DiSCo v2

README.md

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+---
+license: cc-by-nc-sa-4.0
+pretty_name: DiSCo time segments
+tags:
+  - diffusion-mri
+  - monte-carlo
+  - replay-pack
+  - tractography-phantom
+---
+
+# DiSCo time segments
+
+The DiSCo replay walk, contracted at DiSCo's own seven gradient timings so that a whole-grid image costs one pass
+over 125 bytes per walker. It comes from the same 321,138,027 walkers as the replay pack in
+[SubstrateCommons/disco-replay](https://huggingface.co/datasets/SubstrateCommons/disco-replay). It was contracted
+from the walk's unquantised coefficients beside each shard, not from the pack.
+
+**Choose the dataset by the question.** Use this one for an acquisition at one of the timings below, where every
+other knob stays free. Use the replay pack for any other waveform: another δ or Δ, OGSE, an arbitrary gradient.
+
+## What a timing class leaves free
+
+Each class fixes the times at which the gradient may switch; its segments are constant-gradient intervals. Within
+a class, everything else is chosen at replay time:
+
+- the b-value and direction of every measurement, up to the class's gradient amplitude;
+- the per-voxel delivered gradient (gradient nonlinearity, a background gradient);
+- T₂ and T₁ per pool, and surface relaxivity ρ₂ ≤ 3 µm/s;
+- B₀ of 1.5, 3 or 7 T in any direction, and myelin χ up to 2 × 10⁻⁷, isotropic and anisotropic.
+
+| class | timing (ms) | echo |
+|---|---|---|
+| `d7.6-D45.9` | δ 7.6, Δ 45.9 | spin echo, TE 53.5 |
+| `d8-D20` | δ 8, Δ 20 | spin echo, TE 53.5 |
+| `d10.2-D16.7` | δ 10.2, Δ 16.7 | spin echo, TE 53.5 |
+| `d12-D24` | δ 12, Δ 24 | spin echo, TE 53.5 |
+| `d17-D30` | δ 17, Δ 30 | spin echo, TE 53.5 |
+| `d17.7-D35.8` | δ 17.7, Δ 35.8 | spin echo, TE 53.5 |
+| `ste-d7.6-TM38.3` | δ 7.6, mixing time 38.3 | stimulated echo |
+
+Each class's widths are derived at the 1.003 % floor. The layout's own error bound is in `disco/manifest.json` under
+`certificate`, term by term. Against the replay pack, 16 voxels in all seven classes agree to 2.2 × 10⁻⁵, against
+a combined bound of 3.0 × 10⁻³.
+
+## Use me
+
+```
+pip install "git+https://github.com/dmrai-lab/dmipy-sim@main" "huggingface_hub>=0.25"
+```
+
+```python
+import numpy as np
+from dmipy_sim import sequences
+from dmipy_sim.replay.time_segments import TimeSegmentLayout
+from dmipy_sim.spec.tissue import Tissue
+
+layout = TimeSegmentLayout.open("hf://SubstrateCommons/disco-time-segments/disco",
+                                 partitions=["d10.2-D16.7"])   # fetches this class's words and the shared columns
+seq = sequences.pgse([[1, 0, 0], [0, 0, 1]], 0.0102, 0.0167, bvalues=[1e9, 3.094e9], TE=0.0535,
+                     slew_rate=np.inf)                       # square lobes: constant on the class's segments
+S, floor = layout.image(seq)                                 # (40, 40, 40, 2) S/S0 and the per-voxel floor
+wm = Tissue(T2={"intra": 0.05, "extra": 0.055, "myelin": 0.01}, rho2=1e-6, chi_iso=-1e-7, chi_aniso=-1e-7)
+S_3T, _ = layout.image(seq, tissue=wm, scanner=3.0)          # relaxation, wall contact and the sheath field at 3 T
+```
+
+`layout.fit(seq)` names the class a sequence fits, or says why it fits none. `partitions=None` fetches every
+class (40 GB). A whole-grid image keeps the class's
+columns in memory, about 9 GB for bare diffusion and more with the tiers; `resident=False` frees them after the
+call.
+
+## Files
+
+| path | what |
+|---|---|
+| `disco/manifest.json` | grid, classes and their segments, widths, certificate, provenance |
+| `disco/w.npy`, `disco/tiles.npy`, `disco/pool.npy` | each row's weight, its voxel half, and its pool |
+| `disco/p_<class>.npy` | each class's segment vectors, packed |
+| `disco/t_g0.npy`, `disco/t_g1.npy` | the relaxation, contact and field tiers of the spin-echo and stimulated-echo groups |
+| `disco/bg_g0.npy`, `disco/bg_g1.npy` | the background-gradient term of each group |
+
+The rows are in tiles of 128, sorted by voxel. Each voxel's rows are split into two halves, which give the
+split-half floor.
+
+## Licence
+
+**CC BY-NC-SA 4.0**, or a commercial licence on request. The DiSCo substrate is the DiSCo authors' work, CC BY 4.0.
+
+## Citation
+
+- Rafael-Patino J, Girard G, Truffet R, Pizzolato M, Caruyer E, Thiran J-P. The diffusion-simulated connectivity
+  (DiSCo) dataset. Data in Brief 38 (2021) 107429.
+- Fick RHJ. Replayable Monte Carlo MRI: from persistent tissue microphysics to queryable virtual phantoms (in
+  preparation, 2026). dmipy-sim, https://github.com/dmrai-lab/dmipy-sim.

disco/bg_g0.npy

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