Altair® Panopticon


Panopticon Workbook Examples

The AltairPanopticonVisualizationServerWAR_<version number>.zip file includes the bundle of the workbook examples and their associated data files (Examples.exz) that you can import.

These workbooks cover:

q  Visualization Guide

q  Example Use Cases and Sample Dashboard

q  Capabilities and How to Guides


Visualization Guide

The example workbook VizGuide includes demonstration of all snapshot and time series visualizations with usage guidelines.

Example Use Cases and Sample Dashboards

This section of example workbooks includes:

Sample Workbook


Bond Maturity Screening

Bond universe selection and screening.

Displaying Spreads

Spread calculation on selected instruments.

Equity Analysis

Equity portfolio selection and screening.

Equity Universe Screening

Equity universe selection and screening.

GDP Per Capita

Data displayed as a hierarchy (Treemap), Map with scatter points and Choropleth, with each visual emphasizing different aspects of the dataset.

Nano Executions

Nanosecond accuracy executions.


Olympic medals by country, across time.

Order Book

Equity order book imbalance across the S&P 500.

Portfolio Performance

Equity portfolio performance across time, including the playback of performance at each time slice across the 15-month time window.

Shopping Basket Analysis

The display of shopping baskets, constituent products, and the correlation of product purchases based on these baskets. The co-occurrence of products in a basket is demonstrated through use of a self-inner join in the underlying data table.

Supermarket Sales Summary

Supermarket sales and revenues against the target.

US Border Crossings

Periodicity in US border crossings by crossing point.

US Treasury Yield Curves

Demonstrates the manual axis tick marks, time series calculations, Scatter Plot reference lines based off a time series, and the time surface across the last two years.


Capabilities and How to Guides

This section of example workbooks includes:

Sample Workbook


BP Oil Spill Timeline

Use of text time series to display market events, such as news headlines and overlay them on time series displays correlating the event to performance and money flow.

Cross Tab

Display of cross tabbing / trellising into rows and columns across different visuals. Cross tabbing produces a series of trellised smaller visuals which each correspond to a portion of the total dataset as defined by the row and column cross reference.

Financial Time Series

Display of typical financial time series displays such as the Line, OHLC and Candle Stick and Needle graphs for price and volume distributions.  Additionally, the time axis of these displays is configured to show either a calendar axis, a working week axis where Saturdays and Sundays are removed, and a working hour axis, where only a defined time range (Monday to Friday) are displayed.

How to Actions

Examples of how to use Navigation action, URL action, and Script action. Using Action Control parts to set values to parameters that are involved in data connections. How to pick up current time window parameter values from time series visualizations, and how to pick up current axes span parameter values from visualizations.

How to Auto Parameterize

Use of parameters and auto-parameterization to pass context automatically between visualizations on the same dashboard.  Parameters are passed through right-click or double-click mouse events and cause a new data request behind the target visualization.  Unlike filtering, the data request can be pre-defined with parameters reflecting variable components of the pre-defined query, function or stored procedure.

How to Color

Use of the different color settings and properties:

·           sequential or diverging numeric color palettes

·           categorical text color palettes

·           #RGB color source for text columns

·           Alpha value for the level of color transparency/opacity

·           colored shapes through the Shape Legend and Color Legend

·           Line shades based on the Alpha value adjustment in the numeric action slider

·           Configured Custom Single color for visual members in the Time Combination graph which are retrieved in the Timeseries Legend

·           color background of text columns in the visualization table

·           Special examples including mixing of colors using the Action Dropdown or #RGB color source in the Bar Graph. In addition, setting the color gradient or quadrants on the background image, and color codes that are added to the data by using join.

How to Conflate

Use of fixed or auto conflation for time series data sets.

How to Drill

Automatic and manual drill configuration, demonstrating the use of double-clicking to drill through the levels of hierarchy orgranularity of a visualization, and the use of restricted “Level of Details” display, where only a certain number of hierarchy levels can be displayed at a single time, and drilling transverses these levels.

How to Filter

Using filter boxes with Numeric, Text, and Time Series columns. Demonstrating both categorical text filters for specified dimensions, with either selection or wild card entry, and numeric filters for measures, which either demonstrate the range (min to max) and distribution or focus on the distribution with a percentile scale. In addition, visualizations can be used as filters by selecting items and either including or excluding them.

