoolctl=2.1.0
-  - tk=8.6.*
+  - tk=8.6.10
   - tqdm=4.62.0
   - urllib3=1.25.11
   - wheel=0.36.2
-  - whichcraft=0.6.*
+  - whichcraft=0.6.1
   - wrapt=1.12.1
   - xgboost=0.90
-  - xz=5.2.*
+  - xz=5.2.5
-  - yaml=0.2.*
+  - yaml=0.2.5
-  - zipp=3.4.*
+  - zipp=3.4.0
-  - zlib=1.2.*
+  - zlib=1.2.11
   - pip:
-    - amply==0.1.*
+    - amply==0.1.4
+    - bidict==0.22.0
+    - biosppy==0.8.0
+    - cached-property==1.5.2
     - configargparse==0.15.1
-    - decorator==4.4.*
+    - cr-features==0.2.1
-    - ipython-genutils==0.2.*
+    - cycler==0.11.0
-    - jupyter-core==4.6.*
+    - decorator==4.4.2
-    - nbformat==5.0.*
+    - 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
+    - matplotlib==3.5.1
+    - nbformat==5.0.7
+    - opencv-python==4.5.5.64
+    - packaging==21.3
+    - peakutils==1.3.3
+    - pillow==9.1.0
     - pulp==2.4
-    - pyparsing==2.4.*
+    - pyparsing==2.4.7
     - pyrsistent==0.15.5
-    - ratelimiter==1.2.*
+    - pywavelets==1.3.0
+    - ratelimiter==1.2.0.post0
+    - seaborn==0.11.2
+    - shortuuid==1.0.8
     - snakemake==5.30.2
     - toposort==1.5
-    - traitlets==4.3.*
+    - traitlets==4.3.3
-prefix: /usr/local/Caskroom/miniconda/base/envs/rapids202108
+    - typing-extensions==4.2.0
+prefix: /opt/conda/envs/rapids

renv/activate.R

@@ -15,9 +15,6 @@ local({
   Sys.setenv("RENV_R_INITIALIZING" = "true")
   on.exit(Sys.unsetenv("RENV_R_INITIALIZING"), add = TRUE)
 
-  if(grepl("Darwin", Sys.info()["sysname"], fixed = TRUE) & grepl("ARM64", Sys.info()["version"], fixed = TRUE)) # M1 Macs
-    Sys.setenv("TZDIR" = file.path(R.home(), "share", "zoneinfo"))
-
   # signal that we've consented to use renv
   options(renv.consent = TRUE)
 

rules/common.smk

@@ -40,6 +40,26 @@ def find_features_files(wildcards):
             feature_files.extend(expand("data/interim/{{pid}}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", sensor_key=wildcards.sensor_key.lower(), language=get_script_language(provider["SRC_SCRIPT"]), provider_key=provider_key.lower()))
     return(feature_files)
 
+def find_empaticas_standardized_features_files(wildcards):
+    feature_files = []
+    if "empatica" in wildcards.sensor_key:
+        for provider_key, provider in config[(wildcards.sensor_key).upper()]["PROVIDERS"].items():
+            if provider["COMPUTE"] and provider.get("WINDOWS", False) and provider["WINDOWS"]["COMPUTE"]:
+                if "empatica" in wildcards.sensor_key:
+                    feature_files.extend(expand("data/interim/{{pid}}/{sensor_key}_features/z_{sensor_key}_{language}_{provider_key}.csv", sensor_key=wildcards.sensor_key.lower(), language=get_script_language(provider["SRC_SCRIPT"]), provider_key=provider_key.lower()))
+    return(feature_files)
+
+def find_joint_non_empatica_sensor_files(wildcards):
+    joined_files = []
+    for config_key in config.keys():
+        if config_key.startswith(("PHONE", "FITBIT")) and "PROVIDERS" in config[config_key] and isinstance(config[config_key]["PROVIDERS"], dict):
+            for provider_key, provider in config[config_key]["PROVIDERS"].items():
+                if "COMPUTE" in provider.keys() and provider["COMPUTE"]:
+                    joined_files.append("data/processed/features/{pid}/" + config_key.lower() + ".csv")
+                    break
+    return joined_files
+
+
 def optional_steps_sleep_input(wildcards):
     if config["FITBIT_STEPS_INTRADAY"]["EXCLUDE_SLEEP"]["FITBIT_BASED"]["EXCLUDE"]:
         return "data/raw/{pid}/fitbit_sleep_summary_raw.csv"
@@ -62,6 +82,18 @@ def input_merge_sensor_features_for_individual_participants(wildcards):
                     break
     return feature_files
 
+def input_merge_standardized_sensor_features_for_individual_participants(wildcards):
+    feature_files = []
+    for config_key in config.keys():
+        if config_key.startswith(("PHONE", "FITBIT", "EMPATICA")) and "PROVIDERS" in config[config_key] and isinstance(config[config_key]["PROVIDERS"], dict):
+            for provider_key, provider in config[config_key]["PROVIDERS"].items():
+                if "COMPUTE" in provider.keys() and provider["COMPUTE"] and ("STANDARDIZE_FEATURES" in provider.keys() and provider["STANDARDIZE_FEATURES"] or 
+                "WINDOWS" in provider.keys() and "STANDARDIZE_FEATURES" in provider["WINDOWS"].keys() and provider["WINDOWS"]["STANDARDIZE_FEATURES"]):
+                    feature_files.append("data/processed/features/{pid}/z_" + config_key.lower() + ".csv")
+                    break
+    
+    return feature_files
+
 def get_phone_sensor_names():
     phone_sensor_names = []
     for config_key in config.keys():

rules/features.smk

@@ -791,10 +791,25 @@ rule empatica_accelerometer_python_features:
         provider_key = "{provider_key}",
         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}.csv",
+        "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}_windows.csv"
     script:
         "../src/features/entry.py"
 
+rule empatica_accelerometer_python_features_standardization:
+    input:
+        windows_features_data = "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}_windows.csv"
+    params:
+        provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"],
+        provider_key = "{provider_key}",
+        sensor_key = "empatica_accelerometer",
+        provider_main = config["EMPATICA_ACCELEROMETER"]["PROVIDERS"]["CR"]
+    output:
+        "data/interim/{pid}/empatica_accelerometer_features/z_empatica_accelerometer_python_{provider_key}.csv",
+        "data/interim/{pid}/empatica_accelerometer_features/z_empatica_accelerometer_python_{provider_key}_windows.csv"
+    script:
+        "../src/features/standardization/main.py"
+
 rule empatica_accelerometer_r_features:
     input:
         sensor_data = "data/raw/{pid}/empatica_accelerometer_with_datetime.csv",
@@ -817,7 +832,8 @@ rule empatica_heartrate_python_features:
         provider_key = "{provider_key}",
         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}.csv",
+        "data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}_windows.csv"
     script:
         "../src/features/entry.py"
 
@@ -843,10 +859,25 @@ rule empatica_temperature_python_features:
         provider_key = "{provider_key}",
         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}.csv",
+        "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}_windows.csv"
     script:
         "../src/features/entry.py"
 
+rule empatica_temperature_python_features_standardization:
+    input:
+        windows_features_data = "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}_windows.csv"
+    params:
+        provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"],
+        provider_key = "{provider_key}",
+        sensor_key = "empatica_temperature",
+        provider_main = config["EMPATICA_TEMPERATURE"]["PROVIDERS"]["CR"]
+    output:
+        "data/interim/{pid}/empatica_temperature_features/z_empatica_temperature_python_{provider_key}.csv",
+        "data/interim/{pid}/empatica_temperature_features/z_empatica_temperature_python_{provider_key}_windows.csv"
+    script:
+        "../src/features/standardization/main.py"
+
 rule empatica_temperature_r_features:
     input:
         sensor_data = "data/raw/{pid}/empatica_temperature_with_datetime.csv",
@@ -869,10 +900,25 @@ rule empatica_electrodermal_activity_python_features:
         provider_key = "{provider_key}",
         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}.csv",
+        "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}_windows.csv"
     script:
         "../src/features/entry.py"
 
