separate ui logic

src/main/scala/UI.scala

@@ -9,124 +9,46 @@ import PowerTemperatureAnalysis._
   */
 object UI {
 
-  /** Menu options with their associated actions to perform
-    * @return
-    *   a chunk of tuples (name, action), action being a function that takes LoadedData and returns a ZIO effect that will return Unit
-    */
-  val menuOptions: Chunk[(String, (LoadedData) => ZIO[Any, Any, Unit])] = Chunk(
-    "Get stats for a specific day" -> ((data) =>
-      for {
-        _    <- printLine("Please enter a date (dd/MM/yyyy): ")
-        date <- readDate
-        _    <- printLine(s"Stats for $date:")
-      } yield ()
-    ),
-    "Global statistics for a given period" -> ((data) =>
-      for {
-        _         <- printLine("Please enter a start date (dd/MM/yyyy): ")
-        startDate <- readDate
-        _         <- printLine("Please enter an end date (dd/MM/yyyy): ")
-        endDate   <- readDate
-        _ <- printLine(
-          GlobalStatisticsAnalysis.formatStatisticsTable(
-            "Environmental impact of electricity production and consumption",
-            GlobalStatisticsAnalysis.getCarbonIntensityStatistics(data, startDate, endDate),
-            s"Data between $startDate and $endDate"
-          )
-        )
-        _ <- printLine(
-          "\n\n" +
-            GlobalStatisticsAnalysis.formatStatisticsTable(
-              "Electricty consumption and temperature",
-              GlobalStatisticsAnalysis.getTemperatureAndConsumptionStatistics(data, startDate, endDate),
-              s"Data between $startDate and $endDate"
-            )
-        )
-        _ <- printLine(
-          "\n\n" +
-            GlobalStatisticsAnalysis.formatStatisticsTable(
-              "Production (MW) by supply chain",
-              GlobalStatisticsAnalysis.getProductionBySupplyChain(data, startDate, endDate),
-              s"Data between $startDate and $endDate"
-            )
-        )
-      } yield ()
-    ),
-    "Case study: Temperature vs power peak" -> ((data) => printLine(formatPowerPeakAndTemperature(data)))
-  )
-
-  /** Retrieves the information about maximum power peak and minimum temperature entries for each year and formats it into a newline-separated string.
-    *
-    * @param data
-    *   The loaded data containing daily power peak with temperature information.
-    * @return
-    *   A formatted string containing information about maximum power peak and minimum temperature entries for each year, with each entry on a new line.
-    */
-  def formatPowerPeakAndTemperature(data: LoadedData): String = {
-    val title = "+-------------------------------------------------------------------------+\n" +
-      "|                Case Study : Power and Temperature Summary               |\n" +
-      "+-------------------------------------------------------------------------+\n" +
-      "| We want to know the maximum power peak and the minimum temperature for  |\n" +
-      "| each year.                                                              |\n" +
-      "+-------------------------------------------------------------------------+\n"
-
-    val output = powerPeakTemperatureGroupedByYear(data)
-      .flatMap {
-        case (year, yearData) => {
-          val (maxPowerEntry, minTemperatureEntry) = maxPowerPeakAndMinTemperature(yearData)
-          Some(
-            f"|                              Year $year                                  |\n" +
-              "+-------------------------------------------------------------------------+\n" +
-              f"| Max Power Peak:  ${maxPowerEntry.powerPeak}%10.2f MW on ${maxPowerEntry.date}                            |\n" +
-              f"| Min Temperature: ${minTemperatureEntry.meanTemperature}%10.2f C on ${minTemperatureEntry.date}                             |\n" +
-              "+-------------------------------------------------------------------------+\n".stripMargin.trim
-          )
-        }
-      }
-      .mkString("\n")
-
-    val pearsonCoef = temperatureAndPowerPeakPearsonCorrelation(data.dailyPowerPeakWithTemperature)
-    val pearsonCoefFormatted =
-      f"\n| Temperature And PowerPeak Pearson Correlation: $pearsonCoef%5.2f                    |\n" +
-        "+-------------------------------------------------------------------------+\n"
-
-    val conclusion =
-      "|                            Final Conclusion                             |\n" +
-        "+-------------------------------------------------------------------------+\n" +
-        "| - The correlation coefficient indicates a strong correlation.           |\n" +
-        "| - As temperatures decrease, power peak tends to increase.               |\n" +
-        "| - People tend to use more electricity for heating purposes when         |\n" +
-        "|   temperatures fall                                                     |\n" +
-        "+-------------------------------------------------------------------------+\n"
-
-    title + output + pearsonCoefFormatted + conclusion
-  }
-
-  val choiceMenu: String =
-    "Welcome to our energy analysis tool!\n\nHere are all the interesting interactions you can have with it:\n"
-      + menuOptions.zipWithIndex
-        .map { case ((name, _), index) =>
-          s"${index + 1}. $name"
-        }
-        .mkString("\n")
-      + "\n\nPlease enter your choice: "
-
   /** Main ZIO console loop
     * @return
     *   a ZIO effect that will return Unit
     */
   def consoleLoop(data: LoadedData): ZIO[Any, Any, Unit] = {
     for {
-      _     <- printLine(choiceMenu)
+      _     <- printLine(menuString)
       input <- readLine
-      _ <- menuOptions.lift(input.toInt - 1) match {
+      _ <- menuChoices.lift(input.toInt - 1) match {
         case Some((_, action)) => action(data)
         case None              => printLineError("Invalid choice")
       }
       _ <- consoleLoop(data)
     } yield ()
   }
 
