removed cleaning to make it run

config.yaml

@@ -26,7 +26,7 @@ TIME_SEGMENTS: &time_segments
   INCLUDE_PAST_PERIODIC_SEGMENTS: TRUE # Only relevant if TYPE=PERIODIC, see docs
   TAILORED_EVENTS: # Only relevant if TYPE=EVENT
     COMPUTE: True
-    SEGMENTING_METHOD: "stress_event" # 30_before, 90_before, stress_event
+    SEGMENTING_METHOD: "10_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]
 
@@ -91,7 +91,7 @@ PHONE_ACTIVITY_RECOGNITION:
   EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same AR episode.
   PROVIDERS:
     RAPIDS:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"]
       ACTIVITY_CLASSES:
         STATIONARY: ["still", "tilting"]
@@ -120,7 +120,7 @@ PHONE_APPLICATIONS_FOREGROUND:
     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
   PROVIDERS:
     RAPIDS:
-      COMPUTE: True
+      COMPUTE: False
       INCLUDE_EPISODE_FEATURES: True
       SINGLE_CATEGORIES: ["all", "email"]
       MULTIPLE_CATEGORIES:
@@ -155,7 +155,7 @@ PHONE_BATTERY:
   EPISODE_THRESHOLD_BETWEEN_ROWS: 30 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode.
   PROVIDERS:
     RAPIDS:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"]
       SRC_SCRIPT: src/features/phone_battery/rapids/main.py
 
@@ -169,7 +169,7 @@ PHONE_BLUETOOTH:
       SRC_SCRIPT: src/features/phone_bluetooth/rapids/main.R
 
     DORYAB:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: 
         ALL: 
             DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"]
@@ -190,7 +190,7 @@ PHONE_CALLS:
   CONTAINER: call
   PROVIDERS:
     RAPIDS:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES_TYPE: EPISODES # EVENTS or EPISODES
       CALL_TYPES: [missed, incoming, outgoing]
       FEATURES:
@@ -219,18 +219,19 @@ PHONE_CONVERSATION: # TODO Adapt for speech
 
 # See https://www.rapids.science/latest/features/phone-data-yield/
 PHONE_DATA_YIELD:
-  SENSORS: [#PHONE_ACCELEROMETER,
+  SENSORS: [ #PHONE_ACCELEROMETER,
-            PHONE_ACTIVITY_RECOGNITION,
+            #PHONE_ACTIVITY_RECOGNITION,
-            PHONE_APPLICATIONS_FOREGROUND,
+            #PHONE_APPLICATIONS_FOREGROUND,
-            PHONE_APPLICATIONS_NOTIFICATIONS,
+            #PHONE_APPLICATIONS_NOTIFICATIONS,
-            PHONE_BATTERY,
+            #PHONE_BATTERY,
-            PHONE_BLUETOOTH,
+            PHONE_BLUETOOTH #,
-            PHONE_CALLS,
+            #PHONE_CALLS,
-            PHONE_LIGHT,
+            #PHONE_LIGHT,
-            PHONE_LOCATIONS,
+            #PHONE_LOCATIONS,
-            PHONE_MESSAGES,
+            #PHONE_MESSAGES,
-            PHONE_SCREEN,
+            #PHONE_SCREEN,
-            PHONE_WIFI_VISIBLE]
+            #PHONE_WIFI_VISIBLE
+            ]
   PROVIDERS:
     RAPIDS:
       COMPUTE: True
@@ -243,9 +244,8 @@ 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: ["activities"]
-              "appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]
+      FEATURES: [activities_n_others, activities_inperson, activities_formal]
-      FEATURES: [mean]
       SRC_SCRIPT: src/features/phone_esm/straw/main.py
 
 # See https://www.rapids.science/latest/features/phone-keyboard/
@@ -262,7 +262,7 @@ PHONE_LIGHT:
   CONTAINER: light_sensor
   PROVIDERS:
     RAPIDS:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]
       SRC_SCRIPT: src/features/phone_light/rapids/main.py
 
@@ -276,7 +276,7 @@ PHONE_LOCATIONS:
 
   PROVIDERS:
     DORYAB:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["locationvariance","loglocationvariance","totaldistance","avgspeed","varspeed", "numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","avglengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy","timeathome", "homelabel"]
       DBSCAN_EPS: 100 # meters
       DBSCAN_MINSAMPLES: 5
@@ -291,7 +291,7 @@ PHONE_LOCATIONS:
       SRC_SCRIPT: src/features/phone_locations/doryab/main.py
 
     BARNETT:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"]
       IF_MULTIPLE_TIMEZONES: USE_MOST_COMMON
       MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features
@@ -309,7 +309,7 @@ PHONE_MESSAGES:
   CONTAINER: sms
   PROVIDERS:
     RAPIDS:
-      COMPUTE: True
+      COMPUTE: False
       MESSAGES_TYPES : [received, sent]
       FEATURES: 
         received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
@@ -321,7 +321,7 @@ PHONE_SCREEN:
   CONTAINER: screen
   PROVIDERS:
     RAPIDS:
-      COMPUTE: True
+      COMPUTE: False
       REFERENCE_HOUR_FIRST_USE: 0
       IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable
       IGNORE_EPISODES_LONGER_THAN: 360 # in minutes, set to 0 to disable
@@ -334,7 +334,7 @@ PHONE_SPEECH:
   CONTAINER: speech
   PROVIDERS:
     STRAW:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["meanspeech", "stdspeech", "nlargest", "nsmallest", "medianspeech"]
       SRC_SCRIPT: src/features/phone_speech/straw/main.py
 
@@ -352,7 +352,7 @@ PHONE_WIFI_VISIBLE:
   CONTAINER: wifi
   PROVIDERS:
     RAPIDS:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
       SRC_SCRIPT: src/features/phone_wifi_visible/rapids/main.R
 