+rule empatica_electrodermal_activity_python_features_standardization:
+    input:
+        windows_features_data = "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}_windows.csv"
+    params:
+        provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"],
+        provider_key = "{provider_key}",
+        sensor_key = "empatica_electrodermal_activity",
+        provider_main = config["EMPATICA_ELECTRODERMAL_ACTIVITY"]["PROVIDERS"]["CR"]
+    output:
+        "data/interim/{pid}/empatica_electrodermal_activity_features/z_empatica_electrodermal_activity_python_{provider_key}.csv",
+        "data/interim/{pid}/empatica_electrodermal_activity_features/z_empatica_electrodermal_activity_python_{provider_key}_windows.csv"
+    script:
+        "../src/features/standardization/main.py"
+
 rule empatica_electrodermal_activity_r_features:
     input:
         sensor_data = "data/raw/{pid}/empatica_electrodermal_activity_with_datetime.csv",
@@ -895,10 +941,25 @@ rule empatica_blood_volume_pulse_python_features:
         provider_key = "{provider_key}",
         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}.csv",
+        "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows.csv"
     script:
         "../src/features/entry.py"
 
+rule empatica_blood_volume_pulse_python_cr_features_standardization:
+    input:
+        windows_features_data = "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows.csv"
+    params:
+        provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"],
+        provider_key = "{provider_key}",
+        sensor_key = "empatica_blood_volume_pulse",
+        provider_main = config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"]["CR"]
+    output:
+        "data/interim/{pid}/empatica_blood_volume_pulse_features/z_empatica_blood_volume_pulse_python_{provider_key}.csv",
+        "data/interim/{pid}/empatica_blood_volume_pulse_features/z_empatica_blood_volume_pulse_python_{provider_key}_windows.csv"
+    script:
+        "../src/features/standardization/main.py"
+
 rule empatica_blood_volume_pulse_r_features:
     input:
         sensor_data = "data/raw/{pid}/empatica_blood_volume_pulse_with_datetime.csv",
@@ -921,10 +982,25 @@ rule empatica_inter_beat_interval_python_features:
         provider_key = "{provider_key}",
         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}.csv",
+        "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}_windows.csv"
     script:
         "../src/features/entry.py"
 
+rule empatica_inter_beat_interval_python_features_standardization:
+    input:
+        windows_features_data = "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}_windows.csv"
+    params:
+        provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"],
+        provider_key = "{provider_key}",
+        sensor_key = "empatica_inter_beat_interval",
+        provider_main = config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"]["CR"]
+    output:
+        "data/interim/{pid}/empatica_inter_beat_interval_features/z_empatica_inter_beat_interval_python_{provider_key}.csv",
+        "data/interim/{pid}/empatica_inter_beat_interval_features/z_empatica_inter_beat_interval_python_{provider_key}_windows.csv"
+    script:
+        "../src/features/standardization/main.py"
+
 rule empatica_inter_beat_interval_r_features:
     input:
         sensor_data = "data/raw/{pid}/empatica_inter_beat_interval_with_datetime.csv",
@@ -972,6 +1048,38 @@ rule merge_sensor_features_for_individual_participants:
     script:
         "../src/features/utils/merge_sensor_features_for_individual_participants.R"
 
+rule join_standardized_features_from_empatica:
+    input:
+        sensor_features = find_empaticas_standardized_features_files
+    wildcard_constraints:
+        sensor_key = '(empatica).*'
+    output:
+        "data/processed/features/{pid}/z_{sensor_key}.csv"
+    script:
+        "../src/features/utils/join_features_from_providers.R"
+
+rule standardize_features_from_providers_no_empatica:
+    input:
+        sensor_features = find_joint_non_empatica_sensor_files
+    wildcard_constraints:
+        sensor_key = '(phone|fitbit).*'
+    params:
+        provider = config["STANDARDIZATION"]["PROVIDERS"]["OTHER"],
+        provider_key = "OTHER",
+        sensor_key = "{sensor_key}"
+    output:
+        "data/processed/features/{pid}/z_{sensor_key}.csv"
+    script:
+        "../src/features/standardization/main.py"
+
+rule merge_standardized_sensor_features_for_individual_participants:
+    input:
+        feature_files = input_merge_standardized_sensor_features_for_individual_participants
+    output:
+        "data/processed/features/{pid}/z_all_sensor_features.csv"
+    script:
+        "../src/features/utils/merge_sensor_features_for_individual_participants.R"
+
 rule merge_sensor_features_for_all_participants:
     input:
         feature_files = expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])
@@ -980,6 +1088,14 @@ rule merge_sensor_features_for_all_participants:
     script:
         "../src/features/utils/merge_sensor_features_for_all_participants.R"
 
+rule merge_standardized_sensor_features_for_all_participants:
+    input:
+        feature_files = expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"])
+    output:
+        "data/processed/features/all_participants/z_all_sensor_features.csv"
+    script:
+        "../src/features/utils/merge_standardized_sensor_features_for_all_participants.R"
+
 rule clean_sensor_features_for_individual_participants:
     input:
         sensor_data = rules.merge_sensor_features_for_individual_participants.output
@@ -988,11 +1104,12 @@ rule clean_sensor_features_for_individual_participants:
     params:
         provider = lambda wildcards: config["ALL_CLEANING_INDIVIDUAL"]["PROVIDERS"][wildcards.provider_key.upper()],
         provider_key = "{provider_key}",
+        script_extension = "{script_extension}",
         sensor_key = "all_cleaning_individual" 
     output:
-        "data/processed/features/{pid}/all_sensor_features_cleaned_{provider_key}.csv"
+        "data/processed/features/{pid}/all_sensor_features_cleaned_{provider_key}_{script_extension}.csv" # bo predstavljalo probleme za naprej (kako iskati datoteke + standardizacija itd.)
     script:
-        "../src/features/entry.R"
+        "../src/features/entry.{params.script_extension}"
 
 rule clean_sensor_features_for_all_participants:
     input:
@@ -1000,9 +1117,38 @@ rule clean_sensor_features_for_all_participants:
     params:
         provider = lambda wildcards: config["ALL_CLEANING_OVERALL"]["PROVIDERS"][wildcards.provider_key.upper()],
         provider_key = "{provider_key}",
+        script_extension = "{script_extension}",
         sensor_key = "all_cleaning_overall"
     output:
-        "data/processed/features/all_participants/all_sensor_features_cleaned_{provider_key}.csv"
+        "data/processed/features/all_participants/all_sensor_features_cleaned_{provider_key}_{script_extension}.csv"
     script:
-        "../src/features/entry.R"
+        "../src/features/entry.{params.script_extension}"
+
+rule clean_standardized_sensor_features_for_individual_participants:
+    input:
+        sensor_data = rules.merge_standardized_sensor_features_for_individual_participants.output
+    wildcard_constraints:
+        pid = "("+"|".join(config["PIDS"])+")"
+    params:
+        provider = lambda wildcards: config["ALL_CLEANING_INDIVIDUAL"]["PROVIDERS"][wildcards.provider_key.upper()],
+        provider_key = "{provider_key}",
+        script_extension = "{script_extension}",
+        sensor_key = "all_cleaning_individual" 
+    output:
+        "data/processed/features/{pid}/z_all_sensor_features_cleaned_{provider_key}_{script_extension}.csv"
+    script:
+        "../src/features/entry.{params.script_extension}"
+
+rule clean_standardized_sensor_features_for_all_participants:
+    input:
+        sensor_data = rules.merge_standardized_sensor_features_for_all_participants.output
+    params:
+        provider = lambda wildcards: config["ALL_CLEANING_OVERALL"]["PROVIDERS"][wildcards.provider_key.upper()],
+        provider_key = "{provider_key}",
+        script_extension = "{script_extension}",
+        sensor_key = "all_cleaning_overall"
+    output:
+        "data/processed/features/all_participants/z_all_sensor_features_cleaned_{provider_key}_{script_extension}.csv"
+    script:
+        "../src/features/entry.{params.script_extension}"
 

