Module: PredictabilityEngine::VegaVisualizer::BasicCharts

Defined in:
lib/predictability_engine/vega_visualizer/basic_charts.rb

Constant Summary collapse

BAND_COLORS = 
%w[#2ca02c #98df8a #ffdd57 #ff7f0e #d62728 #7b0000].freeze
GRANULARITY_PARAM = 
{
  name: 'granularity',
  value: 'yearweek',
  bind: { input: 'select',
          options: %w[yearday yearweek yearmonth],
          labels: %w[Daily Weekly Monthly],
          name: 'Group by: ' }
}.freeze
PERIOD_EXPR = 
"granularity === 'yearmonth' ? datum.date_month : " \
"(granularity === 'yearday' ? datum.date : datum.date_week)"

Class Method Summary collapse

Class Method Details

.cycle_time_bands(items, title: 'Cycle Time Bands Over Time') ⇒ Object 



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# File 'lib/predictability_engine/vega_visualizer/basic_charts.rb', line 76

def self.cycle_time_bands(items, title: 'Cycle Time Bands Over Time', **)
  labels = RawDataExporter::DONE_THRESHOLD_LABELS
  completed = PredictabilityEngine.completed_items(items)
  return Vega.lite.data([]).title(title) if completed.empty?

  data = completed.map do |item|
    idx = RawDataExporter.threshold_index(item.cycle_time)
    { date: PredictabilityEngine.format_date(item.end_date),
      date_week: PredictabilityEngine.format_year_week(item.end_date),
      date_month: PredictabilityEngine.format_year_month(item.end_date),
      band: labels[idx], band_order: idx }
  end
  VegaVisualizer.apply_standard_dims(
    Vega.lite.data(data)
        .params([GRANULARITY_PARAM])
        .transform([{ calculate: PERIOD_EXPR, as: 'period' }])
        .mark(type: 'area')
        .encoding(
          x: { field: 'period', type: 'ordinal', sort: 'ascending',
               title: nil, axis: VegaVisualizer::LABEL_AXIS },
          y: { aggregate: 'count', type: 'quantitative', title: 'Items Completed' },
          color: { field: 'band', type: 'ordinal', sort: labels,
                   scale: { domain: labels, range: BAND_COLORS },
                   legend: { title: 'Cycle Time', orient: 'bottom', columns: labels.size } },
          order: { field: 'band_order', type: 'quantitative' },
          tooltip: [
            { field: 'period', type: 'ordinal', title: 'Period' },
            { field: 'band', type: 'ordinal', title: 'Cycle Time' },
            { aggregate: 'count', type: 'quantitative', title: 'Items' }
          ]
        ),
    title: title
  )
end

.cycle_time_scatter(items, percentiles, title: 'Cycle Time Scatter Plot') ⇒ Object 



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# File 'lib/predictability_engine/vega_visualizer/basic_charts.rb', line 6

def self.cycle_time_scatter(items, percentiles, title: 'Cycle Time Scatter Plot')
  completed = PredictabilityEngine.completed_items(items)
  data = completed.map do |i|
    { date: PredictabilityEngine.format_date(i.end_date), cycle_time: i.cycle_time, id: i.id,
      title: i.title, title_display: VegaVisualizer.wrap_tooltip_title(i.title), url: i.url }
  end
  pct_data = PredictabilityEngine.mapped_percentiles(items, percentiles)
  VegaVisualizer.apply_standard_dims(
    Vega.lite.data(data + pct_data.map { |p| { type: p[:label], val: p[:val], p: p[:p] } })
        .layer([scatter_points_layer, scatter_rules_layer(pct_data)]),
    title: title
  )
end

.scatter_points_layer ⇒ Object 



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# File 'lib/predictability_engine/vega_visualizer/basic_charts.rb', line 20

def self.scatter_points_layer
  x_axis = VegaVisualizer.date_x_axis(title: 'Completion Date',
                                      tickCount: { interval: 'week' })
  { mark: { type: 'point', opacity: 0.6, size: 20 },
    encoding: { x: x_axis,
                y: VegaVisualizer.quantitative_y_axis('cycle_time', title: 'Cycle Time (days)'),
                color: { value: '#4c78a8' },
                **VegaVisualizer.item_href_and_tooltip(
                  [{ field: 'date', type: 'temporal', title: 'Completion Date' },
                   VegaVisualizer.cycle_time_tooltip_field]
                ) } }
end

.scatter_rules_layer(pct_data) ⇒ Object 



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# File 'lib/predictability_engine/vega_visualizer/basic_charts.rb', line 33

def self.scatter_rules_layer(pct_data)
  count = pct_data.size
  palette = ['#72b7b2', '#e45756', '#b279a2', '#ff9da7', '#ad494a', '#8ca27a']
  # More distinct dash styles and thicker lines
  dash_map = { 50 => [], 75 => [8, 4], 85 => [4, 4], 95 => [2, 2], 98 => [1, 1] }
  width_map = { 50 => 1.5, 75 => 2, 85 => 2.5, 95 => 3, 98 => 3.5 }

  dash_condition = dash_map.map { |p, dash| { test: "datum.p == #{p}", value: dash } }
  width_condition = width_map.map { |p, w| { test: "datum.p == #{p}", value: w } }

  { transform: [{ filter: 'datum.type != null' }],
    mark: { type: 'rule' },
    encoding: { y: VegaVisualizer.quantitative_y_axis('val', title: 'Cycle Time (days)'),
                strokeDash: { condition: dash_condition, value: [4, 4] },
                strokeWidth: { condition: width_condition, value: 1 },
                color: { field: 'type', type: 'nominal', title: 'Percentiles',
                         scale: { range: palette.take(count) },
                         legend: { orient: 'bottom', columns: 3 } },
                tooltip: [{ field: 'type', type: 'nominal', title: 'Percentile' },
                          VegaVisualizer.cycle_time_tooltip_field(field: 'val')] } }
end

.throughput_histogram(items, title: 'Throughput Histogram') ⇒ Object 



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# File 'lib/predictability_engine/vega_visualizer/basic_charts.rb', line 55

def self.throughput_histogram(items, title: 'Throughput Histogram')
  data = Calculators::Throughput.daily(items).values.map { |v| { throughput: v } }
  bar_chart(data, title: title,
                  x: VegaVisualizer.quantitative_x_axis('throughput', bin: true, title: 'Items per Day'),
                  y: VegaVisualizer.quantitative_y_axis('count', aggregate: 'count', title: 'Frequency'))
end