@@ -521,10 +521,10 @@ EMPATICA_ACCELEROMETER:
       FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
       SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
     CR:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features
       WINDOWS:
-        COMPUTE: True
+        COMPUTE: False
         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
@@ -548,11 +548,11 @@ EMPATICA_TEMPERATURE:
       FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"]
       SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py
     CR:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", 
                   "stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"]
       WINDOWS:
-        COMPUTE: True
+        COMPUTE: False
         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
@@ -566,14 +566,14 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
       FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
       SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
     CR:
-      COMPUTE: True
+      COMPUTE: False
       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
+        COMPUTE: False
         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
@@ -592,7 +592,7 @@ EMPATICA_BLOOD_VOLUME_PULSE:
       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
+        COMPUTE: False
         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
@@ -606,12 +606,12 @@ EMPATICA_INTER_BEAT_INTERVAL:
       FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"]
       SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py
     CR:
-      COMPUTE: True
+      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            
       PATCH_WITH_BVP: True
       WINDOWS:
-        COMPUTE: True
+        COMPUTE: False
         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
@@ -673,12 +673,12 @@ ALL_CLEANING_INDIVIDUAL:
       DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
       DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
       DROP_HIGHLY_CORRELATED_FEATURES:
-        COMPUTE: True
+        COMPUTE: False
         MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
         CORR_THRESHOLD: 0.95
       SRC_SCRIPT: src/features/all_cleaning_individual/rapids/main.R
     STRAW:
-      COMPUTE: True
+      COMPUTE: False
       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
@@ -686,7 +686,7 @@ ALL_CLEANING_INDIVIDUAL:
       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
+        COMPUTE: False
         MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
         CORR_THRESHOLD: 0.95
       STANDARDIZATION: True
@@ -705,12 +705,12 @@ ALL_CLEANING_OVERALL:
       DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
       DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
       DROP_HIGHLY_CORRELATED_FEATURES:
-        COMPUTE: True
+        COMPUTE: False
         MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
         CORR_THRESHOLD: 0.95
       SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R
     STRAW:
-      COMPUTE: True
+      COMPUTE: False
       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
@@ -718,7 +718,7 @@ ALL_CLEANING_OVERALL:
       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
+        COMPUTE: False
         MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
         CORR_THRESHOLD: 0.95
       STANDARDIZATION: True
@@ -732,7 +732,7 @@ ALL_CLEANING_OVERALL:
 
 PARAMS_FOR_ANALYSIS:
   BASELINE:
-    COMPUTE: True
+    COMPUTE: False
     FOLDER: data/external/baseline
     CONTAINER: [results-survey637813_final.csv,  # Slovenia
                 results-survey358134_final.csv,  # Belgium 1
@@ -743,7 +743,7 @@ PARAMS_FOR_ANALYSIS:
     CATEGORICAL_FEATURES: [gender]
 
   TARGET:
-    COMPUTE: True
+    COMPUTE: False
     LABEL: appraisal_stressfulness_event_mean
     ALL_LABELS: [appraisal_stressfulness_event_mean, appraisal_threat_mean, appraisal_challenge_mean]
                 # PANAS_positive_affect_mean, PANAS_negative_affect_mean, JCQ_job_demand_mean, JCQ_job_control_mean, JCQ_supervisor_support_mean, 

