Grafana Voron 0 Test Data

Test dataset for Grafana containing simulated metrics from a Voron 0 3D printer. Designed for learning Grafana variables, array-indexed data visualization, and time-series analysis.

Quick Start for Grafana Cloud

Method 1: TestData with CSV Content (Recommended)

1. Open Grafana and navigate to Connections > Data sources > TestData DB
2. Create a new dashboard and add a panel
3. In the query editor, select Scenario: CSV Content
4. Copy and paste the contents from data/voron0_testdata.csv
5. Click Apply to visualize

Method 2: Infinity Plugin with GitHub Raw URL

1. Install the Infinity Plugin: Connections > Add new connection > Infinity
2. Add as data source
3. In the query configuration:
   • Type: CSV
   • Source: URL
   • URL: https://raw.githubusercontent.com/YOUR_USERNAME/Grafana-Voron-0-Metrics/main/data/voron0_testdata.csv
   • Parser: Backend
4. Apply configuration

Method 3: Direct GitHub Raw URL

If you have the CSV Datasource Plugin installed, use the GitHub raw URL directly as the source.

Data Structure

The CSV file contains the following columns:

• timestamp: Time in seconds (0-1233s, loops automatically)
• array_index: Sensor index (1-32)
• sensor_name: Human-readable sensor name
• value: Measured value
• unit: Unit of measurement (mm, C, %, s)

Key Metrics Reference

Variable	Index	Description	Typical Use Case
Layer_Time	21	Time to complete each layer	Track cycle times, identify bottlenecks
X_Position	1	Toolhead X-axis position	Motion analysis, path visualization
Y_Position	2	Toolhead Y-axis position	Motion heatmaps, travel patterns
Z_Position	3	Current build height	Layer progression tracking
X_Speed	4	X-axis velocity	Speed optimization analysis
Y_Speed	5	Y-axis velocity	Acceleration profiling
Extruder_Temp_Actual	10	Current hotend temperature	Thermal stability monitoring
Extruder_Temp_Target	11	Target hotend temperature	Setpoint tracking
Bed_Temp_Actual	12	Current bed temperature	Heat distribution analysis
Bed_Temp_Target	13	Target bed temperature	Thermal control validation
Hotend_Fan_Speed	14	Hotend cooling fan	Thermal management
Part_Cooling_Fan	15	Part cooling fan speed	Print quality correlation
Layer_Number	20	Current layer count	Progress tracking
Total_Time	22	Elapsed print time	Duration analysis
Progress	23	Completion percentage	Real-time status
Flow_Rate	24	Extrusion flow rate	Material usage tracking
Extruder_Position	30	Filament fed distance	Material consumption
Print_Speed	31	Current print velocity	Performance monitoring
Acceleration	32	Motion acceleration	Dynamics analysis

Sensor Index Categories

Index Range	Category	Sensors
1-5	Motion	X/Y/Z position and velocity
10-15	Thermal	Temperatures and fans
20-24	Process	Layer info, timing, progress
30-32	Extruder	Position, speed, acceleration

Creating Variables in Grafana

Example 1: Layer Time Variable

Configuration for a cycle time variable:

1. Navigate to Dashboard Settings > Variables > Add variable
2. Enter configuration:

   Name: cycletime
   Type: Custom
   Label: Layer Time
   
   Custom options:
   Layer Time : 21
   

3. Optional: Enable Multi-value and Include All
4. Save variable

Usage in query:

WHERE array_index = ${cycletime}

Example 2: Temperature Variable Group

Configuration for multiple temperature sensors:

Name: temp_sensor
Type: Custom
Label: Temperature Sensor

Custom options:
Extruder Actual : 10
Extruder Target : 11
Bed Actual : 12
Bed Target : 13

This allows selecting between different temperature measurements in a single panel.

Example 3: Position Variable

Configuration for axis position tracking:

Name: position
Type: Custom
Label: Axis Position

Custom options:
X-Position : 1
Y-Position : 2
Z-Position : 3

Panel Examples

Example 1: Creating a Temperature Monitoring Panel

Step-by-step guide:

1. Add a new panel to your dashboard
2. Select TestData as the data source
3. Choose CSV Content scenario and load your data
4. In the Transform tab:
   • Add transformation: Filter data by name
   • Select columns 10, 11, 12, 13 (temperature metrics)
   • Optional: Add transformation to rename fields for clarity
5. In the panel options:
   • Title: "Temperature Monitoring"
   • Visualization: Time series
   • Y-axis unit: Celsius (C)
   • Y-axis min: 0, max: 250
   • Legend: Show
6. Add thresholds:
   • Warning at 210C (red line)
   • Target at 205C (green line)
7. Apply and save

Example 2: Layer Time Analysis Panel

Configuration:

