#setup ----
library(tidyverse)
library(influxdbclient)
library(rmarkdown)

if(Sys.info()[4] == "pseudotsuga") {
  setwd("~/Documents/dataProjects/laundry_status")
} else {
  setwd("/laundry_status")
}
Sys.setenv(TZ='America/Chicago')
# parameters needed to make connection to Database
token <- substr(read_file("data/api_key"), 1, 88)
org = "home_assistant"
bucket = "home_assistant"
## make connection to the influxDB bucket
home_assistant <- InfluxDBClient$new(url = "https://influxdb.dendroalsia.net",
                                     token = token,
                                     org = org)
longterm_run_time <- ymd_hms("2024-01-01:00:00:00", tz = "America/Chicago")
power_threshold_on <- 10
power_threshold_wash_door <- 3
power_threshold_dry_door <- 1.5

# ---- set variables
entities <- data.frame(name = c("washing machine", "dryer"), entity_id = c("washing_machine_power", "dryer_power"), name_es = c("lavadora", "secadora"))

colors <- data.frame(value = c("off", "on"), color = c("#bcbcbc", "#008080"))

# Define a function to calculate the time difference
calculateTimeAgo <- function(eventTime) {
  current <- Sys.time()  # Get the current time
  timeDiff <- as.duration(current - eventTime)  # Calculate the time difference
  
  # Convert the time difference to appropriate units and format the result
  if (timeDiff >= hours(24)) {
    result <- paste(round(timeDiff / dhours(24)), "days ago")
  } else if (timeDiff >= hours(1)) {
    result <- paste(round(timeDiff / dhours(1)), "hours ago")
  } else if (timeDiff >= minutes(1)) {
    result <- paste(round(timeDiff / dminutes(1)), "minutes ago")
  } else {
    result <- "Just now"
  }
  
  return(result)
}
calculateTimeAgo_es <- function(eventTime) {
  current <- Sys.time()  # Get the current time
  timeDiff <- as.duration(current - eventTime)  # Calculate the time difference
  
  # Convert the time difference to appropriate units and format the result
  if (timeDiff >= hours(24)) {
    result <- paste("hace", round(timeDiff / dhours(24)), "dias")
  } else if (timeDiff >= hours(1)) {
    result <- paste("hace", round(timeDiff / dhours(1)), "horas")
  } else if (timeDiff >= minutes(1)) {
    result <- paste("hace", round(timeDiff / dminutes(1)), "minutos")
  } else {
    result <- "Ahora"
  }
  
  return(result)
}

# ---- update_data function
update_data <- function(){
  Sys.setlocale("LC_TIME", "en_US.UTF-8")
  
  run_time <- Sys.time()
  start_time <- floor_date(run_time - ddays(7), unit = "days")
  values <- home_assistant$query(paste0('from(bucket: "home_assistant") |> range(start: ', 
                                        start_time,
                                        ') |> filter(fn: (r) => r["entity_id"] == "washing_machine_power" or r["entity_id"] == "dryer_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")'),
                                 POSIXctCol = NULL)
  values <- bind_rows(values)
  values <- values %>%
    rename(value = "_value",
           time = "_time")
  values <- values %>%
    mutate(
      time = as.POSIXct(time, tz = "America/Chicago"),
      status = ifelse(value > power_threshold_on, "on", "off")) %>%
    mutate(door_threshold = ifelse(entity_id == "washing_machine_power", power_threshold_wash_door, power_threshold_dry_door)) %>%
    mutate(door = ifelse(value < door_threshold, "open", "closed")) %>%
    filter(time >= start_time)
  
  values_by_entity <- as.list(NULL)
  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)
  
  last_change <- values %>% 
    group_by(entity_id, status) %>% 
    slice_max(n = 1, order_by = time) %>% 
    pivot_wider(id_cols = domain, names_from = c(entity_id, status), values_from = time)
  
  status_door <- values %>% 
    group_by(entity_id) %>% 
    slice_max(n = 1, order_by = time) %>% 
    select(entity_id, door) %>%
    pivot_wider(names_from = entity_id, values_from = door)
  
  current_status <- as.list(NULL)
  for (entity in entities$entity_id){
    current_status[[entity]] <- ifelse(values %>% filter(entity_id %in% entity) %>% tail(1) %>% pull(value) > power_threshold_on, "on", "off")
  }
  current_status <- bind_rows(current_status)
  current_status_update <<- ifelse((current_status$washing_machine_power == "on")|(current_status$dryer_power) == "on", TRUE, FALSE)
  ## add spanish translations
  current_status <- current_status %>%
    mutate(dryer_power_es = ifelse(dryer_power == "on", "encendida", "apagada"),
           washing_machine_power_es = ifelse(washing_machine_power == "on", "encendida", "apagada"))
  
