# 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(ggplot2)
library(reshape2)
library(scales)
library(stringr)
library(data.table)


#################################################################################################################
# Single Day
#################################################################################################################
# ---------------------------------------------------------------------------------------------------------------
# Use the information from technical_data to generate the energy profile across this particular year and day of
# the year with the associated PV and battery capacities
# ---------------------------------------------------------------------------------------------------------------
vis_Day_EnergyProfile <- function(technical_data, year, day_of_year, pv_capacity, battery_capacity) {

  # Search for a valid PV and Battery Index
  pv_index <- 0
  battery_index <- 0
  for (i in 1:nrow(technical_data$Operational)) {
    if (pv_capacity == technical_data$Operational[i,1][[1]]$'PV Capacity (Wp)') {
      pv_index <- i
      break
    }
  }
  for (j in 1:ncol(technical_data$Operational)) {
    if (battery_capacity == technical_data$Operational[1,j][[1]]$'Battery Capacity (Wh)') {
      battery_index <- j
      break
    }
  }

  # Normal Operation
  if ((pv_index > 0) & (battery_index > 0) & (day_of_year > 0) & (day_of_year <= 365) & (year <= technical_data$Inputs$Timeline$Info$'Operational Years')) {

    time_step <- technical_data$Inputs$Timeline$Info$'Time Step (s)'
    start_index <- (day_of_year-1)*24*3600/time_step +1
    end_index <- day_of_year*24*3600/time_step
    n_samples <- end_index - start_index + 1

    timeline <- technical_data$Inputs$Timeline
    timeline$Data$'Time' <- timeline$Data$'Time'[start_index:end_index]

    baseload <- technical_data$Inputs$Baseload
    baseload$Data$'Load (W)' <- baseload$Data$'Load (W)'[start_index:end_index]

    solar <- technical_data$Inputs$Solar
    solar$Info$'Nominal Capacity (Wp)' <- pv_capacity
    solar$Data$'Insolation Reference (W/Wp)' <- solar$Data$'Insolation Reference (W/Wp)'[start_index:end_index]
    if (pv_capacity > 0) {
      gradient <- (solar$Info$'25 Year Capacity (%)' - 1) / (25*365)
      solar_degradation_start <- ((year - 1) * 365 + (day_of_year-1)) * gradient + 1
      solar_degradation_end <- ((year - 1) * 365 + day_of_year) * gradient + 1
      solar_degradation <- solar_degradation_start + (solar_degradation_end - solar_degradation_start)/n_samples*0:(n_samples-1)
      solar$Data$'Generation (W)' <- pv_capacity * solar$Data$'Insolation Reference (W/Wp)' * solar_degradation
    } else {
      solar$Data$'Generation (W)' <- NULL
    }

    battery <- technical_data$Inputs$Battery
    battery$Info$'Rated Capacity (Wh)' <- battery_capacity
    if (battery_capacity > 0) {
      gradient <- (battery$Info$'10 Year Capacity (%)' - 1) / (10*365)
      capacity_start_Wh <- battery_capacity * (((year - 1) * 365 + (day_of_year-1)) * gradient + 1)
      capacity_end_Wh <- battery_capacity * (((year - 1) * 365 + day_of_year) * gradient + 1)
      v_capacity_Wh <- capacity_start_Wh + (capacity_end_Wh - capacity_start_Wh)/n_samples*0:(n_samples-1)
      battery$Data$'Capacity (Wh)' = v_capacity_Wh
      battery$Initial$'SoC (Wh)' = technical_data$Operational[pv_index, battery_index][[1]]$Daily$'B.Soc.Initial (Wh)'[(year-1)*365 + day_of_year]
    } else {
      battery$Data$'Capacity (Wh)' = NULL
      battery$Initial$'SoC (Wh)' = 0
    }

    pv_battery_operation <- process_PV_Battery_Load(timeline, baseload, solar, battery)
    meter_operation <- data.table('Time' = timeline$Data$Time,
                                  'Load (W)' = baseload$Data$'Load (W)',
                                  'PV (W)' = solar$Data$'Generation (W)',
                                  'NetLoad (W)' = pv_battery_operation$'NetLoad (W)',
                                  'PVUsed (W)' = pv_battery_operation$'PVUsed (W)',
                                  'B.Charging (W)' = pv_battery_operation$'B.Charging (W)',
                                  'B.Discharging (W)' = pv_battery_operation$'B.Discharging (W)',
                                  'B.InvLosses (W)' = pv_battery_operation$'B.InvLosses (W)',
                                  'B.CapLosses (Wh)' = pv_battery_operation$'B.CapLosses (Wh)',
                                  'B.SoC (Wh)' = pv_battery_operation$'B.SoC (Wh)',
                                  'Imported (W)' = pv_battery_operation$'Imported (W)',
                                  'Exported (W)' = pv_battery_operation$'Exported (W)')

