Bring back requested fields in config.yaml.

Update coding files based on 7e565c34db98265afcda922a337493781fdd8ed5 in supermodule.

.gitignore

@@ -93,10 +93,17 @@ 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/external/play_store_application_genre_catalogue.csv
+!/data/external/play_store_categories_count.csv
+
+
 data/raw/*
 !/data/raw/.gitkeep
 data/interim/*
@@ -114,3 +121,12 @@ 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 +11,191 @@
 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 master. 
+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.   
+    ```
+
+## Updating RAPIDS
+
+To update RAPIDS, first pull and merge [origin]( https://github.com/carissalow/rapids), such as with:
+
+```commandline
+git fetch --progress "origin" refs/heads/master
+git merge --no-ff origin/master
+```
+
+Next, update the conda and R virtual environment.
+
+```bash
+R -e 'renv::restore(repos = c(CRAN = "https://packagemanager.rstudio.com/all/__linux__/focal/latest"))'
+```
+
+## Custom configuration
+### Credentials
+
+As mentioned under [Database in RAPIDS documentation](https://www.rapids.science/1.6/snippets/database/), a `credentials.yaml` file is needed to connect to a database.
+It should contain:
+
+```yaml
+PSQL_STRAW:
+  database: staw
+  host: 212.235.208.113
+  password: password
+  port: 5432
+  user: staw_db
+```
+
+where`password` needs to be specified as well.
+
+## Possible installation issues
+### Missing dependencies for RPostgres
+
+To install `RPostgres` R package (used to connect to the PostgreSQL database), an error might occur:
+
+```text
+------------------------- ANTICONF ERROR ---------------------------
+Configuration failed because libpq was not found. Try installing:
+   * deb: libpq-dev (Debian, Ubuntu, etc)
+   * rpm: postgresql-devel (Fedora, EPEL)
+   * rpm: postgreql8-devel, psstgresql92-devel, postgresql93-devel, or postgresql94-devel (Amazon Linux)
+   * csw: postgresql_dev (Solaris)
+   * brew: libpq (OSX)
+If libpq is already installed, check that either:
+  (i)  'pkg-config' is in your PATH AND PKG_CONFIG_PATH contains a libpq.pc file; or
+  (ii) 'pg_config' is in your PATH.
+If neither can detect , you can set INCLUDE_DIR
+and LIB_DIR manually via:
+  R CMD INSTALL --configure-vars='INCLUDE_DIR=... LIB_DIR=...'
+--------------------------[ ERROR MESSAGE ]----------------------------
+  <stdin>:1:10: fatal error: libpq-fe.h: No such file or directory
+compilation terminated.
+```
+
+The library requires `libpq` for compiling from source, so install accordingly.
+
+### Timezone environment variable for tidyverse (relevant for WSL2)
+
+One of the R packages, `tidyverse` might need access to the `TZ` environment variable during the installation.
+On Ubuntu 20.04 on WSL2 this triggers the following error:
+
+```text
+> install.packages('tidyverse')
+
+ERROR: configuration failed for package ‘xml2’
+System has not been booted with systemd as init system (PID 1). Can't operate.
+Failed to create bus connection: Host is down
+Warning in system("timedatectl", intern = TRUE) :
+  running command 'timedatectl' had status 1
+Error in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]) :
+  namespace ‘xml2’ 1.3.1 is already loaded, but >= 1.3.2 is required
+Calls: <Anonymous> ... namespaceImportFrom -> asNamespace -> loadNamespace
+Execution halted
+ERROR: lazy loading failed for package ‘tidyverse’
+```
+
+This happens because WSL2 does not use the `timedatectl` service, which provides this variable.
+
+```bash
+~$ timedatectl
+System has not been booted with systemd as init system (PID 1). Can't operate.
+Failed to create bus connection: Host is down
+```
+
+and later 
+
+```bash 
+Warning message:
+In system("timedatectl", intern = TRUE) :
+  running command 'timedatectl' had status 1
+Execution halted
+```
+
+This can be amended by setting the environment variable manually before attempting to install `tidyverse`:
+
+```bash
+export TZ='Europe/Ljubljana'
+```
+
+Note: if this is needed to avoid runtime issues, you need to either define this environment variable in each new terminal window or (better) define it in your `~/.bashrc` or `~/.bash_profile`.
+
+## Possible runtime issues
+### Unix end of line characters
+
+Upon running rapids, an error might occur:
+
+```bash
+/usr/bin/env: ‘python3\r’: No such file or directory
+```
+
+This is due to Windows style end of line characters. 
+To amend this, I added a `.gitattributes` files to force `git` to checkout `rapids` using Unix EOL characters.
+If this still fails, `dos2unix` can be used to change them.
+
+### System has not been booted with systemd as init system (PID 1)
+
+See [the installation issue above](#Timezone-environment-variable-for-tidyverse-(relevant-for-WSL2)).

Snakefile

@@ -171,8 +171,17 @@ 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.append("data/processed/features/all_participants/all_sensor_features.csv")
+        # 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")
+
+for provider in config["PHONE_SPEECH"]["PROVIDERS"].keys():
+    if config["PHONE_SPEECH"]["PROVIDERS"][provider]["COMPUTE"]:
+        files_to_compute.extend(expand("data/raw/{pid}/phone_speech_raw.csv",pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/raw/{pid}/phone_speech_with_datetime.csv",pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/interim/{pid}/phone_speech_features/phone_speech_{language}_{provider_key}.csv",pid=config["PIDS"],language=get_script_language(config["PHONE_SPEECH"]["PROVIDERS"][provider]["SRC_SCRIPT"]),provider_key=provider.lower()))
+        files_to_compute.extend(expand("data/processed/features/{pid}/phone_speech.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")
 
 # 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):
@@ -328,7 +337,7 @@ 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")
-
+     
 for provider in config["EMPATICA_HEARTRATE"]["PROVIDERS"].keys():
     if config["EMPATICA_HEARTRATE"]["PROVIDERS"][provider]["COMPUTE"]:
         files_to_compute.extend(expand("data/raw/{pid}/empatica_heartrate_raw.csv", pid=config["PIDS"]))
@@ -374,7 +383,7 @@ 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 isinstance(config["EMPATICA_TAGS"]["PROVIDERS"], dict):
     for provider in config["EMPATICA_TAGS"]["PROVIDERS"].keys():
         if config["EMPATICA_TAGS"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -408,10 +417,18 @@ 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"]))
+        else:
+            files_to_compute.extend(expand("data/processed/features/{pid}/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":
+            for target in config["PARAMS_FOR_ANALYSIS"]["TARGET"]["ALL_LABELS"]:
+                files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +"_py_(" + target + ").csv"))
+        else:
+            files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +"_R.csv"))     
 
 # Baseline features
 if config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["COMPUTE"]:
@@ -423,9 +440,8 @@ 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/population_model/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"]))
+    for target in config["PARAMS_FOR_ANALYSIS"]["TARGET"]["ALL_LABELS"]:
+        files_to_compute.extend(expand("data/processed/models/population_model/input_" + target + ".csv"))
 
 rule all:
     input:

automl_test.py

@@ -0,0 +1,57 @@
+from pprint import pprint
+import sklearn.metrics
+import autosklearn.regression
+
+import datetime
+import importlib
+import os
+import sys
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+import yaml
+
+from sklearn import linear_model, svm, kernel_ridge, gaussian_process
+from sklearn.model_selection import LeaveOneGroupOut, cross_val_score, train_test_split
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.impute import SimpleImputer
+
+model_input = pd.read_csv("data/processed/models/population_model/input_PANAS_negative_affect_mean.csv") # Standardizirani podatki
+
+model_input.dropna(axis=1, how="all", inplace=True)
+model_input.dropna(axis=0, how="any", subset=["target"], inplace=True)
+
+categorical_feature_colnames = ["gender", "startlanguage"]
+categorical_feature_colnames += [col for col in model_input.columns if "mostcommonactivity" in col or "homelabel" in col]
+categorical_features = model_input[categorical_feature_colnames].copy()
+mode_categorical_features = categorical_features.mode().iloc[0]
+categorical_features = categorical_features.fillna(mode_categorical_features)
+categorical_features = categorical_features.apply(lambda col: col.astype("category"))
+if not categorical_features.empty:
+    categorical_features = pd.get_dummies(categorical_features)
+numerical_features = model_input.drop(categorical_feature_colnames, axis=1)
+model_in = pd.concat([numerical_features, categorical_features], axis=1)
+
+index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
+model_in.set_index(index_columns, inplace=True)
+
+X_train, X_test, y_train, y_test = train_test_split(model_in.drop(["target", "pid"], axis=1), model_in["target"], test_size=0.30)
+
+automl = autosklearn.regression.AutoSklearnRegressor(
+    time_left_for_this_task=7200,
+    per_run_time_limit=120
+)
+automl.fit(X_train, y_train, dataset_name='straw')
+
+print(automl.leaderboard())
+pprint(automl.show_models(), indent=4)
+
+train_predictions = automl.predict(X_train)
+print("Train R2 score:", sklearn.metrics.r2_score(y_train, train_predictions))
+test_predictions = automl.predict(X_test)
+print("Test R2 score:", sklearn.metrics.r2_score(y_test, test_predictions))
+
+import sys
+sys.exit()

config.yaml

@@ -16,14 +16,19 @@ 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
 TIME_SEGMENTS: &time_segments
-  TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT
-  FILE: "data/external/timesegments_daily.csv"
+  TYPE: EVENT # FREQUENCY, PERIODIC, EVENT
+  FILE: "data/external/straw_events.csv"
   INCLUDE_PAST_PERIODIC_SEGMENTS: TRUE # Only relevant if TYPE=PERIODIC, see docs
+  TAILORED_EVENTS: # Only relevant if TYPE=EVENT
+    COMPUTE: True
+    SEGMENTING_METHOD: "30_before" # 30_before, 90_before, stress_event
+    INTERVAL_OF_INTEREST: 10 # duration of event of interest [minutes]
+    IOI_ERROR_TOLERANCE: 5 # interval of interest erorr tolerance (before and after IOI) [minutes]
 
 # See https://www.rapids.science/latest/setup/configuration/#timezone-of-your-study
 TIMEZONE: 
@@ -70,7 +75,6 @@ PHONE_ACCELEROMETER:
       COMPUTE: False
       FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
       SRC_SCRIPT: src/features/phone_accelerometer/rapids/main.py
-    
     PANDA:
       COMPUTE: False
       VALID_SENSED_MINUTES: False
@@ -100,9 +104,9 @@ PHONE_APPLICATIONS_CRASHES:
   CONTAINER: applications_crashes
   APPLICATION_CATEGORIES:
     CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store)
-    CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv"
-    UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE
-    SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway
+    CATALOGUE_FILE: "data/external/play_store_application_genre_catalogue.csv"
+    UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE
+    SCRAPE_MISSING_CATEGORIES: False # whether to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway
   PROVIDERS: # None implemented yet but this sensor can be used in PHONE_DATA_YIELD
 
 # See https://www.rapids.science/latest/features/phone-applications-foreground/
@@ -110,24 +114,32 @@ PHONE_APPLICATIONS_FOREGROUND:
   CONTAINER: applications
   APPLICATION_CATEGORIES:
     CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store)
-    CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv"
-    PACKAGE_NAMES_HASHED: True
-    UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE
-    SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway
+    CATALOGUE_FILE: "data/external/play_store_application_genre_catalogue.csv"
+    # Refer to data/external/play_store_categories_count.csv for a list of categories (genres) and their frequency.
+    UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE
+    SCRAPE_MISSING_CATEGORIES: False # whether to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway
   PROVIDERS:
     RAPIDS:
       COMPUTE: True
       INCLUDE_EPISODE_FEATURES: True
-      SINGLE_CATEGORIES: ["all", "email"]
+      SINGLE_CATEGORIES: ["Productivity", "Tools", "Communication", "Education", "Social"]
       MULTIPLE_CATEGORIES:
-        social: ["socialnetworks", "socialmediatools"]
-        entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"]
+        games: ["Puzzle", "Card", "Casual", "Board", "Strategy", "Trivia", "Word", "Adventure", "Role Playing", "Simulation", "Board, Brain Games", "Racing"]
+        social: ["Communication", "Social", "Dating"]
+        productivity: ["Tools", "Productivity", "Finance", "Education", "News & Magazines", "Business", "Books & Reference"]
+        health: ["Health & Fitness", "Lifestyle", "Food & Drink", "Sports", "Medical", "Parenting"]
+        entertainment: ["Shopping", "Music & Audio", "Entertainment", "Travel & Local", "Photography", "Video Players & Editors", "Personalization", "House & Home", "Art & Design", "Auto & Vehicles", "Entertainment,Music & Video",
+                        "Puzzle", "Card", "Casual", "Board", "Strategy", "Trivia", "Word", "Adventure", "Role Playing", "Simulation", "Board, Brain Games", "Racing" # Add all games.
+        ]
+        maps_weather: ["Maps & Navigation", "Weather"]
       CUSTOM_CATEGORIES:
-        social_media: ["com.google.android.youtube", "com.snapchat.android", "com.instagram.android", "com.zhiliaoapp.musically", "com.facebook.katana"]
-        dating: ["com.tinder", "com.relance.happycouple", "com.kiwi.joyride"]
-      SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps
-      EXCLUDED_CATEGORIES: []
-      EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] # TODO list system apps?
+      SINGLE_APPS: []
+      EXCLUDED_CATEGORIES: ["System", "STRAW"]
+      # Note: A special option here is "is_system_app".
+      # This excludes applications that have is_system_app = TRUE, which is a separate column in the table.
+      # However, all of these applications have been assigned System category.