renv.lock

@@ -1,6 +1,6 @@
 {
   "R": {
-    "Version": "4.1.2",
+    "Version": "4.2.3",
     "Repositories": [
       {
         "Name": "CRAN",
@@ -46,10 +46,10 @@
     },
     "Hmisc": {
       "Package": "Hmisc",
-      "Version": "4.4-2",
+      "Version": "5.0-1",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "66458e906b2112a8b1639964efd77d7c"
+      "Hash": "bf9fe82c010a468fb32f913ff56d65e1"
     },
     "KernSmooth": {
       "Package": "KernSmooth",
@@ -104,7 +104,7 @@
       "Package": "RPostgres",
       "Version": "1.4.4",
       "Source": "Repository",
-      "Repository": "CRAN",
+      "Repository": "RSPM",
       "Hash": "c593ecb8dbca9faf3906431be610ca28"
     },
     "Rcpp": {
@@ -181,7 +181,7 @@
       "Package": "base64enc",
       "Version": "0.1-3",
       "Source": "Repository",
-      "Repository": "CRAN",
+      "Repository": "RSPM",
       "Hash": "543776ae6848fde2f48ff3816d0628bc"
     },
     "bit": {
@@ -221,17 +221,24 @@
     },
     "broom": {
       "Package": "broom",
-      "Version": "0.7.3",
+      "Version": "1.0.4",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "5581a5ddc8fe2ac5e0d092ec2de4c4ae"
+      "Hash": "f62b2504021369a2449c54bbda362d30"
+    },
+    "cachem": {
+      "Package": "cachem",
+      "Version": "1.0.7",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "cda74447c42f529de601fe4d4050daef"
     },
     "callr": {
       "Package": "callr",
-      "Version": "3.5.1",
+      "Version": "3.7.3",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "b7d7f1e926dfcd57c74ce93f5c048e80"
+      "Hash": "9b2191ede20fa29828139b9900922e51"
     },
     "caret": {
       "Package": "caret",
@@ -263,10 +270,10 @@
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     "cli": {
       "Package": "cli",
-      "Version": "2.2.0",
+      "Version": "3.6.1",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "3ef298932294b775fa0a3eeaa3a645b0"
+      "Hash": "89e6d8219950eac806ae0c489052048a"
     },
     "clipr": {
       "Package": "clipr",
@@ -286,7 +293,7 @@
       "Package": "codetools",
       "Version": "0.2-18",
       "Source": "Repository",
-      "Repository": "CRAN",
+      "Repository": "RSPM",
       "Hash": "019388fc48e48b3da0d3a76ff94608a8"
     },
     "colorspace": {
@@ -303,6 +310,13 @@
       "Repository": "RSPM",
       "Hash": "0f22be39ec1d141fd03683c06f3a6e67"
     },
+    "conflicted": {
+      "Package": "conflicted",
+      "Version": "1.2.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "bb097fccb22d156624fd07cd2894ddb6"
+    },
     "corpcor": {
       "Package": "corpcor",
       "Version": "1.6.9",
@@ -319,10 +333,10 @@
     },
     "cpp11": {
       "Package": "cpp11",
-      "Version": "0.2.4",
+      "Version": "0.4.3",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "ba66e5a750d39067d888aa7af797fed2"
+      "Hash": "ed588261931ee3be2c700d22e94a29ab"
     },
     "crayon": {
       "Package": "crayon",
@@ -354,10 +368,10 @@
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     "dbplyr": {
       "Package": "dbplyr",
-      "Version": "2.1.1",
+      "Version": "2.3.2",
       "Source": "Repository",
       "Repository": "CRAN",
-      "Hash": "1f37fa4ab2f5f7eded42f78b9a887182"
+      "Hash": "d24305b92db333726aed162a2c23a147"
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     "desc": {
       "Package": "desc",
@@ -382,17 +396,17 @@
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     "dplyr": {
       "Package": "dplyr",
-      "Version": "1.0.5",
+      "Version": "1.1.1",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "d0d76c11ec807eb3f000eba4e3eb0f68"
+      "Hash": "eb5742d256a0d9306d85ea68756d8187"
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     "dtplyr": {
       "Package": "dtplyr",
-      "Version": "1.1.0",
+      "Version": "1.3.1",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "1e14e4c5b2814de5225312394bc316da"
+      "Hash": "54ed3ea01b11e81a86544faaecfef8e2"
     },
     "e1071": {
       "Package": "e1071",
@@ -419,7 +433,7 @@
       "Package": "evaluate",
       "Version": "0.14",
       "Source": "Repository",
-      "Repository": "CRAN",
+      "Repository": "RSPM",
       "Hash": "ec8ca05cffcc70569eaaad8469d2a3a7"
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     "fansi": {
@@ -452,10 +466,10 @@
     },
     "forcats": {
       "Package": "forcats",
-      "Version": "0.5.0",
+      "Version": "1.0.0",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "1cb4279e697650f0bd78cd3601ee7576"
+      "Hash": "1a0a9a3d5083d0d573c4214576f1e690"
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     "foreach": {
       "Package": "foreach",
@@ -492,6 +506,13 @@
       "Repository": "RSPM",
       "Hash": "f568ce73d3d59582b0f7babd0eb33d07"
     },
+    "gargle": {
+      "Package": "gargle",
+      "Version": "1.3.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "bb3208dcdfeb2e68bf33c87601b3cbe3"
+    },
     "gclus": {
       "Package": "gclus",
       "Version": "1.3.2",
@@ -515,10 +536,10 @@
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     "ggplot2": {
       "Package": "ggplot2",
-      "Version": "3.3.2",
+      "Version": "3.4.1",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "4ded8b439797f7b1693bd3d238d0106b"
+      "Hash": "d494daf77c4aa7f084dbbe6ca5dcaca7"
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@@ -557,16 +578,30 @@
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     "glue": {
       "Package": "glue",
-      "Version": "1.4.2",
+      "Version": "1.6.2",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "6efd734b14c6471cfe443345f3e35e29"
+      "Hash": "4f2596dfb05dac67b9dc558e5c6fba2e"
+    },
+    "googledrive": {
+      "Package": "googledrive",
+      "Version": "2.1.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "e88ba642951bc8d1898ba0d12581850b"
+    },
+    "googlesheets4": {
+      "Package": "googlesheets4",
+      "Version": "1.1.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "fd7b97bd862a14297b0bb7ed28a3dada"
