# Reproducing CryoDynaScape: every figure and table

This guide maps every display item in the manuscript (`cryodynascape_overleaf/main.tex`) to the runs, result files and
commands that produce it. Paths are relative to the project root (`ROOT=/n/netscratch/kempner_mzitnik_lab/Lab/hxu66/cryodynascape`).
Commands were recovered from three sources, in this order:

1. **`logs/<run>.log`, first line `args {...}`.** `run_pipeline.py`, `run_v1.py`, `run_hinge.py` and `run_rigid.py`
   log every argument, so this is the exact configuration of a run. The same dict is stored under the key `args` in
   `checkpoints/<run>/*.pth`, which is on HF. `logs/` itself is not.
2. **`sacct -P -j <id> -o SubmitLine%2000`.** It records `--wrap` commands and `--export=` variables. Variables set
   with a shell `export` before `sbatch` are not recorded. For those wrappers the `RUN/STAGE1/V1` values below come
   from source 1.
3. The project log (`memory/cryodynascape-project.md`) and `results/SUMMARY.md`.

Items marked **[untraced]** could not be recovered exactly (none affects a number in the manuscript: all AU-FSC latents are traced in `results/official/latent_sources.json`). Items marked **[inferred]** were reconstructed from file
names and shapes, not from a recorded command.

## 0. Shortcut: download instead of recompute

Everything the paper reads is public at <https://huggingface.co/datasets/Xu-AI4Science/CryoDynaScape-data>
(26,652 files, 446 GB). It uses the same relative paths as this repository: `results/`, `checkpoints/`,
`baselines/inputs/`, `baselines/runs/`, `data/synth_*`, `data/Spike-MD-128/`, `data/real/empiar10180_128/` and
`data/real/empiar10076_128/`.

```bash
huggingface-cli download Xu-AI4Science/CryoDynaScape-data --repo-type dataset --local-dir $ROOT \
    --include "results/*"          # figures/tables only need results/ (+ data/* for GT-dependent panels)
```
With `results/` in place, every step in Section 4 that runs on CPU (scripts `fig_*`, `*_compose`, `supp_table_paired`
and `table_hier`) regenerates its figure or table without a GPU. The GT-dependent panels (Fig. 3a/c, Table 2) also
need the CryoBench data from Section 2.

The HF dataset does not contain:
- CryoBench originals or EMPIAR raw micrographs/stacks.
- `data/structures/` (templates, AF3 models).
- The CryoSTAR work dirs (`baselines/cryostar/projects/star/work_dirs/`). The CryoSTAR latents
  `.../<run>_0/0061_0096000/z.npy` are therefore only available locally.
- `logs/`.

## 1. Environments

All wrappers hard-code `/n/holylabs/kempner_mzitnik_lab/Lab/hxu66/conda/envs/<env>`. The env migration to netscratch
was cancelled, so `envs/README.md`, which still names `.../cds_envs/<env>`, is stale on location only. Its specs are
current. To run elsewhere, edit `PY=`, `E=` and `RE=` at the top of each wrapper.

| env | spec in `envs/` | kind | used by |
|---|---|---|---|
| `cryodynascape` | `cryodynascape.requirements.txt` + `.pyvenv.cfg` (Mambaforge Py 3.10.13, torch 2.5.1+cu121) | venv | every `scripts/*.py` of ours, AU-FSC scoring (`official_fsc.py`, `prep_official.py`), all figure/table scripts |
| `recovar` | `recovar.requirements.txt` (Py 3.12, jax 0.9.0.1) | venv | RECOVAR baseline and the kernel-regression generator inside every AU-FSC evaluation |
| `cryodrgn` | `cryodrgn.requirements.txt` (Py 3.12) | venv | cryoDRGN baseline, `write_star.py`, `parse_pose_star` checks |
| `cryostar` | `cryostar.yml` (+ `pip install baselines/cryostar`) | conda | CryoSTAR, `baseline_displacement.py --method cryostar` |
| `cryosphere` | `cryosphere.yml` (numpy<2, torchvision 0.16.2) | conda | cryoSPHERE, `cryosphere_resume.py`, `baseline_displacement.py --method cryosphere` |
| `dynamight` | `dynamight.yml` (+ DynaMight git `358db04d5`) | conda | DynaMight, `dynamight_latent.py` |
| `eman2` | `eman2.yml` (`-c cryoem -c conda-forge eman-dev`) | conda | e2gmm (`run_e2gmm.py`) |
| `cds_render` | `cds_render.yml` (conda-forge `mesalib<24.1`) | conda | OSMesa for headless ChimeraX (`scripts/chimerax_offscreen.sh`) |
| `scipion3`, `oXmipp` | `*.yml` | conda | HEMNMA attempt only (reported n.r.) |

Rebuild:
```bash
python -m venv <path>; <path>/bin/pip install -r envs/<env>.requirements.txt --extra-index-url https://download.pytorch.org/whl/cu121
conda env create -p <path> -f envs/<env>.yml          # conda envs
sbatch scripts/rebuild_envs.sh                         # scripted rebuild of all envs (test partition, CPU)
```
Never create envs on the login node: `conda create` gets OOM-killed there. External software:
- **RELION 5:** built from source; recipe in `envs/README.md`. Used by `submit_baseline_prep.sh` (`relion_reconstruct`)
  and `submit_multibody.sh`.
- **ChimeraX 1.8:** el8 RPM extracted to `.../software/chimerax/root`. Extract with `rpm2cpio | cpio -id` *without*
  `-m`, so files get fresh mtimes and survive the purge.