+      # I will therefore filter by that category, which is a superset and is more complete. JL
+      EXCLUDED_APPS: []
       FEATURES: 
         APP_EVENTS: ["countevent", "timeoffirstuse", "timeoflastuse", "frequencyentropy"]
         APP_EPISODES: ["countepisode", "minduration", "maxduration", "meanduration", "sumduration"]
@@ -160,7 +172,7 @@ PHONE_BLUETOOTH:
   CONTAINER: bluetooth
   PROVIDERS:
     RAPIDS:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
       SRC_SCRIPT: src/features/phone_bluetooth/rapids/main.R
 
@@ -239,7 +251,8 @@ PHONE_ESM:
   PROVIDERS:
     STRAW:
       COMPUTE: True
-      SCALES: ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support"]
+      SCALES: ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support", 
+              "appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]
       FEATURES: [mean]
       SRC_SCRIPT: src/features/phone_esm/straw/main.py
 
@@ -324,6 +337,15 @@ PHONE_SCREEN:
       EPISODE_TYPES: ["unlock"]
       SRC_SCRIPT: src/features/phone_screen/rapids/main.py
 
+# Custom added sensor
+PHONE_SPEECH:
+  CONTAINER: speech
+  PROVIDERS:
+    STRAW:
+      COMPUTE: True
+      FEATURES: ["meanspeech", "stdspeech", "nlargest", "nsmallest", "medianspeech"]
+      SRC_SCRIPT: src/features/phone_speech/straw/main.py
+
 # See https://www.rapids.science/latest/features/phone-wifi-connected/
 PHONE_WIFI_CONNECTED:
   CONTAINER: sensor_wifi
@@ -441,7 +463,6 @@ FITBIT_SLEEP_INTRADAY:
         UNIFIED: [awake, asleep]
       SLEEP_TYPES: [main, nap, all]
       SRC_SCRIPT: src/features/fitbit_sleep_intraday/rapids/main.py
-  
     PRICE:
       COMPUTE: False
       FEATURES: [avgduration, avgratioduration, avgstarttimeofepisodemain, avgendtimeofepisodemain, avgmidpointofepisodemain, stdstarttimeofepisodemain, stdendtimeofepisodemain, stdmidpointofepisodemain, socialjetlag, rmssdmeanstarttimeofepisodemain, rmssdmeanendtimeofepisodemain, rmssdmeanmidpointofepisodemain, rmssdmedianstarttimeofepisodemain, rmssdmedianendtimeofepisodemain, rmssdmedianmidpointofepisodemain]
@@ -485,6 +506,7 @@ FITBIT_STEPS_INTRADAY:
       INCLUDE_ZERO_STEP_ROWS: False
       SRC_SCRIPT: src/features/fitbit_steps_intraday/rapids/main.py
 
+
 ########################################################################################################################
 #                                                 EMPATICA                                                             #
 ########################################################################################################################
@@ -506,6 +528,15 @@ 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']
+      SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py
+
 
 # See https://www.rapids.science/latest/features/empatica-heartrate/
 EMPATICA_HEARTRATE:
@@ -524,6 +555,15 @@ 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']
+      SRC_SCRIPT: src/features/empatica_temperature/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-electrodermal-activity/
 EMPATICA_ELECTRODERMAL_ACTIVITY:
@@ -533,6 +573,19 @@ 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]
+        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 +595,15 @@ 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']
+      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 +613,16 @@ 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']
+      SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-tags/
 EMPATICA_TAGS:
@@ -566,7 +638,7 @@ 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 +647,7 @@ 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 +658,7 @@ 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"}
@@ -599,41 +671,71 @@ HEATMAP_FEATURE_CORRELATION_MATRIX:
 ALL_CLEANING_INDIVIDUAL:
   PROVIDERS:
     RAPIDS:
-      COMPUTE: True
+      COMPUTE: False
       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:
+      COMPUTE: True
+      PHONE_DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_MINUTES # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
+      PHONE_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
+      EMPATICA_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
+      ROWS_NAN_THRESHOLD: 0.33 # set to 1 to disable
+      COLS_NAN_THRESHOLD: 0.9 # set to 1 to remove only columns that contains all (100% of) NaN
+      COLS_VAR_THRESHOLD: True
+      DROP_HIGHLY_CORRELATED_FEATURES:
+        COMPUTE: True
+        MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
+        CORR_THRESHOLD: 0.95
+      STANDARDIZATION: True
+      SRC_SCRIPT: src/features/all_cleaning_individual/straw/main.py
 
 ALL_CLEANING_OVERALL:
   PROVIDERS:
     RAPIDS:
-      COMPUTE: True
+      COMPUTE: False
       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:
+      COMPUTE: True
+      PHONE_DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_MINUTES # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
+      PHONE_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
+      EMPATICA_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
+      ROWS_NAN_THRESHOLD: 0.33 # set to 1 to disable
+      COLS_NAN_THRESHOLD: 0.8 # set to 1 to remove only columns that contains all (100% of) NaN
+      COLS_VAR_THRESHOLD: True
+      DROP_HIGHLY_CORRELATED_FEATURES:
+        COMPUTE: True
+        MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
+        CORR_THRESHOLD: 0.95
+      STANDARDIZATION: True
+      TARGET_STANDARDIZATION: False
+      SRC_SCRIPT: src/features/all_cleaning_overall/straw/main.py
+
 
 ########################################################################################################################
-#                                              Analysis Workflow Example                                               #
+#                                                      Baseline                                                        #
 ########################################################################################################################
 
 PARAMS_FOR_ANALYSIS:
@@ -650,4 +752,7 @@ PARAMS_FOR_ANALYSIS:
 
   TARGET:
     COMPUTE: True
-    LABEL: PANAS_negative_affect_mean
+    LABEL: appraisal_stressfulness_event_mean
+    ALL_LABELS: [PANAS_positive_affect_mean, PANAS_negative_affect_mean, JCQ_job_demand_mean, JCQ_job_control_mean, JCQ_supervisor_support_mean, JCQ_coworker_support_mean, appraisal_stressfulness_period_mean]
+                # PANAS_positive_affect_mean, PANAS_negative_affect_mean, JCQ_job_demand_mean, JCQ_job_control_mean, JCQ_supervisor_support_mean, 
+                # JCQ_coworker_support_mean, appraisal_stressfulness_period_mean, appraisal_stressfulness_event_mean, appraisal_threat_mean, appraisal_challenge_mean

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: [4b62a655-cbf0-4ac0-a448-06726f45b56a]
+  PLATFORMS: [android]
+  LABEL: uploader_53573
+  START_DATE: 2021-05-21 09:21:24
+  END_DATE: 2021-07-12 17:32:07
+EMPATICA:
+  DEVICE_IDS: [uploader_53573]
+  LABEL: uploader_53573
+  START_DATE: 2021-05-21 09:21:24
+  END_DATE: 2021-07-12 17:32:07

data/external/play_store_categories_count.csv

@@ -0,0 +1,45 @@
+genre,n
+System,261
+Tools,96
+Productivity,71
+Health & Fitness,60
+Finance,54
+Communication,39
+Music & Audio,39
+Shopping,38
+Lifestyle,33
+Education,28
+News & Magazines,24
+Maps & Navigation,23
+Entertainment,21
+Business,18
+Travel & Local,18
+Books & Reference,16
+Social,16
+Weather,16
+Food & Drink,14
+Sports,14
+Other,13
+Photography,13
+Puzzle,13
+Video Players & Editors,12
+Card,9
+Casual,9
+Personalization,8
+Medical,7
+Board,5
+Strategy,4
+House & Home,3
+Trivia,3
+Word,3
+Adventure,2
+Art & Design,2
+Auto & Vehicles,2
+Dating,2
+Role Playing,2
+STRAW,2
+Simulation,2
+"Board,Brain Games",1
+"Entertainment,Music & Video",1
+Parenting,1
+Racing,1

data/external/timesegments_daily.csv

@@ -1,2 +1,3 @@
 label,start_time,length,repeats_on,repeats_value
 daily,04:00:00,23H 59M 59S,every_day,0
+working_day,04:00:00,18H 00M 00S,every_day,0

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

docs/change-log.md

@@ -1,4 +1,9 @@
 # Change Log
+## v1.8.0
+- Add data stream for AWARE Micro server
+- Fix the NA bug in PHONE_LOCATIONS BARNETT provider
+- Fix the bug of data type for call_duration field
+- Fix the index bug of heatmap_sensors_per_minute_per_time_segment
 ## v1.7.1
 - Update docs for Git Flow section
 - Update RAPIDS paper information

docs/datastreams/aware-micro-mysql.md

@@ -0,0 +1,15 @@
+# `aware_micro_mysql`
+
+This [data stream](../../datastreams/data-streams-introduction) handles iOS and Android sensor data collected with the [AWARE Framework's](https://awareframework.com/) [AWARE Micro](https://github.com/denzilferreira/aware-micro) server and stored in a MySQL database.
+
+## Container
+A MySQL database with a table per sensor, each containing the data for all participants. Sensor data is stored in a JSON field within each table called `data`
+
+The script to connect and download data from this container is at:
+```bash
+src/data/streams/aware_micro_mysql/container.R
+```
+
+## Format
+
+--8<---- "docs/snippets/aware_format.md"

docs/datastreams/data-streams-introduction.md

@@ -16,6 +16,7 @@ For reference, these are the data streams we currently support:
 | Data Stream | Device | Format | Container | Docs
 |--|--|--|--|--|
 | `aware_mysql`| Phone | AWARE app | MySQL | [link](../aware-mysql)
+| `aware_micro_mysql`| Phone | AWARE Micro server | MySQL | [link](../aware-micro-mysql)
 | `aware_csv`| Phone | AWARE app | CSV files | [link](../aware-csv)
 | `aware_influxdb` (beta)| Phone | AWARE app | InfluxDB | [link](../aware-influxdb)
 | `fitbitjson_mysql`| Fitbit | JSON (per [Fitbit's API](https://dev.fitbit.com/build/reference/web-api/)) | MySQL | [link](../fitbitjson-mysql)

donotmakechanges.py

@@ -0,0 +1,39 @@
+"""
+Please do not make any changes, as RAPIDS is running on tmux server ...
+"""
+# !
+# !
+"""
+Please do not make any changes, as RAPIDS is running on tmux server ...
+"""
+# !
+# !
+"""
+Please do not make any changes, as RAPIDS is running on tmux server ...
+"""
+# !
+# !
+"""
+Please do not make any changes, as RAPIDS is running on tmux server ...
+"""
+# !
+# !
+"""
+Please do not make any changes, as RAPIDS is running on tmux server ...
+"""
+# !
+# !
+"""
+Please do not make any changes, as RAPIDS is running on tmux server ...
+"""
+# !
+# !
+"""
+Please do not make any changes, as RAPIDS is running on tmux server ...
+"""
+# !
+# !
+"""
+Please do not make any changes, as RAPIDS is running on tmux server ...
+"""
+# !

environment.yml

@@ -1,116 +1,30 @@
 name: rapids
 channels:
   - conda-forge
-  - defaults
 dependencies:
-  - _py-xgboost-mutex=2.0
-  - appdirs=1.4.*
-  - arrow=0.16.0
-  - asn1crypto=1.4.*
-  - astropy=4.2.*
-  - attrs=20.3.*
-  - binaryornot=0.4.*
-  - blas=1.0
-  - brotlipy=0.7.*
-  - bzip2=1.0.*
-  - ca-certificates
-  - certifi
-  - cffi=1.14.4
-  - chardet=3.0.*
-  - click=7.1.*
-  - cookiecutter=1.6.*
-  - cryptography=3.3.*
-  - datrie=0.8.*
-  - docutils=0.16
-  - future=0.18.2
-  - gitdb=4.0.*
-  - gitdb2=4.0.*
-  - gitpython=3.1.*
-  - idna=2.10
-  - imbalanced-learn=0.6.*
-  - importlib-metadata=2.0.*
-  - importlib_metadata=2.0.*
-  - intel-openmp=2019.4
-  - jinja2=2.11.2
-  - jinja2-time=0.2.*
-  - joblib=1.0.*
-  - jsonschema=3.2.*
-  - libblas=3.8.*
-  - libcblas=3.8.*
-  - libcxx=10.0.*
-  - libedit=3.1.*
-  - libffi=3.3
-  - libgfortran
-  - liblapack=3.8.*
-  - libopenblas=0.3.*
-  - libxgboost=0.90
-  - lightgbm=3.1.*
-  - llvm-openmp=10.0.*
-  - markupsafe=1.1.*
-  - mkl
-  - mkl-service=2.3.*
-  - mkl_fft=1.2.*
-  - mkl_random=1.1.*
-  - more-itertools=8.6.*
-  - ncurses=6.2
-  - numpy=1.19.2
-  - numpy-base=1.19.2
-  - openblas=0.3.*
-  - openssl
-  - pandas=1.1.*
-  - pbr=5.5.*
-  - pip=20.3.*
-  - plotly=4.14.1
-  - poyo=0.5.*
-  - psutil=5.7.*
-  - psycopg2
-  - py-xgboost=0.90
-  - pycparser=2.20
-  - pyerfa=1.7.*
-  - pyopenssl=20.0.*
-  - pyprojroot
-  - pysocks=1.7.*
-  - python=3.7.*
-  - python-dateutil=2.8.*
-  - python-dotenv
-  - python_abi=3.7
-  - pytz=2020.4
-  - pyyaml=5.3.*
-  - readline=8.0
-  - requests=2.25.0
-  - retrying=1.3.*
-  - scikit-learn=0.23.2
-  - scipy=1.5.*
-  - setuptools=51.0.*
-  - six=1.15.0
-  - smmap=3.0.*
-  - smmap2=3.0.*
-  - sqlalchemy
-  - sqlite=3.33.0
-  - threadpoolctl=2.1.*
-  - tk=8.6.*
-  - tqdm=4.62.0
-  - urllib3=1.25.11
-  - wheel=0.36.2
-  - whichcraft=0.6.*
-  - wrapt=1.12.1
-  - xgboost=0.90
-  - xz=5.2.*
-  - yaml=0.2.*
-  - zipp=3.4.*
-  - zlib=1.2.*
-  - pip:
-    - amply==0.1.*
-    - configargparse==0.15.1
-    - decorator==4.4.*
-    - ipython-genutils==0.2.*
-    - jupyter-core==4.6.*
-    - nbformat==5.0.*
-    - pulp==2.4
-    - pyparsing==2.4.*
-    - pyrsistent==0.15.5
-    - ratelimiter==1.2.*
-    - snakemake==5.30.2
-    - toposort==1.5
-    - traitlets==4.3.*
-prefix: /usr/local/Caskroom/miniconda/base/envs/rapids202108
+    - auto-sklearn
+    - hmmlearn
+    - imbalanced-learn
+    - jsonschema
+    - lightgbm
+    - matplotlib
+    - numpy
+    - pandas
+    - peakutils
+    - pip
+    - plotly
+    - python-dateutil
+    - pytz
+    - pywavelets
+    - pyyaml
+    - scikit-learn
+    - scipy
+    - seaborn
+    - setuptools
+    - bioconda::snakemake 
+    - bioconda::snakemake-minimal
+    - tqdm
+    - xgboost
+    - pip:
+        - biosppy
+        - cr_features>=0.2

mkdocs.yml

@@ -85,6 +85,7 @@ nav:
       - Introduction: datastreams/data-streams-introduction.md
       - Phone:
         - aware_mysql: datastreams/aware-mysql.md
+        - aware_micro_mysql: datastreams/aware-micro-mysql.md
         - aware_csv: datastreams/aware-csv.md
         - aware_influxdb (beta): datastreams/aware-influxdb.md
         - Mandatory Phone Format: datastreams/mandatory-phone-format.md

renv/activate.R

@@ -14,9 +14,6 @@ local({
   # signal that we're loading renv during R startup
   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 +40,17 @@ 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_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"

rules/features.smk

@@ -32,7 +32,7 @@ rule phone_data_yield_r_features:
     output:
         "data/interim/{pid}/phone_data_yield_features/phone_data_yield_r_{provider_key}.csv"
     script:
-        "../src/features/entry.R" 
+        "../src/features/entry.R"
 
 rule phone_accelerometer_python_features:
     input:
@@ -345,6 +345,19 @@ rule esm_features:
     script:
         "../src/features/entry.py"
 
+rule phone_speech_python_features:
+    input:
+        sensor_data = "data/raw/{pid}/phone_speech_with_datetime.csv",
+        time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
+    params:
+        provider = lambda wildcards: config["PHONE_SPEECH"]["PROVIDERS"][wildcards.provider_key.upper()],
+        provider_key = "{provider_key}",
+        sensor_key = "phone_speech"
+    output: 
+        "data/interim/{pid}/phone_speech_features/phone_speech_python_{provider_key}.csv"
+    script:
+        "../src/features/entry.py"
+
 rule phone_keyboard_python_features:
     input:
         sensor_data = "data/raw/{pid}/phone_keyboard_with_datetime.csv",
@@ -791,7 +804,8 @@ 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"
 
@@ -817,7 +831,8 @@ 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"
 
@@ -843,7 +858,8 @@ 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"
 
@@ -869,7 +885,8 @@ 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"
 
@@ -895,7 +912,8 @@ 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"
 
@@ -921,7 +939,8 @@ 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"
 
@@ -988,11 +1007,12 @@ rule clean_sensor_features_for_individual_participants:
     params:
         provider = lambda wildcards: config["ALL_CLEANING_INDIVIDUAL"]["PROVIDERS"][wildcards.provider_key.upper()],
         provider_key = "{provider_key}",
-        sensor_key = "all_cleaning_individual"
+        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" 
     script:
-        "../src/features/entry.R"
+        "../src/features/entry.{params.script_extension}"
 
 rule clean_sensor_features_for_all_participants:
     input:
@@ -1000,9 +1020,10 @@ rule clean_sensor_features_for_all_participants:
     params:
         provider = lambda wildcards: config["ALL_CLEANING_OVERALL"]["PROVIDERS"][wildcards.provider_key.upper()],
         provider_key = "{provider_key}",
-        sensor_key = "all_cleaning_overall"
+        script_extension = "{script_extension}",
+        sensor_key = "all_cleaning_overall",
+        target = "{target}"
     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}_({target}).csv"
     script:
-        "../src/features/entry.R"
-
+        "../src/features/entry.{params.script_extension}"

rules/models.smk

@@ -30,7 +30,7 @@ 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}/all_sensor_features_cleaned_straw_py.csv"
     params:
         target_variable = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
     output:
@@ -40,12 +40,13 @@ rule select_target:
 
 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/all_sensor_features_cleaned_straw_py_({target}).csv",
         demographic_features = expand("data/processed/features/{pid}/baseline_features.csv", pid=config["PIDS"]),
     params:
-        target_variable=config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
+        target_variable="{target}"
     output:
-        "data/processed/models/population_model/input.csv"
+        "data/processed/models/population_model/input_{target}.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:
-    params:
