dqmc-advanced
Advanced DQMC features including unequal-time measurements, analytic continuation, and queue system internals. Use when enabling dynamical correlations, performing MaxEnt continuation, or understanding HDF5 data structure.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install edwnh-dqmc-dqmc-advanced
Repository
Skill path: .claude/skills/dqmc-advanced
Advanced DQMC features including unequal-time measurements, analytic continuation, and queue system internals. Use when enabling dynamical correlations, performing MaxEnt continuation, or understanding HDF5 data structure.
Open repositoryBest for
Primary workflow: Analyze Data & AI.
Technical facets: Full Stack, Data / AI.
Target audience: everyone.
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: edwnh.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install dqmc-advanced into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/edwnh/dqmc before adding dqmc-advanced to shared team environments
- Use dqmc-advanced for development workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.
Original source / Raw SKILL.md
--- name: dqmc-advanced description: Advanced DQMC features including unequal-time measurements, analytic continuation, and queue system internals. Use when enabling dynamical correlations, performing MaxEnt continuation, or understanding HDF5 data structure. --- # Advanced Topics ## Unequal-Time Measurements Enable by setting `period_uneqlt > 0` during file generation: ```bash dqmc-util gen period_uneqlt=8 ... ``` Required for: - `nnrw0`, `zzrw0` - Zero-frequency susceptibilities - `dwq0t` - D-wave pair susceptibility - Any time-dependent correlation functions **Note:** Unequal-time measurements significantly increase runtime and memory usage. ## Analytic Continuation Use maximum entropy for continuing imaginary-time data to real frequencies: ```python from dqmc_util import maxent # Solve G = K A given: # - G: binned data, shape (nbin, ntau) # - K: kernel, shape (ntau, nw) # - m: default model, shape (nw,) A_omega = maxent.calc_A(G, K, m) ``` ## HDF5 File Structure ``` /metadata/ # Model info (mu, Nx, Ny, beta) /params/ # Simulation parameters, precomputed matrices /state/ # RNG state, sweep number, aux field config /meas_eqlt/ # Equal-time measurements (n_sample, sign, den, ...) /meas_uneqlt/ # Unequal-time measurements (optional) ``` ## Queue System Internals The sharded queue uses: - 128 shards to avoid lock contention on distributed filesystems - Atomic `rename()` operations for task claiming - Symlinks moved: `todo/` -> `running/` -> `done/` - Checkpointed jobs returned to `todo/` for resumption