    max_x <- max(meter_operation$'PV (W)', meter_operation$'Load (W)')
    min_x <- min(meter_operation$'NetLoad (W)', 0)
    max_y <- last(meter_operation$'Time')
    min_y <- first(meter_operation$'Time')

    df <- data.frame(Time = meter_operation$'Time',
                     Load = meter_operation$'Load (W)',
                     PV = meter_operation$'PV (W)',
                     NetLoad = meter_operation$'NetLoad (W)',
                     SoC = meter_operation$'B.SoC (Wh)'/battery_capacity * max_x)
    melt_df <- melt(df, id="Time")

    plot1 <- ggplot(melt_df, aes(x=Time, y=value, colour=variable, group=variable)) +
             geom_line() +
             coord_cartesian(ylim = c(min_x, max_x), xlim = c(min_y,max_y)) +
             xlab(paste("Year ", year,  " Day ", day_of_year, ", ", format(min_y, "%d/%m"),sep="")) +
             ylab("Power (W)") +
             scale_x_datetime(breaks = date_breaks("2 hours"), minor_breaks = date_breaks("1 hour"), labels = date_format("%H:%M")) +
             ggtitle(paste0("Household Electricity Performance [", pv_capacity/1000, "kWp + ", battery_capacity/1000, "kWh]"),
                     subtitle = paste0("Profile: ", technical_data$Inputs$Baseload$Info$'Profile',
                                       " @ ", technical_data$Inputs$Baseload$Info$'Annual Consumption (MWh)', " MWh/year")) +
             theme(legend.title=element_blank()) +
             theme(plot.title = element_text(hjust = 0.5))

  # Error Case: Return an empty plot
  } else {
    df <- data.frame()
    plot1 <- ggplot(df)
  }

  return(plot1)
}

#################################################################################################################
# Single Year
#################################################################################################################
# ---------------------------------------------------------------------------------------------------------------
# Display the NPV spectrum of every PV and Battery combination and highlight the maximum
# ---------------------------------------------------------------------------------------------------------------
vis_Annual_NPV <- function(simulation_info, technical_summary, financial_data) {

  max_found <- FALSE
  pv_sizes <- technical_summary$'PV Capacity (Wp)' / 1000
  battery_sizes <- technical_summary$'Battery Capacity (Wh)' / 1000
  x_diff <- simulation_info$'PV Delta (Wp)' / 1000
  y_diff <- simulation_info$'Battery Delta (Wh)' / 1000

  mat_npv <- financial_data$'System.NPV'
  colnames(mat_npv) <- paste("batt=", battery_sizes, sep="")
  rownames(mat_npv) <- paste("pv=", pv_sizes, sep="")

  # Determine the level of self-sufficiency for the most profitable system
  v_max <- which((mat_npv==max(mat_npv)) & (mat_npv > 0))
  if (length(v_max) > 0) {
    max_found <- TRUE
    max_battery_index <- ceiling(v_max[1]/length(pv_sizes))
    max_pv_index <- (v_max[1]-1) %% length(pv_sizes) + 1
    self_sufficiency <- round(technical_summary$Total$'Self-Sufficiency (%)'[max_pv_index, max_battery_index] * 100, 2)

    melt_npv <- melt(mat_npv)
    names(melt_npv) <- c("pv","batt","npv")
    melt_npv$pv <- as.numeric(str_sub(melt_npv$pv, str_locate(melt_npv$pv, "=")[1,1] + 1))
    melt_npv$batt <- as.numeric(str_sub(melt_npv$batt, str_locate(melt_npv$batt, "=")[1,1] + 1))
    melt_npv$npv <- sapply(melt_npv$npv, function(x) if (x < 0) -10 else x)
  }

