Julia Makie Recipes
by Krastanov
Create custom Makie plot types using recipes for reusable, themeable visualizations. Use this skill when implementing plot recipes in Makie extensions.
Skill Details
Repository Files
3 files in this skill directory
name: julia-makie-recipes description: Create custom Makie plot types using recipes for reusable, themeable visualizations. Use this skill when implementing plot recipes in Makie extensions.
Julia Makie Recipes
Create custom Makie plot types using recipes. Recipes enable reusable, themeable visualizations that integrate seamlessly with Makie's ecosystem.
This skill focuses on creating recipes as package extensions. See julia-pkgextension for
extension setup.
Recipe Types
Type Recipes (Simple Conversions)
Convert custom types to existing plot types:
function Makie.convert_arguments(P::Type{<:Makie.Heatmap}, data::MyType)
matrix = extract_matrix(data)
return Makie.convert_arguments(P, matrix)
end
Full Recipes (Custom Plot Types)
Makie.@recipe(CircuitPlot, circuit) do scene
Makie.Theme(;
gatewidth = 0.8,
wirecolor = :black,
)
end
@recipe Macro Syntax
Makie.@recipe(PlotTypeName, arg1, arg2, ...) do scene
Makie.Theme(;
attribute_name = default_value,
)
end
Generated automatically:
- Type:
const PlotTypeName{ArgTypes} = Plot{plottypename, ArgTypes} - Functions:
plottypename(args...)andplottypename!(ax, args...)
Implementing plot!
function Makie.plot!(plot::CircuitPlot)
circuit = plot[:circuit][] # Get argument value
# Access attributes
gw = plot.gatewidth[]
# Draw using Makie primitives
Makie.lines!(plot, xs, ys; color = plot.wirecolor)
Makie.scatter!(plot, points; markersize = 10)
Makie.poly!(plot, vertices; color = :blue)
Makie.text!(plot, x, y; text = "label")
return plot # Always return plot!
end
Key points:
- First argument to primitives is
plot(the recipe plot object) - Access attributes with
plot.attribute[]for current value - Access attributes with
plot.attribute(no[]) for Observable (reactive)
Makie Primitives Reference
| Primitive | Use Case |
|---|---|
lines! |
Continuous lines, wires |
linesegments! |
Disconnected line segments |
scatter! |
Points, markers |
poly! |
Filled polygons, rectangles |
text! |
Labels, annotations |
heatmap! |
2D color grids |
Checklist
- Add Makie to
[weakdeps]and[extensions]in Project.toml - Create stub functions in main package (with docstrings)
- Import stub functions in extension
- Define recipe with
Makie.@recipe - Implement
Makie.plot!method - Always
return plotfromplot! - Create
_axisconvenience function for complete figures - Test with CairoMakie and GLMakie
Reference
- Complete Examples - Full extension example with recipes
- Patterns - Attributes, reactivity, axis configuration
Related Skills
julia-pkgextension- Package extension setupjulia-docs- Documenting extension functionality
Related Skills
Attack Tree Construction
Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.
Grafana Dashboards
Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.
Matplotlib
Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.
Scientific Visualization
Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.
Seaborn
Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.
Shap
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Query Writing
For writing and executing SQL queries - from simple single-table queries to complex multi-table JOINs and aggregations
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Scientific Visualization
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
