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README.md

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+---
+license: gpl-3.0
+task_categories:
+  - other
+language:
+  - en
+tags:
+  - electromyography
+  - emg
+  - motor-unit-decomposition
+  - blind-source-separation
+  - dynamic-contraction
+  - synthetic
+  - neuromuscular
+size_categories:
+  - n<1K
+pretty_name: MUniverse Dynamic EMG Benchmark v2
+---
+
+# MUniverse Dynamic EMG Benchmark v2
+
+**336 synthetic non-stationary surface EMG recordings** for benchmarking motor-unit decomposition algorithms under dynamic joint motion. 21 motor-unit pool combinations (26–113 MUs) × 5 simulated subjects × 7 forearm muscles × 8 conditions (sinusoidal/triangular wrist Flexion–Extension at two amplitudes × two SNR levels), at 70-ch and 320-ch electrode configurations.
+
+## Files
+
+| File | Size | Purpose |
+|---|---|---|
+| `recordings.tar.gz` | 4.7 GB | The 336 `.npz` recordings (`benchmark_v2/{ch070,ch320}/`) |
+| `reproducibility_bundle.zip` | 36 KB | Scripts, manifests, NeuroMotion patch, PBS templates |
+| `benchmark_spec.json` | 7 KB | Declarative spec: subjects, muscles, conditions, combos |
+| `README.md` | 8 KB | This page (also inside the bundle, as the canonical reproduction guide) |
+
+SHA256:
+- `recordings.tar.gz`: `715458efd70ca8932e1e45a642698fa11a4446e0352dbe1840e0a7e251544f56`
+- `reproducibility_bundle.zip`: `f4e034726a5c2afd60f408d3e32d63be074a586235316c38926feafd3d3e0988`
+
+## Quick load
+
+```python
+import numpy as np
+d = np.load("benchmark_v2/ch320/SA10-hi_ECU_sub03_N055.npz", allow_pickle=True)
+emg            = d["emg"]              # (T, 320) float32
+spike_mu_0     = d["spike_mu_0"]       # (n_spikes,) int64 — ground-truth indices
+angle_profile  = d["angle_profile"]    # (T,) joint angle in degrees
+effort_profile = d["effort_profile"]   # (T,) effort, fraction MVC
+fs             = int(d["fs"])          # 2048
+```
+
+See `benchmark_spec.json` for the full parameter grid (8 conditions × 21 combos × 2 channel configs = 336).
+
+## Reproduction
+
+The dataset is fully reproducible from open code: NeuroMotion + BioMime → MUAP libraries → crosstalk filtering → NeuroMotion synthesis. Stage-by-stage scripts and PBS templates are inside `reproducibility_bundle.zip`. The original README (which lives at `experiments/v2/README.md` inside the bundle) walks through all five stages.
+
+
+---
+
+## Original reproducibility README
+
+_Below is the canonical reproduction guide as it appears inside `reproducibility_bundle.zip`._
+
+
+Complete configuration, scripts, and metadata to regenerate the **v2 benchmark**: 336 non-stationary EMG recordings across 21 motor-unit pool combinations (26–113 MUs), 5 subjects, 7 muscle types, and 8 conditions (sinusoid/triangular angle × 2 difficulties × 2 SNR).
+
+The benchmark was generated using **MUniverse as-is** — no modifications to the core package except a single small patch to NeuroMotion's Triangular angle-profile generator (included in `scripts/patches/`). The story of this bundle is:
+
+1. Generate 40 MUAP libraries using NeuroMotion+BioMime (5 subjects × 8 muscles at Flex-Ext DOF).
+2. Analyze pairwise MUAP similarity, filter each library to a "crosstalk-clean" subset.
+3. Use NeuroMotion to synthesize 336 EMG recordings from those clean subsets, each exposed to different rotation profiles and noise levels.