  status_door <- status_door %>%
    mutate(dryer_power_es = ifelse(dryer_power == "open", "abierto", "cerado"),
           washing_machine_power_es = ifelse(washing_machine_power == "open", "abierto", "cerado"))
# ---- make plots
  plot_1day <- ggplot(data = values %>% filter(time >= max(values$end_time) - hours(24))) +
    geom_tile(aes(x = time + seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))/2),
                  y = entity_id,
                  width = seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))),
                  height = 0.5,
                  fill = status)) +
    scale_y_discrete(breaks = entities$entity_id, labels = entities$name) +
    scale_x_datetime(breaks = seq(round_date(max(values$end_time), "4 hours") - hours(24), round_date(max(values$end_time), "4 hours"), by = "4 hours"), date_labels = '%I:%M %p', date_minor_breaks = "1 hours") +
    scale_fill_manual(values = colors$color) +
    theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
    labs(title = "Last 24 hours",
         x = NULL,
         y = NULL,
         fill = NULL)
  
  plot_1week_days <- ggplot(data = values %>% 
                              filter(as.Date(time, tz = "America/Chicago") == as.Date(end_time, tz = "America/Chicago")) %>%
                              mutate(date = as.Date(time, tz = "America/Chicago")) %>%
                              left_join(. , entities, by = join_by(entity_id)) %>%
                              mutate(name = factor(name, levels = c("washing machine", "dryer")))) +
    geom_rect(aes(xmin = date - hours(8),
                  xmax = date + hours(8),
                  ymin = ymd_hms(paste("2023-01-01", strftime(time, format = "%H:%M:%S"))),
                  ymax = ymd_hms(paste("2023-01-01", strftime(end_time, format = "%H:%M:%S"))),
                  fill = status)) +
    facet_grid(name ~ .) +
    scale_y_datetime(date_breaks = "4 hours", date_labels = '%I:%M %p',  minor_breaks = "2 hours", expand = expansion(mult = 0)) +
    scale_x_datetime(date_breaks = "1 day", date_labels = '%A', minor_breaks = NULL) +
    scale_fill_manual(values = colors$color) +
    theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
    labs(title = "The past week",
         x = "Day",
         y = "Time of Day",
         fill = NULL)
  
  # ---- generate html
  render("laundry_status.Rmd",
         output_dir = "html",
         output_file = "index.html")
  
  Sys.setlocale("LC_TIME", "es_NI.UTF-8")
  
  # ---- make plots (es)
  plot_1day_es <- ggplot(data = values %>% filter(time >= max(values$end_time) - hours(24)) %>%
                           mutate(status = ifelse(status == "on", "encendida", "apagada"))) +
    geom_tile(aes(x = time + seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))/2),
                  y = entity_id,
                  width = seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))),
                  height = 0.5,
                  fill = status)) +
    scale_y_discrete(breaks = entities$entity_id, labels = entities$name_es) +
    scale_x_datetime(breaks = seq(round_date(max(values$end_time), "4 hours") - hours(24), round_date(max(values$end_time), "4 hours"), by = "4 hours"), date_labels = '%H:%M', date_minor_breaks = "1 hours") +
    scale_fill_manual(values = colors$color) +
    theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
    labs(title = "Hace 24 horas",
         x = NULL,
         y = NULL,
         fill = NULL)
  
  plot_1week_days_es <- ggplot(data = values %>% 
                                 filter(as.Date(time, tz = "America/Chicago") == as.Date(end_time, tz = "America/Chicago")) %>%
                                 mutate(date = as.Date(time, tz = "America/Chicago")) %>%
                                 left_join(. , entities, by = join_by(entity_id)) %>%
                                 mutate(name = factor(name_es, levels = c("lavadora", "secadora"))) %>%
                                 mutate(status = ifelse(status == "on", "encendida", "apagada"))) +
    geom_rect(aes(xmin = date - hours(8),
                  xmax = date + hours(8),
                  ymin = ymd_hms(paste("2023-01-01", strftime(time, format = "%H:%M:%S"))),
                  ymax = ymd_hms(paste("2023-01-01", strftime(end_time, format = "%H:%M:%S"))),
                  fill = status)) +
    facet_grid(name ~ .) +
    scale_y_datetime(date_breaks = "4 hours", date_labels = '%H:%M',  minor_breaks = "2 hours", expand = expansion(mult = 0)) +
    scale_x_datetime(date_breaks = "1 day", date_labels = '%A', minor_breaks = NULL) +
    scale_fill_manual(values = colors$color) +
    theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
    labs(title = "La semana pasada",
         x = "Día",
         y = "Tiempo",
         fill = NULL)
  