-        baseline_folder = "/mnt/e/STRAWbaseline/"
-    output:
-        usernames_file = config["CREATE_PARTICIPANT_FILES"]["USERNAMES_CSV"],
-        timezone_file = config["TIMEZONE"]["MULTIPLE"]["TZ_FILE"]
-    script:
-        "../../participants/prepare_usernames_file.py"
+# rule query_usernames_device_empatica_ids:
+#     params:
+#         baseline_folder = "/mnt/e/STRAWbaseline/"
+#     output:
+#         usernames_file = config["CREATE_PARTICIPANT_FILES"]["USERNAMES_CSV"],
+#         timezone_file = config["TIMEZONE"]["MULTIPLE"]["TZ_FILE"]
+#     script:
+#         "../../participants/prepare_usernames_file.py"
 
 rule prepare_tzcodes_file:
     input:
@@ -247,5 +247,33 @@ rule empatica_readable_datetime:
         include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"]
     output:
         "data/raw/{pid}/empatica_{sensor}_with_datetime.csv"
+    resources:
+        mem_mb=50000
     script:
         "../src/data/datetime/readable_datetime.R"
+
+
+rule extract_event_information_from_esm:
+    input:
+        esm_raw_input = "data/raw/{pid}/phone_esm_raw.csv",
+        pid_file = "data/external/participant_files/{pid}.yaml"
+    params:
+        stage = "extract",
+        pid = "{pid}"
+    output:
+        "data/raw/ers/{pid}_ers.csv",
+        "data/raw/ers/{pid}_stress_event_targets.csv"
+    script:
+        "../src/features/phone_esm/straw/process_user_event_related_segments.py"
+
+rule merge_event_related_segments_files:
+    input:
+        ers_files = expand("data/raw/ers/{pid}_ers.csv", pid=config["PIDS"]),
+        se_files = expand("data/raw/ers/{pid}_stress_event_targets.csv", pid=config["PIDS"])
+    params:
+        stage = "merge"
+    output:
+        "data/external/straw_events.csv",
+        "data/external/stress_event_targets.csv"
+    script:
+        "../src/features/phone_esm/straw/process_user_event_related_segments.py"

src/data/application_categories.R

@@ -29,23 +29,16 @@ get_genre <- function(apps){
 apps <- read.csv(snakemake@input[[1]], stringsAsFactors = F)
 genre_catalogue <- data.frame()
 catalogue_source <- snakemake@params[["catalogue_source"]]
-package_names_hashed <- snakemake@params[["package_names_hashed"]]
 update_catalogue_file <- snakemake@params[["update_catalogue_file"]]
 scrape_missing_genres <- snakemake@params[["scrape_missing_genres"]]
 apps_with_genre <- data.frame(matrix(ncol=length(colnames(apps)) + 1,nrow=0, dimnames=list(NULL, c(colnames(apps), "genre"))))
 
-if (length(package_names_hashed) == 0) {package_names_hashed <- FALSE}
-
 if(nrow(apps) > 0){
   if(catalogue_source == "GOOGLE"){
     apps_with_genre <- apps %>% mutate(genre = NA_character_)
   } else if(catalogue_source == "FILE"){
     genre_catalogue <- read.csv(snakemake@params[["catalogue_file"]], colClasses = c("character", "character"))
-    if (package_names_hashed) {
-      apps_with_genre <- left_join(apps, genre_catalogue, by = "package_hash")
-    } else {
-      apps_with_genre <- left_join(apps, genre_catalogue, by = "package_name")
-    }
+    apps_with_genre <- left_join(apps, genre_catalogue, by = "package_name")
   }
 
   if(catalogue_source == "GOOGLE" || (catalogue_source == "FILE" && scrape_missing_genres)){

src/data/baseline_features.py

@@ -95,7 +95,7 @@ if not participant_info.empty:
                 - limesurvey_demand.loc[rows_demand_reverse, "score_original"]
             )
             baseline_interim = pd.concat([baseline_interim, limesurvey_demand], axis=0, ignore_index=True)
-            if "demand" in requested_features:
+            if "limesurvey_demand" in requested_features:
                 baseline_features.loc[0, "limesurvey_demand"] = limesurvey_demand[
                     "score"
                 ].sum()
@@ -136,9 +136,12 @@ if not participant_info.empty:
                 ].sum()
 
         if "limesurvey_demand_control_ratio" in requested_features:
-            limesurvey_demand_control_ratio = (
-                limesurvey_demand["score"].sum() / limesurvey_control["score"].sum()
-            )
+            if limesurvey_control["score"].sum():
+                limesurvey_demand_control_ratio = (
+                        limesurvey_demand["score"].sum() / limesurvey_control["score"].sum()
+                )
+            else:
+                limesurvey_demand_control_ratio = 0
             if (
                 JCQ_NORMS[participant_info.loc[0, "gender"]][0]
                 <= limesurvey_demand_control_ratio

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/datetime/assign_to_time_segment.R

@@ -5,13 +5,16 @@ options(scipen=999)
 
 assign_rows_to_segments <- function(data, segments){
   # This function is used by all segment types, we use data.tables because they are fast
+
   data <- data.table::as.data.table(data)
   data[, assigned_segments := ""]
   for(i in seq_len(nrow(segments))) {
     segment <- segments[i,]
+
     data[segment$segment_start_ts<= timestamp & segment$segment_end_ts >= timestamp,
          assigned_segments := stringi::stri_c(assigned_segments, segment$segment_id, sep = "|")]
   }
+  
   data[,assigned_segments:=substring(assigned_segments, 2)]
   data
 }

src/data/streams/aware_micro_mysql/container.R

@@ -0,0 +1,85 @@
+# if you need a new package, you should add it with renv::install(package) so your renv venv is updated
+library(RMariaDB)
+library(yaml)
+
+#' @description
+#' Auxiliary function to parse the connection credentials from a specifc group in ./credentials.yaml
+#' You can reause most of this function if you are connection to a DB or Web API.
+#' It's OK to delete this function if you don't need credentials, e.g., you are pulling data from a CSV for example.
+#' @param group the yaml key containing the credentials to connect to a database
+#' @preturn dbEngine a database engine (connection) ready to perform queries
+get_db_engine <- function(group){
+  # The working dir is aways RAPIDS root folder, so your credentials file is always /credentials.yaml
+  credentials <- read_yaml("./credentials.yaml")
+  if(!group %in% names(credentials))
+    stop(paste("The credentials group",group, "does not exist in ./credentials.yaml. The only groups that exist in that file are:", paste(names(credentials), collapse = ","), ". Did you forget to set the group in [PHONE_DATA_STREAMS][aware_mysql][DATABASE_GROUP] in config.yaml?"))
+  dbEngine <- dbConnect(MariaDB(), db = credentials[[group]][["database"]],
+                                    username = credentials[[group]][["user"]],
+                                    password = credentials[[group]][["password"]],
+                                    host = credentials[[group]][["host"]],
+                                    port = credentials[[group]][["port"]])
+  return(dbEngine)
+}
+
+# This file gets executed for each PHONE_SENSOR of each participant
+# If you are connecting to a database the env file containing its credentials is available at "./.env"
+# If you are reading a CSV file instead of a DB table, the @param sensor_container wil contain the file path as set in config.yaml
+# You are not bound to databases or files, you can query a web API or whatever data source you need.
+
+#' @description
+#' RAPIDS allows users to use the keyword "infer" (previously "multiple") to automatically infer the mobile Operative System a device was running.
+#' If you have a way to infer the OS of a device ID, implement this function. For example, for AWARE data we use the "aware_device" table.
+#'  
+#' If you don't have a way to infer the OS, call stop("Error Message") so other users know they can't use "infer" or the inference failed, 
+#' and they have to assign the OS manually in the participant file
+#' 
+#' @param stream_parameters The PHONE_STREAM_PARAMETERS key in config.yaml. If you need specific parameters add them there.
+#' @param device A device ID string
+#' @return The OS the device ran, "android" or "ios"
+
+infer_device_os <- function(stream_parameters, device){
+  dbEngine <- get_db_engine(stream_parameters$DATABASE_GROUP)
+  query <- paste0("SELECT device_id,brand FROM aware_device WHERE device_id = '", device, "'")
+  message(paste0("Executing the following query to infer phone OS: ", query)) 
+  os <- dbGetQuery(dbEngine, query)
+  dbDisconnect(dbEngine)
+  
+  if(nrow(os) > 0)
+    return(os %>% mutate(os = ifelse(brand == "iPhone", "ios", "android")) %>% pull(os))
+  else
+    stop(paste("We cannot infer the OS of the following device id because it does not exist in the aware_device table:", device))
+  
+  return(os)
+}
+
+#' @description
+#' Gets the sensor data for a specific device id from a database table, file or whatever source you want to query
+#' 
+#' @param stream_parameters The PHONE_STREAM_PARAMETERS key in config.yaml. If you need specific parameters add them there.
+#' @param device A device ID string
+#' @param sensor_container database table or file containing the sensor data for all participants. This is the PHONE_SENSOR[CONTAINER] key in config.yaml
+#' @param columns the columns needed from this sensor (we recommend to only return these columns instead of every column in sensor_container)
+#' @return A dataframe with the sensor data for device
+
+pull_data <- function(stream_parameters, device, sensor, sensor_container, columns){
+  dbEngine <- get_db_engine(stream_parameters$DATABASE_GROUP)
+
+  select_items <- c()
+  for (column in columns) {
+    select_items <- append(select_items, paste0("data->>'$.", column, "' ", column))
+  }
+
+  query <- paste0("SELECT ", paste(select_items, collapse = ",")," FROM ", sensor_container, " WHERE ", columns$DEVICE_ID ," = '", device,"'")
+
+  # Letting the user know what we are doing
+  message(paste0("Executing the following query to download data: ", query)) 
+  sensor_data <- dbGetQuery(dbEngine, query)
+  
+  dbDisconnect(dbEngine)
+  
+  if(nrow(sensor_data) == 0)
+    warning(paste("The device '", device,"' did not have data in ", sensor_container))
+
+  return(sensor_data)
+}
+

src/data/streams/aware_micro_mysql/format.yaml

@@ -0,0 +1,337 @@
+PHONE_ACCELEROMETER:
+  ANDROID:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      DOUBLE_VALUES_0: double_values_0
+      DOUBLE_VALUES_1: double_values_1
+      DOUBLE_VALUES_2: double_values_2
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+  IOS:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      DOUBLE_VALUES_0: double_values_0
+      DOUBLE_VALUES_1: double_values_1
+      DOUBLE_VALUES_2: double_values_2
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+
+PHONE_ACTIVITY_RECOGNITION:
+  ANDROID:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      ACTIVITY_NAME: activity_name
+      ACTIVITY_TYPE: activity_type
+      CONFIDENCE: confidence
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+  IOS:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      ACTIVITY_NAME: FLAG_TO_MUTATE
+      ACTIVITY_TYPE: FLAG_TO_MUTATE
+      CONFIDENCE: FLAG_TO_MUTATE
+    MUTATION:
+      COLUMN_MAPPINGS:
+        ACTIVITIES: activities
+        CONFIDENCE: confidence
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+        - "src/data/streams/mutations/phone/aware/activity_recogniton_ios_unification.R"
+
+PHONE_APPLICATIONS_CRASHES:
+  ANDROID:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      PACKAGE_NAME: package_name
+      APPLICATION_NAME: application_name
+      APPLICATION_VERSION: application_version
+      ERROR_SHORT: error_short
+      ERROR_LONG: error_long
+      ERROR_CONDITION: error_condition
+      IS_SYSTEM_APP: is_system_app
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+
+PHONE_APPLICATIONS_FOREGROUND:
+  ANDROID:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      PACKAGE_NAME: package_name
+      APPLICATION_NAME: application_name
+      IS_SYSTEM_APP: is_system_app
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+
+PHONE_APPLICATIONS_NOTIFICATIONS:
+  ANDROID:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      PACKAGE_NAME: package_name
+      APPLICATION_NAME: application_name
+      TEXT: text
+      SOUND: sound
+      VIBRATE: vibrate
+      DEFAULTS: defaults
+      FLAGS: flags
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+
+PHONE_BATTERY:
+  ANDROID:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      BATTERY_STATUS: battery_status
+      BATTERY_LEVEL: battery_level
+      BATTERY_SCALE: battery_scale
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+  IOS:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      BATTERY_STATUS: FLAG_TO_MUTATE
+      BATTERY_LEVEL: battery_level
+      BATTERY_SCALE: battery_scale
+    MUTATION:
+      COLUMN_MAPPINGS:
+        BATTERY_STATUS: battery_status
+      SCRIPTS:
+        - "src/data/streams/mutations/phone/aware/battery_ios_unification.R"
+
+PHONE_BLUETOOTH:
+  ANDROID:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      BT_ADDRESS: bt_address
+      BT_NAME: bt_name
+      BT_RSSI: bt_rssi
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+  IOS:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      BT_ADDRESS: bt_address
+      BT_NAME: bt_name
+      BT_RSSI: bt_rssi
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+
+PHONE_CALLS:
+  ANDROID:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      CALL_TYPE: call_type
+      CALL_DURATION: call_duration
+      TRACE: trace
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+  IOS:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      CALL_TYPE: FLAG_TO_MUTATE
+      CALL_DURATION: call_duration
+      TRACE: trace
+    MUTATION:
+      COLUMN_MAPPINGS:
+        CALL_TYPE: call_type
+      SCRIPTS:
+        - "src/data/streams/mutations/phone/aware/calls_ios_unification.R"
+
+PHONE_CONVERSATION:
+  ANDROID:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      DOUBLE_ENERGY: double_energy
+      INFERENCE: inference
+      DOUBLE_CONVO_START: double_convo_start
+      DOUBLE_CONVO_END: double_convo_end
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+  IOS:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      DOUBLE_ENERGY: double_energy
+      INFERENCE: inference
+      DOUBLE_CONVO_START: double_convo_start
+      DOUBLE_CONVO_END: double_convo_end
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+        - "src/data/streams/mutations/phone/aware/conversation_ios_timestamp.R"
+
+PHONE_KEYBOARD:
+  ANDROID:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      PACKAGE_NAME: package_name
+      BEFORE_TEXT: before_text
+      CURRENT_TEXT: current_text
+      IS_PASSWORD: is_password
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+
+PHONE_LIGHT:
+  ANDROID:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      DOUBLE_LIGHT_LUX: double_light_lux
+      ACCURACY: accuracy
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+
+PHONE_LOCATIONS:
+  ANDROID:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      DOUBLE_LATITUDE: double_latitude
+      DOUBLE_LONGITUDE: double_longitude
+      DOUBLE_BEARING: double_bearing
+      DOUBLE_SPEED: double_speed
+      DOUBLE_ALTITUDE: double_altitude
+      PROVIDER: provider
+      ACCURACY: accuracy
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+  IOS:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      DOUBLE_LATITUDE: double_latitude
+      DOUBLE_LONGITUDE: double_longitude
+      DOUBLE_BEARING: double_bearing
+      DOUBLE_SPEED: double_speed
+      DOUBLE_ALTITUDE: double_altitude
+      PROVIDER: provider
+      ACCURACY: accuracy
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+
+PHONE_LOG:
+  ANDROID:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      LOG_MESSAGE: log_message
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+  IOS:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      LOG_MESSAGE: log_message
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+
+PHONE_MESSAGES:
+  ANDROID:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      MESSAGE_TYPE: message_type
+      TRACE: trace
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+
+PHONE_SCREEN:
+  ANDROID:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      SCREEN_STATUS: screen_status
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+  IOS:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      SCREEN_STATUS: FLAG_TO_MUTATE
+    MUTATION:
+      COLUMN_MAPPINGS:
+        SCREEN_STATUS: screen_status
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+        - "src/data/streams/mutations/phone/aware/screen_ios_unification.R"
+
+PHONE_WIFI_CONNECTED:
+  ANDROID:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      MAC_ADDRESS: mac_address
+      SSID: ssid
+      BSSID: bssid
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+  IOS:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      MAC_ADDRESS: mac_address
+      SSID: ssid
+      BSSID: bssid
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+
+PHONE_WIFI_VISIBLE:
+  ANDROID:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      SSID: ssid
+      BSSID: bssid
+      SECURITY: security
+      FREQUENCY: frequency
+      RSSI: rssi
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+  IOS:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      SSID: ssid
+      BSSID: bssid
+      SECURITY: security
+      FREQUENCY: frequency
+      RSSI: rssi
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+

src/data/streams/aware_postgresql/format.yaml

@@ -349,3 +349,24 @@ PHONE_WIFI_VISIBLE:
       COLUMN_MAPPINGS:
       SCRIPTS: # List any python or r scripts that mutate your raw data
 
