diff --git a/src/data/datetime/assign_to_event_segments.R b/src/data/datetime/assign_to_event_segments.R
new file mode 100644
index 00000000..3b94ab05
--- /dev/null
+++ b/src/data/datetime/assign_to_event_segments.R
@@ -0,0 +1,42 @@
+validate_overlapping_event_segments <- function(segments){
+  # Check for overlapping segments (not allowed because our resampling episode algorithm would have to have a second instead of minute granularity that increases storage and computation time)
+  overlapping <-  segments  %>% 
+    group_by(label) %>% 
+    arrange(segment_start_ts) %>% 
+    mutate(overlaps = if_else(segment_start_ts <= lag(segment_end_ts), TRUE, FALSE),
+           overlapping_segments = glue("a) [{lag(label)},\t{lag(event_timestamp)},\t{lag(length)},\t{lag(shift)},\t{lag(shift_direction)},\t{lag(device_id)}] \n",
+                                       "b) [{label},\t{event_timestamp},\t{length},\t{shift},\t{shift_direction},\t{device_id}]"))
+  if(any(overlapping$overlaps, na.rm = TRUE))
+    stop("One or more event time segments overlap for ",overlapping$device_id[[1]],
+         ", modify their lengths so they don't:\n", paste0(overlapping %>% filter(overlaps == TRUE) %>% pull(overlapping_segments), collapse = "\n"))
+}
+
+infer_event_segments <- function(tz, segments){ 
+  time_format_fn <- stamp("23:51:15", orders="HMS", quiet = TRUE)
+  inferred <- segments %>% 
+    mutate(shift = ifelse(shift == "0", "0seconds", shift),
+           segment_start_ts = event_timestamp + (as.integer(seconds(lubridate::duration(shift))) * ifelse(shift_direction >= 0, 1, -1) * 1000),
+           segment_end_ts = segment_start_ts + (as.integer(seconds(lubridate::duration(length))) * 1000),
+           segment_id_start = lubridate::as_datetime(segment_start_ts/1000, tz = tz), 
+           segment_id_end = lubridate::as_datetime(segment_end_ts/1000, tz = tz),
+           segment_end_ts = segment_end_ts + 999,
+           segment_id = glue("[{label}#{start_date} {start_time},{end_date} {end_time};{segment_start_ts},{segment_end_ts}]",
+                             start_date=lubridate::date(segment_id_start),
+                             start_time=time_format_fn(segment_id_start),
+                             end_date=lubridate::date(segment_id_end),
+                             end_time=time_format_fn(segment_id_end)))
+  validate_overlapping_event_segments(inferred)
+  return(inferred)
+}
+
+assign_to_event_segments <- function(sensor_data, time_segments){
+  sensor_data <- sensor_data %>% 
+    group_by(local_timezone) %>% 
+    nest() %>% 
+    mutate(inferred_time_segments = map(local_timezone, infer_event_segments, time_segments),
+           data = map2(data, inferred_time_segments, assign_rows_to_segments)) %>% 
+    select(-inferred_time_segments) %>% 
+    unnest(data) %>% 
+    arrange(timestamp) %>%
+    ungroup()
+}
\ No newline at end of file
diff --git a/src/data/datetime/assign_to_periodic_segments.R b/src/data/datetime/assign_to_periodic_segments.R
new file mode 100644
index 00000000..e2c13262
--- /dev/null
+++ b/src/data/datetime/assign_to_periodic_segments.R
@@ -0,0 +1,110 @@
+day_type_delay <- function(time_segments, day_type, include_past_periodic_segments){
+  # Return a delay in days to consider or not the first row of data
+  delay <- time_segments %>% 
+    mutate(length_duration = duration(length)) %>%  
+    filter(repeats_on == day_type) %>% arrange(-length_duration) %>% 
+    pull(length_duration) %>% 
+    first()
+  return(if_else(is.na(delay) | include_past_periodic_segments == FALSE, duration("0days"), delay))
+}
+
+get_segment_dates <- function(data, local_timezone, day_type, delay){
+  # Based on the data we are processing we extract unique dates to build segments
+  dates <-  data %>% 
+    distinct(local_date) %>% 
+    mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>% 
+    complete(local_date_obj = seq(date(min(local_date_obj) - delay), date(max(local_date_obj) + delay), by="days")) %>%
+    mutate(local_date = replace_na(as.character(date(local_date_obj))))
+  
+  if(day_type == "every_day")
+    dates <- dates %>% mutate(every_day = 0)
+  else if (day_type == "wday")
+    dates <- dates %>% mutate(wday = wday(local_date_obj, week_start = 1))
+  else if (day_type == "mday")
+    dates <- dates %>% mutate(mday = mday(local_date_obj))
+  else if (day_type == "qday")
+    dates <- dates %>% mutate(qday = qday(local_date_obj))
+  else if (day_type == "yday")
+    dates <- dates %>% mutate(yday = yday(local_date_obj))
+  return(dates)
+}
+
+infer_existent_periodic_segments <- function(existent_dates, segments){
