source("packrat/init.R")

library(dplyr)

unify_ios_battery <- function(ios_battery){
    ios_battery <- ios_battery %>%
        mutate(battery_status = replace(battery_status, battery_status == 3, 5),
            battery_status = replace(battery_status, battery_status == 1, 3))
}

unify_ios_calls <- function(ios_calls){
    # Android’s call types 1=incoming, 2=outgoing, 3=missed
    # iOS' call status 1=incoming, 2=connected, 3=dialing, 4=disconnected
    # iOS' call types based on call status: (1,2,4)=incoming=1, (3,2,4)=outgoing=2, (1,4) or (3,4)=missed=3
    # Sometimes (due to a possible bug in Aware) sequences get logged on the exact same timestamp, thus 3-item sequences can be 2,3,4 or 3,2,4
    # Even tho iOS stores the duration of ringing/dialing for missed calls, we set it to 0 to match Android

    ios_calls <- ios_calls %>%
        arrange(trace, timestamp, call_type) %>% 
        group_by(trace) %>%
                # search for the disconnect event, as it is common to outgoing, received and missed calls
        mutate(completed_call = ifelse(call_type == 4, 2, 0), 
                # assign the same ID to all events before a 4
                completed_call = cumsum(c(1, head(completed_call, -1) != tail(completed_call, -1))), 
                # hack to match ID of last event (4) to that of the previous rows
                completed_call = ifelse(call_type == 4, completed_call - 1, completed_call)) %>% 
        summarise(call_type_sequence = paste(call_type, collapse = ","), # collapse all events before a 4
                    # use this if Android measures calls' duration from pick up to hang up
                    # duration = last(call_duration), 
                    # sanity check, timestamp_diff should be equal or close to duration sum
                    # timestamp_diff = trunc((last(timestamp) - first(timestamp)) / 1000) 
                    # use this if Android measures calls' duration from dialing/ringing to hang up
                    call_duration = sum(call_duration), 

                    timestamp = first(timestamp),
                    utc_date_time = first(utc_date_time),
                    local_date_time = first(local_date_time),
                    local_date = first(local_date),
                    local_time = first(local_time),
                    local_hour = first(local_hour),
                    local_minute = first(local_minute),
                    local_day_segment = first(local_day_segment)
                    ) %>% 
        mutate(call_type = case_when(
            call_type_sequence == "1,2,4" | call_type_sequence == "2,1,4" ~ 1, # incoming
            call_type_sequence == "1,4" ~ 3, # missed
            call_type_sequence == "3,2,4" | call_type_sequence == "2,3,4" ~ 2, # outgoing
            call_type_sequence == "3,4" ~ 4, # outgoing missed, we create this temp missed state to assign a duration of 0 below
            TRUE ~ -1), # other, call sequences without a disconnect (4) event are discarded
            # assign a duration of 0 to incoming and outgoing missed calls
            call_duration = ifelse(call_type == 3 | call_type == 4, 0, call_duration), 
            # get rid of the temp missed call type, set to 3 to match Android
            call_type = ifelse(call_type == 4, 3, call_type) 
        ) %>% 
        # discard sequences without an event 4 (disconnect)
        filter(call_type > 0) %>%
        ungroup() %>%
        arrange(timestamp)

    return(ios_calls)
}


sensor_data <- read.csv(snakemake@input[["sensor_data"]], stringsAsFactors = FALSE)
participant_info <- snakemake@input[["participant_info"]]
sensor <- snakemake@params[["sensor"]]
platform <- readLines(participant_info, n=2)[[2]]

if(sensor == "calls"){
    if(platform == "ios"){
        sensor_data = unify_ios_calls(sensor_data)
    }
    # android calls remain unchanged
} else if(sensor == "battery"){
    if(platform == "ios"){
        sensor_data = unify_ios_battery(sensor_data)
    }
    # android battery remains unchanged
}
write.csv(sensor_data, snakemake@output[[1]], row.names = FALSE)