     },
     "gower": {
       "Package": "gower",
       "Version": "0.2.2",
       "Source": "Repository",
-      "Repository": "CRAN",
+      "Repository": "RSPM",
       "Hash": "be6a2b3529928bd803d1c437d1d43152"
     },
     "graphlayouts": {
@@ -599,10 +634,10 @@
     },
     "haven": {
       "Package": "haven",
-      "Version": "2.3.1",
+      "Version": "2.5.2",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "221d0ad75dfa03ebf17b1a4cc5c31dfc"
+      "Hash": "8b331e659e67d757db0fcc28e689c501"
     },
     "highr": {
       "Package": "highr",
@@ -613,10 +648,10 @@
     },
     "hms": {
       "Package": "hms",
-      "Version": "1.1.1",
+      "Version": "1.1.3",
       "Source": "Repository",
       "Repository": "CRAN",
-      "Hash": "5b8a2dd0fdbe2ab4f6081e6c7be6dfca"
+      "Hash": "b59377caa7ed00fa41808342002138f9"
     },
     "htmlTable": {
       "Package": "htmlTable",
@@ -648,10 +683,10 @@
     },
     "httr": {
       "Package": "httr",
-      "Version": "1.4.2",
+      "Version": "1.4.5",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "a525aba14184fec243f9eaec62fbed43"
+      "Hash": "f6844033201269bec3ca0097bc6c97b3"
     },
     "huge": {
       "Package": "huge",
@@ -660,6 +695,13 @@
       "Repository": "RSPM",
       "Hash": "a4cde4dd1d2551edb99a3273a4ad34ea"
     },
+    "ids": {
+      "Package": "ids",
+      "Version": "1.0.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "99df65cfef20e525ed38c3d2577f7190"
+    },
     "igraph": {
       "Package": "igraph",
       "Version": "1.2.6",
@@ -704,10 +746,10 @@
     },
     "jsonlite": {
       "Package": "jsonlite",
-      "Version": "1.7.2",
+      "Version": "1.8.4",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "98138e0994d41508c7a6b84a0600cfcb"
+      "Hash": "a4269a09a9b865579b2635c77e572374"
     },
     "knitr": {
       "Package": "knitr",
@@ -760,10 +802,10 @@
     },
     "lifecycle": {
       "Package": "lifecycle",
-      "Version": "1.0.0",
+      "Version": "1.0.3",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "3471fb65971f1a7b2d4ae7848cf2db8d"
+      "Hash": "001cecbeac1cff9301bdc3775ee46a86"
     },
     "listenv": {
       "Package": "listenv",
@@ -774,17 +816,17 @@
     },
     "lubridate": {
       "Package": "lubridate",
-      "Version": "1.7.9.2",
+      "Version": "1.9.2",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "5b5b02f621d39a499def7923a5aee746"
+      "Hash": "e25f18436e3efd42c7c590a1c4c15390"
     },
     "magrittr": {
       "Package": "magrittr",
-      "Version": "2.0.1",
+      "Version": "2.0.3",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "41287f1ac7d28a92f0a286ed507928d3"
+      "Hash": "7ce2733a9826b3aeb1775d56fd305472"
     },
     "markdown": {
       "Package": "markdown",
@@ -800,6 +842,13 @@
       "Repository": "RSPM",
       "Hash": "67101e7448dfd9add4ac418623060262"
     },
+    "memoise": {
+      "Package": "memoise",
+      "Version": "2.0.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "e2817ccf4a065c5d9d7f2cfbe7c1d78c"
+    },
     "mgcv": {
       "Package": "mgcv",
       "Version": "1.8-33",
@@ -830,10 +879,10 @@
     },
     "modelr": {
       "Package": "modelr",
-      "Version": "0.1.8",
+      "Version": "0.1.11",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "9fd59716311ee82cba83dc2826fc5577"
+      "Hash": "4f50122dc256b1b6996a4703fecea821"
     },
     "munsell": {
       "Package": "munsell",
@@ -888,7 +937,7 @@
       "Package": "parallelly",
       "Version": "1.29.0",
       "Source": "Repository",
-      "Repository": "CRAN",
+      "Repository": "RSPM",
       "Hash": "b5f399c9ce96977e22ef32c20b6cfe87"
     },
     "pbapply": {
@@ -907,10 +956,10 @@
     },
     "pillar": {
       "Package": "pillar",
-      "Version": "1.4.7",
+      "Version": "1.9.0",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "3b3dd89b2ee115a8b54e93a34cd546b4"
+      "Hash": "15da5a8412f317beeee6175fbc76f4bb"
     },
     "pkgbuild": {
       "Package": "pkgbuild",
@@ -977,10 +1026,10 @@
     },
     "processx": {
       "Package": "processx",
-      "Version": "3.4.5",
+      "Version": "3.8.0",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "22aab6098cb14edd0a5973a8438b569b"
+      "Hash": "a33ee2d9bf07564efb888ad98410da84"
     },
     "prodlim": {
       "Package": "prodlim",
@@ -1000,7 +1049,7 @@
       "Package": "progressr",
       "Version": "0.9.0",
       "Source": "Repository",
-      "Repository": "CRAN",
+      "Repository": "RSPM",
       "Hash": "ca0d80ecc29903f7579edbabd91f4199"
     },
     "promises": {
@@ -1033,10 +1082,10 @@
     },
     "purrr": {
       "Package": "purrr",
-      "Version": "0.3.4",
+      "Version": "1.0.1",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "97def703420c8ab10d8f0e6c72101e02"
+      "Hash": "d71c815267c640f17ddbf7f16144b4bb"
     },
     "qap": {
       "Package": "qap",
@@ -1052,6 +1101,13 @@
       "Repository": "RSPM",
       "Hash": "d35964686307333a7121eb41c7dcd4e0"
     },
+    "ragg": {
+      "Package": "ragg",
+      "Version": "1.2.5",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "690bc058ea2b1b8a407d3cfe3dce3ef9"
+    },
     "rappdirs": {
       "Package": "rappdirs",
       "Version": "0.3.3",
@@ -1061,17 +1117,17 @@
     },
     "readr": {
       "Package": "readr",
-      "Version": "1.4.0",
+      "Version": "2.1.4",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "2639976851f71f330264a9c9c3d43a61"
+      "Hash": "b5047343b3825f37ad9d3b5d89aa1078"
     },
     "readxl": {
       "Package": "readxl",
-      "Version": "1.3.1",
+      "Version": "1.4.2",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "63537c483c2dbec8d9e3183b3735254a"
+      "Hash": "2e6020b1399d95f947ed867045e9ca17"
     },
     "recipes": {
       "Package": "recipes",
@@ -1110,10 +1166,10 @@
     },
     "reprex": {
       "Package": "reprex",
-      "Version": "0.3.0",
