Visualize

Visualize

This package is part of the Axle domain specific language.

The show function is available in the axle.visualize._ package. It can be applied to several types of Axle objects. That package also contains functions for creating files from the images: png, jpeg, gif, bmp.

For example:

show(plot)
png(plot, "plot.png")

XY Plots

axle.visualize.Plot

  import util.Random
  import collection._
  import math.{ Pi, cos, sin }
  import axle.visualize._
  import axle.visualize.Plottable._
  import org.joda.time.DateTime

  val now = new DateTime()

  def randomTimeSeries(i: Int) = {
    val (phase, amp, f) = (Random.nextDouble, Random.nextDouble, Random.nextDouble)
    ("series %d %1.2f %1.2f %1.2f".format(i, phase, amp, f),
      new immutable.TreeMap[DateTime, Double]() ++
      (0 to 100).map(j => (now.plusMinutes(2 * j) -> amp * sin(phase + (j / (10 * f))))).toMap)
  }

  val plot = new Plot((0 until 20).map(i => randomTimeSeries(i)).toList, true,
    title = Some("Random Waves"), xAxis = 0.0, xAxisLabel = Some("time (t)"),
    yAxis = now, yAxisLabel = Some("a * sin(p + t/10f)"))

  show(plot)

Bar Charts

The following example dataset:

val fruits = Vector("apple", "banana", "coconut")

val years = Vector(2011, 2012)

val sales = Map(
  ("apple", 2011) -> 43.0,
  ("apple", 2012) -> 83.8,
  ("banana", 2011) -> 11.3,
  ("banana", 2012) -> 77.9,
  ("coconut", 2011) -> 88.0,
  ("coconut", 2012) -> 10.1
)

Can be grouped in two ways to produce bar charts:

import axle.visualize._

val chart = BarChart(
  fruits,
  years,
  (fruit: String, year: Int) => sales((fruit, year)),
  xAxis = 0.0,
  title = Some("fruit sales")
)

show(chart)

Or:

import axle.visualize._

val chart = BarChart(
  years,
  fruits,
  (year: Int, fruit: String) => sales((fruit, year)),
  xAxis = 0.0,
  title = Some("fruit sales")
)

show(chart)

Animation

Supplying a refresher to the BarChart will cause the data to be recomputed. The first half of the refresher is a function that takes the previous data and returns the new data. The second half is an interval (of type Time.Q that denotes the period at which values are recomputed.

This example keeps the "bar" value steady at 1.0 while assigning a new random Double (between 0 and 1) to "foo" every second.

The visualizing frame polls for updates at a rate of approximately 24 Hz (every 42 ms).

import util.Random.nextDouble
import axle.visualize._
import axle.quanta._
import Time._

val groups = Vector("foo", "bar")
val slices = Vector("bippy")
val initial = Map(("foo", "bippy") -> 1d, ("bar", "bippy") -> 1d)
val tick = (previous: Map[(String, String), Double]) => previous + (("foo", "bippy") -> nextDouble)

val chart = BarChart(
  groups,
  slices,
  initial,
  xAxis = 0d,
  title = Some("random"),
  refresher = Some(tick, 1 *: second)
)

show(chart)

Scatter Plots

TODO: show example of 2-feature KMeans clustering

Graph

TODO: show directed and undirected graph visualizations