Snakefile

@@ -164,16 +164,6 @@ for provider in config["PHONE_CONVERSATION"]["PROVIDERS"].keys():
         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_ESM"]["PROVIDERS"].keys():
-    if config["PHONE_ESM"]["PROVIDERS"][provider]["COMPUTE"]:
-        files_to_compute.extend(expand("data/raw/{pid}/phone_esm_raw.csv",pid=config["PIDS"]))
-        files_to_compute.extend(expand("data/raw/{pid}/phone_esm_with_datetime.csv",pid=config["PIDS"]))
-        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")
-
 # 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):
     for provider in config["PHONE_APPLICATIONS_CRASHES"]["PROVIDERS"].keys():
@@ -413,19 +403,9 @@ 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"))
 
-# Baseline features
-if config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["COMPUTE"]:
-    files_to_compute.extend(expand("data/raw/baseline_merged.csv"))
-    files_to_compute.extend(expand("data/raw/{pid}/participant_baseline_raw.csv", pid=config["PIDS"]))
-    files_to_compute.extend(expand("data/interim/{pid}/baseline_questionnaires.csv", pid=config["PIDS"]))
-    files_to_compute.extend(expand("data/processed/features/{pid}/baseline_features.csv", pid=config["PIDS"]))
-
-# 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"]))
+# Demographic features
+files_to_compute.extend(expand("data/raw/baseline_merged.csv"))
+files_to_compute.extend(expand("data/raw/{pid}/participant_baseline_raw.csv", pid=config["PIDS"]))
 
 rule all:
     input:

config.yaml

@@ -234,15 +234,6 @@ PHONE_DATA_YIELD:
       MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1, minimum percentage of valid minutes in an hour to be considered valid.
       SRC_SCRIPT: src/features/phone_data_yield/rapids/main.R
 
-PHONE_ESM:
-  CONTAINER: esm
-  PROVIDERS:
-    STRAW:
-      COMPUTE: True
-      SCALES: ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support"]
-      FEATURES: [mean]
-      SRC_SCRIPT: src/features/phone_esm/straw/main.py
-
 # See https://www.rapids.science/latest/features/phone-keyboard/
 PHONE_KEYBOARD:
   CONTAINER: keyboard
@@ -570,7 +561,7 @@ HISTOGRAM_PHONE_DATA_YIELD:
 
 # 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:
-  PLOT: False
+  PLOT: True
   TIME: RELATIVE_TIME # ABSOLUTE_TIME or RELATIVE_TIME
 
 # See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#3-heatmap-of-recorded-phone-sensors
@@ -638,16 +629,10 @@ ALL_CLEANING_OVERALL:
 
 PARAMS_FOR_ANALYSIS:
   BASELINE:
-    COMPUTE: True
     FOLDER: data/external/baseline
     CONTAINER: [results-survey637813_final.csv,  # Slovenia
                 results-survey358134_final.csv,  # Belgium 1
                 results-survey413767_final.csv  # Belgium 2
     ]
-    QUESTION_LIST: survey637813+question_text.csv
-    FEATURES: [age, gender, startlanguage, limesurvey_demand, limesurvey_control, limesurvey_demand_control_ratio, limesurvey_demand_control_ratio_quartile]
+    FEATURES: [age, gender]
     CATEGORICAL_FEATURES: [gender]
-
-  TARGET:
-    COMPUTE: True
-    LABEL: PANAS_negative_affect_mean

docs/change-log.md

@@ -1,9 +1,4 @@
 # 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

@@ -1,15 +0,0 @@
-# `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

@@ -16,7 +16,6 @@ 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)

mkdocs.yml

@@ -85,7 +85,6 @@ 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

rules/features.smk

@@ -324,27 +324,6 @@ rule conversation_r_features:
     script:
         "../src/features/entry.R"
 
