# encoding: utf-8

# Dataset is a collection of labeled state records.
# 
class YPetri::Net::DataSet < Hash
  class << self
    alias __new__ new

    def new type: nil
      __new__ do |hsh, missing|
        case missing
        when Float then nil
        else hsh[ missing.to_f ] end
      end.tap { |inst|
        inst.instance_variable_set :@type, type
      }
    end

    private :__new__

    delegate :net, to: :features
    delegate :State, to: :net
    delegate :Marking, :Firing, :Flux, :Gradient, :Delta,
             to: "State()"
  end

  alias events keys
  alias records values

  delegate :features,
           :net,
           :State,
           :Marking, :Firing, :Flux, :Gradient, :Delta,
           to: "self.class"

  attr_reader :type # more like event_type, idea not matured yet

  # Type of the dataset.
  # 
  def timed?
    type == :timed
  end

  # Returns the Record instance corresponding to the given recorded event.
  # 
  def record( event )
    features.load( fetch event )
  end

  # Returns the nearest event smaller or equal to the supplied event-type
  # argument. The second optional ordered argument, true by default, controls
  # whether equality is accepted. If set to false, then the nearest _smaller_
  # event is sought.
  # 
  def floor( event, equal_ok=true )
    e = events.ascending_floor( event, equal_ok )
    e.nil? ? nil : e
  end

  # Returns the nearest event greater or equal to the supplied event-type
  # argument. The second optional ordered argument, true by default, controls
  # whether equality is accepted. If set to false, then the nearest _greater_
  # event is sought.
  # 
  def ceiling( event, equal_ok=true )
    e = events.ascending_ceiling( event, equal_ok )
    e.nil? ? nil : e
  end

  # Revives records from values.
  # 
  def records
    values.map { |value| features.Record.new( value ) }
  end

  # Recreates the simulation at a given event label.
  # 
  def reconstruct event: (fail "No event given!"),
    **settings # settings include marking clamps
    rec = interpolate( event )
    if timed? then
      rec.reconstruct time: event, **settings
    else
      rec.reconstruct **settings
    end
  end

  # Interpolates the recording an the given point (event). Return value is the
  # Record class instance.
  # 
  def interpolate( event )
    begin
      record( event )
    rescue KeyError => msg
      timed? or raise TypeError, "Event #{event} not recorded! (%s)" %
        "simulation type: #{type.nil? ? 'nil' : type}"
      # (Remark: #floor, #ceiling supported by timed datasets only)
      fe = floor( event )
      fail "Event #{event} has no floor!" if fe.nil?
      f = Matrix.column_vector record( fe )
      ce = ceiling( event )
      fail "Event #{event} has no ceiling!" if ce.nil?
      c = Matrix.column_vector record( ce )
      rslt = f + ( c - f ) / ( ce - fe ) * ( event - fe )
      features.load( rslt.column_to_a )
    end
  end

  # Resamples the recording.
  # 
  def resample **nn
    time_range = nn.may_have( :time_range, syn!: :time ) ||
      events.first .. events.last
    sampling = nn.must_have :sampling
    t0, target_time = time_range.begin, time_range.end
    t = t0
    loop.with_object self.class.new do |o|
      o.update t => interpolate( t )
      t += sampling
      return o if t > target_time
    end
  end

  # Computes the distance to another dataset.
  # 
  def distance( other )
    sum_of_sq = events
      .map { |e| [ e, other.interpolate( e ) ] }
      .map { |rec1, rec2| rec1.euclidean_distance rec2 }
      .map { |dist| dist * dist }
      .reduce( :+ )
    sum_of_sq ** 0.5
  end

  # Returns the data series for the specified features.
  # 
  def series arg=nil
    return records.transpose if arg.nil?
    reduce_features( State().features( arg ) ).series
  end

  # Expects a hash of features (:marking (alias :state) of places, :firing
  # of tS transitions, :delta of places and/or transitions) and returns the
  # corresponding mapping of the recording.
  # 
  def reduce_features *args
    Δt = if args.last.is_a? Hash then
           args.last.may_have( :delta_time, syn!: :Δt )
           args.last.delete( :delta_time )
             .tap { args.delete_at( -1 ) if args.last.empty? }
         end
    reduced_features = net.State.features *args
    rf_Record = reduced_features.Record
    reduced_features.new_dataset( type: type ).tap { |dataset|
      ( events >> records ).each_pair { |event, record|
        absent_features = reduced_features - features()
        if absent_features.empty? then # it is a subset
          line = reduced_features.map { |feature| record.fetch feature }
        else # it will require simulation reconstruction
          sim = reconstruct event: event
          if absent_features.any? { |f| f.timed? rescue false } then
            fail ArgumentError, "Reconstruction of timed features requires " +
              "the named arg :delta_time to be given!" unless Δt
            line = reduced_features.map do |feature|
              if absent_features.include? feature then
                if ( feature.timed? rescue false ) then
                  feature.extract_from( sim ).( Δt )
                else
                  feature.extract_from( sim )
                end
              else
                record.fetch feature
              end
            end
          else
            line = reduced_features.map do |feature|
              if absent_features.include? feature then
                feature.extract_from( sim )
              else
                record.fetch feature
              end
            end
          end
        end
        dataset.update event => rf_Record.load( line )
      }
    }
  end