+  /** Menu options with their associated actions to perform
+    * @return
+    *   a chunk of tuples (name, action), action being a function that takes LoadedData and returns a ZIO effect that will return Unit
+    */
+  val menuChoices: Chunk[(String, (LoadedData) => ZIO[Any, Any, Unit])] =
+    Chunk(
+      ("Get stats for a specific day", printDailyStats),
+      ("Global statistics for a given period", printGlobalStats),
+      ("Case study: Temperature vs power peak", printCaseStudy)
+    )
+
+  /** Menu string
+    * @return
+    *   a String containing the menu and its options
+    */
+  val menuString: String =
+    "Welcome to our energy analysis tool!\n\nHere are all the interesting interactions you can have with it:\n"
+      + menuChoices.zipWithIndex
+        .map { case ((name, _), index) =>
+          s"${index + 1}. $name"
+        }
+        .mkString("\n")
+      + "\n\nPlease enter your choice: "
+
   /** Reads a date from the console
     * @return
     *   a ZIO effect that will return a LocalDate
@@ -139,4 +61,36 @@ object UI {
         case None       => printLineError("You entered an invalid date, retry.\n") *> readDate
       }
     } yield date
+
+  def printDailyStats(data: LoadedData): ZIO[Any, Any, Unit] = {
+    for {
+      _    <- printLine("Please enter a date (dd/MM/yyyy): ")
+      date <- readDate
+      _    <- printLine(s"Stats for $date:")
+    } yield ()
+  }
+
+  def printGlobalStats(data: LoadedData): ZIO[Any, Any, Unit] = {
+    for {
+      _         <- printLine("Please enter a start date (dd/MM/yyyy): ")
+      startDate <- readDate
+      _         <- printLine("Please enter an end date (dd/MM/yyyy): ")
+      endDate   <- readDate
+
+      _ <- printLine("\n\n" + GlobalStatisticsAnalysis.carbonIntensityTab(data, startDate, endDate))
+      _ <- printLine("\n\n" + GlobalStatisticsAnalysis.productionBySupplyChainTab(data, startDate, endDate))
+      _ <- printLine("\n\n" + GlobalStatisticsAnalysis.consumptionAndTemperatureTab(data, startDate, endDate) + "\n")
+    } yield ()
+  }
+
+  def printCaseStudy(data: LoadedData): ZIO[Any, Any, Unit] = {
+    for {
+      _ <- printLine(
+        formatPowerPeakAndTemperature(
+          maxPowerPeaksAndMinTemperaturesByYear(data),
+          temperatureAndPowerPeakPearsonCorrelation(data)
+        )
+      )
+    } yield ()
+  }
 }

src/main/scala/analysis/GlobalStatisticsAnalysis.scala

@@ -62,7 +62,7 @@ object GlobalStatisticsAnalysis {
         val count     = values.size
 
