Changelog

All notable changes to nltools are documented here.

Unreleased¶

Features¶

• Feature Implement efficient copying for method chaining (~80% performance improvement)

• Feature Add backend abstraction for CPU/GPU operations

• Feature Add ridge regression algorithms with SVD decomposition

• Feature Add cluster thresholding to Brain_Data.threshold() method

• Feature Complete ridge regression test suite (Cycles 2.2 & 2.3)

• Feature Migrate apply_mask to nilearn for better performance

• Feature Implement BaseModel and Ridge model classes with GPU support

• Feature Extract HyperAlignment class from align() function

• Feature Add Glm model class wrapping nilearn FirstLevelModel

• Feature Add CLI-based benchmarking with dry-run and progress tracking

• Feature Add sklearn-style fit/predict API and deprecate regress()

• Feature Add cross-validation support to Brain_Data.fit()

• Feature Polars migration TDD scaffolding for Design_Matrix

• Feature Implement DesignMatrix Phase 1 - Construction and basic operations

• Feature Implement DesignMatrix Phase 2 - Statistical operations

• Feature Implement DesignMatrix Phase 3 - HRF convolution

• Feature Implement DesignMatrix Phase 4 & Phase 5a/5b - Polynomials and basic append

• Feature Implement DesignMatrix Phase 5c - Polynomial separation (multi-run support)

• Feature Implement DesignMatrix Phase 6 - Diagnostics (VIF and clean)

• Feature Complete DesignMatrix Polars migration - Phase 7 (Utilities)

• Feature feat(polars): Complete DesignMatrix Polars migration with GLM integration

• Feature Implement 2-tier testing strategy with 16× speedup

• Feature Complete file_reader integration with DesignMatrix methods

• Feature Complete Polars DesignMatrix integration - fix Adjacency.regress()

• Feature gpu acceleration one-sample test

• Feature GPU-accelerated inference module with clean architecture

• Feature Add GPU-accelerated correlation permutation test module

• Feature Add Spearman and Kendall correlation metrics to correlation module

• Feature Add matrix permutation test (Mantel test) module

• Feature Add GPU-accelerated Intersubject Correlation (ISC) module

• Feature add three user-requested enhancements for v0.6.0

• Feature Add BrainCollection class for multi-subject data

• Feature Add group inference and transformation methods

• Feature Add searchlight neighborhood caching infrastructure

• Feature Add ISC computation methods

• Feature Add isc_test() for permutation testing

• Feature Add GLM/Ridge workflow helper functions

• Feature Add cortical flatmap visualization

• Feature Add BrainCollection.fit_glm() for group-level GLM

• Feature Add BrainCollection.fit_ridge() for group encoding models

• Feature Add compute_contrasts() and select_feature() methods

• Feature Change fit_ridge() default output to CV scores

• Feature Unified predict() API for timeseries and MVPA decoding

• Feature Add tests for map(axis=1), isc_test, and ISC ROI extraction

• Feature Add unified BrainCollection.fit() API matching BrainData

• Feature Add Phase 2 tests and docs for BrainCollection.fit() API

• Feature Add workflow integration tests (MVPA, Group Inference, ISC)

• Feature Add MNI-aligned VTC masks and update ISC tests

• Feature Add RSA workflow tests

• Feature Add SRM workflow validation tests

• Feature Add cross-subject pooled decoding test for SRM workflow

• Feature Add pipeline infrastructure for fluent CV and Pool API (Phases 1-6)

• Feature Add alignment pipeline step for SRM/HyperAlignment (Phase 7)

• Feature Add Phase 8 terminals and advanced CV (ISC, RSA, Permutation, Nested CV)

• Feature Refactor BrainData.predict() to use Pipeline infrastructure (Phase 9)

• Feature Add LocalAlignment stub for Phase 1 (nltools-oqil)

• Feature Implement LocalAlignment Phase 1 with searchlight alignment (nltools-oqil)

• Feature Add piecewise scheme support to LocalAlignment (nltools-oqil.4)

• Feature Add generator-based batching to LocalAlignment (nltools-pc2i)

