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Tutorial data (not a release of htfa)

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@jeremymanning jeremymanning released this 26 Oct 11:16
· 87 commits to main since this release
tutorial-data-v1
9653702

This pre-release only holds data used by the documentation tutorials. It is not a release of the htfa package.

htfa_mitchell2008_K100.npz

This file is the HTFA fit used by tutorial 09 (docs/tutorials/09_word_decoding_mitchell2008.ipynb), fitted to the nine participants of the Mitchell et al. (2008) dataset. The tutorial downloads it by default; setting REFIT = True in the notebook fits it again from scratch.

  • SHA-256: 635d821194ee1d4a37932a1f4a1ed03592f18838a6453200b1bda0570fa895c4
  • Size: 2,518,508 bytes. It is a NumPy .npz archive with no pickled objects; it loads with np.load(..., allow_pickle=False).
  • Contents:
    • weights (9, 100, 360), centers (9, 100, 3) and widths (9, 100): per participant.
    • global_centers (100, 3) and global_widths (100,): the template.
    • n_iter (10) and converged (False).
    • n_voxels: per participant.
    • settings, htfa_commit, htfa_version and fit_seconds.
  • How it was produced:
    • Call: htfa.fit(Xs, coords=Rs, K=100, max_voxels=10000, init="ncorr", backend="numpy", n_jobs=3, random_state=0).
    • Code: htfa 0.1.0 at commit 9653702.
    • Inputs: the 360 trial images of each data-science-P1.mat to data-science-P9.mat file from the dataset page, with voxel coordinates in MNI mm from the dataset's get_mni_coordinates.m. The tutorial does this loading.
    • Machine: a 12-CPU Apple M2 Max, 1008 s.
    • The fit stopped at 10 global iterations without converging. It is identical to the fit that the tutorial previously computed and cached locally.

htfa_development_fmri_12adults_K50.npz

The HTFA fit used by tutorial 10 (docs/tutorials/10_dynamic_isfc_animation.ipynb). The tutorial downloads it by default; REFIT = True in the notebook fits it again from scratch.

  • SHA-256: 647dc7c2da0eec69b16df12f7c3fcc68a2f33637056dc21532b77da9b3c0cc13
  • Size: 796,190 bytes. NumPy .npz archive without pickled objects (np.load(..., allow_pickle=False)).
  • Contents: weights (12, 50, 168), centers (12, 50, 3), widths (12, 50), global_centers (50, 3), global_widths (50,), n_iter (10), converged (False), subject_ids, n_voxels, settings, data, warnings, htfa_commit, htfa_version, fit_seconds, machine.
  • Data: htfa.datasets.fetch_development_fmri(n_subjects=12), the adults sub-pixar123 to sub-pixar134 of nilearn's development_fmri dataset, masked and cleaned by that function.
  • Produced with htfa.HTFA(K=50, max_voxels=3000, max_global_iter=10, init="ncorr", random_state=0, n_jobs=12).fit(ds.data, ds.coords), htfa 0.1.0 at commit f6691a8, in 103 s on a 12-CPU Apple M2 Max. It stopped at 10 global iterations without converging (one ConvergenceWarning).

htfa_development_fmri_6adults_K30.npz

The HTFA fit used by tutorial 11 (docs/tutorials/11_plotting_and_appearance.ipynb). The tutorial downloads it by default; REFIT = True in the notebook fits it again from scratch.

  • SHA-256: 48d0cdda811a515fad809b2b667f29d470cf5c2c19c53a9c7bc29495ee4ba829
  • Size: 242,010 bytes. NumPy .npz archive without pickled objects (np.load(..., allow_pickle=False)).
  • Contents: weights (6, 30, 168), centers (6, 30, 3), widths (6, 30), global_centers (30, 3), global_widths (30,), n_iter (6), converged (False), subject_ids, n_voxels, settings, data, warnings, htfa_commit, htfa_version, fit_seconds, machine.
  • Data: htfa.datasets.fetch_development_fmri(n_subjects=6), the adults sub-pixar123 to sub-pixar128.
  • Produced with htfa.fit(ds.data, coords=ds.coords, K=30, max_global_iter=6, max_local_iter=5, max_voxels=5000, max_trs=168, init="ncorr", random_state=0, n_jobs=12), htfa 0.1.0 at commit f6691a8, in 43 s on a 12-CPU Apple M2 Max. It stopped at 6 global iterations without converging (one ConvergenceWarning).

The fits for tutorials 10 and 11 may be regenerated when htfa's fitting code changes; each file records the commit that made it.

htfa_mitchell2008_embeddings_Qwen3-Embedding-0.6B.npz

These are the word embeddings of the 60 nouns used by tutorial 09. The tutorial loads them by default; with RECOMPUTE_EMBEDDINGS = True it computes them again.

  • SHA-256: 0f61b70541d0e5c3cb428fb9b81f1cb98df2a835bb76cbd7c76d9f13ce6b2e96
  • Size: 117,751 bytes. It is a NumPy .npz archive with no pickled objects (np.load(..., allow_pickle=False)).
  • Contents:
    • E (60, 1024) float32 and vocab (60,), in the tutorial's vocabulary order.
    • embedding_model, embedding_revision, method and machine.
  • How they were computed: hypertools.tools.format_data(list(vocab), vectorizer="Qwen/Qwen3-Embedding-0.6B", semantic=None, corpus=None, ppca=False)[0], with the model at revision 97b0c614be4d77ee51c0cef4e5f07c00f9eb65b3 (the revision the local Hugging Face cache resolved). This ran on an Apple M2 Max (macOS arm64) on 2026-10-25, through the tutorial's own embedding cache.

htfa_mitchell2008_null_20.npz and htfa_mitchell2008_null_100.npz

These are tutorial 09's permutation nulls for 20 and 100 permutations. The tutorial loads the one matching N_PERMUTATIONS by default; with RECOMPUTE_NULL = True it computes the null again.

  • SHA-256:
    • htfa_mitchell2008_null_20.npz: 69a937cefa24806ac16b842564285434e43869485ce5cf5d0f24e4d90f8e7b05 (4,605 bytes)
    • htfa_mitchell2008_null_100.npz: c756356412a1f4b85eaa6d4c93b00901e41dc0b5b415f5fefd95c4d2c526f337 (7,807 bytes)
  • Format: NumPy .npz archives with no pickled objects.
  • Contents:
    • null (N, 7): the 9-participant means of the 7 statistics for each permutation.
    • statistics: the names of the 7 statistics.
    • n_permutations and seeds (1000 to 1000 + N - 1).
    • seconds and observed (the unpermuted statistics).
    • fit and fit_sha256: htfa_mitchell2008_K100.npz above.
    • embeddings and embeddings_sha256_of_E: the SHA-256 of the bytes of E in the embeddings file above.
    • embedding_model, embedding_revision, htfa_commit, htfa_version and machine.
  • How they were computed: by the tutorial notebook at htfa commit c0a17d8, on a 12-CPU Apple M2 Max. Inputs were the saved fit and the saved embeddings above. It took 1003 s for 20 permutations and 2706 s for 100.
  • Checks: each null is identical to the corresponding null computed earlier with the same inputs. The first 20 rows of the 100-permutation null equal the 20-permutation null.
Assets 16
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