  # Returns a subset of this dataset with only the specified marking features
  # identified by the arguments retained. If no arguments are given, all the
  # marking features from the receiver dataset are selected.
  # 
  def marking ids=nil
    return reduce_features net.State.marking if ids.nil?
    reduce_features marking: ids
  end

  # Returns a subset of this dataset with only the specified firing features
  # identified by the arguments retained. If no arguments are given, all the
  # firing features from the receiver dataset are selected.
  # 
  def firing *args
    Δt = if args.last.is_a? Hash then
           args.last.may_have( :delta_time, syn!: :Δt )
           args.last.delete( :delta_time )
             .tap { args.delete_at( -1 ) if args.last.empty? }
         end
    if Δt then
      return reduce_features net.State.firing, delta_time: Δt if args.empty?
      reduce_features firing: args.first, delta_time: Δt
    else
      return reduce_features net.State.firing if args.empty?
      reduce_features firing: args.first
    end
  end

  # Returns a subset of this dataset with only the specified flux features
  # identified by the arguments retained. If no arguments are given, all the
  # flux features from the receiver dataset are selected.
  # 
  def flux ids=nil
    return reduce_features net.State.flux if ids.nil?
    reduce_features flux: ids
  end

  # Returns a subset of this dataset with only the specified gradient features
  # identified by the arguments retained. If no arguments are given, all the
  # gradient features from the receiver dataset are selected.
  # 
  def gradient *args
    return reduce_features net.State.gradient if args.empty?
    reduce_features gradient: args
  end

  # Returns a subset of this dataset with only the specified delta features
  # identified by the arguments retained. If no arguments are given, all the
  # delta features from the receiver dataset are selected.
  # 
  def delta *args
    Δt = if args.last.is_a? Hash then
           args.last.may_have( :delta_time, syn!: :Δt )
           args.last.delete( :delta_time )
             .tap { args.delete_at( -1 ) if args.last.empty? }
         end
    if Δt then
      return reduce_features net.State.delta, delta_time: Δt if args.empty?
      reduce_features delta: args, delta_time: Δt
    else
      return reduce_features net.State.delta if args.empty?
      reduce_features delta: args
    end
  end

  # Outputs the current recording in CSV format.
  # 
  def to_csv
    map { |lbl, rec| [ lbl, *rec ].join ',' }.join "\n"
  end

  # Plots the dataset. Takes several optional arguments: The list of features
  # to plot as the first ordered argument, 
  # 
  def plot( feature_ids=nil, time: nil, except: [], **nn )
    time = nn.may_have :time, syn!: :time_range
    events = events()
    ff = feature_ids.nil? ? features : net.state.features( feature_ids )
    ff -= net.state.features( except )
    data_ss = series( ff )
    x_range = if time.nil? then
                from = events.first || 0
                to = events.last && events.last > from ? events.last :
                  events.first + 1
                "[#{from}:#{to}]"
              elsif time.is_a? Range then
                "[#{time.begin}:#{time.end}]"
              else
                "[-0:#{SY::Time.magnitude( time ).amount rescue time}]"
              end
    Gnuplot.open do |gp|
      Gnuplot::Plot.new gp do |plot|
        plot.xrange x_range
        plot.title nn[:title] || "#{net} plot"
        plot.ylabel nn[:ylabel] || "Values"
        plot.xlabel nn[:xlabel] || "Time [s]"
        ff.labels.zip( data_ss ).each do |label, data_array|
          plot.data << Gnuplot::DataSet.new( [ events, data_array ] ) { |ds|
            ds.with = "linespoints"
            ds.title = label
          }
        end
      end
    end
  end

  # Returns a string briefly discribing the dataset.
  # 
  def to_s
    "#<DataSet: " +
      "#{keys.size} records, " +
      "features: #{features}" +
      ">"
  end

  # Inspect string of the instance.
  # 
  def inspect
    to_s
  end

  # Pretty print the dataset. Takes +:precision+ and +:distance+ named arguments,
  # that control the shape of the printed table.
  # 
  def print precision: 4, distance: precision + 4
    features.labels.print_as_line precision: precision, distance: distance
    puts '-' * features.size * distance
    records.each { |record|
      record.print_as_line precision: precision, distance: distance
    }
    return nil
  end
end # YPetri::Net::Dataset