How to Maps

Showing features of the map plot visualization as well as an example of how to use the SVG shapes visualization to create a choropleth map.

How to Non Additive

Working with non-additive numbers, where the aggregates must be provided externally, rather than calculated in the product.  This example demonstrates single hierarchies, and multiple hierarchies around a defined leaf column.  In each case, the data table is configured to specify the leaf column, and the value to check for aggregate presence, while the visuals are set to use external aggregates.

How to OrderBook Transform

The transform settings allow for orders to be reconstructed into an Order Book and standardized by conflating into an appropriate granularity for the output display. This allows playback through its values for compliance customers.

To reconstruct the Order Book from the orders, the data must include:

·          Order ID (Unique per Order)

·          Order State/Event Type

·          Update Time

·          Side (Buy/Sell)

·          Price

·          Balance/Remaining Quantity

How to Panel Layout

Shows how to use panels for creating compartments within a dashboard which allow dashboard parts to maximize in a limited way, confined to the space within their panel.

Includes dashboards with or without layout panels.

How to PDF

Uses the configured Paper Size and DPI resolution. Setting the resolution of the workbook to match the output resolution from the PDF settings through the Workbook Style, ensures that what is displayed in the web client matches that output in the PDF.

How to Pivot & Unpivot

Pivoting of data for optimum use by dividing them into Dimensions (Text fields), and Measures (Numeric fields). This example shows how key values are displayed when pivoted, or when data is already pivoted, or when an already pivoted data is unpivoted. They are transformed to provide maximum flexibility.

How to Python

Demonstrates the use of Python as a data source and as data transform. Also, the use of Pyro for Python connectivity. With Python, a list of dictionaries is passed.

This workbook additionally demonstrates enhancing the build in capabilities through Python with the addition of the Numpy and Scipy modules, specifically demonstrating:

·          K Means Clustering

·          Curve Fitting

·          Chi Square Testing

Of course, the full data manipulation capabilities of Python are made available, rather than that just demonstrated in the example dashboards.

How to R

Includes examples and instructions in using Rserve with Panopticon:

·          R environment to use

·          Sample data sets from R (i.e., Seatbelts, Volcano)

·          Univariate Timeseries Forecasting (ARIMA modelling)

·          Unsupervised Machine Learning in the form of K-means cluster analysis on a synthetic, randomized data set

·          Continuous Unsupervised Machine Learning

·          Logistic Regression (machine learning classification)

·          Multiple Linear Regression (Supervised Machine Learning)

·          Anscombe's Quartet of ‘Identical’ Simple Linear Regressions

·          Geographic binning (Interactive transform)

How to Reference Lines

Use of Reference Lines in time series visualizations, both from source columns, and from time series calculations.

How to Retrieve Text and XML

Retrieving Text and XML, together with appropriate parsing from external URLs. This example by design requires a valid direct Internet link, as it retrieves data from external web sites.  Delimited text is retrieved based on a parameterized URL and displayed in a time series graph. RSS is retrieved, parsed through the XML connector, and displayed in a table, and RFD is also retrieved through the XML connector making use of XML name spaces in the XPath definitions to extract data from the source XML.

How to Time Window

Example of how to use Time Axis Minimum Range and Time Axis Increment Step with streaming data.

In addition, time series calculations, based on selected time windows, including time relative calculations such as simple moving averages, time window calculations such as the % Change across the time window, and finally re-baselining of performance values based on a selected time slice (Snapshot).

How to Use JS Dashboard Part

Demonstrates how to include bespoke JS code inside a dashboard such as:

·          how to add a listener for parameter value changes

·          how to update the parameter values

·          data loading

This dashboard part also supports loading data from Panopticon Real Time, inside the same data loading framework as the rest of the dashboard.

How to Use Timeseries Data Formats

Time series retrieval, interpolation and display.  This example shows how line graphs are drawn between known data points, and how gaps are displayed where there is a time slice, but an unknown value (null).  It also demonstrates the use of interpolation to fill the data gap.  Finally, the example shows sparse time data similar to that from multiple sensors.  As the data is not aligned to a standard set of time slices, the gap displays rules take over the visualization, removing most trends lines.  This output is then adjusted to standardize time slices producing appropriate output, where there are values for each series at each given time.

Order Book History

Displays Order Book across time and playback.