ARBE Lambda Star Notebook
A practical and structured notebook for precise color matching, candidate selection, and multi-color group optimization based on CIELAB target values.
The current standalone version works directly with an embedded enriched atlas, so it can run without manual file uploads or external fetch operations.
What this notebook helps you do
- Compare target LAB values with a structured color atlas
- Identify the closest matching atlas entries
- Evaluate full 4-color combinations, not just single matches
- Export results for documentation and further analysis
What is the ARBE Lambda Star Notebook?
The ARBE Lambda Star Notebook is a color analysis tool designed to match defined CIELAB target values against a prepared atlas and identify the most suitable results. It goes beyond simple one-to-one matching by also evaluating how well four selected colors work together as a group.
Single-Color Matching
Each target LAB value is compared against all available atlas entries to find the closest matches.
Candidate Ranking
The notebook produces ranked result lists so you can quickly see the strongest candidates for every target.
Group Optimization
It also evaluates complete 4-color combinations to identify sets that are both accurate and internally consistent.
Who is it for?
This notebook is useful for anyone working with controlled color selection, color development, or structured palette construction.
Typical use cases
- Color development and color specification
- Design systems and product color planning
- Print and prepress color reference workflows
- Material and collection development
- Target-to-atlas comparison for internal color libraries
Main benefits
- Consistent and reproducible evaluation
- Fast ranking of best-fitting colors
- Structured comparison of full color sets
- Exportable results for documentation and collaboration
Technical basis
The notebook works with CIELAB values and evaluates color differences using established color distance methods. This makes it possible to quantify how close each atlas color is to a given target.
In the standalone version, the prepared enriched JSON atlas is embedded directly into the notebook. This removes the need for manual uploads or external downloads during normal use.
In addition to single-color matching, the notebook can score full 4-color groups so that results are not only accurate individually, but also coherent as a set.
What do you need to enter?
The notebook expects four target colors, each defined as a LAB triplet: L*, a*, and b*.
TARGETS_INPUT = [
[86.10, 16.39, -6.99],
[89.09, 6.63, 10.42],
[88.29, -7.94, -9.11],
[90.77, -3.68, 15.10]
]
These values are entered in the first input cell of the editable notebook version. Once they are updated, the remaining cells can be executed in order.
How to use the notebook
The workflow is designed to be simple and repeatable. Update the targets, run the notebook, and review the ranked results.
Enter your target LAB values
Open the notebook and replace the existing values in the target input cell with your own four LAB targets. Each target must contain exactly three numeric values.
Run the notebook from top to bottom
Execute the notebook cells in sequence. The system loads the embedded atlas, calculates the differences, and ranks the best candidates.
Review the top candidates
The first result area shows the strongest individual atlas matches for each target color. Lower distance values indicate better matches.
Evaluate the best groups
The notebook then checks full 4-color combinations. This is useful when the four colors must work together as a coordinated set rather than as isolated matches.
Export and document the results
Final result tables are exported as CSV files so they can be stored, shared, or processed further in spreadsheet or reporting workflows.
What results are generated?
Top Candidates
A ranked list of the best individual atlas matches for each target value.
Best Groups
A ranking of the strongest 4-color combinations based on both target fit and group quality.
Best Group Details
A more detailed view of the best-performing group for deeper inspection and traceability.
How to interpret the results
Individual match quality
Smaller color difference values mean the atlas entry is closer to the target value. In general:
- Low distance: very good match
- Medium distance: visually similar but not identical
- Higher distance: usable mainly as an alternative
Group quality
Group optimization adds another layer of evaluation. A combination may rank higher overall because the full set is more balanced and consistent, even when one single candidate is not the absolute closest standalone result.
Advantages of the standalone version
- No manual upload of supporting files required
- No external fetch step needed during normal use
- Fast startup with embedded atlas data
- Reliable and repeatable workflow
- Simple target editing
- Structured result tables
- CSV export for follow-up work
- Suitable for internal documentation and review
Typical workflow
- Define four LAB target values
- Enter them in the notebook
- Run the notebook completely
- Inspect top candidates
- Compare the best groups
- Select the preferred result
- Export the final data as CSV
Practical recommendations
FAQ
How many target values can I enter?
The current version is designed for exactly four target values because the optimization logic is built around 4-color groups.
Do I need additional files to run the notebook?
No. The standalone JSON-based version is prepared so it can run without manual uploads or fetch operations during normal use.
Can I use the notebook only for single-color matching?
Yes. The Top Candidates table already provides the best individual matches for each target. Group optimization is an additional analysis layer.
What are the CSV files used for?
They are intended for export, documentation, reporting, and further processing in external tools.
What should I do if the results seem implausible?
Check the input values first. Incorrect signs in a* or b*, formatting mistakes, or target values based on inconsistent source data are the most common reasons.
What if the notebook runs but no useful results appear?
Make sure the target input contains four complete LAB triplets and that all notebook cells were executed in order from top to bottom.
Troubleshooting
Input format error
Check brackets, commas, and numeric formatting. The notebook expects a list of four LAB triplets.
Unexpected ranking
Re-check whether the target values were entered correctly and whether the intended LAB reference values are being used.
Missing CSV files
CSV export is performed near the end of the notebook. Make sure all cells completed successfully.
Summary
The ARBE Lambda Star Notebook is a practical tool for precise LAB-based color matching and 4-color group optimization. It helps identify the strongest atlas candidates, compare coordinated color sets, and export structured results for further use.