-rule preprocess_esm:
-    input: "data/raw/{pid}/phone_esm_with_datetime.csv"
-    params:
-        scales=lambda wildcards: config["PHONE_ESM"]["PROVIDERS"]["STRAW"]["SCALES"]
-    output: "data/interim/{pid}/phone_esm_clean.csv"
-    script:
-        "../src/features/phone_esm/straw/preprocess.py"
-
-rule esm_features:
-    input:
-        sensor_data = "data/interim/{pid}/phone_esm_clean.csv",
-        time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
-    params:
-        provider = lambda wildcards: config["PHONE_ESM"]["PROVIDERS"][wildcards.provider_key.upper()],
-        provider_key = "{provider_key}",
-        sensor_key = "phone_esm",
-        scales=lambda wildcards: config["PHONE_ESM"]["PROVIDERS"][wildcards.provider_key.upper()]["SCALES"]
-    output: "data/interim/{pid}/phone_esm_features/phone_esm_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",

rules/models.smk

@@ -14,38 +14,3 @@ rule download_baseline_data:
         "data/raw/{pid}/participant_baseline_raw.csv"
     script:
         "../src/data/download_baseline_data.py"
-
-rule baseline_features:
-    input:
-        "data/raw/{pid}/participant_baseline_raw.csv"
-    params:
-        pid="{pid}",
-        features=config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["FEATURES"],
-        question_filename=config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["FOLDER"] + "/" + config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["QUESTION_LIST"]
-    output:
-        interim="data/interim/{pid}/baseline_questionnaires.csv",
-        features="data/processed/features/{pid}/baseline_features.csv"
-    script:
-        "../src/data/baseline_features.py"
-
-rule select_target:
-    input:
-        cleaned_sensor_features = "data/processed/features/{pid}/all_sensor_features_cleaned_rapids.csv"
-    params:
-        target_variable = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
-    output:
-        "data/processed/models/individual_model/{pid}/input.csv"
-    script:
-        "../src/models/select_targets.py"
-
-rule merge_features_and_targets_for_population_model:
-    input:
-        cleaned_sensor_features = "data/processed/features/all_participants/all_sensor_features_cleaned_rapids.csv",
-        demographic_features = expand("data/processed/features/{pid}/baseline_features.csv", pid=config["PIDS"]),
-    params:
-        target_variable=config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
-    output:
-        "data/processed/models/population_model/input.csv"
-    script:
-        "../src/models/merge_features_and_targets_for_population_model.py"
-

rules/preprocessing.smk

@@ -177,6 +177,7 @@ rule resample_episodes_with_datetime:
     script:
         "../src/data/datetime/readable_datetime.R"
 
+
 rule phone_application_categories:
     input:
         "data/raw/{pid}/phone_applications_{type}_with_datetime.csv"