rules/models.smk

@@ -30,22 +30,43 @@ rule baseline_features:
 
 rule select_target:
     input:
-        cleaned_sensor_features = "data/processed/features/{pid}/all_sensor_features_cleaned_rapids.csv"
+        cleaned_sensor_features = "data/processed/features/{pid}/z_all_sensor_features_cleaned_straw_py.csv"
     params:
         target_variable = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
     output:
-        "data/processed/models/individual_model/{pid}/input.csv"
+        "data/processed/models/individual_model/{pid}/z_input.csv"
     script:
         "../src/models/select_targets.py"
 
 rule merge_features_and_targets_for_population_model:
     input:
-        cleaned_sensor_features = "data/processed/features/all_participants/all_sensor_features_cleaned_rapids.csv",
+        cleaned_sensor_features = "data/processed/features/all_participants/z_all_sensor_features_cleaned_straw_py.csv",
         demographic_features = expand("data/processed/features/{pid}/baseline_features.csv", pid=config["PIDS"]),
     params:
         target_variable=config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
     output:
-        "data/processed/models/population_model/input.csv"
+        "data/processed/models/population_model/z_input.csv"
     script:
         "../src/models/merge_features_and_targets_for_population_model.py"
 
+# rule select_target:
+#     input:
+#         cleaned_sensor_features = "data/processed/features/{pid}/all_sensor_features_cleaned_straw_py.csv"
+#     params:
+#         target_variable = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
+#     output:
+#         "data/processed/models/individual_model/{pid}/input.csv"
+#     script:
+#         "../src/models/select_targets.py"
+
+# rule merge_features_and_targets_for_population_model:
+#     input:
+#         cleaned_sensor_features = "data/processed/features/all_participants/all_sensor_features_cleaned_straw_py.csv",
+#         demographic_features = expand("data/processed/features/{pid}/baseline_features.csv", pid=config["PIDS"]),
+#     params:
+#         target_variable=config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
+#     output:
+#         "data/processed/models/population_model/input.csv"
+#     script:
+#         "../src/models/merge_features_and_targets_for_population_model.py"
+

rules/preprocessing.smk

@@ -4,14 +4,14 @@ rule create_example_participant_files:
     shell:
         "echo 'PHONE:\n  DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524]\n  PLATFORMS: [android]\n  LABEL: test-01\n  START_DATE: 2020-04-23 00:00:00\n  END_DATE: 2020-05-04 23:59:59\nFITBIT:\n  DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524]\n  LABEL: test-01\n  START_DATE: 2020-04-23 00:00:00\n  END_DATE: 2020-05-04 23:59:59\n' >> ./data/external/participant_files/example01.yaml && echo 'PHONE:\n  DEVICE_IDS: [13dbc8a3-dae3-4834-823a-4bc96a7d459d]\n  PLATFORMS: [ios]\n  LABEL: test-02\n  START_DATE: 2020-04-23 00:00:00\n  END_DATE: 2020-05-04 23:59:59\nFITBIT:\n  DEVICE_IDS: [13dbc8a3-dae3-4834-823a-4bc96a7d459d]\n  LABEL: test-02\n  START_DATE: 2020-04-23 00:00:00\n  END_DATE: 2020-05-04 23:59:59\n' >> ./data/external/participant_files/example02.yaml"
 
-rule query_usernames_device_empatica_ids:
+# rule query_usernames_device_empatica_ids:
-    params:
+#     params:
-        baseline_folder = "/mnt/e/STRAWbaseline/"
+#         baseline_folder = "/mnt/e/STRAWbaseline/"
-    output:
+#     output:
-        usernames_file = config["CREATE_PARTICIPANT_FILES"]["USERNAMES_CSV"],
+#         usernames_file = config["CREATE_PARTICIPANT_FILES"]["USERNAMES_CSV"],
-        timezone_file = config["TIMEZONE"]["MULTIPLE"]["TZ_FILE"]
+#         timezone_file = config["TIMEZONE"]["MULTIPLE"]["TZ_FILE"]
-    script:
+#     script:
-        "../../participants/prepare_usernames_file.py"
+#         "../../participants/prepare_usernames_file.py"
 
 rule prepare_tzcodes_file:
     input:

src/data/create_participants_files.R

@@ -58,7 +58,7 @@ participants %>%
       lines <- append(lines, empty_fitbit)
 
     if(add_empatica_section == TRUE && !is.na(row[empatica_device_id_column])){
-      lines <- append(lines, c("EMPATICA:", paste0("  DEVICE_IDS: [",row[empatica_device_id_column],"]"),
+      lines <- append(lines, c("EMPATICA:", paste0("  DEVICE_IDS: [",row$label,"]"),
                                paste("  LABEL:",row$label), paste("  START_DATE:", start_date), paste("  END_DATE:", end_date)))
     } else
       lines <- append(lines, empty_empatica)

src/data/streams/empatica_zip/container.py

@@ -2,11 +2,16 @@ from zipfile import ZipFile
 import warnings
 from pathlib import Path
 import pandas as pd
+import numpy as np
 from pandas.core import indexing
 import yaml
 import csv
 from collections import OrderedDict
 from io import BytesIO, StringIO
+import sys, os
+
+from cr_features.hrv import get_HRV_features, get_patched_ibi_with_bvp
+from cr_features.helper_functions import empatica1d_to_array, empatica2d_to_array
 
 def processAcceleration(x, y, z):
     x = float(x)
@@ -52,6 +57,8 @@ def extract_empatica_data(data,  sensor):
         df = pd.DataFrame.from_dict(ddict, orient='index', columns=[column])
         df[column] = df[column].astype(float)
         df.index.name = 'timestamp'
+        if df.empty:
+            return df
 
     elif sensor == 'EMPATICA_ACCELEROMETER':
         ddict = readFile(sensor_data_file, sensor)
@@ -60,9 +67,16 @@ def extract_empatica_data(data,  sensor):
         df['y'] = df['y'].astype(float)
         df['z'] = df['z'].astype(float)
         df.index.name = 'timestamp'
+        if df.empty:
+            return df
 
     elif sensor == 'EMPATICA_INTER_BEAT_INTERVAL':
-        df = pd.read_csv(sensor_data_file, names=['timestamp', column], header=None)
+
+        df = pd.read_csv(sensor_data_file, names=['timings', column], header=None)
+        df['timestamp'] = df['timings']
+        if df.empty:
+            df = df.set_index('timestamp')
+            return df
         timestampstart = float(df['timestamp'][0])
         df['timestamp'] = (df['timestamp'][1:len(df)]).astype(float) + timestampstart        
         df = df.drop([0])
@@ -84,6 +98,10 @@ def pull_data(data_configuration, device, sensor, container, columns_to_download
     participant_data = pd.DataFrame(columns=columns_to_download.values())
     participant_data.set_index('timestamp', inplace=True)
 