## 2. Data

| dataset | source | local path | preprocessing |
|---|---|---|---|
| IgG-1D, IgG-RL | CryoBench, Zenodo 11629428 | `data/IgG-1D`, `data/IgG-RL` | none (128 px used directly; pipeline auto-detects contrast and pose convention) |
| Ribosembly, Tomotwin-100 | Zenodo 12528292 | `data/Ribosembly`, `data/Tomotwin-100` | none |
| Spike-MD | Zenodo 14941494 | `data/Spike-MD` → `data/Spike-MD-128` | `scripts/prep_spike128.py` (job 49082715); GT maps: `scripts/spike_frames.py` then `scripts/spike_gt_vols.py` → `data/Spike-MD-128/vols/gt` |
| EMPIAR-10180 | EMPIAR + cryoDRGN EMPIAR inputs (github zhonge/cryodrgn_empiar → `data/real/cryodrgn_empiar`) | `data/real/empiar10180` → `data/real/empiar10180_128` | see below |
| EMPIAR-10076 | same | `data/real/empiar10076` → `data/real/empiar10076_128` | see below |
| synthetic | `scripts/make_synth_hier.py` | `data/synth_*` | see Table 5 |
| structures | RCSB (1N0X, 3AVE, 1HZH, 4YBB); AF3 (`scripts/submit_af3_igg.sh`, `submit_af3_iggrl.sh`) | `data/structures/` | `scripts/build_template_model.py` → `igg_templates.pdb`, `iggrl_templates.pdb` (+ `_domains.npy`); exact build args **[untraced]** |

```bash
sbatch scripts/redownload_cryobench.sh            # IgG-1D, IgG-RL, Ribosembly, Tomotwin-100 (size check, unzip -DD)
sbatch scripts/download_ranges.sh <zenodo-url> data/<X>.zip 16   # ranged download used for the large zips (curl -sf)
sbatch scripts/download_empiar.sh 10180 data/real/empiar10180 24
sbatch scripts/download_empiar.sh 10076 data/real/empiar10076 12
sbatch scripts/redownload_empiar_meta.sh          # 10180 Example/ + Mask-and-Ref/, 10076 Parameters.star/.par
# EMPIAR-10180 (job 48615770, test partition):
python scripts/preprocess_empiar.py --star data/real/empiar10180/data/Example/consensus_data.star \
  --datadir data/real/empiar10180/data --ind data/real/cryodrgn_empiar/empiar10180/inputs/filtered.ind.pkl \
  --poses data/real/cryodrgn_empiar/empiar10180/inputs/poses.pkl --ctf data/real/cryodrgn_empiar/empiar10180/inputs/ctf.pkl \
  --D_out 128 --out data/real/empiar10180_128
# EMPIAR-10076 (job 51770970): same with --star data/real/empiar10076/data/Parameters.star, empiar10076 inputs
```
More inputs:
- **10076 symlink:** `run_hier.sh` reads `particles.mrcs`, so EMPIAR-10076 has a symlink `particles.mrcs → particles.128.mrcs`.
- **RECOVAR particle lists:** RECOVAR reads `data/<DSET>_snr0.01_abs.txt`. Each is the per-state `.mrcs` stack list
  with absolute paths, as RECOVAR ≥0.5 requires. They were written by hand **[untraced one-liner]**: one absolute
  path per stack, in the order of `sorted_particles.128.txt`.
- **Baseline inputs:** `baselines/inputs/<DSET>/` holds a RELION-convention stack, a validated star,
  `particles_halves.star` and `relion_recon.mrc`. Build with
  `DSET=... STAGES="<stage1>:<pdb|none> ..." sbatch scripts/submit_baseline_prep.sh`, which calls
  `prep_baseline_inputs.py`, `write_star.py`, a cryoDRGN round trip, `relion_reconstruct` and `check_star.py`. Spike
  (job 51092976): `DSET=Spike-MD-128 PART=images/particles.128.mrcs STAGES=spike_seed:data/Spike-MD/seed_structure.pdb`.
  CryoSTAR/cryoSPHERE configs come from `scripts/make_baseline_configs.py`.

## 3. Pipeline and dependency order

```
stage 1 (v0)      run_pipeline.py      consensus G0, pose convention, beads, low-rank latent z   -> results/<X>_v0
  two-pass (v0c)  run_pipeline.py --bead_map_from <X>_v0   (beads placed on cluster maps)        -> results/<X>_v0c
domains (v1)      run_v1.py --stage1 <X>_v0c                                                     -> results/<X>_v0c_v1
motion            run_hinge.py (IgG-1D) | run_rigid.py (IgG-RL, Spike, EMPIAR)                   -> <X>_v0c_hinge | _rigid
model selection   model_select.py (BIC low-rank vs domain) ; run_discrete.py --tag L1 (Level-1 discrete classes)
Level 1           level1_relate.py --stage1 <X>_v0c                                              -> level1_relate.json
Level 2           level2_blocks.py ; run_rigid.py ... --run <X>_v0c_rigid2 --domains init --merge v1
Level 3           particle_explain.py --null map --tag _mapnull[_full]                           -> particle_explain*.json/npz
decision          hier_decide.py --run <rigid run> --tag _mapnull[_full]                         -> hier_decide*.json
evaluation        submit_ext_eval.sh / submit_ext_serial.sh (AU-FSC); metrics.json (k-NN, displacement)
```
- **Wrappers:** `scripts/run_hier.sh` runs Level 1 → v1 → Level 2 → rigid2 → `particle_explain` → `hier_decide`
  end to end (`NAME=... DATA=... [L1_ARGS=...] sbatch scripts/run_hier.sh`) and resumes on resubmit.
  `scripts/submit_level23.sh` (`S1=... V1=... IN="..." [RIGID_ARGS=...]`) runs Levels 2–3 on an existing v0c/v1 pair.
- **AU-FSC (official protocol):** `prep_official.py` writes `query.npy`, which is `arange(0, N, N//100)`, plus
  `<NAME>_emb.pkl/_targets.txt`. `recovar reconstruct_from_external_embedding` then writes
  `results/official/<TAG>/extp_<NAME>/state###.mrc`. Finally `official_fsc.py` (CryoBench FSC logic) writes
  `score_ext_<NAME>.json`, which holds the mean and the per-query values. Latent specs are `path.h5:key`,
  `path.npy` or `recovar:<dir>:<zdim>`. `theta_all` gives the (cos, sin) of every hinge angle.
  ```bash
  DSET=IgG-1D TAG=igg1d NAME=hinge_v0c LATENT=$ROOT/results/igg1d_v0c_hinge_test_results.h5:theta_all sbatch scripts/submit_ext_eval.sh
  sbatch --export=ALL,EVALS="DSET|TAG|NAME|LATENT DSET|TAG|NAME|LATENT ..." scripts/submit_ext_serial.sh
  ```
  Run evaluations **serially**, one GPU and one at a time: the generator uses 2–5 % of the GPU, which is why the lab
  rule requires serial runs. `submit_ext_serial.sh` skips any eval whose `score_ext_*.json` already exists, so a
  timed-out chain can simply be resubmitted (example: job 51165569). Each eval takes ~1–1.5 h, about 3 h on
  Ribosembly. The serial wrapper caps each at `timeout 170m`.