  # Error Condition: return an empty plot
  if (!max_found) {
    df <- data.frame()
    plot1 <- ggplot(df) +
             geom_point() +
             theme_bw() +
             xlim(0,max(pv_sizes)) + ylim(0,max(battery_sizes)) +
             xlab(paste("Solar PV Capacity (kWp)   $", round(financial_data$Inputs$Costs$'PV Cost ($/kWp)',0), "/kWp", sep="")) +
             ylab(paste("Battery Capacity (kWh)   $", round(financial_data$Inputs$Costs$'Battery Cost ($/kWh)',0), "/kWh", sep="")) +
             ggtitle(paste("System NPV ($) Over ", financial_data$Inputs$Finance$'Analysis Years', " Years", sep=""),
                     subtitle=paste("Consumption = ", round(technical_summary$Total$'Load (kWh)'/financial_data$Inputs$Finance$'Analysis Years'/1000,1), " MWh/year,  "
                                    , "Inverter Limit = ", financial_data$Inputs$Tariff$'FiT System Limit (Wp)' / 1000, "kW\n"
                                    , "Retail Price = ", round(financial_data$Inputs$Tariff$'Import ($/kWh)' * 100,3), "c, "
                                    , "Feed-in Tariff = ", round(financial_data$Inputs$Tariff$'Export ($/kWh)' * 100,3), "c, "
                                    , "Discount Rate = ", financial_data$Inputs$Finance$'Discount Rate (%)' * 100, "%\n"
                                    , "Import Inflation = ", financial_data$Inputs$Tariff$'Import Inflation (%)' * 100, "%, "
                                    , "Export Inflation = ", financial_data$Inputs$Tariff$'Export Inflation (%)' * 100, "%\n"
                                    , "Maximum Profit = none and 0% self-sufficient"
                                    , sep=""))


  # Normal Condition
  } else {

    plot1 <- ggplot(melt_npv) +
             aes(x = pv, y = batt, z = npv, fill = npv) +
             geom_tile() +
             coord_equal(0.5) +
             #            stat_contour(aes(fill=..level..), geom="polygon") + #, binwidth=0.05) +
             geom_contour(color = "white", alpha = 0.5) +
             scale_fill_distiller(palette="Greens", direction=1, na.value=rgb(0,0,0,0), limits=c(0, max(melt_npv$npv))) +
             theme_bw()

    # Add the horizontal and vertical lines to mark the maxima
    plot1 <- plot1 +
             annotate("point", x=pv_sizes[max_pv_index], y=battery_sizes[max_battery_index]) +
             annotate("text", hjust="left", vjust="center", x=pv_sizes[max_pv_index]+x_diff/2, y=battery_sizes[max_battery_index], label=paste("", pv_sizes[max_pv_index], "kWp, ", battery_sizes[max_battery_index], "kWh", sep=""))#, size=3)

    # Format the labels
    plot1 <- plot1 +
             xlab(paste("Solar PV Capacity (kWp)   $", round(financial_data$Inputs$Costs$'PV Cost ($/kWp)',0), "/kWp", sep="")) +
             ylab(paste("Battery Capacity (kWh)   $", round(financial_data$Inputs$Costs$'Battery Cost ($/kWh)',0), "/kWh", sep="")) +
             ggtitle(paste("System NPV ($) Over ", financial_data$Inputs$Finance$'Analysis Years', " Years", sep=""),
                     subtitle=paste("Consumption = ", round(technical_summary$Total$'Load (kWh)'/financial_data$Inputs$Finance$'Analysis Years'/1000,1), " MWh/year,  "
                                    , "Inverter Limit = ", financial_data$Inputs$Tariff$'FiT System Limit (Wp)' / 1000, "kW\n"
                                    , "Retail Price = ", round(financial_data$Inputs$Tariff$'Import ($/kWh)' * 100,3), "c, "
                                    , "Feed-in Tariff = ", round(financial_data$Inputs$Tariff$'Export ($/kWh)' * 100,3), "c, "
                                    , "Discount Rate = ", financial_data$Inputs$Finance$'Discount Rate (%)' * 100, "%\n"
                                    , "Import Inflation = ", financial_data$Inputs$Tariff$'Import Inflation (%)' * 100, "%, "
                                    , "Export Inflation = ", financial_data$Inputs$Tariff$'Export Inflation (%)' * 100, "%\n"
                                    , "Maximum Profit = $", format(max(mat_npv), nsmall = 2), " and "
                                    , self_sufficiency, "% self-sufficient"
                                    , sep="")) +
            guides(fill=guide_colourbar(title="$"))
  }

  return(plot1)
}

# ---------------------------------------------------------------------------------------------------------------
# Display the grid-dependency and annual energy exports for systems above the 5th NPV percentile (NPV Skim Top)
# ---------------------------------------------------------------------------------------------------------------
vis_Annual_Optimal <- function(simulation_info, technical_summary, financial_data, region_indexes, year) {
  num_pv <- length(technical_summary$'PV Capacity (Wp)')
  num_systems <- length(region_indexes)
  legend_type <- 0 #0 = None, #1 = PV Only, #2 = PV-Batteries Only, #3 = PV and PV-Batteries

