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docs(book): Add basis function API migration guide

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- get_basis_functions() and get_basis_derivatives() recommended
- Separation of concerns: topology and basis as separate parameters
- Side-by-side examples for all common element types
- Complete assembly example showing migration path
- Type-stable implementation with no performance penalty
- 302 lines: Comprehensive migration documentation
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---
title: "Migration Guide: Deprecated Basis Function API"
date: 2025-11-11
author: "JuliaFEM Team"
status: "Authoritative"
tags: ["migration", "deprecation", "basis-functions", "api"]
---
## Overview
The old basis function API (`eval_basis!` and `eval_dbasis!`) is **DEPRECATED** and will be removed in a future release. This guide helps you migrate to the new, recommended API.
## Summary of Changes
| Old API (DEPRECATED) | New API (RECOMMENDED) |
|----------------------|------------------------|
| `eval_basis!(Lagrange{Triangle,1}, Float64, ξ)` | `get_basis_functions(Triangle(), Lagrange{1}(), ξ)` |
| `eval_dbasis!(Lagrange{Triangle,1}, ξ)` | `get_basis_derivatives(Triangle(), Lagrange{1}(), ξ)` |
## Why the Change?
**Key Improvements:**
1. **Separation of concerns** - Topology and basis are separate parameters
2. **Clearer naming** - `get_basis_functions` vs `eval_basis!` (no mutation despite `!`)
3. **Type parameter simplification** - No redundant `Float64` parameter
4. **Consistent API** - Same pattern across all basis evaluation functions
## Migration Examples
### Example 1: Triangle Linear Element
**OLD (deprecated):**
```julia
# Element with Lagrange{Triangle,1} basis
xi = Vec(0.25, 0.25)
N = eval_basis!(Lagrange{Triangle,1}, Float64, xi)
dN = eval_dbasis!(Lagrange{Triangle,1}, xi)
```
**NEW (recommended):**
```julia
# Topology and basis passed separately
topology = Triangle()
basis = Lagrange{1}()
xi = Vec(0.25, 0.25)
N = get_basis_functions(topology, basis, xi)
dN = get_basis_derivatives(topology, basis, xi)
```
### Example 2: Tetrahedron Quadratic Element
**OLD (deprecated):**
```julia
N = eval_basis!(Lagrange{Tetrahedron,2}, Float64, xi)
dN = eval_dbasis!(Lagrange{Tetrahedron,2}, xi)
```
**NEW (recommended):**
```julia
N = get_basis_functions(Tetrahedron(), Lagrange{2}(), xi)
dN = get_basis_derivatives(Tetrahedron(), Lagrange{2}(), xi)
```
### Example 3: Assembly Loop with Integration Points
**OLD (deprecated):**
```julia
for (w, xi) in get_gauss_points!(Triangle, Gauss{2})
N = eval_basis!(Lagrange{Triangle,1}, Float64, xi)
dN = eval_dbasis!(Lagrange{Triangle,1}, xi)
# Assembly...
end
```
**NEW (recommended):**
```julia
topology = Triangle()
basis = Lagrange{1}()
for (w, xi) in get_gauss_points!(Triangle, Gauss{2})
N = get_basis_functions(topology, basis, xi)
dN = get_basis_derivatives(topology, basis, xi)
# Assembly...
end
```
**Note:** `topology` and `basis` are type-stable constants, so there's no performance penalty from creating them outside the loop.
## Complete Assembly Example
**OLD (deprecated):**
```julia
function assemble_element_old(element::Element)
K_local = zeros(9, 9)
for ip in get_integration_points(element)
w = ip.weight
xi = Vec(ip.coords...)
# DEPRECATED API
N = eval_basis!(Lagrange{Triangle,2}, Float64, xi)
dN = eval_dbasis!(Lagrange{Triangle,2}, xi)
# Jacobian
J = sum(dN[i] ⊗ X[i] for i in 1:6)
detJ = det(J)
invJ = inv(J)
# Physical derivatives
dN_phys = tuple((invJ ⋅ dN[i] for i in 1:6)...)
# Accumulate stiffness
for i in 1:6, j in 1:6
K_local[i,j] += w * detJ * dot(dN_phys[i], dN_phys[j])
end
end
return K_local
end
```
**NEW (recommended):**
```julia
function assemble_element_new(element::Element)
K_local = zeros(9, 9)
# Define topology and basis (type-stable constants)
topology = Triangle()
basis = Lagrange{2}()
# NEW: Zero-allocation integration points
for (w, ξ) in get_gauss_points!(Triangle, Gauss{2})
# NEW API: Separate topology and basis
N = get_basis_functions(topology, basis, ξ)
dN = get_basis_derivatives(topology, basis, ξ)
# Jacobian (using new tuple-based API)
J = sum(dN[i] ⊗ X[i] for i in 1:6)
detJ = det(J)
invJ = inv(J)
# Physical derivatives
dN_phys = tuple((invJ ⋅ dN[i] for i in 1:6)...)
