• Contents
  • Index

Altair® Panopticon™

 

Capabilities & How to Guides

This section of example workbooks includes:

  • BP Oil Spill Timeline

Use to 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 & 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 & column cross reference.

  • Financial Time Series

Display of typical financial time series displays such as the  Line, OHLC & Candlestick & 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

Use of Parameters, Actions (URL, Script & Navigation), and Action Controls.  The first dashboard demonstrates all three actions, with the Script actions only being available when viewed in a web browser, as they rely on the browser’s JavaScript engine. The navigation action demonstrates the passing of context through parameters from a source dashboard to a target. Action controls are also demonstrated to show that context can be entered through standard form controls (list box, text box, slider, etc.) in addition to being passed between visualizations. Data input is also demonstrated, as are the special time window parameters.

  • How To Auto Parameterize

Use of Parameters & 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

    • 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 Timeseries 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 & Manual Drill configuration, demonstrating the use of double-clicking to drill through the levels of hierarchy / granularity 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 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 Pivot & Unpivot

Pivotting 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 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 Designer matches that output in the PDF.

  • How to Python

This example 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 R & 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 & 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

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

    • Chi Square Testing

    • data loading

This dashboard part also supports loading data from the Panopticon Visualization Server, 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 display 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.