+  # build the actual time segments taking into account the data and users' requested length and repeat schedule
+  # segment datetime labels are computed on UTC
+  crossing(segments, existent_dates) %>%
+    pivot_longer(cols = c(every_day,wday, mday, qday, yday), names_to = "day_type", values_to = "day_value") %>%
+    filter(repeats_on == day_type & repeats_value == day_value) %>%
+    mutate(segment_id_start = lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM")) + period(overlap_duration),
+            segment_id_end = segment_id_start + lubridate::duration(length))
+}
+
+dedup_nonoverlapping_periodic_segments <- function(nested_inferred_time_segments){
+  # Overlapping segments exist when their length is longer than their repeating frequency, e.g. twoday segements starting on every day
+  # In process_time_segments we decompose those segments into non-overlapping ones, e.g. twodayA +0days and twodayB +1days
+  # This means that any date will have more than one non-overlapping instances, that we need to dedup
+  # We choose alternating non-overlapping instances to guarantee any data row is only neeeded in one instance at a time
+  # d1,r1,twoday0
+  # d2,r2,twoday0 twoday1 
+  # d3,r3,twoday1 twoday0 
+  # d4,r4,twoday0 twoday1 
+  new_segments <- data.frame(nested_inferred_time_segments %>% 
+                                group_by(original_label) %>%
+                                mutate(max_groups = max(overlap_id) + 1) %>% 
+                                # select(label, segment_id_start, segment_id_end, overlap_id, max_groups) %>% 
+                                nest() %>%  
+                                mutate(data = map(data, function(nested_data){
+                                  nested_data <- nested_data %>% arrange( segment_id_start, segment_id_end) %>% 
+                                    group_by(segment_id_start) %>% 
+                                    mutate(n_id = ((cur_group_id()-1) %% max_groups)) %>% 
+                                    filter(overlap_id == n_id) %>% 
+                                    # select(label, segment_id_start, overlap_id, n_id) %>% 
+                                    ungroup()
+                                })) %>% 
+                                unnest(cols = data) %>% 
+                                ungroup())
+}
+
+
+
+add_periodic_segment_timestamps_and_id <- function(segments, local_timezone){
+  # segment timestamps are computed on the data's timezone(s)
+  time_format_fn <- stamp("23:51:15", orders="HMS", quiet = TRUE)
+  segments %>% mutate(segment_start_ts = as.numeric(lubridate::force_tz(segment_id_start, tzone = local_timezone)) * 1000,
+                      segment_end_ts = segment_start_ts + as.numeric(lubridate::duration(length)) * 1000 + 999,
+                      segment_id = glue("[{label}#{start_date} {start_time},{end_date} {end_time};{segment_start_ts},{segment_end_ts}]",
+                                        start_date=lubridate::date(segment_id_start),
+                                        start_time=time_format_fn(segment_id_start),
+                                        end_date=lubridate::date(segment_id_end),
+                                        end_time=time_format_fn(segment_id_end) )) %>% 
+                drop_na(segment_start_ts, segment_end_ts)
+}
+
+assign_to_periodic_segments <- function(sensor_data, time_segments, include_past_periodic_segments){
+  time_segments <- time_segments %>% mutate(length_duration = duration(length))
+  every_day_delay <- duration("0days")
+  wday_delay <- day_type_delay(time_segments, "wday", include_past_periodic_segments)
+  mday_delay <- day_type_delay(time_segments, "mday", include_past_periodic_segments)
+  qday_delay <- day_type_delay(time_segments, "qday", include_past_periodic_segments)
+  yday_delay <- day_type_delay(time_segments, "yday", include_past_periodic_segments)
+  
+  sensor_data <- sensor_data %>%
+    group_by(local_timezone) %>% 
+    nest() %>% 
+    mutate(every_date = map2(data, local_timezone, get_segment_dates, "every_day", every_day_delay),
+           week_dates = map2(data, local_timezone, get_segment_dates, "wday", wday_delay),
+           month_dates = map2(data, local_timezone, get_segment_dates, "mday", mday_delay),
+           quarter_dates = map2(data, local_timezone, get_segment_dates, "qday", qday_delay),
+           year_dates = map2(data, local_timezone, get_segment_dates, "yday", yday_delay),
+           existent_dates = pmap(list(every_date, week_dates, month_dates, quarter_dates, year_dates), function(every_date, week_dates, month_dates, quarter_dates, year_dates) reduce(list(every_date, week_dates,month_dates, quarter_dates, year_dates), .f=full_join)),