+
+## Contents
+
+```
+experiments/v2/
+├── README.md                              # This file
+├── benchmark_spec.json                    # Declarative specification (reviewer-readable)
+├── combos.json                            # The 21 subject-muscle-threshold combos used
+├── scripts/                               # Scripts used, in execution order
+│   ├── 01_generate_muap_caches.py         # Stage 1: Run BioMime to make 40 caches (GPU)
+│   ├── 02_analyze_crosstalk.py            # Stage 2: Pairwise similarity analysis
+│   ├── 03_create_clean_caches.py          # Stage 3: Filter each cache to a clean subset
+│   ├── 04_generate_recordings.py          # Stage 4: Run NeuroMotion on clean caches
+│   ├── 05_fix_triangular.py               # Stage 5: Bug fix, regenerate triangular only
+│   ├── build_metadata.py                  # (meta) Generates the manifest CSVs below
+│   └── patches/
+│       └── neuromotion_triangular_symmetric.patch    # NeuroMotion fix
+└── metadata/
+    ├── muap_caches_manifest.csv           # 40 caches with shapes, fiber density, sha256
+    ├── combos_manifest.csv                # 21 clean combo pools
+    ├── conditions_manifest.csv            # 8 condition definitions
+    └── recordings_manifest.csv            # 336 recordings with full parameters
+```
+
+## Prerequisites
+
+1. **MUniverse** — tested against `dynamic_zi_resampling` branch (see `git log`). Clone the repo and install:
+   ```bash
+   git clone https://github.com/dfarinagroup/muniverse muniverse-live
+   cd muniverse-live
+   git checkout dynamic_zi_resampling
+   pip install -e .
+   ```
+
+2. **NeuroMotion Singularity/Docker image** — `pranavm19/muniverse-test:neuromotion`:
+   ```bash
+   cd src/environment
+   singularity pull muniverse-test_neuromotion.sif docker://pranavm19/muniverse-test:neuromotion
+   ```
+
+3. **NeuroMotion triangular patch** — apply to `src/muniverse/data_generation/_run_neuromotion.py`:
+   ```bash
+   cd muniverse-live
+   git apply experiments/v2/scripts/patches/neuromotion_triangular_symmetric.patch
+   ```
+   (Or just copy the `Triangular` branch from that file into your `_run_neuromotion.py` line ~434.)
+
+4. **Hardware**:
+   - Stage 1 (MUAP generation): **GPU** — each of 40 caches takes ~2-3 min on A40 / RTX6000.
+   - Stages 2–5: CPU only.
+
+5. **Disk**:
+   - MUAP caches: ~95 GB permanent
+   - Clean caches: ~8 GB
+   - Recordings: ~5 GB
+   - Raw outputs + configs: ~10 GB ephemeral (safe to delete after gen)
+
+## Reproduction Steps
+
+### Stage 1: Generate 40 MUAP caches (GPU, ~1.5 hours)
+
+Each cache is a `(num_mus, 130, 10, 32, 96)` numpy array — MUAPs over 130 angle steps for each MU on a 10×32 electrode grid.
+
+```bash
+# Generate all 40 (5 subjects × 8 muscles at Flex-Ext DOF)
+python experiments/v2/scripts/01_generate_muap_caches.py
+```
+
+The script edits these hardcoded paths — adjust before running:
+- `CACHE_ROOT`: where to write the caches
+- `RAW_ROOT`: scratch dir for container outputs (ephemeral is fine)
+- `SIF_PATH`: path to the NeuroMotion Singularity image
+
+Output: `<CACHE_ROOT>/cache_sub-simXX/subject_X_{MUSCLE}_Flexion-Extension_muaps.npy` + `_metadata.json` + `_mn_properties.csv`.
+
+### Stage 2: Crosstalk analysis (CPU, ~5 min)
+
+```bash
+python experiments/v2/scripts/02_analyze_crosstalk.py
+```
+
+Discovers all Flex-Ext caches, computes pairwise cosine similarity at mid-pose, and writes clean-subset selections at thresholds 0.75 / 0.80 / 0.85 / 0.90 to:
+`experiments/results/crosstalk_v3/exp_crosstalk_clean_subsets.json`
+
+### Stage 3: Create 21 clean caches (CPU, ~1 min)
+
+```bash
+python experiments/v2/scripts/03_create_clean_caches.py
+```
+
+Reads the crosstalk JSON and writes 21 filtered caches to `experiments/clean_caches_v2/` (organized as `{tier}/sub{N}_{muscle}_thr{XX}/...`). Also writes `combos.json` — the master list consumed by Stage 4.