  # Error condition: return an empty plot
  if (num_systems <= 0) {
    df <- data.frame()
    plot1 <- ggplot(df) +
             geom_point() +
             theme_bw() +
             xlim(0,10) + ylim(0,100) +
             xlab("Annual Energy Exported (MWh)") +
             ylab("Grid Dependency (%)") +
             ggtitle("Near-Optimal DER Configurations",
                     subtitle = paste("Top ", simulation_info$'NPV Skim Top (%)'*100, "% of NPV\n", "Year ", year, "\n\n", sep=""))

  # Normal condition
  } else {
    maximum_index <- which(financial_data$'System.NPV'==max(financial_data$'System.NPV'))
    max_dependence <- NaN
    max_exported <- NaN

    df <- data.frame(exported = numeric(num_systems),
                     dependence = numeric(num_systems),
                     npv = numeric(num_systems),
                     cfg_type = character(num_systems),
                     stringsAsFactors = FALSE)


    for (i in 1:num_systems) {
      battery_index <- ceiling(region_indexes[i]/num_pv)
      pv_index <- (region_indexes[i]-1) %% num_pv + 1

      # Figure out if all systems in the region are either PV-only, PV-Battery or a mix of both
      if (technical_summary$'Battery Capacity (Wh)'[battery_index] == 0) {
        df$cfg_type[i] = "PV"
        if (i == 1) {
          legend_type <- 1 # PV only
        } else {
          if (legend_type == 2) {
            legend_type <- 3 # PV and PV-Batteries
          }
        }
      } else {
        df$cfg_type[i] = "PV-Battery"
        if (i == 1) {
          legend_type <- 2 # PV-Batteries Only
        } else {
          if (legend_type == 1) {
            legend_type <- 3 # PV and PV-Batteries
          }
        }
      }

      # Record the data for the plot
      df$exported[i] <- technical_summary$Total$'Exported (kWh)'[pv_index, battery_index]/1000/technical_summary$Inputs$Timeline$Info$'Operational Years'
      df$dependence[i] <- technical_summary$Total$'Dependence (%)'[pv_index, battery_index]*100
      df$npv[i] <- financial_data$'System.NPV'[pv_index, battery_index]
      if (region_indexes[i] == maximum_index[1]) {
        max_dependence <- technical_summary$Total$'Dependence (%)'[pv_index, battery_index]*100
        max_exported <- df$exported[i]
      }
    }

    # Figure out the length shapes
    if (legend_type == 1) { # All systems in the region are PV-only
      manual_shapes = c(17)
    }
    if (legend_type == 2) { # All systems in the region are PV-Battery
      manual_shapes = c(15)
    }
    if (legend_type == 3) { # All systems in the region are a mix of PV-only and PV-Battery
      manual_shapes = c(17, 15)
    }

    df[order(df$cfg_type, df$npv),]

    plot1 <- ggplot(df) +
             aes(x=exported, y=dependence,z=npv) +
             geom_point(size=4, aes(colour=npv, shape=factor(cfg_type))) + scale_colour_gradient(low = "lightgrey", high = "black") +
             #    scale_shape_identity() +
             scale_shape_manual(values=manual_shapes) +
             theme_bw() +
             coord_cartesian(xlim=c(0,10), ylim=c(0,100)) +
             xlab("Annual Energy Exported (MWh)") +
             ylab("Grid Dependency (%)") +
             annotate("point", size=2, shape=16, colour="green", x=max_exported, y=max_dependence) +
             guides(colour=guide_colourbar(title="NPV $", order=2), shape=guide_legend(title="", label=TRUE, order=1)) +
             ggtitle("Near-Optimal DER Configurations",
                     subtitle = paste("Top ", simulation_info$'NPV Skim Top (%)'*100, "% of NPV\n", "Year ", year, "\n\n", sep=""))
  }

  return(plot1)
}

#################################################################################################################
# Scenario
#################################################################################################################
# ---------------------------------------------------------------------------------------------------------------
#
# ---------------------------------------------------------------------------------------------------------------
vis_Scenario_EnergyDependence <- function(economic_summary) {# plot(vis_Scenario_EnergyDependence(economic_summary))