src/data/baseline_features.py

@@ -1,179 +0,0 @@
-import numpy as np
-import pandas as pd
-
-pid = snakemake.params["pid"]
-requested_features = snakemake.params["features"]
-baseline_interim = pd.DataFrame(columns=["qid", "question", "score_original", "score"])
-baseline_features = pd.DataFrame(columns=requested_features)
-question_filename = snakemake.params["question_filename"]
-
-JCQ_DEMAND = "JobEisen"
-JCQ_CONTROL = "JobControle"
-
-dict_JCQ_demand_control_reverse = {
-    JCQ_DEMAND: {
-        3: " [Od mene se ne zahteva,",
-        4: " [Imam dovolj časa, da končam",
-        5: " [Pri svojem delu se ne srečujem s konfliktnimi",
-    },
-    JCQ_CONTROL: {
-        2: " |Moje delo vključuje veliko ponavljajočega",
-        6: " [Pri svojem delu imam zelo malo svobode",
-    },
-}
-
-LIMESURVEY_JCQ_MIN = 1
-LIMESURVEY_JCQ_MAX = 4
-
-DEMAND_CONTROL_RATIO_MIN = 5 / (9 * 4)
-DEMAND_CONTROL_RATIO_MAX = (4 * 5) / 9
-
-JCQ_NORMS = {
-    "F": {
-        0: DEMAND_CONTROL_RATIO_MIN,
-        1: 0.45,
-        2: 0.52,
-        3: 0.62,
-        4: DEMAND_CONTROL_RATIO_MAX,
-    },
-    "M": {
-        0: DEMAND_CONTROL_RATIO_MIN,
-        1: 0.41,
-        2: 0.48,
-        3: 0.56,
-        4: DEMAND_CONTROL_RATIO_MAX,
-    },
-}
-
-participant_info = pd.read_csv(snakemake.input[0], parse_dates=["date_of_birth"])
-
-if not participant_info.empty:
-    if "age" in requested_features:
-        now = pd.Timestamp("now")
-        baseline_features.loc[0, "age"] = (
-            now - participant_info.loc[0, "date_of_birth"]
-        ).days / 365.25245
-    if "gender" in requested_features:
-        baseline_features.loc[0, "gender"] = participant_info.loc[0, "gender"]
-    if "startlanguage" in requested_features:
-        baseline_features.loc[0, "startlanguage"] = participant_info.loc[
-            0, "startlanguage"
-        ]
-    if (
-        ("limesurvey_demand" in requested_features)
-        or ("limesurvey_control" in requested_features)
-        or ("limesurvey_demand_control_ratio" in requested_features)
-    ):
-        participant_info_t = participant_info.T
-        rows_baseline = participant_info_t.index
-
-        if ("limesurvey_demand" in requested_features) or (
-            "limesurvey_demand_control_ratio" in requested_features
-        ):
-            # Find questions about demand, but disregard time (duration of filling in questionnaire)
-            rows_demand = rows_baseline.str.startswith(
-                JCQ_DEMAND
-            ) & ~rows_baseline.str.endswith("Time")
-            limesurvey_demand = (
-                participant_info_t[rows_demand]
-                .reset_index()
-                .rename(columns={"index": "question", 0: "score_original"})
-            )
-            # Extract question IDs from names such as JobEisen[3]
-            limesurvey_demand["qid"] = (
-                limesurvey_demand["question"].str.extract(r"\[(\d+)\]").astype(int)
-            )
-            limesurvey_demand["score"] = limesurvey_demand["score_original"]
-            # Identify rows that include questions to be reversed.
-            rows_demand_reverse = limesurvey_demand["qid"].isin(
-                dict_JCQ_demand_control_reverse[JCQ_DEMAND].keys()
-            )
-            # Reverse the score, so that the maximum value becomes the minimum etc.
-            limesurvey_demand.loc[rows_demand_reverse, "score"] = (
-                LIMESURVEY_JCQ_MAX
-                + LIMESURVEY_JCQ_MIN
-                - 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:
-                baseline_features.loc[0, "limesurvey_demand"] = limesurvey_demand[
-                    "score"
-                ].sum()
-
-        if ("limesurvey_control" in requested_features) or (
-            "limesurvey_demand_control_ratio" in requested_features
-        ):
-            # Find questions about control, but disregard time (duration of filling in questionnaire)
-            rows_control = rows_baseline.str.startswith(
-                JCQ_CONTROL
-            ) & ~rows_baseline.str.endswith("Time")
-            limesurvey_control = (
-                participant_info_t[rows_control]
-                .reset_index()
-                .rename(columns={"index": "question", 0: "score_original"})
-            )
-            # Extract question IDs from names such as JobControle[3]
-            limesurvey_control["qid"] = (
-                limesurvey_control["question"].str.extract(r"\[(\d+)\]").astype(int)
-            )
-            limesurvey_control["score"] = limesurvey_control["score_original"]
-            # Identify rows that include questions to be reversed.
-            rows_control_reverse = limesurvey_control["qid"].isin(
-                dict_JCQ_demand_control_reverse[JCQ_CONTROL].keys()
-            )
-            # Reverse the score, so that the maximum value becomes the minimum etc.
-            limesurvey_control.loc[rows_control_reverse, "score"] = (
-                LIMESURVEY_JCQ_MAX
-                + LIMESURVEY_JCQ_MIN
-                - limesurvey_control.loc[rows_control_reverse, "score_original"]
-            )
-
-            baseline_interim = pd.concat([baseline_interim, limesurvey_control], axis=0, ignore_index=True)
-
-            if "limesurvey_control" in requested_features:
-                baseline_features.loc[0, "limesurvey_control"] = limesurvey_control[
-                    "score"
-                ].sum()
-
-        if "limesurvey_demand_control_ratio" in requested_features:
-            limesurvey_demand_control_ratio = (
-                limesurvey_demand["score"].sum() / limesurvey_control["score"].sum()
-            )
-            if (
-                JCQ_NORMS[participant_info.loc[0, "gender"]][0]
-                <= limesurvey_demand_control_ratio
-                < JCQ_NORMS[participant_info.loc[0, "gender"]][1]
-            ):
-                limesurvey_quartile = 1
-            elif (
-                JCQ_NORMS[participant_info.loc[0, "gender"]][1]
-                <= limesurvey_demand_control_ratio
-                < JCQ_NORMS[participant_info.loc[0, "gender"]][2]
-            ):
-                limesurvey_quartile = 2
-            elif (
-                JCQ_NORMS[participant_info.loc[0, "gender"]][2]
-                <= limesurvey_demand_control_ratio
-                < JCQ_NORMS[participant_info.loc[0, "gender"]][3]
-            ):
-                limesurvey_quartile = 3
-            elif (
-                JCQ_NORMS[participant_info.loc[0, "gender"]][3]
-                <= limesurvey_demand_control_ratio
-                < JCQ_NORMS[participant_info.loc[0, "gender"]][4]
-            ):
-                limesurvey_quartile = 4
-            else:
-                limesurvey_quartile = np.nan
-
-            baseline_features.loc[
-                0, "limesurvey_demand_control_ratio"
-            ] = limesurvey_demand_control_ratio
-            baseline_features.loc[
-                0, "limesurvey_demand_control_ratio_quartile"
-            ] = limesurvey_quartile
-
-if not baseline_interim.empty:
-    baseline_interim.to_csv(snakemake.output["interim"], index=False, encoding="utf-8")
-
-baseline_features.to_csv(snakemake.output["features"], index=False, encoding="utf-8")

