| name | taylor-diagram |
| description | Create publication-quality Taylor diagrams in Python for multi-algorithm/multi-model metric comparison. Simultaneously displays standard deviation, correlation coefficient, and centered RMS difference on a single polar plot. Automatically handles dynamic standard deviation scaling and full negative correlation support. Triggers on: Taylor diagram, model comparison diagram, multi-model evaluation, algorithm metric comparison, skill metrics plot, 泰勒图, 泰勒图绘制, 模型性能对比图, 多算法指标对比. |
| metadata | {"version":"1.1.0"} |
Taylor Diagram Scientific Skill
Overview
Create publication-quality Taylor diagrams that simultaneously display three key statistical metrics — standard deviation, correlation coefficient, and centered RMS difference — on a single polar coordinate plot. Featuring dynamic standard deviation scaling, full negative correlation support, and premium styling matching high-impact journals (e.g., Nature, Science).
Workflow Decision Tree
User wants Taylor diagram?
├── Has observed + predicted arrays?
│ ├── Yes → Use TaylorDiagram class (Quick Start)
│ └── No → Need to compute from model results first?
│ └── Yes → See Integration with ML Workflow in references/examples.md
├── Prefers SkillMetrics library API?
│ └── Yes → Use plot_taylor_skillmetrics() or sm.taylor_diagram()
├── Need multi-panel (train/val/test)?
│ └── Yes → See Multi-Panel section in references/examples.md
├── Has negative correlations (range -1 to 1)?
│ └── Yes → Set corr_range=(-1, 1) to plot full semicircle
├── Data has raw, unnormalized standard deviations (e.g., standard deviation > 10)?
│ └── Yes → Set normalize=False (limits and ticks will auto-scale dynamically)
└── Many models (>5)?
└── Yes → Use default Nature-inspired safe color cycle or colorbar mapping
Quick Start
Prerequisites
pip install numpy matplotlib
pip install SkillMetrics
Basic Usage
Import TaylorDiagram from scripts/taylor_diagram:
import numpy as np
from taylor_diagram import TaylorDiagram
td = TaylorDiagram(observed, figsize=(8, 8))
td.add_model(pred_a, label="Model A", marker="o")
td.add_model(pred_b, label="Model B", marker="s")
td.plot(title="Taylor Diagram - Model Comparison")
td.savefig("taylor_diagram.png", dpi=300)
Get Statistics Table
print(td.get_stats_table())
Key API Reference
TaylorDiagram Class
| Parameter | Type | Default | Description |
|---|
observed | array-like | (Required) | 1-D array of observed (reference) values |
fig | plt.Figure | None | Existing figure to draw on. If None, created automatically |
rect | int/tuple | 111 | Subplot specification |
figsize | tuple | (8, 8) | Figure size in inches |
normalize | bool | True | If True, normalize standard deviations by observed std (Observed std is at 1.0) |
corr_range | tuple | (0, 1) | Correlation range. Set to (-1, 1) for negative correlations |
label_obs | str | "Observed" | Label for observed reference point |
max_std | float | None | Manual max standard deviation axis limit. If None, calculated dynamically |
| Method | Description |
|---|
add_model(predicted, label, marker, color, markersize) | Add a model prediction. If color is None, auto-selects from premium journal palette |
plot(show_crmsd, crmsd_levels, crmsd_color, ...) | Render the diagram. Generates dynamic ticks and mathematically perfect CRMSD contours |
savefig(filename, dpi, bbox_inches) | Save to file (PNG/PDF/SVG/TIFF) |
get_stats_table() | Return formatted statistics table string |
Premium Customizations & Edge Cases
1. Raw Ticks & Dynamic Scaling (e.g. Standard Deviations > 10)
Setting normalize=False enables plotting raw, unnormalized standard deviations. The axis limits and tick marks automatically adapt to cover the range of the observed and predicted standard deviations:
td = TaylorDiagram(observed, normalize=False)
td.add_model(pred_a, label="SVR")
td.add_model(pred_b, label="Random Forest")
td.plot(title="Raw Taylor Diagram")
2. Full Correlation Range (-1.0 to 1.0)
When models are anti-correlated or correlations are negative, set corr_range=(-1, 1) to automatically extend the Taylor diagram to a full polar semi-circle:
td = TaylorDiagram(observed, corr_range=(-1, 1))
td.add_model(pred_negative, label="Anti-Correlated Model")
td.plot(title="Semicircle Taylor Diagram")
3. Matplotlib 3.9+ Compatibility
The custom implementation avoids the deprecated apply_theta_transforms warnings by using standard polar transformations and explicitly handling layout, ensuring it is future-proof and robust across Matplotlib versions.
Resources
scripts/
taylor_diagram.py — Core Taylor diagram implementation featuring the TaylorDiagram class, compute_taylor_stats(), and plot_taylor_skillmetrics()
references/
taylor-theory.md — Detailed theory, metric interpretation, and math definitionsskillmetrics-api.md — SkillMetrics package API referenceexamples.md — 7 complete examples, updated for dynamic scaling, negative correlation semi-circles, and journal-ready layouts
