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made end_time col accurate

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Ben Varick 2023-11-07 14:18:26 -06:00
parent e22826e492
commit ecc7a3ffa8
Signed by: ben
SSH Key Fingerprint: SHA256:jWnpFDAcacYM5aPFpYRqlsamlDyKNpSj3jj+k4ojtUo
2 changed files with 457 additions and 18 deletions
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html/index.html Normal file
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45
laundry_status.R
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@ -19,11 +19,13 @@ home_assistant <- InfluxDBClient$new(url = "https://influxdb.dendroalsia.net",
org = org) org = org)
update_interval <- 5 update_interval <- 5
cronjob_interval <- 60 cronjob_interval <- 60
power_threshhold <- 5
# ---- set variables # ---- set variables
entities <- data.frame(name = c("washing machine", "dryer"), entity_id = c("lamp_a_power", "lamp_b_power")) entities <- data.frame(name = c("washing machine", "dryer"), entity_id = c("lamp_a_power", "lamp_b_power"))
update_data <- function(){ update_data <- function(){
run_time <- Sys.time()
values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "lamp_b_power" or r["entity_id"] == "lamp_a_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")', values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "lamp_b_power" or r["entity_id"] == "lamp_a_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")',
POSIXctCol = NULL) POSIXctCol = NULL)
values <- bind_rows(values) values <- bind_rows(values)
@ -33,38 +35,45 @@ update_data <- function(){
values <- values %>% values <- values %>%
mutate( mutate(
time = as.POSIXct(time, tz = "America/Chicago"), time = as.POSIXct(time, tz = "America/Chicago"),
status = ifelse(value > 1, "on", "off")) %>% status = ifelse(value > power_threshhold, "on", "off"))
mutate(end_time = time + minutes(1))
washer_last_on <- values %>% filter(entity_id == entities$entity_id[1], value > 5) %>% tail(1) %>% pull(time) values_by_entity <- as.list(NULL)
washer_last_off <- values %>% filter(entity_id == entities$entity_id[1], value < 5) %>% tail(1) %>% pull(time) for(entity in entities$entity_id) {
values_by_entity[[entity]] <- values %>%
filter(entity_id %in% entity) %>%
mutate(end_time = c(time[-1], run_time))
}
values <- bind_rows(values_by_entity)
dryer_last_on <- values %>% filter(entity_id == entities$entity_id[2], value > 5) %>% tail(1) %>% pull(time) washer_last_on <- values %>% filter(entity_id == entities$entity_id[1], value > power_threshhold) %>% tail(1) %>% pull(time)
dryer_last_off <- values %>% filter(entity_id == entities$entity_id[2], value < 5) %>% tail(1) %>% pull(time) washer_last_off <- values %>% filter(entity_id == entities$entity_id[1], value < power_threshhold) %>% tail(1) %>% pull(time)
dryer_last_on <- values %>% filter(entity_id == entities$entity_id[2], value > power_threshhold) %>% tail(1) %>% pull(time)
dryer_last_off <- values %>% filter(entity_id == entities$entity_id[2], value < power_threshhold) %>% tail(1) %>% pull(time)
# ---- generate html # ---- generate html
current_status <- as.list(NULL) current_status <- as.list(NULL)
for (entity in entities$entity_id){ for (entity in entities$entity_id){
current_status[[entity]] <- ifelse(values %>% filter(entity_id %in% entity) %>% tail(1) %>% pull(value) > 5, "on", "off") current_status[[entity]] <- ifelse(values %>% filter(entity_id %in% entity) %>% tail(1) %>% pull(value) > power_threshhold, "on", "off")
} }
plot_1week <- ggplot(data = values) + plot_1week <- ggplot(data = values) +
geom_rect(aes(xmin = end_time, geom_tile(aes(x = time + seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))/2),
xmax = time, y = entity_id,
ymin = ifelse(entity_id == "lamp_a_power", 0, 2) + 0.5, width = seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))),
ymax = ifelse(entity_id == "lamp_a_power", 0, 2) + 1.5, height = 0.5,
fill = status)) + fill = status)) +
scale_y_continuous() + scale_y_discrete(breaks = entities$entity_id, labels = entities$name) +
scale_x_datetime(date_breaks = "24 hours", date_labels = '%A', date_minor_breaks = "6 hours") + scale_x_datetime(date_breaks = "24 hours", date_labels = '%A', date_minor_breaks = "6 hours") +
theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) + theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
labs(title = "Last week") labs(title = "The past week")
plot_1day <- ggplot(data = values %>% filter(time >= max(values$time) - hours(24))) + plot_1day <- ggplot(data = values %>% filter(time >= max(values$time) - hours(24))) +
geom_rect(aes(xmin = end_time, geom_tile(aes(x = time + seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))/2),
xmax = time, y = entity_id,
ymin = ifelse(entity_id == "lamp_a_power", 0, 2) + 0.5, width = seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))),
ymax = ifelse(entity_id == "lamp_a_power", 0, 2) + 1.5, height = 0.5,
fill = status)) + fill = status)) +
scale_y_continuous() + scale_y_discrete(breaks = entities$entity_id, labels = entities$name) +
scale_x_datetime(breaks = seq(round_date(max(values$time), "4 hours") - hours(24), round_date(max(values$time), "4 hours"), by = "4 hours"), date_labels = '%I:%M %p', date_minor_breaks = "1 hours") + scale_x_datetime(breaks = seq(round_date(max(values$time), "4 hours") - hours(24), round_date(max(values$time), "4 hours"), by = "4 hours"), date_labels = '%I:%M %p', date_minor_breaks = "1 hours") +
theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) + theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
labs(title = "Last 24 hours") labs(title = "Last 24 hours")