src/data/merge_baseline_data.py

@@ -24,6 +24,10 @@ baseline = (
 )
 
 baseline.rename(columns=VARIABLES_TO_TRANSLATE, copy=False, inplace=True)
+now = pd.Timestamp("now")
+baseline = baseline.assign(
+    age=lambda x: (now - x.date_of_birth).dt.days / 365.25245,
+)
 
 baseline.to_csv(snakemake.output[0],
                 index=False,

src/data/streams/aware_micro_mysql/container.R

@@ -1,85 +0,0 @@
-# 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

@@ -1,337 +0,0 @@
-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

@@ -183,21 +183,6 @@ PHONE_CONVERSATION:
       SCRIPTS: # List any python or r scripts that mutate your raw data
         - "src/data/streams/mutations/phone/aware/conversation_ios_timestamp.R"
 
-PHONE_ESM:
-  ANDROID:
-    RAPIDS_COLUMN_MAPPINGS:
-      TIMESTAMP: double_esm_user_answer_timestamp
-      DEVICE_ID: device_id
-      ESM_STATUS: esm_status
-      ESM_USER_ANSWER: esm_user_answer
-      ESM_JSON: esm_json
-      ESM_TRIGGER: esm_trigger
-      ESM_SESSION: esm_session
-      ESM_NOTIFICATION_ID: esm_notification_id
-    MUTATION:
-      COLUMN_MAPPINGS:
-      SCRIPTS:
-
 PHONE_KEYBOARD:
   ANDROID:
     RAPIDS_COLUMN_MAPPINGS:

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(as.numeric(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(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