+           inferred_time_segments = map(existent_dates, infer_existent_periodic_segments, time_segments), 
+           inferred_time_segments = map(inferred_time_segments, dedup_nonoverlapping_periodic_segments),
+           inferred_time_segments = map(inferred_time_segments, add_periodic_segment_timestamps_and_id, local_timezone),
+           data = map2(data, inferred_time_segments, assign_rows_to_segments)) %>%
+    select(-existent_dates, -inferred_time_segments, -every_date, -week_dates, -month_dates, -quarter_dates, -year_dates) %>%
+    unnest(cols = data) %>% 
+    arrange(timestamp) %>% 
+    ungroup()
+  
+  return(sensor_data)
+}
\ No newline at end of file
diff --git a/src/data/datetime/assign_to_time_segment.R b/src/data/datetime/assign_to_time_segment.R
index f31807b7..bf2eb551 100644
--- a/src/data/datetime/assign_to_time_segment.R
+++ b/src/data/datetime/assign_to_time_segment.R
@@ -1,81 +1,19 @@
 library("tidyverse")
+library("glue")
 library("lubridate", warn.conflicts = F)
 options(scipen=999)
 
-day_type_delay <- function(time_segments, day_type, include_past_periodic_segments){
-  delay <- time_segments %>% mutate(length_duration = duration(length)) %>%  filter(repeats_on == day_type) %>% arrange(-length_duration) %>% pull(length_duration) %>% first()
-  return(if_else(is.na(delay) | include_past_periodic_segments == FALSE, duration("0days"), delay))
-}
-
-get_segment_dates <- function(data, local_timezone, day_type, delay){
-  dates <-  data %>% 
-            distinct(local_date) %>% 
-            mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>% 
-            complete(local_date_obj = seq(date(min(local_date_obj) - delay), date(max(local_date_obj) + delay), by="days")) %>%
-            mutate(local_date = replace_na(as.character(date(local_date_obj))))
-
-  if(day_type == "every_day")
-    dates <- dates %>% mutate(every_day = 0)
-  else if (day_type == "wday")
-    dates <- dates %>% mutate(wday = wday(local_date_obj, week_start = 1))
-  else if (day_type == "mday")
-    dates <- dates %>% mutate(mday = mday(local_date_obj))
-  else if (day_type == "qday")
-    dates <- dates %>% mutate(qday = qday(local_date_obj))
-  else if (day_type == "yday")
-    dates <- dates %>% mutate(yday = yday(local_date_obj))
-  return(dates)
-}
-
-create_nonoverlapping_periodic_segments <- function(nested_inferred_time_segments){
-  new_segments <- (data.frame(nested_inferred_time_segments %>% 
-    group_by(original_label) %>%
-    mutate(max_groups = max(overlap_id) + 1) %>% 
-    # select(label, segment_id_start, segment_id_end, overlap_id, max_groups) %>% 
-    nest() %>%  
-    mutate(data = map(data, function(nested_data){
-        nested_data <- nested_data %>% arrange( segment_id_start, segment_id_end) %>% 
-        group_by(segment_id_start) %>% 
-        mutate(n_id = ((cur_group_id()-1) %% max_groups)) %>% 
-        filter(overlap_id == n_id) %>% 
-        # select(label, segment_id_start, overlap_id, n_id) %>% 
-        ungroup()
-    })) %>% 
-    unnest(cols = data) %>% 
-    ungroup()
-  ))
-  return(new_segments)
-}
-
-
-assign_rows_to_segments <- function(nested_data, nested_inferred_time_segments){
-  nested_data <- nested_data %>% mutate(assigned_segments = "")
-  for(i in seq_len(nrow(nested_inferred_time_segments))) {
-    segment <- nested_inferred_time_segments[i,]
-    nested_data$assigned_segments <- ifelse(segment$segment_start_ts<= nested_data$timestamp & segment$segment_end_ts >= nested_data$timestamp,
-                                            stringi::stri_c(nested_data$assigned_segments, segment$segment_id, sep = "|"), nested_data$assigned_segments)
+assign_rows_to_segments <- function(data, segments){
+  # This function is used by all segment types, we use data.tables because they are fast
+  data <- data.table::as.data.table(data)
+  data[, assigned_segments := ""]
+  for(i in seq_len(nrow(segments))) {
+    segment <- segments[i,]
+    data[segment$segment_start_ts<= timestamp & segment$segment_end_ts >= timestamp,
+         assigned_segments := stringi::stri_c(assigned_segments, segment$segment_id, sep = "|")]
   }
-  nested_data$assigned_segments <- substring(nested_data$assigned_segments, 2)
-  return(nested_data)
-}
-
-assign_rows_to_segments_frequency <- function(nested_data, nested_timezone, time_segments){
-  for(i in 1:nrow(time_segments)) {
-    segment <- time_segments[i,]
-    nested_data$assigned_segments <- ifelse(segment$segment_start_ts<= nested_data$local_time_obj & segment$segment_end_ts >= nested_data$local_time_obj,