• Feature Add CPU parallelization to LocalAlignment (Phase 3)

• Feature Add GPU/Backend integration to LocalAlignment (Phase 4)

• Feature Add BrainCollection.align() for functional alignment (Phase 5)

• Feature v0.6.0 prep - LocalAlignment complete, GH issue reconciliation

• Feature Add explicit tail options for MCP-compatible p-values (GH #315)

• Feature Complete P1 API improvements for v0.6.0

• Feature Complete P2 API improvements for v0.6.0

• Feature Add type annotations and pipe() tests for v0.6.0 P2 items

• Feature Support unequal sample counts in SRM and LocalAlignment (GH #410)

• Feature add verbose parameter to standardize() to suppress sklearn warnings

• Feature rewrite GLM tutorial and auto-detect duplicate intercept in add_poly

• Feature restore BrainData.ttest / add ttest2

• ⚠ Breaking Feature replace parallel kwarg with device on user-facing methods

• ⚠ Breaking Feature standardize progress flag to progress_bar; default False

• ⚠ Breaking Feature standardize algorithm-choice kwarg to method

• ⚠ Breaking Feature rename plot thr_upper/thr_lower to upper/lower

• ⚠ Breaking Feature standardize permutation count kwarg to n_permute

• ⚠ Breaking Feature flip similarity diagonal flag to include_diag

• ⚠ Breaking Feature rename user-facing radius kwargs to radius_mm

• Feature restore legacy h5 read support for nltools <= 0.5.1 files

• Feature add load_haxby_example offline demo dataset

• Feature append(axis=1) accepts pandas/polars DataFrames

• Feature construct from a numpy array + explicit mask

• ⚠ Breaking Feature ttest returns {mean, t, z, p} uniformly

• Feature compute_contrasts(contrast_type=“all”) + glm_ docs

• ⚠ Breaking Feature load from BIDS file paths + rename polys→confounds

• ⚠ Breaking Feature lazy-fetch niftis from HF dataset

• ⚠ Breaking Feature default model=‘glm’ in fit(); tighten validation

• ⚠ Breaking Feature add design_clean kwargs to fit() for GLM validation

• Feature pyodide async seed + sync fetch

• ⚠ Breaking Feature add limit kwarg + change slices default view to “z”

• Feature IDBFS-backed persistent cache for pyodide

• ⚠ Breaking Feature gate MNI-only plot paths on standard-space data

• ⚠ Breaking Feature per-voxel α + held-out predictions through BrainData CV path

• ⚠ Breaking Feature copy-constructor + read-only convolved/confounds

• ⚠ Breaking Feature HRF-convolve events files by default

• Feature with_columns + pl.Expr in setitem

• ⚠ Breaking Feature find_spikes returns DesignMatrix

• Feature list_resources() for HF dataset discoverability

• ⚠ Breaking Feature rebuild iplot() as anywidget viewer with 4D step-through

• ⚠ Breaking Feature iplot threshold panel + drag-end render fix

• ⚠ Breaking Feature rewrite BrainData.predict() with kwargs API + Predict dataclass

• ⚠ Breaking Feature brain-space Predict fields + ROI per-fold scores + decoding tutorial

• ⚠ Breaking Feature ROI dispatch produces voxel-space weight maps

• ⚠ Breaking Feature spatial_scale axis for ROI/searchlight RSA + predict rename

• Feature implement distance(searchlight) and align(roi)

• Feature cluster reports with anatomical labeling

• ⚠ Breaking Feature rebuild BrainData.iplot() on ipyniivue (niivue)

• Feature add marimo->ipynb converter for JupyterLite tutorials

• Feature add poe docs-jupyterlite task to build the tutorial JupyterLite bundle

• Feature make GLM + MVPA tutorials run in JupyterLite on trimmed HF data

• Feature add “Try it live” JupyterLite nav link + deploy-ready build

• ⚠ Breaking Feature iplot() colorbar + interactive threshold slider

• ⚠ Breaking Feature host pain dataset on HF so fetch_pain() works in Pyodide

• ⚠ Breaking Feature host emotion-rating dataset on HF for fetch_emotion_ratings()

