Skip to main content

Watch List Charts

Watch list charts combine the dynamic data selection of watch lists with Mango's charting capabilities. By using parameter-driven watch lists in combination with dashboard pages, you can create interactive views that allow users to select a data source, device, or tag value and immediately see the corresponding data plotted on a chart.

Chart Types​

Mango supports several chart types for visualizing data point values on watch lists and dashboards:

Line Chart​

The default chart type. Draws a direct line between each data point using the shortest route. Best suited for continuous analog signals such as temperature, pressure, or flow rate.

Smooth Chart​

Applies a smoothing algorithm when drawing the line between points. This produces a visually cleaner curve that reduces the visual noise of minor fluctuations, while still following the overall trend.

Step Chart​

Connects points using only horizontal and vertical segments (90-degree angles). Each value is held constant until the next change. This is the ideal chart type for binary points, multistate points, and any value that represents a discrete state rather than a continuous signal.

Bar Chart​

Draws a vertical column for each data point. Useful for visualizing rollup values (e.g., hourly energy consumption as a bar for each hour) or comparing discrete intervals.

Using Charts with Parameter-Driven Watch Lists​

The most powerful charting configuration uses a Point query watch list with parameters. This creates an interactive chart experience:

  1. Create a point query watch list in the Watch List Builder with one or more parameters (e.g., Device Name, Site tag).
  2. Add the watch list to a dashboard page using dashboard designer components.
  3. Users interact with parameters by selecting values from dropdowns on the dashboard. The watch list query updates dynamically and the chart redraws with the new data points.

This pattern allows a single dashboard page to serve many different locations, equipment groups, or time periods without creating separate pages for each.

Configuring Multiple Series​

When charting multiple data points together, keep the following guidelines in mind:

Axis Assignment​

  • Assign points with similar value ranges to the same Y-axis. For example, put all temperature sensors (range 50-100) on the left axis and all pressure sensors (range 0-500) on the right axis.
  • Mango supports up to four Y-axes: Left 1, Left 2, Right 1, and Right 2. Each axis can have its own color and scale.

Color Selection​

  • Each data point has a default chart color set in its point properties. This color is used unless overridden on the watch list.
  • When comparing related points (e.g., supply and return temperatures), choose contrasting colors to make the lines easy to distinguish.
  • Color values accept HTML hex codes (e.g., #FF5733) or named colors (e.g., red, dodgerblue).

Stacking​

  • Stacked mode fills the area under each series and stacks them on top of each other. This is useful for showing how individual components contribute to a total (e.g., energy consumption by zone).
  • 100% Stacked normalizes the chart to show the relative proportion of each series within the total. This is useful for understanding the distribution of a total across categories.

Rollup Settings on Charts​

Rollup settings control how raw point values are aggregated for display. When viewing long time ranges (days, weeks, months), displaying every raw data point would produce an overwhelming number of points and slow down rendering. Rollups reduce the data to a manageable number of intervals.

Auto Rollup​

By default, Mango uses auto rollup, which automatically selects an appropriate rollup interval based on the time range and the chart width. This ensures a reasonable number of data points are displayed regardless of the time range.

Manual Rollup​

Disable auto rollup to manually specify:

  • Rollup type -- Average, Delta, Minimum, Maximum, Sum, Count, First, Last, etc.
  • Rollup interval -- The time period for each rollup bucket (e.g., 5 minutes, 1 hour, 1 day).

Per-Point Default Rollup​

Each data point can have a default rollup type configured in its point properties. When the chart uses "Point default" as the rollup type, each point uses its own configured rollup. This is valuable when displaying points that need different rollup types on the same chart:

  • A counter point using Delta rollup to show consumption per period.
  • A temperature point using Average rollup to show the mean temperature per period.
  • A pressure point using Maximum rollup to show peak pressure per period.

Simplify Algorithm​

As an alternative to rollups, Mango offers a Simplify option for numeric data points. The simplify algorithm reduces the number of displayed data points while preserving the overall shape of the signal -- keeping important peaks, valleys, and transitions while removing redundant intermediate points.

Configuration options:

  • Target -- The desired number of output data points. The algorithm attempts to reduce the dataset to approximately this many points.
  • Tolerance -- An optional value that fine-tunes the algorithm by specifying the minimum significance of a data variation. Points whose removal would change the signal by less than the tolerance are candidates for elimination.

Simplify is particularly effective for high-frequency data (thousands of values per hour) where rollups would obscure important transient events. The simplified output retains the peaks and valleys that rollups would average away.

Date Range and Auto-Update​

The date range for all charts on a page is controlled by the Header Bar (also called the Date Bar):

  • Preset ranges -- Quick selections like "Last 5 minutes," "Last 1 hour," "Last 24 hours," "Last 7 days," etc.
  • Custom range -- Manually set a start and end date/time.
  • Auto update -- When enabled, the time range automatically refreshes at a configurable interval (e.g., every 30 seconds), re-querying values and updating the chart with the latest data.
  • Manual refresh -- Click the reload icon on the Date Bar to trigger an immediate refresh.

Best Practices​

  • Set default rollup types on points so charts display correctly without manual configuration by the viewer.
  • Use Simplify for analog signals that have high-frequency sampling but where peak and valley visibility matters more than exact interval averages.
  • Use Step chart type for discrete values (binary, multistate) so the chart accurately represents the held state between changes.
  • Keep axis count reasonable. Two axes (one left, one right) is usually sufficient. Using all four axes can make charts difficult to read.
  • Name watch lists descriptively (e.g., "Building A - AHU Temperatures" rather than "WL1") so that users can find the right view quickly.