         Statistics(fieldName, average, stdDev, min, max, count)
-    }.toList
+      }.toList
   }
 
   /** Get a formatted table of statistics.
@@ -215,4 +215,58 @@ object GlobalStatisticsAnalysis {
       end
     )
   }
+
+  /** Get a formatted table of statistics about the carbon intensity of electricity production and consumption, given a period of time.
+    *
+    * @param data
+    *   The data to analyse.
+    * @param startDate
+    *   The start date of the period.
+    * @param endDate
+    *   The end date of the period.
+    * @return
+    *   A formatted table
+    */
+  def carbonIntensityTab(data: LoadedData, startDate: LocalDate, endDate: LocalDate): String =
+    GlobalStatisticsAnalysis.formatStatisticsTable(
+      "Environmental impact of electricity production and consumption",
+      GlobalStatisticsAnalysis.getCarbonIntensityStatistics(data, startDate, endDate),
+      s"Data between $startDate and $endDate"
+    )
+
+    /** Get a formatted table of statistics about the temperature and power consumption, given a period of time.
+      *
+      * @param data
+      *   The data to analyse.
+      * @param startDate
+      *   The start date of the period.
+      * @param endDate
+      *   The end date of the period.
+      * @return
+      *   A formatted table
+      */
+  def consumptionAndTemperatureTab(data: LoadedData, startDate: LocalDate, endDate: LocalDate): String =
+    GlobalStatisticsAnalysis.formatStatisticsTable(
+      "Electricty consumption and temperature",
+      GlobalStatisticsAnalysis.getTemperatureAndConsumptionStatistics(data, startDate, endDate),
+      s"Data between $startDate and $endDate"
+    )
+
+    /** Get a formatted table of statistics about the production by supply chain, given a period of time.
+      *
+      * @param data
+      *   The data to analyse.
+      * @param startDate
+      *   The start date of the period.
+      * @param endDate
+      *   The end date of the period.
+      * @return
+      *   A formatted table
+      */
+  def productionBySupplyChainTab(data: LoadedData, startDate: LocalDate, endDate: LocalDate): String =
+    GlobalStatisticsAnalysis.formatStatisticsTable(
+      "Production (MW) by supply chain",
+      GlobalStatisticsAnalysis.getProductionBySupplyChain(data, startDate, endDate),
+      s"Data between $startDate and $endDate"
+    )
 }

src/main/scala/analysis/PowerTemperatureAnalysis.scala

@@ -12,8 +12,8 @@ object PowerTemperatureAnalysis {
     * @return
     *   A Map where the key is the year and the value is a List of daily power peak with temperature entries for that year.
     */
-  def powerPeakTemperatureGroupedByYear(data: LoadedData): Map[Int, List[DailyPowerPeakWithTemperature]] = {
-    data.dailyPowerPeakWithTemperature.toList.groupBy(pp => pp.date.getYear)
+  def powerPeakTemperatureGroupedByYear(data: LoadedData): Map[Int, Chunk[DailyPowerPeakWithTemperature]] = {
+    data.dailyPowerPeakWithTemperature.groupBy(pp => pp.date.getYear)
   }
 
   /** Finds the maximum power peak and minimum temperature entries in the given list of daily power peak with temperature data for a specific year.
@@ -23,18 +23,77 @@ object PowerTemperatureAnalysis {
     * @return
     *   A tuple containing the maximum power peak entry and the minimum temperature entry for the specified year.
     */
-  def maxPowerPeakAndMinTemperature(yearData: List[DailyPowerPeakWithTemperature]): (DailyPowerPeakWithTemperature, DailyPowerPeakWithTemperature) =
+  def maxPowerPeakAndMinTemperatureOfYear(yearData: Chunk[DailyPowerPeakWithTemperature]): (DailyPowerPeakWithTemperature, DailyPowerPeakWithTemperature) =
     (yearData.maxBy(_.powerPeak), yearData.minBy(_.meanTemperature))
 
