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Version: Java (Groovy)

How to create XY series plots

This guide shows you how to use the plot method to create XY series plots.

XY series plots are generally used to show values over a continuum, such as time. XY Series plots can be represented as a line, a bar, an area or as a collection of points. The X axis is used to show the domain, while the Y axis shows the related values at specific points in the range.

Data sourcing​

XY Series plots can be created using data from Deephaven tables, arrays and functions.

From a table​

When data is sourced from a Deephaven table, the following syntax can be used to create an XY series plot:

.plot("SeriesName", source, "xCol", "yCol").show()

  • plot is the method used to create an XY series plot.
  • "SeriesName" is the name (as a string) you want to use to identify the series on the plot itself.
  • source is the table that holds the data you want to plot.
  • "xCol" is the name of the column of data to be used for the X value.
  • "yCol" is the name of the column of data to be used for the Y value.
  • show tells Deephaven to draw the plot in the console.

The example query below will create an XY series plot that shows the high of Bitcoin for September 8, 2021.

note

Python users must import the appropriate module: from deephaven import Plot or from deephaven import Plot as plt

import static io.deephaven.csv.CsvTools.readCsv

//source the data
source = readCsv("https://media.githubusercontent.com/media/deephaven/examples/main/MetricCentury/csv/metriccentury.csv")

//plot the data
plot_single = plot("Distance",  source.where("SpeedKPH > 0"), "Time", "DistanceMeters").show()

Shared axes​

You can compare multiple series over the same period of time by creating an XY series plot with shared axes. In the following example, two series are plotted, thereby creating two line graphs on the same plot.

plot_shared_axis = plot("Altitude", source, "Time", "AltitudeMeters")\
    .plot("Speed", source, "Time", "SpeedKPH")\
    .show()
tip

You can choose to hide one or more series in the plot. Simply click the name of the series at the right of the plot to hide that series; click the name again to restore it.

Subsequent series can be added to the plot by adding additional plot methods to the query.

Multiple X or Y Axes​

When plotting multiple series in a single plot, the range of the Y axis is an important factor to watch. As the range of the Y axis increases, value changes become harder to assess.

When the scale of the Y axis needs to cover an extremely wide range, the plot may result in relatively flat lines with barely distinguishable differences in values or trend.

This issue can be easily remedied by adding a second Y axis to the plot via the twinX method.

twinX​

The twinX method enables you to use one Y axis for some of the series being plotted and a second Y axis for the others, while sharing the same X axis:

PlotName = figure().plot(...).twinX().plot(...).show()

  • The plot(s) for the series placed before the twinX() method share a common Y axis (on the left).
  • The plot(s) for the series listed after the twinX() method share a common Y axis (on the right).
  • All plots share the same X axis.
plotSharedTwinX = plot("Altitude", source, "Time", "AltitudeMeters")\
    .twinX()\
    .plot("Speed", source, "Time", "SpeedKPH")\
    .show()

The value range for the high value is shown on the left axis and the value range for the low value is shown on the right axis.

twinY​

The twinY method enables you to use one X axis for one set of the values being plotted and a second X axis for another, while sharing the same Y axis:

PlotName = figure().plot(...).twinY().plot(...).show()

  • The plot(s) for the series placed before the twinY() method use the lower X axis.
  • The plot(s) for the series listed after the twinY() method use the upper X axis.

Multiple series​

Multiple plot methods can be used within the same query to produce a chart with multiple series. However, the plotBy methods include an additional argument that enables users to specify the grouping column to be used to plot multiple series. This greatly simplifies and shortens the query structure:

import static io.deephaven.csv.CsvTools.readCsv

//source the data
source = readCsv("https://media.githubusercontent.com/media/deephaven/examples/main/CryptoCurrencyHistory/CSV/CryptoTrades_20210922.csv")

//plot the data
plotBy = plotBy("Sept22", source, "Timestamp", "Price", "Instrument").show()

Plot styles​

The XY series plot in Deephaven defaults to a line plot. However, Deephaven's plotStyle method can be used to format XY series plots as area charts, stacked area charts, bar charts, stacked bar charts, scatter charts and step charts.

In any of the examples below, you can simply swap out the plotStyle argument with the appropriate name; e.g., ("area"), ("stacked_area"), ("step"), etc.

XY Series as a stacked area plot​

In any of the examples below, you can simply swap out the plotStyle argument with the name ("stacked_area"), ("step"), etc.

plot_single_stacked_area= plot("Heart_rate", source, "Time", "HeartRate").plotStyle("stacked_area")\
    .show()

XY Series as a scatter plot​

In the example below, the .plotStyle argument has the name ("scatter"). Other parameters are defined to show the fine tuning detail under control.

plotXYScatter = plot("Speed", source, "Time", "SpeedKPH")\
    .plotStyle("scatter")\
    .pointSize(0.5)\
    .pointColor(colorRGB(0,0,255,50))\
    .pointShape("circle")\
    .twinX()\
    .plot("Distance", source, "Time", "DistanceMeters")\
    .plotStyle("scatter")\
    .pointSize(0.8)\
    .pointColor(colorRGB(255,0,0,100))\
    .pointShape("up_triangle")\
    .chartTitle("Speed and Distance")\
    .show()

XY Series as a step plot​

In the example below, the .plotStyle argument has the name ("step"). Other parameters are defined to show the fine tuning detail under control.

plot_step = plot("HeartRate", source, "Time", "HeartRate")\
    .plotStyle("Step")\
    .lineStyle(lineStyle(3))\
    .show()