@@ -67,16 +67,6 @@ PHONE_CONVERSATION:
   - DOUBLE_CONVO_START
   - DOUBLE_CONVO_END
 
-PHONE_ESM:
-  - TIMESTAMP
-  - DEVICE_ID
-  - ESM_STATUS
-  - ESM_USER_ANSWER
-  - ESM_JSON
-  - ESM_TRIGGER
-  - ESM_SESSION
-  - ESM_NOTIFICATION_ID
-
 PHONE_KEYBOARD:
   - TIMESTAMP
   - DEVICE_ID

src/features/phone_esm/straw/esm_JCQ.py

@@ -1,108 +0,0 @@
-import pandas as pd
-
-JCQ_ORIGINAL_MAX = 4
-JCQ_ORIGINAL_MIN = 1
-
-dict_JCQ_demand_control_reverse = {
-    75: (
-        "I was NOT asked",
-        "Men legde mij geen overdreven",
-        "Men legde mij GEEN overdreven",  # Capitalized in some versions
-        "Od mene se NI zahtevalo",
-    ),
-    76: (
-        "I had enough time to do my work",
-        "Ik had voldoende tijd om mijn werk",
-        "Imela sem dovolj časa, da končam",
-        "Imel sem dovolj časa, da končam",
-    ),
-    77: (
-        "I was free of conflicting demands",
-        "Er werden mij op het werk geen tegenstrijdige",
-        "Er werden mij op het werk GEEN tegenstrijdige",  # Capitalized in some versions
-        "Pri svojem delu se NISEM srečeval",
-    ),
-    79: (
-        "My job involved a lot of repetitive work",
-        "Mijn taak omvatte veel repetitief werk",
-        "Moje delo je vključevalo veliko ponavljajočega",
-    ),
-    85: (
-        "On my job, I had very little freedom",
-        "In mijn taak had ik zeer weinig vrijheid",
-        "Pri svojem delu sem imel zelo malo svobode",
-        "Pri svojem delu sem imela zelo malo svobode",
-    ),
-}
-
-
-def reverse_jcq_demand_control_scoring(
-    df_esm_jcq_demand_control: pd.DataFrame,
-) -> pd.DataFrame:
-    """
-    This function recodes answers in Job content questionnaire by first incrementing them by 1,
-    to be in line with original (1-4) scoring.
-    Then, some answers are reversed (i.e. 1 becomes 4 etc.), because the questions are negatively phrased.
-    These answers are listed in dict_JCQ_demand_control_reverse and identified by their question ID.
-    However, the existing data is checked against literal phrasing of these questions
-        to protect against wrong numbering of questions (differing question IDs).
-
-    Parameters
-    ----------
-    df_esm_jcq_demand_control: pd.DataFrame
-        A cleaned up dataframe, which must also include esm_user_answer_numeric.
-
-    Returns
-    -------
-    df_esm_jcq_demand_control: pd.DataFrame
-        The same dataframe with a column esm_user_score containing answers recoded and reversed.
-    """
-    df_esm_jcq_demand_control_unique_answers = (
-        df_esm_jcq_demand_control.groupby("question_id")
-        .esm_instructions.value_counts()
-        .rename()
-        .reset_index()
-    )
-    # Tabulate all possible answers to each question (group by question ID).
-    for q_id in dict_JCQ_demand_control_reverse.keys():
-        # Look through all answers that need to be reversed.
-        possible_answers = df_esm_jcq_demand_control_unique_answers.loc[
-            df_esm_jcq_demand_control_unique_answers["question_id"] == q_id,
-            "esm_instructions",
-        ]
-        # These are all answers to a given question (by q_id).
-        answers_matches = possible_answers.str.startswith(
-            dict_JCQ_demand_control_reverse.get(q_id)
-        )
-        # See if they are expected, i.e. included in the dictionary.
-        if ~answers_matches.all():
-            print("One of the answers that occur in the data should not be reversed.")
-            print("This was the answer found in the data: ")
-            raise KeyError(possible_answers[~answers_matches])
-            # In case there is an unexpected answer, raise an exception.
-
-    try:
-        df_esm_jcq_demand_control = df_esm_jcq_demand_control.assign(
-            esm_user_score=lambda x: x.esm_user_answer_numeric + 1
-        )
-        # Increment the original answer by 1
-        # to keep in line with traditional scoring (JCQ_ORIGINAL_MIN - JCQ_ORIGINAL_MAX).
-        df_esm_jcq_demand_control[
-            df_esm_jcq_demand_control["question_id"].isin(
-                dict_JCQ_demand_control_reverse.keys()
-            )
-        ] = df_esm_jcq_demand_control[
-            df_esm_jcq_demand_control["question_id"].isin(
-                dict_JCQ_demand_control_reverse.keys()
-            )
-        ].assign(
-            esm_user_score=lambda x: JCQ_ORIGINAL_MAX
-            + JCQ_ORIGINAL_MIN
-            - x.esm_user_score
-        )
-        # Reverse the items that require it.
-    except AttributeError as e:
-        print("Please, clean the dataframe first using features.esm.clean_up_esm.")
-        print(e)
-
-    return df_esm_jcq_demand_control