-                                            # The segment_id is assambled on the fly because it depends on each row's local_date and timezone 
-                                            stringi::stri_c("[",
-                                                            segment[["label"]], "#",
-                                                            nested_data$local_date, " ",
-                                                            segment[["segment_id_start_time"]], ",",
-                                                            nested_data$local_date, " ",
-                                                            segment[["segment_id_end_time"]], ";",
-                                                            as.numeric(lubridate::as_datetime(stringi::stri_c(nested_data$local_date, segment$segment_id_start_time), tz = nested_timezone)) * 1000, ",",
-                                                            as.numeric(lubridate::as_datetime(stringi::stri_c(nested_data$local_date, segment$segment_id_end_time), tz = nested_timezone)) * 1000 + 999,
-                                                            "]"),
-                                            nested_data$assigned_segments)
-  }
-  return(nested_data)
+  data[,assigned_segments:=substring(assigned_segments, 2)]
+  data
 }
 
 assign_to_time_segment <- function(sensor_data, time_segments, time_segments_type, include_past_periodic_segments){
@@ -83,116 +21,14 @@ assign_to_time_segment <- function(sensor_data, time_segments, time_segments_typ
   if(nrow(sensor_data) == 0 || nrow(time_segments) == 0)
     return(sensor_data %>% mutate(assigned_segments = NA))
   
-  if(time_segments_type == "FREQUENCY"){
-    
-    time_segments <- time_segments %>% mutate(start_time = lubridate::hm(start_time),
-                                            end_time = start_time + minutes(length) - seconds(1),
-                                            segment_id_start_time = paste(str_pad(hour(start_time),2, pad="0"), str_pad(minute(start_time),2, pad="0"), str_pad(second(start_time),2, pad="0"),sep =":"),
-                                            segment_id_end_time = paste(str_pad(hour(ymd("1970-01-01") + end_time),2, pad="0"), str_pad(minute(ymd("1970-01-01") + end_time),2, pad="0"), str_pad(second(ymd("1970-01-01") + end_time),2, pad="0"),sep =":"), # add ymd("1970-01-01") to get a real time instead of duration
-                                            segment_start_ts = as.numeric(start_time),
-                                            segment_end_ts = as.numeric(end_time))
-    
-    sensor_data <- sensor_data %>% mutate(local_time_obj = as.numeric(lubridate::hms(local_time)),
-                                          assigned_segments = "")
-    
-    sensor_data <- sensor_data %>%
-      group_by(local_timezone) %>% 
-      nest() %>% 
-      mutate(data = map2(data, local_timezone, assign_rows_to_segments_frequency, time_segments)) %>% 
-      unnest(cols = data) %>% 
-      arrange(timestamp) %>% 
-      select(-local_time_obj) %>% 
-      ungroup()
-    
+  if (time_segments_type == "FREQUENCY" || time_segments_type == "PERIODIC"){ #FREQUENCY segments are just syntactic sugar for PERIODIC
+    source("src/data/datetime/assign_to_periodic_segments.R")
+    sensor_data <- assign_to_periodic_segments(sensor_data, time_segments, include_past_periodic_segments)
     return(sensor_data)
-
     
-  } else if (time_segments_type == "PERIODIC"){
-    
-    # We need to take into account segment start dates that could include the first day of data
-    time_segments <- time_segments %>% mutate(length_duration = duration(length))
-    every_day_delay <- duration("0days")
-    wday_delay <- day_type_delay(time_segments, "wday", include_past_periodic_segments)
-    mday_delay <- day_type_delay(time_segments, "mday", include_past_periodic_segments)
-    qday_delay <- day_type_delay(time_segments, "qday", include_past_periodic_segments)
-    yday_delay <- day_type_delay(time_segments, "yday", include_past_periodic_segments)
-    
-    sensor_data <- sensor_data %>%
-      group_by(local_timezone) %>% 
-      nest() %>% 
-      # get existent days that we need to start segments from
-      mutate(every_date = map2(data, local_timezone, get_segment_dates, "every_day", every_day_delay),
-             week_dates = map2(data, local_timezone, get_segment_dates, "wday", wday_delay),
-             month_dates = map2(data, local_timezone, get_segment_dates, "mday", mday_delay),