+PHONE_SPEECH:
+  ANDROID:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      SPEECH_PROPORTION: speech_proportion
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+  IOS:
+    RAPIDS_COLUMN_MAPPINGS:
+      TIMESTAMP: timestamp
+      DEVICE_ID: device_id
+      SPEECH_PROPORTION: speech_proportion
+    MUTATION:
+      COLUMN_MAPPINGS:
+      SCRIPTS: # List any python or r scripts that mutate your raw data
+
+
+      
+

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)
@@ -60,15 +67,22 @@ 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['timestamp'] = (df['timestamp'][1:len(df)]).astype(float) + timestampstart        
         df = df.drop([0])
         df[column] = df[column].astype(float)
         df = df.set_index('timestamp')
-
+        
     else:
         raise ValueError(
             "sensor has an invalid name: {}".format(sensor))
@@ -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 +129,54 @@ 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, KeyError) as e:
+        # Checks whether IBI.csv is empty
+        # It may raise a KeyError if df is empty here: startTimeStamp = df.time[0]
+        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/mutations/phone/aware/calls_ios_unification.R

@@ -39,7 +39,7 @@ unify_ios_calls <- function(ios_calls){
                         assigned_segments = first(assigned_segments))
         }
         else {
-            ios_calls <- ios_calls %>% summarise(call_type_sequence = paste(call_type, collapse = ","), call_duration = sum(call_duration),  timestamp = first(timestamp), device_id = first(device_id))
+            ios_calls <- ios_calls %>% summarise(call_type_sequence = paste(call_type, collapse = ","), call_duration = sum(as.numeric(call_duration)),  timestamp = first(timestamp), device_id = first(device_id))
         }
         ios_calls <- ios_calls %>% mutate(call_type = case_when(
             call_type_sequence == "1,2,4" | call_type_sequence == "2,1,4" ~ 1, # incoming

src/data/streams/rapids_columns.yaml

@@ -118,6 +118,11 @@ PHONE_SCREEN:
   - DEVICE_ID
   - SCREEN_STATUS
 
+PHONE_SPEECH:
+  - TIMESTAMP
+  - DEVICE_ID
+  - SPEECH_PROPORTION
+
 PHONE_WIFI_CONNECTED:
   - TIMESTAMP
   - DEVICE_ID
@@ -227,6 +232,7 @@ EMPATICA_INTER_BEAT_INTERVAL:
   - TIMESTAMP
   - DEVICE_ID
   - INTER_BEAT_INTERVAL
+  - TIMINGS
 
 EMPATICA_TAGS:
   - TIMESTAMP

src/features/all_cleaning_individual/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_individual/straw/main.py

@@ -0,0 +1,180 @@
+import pandas as pd
+import numpy as np
+import math, sys, random
+import yaml
+
+from sklearn.impute import KNNImputer
+from sklearn.preprocessing import StandardScaler
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+sys.path.append('/rapids/')
+from src.features import empatica_data_yield as edy
+
+pd.set_option('display.max_columns', 20)
+
+def straw_cleaning(sensor_data_files, provider):
+    
+    features = pd.read_csv(sensor_data_files["sensor_data"][0])
+    
+    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
+
+    with open('config.yaml', 'r') as stream:
+        config = yaml.load(stream, Loader=yaml.FullLoader)
+
+    excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
+
+    # (1) FILTER_OUT THE ROWS THAT DO NOT HAVE THE TARGET COLUMN AVAILABLE
+    if config['PARAMS_FOR_ANALYSIS']['TARGET']['COMPUTE']:
+        target = config['PARAMS_FOR_ANALYSIS']['TARGET']['LABEL'] # get target label from config
+        if 'phone_esm_straw_' + target in features:
+            features = features[features['phone_esm_straw_' + target].notna()].reset_index(drop=True)
+        else:
+            return features
+
+    # (2.1) QUALITY CHECK (DATA YIELD COLUMN) deletes the rows where E4 or phone data is low quality
+    phone_data_yield_unit = provider["PHONE_DATA_YIELD_FEATURE"].split("_")[3].lower()
+    phone_data_yield_column = "phone_data_yield_rapids_ratiovalidyielded" + phone_data_yield_unit
+
+    if features.empty:
+        return features
+
+    features = edy.calculate_empatica_data_yield(features)
+
+    if not phone_data_yield_column in features.columns and not "empatica_data_yield" in features.columns:
+        raise KeyError(f"RAPIDS provider needs to clean the selected event features based on {phone_data_yield_column} and empatica_data_yield columns. For phone data yield, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded{data_yield_unit}' in [FEATURES].")
+        
+    # Drop rows where phone data yield is less then given threshold
+    if provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]:
+        features = features[features[phone_data_yield_column] >= provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
+    
+    # Drop rows where empatica data yield is less then given threshold
+    if provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]:
+        features = features[features["empatica_data_yield"] >= provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
+
+    if features.empty:
+        return features
+    
+    # (2.2) DO THE ROWS CONSIST OF ENOUGH NON-NAN VALUES?
+    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) # Thresh => at least this many not-nans
+
+    # (3) REMOVE COLS IF THEIR NAN THRESHOLD IS PASSED (should be <= if even all NaN columns must be preserved - this solution now drops columns with all NaN rows)
+    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
+
+    features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]
+
+    # 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]
+
+    # (4) CONTEXTUAL IMPUTATION
+
+    # Impute selected phone features with a high number
+    impute_w_hn = [col for col in features.columns if \
+        "timeoffirstuse" in col or
+        "timeoflastuse" in col or
+        "timefirstcall" in col or
+        "timelastcall" in col or
+        "firstuseafter" in col or
+        "timefirstmessages" in col or
+        "timelastmessages" in col]
+    features[impute_w_hn] = features[impute_w_hn].fillna(1500)
+
+
+    # Impute special case (mostcommonactivity) and (homelabel)
+    impute_w_sn = [col for col in features.columns if "mostcommonactivity" in col]
+    features[impute_w_sn] = features[impute_w_sn].fillna(4) # Special case of imputation - nominal/ordinal value
+
+    impute_w_sn2 = [col for col in features.columns if "homelabel" in col]
+    features[impute_w_sn2] = features[impute_w_sn2].fillna(1) # Special case of imputation - nominal/ordinal value
+
+    impute_w_sn3 = [col for col in features.columns if "loglocationvariance" in col]
+    features[impute_w_sn2] = features[impute_w_sn2].fillna(-1000000) # Special case of imputation - nominal/ordinal value
+
+
+    # Impute selected phone features with 0
+    impute_zero = [col for col in features if \
+        col.startswith('phone_applications_foreground_rapids_') or
+        col.startswith('phone_battery_rapids_') or
+        col.startswith('phone_bluetooth_rapids_') or
+        col.startswith('phone_light_rapids_') or
+        col.startswith('phone_calls_rapids_') or
+        col.startswith('phone_messages_rapids_') or
+        col.startswith('phone_screen_rapids_') or
+        col.startswith('phone_wifi_visible')]
+
+    features[impute_zero+list(esm_cols.columns)] = features[impute_zero+list(esm_cols.columns)].fillna(0)
+
+    ## (5) STANDARDIZATION 
+    if provider["STANDARDIZATION"]:
+        features.loc[:, ~features.columns.isin(excluded_columns)] = StandardScaler().fit_transform(features.loc[:, ~features.columns.isin(excluded_columns)])
+
+    # (6) IMPUTATION: IMPUTE DATA WITH KNN METHOD
+    impute_cols = [col for col in features.columns if col not in excluded_columns]
+    features.reset_index(drop=True, inplace=True)
+    features[impute_cols] = impute(features[impute_cols], method="knn")
+
+    # (7) REMOVE COLS WHERE VARIANCE IS 0
+    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')]
+
+    if provider["COLS_VAR_THRESHOLD"]:
+        features.drop(features.std(numeric_only=True)[features.std(numeric_only=True) == 0].index.values, axis=1, inplace=True)
+
+    fe5 = features.copy()
+
+    # (8) DROP HIGHLY CORRELATED FEATURES
+    drop_corr_features = provider["DROP_HIGHLY_CORRELATED_FEATURES"]
+    if drop_corr_features["COMPUTE"] and features.shape[0]: # If small amount of segments (rows) is present, do not execute correlation check
+        
+        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]]
+
+        corr_matrix = valid_features.corr().abs()
+        upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))
+        to_drop = [column for column in upper.columns if any(upper[column] > drop_corr_features["CORR_THRESHOLD"])]
+
+        features.drop(to_drop, 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]
+
+    # (9) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
+    if features.isna().any().any():
+        raise ValueError("There are still some NaNs present in the dataframe. Please check for implementation errors.")
+
+    return features
+
+
+def k_nearest(df):
+    pd.set_option('display.max_columns', None)
+    imputer = KNNImputer(n_neighbors=3)
+    return pd.DataFrame(imputer.fit_transform(df), columns=df.columns)
+
+
+def impute(df, method='zero'):
+
+    return {
+        'zero': df.fillna(0),
+        'high_number': df.fillna(1500),
+        'mean': df.fillna(df.mean()),
+        'median': df.fillna(df.median()),
+        'knn': k_nearest(df) 
+    }[method]
+
+
+def graph_bf_af(features, phase_name, plt_flag=False):
+    if plt_flag:
+        sns.set(rc={"figure.figsize":(16, 8)})
+        sns.heatmap(features.isna(), cbar=False) #features.select_dtypes(include=np.number)
+        plt.savefig(f'features_overall_nans_{phase_name}.png', bbox_inches='tight')
+
+    print(f"\n-------------{phase_name}-------------")
+    print("Rows number:", features.shape[0])
+    print("Columns number:", len(features.columns))
+    print("---------------------------------------------\n")

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 +1,275 @@
+import pandas as pd
+import numpy as np
+import math, sys, random, warnings, yaml
+
+from sklearn.impute import KNNImputer
+from sklearn.preprocessing import StandardScaler, minmax_scale 
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+sys.path.append('/rapids/')
+from src.features import empatica_data_yield as edy
+
+def straw_cleaning(sensor_data_files, provider, target):
+
+    features = pd.read_csv(sensor_data_files["sensor_data"][0])
+
+    with open('config.yaml', 'r') as stream:
+        config = yaml.load(stream, Loader=yaml.FullLoader)
+
+    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
+
+    excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
+
+    graph_bf_af(features, "1target_rows_before")
+
+    # (1.0) OVERRIDE STRESSFULNESS EVENT TARGETS IF ERS SEGMENTING_METHOD IS "STRESS_EVENT"
+    if config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["SEGMENTING_METHOD"] == "stress_event": 
+    
+        stress_events_targets = pd.read_csv("data/external/stress_event_targets.csv")   
+
+        if "appraisal_stressfulness_event_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
+            features.drop(columns=['phone_esm_straw_appraisal_stressfulness_event_mean'], inplace=True)
+            features = features.merge(stress_events_targets[["label", "appraisal_stressfulness_event"]] \
+                        .rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
+                        .rename(columns={'appraisal_stressfulness_event': 'phone_esm_straw_appraisal_stressfulness_event_mean'})
+
+        if "appraisal_threat_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
+            features.drop(columns=['phone_esm_straw_appraisal_threat_mean'], inplace=True)
+            features = features.merge(stress_events_targets[["label", "appraisal_threat"]] \
+                        .rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
+                        .rename(columns={'appraisal_threat': 'phone_esm_straw_appraisal_threat_mean'})
+
+        if "appraisal_challenge_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
+            features.drop(columns=['phone_esm_straw_appraisal_challenge_mean'], inplace=True)
+            features = features.merge(stress_events_targets[["label", "appraisal_challenge"]] \
+                        .rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
+                        .rename(columns={'appraisal_challenge': 'phone_esm_straw_appraisal_challenge_mean'})
+
+    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
+
+    # (1.1) FILTER_OUT THE ROWS THAT DO NOT HAVE THE TARGET COLUMN AVAILABLE
+    if config['PARAMS_FOR_ANALYSIS']['TARGET']['COMPUTE']:
+        features = features[features['phone_esm_straw_' + target].notna()].reset_index(drop=True)
+    
+    if features.empty:
+        return pd.DataFrame(columns=excluded_columns)
+
+    graph_bf_af(features, "2target_rows_after")
+
+    # (2) QUALITY CHECK (DATA YIELD COLUMN) drops the rows where E4 or phone data is low quality
+    phone_data_yield_unit = provider["PHONE_DATA_YIELD_FEATURE"].split("_")[3].lower()
+    phone_data_yield_column = "phone_data_yield_rapids_ratiovalidyielded" + phone_data_yield_unit
+
+    features = edy.calculate_empatica_data_yield(features)
+
+    if not phone_data_yield_column in features.columns and not "empatica_data_yield" in features.columns:
+        raise KeyError(f"RAPIDS provider needs to clean the selected event features based on {phone_data_yield_column} and empatica_data_yield columns. For phone data yield, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded{data_yield_unit}' in [FEATURES].")
+
+    hist = features[["empatica_data_yield", phone_data_yield_column]].hist()
+    plt.savefig(f'phone_E4_histogram.png', bbox_inches='tight')
+
+    # Drop rows where phone data yield is less then given threshold
+    if provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]:
+        hist = features[phone_data_yield_column].hist(bins=5)
+        plt.close()
+        features = features[features[phone_data_yield_column] >= provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
+
+    # Drop rows where empatica data yield is less then given threshold
+    if provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]:
+        features = features[features["empatica_data_yield"] >= provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
+
+    if features.empty:
+        return pd.DataFrame(columns=excluded_columns)
+
+    graph_bf_af(features, "3data_yield_drop_rows")
+
+    if features.empty:
+        return pd.DataFrame(columns=excluded_columns)
+
+
+    # (3) CONTEXTUAL IMPUTATION
+
+    # Impute selected phone features with a high number
+    impute_w_hn = [col for col in features.columns if \
+        "timeoffirstuse" in col or
+        "timeoflastuse" in col or
+        "timefirstcall" in col or
+        "timelastcall" in col or
+        "firstuseafter" in col or
+        "timefirstmessages" in col or
+        "timelastmessages" in col]
+    features[impute_w_hn] = features[impute_w_hn].fillna(1500)
+
+    # Impute special case (mostcommonactivity) and (homelabel)
+    impute_w_sn = [col for col in features.columns if "mostcommonactivity" in col]
+    features[impute_w_sn] = features[impute_w_sn].fillna(4) # Special case of imputation - nominal/ordinal value
+
+    impute_w_sn2 = [col for col in features.columns if "homelabel" in col]
+    features[impute_w_sn2] = features[impute_w_sn2].fillna(1) # Special case of imputation - nominal/ordinal value
+
+    impute_w_sn3 = [col for col in features.columns if "loglocationvariance" in col]
+    features[impute_w_sn3] = features[impute_w_sn3].fillna(-1000000) # Special case of imputation - loglocation
+
+    # Impute location features
+    impute_locations = [col for col in features \
+        if col.startswith('phone_locations_doryab_') and
+        'radiusgyration' not in col    
+    ]
+
+    # Impute selected phone, location, and esm features with 0
+    impute_zero = [col for col in features if \
+        col.startswith('phone_applications_foreground_rapids_') or
+        col.startswith('phone_activity_recognition_') or
+        col.startswith('phone_battery_rapids_') or
+        col.startswith('phone_bluetooth_rapids_') or
+        col.startswith('phone_light_rapids_') or
+        col.startswith('phone_calls_rapids_') or
+        col.startswith('phone_messages_rapids_') or
+        col.startswith('phone_screen_rapids_') or
+        col.startswith('phone_bluetooth_doryab_') or
+        col.startswith('phone_wifi_visible')
+        ]
+
+    features[impute_zero+impute_locations+list(esm_cols.columns)] = features[impute_zero+impute_locations+list(esm_cols.columns)].fillna(0)
+
+    pd.set_option('display.max_rows', None)
+
+    graph_bf_af(features, "4context_imp")
+ 
+    # (4) REMOVE COLS IF THEIR NAN THRESHOLD IS PASSED (should be <= if even all NaN columns must be preserved - this solution now drops columns with all NaN rows)
+    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
+
+    features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]
+
+    graph_bf_af(features, "5too_much_nans_cols")
+    # (5) REMOVE COLS WHERE VARIANCE IS 0
+
+    if provider["COLS_VAR_THRESHOLD"]:
+        features.drop(features.std(numeric_only=True)[features.std(numeric_only=True) == 0].index.values, axis=1, inplace=True)
+
+    graph_bf_af(features, "6variance_drop")
+
+    # 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]
+    
+    # (6) DO THE ROWS CONSIST OF ENOUGH NON-NAN VALUES?