### 3.1 Recovered commands for the core runs

The variables in front of `sbatch` are taken from the args in `logs/<run>.log`. Job IDs come from `logs/queue_*.log`
and sacct.

| run(s) | command (job) |
|---|---|
| `igg1d_v0` | `sbatch scripts/submit_igg1d.sh` (48235447) |
| `igg1d_v0c` → `_v1` → `_hinge` | `RUN=igg1d_v0c sbatch scripts/submit_igg1d.sh --bead_map_from igg1d_v0` (48911691); `RUN=igg1d_v0c_v1 STAGE1=igg1d_v0c sbatch scripts/submit_v1_igg1d.sh` (48911692); `RUN=igg1d_v0c_hinge STAGE1=igg1d_v0c V1=igg1d_v0c_v1 sbatch scripts/submit_hinge_igg1d.sh --n_axes 100 --rest x0` (48911693) |
| `iggrl_v0`, `iggrl_v0c` → `_v1` → `_rigid` | `DSET=IgG-RL PERIOD=0 RUN=iggrl_v0 sbatch scripts/submit_igg1d.sh`; then `RUN=iggrl_v0c ... --bead_map_from iggrl_v0` (48912675); `RUN=iggrl_v0c_v1 STAGE1=iggrl_v0c DSET=IgG-RL PERIOD=0 sbatch scripts/submit_v1_igg1d.sh` (48912676); `RUN=iggrl_v0c_rigid STAGE1=iggrl_v0c V1=iggrl_v0c_v1 sbatch scripts/submit_rigid.sh --rest x0 --em_rounds 4 --flip_pool 50 --fast_coarse --c2f 0:25:32,14:12:8,7:8:2,3.5:6:1` (48912677) |
| `ribo_v0`, `ribo_v0c` → `_v1` → `_rigid` | `ribo_v0`: `run_pipeline.py ... --n_beads 2000 --K 8 --epochs_homo 3 --epochs 30 --n_clusters 16` (see log); `ribo_v0c` = same `--bead_map_from ribo_v0 --labels ... --gt_vols data/Ribosembly/vols/128_org` (48931595, `--wrap`); `run_v1.py --stage1 ribo_v0c --run ribo_v0c_v1 --Ke 4 --epochs 20` (48931632); `run_rigid.py --stage1 ribo_v0c --v1 ribo_v0c_v1 --run ribo_v0c_rigid --rest x0 ...` (48931635; full args in log) |
| `tomo_v0`, `tomo_v0c` → `_v1` → `_rigid` | `DSET=Tomotwin-100 PERIOD=0 RUN=tomo_v0 sbatch scripts/submit_igg1d.sh` (48943326); `RUN=tomo_v0c ... --bead_map_from tomo_v0` (48943329); v1 (48943337); `submit_rigid.sh --rest x0 --em_rounds 1 --sweeps 1 --fast_coarse --pop_prior 50 --c2f ...` (48943338) |
| `spike_v0` → `spike_v0c` → `_v1` → `_rigid` | `--wrap` jobs 49083864/65/67/71: `run_pipeline.py --particles data/Spike-MD-128/images/particles.128.mrcs ... --run spike_v0 --n_beads 2000 --K 8 --epochs_homo 3 --epochs 30 --n_clusters 20`; then v0c (`--bead_map_from spike_v0`), `run_v1.py --Ke 4 --epochs 20`, `run_rigid.py --rest x0 --em_rounds 4 --flip_pool 50 ...` |
| model selection | `model_select.py --stage1 <X>_v0c --domain_run <X>_v0c_hinge|_rigid --kind hinge|rigid ...` (48913204, 48923258, 48931636, 48943341) → `results/*/model_select_*.json` |
| Level-1 discrete (Ribosembly, Tomotwin) | `run_discrete.py --stage1 ribo_v0 ... --n_sub 20000 --Ds 64 --Ks 5,10,16,25 --n_iter 6 --domain_run ribo_v0c_rigid --domain_kind rigid --domain_stage1 ribo_v0c --apply_all --tag L1` (48974832) → `results/ribo_v0/L1_posterior_K16.npy`; Tomotwin the same with `--stage1 tomo_v0 --Ks 75,100,125 --domain_run tomo_v0c_rigid` (48974831) → `results/tomo_v0/L1_posterior_K100.npy`; Spike: `run_discrete.py --stage1 spike_v0 ... --Ks 1,10,50,100,200 --domain_run spike_v0c_rigid` (49083885) → low-rank selected (`results/spike_v0/L1.json`) |

## 4. Display items

### Fig. 1 — Overview (`figures/fig1_overview.pdf` = `results/figures/overview.pdf`)
- Inputs: icon renders `results/figures/ribo/test.png`, made with `bash scripts/chimerax_offscreen.sh scripts/diag/cx_test.cxc`
  (job 51430342). It also uses `ribo/class05,12,06.png` (Fig. 4 render) and `morph/igg1d_still0,2.png` (Fig. 3b).
- Command: `python scripts/fig_overview.py --out results/figures/overview` (job 51505455, test partition).