• Feature serve tutorials as interactive marimo/WASM notebooks

• Feature land core helpers, init, indexing, parallel _apply

• Feature implement load/unload/write/read/cleanup + memory_estimate

• Feature GLM fit + HDF5 bundles + compute_contrasts

• Feature reductions, perm tests, ISC, align, from_bids/from_glob, predict dispatch

• Feature ridge fit bundles + bc.predict(X_new=) per-subject path

• Feature stage encoding + ISC datasets for nltools/niftis

• Feature wire max_gpu_memory_gb into ridge_svd GPU batching (F022)

• ⚠ Breaking Feature user-controlled scale/standardize preprocessing

• Feature GLM predict(X)/coef_ parity + nilearn report (F182)

• ⚠ Breaking Feature append(axis=1) refuses bitwise-duplicate columns

• Feature make the rank-deficiency warning diagnostic and actionable

• ⚠ Breaking Feature machine-enforce the canonical kwarg vocabulary from api-vocabulary.yml

• ⚠ Breaking Feature one canonical tail= vocabulary across every p-value (#474)

• ⚠ Breaking Feature reserve the .nl_ namespace for generated columns

• ⚠ Breaking Feature one core GPU execution layer — measured budgets, OOM recovery, run-or-raise

• ⚠ Breaking Feature carve out predict_group(); remove the legacy cv() pipeline

• ⚠ Breaking Feature iplot robust autoscaling + shared zero-aware percentile thresholds

• ⚠ Breaking Feature sklearn-style cv names — ‘logo’/‘loo’ replace ‘loso’/‘loro’

• ⚠ Breaking Feature predict() decodes against the stored .Y slot — labels travel with the data

• Feature PredictCollection — per-subject decoding results container

• ⚠ Breaking Feature predict(y=) maps per-subject decoding — closes the #478 map-reduce gap

• ⚠ Breaking Feature predict_group permutation null for roi and searchlight

• Feature warn on near-collinear full-rank designs

Improvements¶

• Improvement refactor and improve brain data dunder math. improve first tutorial

• Improvement refactor onsets_to_dm to wrap new nilearn functionality instead

• Improvement Convert .shape(), .isempty(), .dtype() to properties + quick fixes

• Improvement Convert shape() and isempty() to properties

• Improvement Reorganize Brain_Data tests into class-based structure

• Improvement Reorganize Adjacency tests into class-based structure

• Improvement Reorganize Design_Matrix tests into class-based structure

• Improvement Organize test_stats.py with section headers and docstrings

• Improvement Organize test suite into subdirectories following architectural patterns

• Improvement Integrate Glm model into Brain_Data.regress()

• Improvement Optimize DesignMatrix with idiomatic Polars patterns

• Improvement Optimize DesignMatrix with selectors and enhanced errors

• Improvement Consolidate design_matrix files and remove old implementation

• Improvement Remove dead Design_Matrix_Series code

• Improvement Standardize on DesignMatrix naming throughout codebase

• Improvement Complete Polars optimization with native resampling

• Improvement refactor inference tests and add two sample statistica correcteness tests

• Improvement improve isc group

• Improvement refactor tests

• Improvement refactor tests

• Improvement refactoring tests

• Improvement improve resampling

• Improvement move Adjacency to subdirectory structure

• Improvement Complete P0 deprecations for v0.6.0 release

• Improvement split brain_data.py into braindata/ subpackage with facade pattern

• Improvement split design_matrix.py into design_matrix/ subpackage

• Improvement rename design_matrix/ to designmatrix/

• Improvement split adjacency/init.py into submodules

• Improvement split collection.py into collection/ subpackage

• Improvement remove leading underscores from module filenames

• Improvement restructure data/ to use only subpackage dirs

• Improvement clean BrainData facade — no private methods, alphabetical ordering

• Improvement clean Adjacency facade — no private methods, alphabetical ordering

• Improvement restructure BrainData tests into braindata/ subdir

• Improvement clean DesignMatrix facade — no private methods, alphabetical ordering