+    with open('config.yaml', 'r') as stream:
+        config = yaml.load(stream, Loader=yaml.FullLoader)
+    cr_ibi_provider = config['EMPATICA_INTER_BEAT_INTERVAL']['PROVIDERS']['CR']
+
     available_zipfiles = list((Path(data_configuration["FOLDER"]) / Path(device)).rglob("*.zip"))
     if len(available_zipfiles) == 0:
         warnings.warn("There were no zip files in: {}. If you were expecting data for this participant the [EMPATICA][DEVICE_IDS] key in their participant file is missing the pid".format((Path(data_configuration["FOLDER"]) / Path(device))))
@@ -94,7 +112,13 @@ def pull_data(data_configuration, device, sensor, container, columns_to_download
             listOfFileNames = zipFile.namelist()
             for fileName in listOfFileNames:
                 if fileName == sensor_csv:
-                    participant_data = pd.concat([participant_data, extract_empatica_data(zipFile.read(fileName),  sensor)], axis=0)
+                    if sensor == "EMPATICA_INTER_BEAT_INTERVAL" and cr_ibi_provider.get('PATCH_WITH_BVP', False):
+                        participant_data = \
+                            pd.concat([participant_data, patch_ibi_with_bvp(zipFile.read('IBI.csv'), zipFile.read('BVP.csv'))], axis=0)
+                        #print("patch with ibi")
+                    else:
+                        participant_data = pd.concat([participant_data, extract_empatica_data(zipFile.read(fileName), sensor)], axis=0)
+                        #print("no patching")
                     warning = False
             if warning:
                 warnings.warn("We could not find a zipped file for {} in {} (we tried to find {})".format(sensor, zipFile, sensor_csv))
@@ -105,4 +129,53 @@ def pull_data(data_configuration, device, sensor, container, columns_to_download
     participant_data["device_id"] = device
     return(participant_data)
 
+def patch_ibi_with_bvp(ibi_data, bvp_data):
+    ibi_data_file = BytesIO(ibi_data).getvalue().decode('utf-8')
+    ibi_data_file = StringIO(ibi_data_file)
+
+    # Begin with the cr-features part
+    try:
+        ibi_data, ibi_start_timestamp = empatica2d_to_array(ibi_data_file)
+    except IndexError as e:
+        # Checks whether IBI.csv is empty
+        df_test = pd.read_csv(ibi_data_file, names=['timings', 'inter_beat_interval'], header=None)
+        if df_test.empty:
+            df_test['timestamp'] = df_test['timings']
+            df_test = df_test.set_index('timestamp')
+            return df_test
+        else:
+            raise IndexError("Something went wrong with indices. Error that was previously caught:\n", repr(e))
+
+    bvp_data_file = BytesIO(bvp_data).getvalue().decode('utf-8')
+    bvp_data_file = StringIO(bvp_data_file)
+
+    bvp_data, bvp_start_timestamp, sample_rate = empatica1d_to_array(bvp_data_file)
+
+    hrv_time_and_freq_features, sample, bvp_rr, bvp_timings, peak_indx = \
+        get_HRV_features(bvp_data, ma=False, 
+                        detrend=False, m_deternd=False, low_pass=False, winsorize=True, 
+                        winsorize_value=25, hampel_fiter=False, median_filter=False, 
+                        mod_z_score_filter=True, sampling=64, feature_names=['meanHr'])
+    
+    ibi_timings, ibi_rr = get_patched_ibi_with_bvp(ibi_data[0], ibi_data[1], bvp_timings, bvp_rr)
+
+    df = \
+        pd.DataFrame(np.array([ibi_timings, ibi_rr]).transpose(), columns=['timestamp', 'inter_beat_interval'])
+    df.loc[-1] = [ibi_start_timestamp, 'IBI']  # adding a row
+    df.index = df.index + 1  # shifting index
+    df = df.sort_index()  # sorting by index
+
+    # Repeated as in extract_empatica_data for IBI
+    df['timings'] = df['timestamp']
+    timestampstart = float(df['timestamp'][0])
+    df['timestamp'] = (df['timestamp'][1:len(df)]).astype(float) + timestampstart        
+    df = df.drop([0])
+    df['inter_beat_interval'] = df['inter_beat_interval'].astype(float)
+    df = df.set_index('timestamp')
+
+    # format timestamps
+    df.index *= 1000
+    df.index = df.index.astype(int)
+    return(df)
+
 # print(pull_data({'FOLDER': 'data/external/empatica'}, "e01", "EMPATICA_accelerometer", {'TIMESTAMP': 'timestamp', 'DEVICE_ID': 'device_id', 'DOUBLE_VALUES_0': 'x', 'DOUBLE_VALUES_1': 'y', 'DOUBLE_VALUES_2': 'z'}))

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

@@ -227,6 +227,7 @@ EMPATICA_INTER_BEAT_INTERVAL:
   - TIMESTAMP
   - DEVICE_ID
   - INTER_BEAT_INTERVAL
+  - TIMINGS
 
 EMPATICA_TAGS:
   - TIMESTAMP

src/features/all_cleaning_individual/rapids/main.R

@@ -39,8 +39,10 @@ rapids_cleaning <- function(sensor_data_files, provider){
     if(!data_yield_column %in% colnames(clean_features)){
         stop(paste0("Error: RAPIDS provider needs to clean data based on ", data_yield_column, " column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded", data_yield_unit, "' in [FEATURES]."))
     }
-    clean_features <- clean_features %>% 
+    if (data_yield_ratio_threshold > 0) {
+        clean_features <- clean_features %>% 
         filter(.[[data_yield_column]] >= data_yield_ratio_threshold)
+    }
 
     # Drop columns with a percentage of NA values above cols_nan_threshold
     if(nrow(clean_features))

src/features/all_cleaning_individual/straw/main.py

@@ -0,0 +1,88 @@
+import pandas as pd
+import numpy as np
+import math, sys
+
+def straw_cleaning(sensor_data_files, provider):
+    
+    features = pd.read_csv(sensor_data_files["sensor_data"][0])
+
+    # TODO: reorder the cleaning steps so it makes sense for the analysis
+    # TODO: add conditions that differentiates cleaning steps for standardized and nonstandardized features, for this
+    # the snakemake rules will also have to come with additional parameter (in rules/features.smk)
+
+    # Impute selected features event
+    impute_phone_features = provider["IMPUTE_PHONE_SELECTED_EVENT_FEATURES"]
+    if impute_phone_features["COMPUTE"]:
+        if not 'phone_data_yield_rapids_ratiovalidyieldedminutes' in features.columns:
+            raise KeyError("RAPIDS provider needs to impute the selected event features based on phone_data_yield_rapids_ratiovalidyieldedminutes column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyieldedminutes' in [FEATURES].")
+        
+        # TODO: if the type of the imputation will vary for different groups of features make conditional imputations here 
+        phone_cols = [col for col in features if \
+            col.startswith('phone_applications_foreground_rapids_') or
+            col.startswith('phone_battery_rapids_') or
+            col.startswith('phone_calls_rapids_') or
+            col.startswith('phone_keyboard_rapids_') or
+            col.startswith('phone_messages_rapids_') or
+            col.startswith('phone_screen_rapids_') or
+            col.startswith('phone_wifi_')]
+
+        mask = features['phone_data_yield_rapids_ratiovalidyieldedminutes'] > impute_phone_features['MIN_DATA_YIELDED_MINUTES_TO_IMPUTE']
+        features.loc[mask, phone_cols] = impute(features[mask][phone_cols], method=impute_phone_features["TYPE"].lower())
+
+    # Drop rows with the value of data_yield_column less than data_yield_ratio_threshold
+    data_yield_unit = provider["DATA_YIELD_FEATURE"].split("_")[3].lower()
+    data_yield_column = "phone_data_yield_rapids_ratiovalidyielded" + data_yield_unit
+
+    if not data_yield_column in features.columns:
+        raise KeyError(f"RAPIDS provider needs to impute the selected event features based on {data_yield_column} column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded{data_yield_unit}' in [FEATURES].")
+        
+    if provider["DATA_YIELD_RATIO_THRESHOLD"]:
+        features = features[features[data_yield_column] >= provider["DATA_YIELD_RATIO_THRESHOLD"]]
+
+    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm')] # For later preservation of esm_cols
+
+    # Remove cols if threshold of NaN values is passed
+    features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]    
+    
+    # Remove cols where variance is 0
+    if provider["COLS_VAR_THRESHOLD"]:
+        features.drop(features.std()[features.std() == 0].index.values, axis=1, inplace=True)
+    
+    # Preserve esm cols if deleted (has to come after drop cols operations)
+    for esm in esm_cols:
+        if esm not in features:
+            features[esm] = esm_cols[esm]
+    
+    # Drop highly correlated features - To-Do še en thershold var, ki je v config + kako se tretirajo NaNs?
+    drop_corr_features = provider["DROP_HIGHLY_CORRELATED_FEATURES"]
+    if drop_corr_features["COMPUTE"]:
+        
+        numerical_cols = features.select_dtypes(include=np.number).columns.tolist()
+
+        # Remove columns where NaN count threshold is passed
+        valid_features = features[numerical_cols].loc[:, features[numerical_cols].isna().sum() < drop_corr_features['MIN_OVERLAP_FOR_CORR_THRESHOLD'] * features[numerical_cols].shape[0]]
+
+        cor_matrix = valid_features.corr(method='spearman').abs()
+        upper_tri = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
+        to_drop = [column for column in upper_tri.columns if any(upper_tri[column] > drop_corr_features["CORR_THRESHOLD"])]
+
+        features.drop(to_drop, axis=1, inplace=True)
+
+    # Remove rows if threshold of NaN values is passed
+    min_count =  math.ceil((1 - provider["ROWS_NAN_THRESHOLD"]) * features.shape[1]) # minimal not nan values in row
+    features.dropna(axis=0, thresh=min_count, inplace=True)
+
+    return features
+
+def impute(df, method='zero'):
+    
+    def k_nearest(df): # TODO: if needed, implement k-nearest imputation / interpolation
+        pass
+
+    return { # rest of the columns should be imputed with the selected method
+        'zero': df.fillna(0),
+        'mean': df.fillna(df.mean()),
+        'median': df.fillna(df.median()),
+        'k-nearest': k_nearest(df) 
+    }[method]
+    

