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tcfuse-visualize

النجوم٠

التفرعات٠

آخر تحديث٩ يونيو ٢٠٢٦ في ١٢:٠٢

Publication-quality TC-Fuse visualizations for journal articles and preprints — matplotlib + cartopy + cmocean, SVG output via `save_fig`, shared style constants (`COL1`, `COL2`, `AR_GOLDEN`, `INTENSITY_COLORS`, `SOURCE_COLORS`), thematic modules under `src/tcfuse/data/visualization/` (fields, tracks, profiles, timeline, storm_data_visu, training). Use when writing or modifying any plotting function, adding a new figure type, or touching `style.py`.

التثبيت

التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.

تشغيل في Manus

المصدر

dauvillc

dauvillc/tcfuse

فتح مستودع GitHub عرض مستودعات المنشئ

تنزيل

تشغيل في Manus

المهن ذات الصلةSOC

استنادا إلى تصنيف SOC المهني

مطوّرو البرمجياتمهن الحاسوب والرياضيات·SOC 15-1252

SKILL.md

readonly

name

tcfuse-visualize

description

Content has moved. The full skill documentation is in .agents/visualize.md. Read that file for agent behavior rules, style conventions, module layout, function signature patterns, and priority plot types.

المزيد من هذا المستودع

نفس المستودع

tcfuse-sweep

dauvillc/tcfuse

TC-Fuse hyperparameter search — Hydra Optuna sweeper + submitit launcher running parallel single-GPU SLURM trials (offline-safe, works on Jean-Zay). Search space in conf/hydra/sweeper/*.yaml; launcher SLURM spec in conf/hydra/launcher/jz_*_sweep.yaml; short-budget experiment in conf/experiment/pmw_gmi_sweep.yaml. With submitit=false, scripts/train/train.py returns the best val/loss as the objective Optuna minimizes. Respect the Perceiver's head-divisibility invariants when defining the space. Use when running a sweep, adding a search space, or changing the optimized metric.

2026-06-300

tcfuse-evaluate

dauvillc/tcfuse

TC-Fuse evaluation pipeline — computing metrics and building figures from saved predictions with `scripts/evaluation/evaluate.py` (Hydra, `conf/evaluation.yaml`). Plugin-based: each `Evaluation` (`src/tcfuse/evaluation/`) is enabled via the `conf/evaluation/` config group and writes into its own subfolder under `paths.results/<run_id>/<experiment_name>/`. The base contract imposes no data shape (flattening is plugin-dependent); the `quantitative_metrics` plugin computes RMSE/MAE/R2/MAPE per source/channel with numpy/scikit-learn, independent of torchmetrics. Use when evaluating a prediction run, adding a new evaluation plugin, or changing the results layout.

2026-06-290

tcfuse-inference

dauvillc/tcfuse

TC-Fuse inference and prediction pipeline — running a trained checkpoint over a split with `scripts/inference/infer.py` (Hydra, `conf/inference.yaml`), the single-GPU `trainer.predict` flow with task-specific target masking, the `PredictionWriter` callback, and the `SamplePrediction` / `PredictionRun` output format (per-window HDF5 + `index.parquet` + `manifest.yaml`) with its `compute_metrics` evaluation (bias, RMSE, MAE, R2 per channel). Use when running inference, evaluating a checkpoint, changing the prediction output format, or working in `src/tcfuse/data/predictions/` or `src/tcfuse/lightning/prediction_writer.py`.

2026-06-280

tcfuse-cleps

dauvillc/tcfuse

Drive the CLEPS (Inria Paris) cluster from the local machine — rsync sync, preflight, SLURM submission via submitit, job monitoring, checkpoint resume, and storage quota. Use whenever a task involves `ssh cleps`, $SCRATCH, CLEPS SLURM partitions (cpu_devel / gpu / arches), pixi on the cluster, `submitit`, `setup=cleps_*`, `paths=cleps`, or running jobs on CLEPS. CLEPS uses pixi (no modules) and W&B online (no offline sync), and its scratch is persistent (no archive step).

2026-06-250

tcfuse-architecture

dauvillc/tcfuse

TC-Fuse model backbone design — the architecture-agnostic embedding layer (TorchSource → EmbeddedSource via MultiSourceEncoder) and un-embedding layer (EmbeddedBatch → DecodedBatch via MultiSourceDecoder), the swappable encoder interface, candidate backbones to benchmark (Perceiver, cross-attention Transformer, hierarchical windowed attention), and the masked-source-reconstruction self-supervised pre-training task. Use when working in `src/tcfuse/models/`, adding or benchmarking a backbone, or changing the encoder/decoder interface.

2026-06-230

pyright-typing-fixer

dauvillc/tcfuse

Fix basedpyright diagnostics with clean, minimal Python typing changes. Use when the user mentions Pyright, basedpyright, type errors, typing fixes, type checking, or Python typing diagnostics.

2026-06-090

name

tcfuse-visualize

description

Content has moved. The full skill documentation is in .agents/visualize.md. Read that file for agent behavior rules, style conventions, module layout, function signature patterns, and priority plot types.