• Improvement restructure Adjacency tests into adjacency/ subdir

• Improvement restructure DesignMatrix tests into designmatrix/ subdir

• Improvement rename tests/shell → tests/data, tests/data → tests/fixtures

• Improvement restructure model tests into models/ subdir

• Improvement move models tests from tests/core/ to tests/models/

• Improvement move Simulator and Roc into nltools/data/ packages

• Improvement move neighborhoods and cache into data/braindata/

• Improvement move check_brain_data helpers into braindata/utils.py

• Improvement consolidate template resolution into MNI_Template_Factory

• Improvement create nltools/io/ package, move HDF5 and file_reader into it

• Improvement add tests/io_tests/ with h5 and file_reader test classes

• Improvement move BrainData Args to class docstring, drop params-move logic

• Improvement reorganize nltools/stats.py into focused subpackage

• Improvement move stats tests to tests/stats/ for flatter hierarchy

• Improvement add stats/permutation.py facade and update imports

• Improvement reorganize nltools/plotting.py into focused subpackage

• Improvement consolidate alignment algorithms and move validation into inference

• Improvement consolidate ridge/backends into top-level Backend class

• Improvement remove deprecated neurovault download shims

• Improvement replace prefs.MNI_Template with templates/ module

• Improvement lazy-import pandas across nltools modules

• Improvement rewrite plotting/adjacency label-distance and silhouette as polars-native

• Improvement polars-first stats.outliers.zscore and stats.intersubject input boundaries

• Improvement sweep pandas out of scattered small modules

• Improvement relocate backends to algorithms/ and thread resolve_backend

• Improvement validate_frame returns polars, accepts pandas/numpy/csv

• Improvement BrainData.X/.Y are polars DataFrames, never None

• Improvement drop pandas from braindata/analysis.py

• Improvement drop vestigial pandas branch in parse_contrast_string

• Improvement rewrite BrainData h5 persistence on h5py + polars

• Improvement Simulator.create_data builds polars frames and writes via pl

• Improvement Adjacency.Y invariant is polars via property setter

• Improvement drop pandas from Adjacency CSV read/write

• Improvement rewrite Adjacency h5 persistence on h5py + polars

• Improvement numpy-native Adjacency label-distance and cluster helpers

• Improvement BrainCollection.metadata invariant is polars

• Improvement build BrainCollection result metadata as polars

• Improvement rename plotting functions to plot_* convention

• Improvement lean on nilearn for HRFs and resample plumbing

• Improvement drop BrainData.nifti_masker, use functional apply_mask/unmask

• Improvement drop Simulator.nifti_masker, use functional apply_mask/unmask

• Improvement drop pandas-compat shims from stats and DesignMatrix

• Improvement drop legacy PyTables h5 support and plotting underscore re-exports

• Improvement drop glover_hrf lambda wrapper in onsets_to_dm

• ⚠ Breaking Improvement canonical trailing-kwarg order across facades

• ⚠ Breaking Improvement replace **kwargs passthroughs with explicit signatures (1/2)

• ⚠ Breaking Improvement replace **kwargs passthroughs with explicit signatures (2/2)

• ⚠ Breaking Improvement keyword-only marker for init and complex methods

• Improvement route progress_bar to nilearn verbose

• Improvement simplify extract_roi PCA, stop mutating caller headers

• Improvement broaden check_brain_data to accept Niimg-like inputs

• Improvement use np.trunc instead of deprecated np.fix

• Improvement expand ruff with UP/C4/PIE/RUF022/RET/SIM families

• Improvement improve first tutorial and make plotting fixes

• ⚠ Breaking Improvement polars passthrough, unify data attr, drop details()

• ⚠ Breaking Improvement drop fetch_haxby; migrate GLM tutorial to localizer

• Improvement rename fetch_nifti → fetch_resource

• Improvement extract indexing helpers to indexing.py

• Improvement extract aggregation helpers to aggregation.py

• Improvement extract conversions to conversions.py

• Improvement move memory_estimate/load/unload to io.py

• Improvement consolidate stateless helpers in core.py

• Improvement drop unused TypeVar import

• ⚠ Breaking Improvement scaffold v0.6.0 BrainCollection redesign

• ⚠ Breaking Improvement tighten decompose signature

• ⚠ Breaking Improvement drop final_weight_map, weight_map = all-data refit

• ⚠ Breaking Improvement generalize .plot() into method= dispatcher; add .corr()