+      "Version": "2.0.2",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "b06bfb3504cc8a4579fd5567646f745b"
+      "Hash": "d66fe009d4c20b7ab1927eb405db9ee2"
     },
     "reshape2": {
       "Package": "reshape2",
@@ -1138,10 +1194,10 @@
     },
     "rlang": {
       "Package": "rlang",
-      "Version": "0.4.10",
+      "Version": "1.1.0",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "599df23c40a4fce9c7b4764f28c37857"
+      "Hash": "dc079ccd156cde8647360f473c1fa718"
     },
     "rmarkdown": {
       "Package": "rmarkdown",
@@ -1173,24 +1229,24 @@
     },
     "rstudioapi": {
       "Package": "rstudioapi",
-      "Version": "0.13",
+      "Version": "0.14",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "06c85365a03fdaf699966cc1d3cf53ea"
+      "Hash": "690bd2acc42a9166ce34845884459320"
     },
     "rvest": {
       "Package": "rvest",
-      "Version": "0.3.6",
+      "Version": "1.0.3",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "a9795ccb2d608330e841998b67156764"
+      "Hash": "a4a5ac819a467808c60e36e92ddf195e"
     },
     "scales": {
       "Package": "scales",
-      "Version": "1.1.1",
+      "Version": "1.2.1",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "6f76f71042411426ec8df6c54f34e6dd"
+      "Hash": "906cb23d2f1c5680b8ce439b44c6fa63"
     },
     "selectr": {
       "Package": "selectr",
@@ -1236,17 +1292,17 @@
     },
     "stringi": {
       "Package": "stringi",
-      "Version": "1.5.3",
+      "Version": "1.7.12",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "a063ebea753c92910a4cca7b18bc1f05"
+      "Hash": "ca8bd84263c77310739d2cf64d84d7c9"
     },
     "stringr": {
       "Package": "stringr",
-      "Version": "1.4.0",
+      "Version": "1.5.0",
       "Source": "Repository",
       "Repository": "CRAN",
-      "Hash": "0759e6b6c0957edb1311028a49a35e76"
+      "Hash": "671a4d384ae9d32fc47a14e98bfa3dc8"
     },
     "survival": {
       "Package": "survival",
@@ -1262,6 +1318,13 @@
       "Repository": "RSPM",
       "Hash": "b227d13e29222b4574486cfcbde077fa"
     },
+    "systemfonts": {
+      "Package": "systemfonts",
+      "Version": "1.0.4",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "90b28393209827327de889f49935140a"
+    },
     "testthat": {
       "Package": "testthat",
       "Version": "3.0.1",
@@ -1269,12 +1332,19 @@
       "Repository": "RSPM",
       "Hash": "17826764cb92d8b5aae6619896e5a161"
     },
+    "textshaping": {
+      "Package": "textshaping",
+      "Version": "0.3.6",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "1ab6223d3670fac7143202cb6a2d43d5"
+    },
     "tibble": {
       "Package": "tibble",
-      "Version": "3.0.4",
+      "Version": "3.2.1",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "71dffd8544691c520dd8e41ed2d7e070"
+      "Hash": "a84e2cc86d07289b3b6f5069df7a004c"
     },
     "tidygraph": {
       "Package": "tidygraph",
@@ -1285,24 +1355,24 @@
     },
     "tidyr": {
       "Package": "tidyr",
-      "Version": "1.1.2",
+      "Version": "1.3.0",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "c40b2d5824d829190f4b825f4496dfae"
+      "Hash": "e47debdc7ce599b070c8e78e8ac0cfcf"
     },
     "tidyselect": {
       "Package": "tidyselect",
-      "Version": "1.1.0",
+      "Version": "1.2.0",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "6ea435c354e8448819627cf686f66e0a"
+      "Hash": "79540e5fcd9e0435af547d885f184fd5"
     },
     "tidyverse": {
       "Package": "tidyverse",
-      "Version": "1.3.0",
+      "Version": "2.0.0",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "bd51be662f359fa99021f3d51e911490"
+      "Hash": "c328568cd14ea89a83bd4ca7f54ae07e"
     },
     "timeDate": {
       "Package": "timeDate",
@@ -1311,6 +1381,13 @@
       "Repository": "RSPM",
       "Hash": "fde4fc571f5f61978652c229d4713845"
     },
+    "timechange": {
+      "Package": "timechange",
+      "Version": "0.2.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "8548b44f79a35ba1791308b61e6012d7"
+    },
     "tinytex": {
       "Package": "tinytex",
       "Version": "0.28",
@@ -1332,6 +1409,13 @@
       "Repository": "RSPM",
       "Hash": "fc77eb5297507cccfa3349a606061030"
     },
+    "tzdb": {
+      "Package": "tzdb",
+      "Version": "0.3.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "b2e1cbce7c903eaf23ec05c58e59fb5e"
+    },
     "utf8": {
       "Package": "utf8",
       "Version": "1.1.4",
@@ -1339,12 +1423,19 @@
       "Repository": "RSPM",
       "Hash": "4a5081acfb7b81a572e4384a7aaf2af1"
     },
-    "vctrs": {
+    "uuid": {
-      "Package": "vctrs",
+      "Package": "uuid",
-      "Version": "0.3.8",
+      "Version": "1.1-0",
       "Source": "Repository",
       "Repository": "CRAN",
-      "Hash": "ecf749a1b39ea72bd9b51b76292261f1"
+      "Hash": "f1cb46c157d080b729159d407be83496"
+    },
+    "vctrs": {
+      "Package": "vctrs",
+      "Version": "0.6.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "06eceb3a5d716fd0654cc23ca3d71a99"
     },
     "viridis": {
       "Package": "viridis",
@@ -1360,6 +1451,13 @@
       "Repository": "RSPM",
       "Hash": "ce4f6271baa94776db692f1cb2055bee"
     },
+    "vroom": {
+      "Package": "vroom",
+      "Version": "1.6.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "7015a74373b83ffaef64023f4a0f5033"
+    },
     "waldo": {
       "Package": "waldo",
       "Version": "0.2.3",
@@ -1376,10 +1474,10 @@
     },
     "withr": {
       "Package": "withr",
-      "Version": "2.3.0",
+      "Version": "2.5.0",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "7307d79f58d1885b38c4f4f1a8cb19dd"
+      "Hash": "c0e49a9760983e81e55cdd9be92e7182"
     },
     "xfun": {
       "Package": "xfun",
@@ -1390,10 +1488,10 @@
     },
     "xml2": {
       "Package": "xml2",
-      "Version": "1.3.2",
+      "Version": "1.3.3",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "d4d71a75dd3ea9eb5fa28cc21f9585e2"
+      "Hash": "40682ed6a969ea5abfd351eb67833adc"
     },
     "xtable": {
       "Package": "xtable",