src/features/all_cleaning_overall/rapids/main.R

@@ -39,16 +39,18 @@ rapids_cleaning <- function(sensor_data_files, provider){
     if(!data_yield_column %in% colnames(clean_features)){
         stop(paste0("Error: RAPIDS provider needs to clean data based on ", data_yield_column, " column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded", data_yield_unit, "' in [FEATURES]."))
     }
-    clean_features <- clean_features %>% 
+    if (data_yield_ratio_threshold > 0) {
+        clean_features <- clean_features %>% 
         filter(.[[data_yield_column]] >= data_yield_ratio_threshold)
+    }
 
     # Drop columns with a percentage of NA values above cols_nan_threshold
     if(nrow(clean_features))
-        clean_features <- clean_features %>% select_if(~ sum(is.na(.)) / length(.) <= cols_nan_threshold )
+        clean_features <- clean_features %>% select(where(~ sum(is.na(.)) / length(.) <= cols_nan_threshold ), starts_with("phone_esm"))
 
     # Drop columns with zero variance
     if(drop_zero_variance_columns)
-    clean_features <- clean_features %>% select_if(grepl("pid|local_segment|local_segment_label|local_segment_start_datetime|local_segment_end_datetime",names(.)) | sapply(., n_distinct, na.rm = T) > 1)
+        clean_features <- clean_features %>% select_if(grepl("pid|local_segment|local_segment_label|local_segment_start_datetime|local_segment_end_datetime|phone_esm",names(.)) | sapply(., n_distinct, na.rm = T) > 1)
 
     # Drop highly correlated features
     if(as.logical(drop_highly_correlated_features$COMPUTE)){

src/features/all_cleaning_overall/straw/main.py

@@ -0,0 +1,88 @@
+import pandas as pd
+import numpy as np
+import math, sys
+
+def straw_cleaning(sensor_data_files, provider):
+    
+    features = pd.read_csv(sensor_data_files["sensor_data"][0])
+
+    # TODO: reorder the cleaning steps so it makes sense for the analysis
+    # TODO: add conditions that differentiates cleaning steps for standardized and nonstandardized features, for this
+    # the snakemake rules will also have to come with additional parameter (in rules/features.smk)
+
+    # Impute selected features event
+    impute_phone_features = provider["IMPUTE_PHONE_SELECTED_EVENT_FEATURES"]
+    if impute_phone_features["COMPUTE"]:
+        if not 'phone_data_yield_rapids_ratiovalidyieldedminutes' in features.columns:
+            raise KeyError("RAPIDS provider needs to impute the selected event features based on phone_data_yield_rapids_ratiovalidyieldedminutes column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyieldedminutes' in [FEATURES].")
+        
+        # TODO: if the type of the imputation will vary for different groups of features make conditional imputations here 
+        phone_cols = [col for col in features if \
+            col.startswith('phone_applications_foreground_rapids_') or
+            col.startswith('phone_battery_rapids_') or
+            col.startswith('phone_calls_rapids_') or
+            col.startswith('phone_keyboard_rapids_') or
+            col.startswith('phone_messages_rapids_') or
+            col.startswith('phone_screen_rapids_') or
+            col.startswith('phone_wifi_')]
+
+        mask = features['phone_data_yield_rapids_ratiovalidyieldedminutes'] > impute_phone_features['MIN_DATA_YIELDED_MINUTES_TO_IMPUTE']
+        features.loc[mask, phone_cols] = impute(features[mask][phone_cols], method=impute_phone_features["TYPE"].lower())
+
+    # Drop rows with the value of data_yield_column less than data_yield_ratio_threshold
+    data_yield_unit = provider["DATA_YIELD_FEATURE"].split("_")[3].lower()
+    data_yield_column = "phone_data_yield_rapids_ratiovalidyielded" + data_yield_unit
+
+    if not data_yield_column in features.columns:
+        raise KeyError(f"RAPIDS provider needs to impute the selected event features based on {data_yield_column} column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded{data_yield_unit}' in [FEATURES].")
+        
+    if provider["DATA_YIELD_RATIO_THRESHOLD"]:
+        features = features[features[data_yield_column] >= provider["DATA_YIELD_RATIO_THRESHOLD"]]
+
+    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm')] # For later preservation of esm_cols
+
+    # Remove cols if threshold of NaN values is passed
+    features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]    
+    
+    # Remove cols where variance is 0
+    if provider["COLS_VAR_THRESHOLD"]:
+        features.drop(features.std()[features.std() == 0].index.values, axis=1, inplace=True)
+    
+    # Preserve esm cols if deleted (has to come after drop cols operations)
+    for esm in esm_cols:
+        if esm not in features:
+            features[esm] = esm_cols[esm]
+    
+    # Drop highly correlated features - To-Do še en thershold var, ki je v config + kako se tretirajo NaNs?
+    drop_corr_features = provider["DROP_HIGHLY_CORRELATED_FEATURES"]
+    if drop_corr_features["COMPUTE"]:
+        
+        numerical_cols = features.select_dtypes(include=np.number).columns.tolist()
+
+        # Remove columns where NaN count threshold is passed
+        valid_features = features[numerical_cols].loc[:, features[numerical_cols].isna().sum() < drop_corr_features['MIN_OVERLAP_FOR_CORR_THRESHOLD'] * features[numerical_cols].shape[0]]
+
+        cor_matrix = valid_features.corr(method='spearman').abs()
+        upper_tri = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
+        to_drop = [column for column in upper_tri.columns if any(upper_tri[column] > drop_corr_features["CORR_THRESHOLD"])]
+
+        features.drop(to_drop, axis=1, inplace=True)
+
+    # Remove rows if threshold of NaN values is passed
+    min_count =  math.ceil((1 - provider["ROWS_NAN_THRESHOLD"]) * features.shape[1]) # minimal not nan values in row
+    features.dropna(axis=0, thresh=min_count, inplace=True)
+
+    return features
+
+def impute(df, method='zero'):
+    
+    def k_nearest(df): # TODO: if needed, implement k-nearest imputation / interpolation
+        pass
+
+    return { # rest of the columns should be imputed with the selected method
+        'zero': df.fillna(0),
+        'mean': df.fillna(df.mean()),
+        'median': df.fillna(df.median()),
+        'k-nearest': k_nearest(df) 
+    }[method]
+    