+    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) # Thresh => at least this many not-nans
+
+    graph_bf_af(features, "7too_much_nans_rows")
+
+    if features.empty:
+        return pd.DataFrame(columns=excluded_columns)
+
+    # (7) STANDARDIZATION
+    if provider["STANDARDIZATION"]:
+        nominal_cols = [col for col in features.columns if "mostcommonactivity" in col or "homelabel" in col] # Excluded nominal features
+        # Expected warning within this code block
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=RuntimeWarning)
+            if provider["TARGET_STANDARDIZATION"]:
+                features.loc[:, ~features.columns.isin(excluded_columns + ["pid"] + nominal_cols)] = \
+                    features.loc[:, ~features.columns.isin(excluded_columns + nominal_cols)].groupby('pid').transform(lambda x: StandardScaler().fit_transform(x.values[:,np.newaxis]).ravel())
+            else:
+                features.loc[:, ~features.columns.isin(excluded_columns + ["pid"] + nominal_cols + ['phone_esm_straw_' + target])] = \
+                    features.loc[:, ~features.columns.isin(excluded_columns + nominal_cols + ['phone_esm_straw_' + target])].groupby('pid').transform(lambda x: StandardScaler().fit_transform(x.values[:,np.newaxis]).ravel())
+
+    graph_bf_af(features, "8standardization")
+
+    # (8) IMPUTATION: IMPUTE DATA WITH KNN METHOD
+    features.reset_index(drop=True, inplace=True)
+    impute_cols = [col for col in features.columns if col not in excluded_columns and col != "pid"]
+
+    features[impute_cols] = impute(features[impute_cols], method="knn")
+
+    graph_bf_af(features, "9knn_after")
+
+
+    # (9) DROP HIGHLY CORRELATED FEATURES
+    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')]
+
+    drop_corr_features = provider["DROP_HIGHLY_CORRELATED_FEATURES"]
+    if drop_corr_features["COMPUTE"] and features.shape[0] > 5: # If small amount of segments (rows) is present, do not execute correlation check
+        
+        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]]
+
+        corr_matrix = valid_features.corr().abs()
+        upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))
+        to_drop = [column for column in upper.columns if any(upper[column] > drop_corr_features["CORR_THRESHOLD"])]
+
+        # sns.heatmap(corr_matrix, cmap="YlGnBu")
+        # plt.savefig(f'correlation_matrix.png', bbox_inches='tight')
+        # plt.close()
+
+        # s = corr_matrix.unstack()
+        # so = s.sort_values(ascending=False)
+
+        # pd.set_option('display.max_rows', None)
+        # sorted_upper = upper.unstack().sort_values(ascending=False)
+        # print(sorted_upper[sorted_upper > drop_corr_features["CORR_THRESHOLD"]])
+
+        features.drop(to_drop, 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]
+
+    graph_bf_af(features, "10correlation_drop")
+
+    # Transform categorical columns to category dtype
+
+    cat1 = [col for col in features.columns if "mostcommonactivity" in col]
+    if cat1: # Transform columns to category dtype (mostcommonactivity)
+        features[cat1] = features[cat1].astype(int).astype('category')
+
+    cat2 = [col for col in features.columns if "homelabel" in col]
+    if cat2: # Transform columns to category dtype (homelabel)
+        features[cat2] = features[cat2].astype(int).astype('category')
+
+    # (10) DROP ALL WINDOW RELATED COLUMNS
+    win_count_cols = [col for col in features if "SO_windowsCount" in col]
+    if win_count_cols:
+        features.drop(columns=win_count_cols, inplace=True)
+
+    # (11) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
+    if features.isna().any().any():
+        raise ValueError("There are still some NaNs present in the dataframe. Please check for implementation errors.")
+
+
+    return features
+
+
+def k_nearest(df):
+    imputer = KNNImputer(n_neighbors=3)
+    return pd.DataFrame(imputer.fit_transform(df), columns=df.columns)
+
+
+def impute(df, method='zero'):
+
+    return {
+        'zero': df.fillna(0),
+        'high_number': df.fillna(1500),
+        'mean': df.fillna(df.mean()),
+        'median': df.fillna(df.median()),
+        'knn': k_nearest(df) 
+    }[method]
+
+
+def graph_bf_af(features, phase_name, plt_flag=False):
+    if plt_flag:
+        sns.set(rc={"figure.figsize":(16, 8)})
+        sns.heatmap(features.isna(), cbar=False) #features.select_dtypes(include=np.number)
+        plt.savefig(f'features_overall_nans_{phase_name}.png', bbox_inches='tight')
+
+    print(f"\n-------------{phase_name}-------------")
+    print("Rows number:", features.shape[0])
+    print("Columns number:", len(features.columns))
+    print("NaN values:", features.isna().sum().sum())
+    print("---------------------------------------------\n")

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(numeric_only=True).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(numeric_only=True).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(numeric_only=True).fillna(0).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 +1,75 @@
+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):
+    
+    data_types = {'local_timezone': 'str', 'device_id': 'str', 'timestamp': 'int64', 'double_values_0': 'float64',
+                    'double_values_1': 'float64', 'double_values_2': 'float64', 'local_date_time': 'str', 'local_date': "str",
+                    'local_time': "str", 'local_hour': "str", 'local_minute': "str", 'assigned_segments': "str"}
+    acc_intraday_data = pd.read_csv(sensor_data_files["sensor_data"], dtype=data_types)    
+
+    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_data_yield.py

@@ -0,0 +1,32 @@
+import pandas as pd
+import numpy as np
+from datetime import datetime
+
+import sys, yaml
+
+def calculate_empatica_data_yield(features): # TODO
+
+    # Get time segment duration in seconds from all segments in features dataframe
+    datetime_start =  pd.to_datetime(features['local_segment_start_datetime'], format='%Y-%m-%d %H:%M:%S')
+    datetime_end = pd.to_datetime(features['local_segment_end_datetime'], format='%Y-%m-%d %H:%M:%S')
+    tseg_duration = (datetime_end - datetime_start).dt.total_seconds()
+
+    with open('config.yaml', 'r') as stream:
+        config = yaml.load(stream, Loader=yaml.FullLoader)
+        
+    sensors = ["EMPATICA_ACCELEROMETER", "EMPATICA_TEMPERATURE", "EMPATICA_ELECTRODERMAL_ACTIVITY", "EMPATICA_INTER_BEAT_INTERVAL"]
+    for sensor in sensors:
+        features[f"{sensor.lower()}_data_yield"] = \
+            (features[f"{sensor.lower()}_cr_SO_windowsCount"] * config[sensor]["PROVIDERS"]["CR"]["WINDOWS"]["WINDOW_LENGTH"]) / tseg_duration \
+            if f'{sensor.lower()}_cr_SO_windowsCount' in features else 0
+
+    empatica_data_yield_cols = [sensor.lower() + "_data_yield" for sensor in sensors]
+    pd.set_option('display.max_rows', None)
+
+    # Assigns 1 to values that are over 1 (in case of windows not being filled fully)
+    features[empatica_data_yield_cols] = features[empatica_data_yield_cols].apply(lambda x: [y if y <= 1 or np.isnan(y) else 1 for y in x])
+    
+    features["empatica_data_yield"] = features[empatica_data_yield_cols].mean(axis=1, numeric_only=True).fillna(0)
+    features.drop(empatica_data_yield_cols, axis=1, inplace=True) # In case of if the advanced operations will later not be needed (e.g., weighted average)
+
+    return features

src/features/empatica_electrodermal_activity/cr/main.py

@@ -0,0 +1,82 @@
+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):
+
+    data_types = {'local_timezone': 'str', 'device_id': 'str', 'timestamp': 'int64', 'electrodermal_activity': 'float64', 'local_date_time': 'str', 
+                  'local_date': "str", 'local_time': "str", 'local_hour': "str", 'local_minute': "str", 'assigned_segments': "str"}
+
+    eda_intraday_data = pd.read_csv(sensor_data_files["sensor_data"], dtype=data_types)
+
+    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 +1,83 @@
+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):
+
+    data_types = {'local_timezone': 'str', 'device_id': 'str', 'timestamp': 'int64', 'inter_beat_interval': 'float64', 'timings': 'float64', 'local_date_time': 'str', 
+                  'local_date': "str", 'local_time': "str", 'local_hour': "str", 'local_minute': "str", 'assigned_segments': "str"}
+
+    ibi_intraday_data = pd.read_csv(sensor_data_files["sensor_data"], dtype=data_types)
+
+    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 +1,68 @@
+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):
+    data_types = {'local_timezone': 'str', 'device_id': 'str', 'timestamp': 'int64', 'temperature': 'float64', 'local_date_time': 'str', 
+                'local_date': "str", 'local_time': "str", 'local_hour': "str", 'local_minute': "str", 'assigned_segments': "str"}
+
+    temperature_intraday_data = pd.read_csv(sensor_data_files["sensor_data"], dtype=data_types)
+
+    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

@@ -1,19 +1,38 @@
 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)
+    if "overall" in sensor_key:
+        sensor_features = run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data_files, snakemake.params["target"])
+    else:
+        sensor_features = run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data_files)
 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_activity_recognition/rapids/main.py

@@ -37,6 +37,6 @@ def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_se
             ar_features.index.names = ["local_segment"]
             ar_features = ar_features.reset_index()
     
-    ar_features.fillna(value={"count": 0, "countuniqueactivities": 0, "durationstationary": 0, "durationmobile": 0, "durationvehicle": 0}, inplace=True)
+    ar_features.fillna(value={"count": 0, "countuniqueactivities": 0, "durationstationary": 0, "durationmobile": 0, "durationvehicle": 0, "mostcommonactivity": 4}, inplace=True)
 
     return ar_features

src/features/phone_applications_foreground/rapids/main.py

@@ -9,19 +9,19 @@ def compute_features(filtered_data, apps_type, requested_features, apps_features
     if "timeoffirstuse" in requested_features:
         time_first_event = filtered_data.sort_values(by="timestamp", ascending=True).drop_duplicates(subset="local_segment", keep="first").set_index("local_segment")
         if time_first_event.empty:
-            apps_features["timeoffirstuse" + apps_type] = np.nan
+            apps_features["timeoffirstuse" + apps_type] = 1500 # np.nan
         else:
             apps_features["timeoffirstuse" + apps_type] = time_first_event["local_hour"] * 60 + time_first_event["local_minute"]
     if "timeoflastuse" in requested_features:
         time_last_event = filtered_data.sort_values(by="timestamp", ascending=False).drop_duplicates(subset="local_segment", keep="first").set_index("local_segment")
         if time_last_event.empty:
-            apps_features["timeoflastuse" + apps_type] = np.nan
+            apps_features["timeoflastuse" + apps_type] = 1500 # np.nan
         else:
             apps_features["timeoflastuse" + apps_type] = time_last_event["local_hour"] * 60 + time_last_event["local_minute"]
     if "frequencyentropy" in requested_features:
         apps_with_count = filtered_data.groupby(["local_segment","application_name"]).count().sort_values(by="timestamp", ascending=False).reset_index()
         if (len(apps_with_count.index) < 2 ):
-            apps_features["frequencyentropy" + apps_type] = np.nan
+            apps_features["frequencyentropy" + apps_type] = 0 # np.nan
         else:    
             apps_features["frequencyentropy" + apps_type] = apps_with_count.groupby("local_segment")["timestamp"].agg(entropy)
     if "countevent" in requested_features:
@@ -43,6 +43,7 @@ def compute_features(filtered_data, apps_type, requested_features, apps_features
         apps_features["sumduration" + apps_type] = filtered_data.groupby(by = ["local_segment"])["duration"].sum()
     
     apps_features.index.names = ["local_segment"]
+    
     return apps_features
 
 def process_app_features(data, requested_features, time_segment, provider, filter_data_by_segment):

src/features/phone_bluetooth/doryab/main.py

@@ -14,8 +14,8 @@ def deviceFeatures(devices, ownership, common_devices, features_to_compute, feat
         features = features.join(device_value_counts.groupby("local_segment")["bt_address"].nunique().to_frame("uniquedevices" + ownership), how="outer")
     if "meanscans" in features_to_compute:
         features = features.join(device_value_counts.groupby("local_segment")["scans"].mean().to_frame("meanscans" + ownership), how="outer")
-    if "stdscans" in features_to_compute:
-        features = features.join(device_value_counts.groupby("local_segment")["scans"].std().to_frame("stdscans" + ownership), how="outer")
+    if "stdscans" in features_to_compute: 
+        features = features.join(device_value_counts.groupby("local_segment")["scans"].std().to_frame("stdscans" + ownership).fillna(0), how="outer")
     # Most frequent device within segments, across segments, and across dataset
     if "countscansmostfrequentdevicewithinsegments" in features_to_compute:
         features = features.join(device_value_counts.groupby("local_segment")["scans"].max().to_frame("countscansmostfrequentdevicewithinsegments" + ownership), how="outer")

src/features/phone_calls/rapids/main.R

@@ -88,6 +88,16 @@ rapids_features <- function(sensor_data_files, time_segment, provider){
         features <- call_features_of_type(calls_of_type, features_type, call_type, time_segment, requested_features)
         call_features <- merge(call_features, features, all=TRUE)
     }
-    call_features <- call_features %>% mutate_at(vars(contains("countmostfrequentcontact") | contains("distinctcontacts") | contains("count") | contains("sumduration") | contains("minduration") | contains("maxduration") | contains("meanduration") | contains("modeduration")), list( ~ replace_na(., 0)))
+
+    # Fill seleted columns with a high number
+    time_cols <- select(call_features, contains("timefirstcall") |  contains("timelastcall")) %>% 
+        colnames(.)
+
+    call_features <- call_features %>% 
+        mutate_at(., time_cols, ~replace(., is.na(.), 1500))
+
+    # Fill NA values with 0
+    call_features <- call_features %>% mutate_all(~replace(., is.na(.), 0))
+
     return(call_features)
 }

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/phone_esm/straw/esm.py

@@ -0,0 +1,274 @@
+from collections.abc import Collection
+
+import numpy as np
+import pandas as pd
+from pytz import timezone
+import datetime, json
+
+# from config.models import ESM, Participant
+# from features import helper
+
+ESM_STATUS_ANSWERED = 2
+
+GROUP_SESSIONS_BY = ["device_id", "esm_session"] # 'participant_id
+
+SESSION_STATUS_UNANSWERED = "ema_unanswered"
+SESSION_STATUS_DAY_FINISHED = "day_finished"
+SESSION_STATUS_COMPLETE = "ema_completed"
+
+ANSWER_DAY_FINISHED = "DayFinished3421"
+ANSWER_DAY_OFF = "DayOff3421"
+ANSWER_SET_EVENING = "DayFinishedSetEvening"
+
+MAX_MORNING_LENGTH = 3
+# When the participants was not yet at work at the time of the first (morning) EMA,
+# only three items were answered.
+# Two sleep related items and one indicating NOT starting work yet.
+# Daytime EMAs are all longer, in fact they always consist of at least 6 items.
