2020-05-02 01:46:04 +02:00
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source("renv/activate.R")
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2019-11-05 16:47:55 +01:00
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library("tidyverse")
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battery <- read.csv(snakemake@input[[1]])
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2019-11-13 20:53:28 +01:00
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if(nrow(battery) > 0){
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consumption <- battery %>%
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2019-11-05 16:47:55 +01:00
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mutate(group = ifelse(lag(battery_status) != battery_status, 1, 0) %>% coalesce(0),
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2019-11-13 20:53:28 +01:00
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group_id = cumsum(group) + 1) %>%
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2020-04-09 18:07:35 +02:00
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filter(battery_status == 2 | battery_status == 3) %>%
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2019-11-05 16:47:55 +01:00
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group_by(group_id) %>%
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summarize(battery_diff = first(battery_level) - last(battery_level),
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2019-11-13 20:53:28 +01:00
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time_diff = (last(timestamp) - first(timestamp)) / (1000 * 60 * 60),
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local_start_date_time = first(local_date_time),
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local_end_date_time = last(local_date_time),
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local_start_date = first(local_date),
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2019-11-21 17:34:30 +01:00
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local_end_date = last(local_date),
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2019-11-21 18:39:52 +01:00
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local_start_day_segment = first(local_day_segment),
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local_end_day_segment = last(local_day_segment)) %>%
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2019-11-05 16:47:55 +01:00
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select(-group_id) %>%
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2019-11-05 17:35:01 +01:00
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filter(time_diff > 0.1) # Avoids including quick cycles
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2019-11-13 20:53:28 +01:00
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} else {
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consumption <- data.frame(battery_diff = numeric(),
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time_diff = numeric(),
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local_start_date_time = character(),
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local_end_date_time = character(),
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local_start_date = character(),
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2019-11-21 17:34:30 +01:00
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local_end_date = character(),
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2019-11-21 18:39:52 +01:00
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local_start_day_segment = character(),
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local_end_day_segment = character())
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2019-11-13 20:53:28 +01:00
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}
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2019-11-05 16:47:55 +01:00
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2019-11-08 18:05:41 +01:00
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write.csv(consumption, snakemake@output[[1]], row.names = FALSE)
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