src/features/cr_features_helper_methods.py

@@ -0,0 +1,59 @@
+import pandas as pd
+import numpy as np
+import math as m
+
+import sys
+
+def extract_second_order_features(intraday_features, so_features_names, prefix=""):
+    
+    if prefix:
+        groupby_cols = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
+    else:
+        groupby_cols = ['local_segment']
+
+    if not intraday_features.empty:
+        so_features = pd.DataFrame()
+        #print(intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).nsmallest())
+        if "mean" in so_features_names:
+            so_features = pd.concat([so_features, intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols).mean().add_suffix("_SO_mean")], axis=1)
+        
+        if "median" in so_features_names:
+            so_features = pd.concat([so_features, intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols).median().add_suffix("_SO_median")], axis=1)
+        
+        if "sd" in so_features_names:
+            so_features = pd.concat([so_features, intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols).std().add_suffix("_SO_sd")], axis=1)
+        
+        if "nlargest" in so_features_names: # largest 5 -- maybe there is a faster groupby solution?
+            for column in intraday_features.loc[:, ~intraday_features.columns.isin(groupby_cols+[prefix+"level_1"])]:
+                so_features[column+"_SO_nlargest"] = intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols)[column].apply(lambda x: x.nlargest(5).mean())
+        
+        if "nsmallest" in so_features_names: # smallest 5 -- maybe there is a faster groupby solution?
+            for column in intraday_features.loc[:, ~intraday_features.columns.isin(groupby_cols+[prefix+"level_1"])]:
+                so_features[column+"_SO_nsmallest"] = intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols)[column].apply(lambda x: x.nsmallest(5).mean())
+        
+        if "count_windows" in so_features_names:
+            so_features["SO_windowsCount"] = intraday_features.groupby(groupby_cols).count()[prefix+"level_1"]
+
+        # numPeaksNonZero specialized for EDA sensor
+        if "eda_num_peaks_non_zero" in so_features_names and prefix+"numPeaks" in intraday_features.columns:
+            so_features[prefix+"SO_numPeaksNonZero"] = intraday_features.groupby(groupby_cols)[prefix+"numPeaks"].apply(lambda x: (x!=0).sum())
+
+        # numWindowsNonZero specialized for BVP and IBI sensors
+        if "hrv_num_windows_non_nan" in so_features_names and prefix+"meanHr" in intraday_features.columns:
+            so_features[prefix+"SO_numWindowsNonNaN"] = intraday_features.groupby(groupby_cols)[prefix+"meanHr"].apply(lambda x: (~np.isnan(x)).sum())
+            
+        so_features.reset_index(inplace=True)
+
+    else:
+        so_features = pd.DataFrame(columns=groupby_cols)
+
+    return so_features
+
+def get_sample_rate(data): # To-Do get the sample rate information from the file's metadata
+    try:
+        timestamps_diff = data['timestamp'].diff().dropna().mean()
+        print("Timestamp diff:", timestamps_diff)
+    except:
+        raise Exception("Error occured while trying to get the mean sample rate from the data.")
+
+    return m.ceil(1000/timestamps_diff)

src/features/empatica_accelerometer/cr/main.py

@@ -0,0 +1,71 @@
+import pandas as pd
+from scipy.stats import entropy
+
+from cr_features.helper_functions import convert_to2d, accelerometer_features, frequency_features
+from cr_features.calculate_features_old import calculateFeatures
+from cr_features.calculate_features import calculate_features
+from cr_features_helper_methods import extract_second_order_features
+
+import sys
+
+def extract_acc_features_from_intraday_data(acc_intraday_data, features, window_length, time_segment, filter_data_by_segment):
+    acc_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
+
+    if not acc_intraday_data.empty:   
+        sample_rate = 32
+     
+        acc_intraday_data = filter_data_by_segment(acc_intraday_data, time_segment)
+
+        if not acc_intraday_data.empty:
+
+            acc_intraday_features = pd.DataFrame()
+
+            # apply methods from calculate features module
+            if window_length is None:
+                acc_intraday_features = \
+                    acc_intraday_data.groupby('local_segment').apply(lambda x: calculate_features( \
+                    convert_to2d(x['double_values_0'], x.shape[0]), \
+                    convert_to2d(x['double_values_1'], x.shape[0]), \
+                    convert_to2d(x['double_values_2'], x.shape[0]), \
+                    fs=sample_rate, feature_names=features, show_progress=False)) 
+            else:
+                acc_intraday_features = \
+                    acc_intraday_data.groupby('local_segment').apply(lambda x: calculate_features( \
+                    convert_to2d(x['double_values_0'], window_length*sample_rate), \
+                    convert_to2d(x['double_values_1'], window_length*sample_rate), \
+                    convert_to2d(x['double_values_2'], window_length*sample_rate), \
+                    fs=sample_rate, feature_names=features, show_progress=False)) 
+
+            acc_intraday_features.reset_index(inplace=True)
+
+    return acc_intraday_features
+
+
+
+def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
+    acc_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
+
+    requested_intraday_features = provider["FEATURES"]
+    
+    calc_windows = kwargs.get('calc_windows', False)
+
+    if provider["WINDOWS"]["COMPUTE"] and calc_windows:
+        requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
+    else:
+        requested_window_length = None
+
+    # name of the features this function can compute
+    base_intraday_features_names = accelerometer_features + frequency_features
+    # 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
+    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

@@ -0,0 +1,73 @@
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+
+from cr_features.helper_functions import convert_to2d, hrv_features
+from cr_features.hrv import extract_hrv_features_2d_wrapper
+from cr_features_helper_methods import extract_second_order_features
+
+import sys
+
+# pd.set_option('display.max_rows', 1000)
+pd.set_option('display.max_columns', None)
+
+def extract_bvp_features_from_intraday_data(bvp_intraday_data, features, window_length, time_segment, filter_data_by_segment):
+    bvp_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
+
+    if not bvp_intraday_data.empty:
+        sample_rate = 64
+     
+        bvp_intraday_data = filter_data_by_segment(bvp_intraday_data, time_segment)
+
+        if not bvp_intraday_data.empty:
+
+            bvp_intraday_features = pd.DataFrame()
+
+            # apply methods from calculate features module
+            if window_length is None:
+                bvp_intraday_features = \
+                    bvp_intraday_data.groupby('local_segment').apply(\
+                    lambda x: 
+                        extract_hrv_features_2d_wrapper(
+                            convert_to2d(x['blood_volume_pulse'], x.shape[0]), 
+                            sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features))
+
+            else:
+                bvp_intraday_features = \
+                    bvp_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)) 
+
+            bvp_intraday_features.reset_index(inplace=True)
+
+    return bvp_intraday_features
+
+
+def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
+    bvp_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
+
+    requested_intraday_features = provider["FEATURES"]
+    
+    calc_windows = kwargs.get('calc_windows', False)
+
+    if provider["WINDOWS"]["COMPUTE"] and calc_windows:
+        requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
+    else:
+        requested_window_length = None
+
+    # name of the features this function can compute
+    base_intraday_features_names = hrv_features
+    # 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
+    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