+
+
+TZ_LJ = timezone("Europe/Ljubljana")
+COLUMN_TIMESTAMP = "timestamp"
+COLUMN_TIMESTAMP_ESM = "double_esm_user_answer_timestamp"
+
+
+def get_date_from_timestamp(df_aware) -> pd.DataFrame:
+    """
+    Transform a UNIX timestamp into a datetime (with Ljubljana timezone).
+    Additionally, extract only the date part, where anything until 4 AM is considered the same day.
+
+    Parameters
+    ----------
+    df_aware: pd.DataFrame
+        Any AWARE-type data as defined in models.py.
+
+    Returns
+    -------
+    df_aware: pd.DataFrame
+        The same dataframe with datetime_lj and date_lj columns added.
+
+    """
+    if COLUMN_TIMESTAMP_ESM in df_aware:
+        column_timestamp = COLUMN_TIMESTAMP_ESM
+    else:
+        column_timestamp = COLUMN_TIMESTAMP
+
+    df_aware["datetime_lj"] = df_aware[column_timestamp].apply(
+        lambda x: datetime.datetime.fromtimestamp(x / 1000.0, tz=TZ_LJ)
+    )
+    df_aware = df_aware.assign(
+        date_lj=lambda x: (x.datetime_lj - datetime.timedelta(hours=4)).dt.date
+    )
+    # Since daytime EMAs could *theoretically* last beyond midnight, but never after 4 AM,
+    # the datetime is first translated to 4 h earlier.
+
+    return df_aware
+
+
+def preprocess_esm(df_esm: pd.DataFrame) -> pd.DataFrame:
+    """
+    Convert timestamps into human-readable datetimes and dates
+    and expand the JSON column into several Pandas DF columns.
+
+    Parameters
+    ----------
+    df_esm: pd.DataFrame
+        A dataframe of esm data.
+
+    Returns
+    -------
+    df_esm_preprocessed: pd.DataFrame
+        A dataframe with added columns: datetime in Ljubljana timezone and all fields from ESM_JSON column.
+    """
+    df_esm = get_date_from_timestamp(df_esm)
+
+    df_esm_json = df_esm["esm_json"].apply(json.loads)
+    df_esm_json = pd.json_normalize(df_esm_json).drop(
+        columns=["esm_trigger"]
+    )  # The esm_trigger column is already present in the main df.
+    return df_esm.join(df_esm_json)
+
+
+def classify_sessions_by_completion(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
+    """
+    For each distinct EMA session, determine how the participant responded to it.
+    Possible outcomes are: SESSION_STATUS_UNANSWERED, SESSION_STATUS_DAY_FINISHED, and SESSION_STATUS_COMPLETE
+
+    This is done in three steps.
+
+    First, the esm_status is considered.
+    If any of the ESMs in a session has a status *other than* "answered", then this session is taken as unfinished.
+
+    Second, the sessions which do not represent full questionnaires are identified.
+    These are sessions where participants only marked they are finished with the day or have not yet started working.
+
+    Third, the sessions with only one item are marked with their trigger.
+    We never offered questionnaires with single items, so we can be sure these are unfinished.
+
+    Finally, all sessions that remain are marked as completed.
+    By going through different possibilities in expl_esm_adherence.ipynb, this turned out to be a reasonable option.
+
+    Parameters
+    ----------
+    df_esm_preprocessed: pd.DataFrame
+        A preprocessed dataframe of esm data, which must include the session ID (esm_session).
+
+    Returns
+    -------
+    df_session_counts: pd.Dataframe
+        A dataframe of all sessions (grouped by GROUP_SESSIONS_BY) with their statuses and the number of items.
+    """
+    sessions_grouped = df_esm_preprocessed.groupby(GROUP_SESSIONS_BY)
+
+    # 0. First, assign all session statuses as NaN.
+    df_session_counts = pd.DataFrame(sessions_grouped.count()["timestamp"]).rename(
+        columns={"timestamp": "esm_session_count"}
+    )
+    df_session_counts["session_response"] = np.nan
+
+    # 1. Identify all ESMs with status other than answered.
+    esm_not_answered = sessions_grouped.apply(
+        lambda x: (x.esm_status != ESM_STATUS_ANSWERED).any()
+    )
+    df_session_counts.loc[
+        esm_not_answered, "session_response"
+    ] = SESSION_STATUS_UNANSWERED
+
+    # 2. Identify non-sessions, i.e. answers about the end of the day.
+    non_session = sessions_grouped.apply(
+        lambda x: (
+            (x.esm_user_answer == ANSWER_DAY_FINISHED)  # I finished working for today.
+            | (x.esm_user_answer == ANSWER_DAY_OFF)  # I am not going to work today.
+            | (
+                x.esm_user_answer == ANSWER_SET_EVENING
+            )  # When would you like to answer the evening EMA?
+        ).any()
+    )
+    df_session_counts.loc[non_session, "session_response"] = SESSION_STATUS_DAY_FINISHED
+
+    # 3. Identify sessions appearing only once, as those were not true EMAs for sure.
+    singleton_sessions = (df_session_counts.esm_session_count == 1) & (
+        df_session_counts.session_response.isna()
+    )
+    df_session_1 = df_session_counts[singleton_sessions]
+    df_esm_unique_session = df_session_1.join(
+        df_esm_preprocessed.set_index(GROUP_SESSIONS_BY), how="left"
+    )
+    df_esm_unique_session = df_esm_unique_session.assign(
+        session_response=lambda x: x.esm_trigger
+    )["session_response"]
+    df_session_counts.loc[
+        df_esm_unique_session.index, "session_response"
+    ] = df_esm_unique_session
+
+    # 4. Mark the remaining sessions as completed.
+    df_session_counts.loc[
+        df_session_counts.session_response.isna(), "session_response"
+    ] = SESSION_STATUS_COMPLETE
+
+    return df_session_counts
+
+
+def classify_sessions_by_time(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
+    """
+    For each EMA session, determine the time of the first user answer and its time type (morning, workday, or evening.)
+
+    Parameters
+    ----------
+    df_esm_preprocessed: pd.DataFrame
+        A preprocessed dataframe of esm data, which must include the session ID (esm_session).
+
+    Returns
+    -------
+    df_session_time: pd.DataFrame
+        A dataframe of all sessions (grouped by GROUP_SESSIONS_BY) with their time type and timestamp of first answer.
+    """
+    df_session_time = (
+        df_esm_preprocessed.sort_values(["datetime_lj"]) # "participant_id"
+        .groupby(GROUP_SESSIONS_BY)
+        .first()[["time", "datetime_lj"]]
+    )
+    return df_session_time
+
+
+def classify_sessions_by_completion_time(
+    df_esm_preprocessed: pd.DataFrame,
+) -> pd.DataFrame:
+    """
+    The point of this function is to not only classify sessions by using the previously defined functions.
+    It also serves to "correct" the time type of some EMA sessions.
+
+    A morning questionnaire could seamlessly transition into a daytime questionnaire,
+        if the participant was already at work.
+    In this case, the "time" label changed mid-session.
+    Because of the way classify_sessions_by_time works, this questionnaire was classified as "morning".
+    But for all intents and purposes, it can be treated as a "daytime" EMA.
+
+    The way this scenario is differentiated from a true "morning" questionnaire,
+        where the participants NOT yet at work, is by considering their length.
+
+    Parameters
+    ----------
+    df_esm_preprocessed: pd.DataFrame
+        A preprocessed dataframe of esm data, which must include the session ID (esm_session).
+
+    Returns
+    -------
+    df_session_counts_time: pd.DataFrame
+        A dataframe of all sessions (grouped by GROUP_SESSIONS_BY) with statuses, the number of items,
+            their time type (with some morning EMAs reclassified) and timestamp of first answer.
+
+    """
+    df_session_counts = classify_sessions_by_completion(df_esm_preprocessed)
+    df_session_time = classify_sessions_by_time(df_esm_preprocessed)
+
+    df_session_counts_time = df_session_time.join(df_session_counts)
+
+    morning_transition_to_daytime = (df_session_counts_time.time == "morning") & (
+        df_session_counts_time.esm_session_count > MAX_MORNING_LENGTH
+    )
+
+    df_session_counts_time.loc[morning_transition_to_daytime, "time"] = "daytime"
+
+    return df_session_counts_time
+
+
+# def clean_up_esm(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
+#     """
+#     This function eliminates invalid ESM responses.
+#     It removes unanswered ESMs and those that indicate end of work and similar.
+#     It also extracts a numeric answer from strings such as "4 - I strongly agree".
+
+#     Parameters
+#     ----------
+#     df_esm_preprocessed: pd.DataFrame
+#         A preprocessed dataframe of esm data.
+
+#     Returns
+#     -------
+#     df_esm_clean: pd.DataFrame
+#         A subset of the original dataframe.
+
+#     """
+#     df_esm_clean = df_esm_preprocessed[
+#         df_esm_preprocessed["esm_status"] == ESM_STATUS_ANSWERED
+#     ]
+#     df_esm_clean = df_esm_clean[
+#         ~df_esm_clean["esm_user_answer"].isin(
+#             [ANSWER_DAY_FINISHED, ANSWER_DAY_OFF, ANSWER_SET_EVENING]
+#         )
+#     ]
+#     df_esm_clean["esm_user_answer_numeric"] = np.nan
+#     esm_type_numeric = [
+#         ESM.ESM_TYPE.get("radio"),
+#         ESM.ESM_TYPE.get("scale"),
+#         ESM.ESM_TYPE.get("number"),
+#     ]
+#     df_esm_clean.loc[
+#         df_esm_clean["esm_type"].isin(esm_type_numeric)
+#     ] = df_esm_clean.loc[df_esm_clean["esm_type"].isin(esm_type_numeric)].assign(
+#         esm_user_answer_numeric=lambda x: x.esm_user_answer.str.slice(stop=1).astype(
+#             int
+#         )
+#     )
+#     return df_esm_clean

src/features/phone_esm/straw/main.py

@@ -42,7 +42,8 @@ def straw_features(sensor_data_files, time_segment, provider, filter_data_by_seg
     requested_features = provider["FEATURES"]
     # name of the features this function can compute
     requested_scales = provider["SCALES"]
-    base_features_names = ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support"]
+    base_features_names = ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support", 
+                            "appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]
     #TODO Check valid questionnaire and feature names.
     # the subset of requested features this function can compute
     features_to_compute = list(set(requested_features) & set(base_features_names))
@@ -52,7 +53,6 @@ def straw_features(sensor_data_files, time_segment, provider, filter_data_by_seg
 
         if not esm_data.empty:
             esm_features = pd.DataFrame()
-
             for scale in requested_scales:
                 questionnaire_id = QUESTIONNAIRE_IDS[scale]
                 mask = esm_data["questionnaire_id"] == questionnaire_id
@@ -60,4 +60,7 @@ def straw_features(sensor_data_files, time_segment, provider, filter_data_by_seg
                 #TODO Create the column esm_user_score in esm_clean. Currently, this is only done when reversing.
 
             esm_features = esm_features.reset_index()
+            if 'index' in esm_features: # In calse of empty esm_features df 
+                esm_features.rename(columns={'index': 'local_segment'}, inplace=True)
+
     return esm_features

src/features/phone_esm/straw/process_user_event_related_segments.py

@@ -0,0 +1,260 @@
+import pandas as pd
+import numpy as np
+import datetime
+
+import math, sys, yaml
+
+from esm_preprocess import clean_up_esm
+from esm import classify_sessions_by_completion_time, preprocess_esm
+
+input_data_files = dict(snakemake.input)
+
+def format_timestamp(x):
+    """This method formates inputed timestamp into format "HH MM SS". Including spaces. If there is no hours or minutes present
+    that part is ignored, e.g., "MM SS" or just "SS". 
+
+    Args:
+        x (int): unix timestamp in seconds
+
+    Returns:
+        str: formatted timestamp using "HH MM SS" sintax
+    """
+    tstring=""
+    space = False
+    if x//3600 > 0:
+        tstring += f"{x//3600}H"
+        space = True
+    if x % 3600 // 60 > 0:
+        tstring += f" {x % 3600 // 60}M" if "H" in tstring else f"{x % 3600 // 60}M"
+    if x % 60 > 0:  
+        tstring += f" {x % 60}S" if "M" in tstring or "H" in tstring else f"{x % 60}S"
+    
+    return tstring
+
+
+def extract_ers(esm_df):
+    """This method has two major functionalities: 
+        (1) It prepares STRAW event-related segments file with the use of esm file. The execution protocol is depended on 
+            the segmenting method specified in the config.yaml file.
+        (2) It prepares and writes csv with targets and corresponding time segments labels. This is later used 
+            in the overall cleaning script (straw).
+    
+    Details about each segmenting method are listed below by each corresponding condition. Refer to the RAPIDS documentation for the 
+    ERS file format: https://www.rapids.science/1.9/setup/configuration/#time-segments -> event segments
+
+    Args:
+        esm_df (DataFrame): read esm file that is dependend on the current participant.
+
+    Returns:
+        extracted_ers (DataFrame): dataframe with all necessary information to write event-related segments file 
+        in the correct format.
+    """
+
+    pd.set_option("display.max_rows", 100)
+    pd.set_option("display.max_columns", None)
+
+    with open('config.yaml', 'r') as stream:
+        config = yaml.load(stream, Loader=yaml.FullLoader)
+
+    pd.DataFrame(columns=["label"]).to_csv(snakemake.output[1]) # Create an empty stress_events_targets file 
+
+    esm_preprocessed = clean_up_esm(preprocess_esm(esm_df))
+
+    # Take only ema_completed sessions responses
+    classified = classify_sessions_by_completion_time(esm_preprocessed)
+    esm_filtered_sessions = classified[classified["session_response"] == 'ema_completed'].reset_index()[['device_id', 'esm_session']]
+    esm_df = esm_preprocessed.loc[(esm_preprocessed['device_id'].isin(esm_filtered_sessions['device_id'])) & (esm_preprocessed['esm_session'].isin(esm_filtered_sessions['esm_session']))]
+    
+    segmenting_method = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["SEGMENTING_METHOD"]
+    
+    if segmenting_method in ["30_before", "90_before"]: # takes 30-minute peroid before the questionnaire + the duration of the questionnaire
+        """ '30-minutes and 90-minutes before' have the same fundamental logic with couple of deviations that will be explained below.
+        Both take x-minute period before the questionnaire that is summed with the questionnaire duration.
+        All questionnaire durations over 15 minutes are excluded from the querying.
+        """
+        # Extract time-relevant information
+        extracted_ers = esm_df.groupby(["device_id", "esm_session"])['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index() # questionnaire length
+        extracted_ers["label"] = f"straw_event_{segmenting_method}_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3) 
+        extracted_ers[['event_timestamp', 'device_id']] = esm_df.groupby(["device_id", "esm_session"])['timestamp'].min().reset_index()[['timestamp', 'device_id']]
+        extracted_ers = extracted_ers[extracted_ers["timestamp"] <= 15 * 60].reset_index(drop=True) # ensure that the longest duration of the questionnaire anwsering is 15 min 
+        extracted_ers["shift_direction"] = -1 
+
+        if segmenting_method == "30_before":
+            """The method 30-minutes before simply takes 30 minutes before the questionnaire and sums it with the questionnaire duration.
+            The timestamps are formatted with the help of format_timestamp() method.
+            """
+            time_before_questionnaire = 30 * 60 # in seconds (30 minutes)
+
+            extracted_ers["length"] = (extracted_ers["timestamp"] + time_before_questionnaire).apply(lambda x: format_timestamp(x))
+            extracted_ers["shift"] = time_before_questionnaire
+            extracted_ers["shift"] = extracted_ers["shift"].apply(lambda x: format_timestamp(x))
+        
+        elif segmenting_method == "90_before":
+            """The method 90-minutes before has an important condition. If the time between the current and the previous questionnaire is
+            longer then 90 minutes it takes 90 minutes, otherwise it takes the original time difference between the questionnaires.