-             quarter_dates = map2(data, local_timezone, get_segment_dates, "qday", qday_delay),
-             year_dates = map2(data, local_timezone, get_segment_dates, "yday", yday_delay),
-             existent_dates = pmap(list(every_date, week_dates, month_dates, quarter_dates, year_dates),
-                                   function(every_date, week_dates, month_dates, quarter_dates, year_dates) reduce(list(every_date, week_dates,month_dates, quarter_dates, year_dates), .f=full_join)),
-             # build the actual time segments taking into account the users requested length and repeat schedule
-             inferred_time_segments = map(existent_dates,
-                                         ~ crossing(time_segments, .x) %>%
-                                           pivot_longer(cols = c(every_day,wday, mday, qday, yday), names_to = "day_type", values_to = "day_value") %>%
-                                           filter(repeats_on == day_type & repeats_value == day_value) %>%
-                                           # The segment ids (segment_id_start and segment_id_end) are computed in UTC to avoid having different labels for instances of a segment that happen in different timezones
-                                           mutate(segment_id_start = lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM")) + period(overlap_duration),
-                                                  segment_id_end = segment_id_start + lubridate::duration(length),
-                                                  # The actual segments are computed using timestamps taking into account the timezone
-                                                  segment_start_ts = as.numeric(lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = local_timezone) + period(overlap_duration)) * 1000,
-                                                  segment_end_ts = segment_start_ts + as.numeric(lubridate::duration(length)) * 1000 + 999,
-                                                  segment_id = paste0("[",
-                                                                      paste0(label,"#",
-                                                                             paste0(lubridate::date(segment_id_start), " ",
-                                                                                    paste(str_pad(hour(segment_id_start),2, pad="0"), str_pad(minute(segment_id_start),2, pad="0"), str_pad(second(segment_id_start),2, pad="0"),sep =":"), ",",
-                                                                                    lubridate::date(segment_id_end), " ",
-                                                                                    paste(str_pad(hour(segment_id_end),2, pad="0"), str_pad(minute(segment_id_end),2, pad="0"), str_pad(second(segment_id_end),2, pad="0"),sep =":")),";",
-                                                                             paste0(segment_start_ts, ",", segment_end_ts)),
-                                                                      "]")) %>% 
-                                           # drop time segments with an invalid start or end time (mostly due to daylight saving changes, e.g. 2020-03-08 02:00:00 EST does not exist, clock jumps from 01:59am to 03:00am)
-                                           drop_na(segment_start_ts, segment_end_ts)), 
-              inferred_time_segments = map(inferred_time_segments, create_nonoverlapping_periodic_segments),
-             data = map2(data, inferred_time_segments, assign_rows_to_segments)
-      ) %>%
-      select(-existent_dates, -inferred_time_segments, -every_date, -week_dates, -month_dates, -quarter_dates, -year_dates) %>%
-      unnest(cols = data) %>% 
-      arrange(timestamp)
-
   } else if ( time_segments_type == "EVENT"){
-
-    sensor_data <- sensor_data %>% 
-      group_by(local_timezone) %>% 
-      nest() %>% 
-      mutate(inferred_time_segments = map(local_timezone, function(tz){ 
-                                         inferred <- time_segments %>% 
-                                           mutate(shift = ifelse(shift == "0", "0seconds", shift),
-                                                  segment_start_ts = event_timestamp + (as.integer(seconds(lubridate::duration(shift))) * ifelse(shift_direction >= 0, 1, -1) * 1000),
-                                                  segment_end_ts = segment_start_ts + (as.integer(seconds(lubridate::duration(length))) * 1000),
-                                                  # these start and end datetime objects are for labeling only
-                                                  segment_id_start = lubridate::as_datetime(segment_start_ts/1000, tz = tz), 