src/features/phone_esm/straw/esm_preprocess.py

@@ -1,135 +0,0 @@
-import json
-
-import numpy as np
-import pandas as pd
-
-ESM_TYPE = {
-    "text": 1,
-    "radio": 2,
-    "checkbox": 3,
-    "likert": 4,
-    "quick_answers": 5,
-    "scale": 6,
-    "datetime": 7,
-    "pam": 8,
-    "number": 9,
-    "web": 10,
-    "date": 11,
-}
-
-QUESTIONNAIRE_IDS = {
-    "sleep_quality": 1,
-    "PANAS_positive_affect": 8,
-    "PANAS_negative_affect": 9,
-    "JCQ_job_demand": 10,
-    "JCQ_job_control": 11,
-    "JCQ_supervisor_support": 12,
-    "JCQ_coworker_support": 13,
-    "PFITS_supervisor": 14,
-    "PFITS_coworkers": 15,
-    "UWES_vigor": 16,
-    "UWES_dedication": 17,
-    "UWES_absorption": 18,
-    "COPE_active": 19,
-    "COPE_support": 20,
-    "COPE_emotions": 21,
-    "balance_life_work": 22,
-    "balance_work_life": 23,
-    "recovery_experience_detachment": 24,
-    "recovery_experience_relaxation": 25,
-    "symptoms": 26,
-    "appraisal_stressfulness_event": 87,
-    "appraisal_threat": 88,
-    "appraisal_challenge": 89,
-    "appraisal_event_time": 90,
-    "appraisal_event_duration": 91,
-    "appraisal_event_work_related": 92,
-    "appraisal_stressfulness_period": 93,
-    "late_work": 94,
-    "work_hours": 95,
-    "left_work": 96,
-    "activities": 97,
-    "coffee_breaks": 98,
-    "at_work_yet": 99,
-}
-
-ESM_STATUS_ANSWERED = 2
-
-GROUP_SESSIONS_BY = ["participant_id", "device_id", "esm_session"]
-
-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.
-
-
-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_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 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_TYPE.get("radio"),
-        ESM_TYPE.get("scale"),
-        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

@@ -1,63 +0,0 @@
-import pandas as pd
-
-QUESTIONNAIRE_IDS = {
-    "sleep_quality": 1,
-    "PANAS_positive_affect": 8,
-    "PANAS_negative_affect": 9,
-    "JCQ_job_demand": 10,
-    "JCQ_job_control": 11,
-    "JCQ_supervisor_support": 12,
-    "JCQ_coworker_support": 13,
-    "PFITS_supervisor": 14,
-    "PFITS_coworkers": 15,
-    "UWES_vigor": 16,
-    "UWES_dedication": 17,
-    "UWES_absorption": 18,
-    "COPE_active": 19,
-    "COPE_support": 20,
-    "COPE_emotions": 21,
-    "balance_life_work": 22,
-    "balance_work_life": 23,
-    "recovery_experience_detachment": 24,
-    "recovery_experience_relaxation": 25,
-    "symptoms": 26,
-    "appraisal_stressfulness_event": 87,
-    "appraisal_threat": 88,
-    "appraisal_challenge": 89,
-    "appraisal_event_time": 90,
-    "appraisal_event_duration": 91,
-    "appraisal_event_work_related": 92,
-    "appraisal_stressfulness_period": 93,
-    "late_work": 94,
-    "work_hours": 95,
-    "left_work": 96,
-    "activities": 97,
-    "coffee_breaks": 98,
-    "at_work_yet": 99,
-}
-
-
-def straw_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
-    esm_data = pd.read_csv(sensor_data_files["sensor_data"])
-    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"]
-    #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))
-    esm_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
-    if not esm_data.empty:
-        esm_data = filter_data_by_segment(esm_data, time_segment)
-
-        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
-                esm_features[scale + "_mean"] = esm_data.loc[mask].groupby(["local_segment"])["esm_user_score"].mean()
-                #TODO Create the column esm_user_score in esm_clean. Currently, this is only done when reversing.
-
-            esm_features = esm_features.reset_index()
-    return esm_features