+            """
+            time_before_questionnaire = 90 * 60 # in seconds (90 minutes)
+
+            extracted_ers[['end_event_timestamp', 'device_id']] = esm_df.groupby(["device_id", "esm_session"])['timestamp'].max().reset_index()[['timestamp', 'device_id']]
+
+            extracted_ers['diffs'] = extracted_ers['event_timestamp'].astype('int64') - extracted_ers['end_event_timestamp'].shift(1, fill_value=0).astype('int64')
+            extracted_ers.loc[extracted_ers['diffs'] > time_before_questionnaire * 1000, 'diffs'] = time_before_questionnaire * 1000
+            
+            extracted_ers["diffs"] = (extracted_ers["diffs"] / 1000).apply(lambda x: math.ceil(x))
+
+            extracted_ers["length"] = (extracted_ers["timestamp"] + extracted_ers["diffs"]).apply(lambda x: format_timestamp(x))
+            extracted_ers["shift"] = extracted_ers["diffs"].apply(lambda x: format_timestamp(x))
+
+    elif segmenting_method == "stress_event":
+        """
+        TODO: update documentation for this condition
+        This is a special case of the method as it consists of two important parts:
+            (1) Generating of the ERS file (same as the methods above) and
+            (2) Generating targets file alongside with the correct time segment labels.
+        
+        This extracts event-related segments, depended on the event time and duration specified by the participant in the next
+        questionnaire. Additionally, 5 minutes before the specified start time of this event is taken to take into a account the 
+        possiblity of the participant not remembering the start time percisely => this parameter can be manipulated with the variable
+        "time_before_event" which is defined below. 
+        
+        In case if the participant marked that no stressful event happened, the default of 30 minutes before the event is choosen. 
+        In this case, se_threat and se_challenge are NaN.
+        
+        By default, this method also excludes all events that are longer then 2.5 hours so that the segments are easily comparable. 
+        """
+
+        ioi = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["INTERVAL_OF_INTEREST"] * 60 # interval of interest in seconds
+        ioi_error_tolerance = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["IOI_ERROR_TOLERANCE"]  * 60 # interval of interest error tolerance in seconds 
+
+        # Get and join required data
+        extracted_ers = esm_df.groupby(["device_id", "esm_session"])['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index().rename(columns={'timestamp': 'session_length'}) # questionnaire length
+        extracted_ers = extracted_ers[extracted_ers["session_length"] <= 15 * 60].reset_index(drop=True) # ensure that the longest duration of the questionnaire answering is 15 min
+        session_start_timestamp = esm_df.groupby(['device_id', 'esm_session'])['timestamp'].min().to_frame().rename(columns={'timestamp': 'session_start_timestamp'}) # questionnaire start timestamp
+        session_end_timestamp = esm_df.groupby(['device_id', 'esm_session'])['timestamp'].max().to_frame().rename(columns={'timestamp': 'session_end_timestamp'}) # questionnaire end timestamp
+        
+        # Users' answers for the stressfulness event (se) start times and durations 
+        se_time = esm_df[esm_df.questionnaire_id == 90.].set_index(['device_id', 'esm_session'])['esm_user_answer'].to_frame().rename(columns={'esm_user_answer': 'se_time'})
+        se_duration = esm_df[esm_df.questionnaire_id == 91.].set_index(['device_id', 'esm_session'])['esm_user_answer'].to_frame().rename(columns={'esm_user_answer': 'se_duration'})
+
+        # Make se_durations to the appropriate lengths
+
+        # Extracted 3 targets that will be transfered in the csv file to the cleaning script. 
+        se_stressfulness_event_tg = esm_df[esm_df.questionnaire_id == 87.].set_index(['device_id', 'esm_session'])['esm_user_answer_numeric'].to_frame().rename(columns={'esm_user_answer_numeric': 'appraisal_stressfulness_event'})
+        se_threat_tg = esm_df[esm_df.questionnaire_id == 88.].groupby(["device_id", "esm_session"]).mean(numeric_only=True)['esm_user_answer_numeric'].to_frame().rename(columns={'esm_user_answer_numeric': 'appraisal_threat'})
+        se_challenge_tg = esm_df[esm_df.questionnaire_id == 89.].groupby(["device_id", "esm_session"]).mean(numeric_only=True)['esm_user_answer_numeric'].to_frame().rename(columns={'esm_user_answer_numeric': 'appraisal_challenge'})
+
+        # All relevant features are joined by inner join to remove standalone columns (e.g., stressfulness event target has larger count)
+        extracted_ers = extracted_ers.join(session_start_timestamp, on=['device_id', 'esm_session'], how='inner') \
+                                     .join(session_end_timestamp, on=['device_id', 'esm_session'], how='inner') \
+                                     .join(se_stressfulness_event_tg, on=['device_id', 'esm_session'], how='inner') \
+                                     .join(se_time, on=['device_id', 'esm_session'], how='left') \
+                                     .join(se_duration, on=['device_id', 'esm_session'], how='left') \
+                                     .join(se_threat_tg, on=['device_id', 'esm_session'], how='left') \
+                                     .join(se_challenge_tg, on=['device_id', 'esm_session'], how='left')
+
+        # Filter-out the sessions that are not useful. Because of the ambiguity this excludes: 
+        # (1) straw event times that are marked as "0 - I don't remember"
+        extracted_ers = extracted_ers[~extracted_ers.se_time.astype(str).str.startswith("0 - ")]
+        extracted_ers.reset_index(drop=True, inplace=True)
+
+        extracted_ers.loc[extracted_ers.se_duration.astype(str).str.startswith("0 - "), 'se_duration'] = 0
+
+        # Add default duration in case if participant answered that no stressful event occured
+        extracted_ers["se_duration"] = extracted_ers["se_duration"].fillna(int((ioi + 2*ioi_error_tolerance) * 1000))
+
+        # Prepare data to fit the data structure in the CSV file ...
+        # Add the event time as the end of the questionnaire if no stress event occured
+        extracted_ers['se_time'] = extracted_ers['se_time'].fillna(extracted_ers['session_start_timestamp'])
+        # Type could be an int (timestamp [ms]) which stays the same, and datetime str which is converted to timestamp in miliseconds 
+        extracted_ers['event_timestamp'] = extracted_ers['se_time'].apply(lambda x: x if isinstance(x, int) else pd.to_datetime(x).timestamp() * 1000).astype('int64')
+        extracted_ers['shift_direction'] = -1
+
+        """>>>>> begin section (could be optimized) <<<<<"""
+
+        # Checks whether the duration is marked with "1 - It's still ongoing" which means that the end of the current questionnaire
+        # is taken as end time of the segment. Else the user input duration is taken. 
+        extracted_ers['se_duration'] = \
+            np.where(
+                extracted_ers['se_duration'].astype(str).str.startswith("1 - "),
+                extracted_ers['session_end_timestamp'] - extracted_ers['event_timestamp'], 
+                extracted_ers['se_duration']
+            )
+
+        # This converts the rows of timestamps in miliseconds and the rows with datetime... to timestamp in seconds.
+        extracted_ers['se_duration'] = \
+            extracted_ers['se_duration'].apply(lambda x: math.ceil(x / 1000) if isinstance(x, int) else (pd.to_datetime(x).hour * 60 + pd.to_datetime(x).minute) * 60)
+
+        # Check explicitley whether min duration is at least 0. This will eliminate rows that would be investigated after the end of the questionnaire.
+        extracted_ers = extracted_ers[extracted_ers['session_end_timestamp'] - extracted_ers['event_timestamp'] >= 0]
+        # Double check whether min se_duration is at least 0. Filter-out the rest. Negative values are considered invalid.
+        extracted_ers = extracted_ers[extracted_ers["se_duration"] >= 0].reset_index(drop=True)
+
+        """>>>>> end section <<<<<"""
+
+        # Simply override all durations to be of an equal amount
+        extracted_ers['se_duration'] = ioi + 2*ioi_error_tolerance 
+
+        # If target is 0 then shift by the total stress event duration, otherwise shift it by ioi_tolerance
+        extracted_ers['shift'] = \
+            np.where(
+                extracted_ers['appraisal_stressfulness_event'] == 0,
+                extracted_ers['se_duration'], 
+                ioi_error_tolerance
+            )
+
+        extracted_ers['shift'] = extracted_ers['shift'].apply(lambda x: format_timestamp(int(x)))
+        extracted_ers['length'] = extracted_ers['se_duration'].apply(lambda x: format_timestamp(int(x)))
+
+        # Drop event_timestamp duplicates in case in the user is referencing the same event over multiple questionnaires
+        extracted_ers.drop_duplicates(subset=["event_timestamp"], keep='first', inplace=True)
+        extracted_ers.reset_index(drop=True, inplace=True)
+
+        extracted_ers["label"] = f"straw_event_{segmenting_method}_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3)
+
+        # Write the csv of extracted ERS labels with targets related to stressfulness event   
+        extracted_ers[["label", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]].to_csv(snakemake.output[1], index=False)
+        
+    else:
+        raise Exception("Please select correct target method for the event-related segments.")
+        extracted_ers = pd.DataFrame(columns=["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"])
+
+    return extracted_ers[["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"]]
+
+
+"""
+Here the code is executed - this .py file is used both for extraction of the STRAW time_segments file for the individual
+participant, and also for merging all participant's files into one combined file which is later used for the time segments 
+to all sensors assignment.
+
+There are two files involved (see rules extract_event_information_from_esm and merge_event_related_segments_files in preprocessing.smk)
+(1) ERS file which contains all the information about the time segment timings and
+(2) targets file which has corresponding target value for the segment label which is later used to merge with other features in the cleaning script.
+For more information, see the comment in the method above.
+"""
+if snakemake.params["stage"] == "extract": 
+    esm_df = pd.read_csv(input_data_files['esm_raw_input'])
+
+    extracted_ers = extract_ers(esm_df)
+
+    extracted_ers.to_csv(snakemake.output[0], index=False)
+
+elif snakemake.params["stage"] == "merge":
+
+    input_data_files = dict(snakemake.input)
+    straw_events = pd.DataFrame(columns=["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"])
+    stress_events_targets = pd.DataFrame(columns=["label", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"])
+
+    for input_file in input_data_files["ers_files"]:
+        ers_df = pd.read_csv(input_file)
+        straw_events = pd.concat([straw_events, ers_df], axis=0, ignore_index=True)
+
+    straw_events.to_csv(snakemake.output[0], index=False)
+
+    for input_file in input_data_files["se_files"]:
+        se_df = pd.read_csv(input_file)
+        stress_events_targets = pd.concat([stress_events_targets, se_df], axis=0, ignore_index=True)
+
+    stress_events_targets.to_csv(snakemake.output[1], index=False)
+
+
+

src/features/phone_light/rapids/main.py

@@ -29,7 +29,7 @@ def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_se
             if "medianlux" in features_to_compute:
                 light_features["medianlux"] = light_data.groupby(["local_segment"])["double_light_lux"].median()
             if "stdlux" in features_to_compute:
-                light_features["stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std()
+                light_features["stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std().fillna(0)
             
             light_features = light_features.reset_index()
 

src/features/phone_locations/barnett/daily_features.R

@@ -25,9 +25,11 @@ barnett_daily_features <- function(snakemake){
   datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59"
   location <- location %>% 
     mutate(is_daily = str_detect(assigned_segments, paste0(".*#", datetime_start_regex, ",", datetime_end_regex, ".*")))
-  
-  if(nrow(segment_labels) == 0 || nrow(location) == 0 || all(location$is_daily == FALSE) || (max(location$timestamp) - min(location$timestamp) < 86400000)){
-    warning("Barnett's location features cannot be computed for data or time segments that do not span one or more entire days (00:00:00 to 23:59:59). Values below point to the problem:",
+
+  does_not_span = nrow(segment_labels) == 0 || nrow(location) == 0 || all(location$is_daily == FALSE) || (max(location$timestamp) - min(location$timestamp) < 86400000)
+
+  if(is.na(does_not_span) || does_not_span){
+      warning("Barnett's location features cannot be computed for data or time segments that do not span one or more entire days (00:00:00 to 23:59:59). Values below point to the problem:",
             "\nLocation data rows within a daily time segment: ", nrow(filter(location, is_daily)),
             "\nLocation data time span in days: ", round((max(location$timestamp) - min(location$timestamp)) / 86400000, 2)
             )

src/features/phone_locations/doryab/add_doryab_extra_columns.py

@@ -115,7 +115,7 @@ cluster_on = provider["CLUSTER_ON"]
 strategy = provider["INFER_HOME_LOCATION_STRATEGY"]
 days_threshold = provider["MINIMUM_DAYS_TO_DETECT_HOME_CHANGES"]
 
-if not location_data.timestamp.is_monotonic:
+if not location_data.timestamp.is_monotonic_increasing:
     location_data.sort_values(by=["timestamp"], inplace=True)
 
 location_data["duration_in_seconds"] = -1 * location_data.timestamp.diff(-1) / 1000

src/features/phone_locations/doryab/main.py

@@ -37,7 +37,8 @@ def variance_and_logvariance_features(location_data, location_features):
     location_data["longitude_for_wvar"] = (location_data["double_longitude"] - location_data["longitude_wavg"]) ** 2 * location_data["duration"] * 60
 
     location_features["locationvariance"] = ((location_data_grouped["latitude_for_wvar"].sum() + location_data_grouped["longitude_for_wvar"].sum()) / (location_data_grouped["duration"].sum() * 60 - 1)).fillna(0)
-    location_features["loglocationvariance"] = np.log10(location_features["locationvariance"]).replace(-np.inf, np.nan)
+    
+    location_features["loglocationvariance"] = np.log10(location_features["locationvariance"]).replace(-np.inf, -1000000)
 
     return location_features
 

src/features/phone_messages/rapids/main.R

@@ -65,6 +65,15 @@ rapids_features <- function(sensor_data_files, time_segment, provider){
         features <- message_features_of_type(messages_of_type, message_type, time_segment, requested_features)
         messages_features <- merge(messages_features, features, all=TRUE)
     }
-    messages_features <- messages_features %>% mutate_at(vars(contains("countmostfrequentcontact") | contains("distinctcontacts") | contains("count")), list( ~ replace_na(., 0)))
+    # Fill seleted columns with a high number
+    time_cols <- select(messages_features, contains("timefirstmessages") |  contains("timelastmessages")) %>% 
+    colnames(.)