  render("laundry_status_es.Rmd",
         output_dir = "html",
         output_file = "es.html")
}

update_longterm_data <- function(){
  Sys.setlocale("LC_TIME", "en_US.UTF-8")
  
  run_time <- Sys.time()
  longterm_run_time <<- run_time
  start_time <- floor_date(run_time - ddays(365), unit = "days")
  values <- home_assistant$query(paste0('from(bucket: "home_assistant") |> range(start: ', 
                                        start_time,
                                        ') |> filter(fn: (r) => r["entity_id"] == "washing_machine_power" or r["entity_id"] == "dryer_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")'),
                                 POSIXctCol = NULL)
  values <- bind_rows(values)
  values <- values %>%
    rename(value = "_value",
           time = "_time")
  values <- values %>%
    mutate(
      time = as.POSIXct(time, tz = "America/Chicago"),
      status = ifelse(value > power_threshold_on, "on", "off")) %>%
    mutate(door_threshold = ifelse(entity_id == "washing_machine_power", power_threshold_wash_door, power_threshold_dry_door)) %>%
    mutate(door = ifelse(value < door_threshold, "open", "closed")) %>%
    filter(time >= start_time)
  
  values_by_entity <- as.list(NULL)
  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)
  
  values["weekday"] <- wday(values$time, label = TRUE)
  values["hourofday"] <- floor_date(ymd_hms(paste("2023_01_01",strftime(values$time, format="%H:%M:%S"))), unit = "hours")
  long_term <- values %>%
    filter(status =="on") %>%
    group_by(weekday, hourofday, entity_id, status) %>%
    summarise(count = n())
    # ---- make plots
  plot_all_week_days <- ggplot(data = long_term %>%
                                 left_join(. , entities, by = join_by(entity_id)) %>%
                                 mutate(name = factor(name, levels = c("washing machine", "dryer")))) +
    geom_tile(aes(x = weekday,
                  y = hourofday,
                  alpha = count,
                  fill = status)) +
    facet_grid(name ~ .) +
    scale_y_datetime(date_breaks = "4 hours", date_labels = '%I:%M %p',  minor_breaks = "2 hours", expand = expansion(mult = 0)) +
    scale_x_discrete() +
    scale_fill_manual(values = colors$color[2], guide = NULL) +
    theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
    labs(title = "Long term trends",
         subtitle = paste0("data from ",strftime(min(values$time), format = "%B %d, %Y"), " to ", strftime(run_time, format = "%B %d, %Y")),
         x = "Weekday",
         y = "Time of Day",
         alpha = "Frequency")
  
  # ---- generate html
  render("laundry_longterm_data.Rmd",
         output_dir = "html",
         output_file = "longterm_data.html")
  
  # ---- spanish translation
  
  Sys.setlocale("LC_TIME", "es_NI.UTF-8")
  
  values["weekday"] <- wday(values$time, label = TRUE)
  values["hourofday"] <- floor_date(ymd_hms(paste("2023_01_01",strftime(values$time, format="%H:%M:%S"))), unit = "hours")
  long_term <- values %>%
    filter(status =="on") %>%
    group_by(weekday, hourofday, entity_id, status) %>%
    summarise(count = n())
  # ---- make plots
  
  plot_all_week_days_es <- ggplot(data = long_term %>%
                                 left_join(. , entities, by = join_by(entity_id)) %>%
                                 mutate(name = factor(name_es, levels = c("lavadora", "secadora")))) +
    geom_tile(aes(x = weekday,
                  y = hourofday,
                  alpha = count,
                  fill = status)) +
    facet_grid(name ~ .) +
    scale_y_datetime(date_breaks = "4 hours", date_labels = '%H:%M',  minor_breaks = "2 hours", expand = expansion(mult = 0)) +
    scale_x_discrete() +
    scale_fill_manual(values = colors$color[2], guide = NULL) +
    theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
    labs(title = "Tendencias a largo plazo",
         subtitle = paste0("datos desde el ",strftime(min(values$time), format = "%d de %B de %Y"), " hasta el ", strftime(run_time, format = "%d de %B de %Y")),
         x = "Día de la semana",
         y = "Tiempo",
         alpha = "Frecuencia")
  
  render("laundry_longterm_data_es.Rmd",
         output_dir = "html",
         output_file = "longterm_data_es.html")
}

continue <- TRUE
while(continue){
  message(Sys.time())
  update_data()
  if (as.double(difftime(Sys.time(), longterm_run_time, units = "hours", tz = "America/Chicago")) > 24) {
    update_longterm_data()
  }
  if (current_status_update == TRUE) {
    message("sleeping for 1 minute")
    Sys.sleep(60*1)
  } else {
    message("sleeping for 5 minutes")
    Sys.sleep(60*5)
  }
  
}