+
+### Stage 4: Generate 336 recordings (CPU, ~55 min)
+
+```bash
+python experiments/v2/scripts/04_generate_recordings.py \
+    --config experiments/clean_caches_v2/combos.json
+```
+
+For each combo, runs the container 8 times (one per condition), post-processes into 2 channel configs (ch070 + ch320), and writes:
+`<BENCHMARK_ROOT>/ch{070,320}/{cond}_{muscle}_sub{N}_N{K}.npz`
+
+### Stage 5 (only if you hit the bug): Fix triangular recordings
+
+If you run Stage 4 **without** the NeuroMotion patch applied, the 168 triangular recordings will have flat 0° angle profiles. To detect: check any `TA*.npz` file — `angle_profile` should oscillate ±sin_amplitude.
+
+If buggy, patch NeuroMotion and regenerate just the triangular recordings:
+
+```bash
+# Submit as array job (21 combos × ~3 min each)
+qsub experiments/v2/run_regenerate_triangular.pbs
+
+# Or run interactively, one combo at a time
+for i in {0..20}; do
+    python experiments/v2/scripts/05_fix_triangular.py --combo-idx $i
+done
+```
+
+### Rebuild metadata CSVs
+
+```bash
+python experiments/v2/scripts/build_metadata.py
+```
+
+Regenerates `metadata/*.csv` from the actual files on disk (useful for validation).
+
+## Data Format
+
+Each recording is a `.npz` file following `/BENCHMARK_DATA_FORMAT.md` with:
+
+| Key | Shape | Description |
+|-----|-------|-------------|
+| `emg` | `(T, M)` float32 | EMG samples-first |
+| `spike_mu_{i}` | `(n_spikes,)` int64 | GT spike sample indices per MU (empty for inactive) |
+| `angle_profile` | `(T,)` | Joint angle over time (deg) |
+| `effort_profile` | `(T,)` | Effort over time (fraction MVC) |
+| `fs`, `n_channels`, `n_samples`, `muscle`, `subject_seed`, `af`, ... | scalars | Full parameters |
+
+## Conditions Summary
+
+All 8 conditions use **constant effort at 50% MVC** with **0.3 Hz** sinusoid/triangle. Duration 10s, fs=2048.
+
+| ID | Angle Profile | af (fraction of ±65°) | ±deg | SNR (dB) |
+|----|---------------|----------------------|------|----------|
+| SA05-hi | Sinusoid   | 0.5 | ±32.5 | 25 |
+| SA05-lo | Sinusoid   | 0.5 | ±32.5 | 20 |
+| SA10-hi | Sinusoid   | 1.0 | ±65.0 | 25 |
+| SA10-lo | Sinusoid   | 1.0 | ±65.0 | 20 |
+| TA05-hi | Triangular | 0.5 | ±32.5 | 25 |
+| TA05-lo | Triangular | 0.5 | ±32.5 | 20 |
+| TA10-hi | Triangular | 1.0 | ±65.0 | 25 |
+| TA10-lo | Triangular | 1.0 | ±65.0 | 20 |
+
+## Combo Summary
+
+21 combos grouped into 3 pool-size tiers:
+
+| Tier | # Combos | Pool range | Typical recording |
+|------|:-:|:-:|---|
+| **Small**  | 6 | 26–45 MUs | PL × 3 subjects + FCU_h + ECRL + FDSI |
+| **Medium** | 8 | 47–74 MUs | ECU, FDSI, EDI, ECRB, FCU_u |
+| **Large**  | 7 | 77–113 MUs | EDI, ECU, ECRB, FCU_u at threshold 0.90 |
+
+See `combos.json` / `metadata/combos_manifest.csv` for the exact list.
+
+## Verification
+
+After reproduction, verify the dataset is correct:
+
+```bash
+# Compare against recordings_manifest.csv (336 rows)
+python experiments/v2/scripts/build_metadata.py
+diff <(sort experiments/v2/metadata/recordings_manifest.csv) <(sort <your-regenerated>/recordings_manifest.csv)
+```
+
+MUAP cache SHA256 hashes (first 16 hex chars) are in `metadata/muap_caches_manifest.csv` for integrity checking of the Stage 1 outputs.
+
+## Citation
+
+```
+<pending — paper in prep>
+```
+
+## License
+
+Same as MUniverse core (GPL-3.0).