42 lines
2.5 KiB
R
42 lines
2.5 KiB
R
validate_overlapping_event_segments <- function(segments){
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# 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)
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overlapping <- segments %>%
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group_by(label) %>%
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arrange(segment_start_ts) %>%
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mutate(overlaps = if_else(segment_start_ts <= lag(segment_end_ts), TRUE, FALSE),
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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",
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"b) [{label},\t{event_timestamp},\t{length},\t{shift},\t{shift_direction},\t{device_id}]"))
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if(any(overlapping$overlaps, na.rm = TRUE))
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stop("One or more event time segments overlap for ",overlapping$device_id[[1]],
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", modify their lengths so they don't:\n", paste0(overlapping %>% filter(overlaps == TRUE) %>% pull(overlapping_segments), collapse = "\n"))
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}
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infer_event_segments <- function(tz, segments){
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time_format_fn <- stamp("23:51:15", orders="HMS", quiet = TRUE)
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inferred <- segments %>%
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mutate(shift = ifelse(shift == "0", "0seconds", shift),
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segment_start_ts = event_timestamp + (as.integer(seconds(lubridate::duration(shift))) * ifelse(shift_direction >= 0, 1, -1) * 1000),
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segment_end_ts = segment_start_ts + (as.integer(seconds(lubridate::duration(length))) * 1000),
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segment_id_start = lubridate::as_datetime(segment_start_ts/1000, tz = tz),
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segment_id_end = lubridate::as_datetime(segment_end_ts/1000, tz = tz),
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segment_end_ts = segment_end_ts + 999,
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segment_id = glue("[{label}#{start_date} {start_time},{end_date} {end_time};{segment_start_ts},{segment_end_ts}]",
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start_date=lubridate::date(segment_id_start),
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start_time=time_format_fn(segment_id_start),
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end_date=lubridate::date(segment_id_end),
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end_time=time_format_fn(segment_id_end)))
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validate_overlapping_event_segments(inferred)
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return(inferred)
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}
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assign_to_event_segments <- function(sensor_data, time_segments){
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sensor_data <- sensor_data %>%
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group_by(local_timezone) %>%
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nest() %>%
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mutate(inferred_time_segments = map(local_timezone, infer_event_segments, time_segments),
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data = map2(data, inferred_time_segments, assign_rows_to_segments)) %>%
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select(-inferred_time_segments) %>%
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unnest(data) %>%
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arrange(timestamp) %>%
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ungroup()
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} |