-                                                  segment_id_end = lubridate::as_datetime(segment_end_ts/1000, tz = tz),
-                                                  segment_end_ts = segment_end_ts + 999,
-                                                  segment_id = paste0("[",
-                                                                      paste0(label,"#",
-                                                                             paste0(lubridate::date(segment_id_start), " ",
-                                                                                    paste(str_pad(hour(segment_id_start),2, pad="0"), str_pad(minute(segment_id_start),2, pad="0"), str_pad(second(segment_id_start),2, pad="0"),sep =":"), ",",
-                                                                                    lubridate::date(segment_id_end), " ",
-                                                                                    paste(str_pad(hour(segment_id_end),2, pad="0"), str_pad(minute(segment_id_end),2, pad="0"), str_pad(second(segment_id_end),2, pad="0"),sep =":")),";",
-                                                                             paste0(segment_start_ts, ",", segment_end_ts)),
-                                                                      "]"))
-                                         # Check that for overlapping segments (not allowed because our resampling episode algorithm would have to have a second instead of minute granularity that increases storage and computation time)
-                                         overlapping <-  inferred  %>% group_by(label) %>% arrange(segment_start_ts) %>% 
-                                           mutate(overlaps = if_else(segment_start_ts <= lag(segment_end_ts), TRUE, FALSE),
-                                                  overlapping_segments = paste(paste(lag(label), lag(event_timestamp), lag(length), lag(shift), lag(shift_direction), lag(device_id), sep = ","),"and",
-                                                                                paste(label, event_timestamp, length, shift, shift_direction, device_id, sep = ",")))
-                                         if(any(overlapping$overlaps, na.rm = TRUE)){
-                                           stop(paste0("\n\nOne or more event time segments overlap for ",overlapping$device_id[[1]],", modify their lengths so they don't:\n", paste0(overlapping %>% filter(overlaps == TRUE) %>% pull(overlapping_segments), collapse = "\n"), "\n\n"))
-                                         } else{
-                                           return(inferred)
-                                         }}),
-             data = map2(data, inferred_time_segments, assign_rows_to_segments)) %>% 
-      select(-inferred_time_segments) %>% 
-      unnest(data) %>% 
-      arrange(timestamp)
+    source("src/data/datetime/assign_to_event_segments.R")
+    sensor_data <- assign_to_event_segments(sensor_data, time_segments)
+    return(sensor_data)
   }
-
-  return(sensor_data %>% ungroup())
 }
\ No newline at end of file
diff --git a/src/data/datetime/process_time_segments.R b/src/data/datetime/process_time_segments.R
index 92ebc249..96d6d634 100644
--- a/src/data/datetime/process_time_segments.R
+++ b/src/data/datetime/process_time_segments.R
@@ -105,16 +105,22 @@ validate_frequency_segments <- function(segments){
 }
 
 prepare_frequency_segments <- function(segments){
+  #FREQUENCY segments are just syntactic sugar for PERIODIC
   validate_frequency_segments(segments)
-  stamp_fn <- stamp("23:10", orders = c("HM"), quiet = TRUE)
+  stamp_fn <- stamp("23:10:00", orders = c("HMS"), quiet = TRUE)
   new_segments <- data.frame(start_time = seq.POSIXt(from = ymd_hms("2020-01-01 00:00:00"), 
                                                         to=ymd_hms("2020-01-02 00:00:00"), 
                                                         by=paste(segments$length, "min")))
   new_segments <- new_segments %>%
     head(-1) %>% 
-    mutate(start_time = stamp_fn(start_time),
-           length = segments$length,
-           label = paste0(segments$label, str_pad(row_number()-1, width = 4, pad = "0")))
+    mutate(label = paste0(segments$label, str_pad(row_number()-1, width = 4, pad = "0")),
+           start_time = stamp_fn(start_time),
+           length = paste0((segments$length * 60)-1, "S"),
+           repeats_on = "every_day",
+           repeats_value=0,
+           overlap_id = 0,
+           original_label = label,
+           overlap_duration = "0D")
     
 }