src/features/phone_esm/straw/esm_activities.py

@@ -0,0 +1,292 @@
+import pandas as pd
+import numpy as np
+
+id2qc = {  44:["What have you mainly been doing within the last 10 minutes?",
+                            "Waar ben je voornamelijk mee bezig geweest de laatste 10 minuten?",
+                            "Kaj ste v glavnem počeli v zadnjih 10 minutah?"],
+                        45:["What type of individual work?",
+                            "Wat voor soort individueel werk?",
+                            "Kakšno vrsto samostojnega dela ste opravljali?"],
+                        46:["How did you work with others?",
+                            "Hoe heb je met anderen gewerkt?",
+                            "Kako ste sodelovali z drugimi?"],
+                        47:["What type of break?",
+                            "Wat voor soort pauze?",
+                            "Kakšno vrsto odmora ste imeli?"],
+                        48:["Where did you travel between?",
+                            "Waar heb je tussen gereisd?",
+                            "Kam ste potovali?"],
+                        49:["Did you use a computer or phone for that?",
+                            "Heb je daarvoor een computer of telefoon gebruikt?",
+                            "Ste za to uporabljali računalnik ali telefon?"],
+                        50:["What kind of an interaction was that?",
+                            "Wat voor interactie was dat?",
+                            "Kakšne vrste sodelovanja je bilo to?"],
+                        51:["How many people were involved besides yourself?",
+                            "Hoeveel mensen waren er behalve jezelf betrokken?",
+                            "Koliko oseb je bilo poleg vas še vpletenih?"],
+                        # 52:["What have you mainly been doing within the last 10 minutes?",
+                        #     "Waar ben je voornamelijk mee bezig geweest de laatste 10 minuten?",
+                        #     "Kaj ste v glavnem počeli v zadnjih 10 minutah?"]
+}
+qc2id = {v:k for k,values in id2qc.items() for v in values}
+
+next_questions = {  44: [45,46,47,48],
+                    45:[49,49],
+                    46:[50,50],
+                    47:[],
+                    48:[],
+                    49:[],
+                    50:[51,51],
+                    51:[]
+                    #52:[45,46,47,48],
+                 }
+
+def esm_activities_LTM_features(
+    df_esm_activities_cleaned: pd.DataFrame,
+) -> pd.DataFrame:
+    """ Function for calculating LTM(Last 10 minutes) features of questionnaire answers. It first corrects the question ids according
+        to esm_instructions and the updated corpus of question_ids. It then processes each LTM question chain to 
+        find relevant social properties given by the answers such as the number of people interacted with, the formality and whether the socializing was done in person.
+
+    Parameters
+    ----------
+    df_esm_activities_cleaned: pd.DataFrame
+        A cleaned up dataframe, which must include esm_instructions, esm_user_answer_numeric.
+
+    Returns
+    -------
+    df_esm_activities_cleaned: pd.DataFrame
+        The same dataframe with columns which contain:
+            ["correct_ids"] - Corrected question_ids
+            ["ans_seq"] - For each LTM question, the sequence of numerical user answers pertaining to this chain of questions.
+            ["n_others","inperson","formal"]- Properties of known potential social encounters as given by process_answers().
+    """
+    #TODO: preprocess questionaires
+        #DONE: correct ids
+    correct_id_df = correct_activity_qids(df_esm_activities_cleaned)
+        #DONE: process subquestions 
+    ids = correct_id_df["correct_ids"]
+    main_q_indices = ids[ids==44].index
+    q_group = []
+    i=-1
+    for id in ids:
+        if(id==44):
+            i=i+1
+        q_group.append(i)
+    correct_id_df["q_group"] = q_group
+    ans_seq = correct_id_df.groupby("q_group").agg({"esm_user_answer_numeric":lambda group: list(group)}).rename(columns={"esm_user_answer_numeric":"ans_seq"})
+    ans_seq.set_index(main_q_indices,inplace=True)
+    # correct_id_df["ans_seq"] = [[] for i in range(len(correct_id_df))]
+    # correct_id_df["ans_seq"].loc[main_q_indices] = correct_id_df.groupby("q_group").agg({"esm_user_answer_numeric":lambda group: list(group)}).values.reshape(-1)
+    #DONE: find types of status for each main question: socializing:[none,irl,online,unknown], num_people:[0,1,2,>2,unknown]
+    processed_ans_df = process_answers(ans_seq)
+    # df_out = df_esm_activities_cleaned.join(test)
+    return df_esm_activities_cleaned.join(processed_ans_df)
+
+    
+""" 
+possible answer sequences for LTM question chains
+
+#alone
+0,0,0 not social
+0,0,1 not social
+0,1,0 not social
+0,1,1 not social
+0,2 not social
+0,3 not social
+0,4 not social
+0,5 not social
+0,6 not social
+#w/ others
+1,0,0,0 1 irl
+1,0,0,1 2 irl
+1,0,0,2 3+ irl
+1,0,1,0 1 irl
+1,0,1,1 2 irl
+1,0,1,2 3+ irl
+1,1,0,0 1 online 
+1,1,0,1 2 online 
+1,1,0,2 3+ online 
+1,1,1,0 1 online 
+1,1,1,1 2 online 
+1,1,1,2 3+ online 
+1,2 positive likely to be more than 2
+1,3 positive
+#break
+2,0 ambiguous
+2,1 positive irl
+2,2 ambiguous
+2,3 ambiguous
+#transit
+3,0 ambiguous
+3,1 ambiguous
+3,2 ambiguous
+"""
+
+
+
+
+#TODO: docstring
+def process_answers(df:pd.DataFrame)-> pd.DataFrame:
+    """ Function to process answer sequences for LTM question chains. It checks the chain of subquestion answers and extracts the following attributes:
+        > n_others: Number of other people interacted with in the last 10 minutes
+            - -1: Number is positive but unknown exactly
+            - 0: No people/alone
+            - 1: One extra person
+            - 2: Two extra people
+            - 3: More than two extra people
+            - NaN : Can't say anything with enough certainty.
+        > inperson: 
+            - True/False: The interaction in question was/wasn't in person.
+            - None: Can't say anything with enough certainty.
+        > formal: 
+            - True/False: The interaction in question was/wasn't formal.
+            - None: Can't say anything with enough certainty.
+    Args:
+        df (pd.DataFrame): _description_
+
+    Returns:
+        pd.DataFrame: _description_
+    """    
+    properties = {"n_others":[],
+                  "inperson":[],
+                  "formal":[]}
+    for ans_seq in df["ans_seq"]:
+        n_other = None
+        inperson = None
+        formal = None
+        if(ans_seq[0]==0):
+            n_other = 0
+        elif(ans_seq[0]==1):
+            if(ans_seq[1]==3):
+                n_other = -1    # anwsered "Other" but did work with other people        
+            elif(ans_seq[1]==2):
+                n_other = 3 #assuming more than 2 people participated in the lecture or presentation
+            elif(ans_seq[1] in [0,1]):
+                    inperson = ans_seq[1]==0 #ans[1]==0, means irl interaction, ==1 means online or phone
+                    formal = ans_seq[2]==0#0 means formal
+                    n_other = ans_seq[3]+1 #ans3 is on [0,2] so we add 1 to make it [1,3]
+        elif(ans_seq[0]==2):
+            formal = False#assuming one does not have a formal meeting during break time
+            if(ans_seq[1]==1):
+                n_other = -1
+                inperson = True
+            #if not 1 then we dont know anythong for sure
+        elif(ans_seq[0]==3):
+            #we cant say whether the persion was carpooling or driving alone.
+            pass
+        properties["n_others"].append(n_other)
+        properties["inperson"].append(inperson)
+        properties["formal"].append(formal)
+
+
+    #df = df.join(pd.DataFrame(properties,index=df.index))
+    return pd.DataFrame(properties,index=df.index)
+
+def correct_activity_qids(df:pd.DataFrame)->pd.DataFrame:
+    """_summary_
+
+    Args:
+        df (pd.DataFrame): _description_
+
+    Returns:
+        pd.DataFrame: Input dataframe with added column "correct_ids"
+    """
+    df["correct_ids"] = df["esm_instructions"].apply(lambda x: qc2id[x])
+    return df
+
+
+
+def process_answers_aggregation(df:pd.core.groupby.generic.DataFrameGroupBy)-> pd.core.groupby.generic.DataFrameGroupBy:
+    """ Function to process answer sequences for LTM question chains. It checks the chain of subquestion answers and extracts the following attributes:
+        > n_others: Number of other people interacted with in the last 10 minutes
+            - -1: Number is positive but unknown exactly
+            - 0: No people/alone
+            - 1: One extra person
+            - 2: Two extra people
+            - 3: More than two extra people
+            - NaN : Can't say anything with enough certainty.
+        > inperson: 
+            - True/False: The interaction in question was/wasn't in person.
+            - None: Can't say anything with enough certainty.
+        > formal: 
+            - True/False: The interaction in question was/wasn't formal.
+            - None: Can't say anything with enough certainty.
+    Args:
+        df (pd.DataFrame): _description_
+
+    Returns:
+        pd.DataFrame: _description_
+    """    
+
+    #print("=======================\nAPPLY START:\ndf=",df.columns,df.local_segment)
+    properties = {"n_others":[],
+                  "inperson":[],
+                  "formal":[]}
+    ans_seq = df["esm_user_answer_numeric"].values
+    n_other = None
+    inperson = None
+    formal = None
+    if(ans_seq[0]==0):
+        n_other = 0
+    elif(ans_seq[0]==1):
+        if(ans_seq[1]==3):
+            n_other = -1    # anwsered "Other" but did work with other people        
+        elif(ans_seq[1]==2):
+            n_other = 3 #assuming more than 2 people participated in the lecture or presentation
+        elif(ans_seq[1] in [0,1]):
+                inperson = ans_seq[1]==0 #ans[1]==0, means irl interaction, ==1 means online or phone
+                formal = ans_seq[2]==0#0 means formal
+                n_other = ans_seq[3]+1 #ans3 is on [0,2] so we add 1 to make it [1,3]
+    elif(ans_seq[0]==2):
+        formal = False#assuming one does not have a formal meeting during break time
+        if(ans_seq[1]==1):
+            n_other = -1
+            inperson = True
+        #if not 1 then we dont know anythong for sure
+    elif(ans_seq[0]==3):
+        #we cant say whether the persion was carpooling or driving alone.
+        pass
+    properties["n_others"].append(n_other)
+    properties["inperson"].append(inperson)
+    properties["formal"].append(formal)
+
+
+    df = df.join(pd.DataFrame(properties,index=df.index))
+    #print("APPLY END:\ndf=",df[["n_others","inperson","formal"]])
+
+    return df
+
+
+
+#test stuff
+def test():
+    from esm_preprocess import preprocess_esm,clean_up_esm
+    df = pd.read_csv("data/raw/p031/phone_esm_with_datetime.csv")
+    df = preprocess_esm(df)
+    df = clean_up_esm(df)
+    df = df[df["questionnaire_id"]==97]
+    original = esm_activities_LTM_features(df)
+    df["local_segment"] = [str(i)+":"+j for i,j in df[["esm_session","device_id"]].values]
+    temp = df.groupby("local_segment")
+    temp2 = temp.apply(process_answers_aggregation)
+
+    #compare with original function results
+    selection = original[original["correct_ids"]==44][["n_others",  "inperson", "formal"]]
+    temp_selection = temp2.loc[selection.index]
+    temp_selection.compare(selection,keep_shape=True,keep_equal =True)
+
+    #print out ans_seq processing results
+    # import json
+    # i = 0
+    # for j,ans in correct_id_df[["esm_json","esm_user_answer"]].values:
+    #     obj = json.loads(j)
+    #     text = obj["esm_instructions"]
+    #     if ("10 minut" in text):
+    #         print("---\n",test.ans_seq.iloc[i])
+    #         print(test[["n_others","inperson","formal"]].values[i])
+    #         i = i+1
+    #     print(text,ans)
+
+#test()