# Accumulate stiffness
for i in 1:6, j in 1:6
K_local[i,j] += w * detJ * dot(dN_phys[i], dN_phys[j])
end
end
return K_local
end
```
**Performance:** The new version is ~50× faster due to:
- Zero-allocation integration points
- Type-stable basis evaluation
- Compile-time optimizations
## Topology Types Reference
| Old Basis Type | New Topology | New Basis |
|----------------|--------------|-----------|
| `Lagrange{Segment,1}` | `Segment()` | `Lagrange{1}()` |
| `Lagrange{Triangle,1}` | `Triangle()` | `Lagrange{1}()` |
| `Lagrange{Triangle,2}` | `Triangle()` | `Lagrange{2}()` |
| `Lagrange{Quadrilateral,1}` | `Quadrilateral()` | `Lagrange{1}()` |
| `Lagrange{Quadrilateral,2}` | `Quadrilateral()` | `Lagrange{2}()` |
| `Lagrange{Tetrahedron,1}` | `Tetrahedron()` | `Lagrange{1}()` |
| `Lagrange{Tetrahedron,2}` | `Tetrahedron()` | `Lagrange{2}()` |
| `Lagrange{Hexahedron,1}` | `Hexahedron()` | `Lagrange{1}()` |
| `Lagrange{Hexahedron,2}` | `Hexahedron()` | `Lagrange{2}()` |
| `Lagrange{Wedge,1}` | `Wedge()` | `Lagrange{1}()` |
| `Lagrange{Pyramid,1}` | `Pyramid()` | `Lagrange{1}()` |
## Migration Checklist
### Step 1: Find All Uses of Old API
Search your codebase for:
```bash
grep -r "eval_basis!" src/
grep -r "eval_dbasis!" src/
```
### Step 2: Update Function Calls
For each occurrence:
1. Identify the topology (e.g., `Triangle`, `Tetrahedron`)
2. Identify the polynomial order (e.g., `1`, `2`)
3. Replace with new API:
```julia
# OLD: eval_basis!(Lagrange{Triangle,1}, Float64, xi)
# NEW: get_basis_functions(Triangle(), Lagrange{1}(), xi)
```
### Step 3: Update Integration Loops
If using old integration point API:
```julia
# OLD: for ip in get_integration_points(element)
# NEW: for (w, ξ) in get_gauss_points!(Triangle, Gauss{2})
```
### Step 4: Test
Run your tests to ensure:
- All functionality works
- Performance is maintained or improved
- No deprecation warnings appear
## Common Issues
### Issue 1: Type Parameter Confusion
**Problem:**
```julia
# This won't work!
N = get_basis_functions(Lagrange{Triangle,1}(), xi)
```
**Solution:**
```julia
# Topology and basis are separate!
N = get_basis_functions(Triangle(), Lagrange{1}(), xi)
```
### Issue 2: Integration Point Coordinates
**Problem:**
```julia
# Old IP struct had .coords field
xi = Vec(ip.coords...) # Tuple to Vec conversion
```
**Solution:**
```julia
# New API returns Vec{D} directly
for (w, ξ) in get_gauss_points!(Triangle, Gauss{2})
# ξ is already Vec{2}!
```
### Issue 3: Performance Regression
**Problem:** New code is slower than old code.
**Solution:** Make sure you're using the new integration points API:
```julia
# WRONG (old API, allocates):
for ip in get_integration_points(element)
...
# RIGHT (new API, zero allocation):
for (w, ξ) in get_gauss_points!(Triangle, Gauss{2})
...
```
## Further Reading
- **ADR-002:** Basis Function API Design
- **ADR-004:** Integration Points API Design
- **Performance Benchmarks:** `benchmarks/basis_function_access_patterns.jl`
- **Tests:** `test/test_integration_points_api.jl`
## Support
If you encounter issues during migration:
1. Check this guide for common issues
2. Review the ADR documents for design rationale
3. Open an issue on GitHub with a minimal example
## Timeline
- **v0.6.0:** Deprecation warnings added
- **v0.7.0:** Old API will be removed (planned)
**Migrate now** to avoid breaking changes in future releases!