src/features/phone_esm/straw/preprocess.py

@@ -1,25 +0,0 @@
-from esm_preprocess import *
-from esm_JCQ import reverse_jcq_demand_control_scoring
-
-requested_scales = snakemake.params["scales"]
-
-df_esm = pd.read_csv(snakemake.input[0])
-df_esm_preprocessed = preprocess_esm(df_esm)
-
-if not all([scale in QUESTIONNAIRE_IDS for scale in requested_scales]):
-    unknown_scales = set(requested_scales) - set(QUESTIONNAIRE_IDS.keys())
-    print("The requested questionnaire name should be one of the following:")
-    print(QUESTIONNAIRE_IDS.keys())
-    raise ValueError("You requested scales not collected: ", unknown_scales)
-
-df_esm_clean = clean_up_esm(df_esm_preprocessed)
-df_esm_clean["esm_user_score"] = df_esm_clean["esm_user_answer_numeric"]
-
-for scale in requested_scales:
-    questionnaire_id = QUESTIONNAIRE_IDS[scale]
-    mask = df_esm_clean["questionnaire_id"] == questionnaire_id
-    if scale.startswith("JCQ"):
-        df_esm_clean.loc[mask] = reverse_jcq_demand_control_scoring(df_esm_clean.loc[mask])
-    #TODO Reverse other questionnaires if needed and/or adapt esm_user_score to original scoring.
-
-df_esm_clean.to_csv(snakemake.output[0], index=False)

src/features/phone_locations/barnett/daily_features.R

@@ -25,11 +25,9 @@ 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, ".*")))
-
-  does_not_span = nrow(segment_labels) == 0 || nrow(location) == 0 || all(location$is_daily == FALSE) || (max(location$timestamp) - min(location$timestamp) < 86400000)
-
-  if(is.na(does_not_span) || does_not_span){
-      warning("Barnett's location features cannot be computed for data or time segments that do not span one or more entire days (00:00:00 to 23:59:59). Values below point to the problem:",
+  
+  if(nrow(segment_labels) == 0 || nrow(location) == 0 || all(location$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:",
             "\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/models/helper.py

@@ -1,18 +0,0 @@
-import pandas as pd
-
-
-def retain_target_column(df_input: pd.DataFrame, target_variable_name: str):
-    column_names = df_input.columns
-    esm_names_index = column_names.str.startswith("phone_esm_straw")
-    # Find all columns coming from phone_esm, since these are not features for our purposes and we will drop them.
-    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")
-    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.
-    # Add it back to the very and of the data frame and rename it to target.
-    return sensor_features_plus_target

src/models/merge_features_and_targets_for_population_model.py

@@ -1,20 +0,0 @@
-import pandas as pd
-
-from helper import retain_target_column
-
-sensor_features = pd.read_csv(snakemake.input["cleaned_sensor_features"])
-
-all_baseline_features = pd.DataFrame()
-for baseline_features_path in snakemake.input["demographic_features"]:
-    pid = baseline_features_path.split("/")[3]
-    baseline_features = pd.read_csv(baseline_features_path)
-    baseline_features = baseline_features.assign(pid=pid)
-    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")
-
-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)

src/models/select_targets.py

@@ -1,11 +0,0 @@
-import pandas as pd
-
-from helper import retain_target_column
-
-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)

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")