Graph Editor Integration + Standalone Data Capturing
The Mini-Graph is the new Editor Integration to capture any values with just a few clicks
The Data-Tracker is now the Mini-Graph
Collect and plot any signal with a simple mouse-click
- 3D/VR Displays
- Blueprint /or C++
- Data Export (csv)
- Well commented
Capturing Velocity of Objects in Race Level
Example Map: Download (52 MB)
Step 1: Install the plugin (from the marketplace or copy the plugin folder into the project folder). Activate the Plugin from the Editor Plugins Console.
Step 2a: Mini-Graph Editor Integration** A new Editor Widget will appear to bring up the Mini-Graph window. Right-Clicking an Object will expose its variables to add for tracking.
**Step 2b: ** Collect Data in Blueprint by sending variables into an UpdateDataset Node. If the data set does not exist, a new one is created under the given name.
Step3: Save and Load Data-Sets if desired, using the Mini-Graph interface or Blueprint (See the DataTracker_Showcase Actor for the available functions).
Step 4: In-Game Displays Go to the Widgets (WBP) and the 3D VR-Ready Widgets (BP) event graphs to bind the signals you want to display and set widget preferences (f.i. resolution). By giving the ‘name’ of a data set, the widgets will start to display the corresponding samples.
- Sample Value
- TotalN of Samples
- for any kind of dichotome/categorial input
- Samples A, B
- Probability/Chance A, B
Mean and Standard Deviation:
- description of a normal distribution (central tendency and variance)
- Mean = central tendency of the sample set (average of all samples)
- Variance = average of the squared distance between each value in a distribution and the mean of all samples
- StandardDeviation (SD) = square root of the variance (always ~68% of all samples fall within ± 1 SD of the mean)
- StandardErrorofMean (SEM) = Precision of the estimation of the true population mean from the sample set (a range within which the true value of a parameter probably lies)
- 95-% ConfidenceInterval (CI) = Precision of the estimation of the true population mean from the sample set (calculated with SE).
- for a more stable statistical value of the central tendency of a distribution
- the linear change-over-time
- y-offset (a)
- Slope (b) of the linear regression
- Correlation Coefficient (r) = standardized slope (beta) = regression weight
- Determination Coefficient (r²)
- Shows basic descriptive Statistics in a table
- Signal Display
– Shows the Signals
– Regression/Trend Lines
- Distribution Display
– Histogram with all samples (normalized for each sample set)
– Normal Distribution (an approximation of a normal gauss curve on the sample set)
– Standardized Regression Slope / Standardized Trendline
Platform: Win64 // UE 4.13+
Contact: Thauros-Development ✧@✧ outlook.com