  # Combine the annual system information into a format for ggplot
  y <- data.table(year = economic_summary$'Economic Outcomes'$year,
                  '% Grid Dependence' =  economic_summary$'Economic Outcomes'$dependence,
                  '% Dispatchable Self Consumption' =  economic_summary$'Economic Outcomes'$gen_dispatch,
                  '% Non Dispatchable Export' =  economic_summary$'Economic Outcomes'$gen_nondispatch)
  y_melt <- melt(y, id.vars = c("year"), variable.name = "group", value.name = "y")
  ymin <- data.table(year =  economic_summary$'Economic Outcomes'$year,
                     '% Grid Dependence' =  economic_summary$'Economic Outcomes'$dependence.min,
                     '% Dispatchable Self Consumption' =  economic_summary$'Economic Outcomes'$gen_dispatch.min,
                     '% Non Dispatchable Export' =  economic_summary$'Economic Outcomes'$gen_nondispatch.min)
  ymin_melt <- melt(ymin, id.vars = c("year"), variable.name = "group", value.name = "ymin")
  ymax <- data.table(year =  economic_summary$'Economic Outcomes'$year,
                     '% Grid Dependence' =  economic_summary$'Economic Outcomes'$dependence.max,
                     '% Dispatchable Self Consumption' =  economic_summary$'Economic Outcomes'$gen_dispatch.max,
                     '% Non Dispatchable Export' =  economic_summary$'Economic Outcomes'$gen_nondispatch.max)
  ymax_melt <- melt(ymax, id.vars = c("year"), variable.name = "group", value.name = "ymax")
  combined <- merge(y_melt, ymin_melt, by=c('year', 'group'))
  combined <- merge(combined, ymax_melt, by=c('year', 'group'))

  # Determine breaks
  initial_year <- min(economic_summary$'Economic Outcomes'$year)
  final_year <- max(economic_summary$'Economic Outcomes'$year)
  y_lim_max <- ceiling(max( economic_summary$'Economic Outcomes'$dependence,  economic_summary$'Economic Outcomes'$gen_dispatch.max,  economic_summary$'Economic Outcomes'$gen_nondispatch.max, 100) / 20) * 20
  y_breaks <- 0:(y_lim_max/20)*20

  # Setup the colour palette
  colour_fill <- rep("", 3)
  names(colour_fill) <- levels(combined$group)
  colour_fill['% Grid Dependence'] <- 'darkolivegreen3'
  colour_fill['% Dispatchable Self Consumption'] <- 'cadetblue3'
  colour_fill['% Non Dispatchable Export'] <- 'lightpink2'

  colour_line <- rep("", 3)
  names(colour_line) <- levels(combined$group)
  colour_line['% Grid Dependence'] <- 'forestgreen'
  colour_line['% Dispatchable Self Consumption'] <- 'darkturquoise'
  colour_line['% Non Dispatchable Export'] <- 'red4'

  # Prepare the subtitle
  subtitle <- paste0("Household: ", economic_summary$Scenario$Load$Profile, " @ ", economic_summary$Scenario$Load$'Annual Consumption (MWh)', " MWh pa\n",
                     "Import: ", economic_summary$Scenario$`Electricity Tariffs`$`Import ($/kWh)`*100, "c/kWh, ", print_Growth_Rate(economic_summary$Scenario$`Electricity Tariffs`$`Import Inflation (%)`), ";  ",
                     "Export: ", economic_summary$Scenario$`Electricity Tariffs`$`Export ($/kWh)`*100, "c/kWh, ", print_Growth_Rate(economic_summary$Scenario$`Electricity Tariffs`$`Export Inflation (%)`), "\n",
                     "PV: $", economic_summary$Scenario$`System Costs`$`PV Cost ($/kWp)`, "/kWp, ", print_Growth_Rate(economic_summary$Scenario$`System Costs`$`PV Cost Change (%)`), ";  ",
                     "Battery: $", economic_summary$Scenario$`System Costs`$'Battery Cost ($/kWh)', "/kWh, ", print_Growth_Rate(economic_summary$Scenario$`System Costs`$`Battery Cost Change (%)`), ";  ",
                     "Discount Rate: ", economic_summary$Scenario$Financial$`Discount Rate (%)`*100, "%")

