Visualisation

Introducing Elite Dangerous Timeline

A vis.js visual of game events with a DynamoDB backend and Flask API.

James Montgomery

4 minute read

TL; DR I created a timeline visual from Elite Dangerous in-game events. Why? On the one hand, to further my Python learning and explore AWS DynamoDB. On the other hand, because I ultimately would like to provide some context to me in the game which is not present natively. By focusing on visualising a subset of the available events, I gain a foothold in parsing the game data into a database and putting an API in front of it.

Producing live visuals from a PowerShell REST API

Using vis.js and PowerShell Universal Dashboard

James Montgomery

3 minute read

TL; DR In this post, I’d like to share an example of bringing to life the data returned by an API. One PowerShell example takes the output of Get-NetTCPConnection and Get-Process to create a JSON representation of process IDs and their network connections. Universal Dashboard provides the means to expose this via REST API. Lastly, the vis.js network module creates a visual representation of this data. Here is an example video:

Visualising your DNS cache with PSGraph

Exploring how to create a visual with Powershell

James Montgomery

5 minute read

TL; DR In this post, I’d like to introduce you to how I’ve used PSGraph to create visuals from code. I’ll show you how to represent the Windows DNS cache graphically. Here is an example output (click for a full size image): Below I cover how to arrive at that outcome. You can find all scripts used at my GitHub page. Why DNS The hierarchical nature of DNS (subdomain.topleveldomain.rootdomain) lends itself to graphing.

A punnet of raspberries with a side of visuals

Pi-hole and DNS over DNSCrypt with a PowerShell dashboard.

James Montgomery

5 minute read

TL; DR I finally dusted off that Raspberry Pi in the corner. Initially, I was putting Pi-hole through its paces. One thing led to another as a new rabbit hole emerged in the form of adding encrypted transport to forwarded queries (a subject I visited in the past). I purchased another Pi for resiliency. I then explored how I might visualise the performance of the solution. Lastly, a first for me, I have shared this code on my GitHub page.