### Fig. 2 — AU-FSC benchmark (`fig2_benchmark.pdf` = `results/figures/benchmark.pdf`)
- Reads the numbers from `tab:bench` in `main.tex`, so it always matches Extended Data Table 1. Regenerate the table
  first (ED Table 1 below).
- `python scripts/fig_benchmark.py --tex ../cryodynascape_overleaf/main.tex --out results/figures/benchmark` (job 51405401).

### Fig. 3 — Recovered motion (`fig3_motion.pdf` = `results/figures/fig3.pdf`)
| panel | run | data | command |
|---|---|---|---|
| a | `igg1d_v0c_hinge` | `results/igg1d_v0c_hinge_test_results.h5` (`theta_mean_dom1`, `labels`) | composed in `fig3_compose.py` |
| b | `igg1d_v0c_hinge` | `results/igg1d_v0c_hinge/hinge1_traj_ca.pdb` | `python scripts/fig_morph.py --traj results/igg1d_v0c_hinge/hinge1_traj_ca.pdb --h5 results/igg1d_v0c_hinge_test_results.h5 --out results/figures/morph/igg1d && bash scripts/chimerax_offscreen.sh results/figures/morph/igg1d.cxc` (job 51458988; the still/strip post-processing inline `python -c` in that job is **[untraced]**, truncated in sacct) |
| c | `iggrl_rigid_restx0` (oracle start, rest re-estimated) | `results/iggrl_rigid_restx0/motion_eval.npz` | `python scripts/fig_state_overlay.py --run results/iggrl_rigid_restx0 --out results/figures/iggrl_states && bash scripts/chimerax_offscreen.sh results/figures/iggrl_states/render.cxc` (job 51602507) |
| d | `spike_v0` vs all Spike evals | `results/official/spike/score_ext_*.json` (`per_query`) | `python scripts/fig_spike_paired.py --out results/figures/spike_paired` (job 51622677) |
Compose: `python scripts/fig3_compose.py --out results/figures/fig3` (job 51920932).

### Fig. 4 — Ribosembly containment graph (`fig4_ribosembly.pdf` = `results/figures/ribo/ribo_graph.pdf`)
- Run: `ribo_v0c` with Level 1 from `level1_relate.py --stage1 ribo_v0c ... --kgrid 1,2,3,5,8,10,16,20,30`
  (`scripts/run_level1_all.sh`, job 49858198). Outputs: `results/ribo_v0c/level1_relate.json`, `level1r_class00–15.mrc`.
- Commands (job 51430859, then 52017444):
  ```bash
  python scripts/fig_ribo.py render  --run results/ribo_v0c --out results/figures/ribo
  bash scripts/chimerax_offscreen.sh results/figures/ribo/render.cxc
  python scripts/fig_ribo.py compose --run results/ribo_v0c --out results/figures/ribo
  ```

### Fig. 5 — EMPIAR-10180 (`fig6_e10180.pdf` = `results/figures/fig6.pdf`; file name keeps the old number)

Runs (all on `data/real/empiar10180_128`, 139,722 particles):
1. `DATA=data/real/empiar10180_128 RUN=e10180_v0 sbatch scripts/submit_real.sh` (48634518; 2000 beads, 30 epochs).
2. `DATA=... STAGE=v1 STAGE1=e10180_v0 RUN=e10180_v1 sbatch scripts/submit_real_stage.sh` (48634888).
3. `DATA=... STAGE=rigid STAGE1=e10180_v0 V1=e10180_v1 RUN=e10180_rigid_gate sbatch scripts/submit_real_stage.sh --em_rounds 1 --sweeps 1 --fast_coarse --bic_gate` (49367475).
4. Map-null labels on all particles, one job per domain *j* = 1..4 (49672084–087):
   `particle_explain.py --run e10180_rigid_gate --stage1 e10180_v0 --particles data/real/empiar10180_128/particles.128.mrcs --poses .../poses.pkl --ctf .../ctf.pkl --n 0 --domain j --null map --tag _mapnull_full_dj`
5. Decision: `hier_decide.py --run e10180_rigid_gate --tag _mapnull_full` → `hier_decide_mapnull_full.json`. The
   exact invocation is **[inferred]** from the usage string; no recorded job.
6. Validation against RELION (49695252):
   `validate_labels.py --run e10180_rigid_gate --stage1 e10180_v0 <inputs> --label_tag _mapnull_full_d --rotvec_tag _mapnull_full_d --out_tag _mapnull`.
   It needs RELION multi-body `results/relion_multibody/` (`sbatch scripts/submit_multibody.sh`, 48885317, 2 H100,
   4 h 57 min) and the authors' masks `data/real/empiar10180/data/Mask-and-Ref`.
7. Motion ends (51541669): `motion_bins.py --run e10180_rigid_gate --stage1 e10180_v0 <inputs> --domains 1 3`
   → `results/e10180_rigid_gate/motion_bins/`.
   Quantify (51590700): `fig_e10180_bins.py --bins results/e10180_rigid_gate/motion_bins --out results/figures/e10180/bins`.