@@ -0,0 +1,78 @@
+import pandas as pd
+import numpy as np
+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.gsr import extractGsrFeatures2D
+from cr_features_helper_methods import extract_second_order_features
+
+import sys
+
+#pd.set_option('display.max_columns', None)
+#pd.set_option('display.max_rows', None)
+#np.seterr(invalid='ignore')
+
+
+def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_length, time_segment, filter_data_by_segment):
+    eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
+
+    if not eda_intraday_data.empty:   
+        sample_rate = 4  
+     
+        eda_intraday_data = filter_data_by_segment(eda_intraday_data, time_segment)
+
+        if not eda_intraday_data.empty: 
+
+            eda_intraday_features = pd.DataFrame()
+
+            # apply methods from calculate features module 
+            if window_length is None:
+                eda_intraday_features = \
+                    eda_intraday_data.groupby('local_segment').apply(\
+                    lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], x.shape[0]), sampleRate=sample_rate, featureNames=features,
+                    threshold=.01, offset=1, riseTime=5, decayTime=15)) 
+            else:
+                eda_intraday_features = \
+                    eda_intraday_data.groupby('local_segment').apply(\
+                    lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), sampleRate=sample_rate, featureNames=features,
+                    threshold=.01, offset=1, riseTime=5, decayTime=15)) 
+
+            eda_intraday_features.reset_index(inplace=True)
+
+    return eda_intraday_features
+
+
+def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
+    eda_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
+
+    requested_intraday_features = provider["FEATURES"]
+    
+    calc_windows = kwargs.get('calc_windows', False)
+
+    if provider["WINDOWS"]["COMPUTE"] and calc_windows:
+        requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
+    else:
+        requested_window_length = None
+
+    # name of the features this function can compute
+    base_intraday_features_names = gsr_features
+    # 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
+    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:
+        if provider["WINDOWS"]["IMPUTE_NANS"]:
+            eda_intraday_features[eda_intraday_features["numPeaks"] == 0] = \
+                eda_intraday_features[eda_intraday_features["numPeaks"] == 0].fillna(0)
+            pd.set_option('display.max_columns', None)
+            
+        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

@@ -0,0 +1,79 @@
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+import numpy as np
+
+from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features
+from cr_features.hrv import extract_hrv_features_2d_wrapper, get_HRV_features
+from cr_features_helper_methods import extract_second_order_features
+
+import math
+import sys
+
+# pd.set_option('display.max_rows', 1000)
+pd.set_option('display.max_columns', None)
+
+
+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)
+
+    if not ibi_intraday_data.empty:   
+
+        ibi_intraday_data = filter_data_by_segment(ibi_intraday_data, time_segment)
+
+        if not ibi_intraday_data.empty:
+
+            ibi_intraday_features = pd.DataFrame()
+
+            # 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(
+                            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(
+                            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)
+
+    return ibi_intraday_features
+
+
+def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
+    ibi_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
+
+    requested_intraday_features = provider["FEATURES"]
+    
+    calc_windows = kwargs.get('calc_windows', False)
+
+    if provider["WINDOWS"]["COMPUTE"] and calc_windows:
+        requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
+    else:
+        requested_window_length = None
+
+    # name of the features this function can compute
+    base_intraday_features_names = hrv_features
+    # 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
+    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

@@ -0,0 +1,65 @@
+import pandas as pd
+from scipy.stats import entropy
+
+from cr_features.helper_functions import convert_to2d, generic_features
+from cr_features.calculate_features_old import calculateFeatures
+from cr_features.calculate_features import calculate_features
+from cr_features_helper_methods import extract_second_order_features
+
+import sys
+
+def extract_temp_features_from_intraday_data(temperature_intraday_data, features, window_length, time_segment, filter_data_by_segment):
+    temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
+
+    if not temperature_intraday_data.empty:
+        sample_rate = 4
+
+        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
+            if window_length is None:
+                temperature_intraday_features = \
+                    temperature_intraday_data.groupby('local_segment').apply(\
+                    lambda x: calculate_features(convert_to2d(x['temperature'], x.shape[0]), fs=sample_rate, feature_names=features, show_progress=False))
+            else:
+                temperature_intraday_features = \
+                    temperature_intraday_data.groupby('local_segment').apply(\
+                    lambda x: calculate_features(convert_to2d(x['temperature'], window_length*sample_rate), fs=sample_rate, feature_names=features, show_progress=False))
+
+
+            temperature_intraday_features.reset_index(inplace=True)
+
+    return temperature_intraday_features
+
+
+def cr_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"]
+
+    calc_windows = kwargs.get('calc_windows', False)
+
+    if provider["WINDOWS"]["COMPUTE"] and calc_windows:
+        requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
+    else:
+        requested_window_length = None
+    
+    # name of the features this function can compute
+    base_intraday_features_names = generic_features
+    # 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 = 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

@@ -1,12 +1,16 @@
 import pandas as pd
 from utils.utils import fetch_provider_features, run_provider_cleaning_script
 
+import sys
+
 sensor_data_files = dict(snakemake.input)
 
 provider = snakemake.params["provider"]
 provider_key = snakemake.params["provider_key"]
 sensor_key = snakemake.params["sensor_key"]
 
+calc_windows = True if (provider.get("WINDOWS", False) and provider["WINDOWS"].get("COMPUTE", False)) else False
+
 if sensor_key == "all_cleaning_individual" or sensor_key == "all_cleaning_overall":
     # Data cleaning
     sensor_features = run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data_files)
@@ -14,6 +18,18 @@ else:
     # Extract sensor features
     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)
 
-sensor_features.to_csv(snakemake.output[0], index=False)
+    if calc_windows:
+        window_features, second_order_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=True)        
+
+        window_features.to_csv(snakemake.output[1], index=False)
+        second_order_features.to_csv(snakemake.output[0], index=False)
+
+    elif "empatica" in sensor_key: 
+        pd.DataFrame().to_csv(snakemake.output[1], index=False)
+
+    if not calc_windows:
+        sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=False)
+
+if not calc_windows:
+    sensor_features.to_csv(snakemake.output[0], index=False)

src/features/phone_data_yield/rapids/main.R

@@ -3,9 +3,11 @@ library(tidyr)
 library(readr)
 
 compute_data_yield_features <- function(data, feature_name, time_segment, provider){
+  
   data <- data %>% filter_data_by_segment(time_segment)
-  if(nrow(data) == 0)
+  if(nrow(data) == 0){
     return(tibble(local_segment = character(), ratiovalidyieldedminutes = numeric(), ratiovalidyieldedhours = numeric()))
+  }
   features <- data %>%
     separate(timestamps_segment, into = c("start_timestamp", "end_timestamp"), convert = T, sep = ",") %>% 
     mutate(duration_minutes = (end_timestamp - start_timestamp) / 60000,

src/features/standardization/main.py

@@ -0,0 +1,50 @@
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+
+import sys
+
+sensor_data_files = dict(snakemake.input)
+
+provider = snakemake.params["provider"]
+provider_key = snakemake.params["provider_key"]
+sensor_key = snakemake.params["sensor_key"]
+
+pd.set_option('display.max_columns', None)
+
+if provider_key == "cr":
+    sys.path.append('/rapids/src/features/')
+    from cr_features_helper_methods import extract_second_order_features
+
+    provider_main = snakemake.params["provider_main"]
+    prefix = sensor_key + "_" + provider_key + "_"
+
+    windows_features_data = pd.read_csv(sensor_data_files["windows_features_data"])
+    excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime', prefix + "level_1"]
+    
+    if windows_features_data.empty:
+        windows_features_data.to_csv(snakemake.output[1], index=False)
+        windows_features_data.to_csv(snakemake.output[0], index=False)
+    else:
+        windows_features_data.loc[:, ~windows_features_data.columns.isin(excluded_columns)] = StandardScaler().fit_transform(windows_features_data.loc[:, ~windows_features_data.columns.isin(excluded_columns)])
+
+        windows_features_data.to_csv(snakemake.output[1], index=False)
+
+        if provider_main["WINDOWS"]["COMPUTE"] and "SECOND_ORDER_FEATURES" in provider_main["WINDOWS"]:
+            so_features_names = provider_main["WINDOWS"]["SECOND_ORDER_FEATURES"]
+            windows_so_features_data = extract_second_order_features(windows_features_data, so_features_names, prefix)
+            windows_so_features_data.to_csv(snakemake.output[0], index=False)
+        else:
+            pd.DataFrame().to_csv(snakemake.output[0], index=False)
+
+else: 
+    for sensor_features in sensor_data_files["sensor_features"]:
+        if "/" + sensor_key + ".csv" in sensor_features:
+            sensor_data = pd.read_csv(sensor_features)
+            excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
+
+            if not sensor_data.empty:
+                sensor_data.loc[:, ~sensor_data.columns.isin(excluded_columns)] = StandardScaler().fit_transform(sensor_data.loc[:, ~sensor_data.columns.isin(excluded_columns)])
+            
+            sensor_data.to_csv(snakemake.output[0], index=False)
+            break