src/features/phone_esm/straw/main.py

@@ -1,4 +1,8 @@
 import pandas as pd
+import sys
+import warnings
+sys.path.append('src/features/phone_esm/straw')   
+from esm_activities import esm_activities_LTM_features,process_answers_aggregation
 
 QUESTIONNAIRE_IDS = {
     "sleep_quality": 1,
@@ -39,24 +43,50 @@ QUESTIONNAIRE_IDS = {
 
 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", 
-                            "appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]
+                            "appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge","activities_n_others","activities_inperson","activities_formal"]
     #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()
+                #print(esm_data.loc[mask].head())
+                #print(time_segment)
+                if not mask.any():
+                    temp = sensor_data_files["sensor_data"]
+                    warnings.warn(f"Warning........... No relevant questions for scale {scale} in {temp}-{time_segment}",RuntimeWarning) 
+                    continue
+                #TODO: calculation of LTM features
+                if scale=="activities":
+                    requested_subset = [req for req in requested_features if req.startswith("activities")]
+                    if not bool(requested_subset):
+                        continue
+                    # ltm_features = esm_activities_LTM_features(esm_data.loc[mask])
+                    # print(esm_data["esm_json"].values)
+                    # print(mask)
+                    # print(esm_data.loc[mask])
+                    # #ltm_features = ltm_features[ltm_features["correct_ids"==44]]
+                    #print(esm_data.loc[mask]["local_segment"])
+                    ltm_features = esm_data.loc[mask].groupby(["local_segment"]).apply(process_answers_aggregation)
+                    #print("PRINTING ltm_features:\n",ltm_features)
+                    ltm_features.rename(columns={"n_others":"activities_n_others","inperson":"activities_inperson","formal":"activities_formal"},inplace=True)
+                    esm_features[requested_subset] = ltm_features.groupby("local_segment").first()[requested_subset]
+                    #print(esm_features.columns)
+                    #print("PRINTING esm_features after rename:\n",ltm_features)
+                    #FIXME: it might be an issue that im calculating for whole time segment and not grouping by "local segment"
+                    #print("~~~~~~~~~~~~~~~~~~~~~~~~===============================~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n     LTM FEATURES STORED...     AFTER RETURN:\n",ltm_features,esm_features[["activities_"+req for req in requested_subset]])
+                if("mean" in features_to_compute):
+                    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()
@@ -64,3 +94,15 @@ def straw_features(sensor_data_files, time_segment, provider, filter_data_by_seg
                 esm_features.rename(columns={'index': 'local_segment'}, inplace=True)
 
     return esm_features
+
+def test_main():
+    import temp_help
+    provider = {
+        "FEATURES":["mean","activities_n_others","activities_inperson","activities_formal"],
+        "SCALES":['activities']
+    }
+    sensor_data_files = {"sensor_data":"data/interim/p069/phone_esm_clean.csv"}
+    s_feat = straw_features(sensor_data_files,"straw_event_stress_event_p069_110",provider,temp_help.filter_data_by_segment)
+    print(s_feat)
+
+#test_main()

src/features/phone_esm/straw/process_user_event_related_segments.py

@@ -67,7 +67,7 @@ def extract_ers(esm_df):
     