Render and compose:
- Render: `bash scripts/fig_e10180_render.sh` (writes `results/figures/e10180/render.cxc`, identical to the one used
  in job 51563165, and runs it with `scripts/chimerax_offscreen.sh`).
- Compose: `python scripts/fig6_compose.py --out results/figures/fig6` (job 51653150).

Text-only analyses (no figure panel):
- `e10180_latent_agreement.py --out results/e10180_rigid_gate/latent_agreement.json`
- `e10180_view_leak.py`

Both need `results/cryodrgn_e10180` and `results/recovar_e10180` (Section 5, caveat 3).

### Fig. 6 — EMPIAR-10076 (`fig7_e10076.pdf` = `results/figures/e10076/e10076_panel.pdf`)
- Stage 1 (51770971): `DATA=data/real/empiar10076_128 RUN=e10076_v0 sbatch scripts/submit_real.sh`.
- Hierarchy (51780442): `NAME=e10076 DATA=data/real/empiar10076_128 L1_ARGS="--kgrid 1,2,3,5,8,10,16,20,30" sbatch scripts/run_hier.sh`.
  `L1_ARGS` was set exactly as shown (recorded in the project log; the BIC keys in
  `results/e10076_v0/level1_relate.json` agree). This
  produces `e10076_v1`, `e10076_rigid2` and the 20k-particle `particle_explain.json` that the figure renders.
- Full labels (51849317): `particle_explain.py --run e10076_rigid2 --stage1 e10076_v0 --particles data/real/empiar10076_128/particles.mrcs ... --n 0 --null map --tag _mapnull_full`.
- Then (51860267): `hier_decide.py --run e10076_rigid2 --tag _mapnull_full` and `occupancy_check.py --run e10076_rigid2 --stage1 e10076_v0 <inputs> --domain 2 --tag _mapnull_full`.
  The 20k version, which the figure uses, is the same command without `--tag` (51809938).
- Block → 50S chains: `bash scripts/chimerax_offscreen.sh scripts/cx_fit_50s.py`, then
  `python scripts/e10076_block_map.py --write_beads && bash scripts/chimerax_offscreen.sh scripts/cx_fit_50s_beads.py`
  (51850681, 51851250). Needs `data/structures/4ybb.cif`.
- Figure (51823516): `python scripts/fig_e10076.py render --out results/figures/e10076 && bash scripts/chimerax_offscreen.sh results/figures/e10076/render.cxc && python scripts/fig_e10076.py compose --out results/figures/e10076`.

### Extended Data Fig. 1 — Calibration contrast (`results/figures/calibration_contrast.{json,pdf}`)
- Data: per-domain labelled fractions under three calibrations:
  - BIC penalty: `particle_explain_full_d*.json`
  - bead-rendered null: `particle_explain*.json` without `_mapnull`
  - consensus-map null: `particle_explain_mapnull*.json`
- Runs behind it:
  - synthetic species: `synth_hier_class{0,1}_rigid2` (49665450/464)
  - EMPIAR-10180: `e10180_rigid_gate`; bead null 49424308–315, map null 49672084–087
- Regenerate: `python scripts/fig_calibration.py` rebuilds `calibration_contrast.{json,pdf,png}` from the
  particle_explain JSON files above (checked 2026-10-12: identical JSON to the original, max difference 0).

### Extended Data Table 1 — AU-FSC (`tab:bench`)

Published values come from CryoBench. Cells marked † were evaluated here, each from
`results/official/<tag>/score_ext_<NAME>.json` (`AU-FSC_mean`):

| row | IgG-1D (`igg1d`) | IgG-RL (`iggrl`) | Ribosembly (`ribo`) | Tomotwin (`tomo`) | Spike (`spike`) |
|---|---|---|---|---|---|
| CDS (official) | `hinge_v0c` ← `igg1d_v0c_hinge:theta_all` | `rigid_v0c` ← `iggrl_v0c_rigid:features_pca` | `L1_discrete` ← `results/ribo_v0/L1_posterior_K16.npy` | `L1_discrete` ← `results/tomo_v0/L1_posterior_K100.npy` | `lowrank_v0` ← `spike_v0_test_results.h5:z_pca` |
| RECOVAR† | `RECOVAR` ← `recovar:results/recovar_igg1d:10` | same, `recovar_iggrl` | `recovar_ribo` | `recovar_tomo` (0.201; table prints published 0.258) | `recovar_spike` |
| e2gmm† | `e2gmm` ← `baselines/runs/e2gmm_<DSET>/latent.npy` (all five) | | | | |
| DynaMight† | `dynamight` ← `baselines/runs/dynamight_<DSET>/latent.npy` (via `submit_dynamight_eval.sh`) | | n.c. | n.c. | DIAM 300 |
| CryoSTAR† | `cryostar_tpl` ← `baselines/cryostar/projects/star/work_dirs/<DSET>_<s1>_tpl_0/0061_0096000/z.npy` | | — | — | `cryostar_seed` |
| cryoSPHERE† | `cryosphere_tpl` ← `baselines/runs/cryosphere_<DSET>_<s1>_tpl/analysis/z.npy` | | — | — | `cryosphere_seed` (resumed, Section 5) |
| CDS (structure) | `hinge_tpl` ← `igg1d_tpl_hinge:theta_all` | `rigid_tpl` ← `iggrl_tpl_rigid_dinit:features_pca` | — | — | `rigid_seed` ← `spike_seed_rigid:features_pca` |

The latent specs above are recorded in sacct (`--export` / `EVALS`) for these evaluations: e2gmm IgG-RL/Tomotwin/
Ribosembly/Spike, CryoSTAR Spike, `rigid_seed`, the AF3 rows, `random_latent` and cryoSPHERE/DynaMight (wrappers).
For the evaluations of 2026-09-25 to 09-28 (48913202–49202654) `LATENT` was exported in the shell and not recorded.
The exact source of **every** official embedding (53 of 53) was recovered by matching each saved
`results/official/<tag>/<name>_emb.pkl` against all candidate latents (`python scripts/trace_latents.py` →
`results/official/latent_sources.json`); use the listed source as `LATENT` (for hinge runs the key `theta_all`).