  # Generate the plot
  plot1 <- ggplot(combined, aes(x=year, y=y, group=group, colour=group)) +
    scale_colour_manual(name = paste0(">", print_percentile(1-economic_summary$Scenario$Simulation$`NPV Skim Top (%)`), " (NPV)"),
                        values = colour_line) +
    scale_fill_manual(name = paste0(">", print_percentile(1-economic_summary$Scenario$Simulation$`NPV Skim Top (%)`), " (NPV)"),
                      values = colour_fill) +
    geom_ribbon(aes(ymin=ymin,ymax=ymax,fill=group),colour=NA,alpha=0.2) +
    geom_line(size = 1) +
    ggtitle(paste0("Most Profitable Customer DER Systems, ", initial_year, "-", final_year, " [", economic_summary$Scenario$'Name', "]"), subtitle = subtitle) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5)) +
    scale_y_continuous(name="% of Base Household Energy Load", breaks=y_breaks) +
    scale_x_continuous(name="", breaks=economic_summary$'Economic Outcomes'$year, minor_breaks=NULL, expand=c(0.01,0)) +
    coord_cartesian(ylim=c(0,y_lim_max)) +
    theme(legend.position = c(0.99, 0.99),
          legend.justification=c("right","top"),
          legend.background = element_rect(fill=alpha('white', 0.05)))

  return(plot1)
}

# ---------------------------------------------------------------------------------------------------------------
#
# ---------------------------------------------------------------------------------------------------------------
vis_scenario_ResultsNPV <- function(df) {
  initial_year <- df$year[1]
  final_year <- df$year[length(df$year)]
  y_lim_max <- ceiling(max(df$npv_max.max) / 2000) * 2000
  y_breaks <- 0:(y_lim_max/2000)*2000
  plot1 <- ggplot( data = df, aes(x=year)) +
    geom_ribbon(aes(ymin=npv_max.min, ymax=npv_max.max), fill="darkolivegreen3", alpha=0.2) +
    geom_line(aes(y=npv_max), colour="forestgreen", size = 1) +
    ggtitle(paste0("Change in Customer NPV ", initial_year, "-", final_year)) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5)) +
    scale_y_continuous(name="NPV ($)", breaks=y_breaks) +
    scale_x_continuous(name="", breaks=df$year, minor_breaks=NULL, expand=c(0.01,0)) +
    coord_cartesian(ylim=c(0,y_lim_max))
  return(plot1)
}

# ---------------------------------------------------------------------------------------------------------------
#
# ---------------------------------------------------------------------------------------------------------------
vis_Scenario_ResultsPayback <- function(df) {
  initial_year <- df$year[1]
  final_year <- df$year[length(df$year)]
  y_lim_max <- ceiling(max(df$payback.max) / 2) * 2
  y_breaks <- 0:(y_lim_max/2)*2
  plot1 <- ggplot( data = df, aes(x=year)) +
    geom_ribbon(aes(ymin=payback.min, ymax=payback.max), fill="darkolivegreen3", alpha=0.2) +
    geom_line(aes(y=payback), colour="forestgreen", size = 1) +
    ggtitle(paste0("Change in Customer Payback Periods ", initial_year, "-", final_year)) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5)) +
    scale_y_continuous(name="Years", breaks=y_breaks) +
    scale_x_continuous(name="", breaks=df$year, minor_breaks=NULL, expand=c(0.01,0))+
    coord_cartesian(ylim=c(0,y_lim_max))
  return(plot1)
}

# ---------------------------------------------------------------------------------------------------------------
#
# ---------------------------------------------------------------------------------------------------------------
vis_Scenario_FinancialAssumptions <- function(df) {
  initial_year <- df$year[1]
  final_year <- df$year[length(df$year)]
  df_flat <- melt(df, id="year")

  plot1 <- ggplot( data = df_flat, aes( x= year, y=value, colour=variable )) +
    geom_line() +
    ggtitle(paste("Economic Changes ", initial_year, "-", final_year, sep="")) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5)) +
    scale_y_continuous(name="% change") +
    scale_x_continuous(name="", breaks=df_flat$year, minor_breaks=NULL, expand=c(0.01,0)) +
    coord_cartesian(ylim=c(-100,max(100, max(df$growth_tariff_import, df$growth_tariff_export))))

  return(plot1)
}