src/features/utils/merge_standardized_sensor_features_for_all_participants.R

@@ -0,0 +1,17 @@
+source("renv/activate.R")
+
+library(tidyr)
+library(purrr)
+library("dplyr", warn.conflicts = F)
+library(stringr)
+
+feature_files  <- snakemake@input[["feature_files"]]
+
+
+features_of_all_participants <- tibble(filename = feature_files) %>% # create a data frame
+  mutate(file_contents = map(filename, ~ read.csv(., stringsAsFactors = F, colClasses = c(local_segment = "character", local_segment_label = "character", local_segment_start_datetime="character", local_segment_end_datetime="character"))),
+         pid = str_match(filename, ".*/(.*)/z_all_sensor_features.csv")[,2]) %>%
+  unnest(cols = c(file_contents)) %>%
+  select(-filename)
+
+write.csv(features_of_all_participants, snakemake@output[[1]], row.names = FALSE)

src/features/utils/utils.py

@@ -88,11 +88,13 @@ def chunk_episodes(sensor_episodes):
 
     return merged_sensor_episodes
 
-def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file):
+def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=False):
     import pandas as pd
     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()))
@@ -106,23 +108,57 @@ 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)
+
-            if not "local_segment" in features.columns:
+            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)
-                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]
+            # In case of calc_window = True
-            sensor_features = pd.concat([sensor_features, features], axis=0, sort=False)
+            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_fo_features, features[0]], axis=0, sort=False)                
+                if not features[1].empty:
+                    sensor_so_features = pd.concat([sensor_so_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]
+                sensor_features = pd.concat([sensor_features, features], axis=0, sort=False)
     else:
         for feature in provider["FEATURES"]:
             sensor_features[feature] = None
-    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)
-    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_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]])
     
-    return sensor_features
+    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)
+        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_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]])
+
+        return sensor_features
 
 def run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data_files):
     from importlib import import_module, util

src/models/helper.py

@@ -10,7 +10,7 @@ def retain_target_column(df_input: pd.DataFrame, target_variable_name: str):
     if all(~target_variable_index):
         raise ValueError("The requested target (", target_variable_name,
                          ")cannot be found in the dataset.",
-                         "Please check the names of phone_esm_ columns in all_sensor_features_cleaned_rapids.csv")
+                         "Please check the names of phone_esm_ columns in z_all_sensor_features_cleaned_straw_py.csv")
     sensor_features_plus_target = df_input.drop(esm_names, axis=1)
     sensor_features_plus_target["target"] = df_input[esm_names[target_variable_index]]
     # We will only keep one column related to phone_esm and that will be our target variable.

tests/scripts/missing_vals.py

@@ -0,0 +1,39 @@
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+
+participant = "p031"
+all_sensors = ["eda", "bvp", "ibi", "temp", "acc"]
+
+for sensor in all_sensors:
+
+    if sensor == "eda":
+        path = f"/rapids/data/interim/{participant}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv"
+    elif sensor == "bvp":
+        path = f"/rapids/data/interim/{participant}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_cr_windows.csv"
+    elif sensor == "ibi":
+        path = f"/rapids/data/interim/{participant}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_cr_windows.csv" 
+    elif sensor == "acc":
+        path = f"/rapids/data/interim/{participant}/empatica_accelerometer_features/empatica_accelerometer_python_cr_windows.csv" 
+    elif sensor == "temp":
+        path = f"/rapids/data/interim/{participant}/empatica_temperature_features/empatica_temperature_python_cr_windows.csv" 
+    else:
+        path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv" # all features all participants
+
+
+    df = pd.read_csv(path)
+    print(df)
+    is_NaN = df.isnull()
+    row_has_NaN = is_NaN.any(axis=1)
+    rows_with_NaN = df[row_has_NaN]
+
+    print("All rows:", len(df.index))
+    print("\nCount NaN vals:", rows_with_NaN.size)
+    print("\nDf mean:")
+    print(df.mean())
+
+    sns.heatmap(df.isna(), cbar=False)
+    plt.savefig(f'{sensor}_{participant}_windows_NaN.png', bbox_inches='tight')
+
+

tests/scripts/phone_vals.py

@@ -0,0 +1,23 @@
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+import os, sys
+
+participant = "p032"
+
+folder = f"/rapids/data/processed/features/{participant}/"
+for filename in os.listdir(folder):
+    if filename.startswith("phone_"):
+        df = pd.read_csv(f"{folder}{filename}")
+        plt.figure()
+        sns.heatmap(df[[col for col in df if col.startswith('phone_')]], cbar=True)
+        plt.savefig(f'{participant}_{filename}.png', bbox_inches='tight')
+        plt.close()
+
+        plt.figure()
+        sns.heatmap(df[[col for col in df if col.startswith('phone_')]].isna(), cbar=True)
+        plt.savefig(f'is_na_{participant}_{filename}.png', bbox_inches='tight')
+        plt.close()
+        
+        

tests/scripts/zero_vals.py

@@ -0,0 +1,48 @@
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from itertools import compress
+
+
+participant = "p031"
+sensor = "eda"
+
+if sensor == "eda":
+    path = f"/rapids/data/interim/{participant}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv"
+elif sensor == "bvp":
+    path = f"/rapids/data/interim/{participant}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_cr_windows.csv"
+elif sensor == "ibi":
+    path = f"/rapids/data/interim/{participant}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_cr_windows.csv" 
+elif sensor == "acc":
+    path = f"/rapids/data/interim/{participant}/empatica_accelerometer_features/empatica_accelerometer_python_cr_windows.csv" 
+elif sensor == "temp":
+    path = f"/rapids/data/interim/{participant}/empatica_temperature_features/empatica_temperature_python_cr_windows.csv" 
+else:
+    path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv" # all features all participants" 
+
+
+df = pd.read_csv(path)
+df_num_peaks_zero = df[df["empatica_electrodermal_activity_cr_numPeaks"] == 0]
+columns_num_peaks_zero = df_num_peaks_zero.columns[df_num_peaks_zero.isna().any()].tolist()
+
+df_num_peaks_non_zero = df[df["empatica_electrodermal_activity_cr_numPeaks"] != 0]
+df_num_peaks_non_zero = df_num_peaks_non_zero[columns_num_peaks_zero]
+
+pd.set_option('display.max_columns', None)
+
+df_q = pd.DataFrame()
+for col in df_num_peaks_non_zero:
+    df_q[col] = pd.to_numeric(pd.cut(df_num_peaks_non_zero[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q)
+plt.savefig(f'eda_{participant}_window_non_zero_peak_other_vals.png', bbox_inches='tight')
+plt.close()
+
+# Filter columns that do not contain 0
+non_zero_cols = list(compress(columns_num_peaks_zero, df_num_peaks_non_zero.all().tolist()))
+zero_cols = list(set(columns_num_peaks_zero) - set(non_zero_cols))
+
+print(non_zero_cols, "\n")
+print(zero_cols)
+
+