1. Add new panel with TestData source
2. Load CSV data
3. Transform: Filter to column 21 (Layer_Time)
4. Visualization: Bar chart or Time series
5. Panel settings:
   • Title: "Layer Time Analysis"
   • Y-axis unit: seconds (s)
   • Add threshold at 10 seconds for slow layer detection
6. Optional: Add stat panel showing average layer time

Example 3: Motion Heatmap

Configuration:

1. Create panel with TestData source
2. Filter columns 1 and 2 (X_Position, Y_Position)
3. Visualization: Scatter plot or Canvas with heatmap overlay
4. Settings:
   • Title: "Toolhead Motion Pattern"
   • X-axis: X_Position (0-120mm)
   • Y-axis: Y_Position (0-120mm)
   • Point size: Based on speed or layer time

Query Templates

Basic Filtering

For TestData CSV datasource:

WHERE array_index = 21

SQL-Style Queries

For SQL-compatible datasources:

SELECT 
  timestamp as time,
  value
FROM voron0_testdata
WHERE array_index = ${sensor}
  AND timestamp >= $__from 
  AND timestamp <= $__to
ORDER BY timestamp

Multiple Sensor Query

Query multiple related sensors:

SELECT 
  timestamp as time,
  sensor_name as metric,
  value
FROM voron0_testdata
WHERE array_index IN (10, 11, 12, 13)
ORDER BY timestamp

Aggregation Query

Calculate statistics over time windows:

SELECT 
  $__timeGroup(timestamp, '1m') as time,
  AVG(value) as avg_value,
  MAX(value) as max_value,
  MIN(value) as min_value
FROM voron0_testdata
WHERE array_index = 21
GROUP BY time
ORDER BY time

Beginner-Friendly Tips

Getting Started

1. Start with a single metric: Begin by visualizing one temperature sensor (index 10)
2. Add complexity gradually: Once comfortable, add more metrics to the same panel
3. Use transformations: Learn to filter, rename, and calculate derived fields
4. Experiment with visualizations: Try different chart types for the same data
5. Create meaningful dashboards: Group related metrics logically

Best Practices for Data Visualization

Units and Scales

• Always set appropriate units (C for temperature, mm for position, % for rates)
• Don't rely on auto-scaling for temperature plots
• Use consistent scales across related panels

Color Coding

• Use intuitive colors: red for hot, blue for cold, yellow for warnings
• Maintain consistency: same metric should have same color across panels
• Use threshold colors to highlight out-of-range conditions

Panel Organization

• Group related metrics together (all temperatures in one panel)
• Use rows to separate categories (Motion, Thermal, Process)
• Add text panels for context and documentation

Variable Usage

• Create variables for frequently changed parameters
• Use descriptive labels instead of numeric indices
• Enable multi-value selection for comparison views

Common Use Cases

Performance Monitoring

• Track layer times (index 21) to identify slow layers
• Monitor speeds (indices 4, 5, 31) for optimization
• Compare actual vs target temperatures (10 vs 11, 12 vs 13)

Quality Analysis

• Correlate temperature stability with print quality
• Analyze acceleration patterns during layer changes
• Track flow rate consistency (index 24)

Troubleshooting

• Identify thermal runaway conditions
• Detect motion anomalies through position tracking
• Monitor fan behavior during transitions

Dataset Details

• Format: CSV with headers
• Duration: 1233 seconds (20 minutes 33 seconds)
• Data Points: Variable intervals (5-150 seconds)
• Metrics: 15 unique sensors across 4 categories
• Loop Behavior: Data automatically restarts after completion
• Device: Voron 0 (120×120×120mm build volume)
• Layers: 150 layers, 0.2mm layer height

Repository Structure

├── data/
│   └── voron0_testdata.csv     # Main CSV with all sensor data
├── docs/
│   └── sensor_mapping.md       # Detailed sensor documentation
└── README.md                   # This file

Additional Documentation

For detailed sensor specifications, value ranges, and advanced usage patterns, see docs/sensor_mapping.md.

Use Cases

This dataset is ideal for:

• Learning Grafana query syntax and transformations
• Understanding time-series data visualization
• Practicing dashboard design and layout
• Testing alert and notification configurations
• Training sessions and workshops
• Prototyping monitoring solutions

Technical Notes

Data Loop The dataset runs from timestamp 0 to 1233 seconds, then automatically loops back to the beginning. This simulates continuous operation for testing and demonstration purposes.

Query Performance For optimal performance with large time ranges:

• Use appropriate time filters
• Leverage query caching where available
• Consider aggregation for overview panels

Variable Best Practices

• Use meaningful variable names (e.g., temp_sensor not var1)
• Set appropriate default values
• Enable multi-value for comparison scenarios
• Use query variables for dynamic sensor lists

License

This dataset is provided for educational and testing purposes. Free to use for learning, prototyping, and demonstrations.

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Ready to start visualizing? Load the CSV into Grafana and explore the data!