+
+    messages_features <- messages_features %>% 
+        mutate_at(., time_cols, ~replace(., is.na(.), 1500))
+    
+    # Fill NA values with 0
+    messages_features <- messages_features %>% mutate_all(~replace(., is.na(.), 0))
+    
     return(messages_features)
 }

src/features/phone_screen/rapids/main.py

@@ -15,7 +15,7 @@ def getEpisodeDurationFeatures(screen_data, time_segment, episode, features, ref
     if "avgduration" in features:
         duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].mean().rename(columns = {"duration":"avgduration" + episode})], axis = 1)
     if "stdduration" in features:
-        duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].std().rename(columns = {"duration":"stdduration" + episode})], axis = 1)
+        duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].std().fillna(0).rename(columns = {"duration":"stdduration" + episode})], axis = 1)
     if "firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) in features:
         screen_data_episode_after_hour = screen_data_episode.copy()
         screen_data_episode_after_hour["hour"] = pd.to_datetime(screen_data_episode["local_start_date_time"]).dt.hour

src/features/phone_speech/straw/main.py

@@ -0,0 +1,30 @@
+import pandas as pd
+
+
+def straw_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
+    speech_data = pd.read_csv(sensor_data_files["sensor_data"])
+    requested_features = provider["FEATURES"]
+    # name of the features this function can compute+
+    base_features_names = ["meanspeech", "stdspeech", "nlargest", "nsmallest", "medianspeech"]
+    features_to_compute = list(set(requested_features) & set(base_features_names))
+    speech_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
+    
+    if not speech_data.empty:
+        speech_data = filter_data_by_segment(speech_data, time_segment)
+
+        if not speech_data.empty:
+            speech_features = pd.DataFrame()
+            if "meanspeech" in features_to_compute:
+                speech_features["meanspeech"] = speech_data.groupby(["local_segment"])['speech_proportion'].mean()
+            if "stdspeech" in features_to_compute:
+                speech_features["stdspeech"] = speech_data.groupby(["local_segment"])['speech_proportion'].std()
+            if "nlargest" in features_to_compute:
+                speech_features["nlargest"] = speech_data.groupby(["local_segment"])['speech_proportion'].apply(lambda x: x.nlargest(5).mean())
+            if "nsmallest" in features_to_compute:
+                speech_features["nsmallest"] = speech_data.groupby(["local_segment"])['speech_proportion'].apply(lambda x: x.nsmallest(5).mean())
+            if "medianspeech" in features_to_compute:
+                speech_features["medianspeech"] = speech_data.groupby(["local_segment"])['speech_proportion'].median()
+            
+            speech_features = speech_features.reset_index()
+
+    return speech_features

src/features/phone_wifi_visible/rapids/main.R

@@ -9,21 +9,26 @@ compute_wifi_feature <- function(data, feature, time_segment){
               "countscans" = data %>% summarise(!!feature := n()),
               "uniquedevices" = data %>% summarise(!!feature := n_distinct(bssid)))
     return(data)
+
    } else if(feature == "countscansmostuniquedevice"){
      # Get the most scanned device
-    mostuniquedevice <- data %>% 
+    mostuniquedevice <- data %>%
+      filter(bssid != "") %>% 
       group_by(bssid) %>% 
       mutate(N=n()) %>% 
       ungroup() %>%
       filter(N == max(N)) %>% 
       head(1) %>% # if there are multiple device with the same amount of scans pick the first one only
       pull(bssid)
+
     data <- data %>% filter_data_by_segment(time_segment)
+
     return(data %>% 
              filter(bssid == mostuniquedevice) %>%
              group_by(local_segment) %>% 
-             summarise(!!feature := n()) %>%
-             replace(is.na(.), 0))
+             summarise(!!feature := n())
+    )
+
   }
 }
 
@@ -43,6 +48,6 @@ rapids_features <- function(sensor_data_files, time_segment, provider){
     feature <- compute_wifi_feature(wifi_data, feature_name, time_segment)
     features <- merge(features, feature, by="local_segment", all = TRUE)
   }
-
+  features <- features %>% mutate_all(~replace(., is.na(.), 0))
   return(features)
 }

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()))
@@ -106,30 +108,68 @@ 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:
-                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)
+
+            features = feature_function(sensor_data_files, time_segment, provider, filter_data_by_segment=filter_data_by_segment, chunk_episodes=chunk_episodes, calc_windows=calc_windows)
+            
+            # In case of calc_window = True
+            if isinstance(features, tuple):
+                if not "local_segment" in features[0].columns or not "local_segment" in features[1].columns:
+                        raise ValueError("The dataframe returned by the " + sensor_key + " provider '" + provider_key + "' is missing the 'local_segment' column added by the 'filter_data_by_segment()' function. Check the provider script is using such function and is not removing 'local_segment' by accident (" + provider["SRC_SCRIPT"] + ")\n  The 'local_segment' column is used to index a provider's features (each row corresponds to a different time segment instance (e.g. 2020-01-01, 2020-01-02, 2020-01-03, etc.)")
+                features[0].columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features[0].columns]
+                features[1].columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features[1].columns]
+                if not features[0].empty:
+                    sensor_fo_features = pd.concat([sensor_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]])
+    
+    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_features
+        return sensor_fo_features, sensor_so_features
 
-def run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data_files):
+    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, target=False):
     from importlib import import_module, util
     print("{} Processing {} {}".format(rapids_log_tag, sensor_key, provider_key))
 
     cleaning_module = import_path(provider["SRC_SCRIPT"])
     cleaning_function = getattr(cleaning_module,  provider_key.lower() + "_cleaning")
-    sensor_features = cleaning_function(sensor_data_files, provider)
 
-    return sensor_features
+    if target:
+        sensor_features = cleaning_function(sensor_data_files, provider, target)
+    else:
+        sensor_features = cleaning_function(sensor_data_files, provider)
+
+    return sensor_features

src/models/helper.py

@@ -1,5 +1,6 @@
 import pandas as pd
-
+import sys
+import warnings
 
 def retain_target_column(df_input: pd.DataFrame, target_variable_name: str):
     column_names = df_input.columns
@@ -8,9 +9,9 @@ def retain_target_column(df_input: pd.DataFrame, target_variable_name: str):
     esm_names = column_names[esm_names_index]
     target_variable_index = esm_names.str.contains(target_variable_name)
     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")
+        warnings.warn(f"The requested target (, {target_variable_name} ,)cannot be found in the dataset. Please check the names of phone_esm_ columns in cleaned python file")
+        return None
+
     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.

src/models/merge_features_and_targets_for_population_model.py

@@ -12,9 +12,13 @@ for baseline_features_path in snakemake.input["demographic_features"]:
     all_baseline_features = pd.concat([all_baseline_features, baseline_features], axis=0)
 
 # merge sensor features and baseline features
-features = sensor_features.merge(all_baseline_features, on="pid", how="left")
+if not sensor_features.empty:
+    features = sensor_features.merge(all_baseline_features, on="pid", how="left")
 
-target_variable_name = snakemake.params["target_variable"]
-model_input = retain_target_column(features, target_variable_name)
+    target_variable_name = snakemake.params["target_variable"]
+    model_input = retain_target_column(features, target_variable_name)
 
-model_input.to_csv(snakemake.output[0], index=False)
+    model_input.to_csv(snakemake.output[0], index=False)
+    
+else:
+    sensor_features.to_csv(snakemake.output[0], index=False)

src/models/select_targets.py

@@ -6,6 +6,8 @@ cleaned_sensor_features = pd.read_csv(snakemake.input["cleaned_sensor_features"]
 target_variable_name = snakemake.params["target_variable"]
 
 model_input = retain_target_column(cleaned_sensor_features, target_variable_name)
-model_input.dropna(axis ="index", how="any", subset=["target"], inplace=True)
 
-model_input.to_csv(snakemake.output[0], index=False)
+if model_input is None:
+    pd.DataFrame().to_csv(snakemake.output[0])
+else:
+    model_input.to_csv(snakemake.output[0], index=False)

src/visualization/heatmap_sensors_per_minute_per_time_segment.py

@@ -24,12 +24,12 @@ def colors2colorscale(colors):
 def getDataForPlot(phone_data_yield_per_segment):
     # calculate the length (in minute) of per segment instance
     phone_data_yield_per_segment["length"] = phone_data_yield_per_segment["timestamps_segment"].str.split(",").apply(lambda x: int((int(x[1])-int(x[0])) / (1000 * 60)))
-    # calculate the number of sensors logged at least one row of data per minute.
-    phone_data_yield_per_segment = phone_data_yield_per_segment.groupby(["local_segment", "length", "local_date", "local_hour", "local_minute"])[["sensor", "local_date_time"]].max().reset_index()
     # extract local start datetime of the segment from "local_segment" column
     phone_data_yield_per_segment["local_segment_start_datetimes"] = pd.to_datetime(phone_data_yield_per_segment["local_segment"].apply(lambda x: x.split("#")[1].split(",")[0]))
     # calculate the number of minutes after local start datetime of the segment
     phone_data_yield_per_segment["minutes_after_segment_start"] = ((phone_data_yield_per_segment["local_date_time"] - phone_data_yield_per_segment["local_segment_start_datetimes"]) / pd.Timedelta(minutes=1)).astype("int")
+    # calculate the number of sensors logged at least one row of data per minute.
+    phone_data_yield_per_segment = phone_data_yield_per_segment.groupby(["local_segment", "length", "local_segment_start_datetimes", "minutes_after_segment_start"])[["sensor"]].max().reset_index()
     
     # impute missing rows with 0
     columns_for_full_index = phone_data_yield_per_segment[["local_segment_start_datetimes", "length"]].drop_duplicates(keep="first")

tests/scripts/missing_vals.py

@@ -0,0 +1,39 @@
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+
+participant = "p01"
+all_sensors = ["eda", "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_feats.py

@@ -0,0 +1,285 @@
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+
+path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv"
+df = pd.read_csv(path)
+
+# activity_recognition
+
+cols = [col for col in df.columns if "activity_recognition" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'activity_recognition_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_activity_recognition_values', bbox_inches='tight')
+plt.close()
+
+# applications_foreground
+
+cols = [col for col in df.columns if "applications_foreground" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'applications_foreground_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_applications_foreground_values', bbox_inches='tight')
+plt.close()
+
+# battery
+
+cols = [col for col in df.columns if "phone_battery" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'phone_battery_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_phone_battery_values', bbox_inches='tight')
+plt.close()
+
+# bluetooth_doryab
+
+cols = [col for col in df.columns if "bluetooth_doryab" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'bluetooth_doryab_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_bluetooth_doryab_values', bbox_inches='tight')
+plt.close()
+
+# bluetooth_rapids
+
+cols = [col for col in df.columns if "bluetooth_rapids" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'bluetooth_rapids_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_bluetooth_rapids_values', bbox_inches='tight')
+plt.close()
+
+# calls
+
+cols = [col for col in df.columns if "phone_calls" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'phone_calls_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_phone_calls_values', bbox_inches='tight')
+plt.close()
+
+# data_yield
+
+cols = [col for col in df.columns if "data_yield" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'data_yield_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_data_yield_values', bbox_inches='tight')
+plt.close()
+
+# esm
+
+cols = [col for col in df.columns if "phone_esm" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'phone_esm_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_phone_esm_values', bbox_inches='tight')
+plt.close()
+
+# light
+
+cols = [col for col in df.columns if "phone_light" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'phone_light_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_phone_light_values', bbox_inches='tight')
+plt.close()
+
+# locations_doryab
+
+cols = [col for col in df.columns if "locations_doryab" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'locations_doryab_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_locations_doryab_values', bbox_inches='tight')
+plt.close()
+
+# locations_barnett
+
+# Not working
+
+# messages
+
+cols = [col for col in df.columns if "phone_messages" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'phone_messages_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_phone_messages_values', bbox_inches='tight')
+plt.close()
+
+# screen
+
+cols = [col for col in df.columns if "phone_screen" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'phone_screen_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_phone_screen_values', bbox_inches='tight')
+plt.close()
+
+# wifi_visible
+
+cols = [col for col in df.columns if "wifi_visible" in col]
+df_x = df[cols]
+
+print(len(cols))
+print(df_x)
+
+df_x = df_x.dropna(axis=0, how="all")
+sns.heatmap(df_x.isna(), xticklabels=1)
+plt.savefig(f'wifi_visible_values', bbox_inches='tight')
+
+df_q = pd.DataFrame()
+for col in df_x:
+    df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
+
+sns.heatmap(df_q, cbar=False, xticklabels=1)
+plt.savefig(f'cut_wifi_visible_values', bbox_inches='tight')
+plt.close()
+
+# All features
+
+print(len(df))
+print(df)
+
+# df = df.dropna(axis=0, how="all")
+# df = df.dropna(axis=1, how="all")
+sns.heatmap(df.isna())
+plt.savefig(f'all_features', bbox_inches='tight')
+
+print(df.columns[df.isna().all()].tolist())
+print("All NaNs:", df.isna().sum().sum())
+print("Df shape NaNs:", df.shape)

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/standardization_methods_test.py

@@ -0,0 +1,70 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt 
+from sklearn.preprocessing import StandardScaler
+import sys
+
+sys.path.append('/rapids/')
+from src.features import cr_features_helper_methods as crhm
+
+pd.set_option("display.max_columns", None)
+features_win = pd.read_csv("data/interim/p031/empatica_temperature_features/empatica_temperature_python_cr_windows.csv", usecols=[0, 1, 2, 3, 4, 5])
+
+# First standardization method
+excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime', "empatica_temperature_cr_level_1"]
+z1_windows = features_win.copy()
+z1_windows.loc[:, ~z1_windows.columns.isin(excluded_columns)] = StandardScaler().fit_transform(z1_windows.loc[:, ~z1_windows.columns.isin(excluded_columns)])
+z1 = crhm.extract_second_order_features(z1_windows, ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows'], prefix="empatica_temperature_cr_")
+z1 = z1.iloc[:,4:]
+# print(z1)
+
+# Second standardization method
+so_features_reg = crhm.extract_second_order_features(features_win, ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows'], prefix="empatica_temperature_cr_")
+so_features_reg = so_features_reg.iloc[:,4:]
+z2 = pd.DataFrame(StandardScaler().fit_transform(so_features_reg), columns=so_features_reg.columns)
+# print(z2)
+
+# Standardization of the first standardization method values
+z1_z = pd.DataFrame(StandardScaler().fit_transform(z1), columns=z1.columns)
+# print(z1_z)
+
+# For SD
+fig, axs = plt.subplots(3, figsize=(8, 10))
+axs[0].plot(z1['empatica_temperature_cr_squareSumOfComponent_X_SO_sd'])
+axs[0].set_title("Z1 - standardizirana okna, nato ekstrahiranje značilk SO")
+
+axs[1].plot(z2['empatica_temperature_cr_squareSumOfComponent_X_SO_sd'])
+axs[1].set_title("Z2 - ekstrahirane značilke SO 'normalnih' vrednosti, nato standardizacija")
+
+axs[2].plot(z1_z['empatica_temperature_cr_squareSumOfComponent_X_SO_sd'])
+axs[2].set_title("Standardiziran Z1")
+
+fig.suptitle('Z-Score methods for temperature_squareSumOfComponent_SO_sd')
+plt.savefig('z_score_comparison_temperature_squareSumOfComponent_X_SO_sd', bbox_inches='tight')
+
+showcase = pd.DataFrame()
+showcase['Z1__SD'] = z1['empatica_temperature_cr_squareSumOfComponent_X_SO_sd']
+showcase['Z2__SD'] = z2['empatica_temperature_cr_squareSumOfComponent_X_SO_sd']
+showcase['Z1__SD_STANDARDIZED'] = z1_z['empatica_temperature_cr_squareSumOfComponent_X_SO_sd']
+print(showcase)
+
+# For 
+fig, axs = plt.subplots(3, figsize=(8, 10))
+axs[0].plot(z1['empatica_temperature_cr_squareSumOfComponent_X_SO_nlargest'])
+axs[0].set_title("Z1 - standardizirana okna, nato ekstrahiranje značilk SO")
+
+axs[1].plot(z2['empatica_temperature_cr_squareSumOfComponent_X_SO_nlargest'])
+axs[1].set_title("Z2")
+
+axs[2].plot(z1_z['empatica_temperature_cr_squareSumOfComponent_X_SO_nlargest'])
+axs[2].set_title("Standardized Z1")
+
+fig.suptitle('Z-Score methods for temperature_squareSumOfComponent_SO_nlargest')
+plt.savefig('z_score_comparison_temperature_squareSumOfComponent_X_SO_nlargest', bbox_inches='tight')
+
+showcase2 = pd.DataFrame()
+showcase2['Z1__nlargest'] = z1['empatica_temperature_cr_squareSumOfComponent_X_SO_nlargest']
+showcase2['Z2__nlargest'] = z2['empatica_temperature_cr_squareSumOfComponent_X_SO_nlargest']
+showcase2['Z1__nlargest_STANDARDIZED'] = z1_z['empatica_temperature_cr_squareSumOfComponent_X_SO_nlargest']
+print(showcase2)
+

tests/scripts/test_acc.py

@@ -0,0 +1,38 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+import sys
+
+df = pd.read_csv(f"/rapids/data/raw/p03/empatica_accelerometer_raw.csv")
+
+
+df['date'] = pd.to_datetime(df['timestamp'],unit='ms')
+df.set_index('date', inplace=True)
+print(df)
+df = df['double_values_0'].resample("31ms").mean()
+print(df)
+
+st='2021-05-21 12:28:27'
+en='2021-05-21 12:59:12'
+
+df = df.loc[(df.index > st) & (df.index < en)]
+plt.plot(df)
+
+plt.savefig(f'NaN.png')
+sys.exit()
+
+
+plt.plot(df)
+
+esm = pd.read_csv(f"/rapids/data/raw/p03/phone_esm_raw.csv")
+
+esm['date'] = pd.to_datetime(esm['timestamp'],unit='ms')
+esm = esm[esm['date']]
+esm.set_index('date', inplace=True)
+print(esm)
+
+esm = esm['esm_session'].resample("2900ms").mean()
+
+plt.plot(esm)
+plt.savefig(f'NaN.png')

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)
+
+