Baseline runs:
- **RECOVAR:** `DSET=<DSET> TAG=<tag> sbatch scripts/submit_recovar.sh` (zdim 1,2,4,10; `--mem=300G` for
  Ribosembly). Jobs: IgG-1D 48256679 (`submit_recovar_igg1d.sh`), IgG-RL 48312921, Ribosembly 48344994,
  Tomotwin 48944700, Spike 49197320.
- **cryoDRGN:** `DSET=... PART=... sbatch scripts/submit_cryodrgn.sh` (zdim 8, 20 epochs), then
  `DSET=... TAG=... PERIOD=100|0 sbatch scripts/submit_eval_cryodrgn.sh` → `score_drgn_own.json` (own decoder; used
  in S1). Published numbers are shown in the table.
- **CryoSTAR:** `NAME=<DSET>_<s1>_tpl sbatch scripts/submit_cryostar.sh` (Spike: `NAME=Spike-MD-128_spike_seed`, 51104007).
- **cryoSPHERE:** `NAME=... sbatch [--partition=kempner_h200] scripts/submit_cryosphere.sh` (IgG-1D 48888020,
  IgG-RL 48942834, Spike 51104008), then
  `NAME=... DSET=... TAG=... PERIOD=... EPOCH=299 [EXTNAME=cryosphere_seed] sbatch scripts/submit_cryosphere_eval.sh`.
- **DynaMight:** `DSET=... DIAM=250 sbatch scripts/submit_dynamight.sh` (Spike `DIAM=300`, 51104009), then
  `DSET=... TAG=... DIAM=... PERIOD=... sbatch scripts/submit_dynamight_eval.sh`.
- **e2gmm:**
  - Conversion on CPU: `run_e2gmm.py --dset <DSET> --apix 3.0|4.5 --box 128 --prep_only --chunks 8` (49934015–020,
    test partition).
  - Training: `DSET=... APIX=... sbatch scripts/submit_e2gmm.sh`. Ribosembly ran fully on CPU, test partition,
    180 G (50057962).
- **HEMNMA:** not run (`scripts/hemnma_scipion.py`, `scripts/scipion_compat/`; incompatibility in the caption).
- **Spike random-latent floor and map regeneration:** `sbatch scripts/submit_spike_extra.sh` (51541332) →
  `score_ext_random_latent.json` and `score_ext_lowrank_v0_maps.json`, which must equal 0.4258.

### Extended Data Table 2 — State recovery / displacement (`tab:motioncmp`)
| row | source file(s) |
|---|---|
| RECOVAR | `results/recovar_<tag>/cds_metrics.json` from `eval_recovar.py --recovar_out results/recovar_<tag> --labels ... --poses ... --label_period 100|0 --zdims ...` (48315826, 48366426) |
| cryoDRGN | `results/cryodrgn_<DSET>/latent_metrics.json` (`submit_eval_cryodrgn.sh`) |
| CryoSTAR | `baselines/cryostar/projects/star/work_dirs/<DSET>_<s1>_tpl_0/latent_metrics.json` (producer **[untraced]**); displacement `results/baseline_disp_cryostar_{igg1d,iggrl}.json` (50984766) |
| cryoSPHERE | `baselines/runs/cryosphere_*/latent_metrics.json`; displacement `results/baseline_disp_cryosphere_*.json` (50984767) |
| DynaMight, e2gmm | `baselines/runs/{dynamight,e2gmm}_<DSET>/latent_metrics.json` |
| CDS no structure | `results/igg1d_v0c_hinge/metrics.json` (`gt_knn_label_err`), `iggrl_v0c_rigid/metrics.json` (`gt_knn_purity`), `ribo_v0c` metrics |
| CDS GT start | `results/igg1d_hinge_restx0/metrics.json`, `iggrl_rigid_restx0/metrics.json` (`motion_disp_rmsd_A`) |

Displacement command, per method and dataset:
`<env>/python scripts/baseline_displacement.py --method cryostar|cryosphere --work_dir <run dir> --stage1 <s1>_tpl --gt_pdbs data/<DSET>/pdbs --labels data/<DSET>/gt_latents.pkl --out results/baseline_disp_<method>_<s1>.json`.

### Extended Data Table 3 — Starting structure (`tab:structure`)

Metrics come from `results/<run>/metrics.json`; AU-FSC from `results/official/{igg1d,iggrl}/score_ext_<NAME>.json`.

