python-algorithm-porter Skill

Context

When porting algorithms from upstream Python repositories (e.g., docling, docling-core, docling-ibm-models) to the .NET port, direct translation of syntax often leads to poor performance or subtle bugs. Python relies heavily on dynamic typing, implicit broadcasting (numpy), and C-backed native libraries (rtree, bisect). .NET relies on static typing, explicit memory management (Span<T>, Memory<T>), and JIT inlining.

Trigger

Use this skill whenever you are tasked with porting a specific algorithm, mathematical function, or complex data transformation from Python to C#.

Workflow

1. Isolate and Analyze (The Python Baseline)

• •Locate the target Python function.
• •Identify the input shapes and data types (e.g., np.ndarray of float32 with shape [1, 3, 640, 640], or a list of dict objects).
• •Identify the algorithmic complexity (e.g., $O(N^2)$ nested loops, $O(\log N)$ binary searches).
• •Identify any hidden native dependencies (e.g., bisect, rtree, shapely, scipy).

2. Design the .NET Data Structures

• •Avoid Classes for Small Data: Prefer readonly struct for geometric data (BoundingBox, Point) to avoid heap allocation and GC pressure.
• •Avoid Boxing: Use strongly-typed generic collections (List<T>, Dictionary<TKey, TValue>). Never use object or dynamic.
• •Memory Efficiency: If an array is created and discarded within a method (e.g., a temporary buffer for image pixels), consider ArrayPool<T>.Shared.Rent or stackalloc for small allocations.

3. Porting Strategies

A. Numpy/Broadcasting -> C# Loops/SIMD

Python:

# Normalize image: (img - mean) / std
img = (img / 255.0 - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]

C# Port:

• •Do not use System.Drawing.Bitmap.GetPixel().
• •Do use unsafe pointers, Span<T>, or MemoryMarshal.Cast to iterate over flat byte arrays of pixel data.
• •Do manually unroll the broadcasting into explicit R, G, B scalar operations within a tight for loop.

B. bisect (Sorted Intervals) -> C# List<T>.BinarySearch

Python:

import bisect
bisect.insort(my_list, new_item)

C# Port:

int index = myList.BinarySearch(newItem);
if (index < 0) index = ~index;
myList.Insert(index, newItem);

C. rtree / libspatialindex -> C# Spatial Index

Python:

from rtree import index
idx = index.Index()
idx.insert(id, (l, b, r, t))
candidates = idx.intersection((query_l, query_b, query_r, query_t))

C# Port:

• •If $N < 5000$, a flat List<T> with a brute-force $O(N)$ linear scan is often faster in .NET due to cache locality and zero FFI overhead.
• •For optimized lookup, use the custom DoclingDotNet.Algorithms.Spatial.SpatialIndex<T> (a flat array optimized for zero-allocation intersections).

D. Pandas DataFrames -> C# LINQ

Python:

df.groupby('label').agg({'confidence': 'mean'})

C# Port:

items.GroupBy(x => x.Label)
     .Select(g => new { Label = g.Key, MeanConfidence = g.Average(x => x.Confidence) });

4. Implementation Rules

1. •No dynamic: Everything must be strictly typed.
2. •Zero Allocations in Loops: Avoid instantiating new objects (e.g., new List<int>()) inside tight inner loops. Reuse buffers.
3. •Explicit Math: Use Math.Max, Math.Min, Math.Abs, and Math.Round(..., MidpointRounding.AwayFromZero) strictly. Python's round() behaves differently (Banker's rounding) than standard expectations; verify the exact rounding semantics required.

5. Validation

• •Always write a short, deterministic unit test in DoclingDotNet.Tests demonstrating that the ported C# algorithm produces the exact same numerical or structural output as the Python original for a given dummy input.