• ⚠ Breaking Improvement rename BrainCollectionPipeline.normalize → standardize

• ⚠ Breaking Improvement BrainCollectionPipeline.predict returns BrainData

• ⚠ Breaking Improvement align bc.predict with new BD.predict + GLM tutorial

• Improvement memoize fetch_resource, skip network revalidation

• Improvement add coalesced_gc() and wrap masking-heavy operations

• Improvement validate GLM-fit result mask once per map-list

• ⚠ Breaking Improvement remove superseded standalone Pipeline/MultiSubject orchestration

• Improvement extract API-doc postprocess into its own module

• ⚠ Breaking Improvement remove dead standalone pipeline surface

• ⚠ Breaking Improvement strip ICC functionality entirely (F012/F048/F140/F177/F194)

• ⚠ Breaking Improvement canonical-kwarg sweep — banned-kwarg renames + **kwargs hygiene

• ⚠ Breaking Improvement add keyword-only * marker to 61 public functions (F-kwonly)

• ⚠ Breaking Improvement rename compute_contrasts statistic selector method→statistic

• ⚠ Breaking Improvement canonicalize remaining kwarg names across facades

• ⚠ Breaking Improvement close AUDIT-0.6.0 dead-code bucket (23 findings)

• ⚠ Breaking Improvement remove orphaned pool.py two-stage aggregation (F118/F111)

• Improvement functional GLM map extraction, no per-contrast round-trips

• Improvement functional compute_contrasts, no Nifti round-trip

• Improvement annotate models layer + fix Any/hint gaps (F107/F009/F017/F046/F085)

• Improvement single-source helpers + frozen dataclass cleanups (F006/F061/F154/F183/F192)

• ⚠ Breaking Improvement co-locate pipeline primitives under data/collection/pipesteps

• ⚠ Breaking Improvement canonicalize spatial_scale vocab in LocalAlignment

• Improvement remove 7 dead validation/utility functions

• Improvement rewrite as a speed+memory harness over the current API

• ⚠ Breaking Improvement canonicalize device selection to device= across facades

• Improvement run pairwise ISC bootstrap on the GPU

• ⚠ Breaking Improvement rebuild BrainData.iplot() on a self-owned niivue anywidget

• Improvement make find_spikes deduplication unconditional; drop clean=

• ⚠ Breaking Improvement stop cleaning the design in fit(); warn on rank deficiency

• Improvement make maybe_tqdm/make_progress_bar the single library-wide progress mechanism

• ⚠ Breaking Improvement enforce the keyword-only marker convention uniformly across the package

• ⚠ Breaking Improvement remove nltools.stats; consolidate the functional core into nltools.algorithms

• ⚠ Breaking Improvement canonicalize the ISC vocabulary (summary=/metric=/null_dist)

• ⚠ Breaking Improvement canonicalize cluster_summary and extract_roi kwargs

• Improvement isc_test p-values via the shared _compute_pvalue helper

• Improvement thread tail through the bootstrap engines; drop the facade closures

• Improvement one GPU bootstrap driver, two thin wrappers

• Improvement stop materializing np.abs(arr) three times per iplot window

• Improvement one shared manifest module for the lint-api trio

Bug Fixes¶

• Bug Fix fix formatting

• Bug Fix fix warnings 1

• Bug Fix fix warnings 2

• Bug Fix fix us few more things

• Bug Fix fix neurovault downloaders and add tests

• Bug Fix fix tutorial and log

• Bug Fix fix nilearn resampling messages

• Bug Fix fix Brain_Data eq. Update tests

• Bug Fix fixup collection and tutorial v1

• Bug Fix fix up collection issue

• Bug Fix Handle model attributes in Brain_Data.copy() to prevent pickle errors

• Bug Fix Complete Round 1 audit fixes - 4 critical bugs resolved

• Bug Fix Achieve perfect backward compatibility via deterministic RNG pattern

• Bug Fix Achieve perfect cross-backend determinism for all permutation tests

• Bug Fix fix statistical correctness of inference module

• Bug Fix fix docs

• Bug Fix fix ruff complaints

• Bug Fix fix adjacenecy bootstrap. Add gpu optimized correlation and timeseries functions