| row | IgG-1D run → eval NAME | IgG-RL run → eval NAME |
|---|---|---|
| no structure, consensus beads | `igg1d_ns_v1`→`igg1d_ns_hinge` (from `igg1d_v0`) → `hinge_ns` | `iggrl_v0`→`iggrl_ns_v1`→`iggrl_ns_rigid` (48830518/21/25) → `rigid_ns_consensus` |
| no structure, cluster-map beads | `igg1d_v0c_hinge` → `hinge_v0c` | `iggrl_v0c_rigid` → `rigid_v0c` |
| AF3 whole | `igg1d_af3`→`_v1`→`igg1d_af3_hinge` → `hinge_af3` (50059423) | `VARIANT=whole sbatch scripts/submit_iggrl_af3.sh` (50977590) → `rigid_af3` |
| AF3 PAE domains | `igg1d_af3hier`→`_hinge` (`--init_pae ... --pae_hierarchical`, see log) → `hinge_af3hier` (50059425) | `VARIANT=hier ...` (50977591, timed out in round 4), resumed by `run_rigid.py ... --run iggrl_af3hier_rigid --resume_from iggrl_af3hier_rigid --extra_rounds 1` (51092974) → `rigid_af3hier` |
| PDB 1N0X+3AVE | `igg1d_tpl` (`--init_model data/structures/igg_templates.pdb --init_domains ..._domains.npy`) → `igg1d_tpl_hinge` → `hinge_tpl` | `iggrl_tpl`→`iggrl_tpl_rigid_dinit` → `rigid_tpl` |
| oracle, rest re-estimated | `igg1d_hinge_restx0` (`--rest x0`, from `igg1d_ca`) → `hinge_nostruct` | `iggrl_rigid_restx0` → `rigid_nostruct` |
| oracle (1HZH) | `igg1d_hinge` (`submit_hinge_igg1d.sh --n_axes 100`, 48274787) → `hinge_pdb` | `iggrl_rigid_em4_flip` → `rigid_pdb` |

Notes:
- The `igg1d_ca`/`iggrl_ca` stage-1 runs used
  `submit_igg1d.sh --init_pdb data/<DSET>/pdbs/000.pdb --gt_pdbs data/<DSET>/pdbs` (48262209, 48286174).
- AF3 inputs: `submit_af3_igg.sh` (48370160) and `submit_af3_iggrl.sh` (48831257) → `data/structures/af3*/`.

### Extended Data Table 4 — Ablations (`tab:ablation`)

(a) Oracle rest shape; values from `results/<run>/metrics.json`:

| dataset | run per row |
|---|---|
| IgG-1D | `igg1d_hinge`; `igg1d_ca`; `igg1d_v1`; `igg1d_hinge_v12a` (pivot fixed at the interface; mapping **[inferred]** from metrics); `igg1d_hinge_soft` |
| IgG-RL | `iggrl_rigid_em4_flip`; `iggrl_rigid_attempt1` (centroid); `iggrl_rigid_grid`; `iggrl_rigid` (keep 8); `iggrl_rigid_c2f32` (keep 32); `iggrl_rigid_flip` (naive); `iggrl_rigid_flip2` (aligned); `iggrl_rigid_c2f32_em2`; `iggrl_rigid_c2f32_fast_prior`; `iggrl_rigid_c2f32_fast` |
| Ribosembly | `ribo_v0`; `ribo_comp`; `ribo_icm16` |

Exact args for each run are in `logs/<run>.log`.

(b) IgG-RL, official setting.
- Six rigid runs `abl_{no_flip,grid_only,centroid_pivot,no_merge,em1,with_prior}`, each followed by its AU-FSC eval
  (`logs/queue_ablation.log`). Each is
  `RUN=abl_<x> STAGE1=iggrl_v0c V1=iggrl_v0c_v1 sbatch scripts/submit_rigid.sh --rest x0 --fast_coarse --c2f 0:25:32,14:12:8,7:8:2,3.5:6:1 --em_rounds 4 --flip_pool 50 <change>`,
  where `<change>` is one of:
  - `--flip_pool 0` (48987090)
  - `--search grid` (48988015)
  - `--pivot centroid` (48992586)
  - `--merge v1` (48998119)
  - `--em_rounds 1` (48998125)
  - `--pop_prior 50` (49011449)
- Evals: `NAME=abl_<x> LATENT=results/abl_<x>_test_results.h5:features_pca`.
- "consensus beads" = `rigid_ns_consensus`. "low-rank only" = `score_ext_lowrank_ns.json`, latent
  `results/iggrl_v0_test_results.h5:z_pca` (IgG-1D: `results/igg1d_v0_test_results.h5:z_pca`; from latent_sources.json).

### Extended Data Table 5 — Hierarchical selection (`tab:hier`)

(a) Level 1:
- Synthetic variants:
  `make_synth_hier.py --out data/<v> --N 50000 [--frac_b 0 | --snr 0.01 | --snr 0.005 | --frac_b 0.1 | --b_shape globule]`
  (`--N 20000` for `synth_n20k`). Then `run_pipeline.py ... --run <v>_v0 --n_beads 1000 --K 8 --epochs_homo 3 --epochs 40 --batch 256 --n_clusters 10`
  and `level1_relate.py --stage1 <v>_v0 ... --species_gt data/<v>/gt.npz`. Jobs: 49424982, 49490302/324, 49543147–151.
- All variants plus CryoBench in one job: `sbatch scripts/run_level1_all.sh` (49858198) → `results/*_v0[c]/level1_relate.json`.
- Tomotwin discrete row:
  `level1_relate.py --stage1 tomo_v0 ... --posterior results/tomo_v0/L1_posterior_K100.npy --min_class 300` (49918976).
- Synthetic summary: `python scripts/table_hier.py --tag _mapnull` → `results/table_hier_mapnull.json`.

(b) Levels 2–3, files `results/<run>/hier_decide_mapnull.json`:
- **Synthetic:** `NAME=synth_hier DATA=data/synth_hier sbatch scripts/run_hier.sh` (49556003). Then map-null
  re-labels of `synth_hier_class{0,1}_rigid2` (49665450, 49665464). The `--wrap` commands are in Section 3, pattern
  `particle_explain.py ... --n 20000 --null map --tag _mapnull && hier_decide.py --tag _mapnull`.
- **CryoBench:** `submit_level23.sh` with `S1=<X>_v0c V1=<X>_v0c_v1 IN="<inputs>"`. Jobs: IgG-1D 49918979,
  IgG-RL 49806450, Spike 49842278, Ribosembly 49842245 (`RIGID_TO=230m RIGID_ARGS="--em_rounds 1 --sweeps 1"`).
- **Ribosembly second round:**
  `run_rigid.py --stage1 ribo_v0c --v1 ribo_v0c_v1 --run ribo_v0c_rigid2_r2 ... --resume_from ... --extra_rounds 1`
  (49857979; full args in `logs/ribo_v0c_rigid2_r2.log`), then `particle_explain` + `hier_decide` on `_r2` (49874342).

