# Copyright (c) 2018 Kelvin Say
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

library(lubridate)
library(readr)

preprocess_Read_Scenario <- function(i.file_name_load = "pattern_double_peak.csv",
                                     i.file_path_load = file.path(getwd(), "_Scenarios/ConsumptionPatterns_SunWiz"),
                                     i.file_name_insolation = "pvwatts_perth_hourly.csv",
                                     i.file_path_insolation = file.path(getwd(), "_Scenarios/Insolation_PVWatts"),
                                     i.annual_consumption_kWh = 5600,
                                     i.sample_period_sec = 900) {
  load_profile <- preprocess_Generate_Load_Pattern(file.path(i.file_path_load, i.file_name_load), i.annual_consumption_kWh/365, i.sample_period_sec);
  fwrite(load_profile, file.path(getwd(), "_Inputs", "HouseholdLoad.csv"))

  insolation <- preprocess_Generate_Isolation_Pattern(file.path(i.file_path_insolation, i.file_name_insolation), i.sample_period_sec)
  fwrite(insolation, file.path(getwd(), "_Inputs", "UnitInsolation.csv"))

  inputs <- list("Load Filename" = i.file_name_load,
                 "Load Filepath" = i.file_path_load,
                 "Insolation Filename" = i.file_name_insolation,
                 "Insolation Filepath" = i.file_path_insolation,
                 "Annual Consumption (kWh)" = i.annual_consumption_kWh,
                 "Sample Period (s)" = i.sample_period_sec)
  saveRDS(inputs, file.path(getwd(), "_Inputs", "load_insolation_info.rds"))
  sink(file.path(getwd(), "_Inputs", "load_insolation_info.txt"))
  print(inputs)
  sink()

}


# x <- preprocess_Generate_Load_Pattern(file.path(getwd(), "Inputs/Demand", "pattern_day_focus.csv"), 5600/365, 900)
preprocess_Generate_Load_Pattern <- function(i.pattern_file, i.daily_consumption_kWh, i.sample_period_sec) {
  raw_pattern <- fread(i.pattern_file, data.table = FALSE)
  raw_pattern$StartDatetime <- as.POSIXct(strptime(raw_pattern$StartDatetime, "%Y-%m-%dT%H:%M:%SZ"))
  raw_pattern$FinishDatetime <- as.POSIXct(strptime(raw_pattern$FinishDatetime, "%Y-%m-%dT%H:%M:%SZ"))

  time_start <- raw_pattern$StartDatetime[1]
  time_end <- raw_pattern$FinishDatetime[length(raw_pattern$FinishDatetime)]
  time_period <- as.numeric(difftime(raw_pattern$FinishDatetime[1], raw_pattern$StartDatetime[1], units = "secs"))
  num_samples <- floor(as.numeric(difftime(time_end, time_start, units = "secs")) / i.sample_period_sec)
  time_series <- time_start + seconds(i.sample_period_sec * (0:(num_samples-1)))

  raw_pattern$NetPower <- as.integer(raw_pattern$Daily1kWh_LoadWh * 3600 / time_period * i.daily_consumption_kWh)

  # Massage the last same through to midnight using the first value of the day (needed to get an even transition)
  num_raw_pattern <- length(raw_pattern$StartDatetime) + 1
  raw_pattern[num_raw_pattern,] <- raw_pattern[1,]
  raw_pattern$StartDatetime[num_raw_pattern] <- raw_pattern$StartDatetime[num_raw_pattern] + days(1)
  raw_pattern$FinishDatetime[num_raw_pattern] <- raw_pattern$FinishDatetime[num_raw_pattern] + days(1)

  resample_func <- approxfun(raw_pattern$StartDatetime, raw_pattern$NetPower, method = "linear", f = 0, rule = 2)
  daily_load_pattern <- data.frame(Time = time_series, NetPower = as.integer(resample_func(time_series)))
  num_pattern_elements <- length(daily_load_pattern$NetPower)

  for (i in 1:365) {
    if (i == 1) {
      num_samples <- floor(as.numeric(difftime(ymd_hm("201801010000"), ymd_hm("201701010000"), units = "secs")) / i.sample_period_sec)
      full_time_series <- ymd_hm("201701010000") + seconds(i.sample_period_sec * (0:(num_samples-1)))
      year_load_pattern <- data.table(Time = full_time_series, NetPower = integer(length(full_time_series)))
    }
    index_start <- (i-1) * num_pattern_elements + 1
    index_end <- i * num_pattern_elements
    year_load_pattern$NetPower[index_start:index_end] <- daily_load_pattern$NetPower
  }
  return(year_load_pattern)
}

preprocess_Generate_Isolation_Pattern <- function(i.insolation_file, i.sample_period_sec) {
  raw_year_insolation <- fread(i.insolation_file, data.table = FALSE)

  raw_time_series <- ymd_h(paste0("2017", sprintf("%02i", raw_year_insolation$Month), sprintf("%02i", raw_year_insolation$Day), sprintf("%02i", raw_year_insolation$Hour)))
  raw_insolation_power <- raw_year_insolation$`AC System Output (W)`

  # Massage the last same through to midnight using the first value of the day (needed to get an even transition)
  raw_time_series[length(raw_time_series)+1] <- raw_time_series[1] + years(1)
  raw_insolation_power[length(raw_insolation_power)+1] <- raw_insolation_power[1]

  time_start <- raw_time_series[1]
  time_end <- raw_time_series[length(raw_time_series)]
  num_samples <- floor(as.numeric(difftime(time_end, time_start, units = "secs")) / i.sample_period_sec)
  time_series <- time_start + seconds(i.sample_period_sec * (0:(num_samples-1)))

  resample_func <- approxfun(raw_time_series, raw_insolation_power, method = "linear", f = 0, rule = 2)
  year_insolation <- data.frame(Time = time_series, Power = resample_func(time_series))
  return(year_insolation)
}