# 16️⃣ `16_aliasing.md`
# Aliasing for Clarity and Efficiency
### Principle
Create **local aliases** for long or frequently accessed objects to improve readability and reduce repeated lookups.
Aliasing helps code read cleanly, avoid excessive dot chains, and, in some cases, improve runtime performance.
---
### ✅ Prefer
```python
grid = dataset.weather_model.grid
length = grid.temporal.bounds.length
for i in range(length):
process(grid, i)
````
### ❌ Avoid
```python
for i in range(dataset.weather_model.grid.temporal.bounds.length):
process(dataset.weather_model.grid, i)
```
---
### Why
* **Readability** — shorter, semantically meaningful references
* **Maintainability** — clearer local context and intent
* **Performance** — fewer repeated attribute lookups in tight loops
* **Line length** — avoids horizontal sprawl and cognitive clutter
---
### Example: Reusing Bound Methods
```python
get = cache.get # reuse inside loop
for key in keys:
value = get(key)
```
### Example: Semantic Clarity
```python
pipeline = context.workflow.pipeline
for stage in pipeline.stages:
cfg = stage.config
run_stage(cfg)
```
---
### ⚠️ Avoid Overuse
* Don’t alias imports like `import numpy as np → n` — follow community norms
* Don’t shorten meaningful names into cryptic ones (`c = config`)
* Don’t alias something used only once
---
### 🤝 Our Philosophy
Aliasing isn’t “lazy naming”, it’s narrative optimization.
Use it to make code flow naturally, reduce noise, and guide the reader’s eye to what matters.