+  /** Retrieves the information about maximum power peak and minimum temperature entries for each year.
+    *
+    * @param data
+    *   The loaded data containing daily power peak with temperature information.
+    * @return
+    *   A chunk of tuples containing the year, the maximum power peak entry for that year and the minimum temperature entry for that year.
+    */
+  def maxPowerPeaksAndMinTemperaturesByYear(data: LoadedData): Chunk[(Int, DailyPowerPeakWithTemperature, DailyPowerPeakWithTemperature)] =
+    Chunk
+      .fromIterable(
+        powerPeakTemperatureGroupedByYear(data)
+          .map((year, yearData) => {
+            val (maxPowerPeak, minTemperature) = maxPowerPeakAndMinTemperatureOfYear(yearData)
+            (year, maxPowerPeak, minTemperature)
+          })
+      )
+      .sortBy(_._1)
+
   /** Calculates the Pearson correlation coefficient between the given power peaks and temperatures.
     *
     * @param data
     *   The loaded chunk of data containing daily power peaks with temperature information.
     * @return
     *   The Pearson correlation coefficient between the given power peaks and temperatures.
     */
-  def temperatureAndPowerPeakPearsonCorrelation(data: Chunk[DailyPowerPeakWithTemperature]): Double =
-    val temperatures: Chunk[Temperature.Celsius] = data.map(line => line.meanTemperature)
-    val powerPeaks: Chunk[Power.MW]              = data.map(line => line.powerPeak)
+  def temperatureAndPowerPeakPearsonCorrelation(data: LoadedData): Double =
+    val temperatures: Chunk[Temperature.Celsius] = data.dailyPowerPeakWithTemperature.map(line => line.meanTemperature)
+    val powerPeaks: Chunk[Power.MW]              = data.dailyPowerPeakWithTemperature.map(line => line.powerPeak)
     linearCorrelationCoefficient(powerPeaks, temperatures)
+
+    /** Retrieves the information about maximum power peak and minimum temperature entries for each year and formats it into a newline-separated string.
+      *
+      * @param data
+      *   The loaded data containing daily power peak with temperature information.
+      * @return
+      *   A formatted string containing information about maximum power peak and minimum temperature entries for each year, with each entry on a new line.
+      */
+  def formatPowerPeakAndTemperature(yearsData: Chunk[(Int, DailyPowerPeakWithTemperature, DailyPowerPeakWithTemperature)], correlationCoef: Double): String = {
+    val title = "+-------------------------------------------------------------------------+\n" +
+      "|                Case Study : Power and Temperature Summary               |\n" +
+      "+-------------------------------------------------------------------------+\n" +
+      "| We want to know the maximum power peak and the minimum temperature for  |\n" +
+      "| each year.                                                              |\n" +
+      "+-------------------------------------------------------------------------+\n"
+
+    val yearsSummaries = yearsData
+      .map((year, maxPowerEntry, minTemperatureEntry) =>
+        f"|                              Year $year                                  |\n" +
+          "+-------------------------------------------------------------------------+\n" +
+          f"| Max Power Peak:  ${maxPowerEntry.powerPeak}%10.2f MW on ${maxPowerEntry.date}                            |\n" +
+          f"| Min Temperature: ${minTemperatureEntry.meanTemperature}%10.2f C on ${minTemperatureEntry.date}                             |\n" +
+          "+-------------------------------------------------------------------------+\n".stripMargin.trim
+      )
+      .mkString("\n")
+
+    val pearsonCoefFormatted =
+      f"\n| Temperature And PowerPeak Pearson Correlation: $correlationCoef%5.2f                    |\n" +
+        "+-------------------------------------------------------------------------+\n"
+
+    val conclusion =
+      "|                            Final Conclusion                             |\n" +
+        "+-------------------------------------------------------------------------+\n" +
+        "| - The correlation coefficient indicates a strong enough correlation.    |\n" +
+        "| - As daily temperatures decrease, daily power peak tends to increase.   |\n" +
+        "| - People tend to use more electricity for heating purposes when         |\n" +
+        "|   temperatures fall                                                     |\n" +
+        "+-------------------------------------------------------------------------+\n"
+
+    title + yearsSummaries + pearsonCoefFormatted + conclusion
+  }
 }