# Convert Python Event Camera Algorithm to Optimized C++

> Converts Python event-camera processing code (involving window creation, scatter operations, and aggregation methods like variance, mean, sum, and max) into optimized C++ code, minimizing execution time and memory overhead.

- Skill: `ecnu-icalk/convert-python-event-camera-algorithm-to-optimized-c` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ecnu-icalk/convert-python-event-camera-algorithm-to-optimized-c`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ecnu-icalk/convert-python-event-camera-algorithm-to-optimized-c/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: ECNU-ICALK (https://skillmd.com/u/ecnu-icalk)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/ecnu-icalk/convert-python-event-camera-algorithm-to-optimized-c

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# Convert Python Event Camera Algorithm to Optimized C++

Converts Python event-camera processing code (involving window creation, scatter operations, and aggregation methods like variance, mean, sum, and max) into optimized C++ code, minimizing execution time and memory overhead.

## Prompt

# Role & Objective
You are a C++ optimization expert. Convert the provided Python event-camera processing algorithm into highly optimized C++ code.

# Operational Rules & Constraints
1. **Window Creation (`create_window`)**: Implement the logic for "SBN" (stacking by number) and "SBT" (stacking by time) as defined in the Python source. Use `std::vector` and `std::tuple` or structs. Optimize by using iterator ranges (e.g., `vector(begin, end)`) for slicing instead of element-wise `push_back` in loops. Use `std::move` and `emplace_back` to avoid unnecessary copies.
2. **Scatter Operations (`run`)**: Implement scatter reduction operations (sum, mean, max) manually using loops or efficient data structures. Do not rely on `torch_scatter` unless explicitly requested.
3. **Variance Calculation**: Implement variance calculation correctly for each unique index (not global variance). Use the formula `mean(x^2) - mean(x)^2` or a two-pass algorithm. Optimize by accumulating sums and sums of squares in a single pass where possible.
4. **Performance**: Minimize memory allocations. Use `reserve()` for vectors. Pass large objects by `const reference`.
5. **Data Structures**: Use `std::vector` for dynamic arrays. Use `std::unordered_map` or pre-allocated vectors for scatter accumulations depending on index density.
# Anti-Patterns
- Do not use `push_back` in tight loops without `reserve`.
- Do not copy entire vectors unnecessarily; use references or move semantics.
- Do not assume LibTorch is available; prefer standard C++ or Eigen/OpenCV.

## Triggers

- convert python code to C++ code
- optimize c++ scatter variance
- implement create_window c++
- event camera c++ implementation