### Extended Data Table 6 — EMPIAR-10180 labels (`tab:real`)
- Labels and fractions: `results/e10180_rigid_gate/particle_explain_mapnull_full_d{1..4}.json`, and the decision in
  `hier_decide_mapnull_full.json` (Fig. 5 steps 4–5).
- RELION columns: `validate_labels_mapnull.json` (step 6).
- Body overlap: `scripts/diag/label_domains_by_masks.py`; its arguments are **[untraced]**.

### Extended Data Table 7 — Runtime (`tab:runtime`)
- CDS component times: `results/SUMMARY.md` § "Runtime", taken from sacct Elapsed of the jobs in Section 3.1.
- Baselines (sacct Elapsed):
  - RECOVAR 48256679 / 48312921 / 48344994; EMPIAR-10180 50977593 (18:09)
  - cryoDRGN 48832407 / 48860117 / 48865079; EMPIAR-10180 50084984 (45:43)
  - RELION multi-body 48885317

### Supplementary Table S1 — Per-query paired tests (`tab:supp_paired`)
- Per-query AU-FSC of directories that predate `score_ext` per-query storage:
  `python scripts/paired_test.py` (49096193, test partition) → `results/official/<tag>/pq_*.json` and `paired_tests.json`.
  It re-scores `extp_*/state###.mrc` and `drgn_own/vol_*.mrc`.
- Table: `python scripts/supp_table_paired.py --out results/official/supp_paired.tex` (51875037) →
  `supp_paired.{tex,json}`, pasted into `main.tex`. The reference is CDS: `hinge_v0c`, `rigid_v0c`, `L1_discrete`
  ×2, `lowrank_v0`.

## 5. Known caveats

1. **cryoSPHERE on Spike-MD was resumed.** The run (51104008) hit the 16 h limit at epoch ~181/300. It was continued
   with `NAME=Spike-MD-128_spike_seed sbatch --dependency=afterany:51104008 scripts/submit_cryosphere_resume.sh`
   (51239687). That job uses `scripts/cryosphere_resume.py`: same epoch counter and loss schedules, Adam moments
   re-initialised. Evaluation: `submit_cryosphere_eval.sh` with `EXTNAME=cryosphere_seed` (51239688).
   An uninterrupted 300-epoch run will differ slightly.
2. **Ribosembly used one EM round.** Its Level 2–3 run (`ribo_v0c_rigid2`) used `--em_rounds 1 --sweeps 1`; every
   other dataset used 2. A resumed second round is reported as a range in `tab:hier`. Per-particle rotations had not
   converged; the decision was unchanged.
3. **EMPIAR-10180 has three non-finite images** (indices 9955, 40867, 108052). CDS zeroes them in its loader. The
   baselines must exclude them with `--ind data/real/empiar10180_128/good_ind.pkl` (139,719 particles). Without
   this flag RECOVAR and cryoDRGN produce NaN (`results/*_e10180.failed_nan`). RECOVAR needs `--mem=160G`.
   EMPIAR-10076 has one corrupt image (7234), which is zeroed.
4. **Netscratch purge.** On 2026-10-08 the purge emptied `data/{IgG-1D,IgG-RL,Ribosembly,Tomotwin-100}`, the
   ChimeraX install and EMPIAR metadata, because extracted files keep old archive mtimes. Restore them with
   `redownload_cryobench.sh` (`unzip -DD`), `download_ranges.sh` (`curl -sf`; a 107-byte HTTP error body once
   corrupted a Tomotwin part) and `redownload_empiar_meta.sh`.
   - Not restored: the EMPIAR-10180 raw micrograph stacks. `data/real/empiar10180_128` is intact and on HF.
   - Deleted on purpose and regenerable: half/unfiltered generator maps, the zips, Spike 256-px images, and
     intermediate cryoSPHERE/DynaMight checkpoints. See `notes/DELETED_MANIFEST.md`.
   - Never delete anything in `notes/FIGURE_DATA.md`.
5. **ChimeraX offscreen** needs the `cds_render` env, which provides OSMesa (`mesalib<24.1`), and the symlinks in
   `.../software/osmesa_link`. Always call it through `scripts/chimerax_offscreen.sh`. Other requirements:
   - `.cxc` paths must be absolute.
   - Do not pipe ChimeraX output into `head`.
   - A map written from an (x, y, z) array is read by ChimeraX as (z, y, x).
6. **Spike-MD AU-FSC is evaluated here at 128 px.** The GT maps come from the query frames via CryoBench's ChimeraX
   recipe, so the values are not comparable to published Spike-MD numbers. The floor is the random latent at 0.380.
7. **Post-hoc thresholds** (disclosed in the paper):
   - The Level-1 link rule uses corr ≥ 0.7, stable over 0.60–0.80.
   - `hier_decide --mode_ratio 0.35` was chosen after an absolute threshold mislabelled Ribosembly.
   - The defaults in the scripts are the values used in the paper.
8. **No fixed seeds.** GPU fits are not bit-reproducible. Rerunning AU-FSC from the stored `_emb.pkl`/`_targets.txt`
   reproduces scores to about 1e-7 (Spike check: 0.425817 vs 0.425817).