• Bug Fix fix/add gpu optimizations

• Bug Fix fix up bootstrapping with ridge

• Bug Fix fix up docs for algorithms

• Bug Fix fix cross-validation

• Bug Fix fix brain-data fit progress bar

• Bug Fix fix resampling

• Bug Fix fix beads git tracking and add GitHub issues

• Bug Fix fix Brain_Data squeeze flattening single-item lists (#449)

• Bug Fix fix nilearn darkness parameter deprecation warning

• Bug Fix fix nilearn nearest interpolation deprecation warning

• Bug Fix fix NiftiMasker mask warning in simulator

• Bug Fix fix memory resampling warnings in tests

• Bug Fix convert Backend object to parallel string in Ridge CV calls

• Bug Fix handle NaN values in Adjacency.similarity() (#432)

• Bug Fix resolve API mismatches in BrainData and test expectations

• Bug Fix prevent fitted model state from propagating to copies

• Bug Fix resolve pandas deprecation and logic bug in Adjacency

• Bug Fix replace legacy tier1/tier2 markers with slow/gpu

• Bug Fix Adjacency.regress() now correctly sets is_single_matrix for single-regressor DesignMatrix case

• Bug Fix ISC calculation in align() now correctly handles all axis/data_type combos

• Bug Fix atlas/label data now correctly uses nearest-neighbor interpolation (#446)

• Bug Fix Adjacency.similarity() NaN handling with perm_type=‘2d’ (#432)

• Bug Fix Brain_Data.threshold() now works when upper=0 or lower=0 (#370)

• Bug Fix fix docs build: add jupytext config for .py tutorials

• Bug Fix Adjacency.shape returns (n_nodes, n_nodes) for API consistency

• Bug Fix Remove incorrect @pytest.mark.slow markers from fast tests

• Bug Fix MultiSubjectPipeline.align() now works with LOSO CV (nltools-7j3g)

• Bug Fix Skip CI for beads sync commits, skip surface tests when files missing

• Bug Fix CI test failures - FittedBrainCollection, thresholds, tolerance

• Bug Fix More robust CI tests - tolerance, nan handling, constant input checks

• Bug Fix Use unbiased sigma estimator in regression (GH #287)

• Bug Fix Correct type annotations for ty type checker

• Bug Fix relax SVD reconstruction tolerance for float32 precision

• Bug Fix seed RNG in SVD test to eliminate flaky precision failures

• Bug Fix configure ty type checker and resolve 18 type errors

• Bug Fix fixup tutorials

• Bug Fix resolve ty type-checking errors

• Bug Fix guard scale_data grand-mean branch against divide-by-zero

• Bug Fix fix up docs and poe commands

• Bug Fix fix toc

• Bug Fix update ridge benchmark to parallel API

• Bug Fix rewrite mvpa_roi for correct per-ROI MVPA decoding

• Bug Fix serialize polars frames to h5 via Arrow IPC

• ⚠ Breaking Bug Fix make out-of-mask voxels transparent in BrainData.plot

• Bug Fix make slices + flatmap plotting usable again

• Bug Fix fixup plotting and add plot_surf()

• Bug Fix fix up design matrix, autoscale for plotting, include constant for .add_dct_basis by default to match .add_poly

• Bug Fix fix up design mat

• ⚠ Breaking Bug Fix compute_contrasts returns real t-stats, not beta sums

• ⚠ Breaking Bug Fix return matplotlib Figure from BrainData/DesignMatrix .plot()

• Bug Fix honor fit_intercept and CV splitters; add BrainData.size

• Bug Fix re-stub predict_multi; bounds-trim default slice cut_coords

• ⚠ Breaking Bug Fix always suffix convolved columns with _c{i}

• Bug Fix make .convolve() idempotent over already-convolved columns

• Bug Fix relax accidental numpy floor; pin nilearn in pyodide smoke test

• Bug Fix restore the interactive_plots optional extra (ipywidgets)