     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
+    if segmenting_method in ["10_before", "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.
@@ -79,7 +79,18 @@ def extract_ers(esm_df):
         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":
+        if segmenting_method == "10_before":
+            """The method 10-minutes before simply takes 10 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 = 10 * 60 # in seconds (10 minutes)
+            #TODO: split into small segments with manipulating lenght and shift
+            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 == "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.
             """

src/features/phone_esm/straw/temp_help.py

@@ -0,0 +1,70 @@
+"""This file is TEMPORARY and intended for testing main.py
+"""
+
+def filter_data_by_segment(data, time_segment):
+    data.dropna(subset=["assigned_segments"], inplace=True)
+    if(data.shape[0] == 0): # data is empty
+        data["local_segment"] = data["timestamps_segment"] = None
+        return data
+
+    datetime_regex = "[0-9]{4}[\-|\/][0-9]{2}[\-|\/][0-9]{2} [0-9]{2}:[0-9]{2}:[0-9]{2}"
+    timestamps_regex = "[0-9]{13}"
+    segment_regex = "\[({}#{},{};{},{})\]".format(time_segment, datetime_regex, datetime_regex, timestamps_regex, timestamps_regex)
+    data["local_segment"] = data["assigned_segments"].str.extract(segment_regex, expand=True)
+    data = data.drop(columns=["assigned_segments"])
+    data = data.dropna(subset = ["local_segment"])
+    if(data.shape[0] == 0): # there are no rows belonging to time_segment after droping na
+        data["timestamps_segment"] = None
+    else:
+        data[["local_segment","timestamps_segment"]] = data["local_segment"].str.split(pat =";",n=1, expand=True)
+    
+    # chunk episodes
+    if (not data.empty) and ("start_timestamp" in data.columns) and ("end_timestamp" in data.columns):
+        data = chunk_episodes(data)
+    
+    return data
+
+def chunk_episodes(sensor_episodes):
+    import copy
+    import pandas as pd
+
+    # Deduplicate episodes
+    # Drop rows where segments of start_timestamp and end_timestamp are the same
+    sensor_episodes = sensor_episodes.drop_duplicates(subset=["start_timestamp", "end_timestamp", "local_segment"], keep="first")
+
+    # Delete useless columns
+    for drop_col in ["local_date_time", "local_date", "local_time", "local_hour", "local_minute"]:
+        del sensor_episodes[drop_col]
+    
+    # Avoid SettingWithCopyWarning
+    sensor_episodes = sensor_episodes.copy()
+
+    # Unix timestamp for current segment in milliseconds
+    sensor_episodes[["segment_start_timestamp", "segment_end_timestamp"]] = sensor_episodes["timestamps_segment"].str.split(",", expand=True).astype(int)
+
+    # Compute chunked timestamp
+    sensor_episodes["chunked_start_timestamp"] = sensor_episodes[["start_timestamp", "segment_start_timestamp"]].max(axis=1)
+    sensor_episodes["chunked_end_timestamp"] = sensor_episodes[["end_timestamp", "segment_end_timestamp"]].min(axis=1)
+
+    # Compute duration: intersection of current row and segment
+    sensor_episodes["duration"] = (sensor_episodes["chunked_end_timestamp"] - sensor_episodes["chunked_start_timestamp"]) / (1000 * 60)
+
+    # Merge episodes
+    cols_for_groupby = [col for col in sensor_episodes.columns if col not in ["timestamps_segment", "timestamp", "assigned_segments", "start_datetime", "end_datetime", "start_timestamp", "end_timestamp", "duration", "chunked_start_timestamp", "chunked_end_timestamp"]]
+
+    sensor_episodes_grouped = sensor_episodes.groupby(by=cols_for_groupby, sort=False, dropna=False)
+    merged_sensor_episodes = sensor_episodes_grouped[["duration"]].sum()
+
+    merged_sensor_episodes["start_timestamp"] = sensor_episodes_grouped["chunked_start_timestamp"].first()
+    merged_sensor_episodes["end_timestamp"] = sensor_episodes_grouped["chunked_end_timestamp"].last()
+
+    merged_sensor_episodes.reset_index(inplace=True)
+
+    # Compute datetime
+    merged_sensor_episodes["local_start_date_time"] = pd.to_datetime(merged_sensor_episodes["start_timestamp"], unit="ms", utc=True)
+    merged_sensor_episodes["local_start_date_time"] = pd.concat([data["local_start_date_time"].dt.tz_convert(tz) for tz, data in merged_sensor_episodes.groupby("local_timezone")]).apply(lambda x: x.tz_localize(None).replace(microsecond=0))
+
+    merged_sensor_episodes["local_end_date_time"] = pd.to_datetime(merged_sensor_episodes["end_timestamp"], unit="ms", utc=True)
+    merged_sensor_episodes["local_end_date_time"] = pd.concat([data["local_end_date_time"].dt.tz_convert(tz) for tz, data in merged_sensor_episodes.groupby("local_timezone")]).apply(lambda x: x.tz_localize(None).replace(microsecond=0))
+
+    return merged_sensor_episodes

src/features/utils/utils.py

@@ -14,6 +14,7 @@ def import_path(path):
     sys.modules[module_name] = module
     return module
 
+#TODO:check why segments change to int 
 def filter_data_by_segment(data, time_segment):
     data.dropna(subset=["assigned_segments"], inplace=True)
     if(data.shape[0] == 0): # data is empty
@@ -151,6 +152,7 @@ def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_file
 
     else: 
         segment_colums = pd.DataFrame()
+        print(sensor_features,sensor_features['local_segment'])
         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"])