• Bug Fix deterministic cell ids for generated tutorial notebooks

• Bug Fix deterministic tiebreak for same-second step subdirs

• Bug Fix strip leaked RST directives from re-exported docstrings

• Bug Fix correct Attributes-section removal over-match in API generation

• Bug Fix correct invalid p-values/CIs and permutation nulls (0.6.0 audit)

• Bug Fix crashes/silent-empty on realistic inputs (0.6.0 audit)

• Bug Fix silent metadata loss and threshold/stack bugs (0.6.0 audit)

• Bug Fix seaborn 0.13.2 crashes, dropped returns, triangle swap (0.6.0 audit)

• Bug Fix always-raising ctor, copy-paste + wrong SE bugs (0.6.0 audit)

• Bug Fix schema check + non-integer downsample ratios (0.6.0 audit)

• Bug Fix all attribute errors and mutable default (0.6.0 audit)

• Bug Fix filter crash, resource leaks, CV-index + MVPA seed bugs (0.6.0 audit)

• Bug Fix NaN-poisoning, silent-wrong + crash bugs (0.6.0 audit)

• Bug Fix n_subjects referenced nonexistent BrainCollection.n_images (0.6.0 audit)

• Bug Fix restore ISC bootstrap null centering dropped in refactor (F066)

• Bug Fix HyperAlignment auto_pad zero-pads instead of truncating (F001)

• Bug Fix implement PooledData.repool for real fitted_state shapes (F111)

• Bug Fix share one integer cluster_id space between peaks and clusters (F043)

• Bug Fix complete parallel→device + contrast_type→method renames missed in 9b1b0eb4

• ⚠ Breaking Bug Fix drop invalid permutation CVScheme, add predict(n_permute=) null (F112)

• ⚠ Breaking Bug Fix stop advertising kwargs that do nothing (F068/F021/F182)

• Bug Fix apply_mask inherits target space for raw Niimg masks

• Bug Fix clearer errors + robust input handling (F098/F031/F159/F157)

• Bug Fix to_nifti preserves data precision instead of quantizing to mask dtype

• Bug Fix align feeds LocalAlignment correct orientation + wire cache= (F073)

• Bug Fix repair docstring rendering bugs in API reference

• Bug Fix make marimo-WASM tutorials boot in Pyodide

• Bug Fix run GPU legs on CUDA hosts, not just MPS

• Bug Fix stream leave-one-out ISC instead of materializing all subjects

• Bug Fix correct regress standard errors, all_same, copy() docs

• ⚠ Breaking Bug Fix wire GPU into pairwise ISC; fail fast on unsupported metric

• Bug Fix sequential micropip install for marimo-WASM; adopt niivue viewer

• Bug Fix use Python # comments in Pyodide micropip.install string

• Bug Fix make h5py a core dependency; BrainCollection.fit() requires it

• Bug Fix stop find_spikes() emitting duplicate spike regressors

• Bug Fix a design matrix with no regressors keeps its row count

• Bug Fix make n_rows survive copies and reject conflicting values

• Bug Fix point the rank-deficiency warning at regularization, not deletion

• ⚠ Breaking Bug Fix add progress_bar to the permutation and bootstrap family

• Bug Fix thread progress_bar through the bootstrap and Adjacency stat facades

• ⚠ Breaking Bug Fix make options keyword-only across the inference layer

• ⚠ Breaking Bug Fix make write() and the file constructor round-trip

• Bug Fix compare OOM-recovered results at float32-ulp tolerance, not bitwise

• ⚠ Breaking Bug Fix timeseries GPU draws match CPU exactly; conjugate pairing fixed in batched phase randomization

• Bug Fix relay worker warnings to the parent — deduplicated, categories preserved

• Bug Fix adjacency plots rendered twice

• ⚠ Breaking Bug Fix GPU Spearman ranks ties correctly; device validation is run-or-raise

• Bug Fix one-tailed z maps stay finite; single shared z-from-p helper

• ⚠ Breaking Bug Fix ttest(popmean=X, permutation=True) tests mean != popmean

• Bug Fix string class labels decode and persist end to end

• Bug Fix predict worker closures no longer capture the collection

• Bug Fix classify .h5 items by bundle_kind, not bare suffix

• Bug Fix predict-bundle model_spec is a real refit spec, not a repr

• Bug Fix plot_between_label_distance crashed on its default permutation path

• Bug Fix validate the separator-recovery re-parse instead of trusting the header hint

• Bug Fix translate pre-.nl_ generated names when loading a legacy h5

• Bug Fix make the GPU bootstrap per-sample hooks private

• Bug Fix qualify vocabulary suppressions by module path

• Bug Fix move check_kwonly’s inline EXEMPT dict into the vocabulary manifest

• Bug Fix move the _NullProgressBar nosemgrep suppressions into the semgrep config

• Bug Fix serialize in-memory masks in BrainData h5 files

• Bug Fix GPU bootstrap guard accepts torch-cuda

• Bug Fix keep refit alpha indices on the compute device

• Bug Fix cap measured-budget batch sizing at a saturation ceiling

• Bug Fix bench_inference GPU leg passed the harness probe string as device=

• Bug Fix provenance label auto-detects the GPU; record the benchmarked commit

Documentation¶

• Docs Streamline CLAUDE.md and add token-efficient pytest guidance

• Docs Update documentation to reflect completed test suite refactoring

• Docs Update documentation for R², effect variance, and filter method

• Docs Update nilearn-log.md with Phase 1 & 2 completion status

• Docs Update nilearn-log.md with Phase 3 completion status

• Docs Update REFACTORING_PLAN.md with Priority 2.5 completion

• Docs Add systematic benchmarking framework and update project specs

• Docs Add remaining v0.6.0 tasks to refactoring plan

• Docs Improve API documentation infrastructure and organization

• Docs Refactor documentation into focused, purpose-built files

• Docs suppress sphinx build warnings with exclude patterns

• Docs eliminate all Sphinx build warnings (45→0)

• Docs docs scaffold

• Docs Document parallel testing safety with pytest-xdist

• Docs Convert all docstrings from NumPy to Google style

• Docs Enforce parallel-first and permission-gated tier2 testing

• Docs Update refactor docs and archive completed research

• Docs Complete documentation update for GPU-accelerated inference module

• Docs add GitHub issues audit for v0.6.0 planning

• Docs add update notices to tutorials using deprecated localizer dataset

• Docs document API issues in migration guide

• Docs Add BrainCollection tutorial

• Docs Update migration guide and API docs for v0.6.0

• Docs Add encoding models tutorial (08_encoding_models.py)

• Docs Consolidate group_analysis + thresholding tutorials

• Docs Heavy prune 01_glm.py tutorial

• Docs Prune tutorials removing pedagogy, keeping practical code

• Docs Add CHANGELOG.md with pipeline infrastructure release notes

• Docs Add Pipeline workflow tutorials

• Docs Document predict algorithms and class_weight=‘balanced’ (GH #182, #177)

• Docs Add comprehensive v0.6.0 codebase audit

• Docs Fix all 236 documentation build warnings

• Docs standardize docstrings to Google-style and fill gaps

• Docs add API doc pages for pipelines, simulator, neighborhoods, and cache

• Docs fix migration guide inaccuracies and add missing SRM docs

• Docs clarify mask handling behavior in migration guide

• Docs organize BrainData API page into navigable grouped sections

• Docs fix docstrings across braindata subpackage

• Docs fix docstrings and add API pages for design_matrix subpackage

• Docs add API pages for collection subpackage, clean up braindata imports

• Docs migrate from Jupyter Book v1 to v2 (mystmd)

• Docs rewrite BrainData tutorial with execution support

• Docs add class renames, import paths, and ttest removal to migration guide

• Docs rewrite DesignMatrix tutorial with execution support

• Docs rewrite Adjacency tutorial with execution support

• Docs improve BrainData API page layout and griffe2md postprocessing

• Docs apply same API page improvements to DesignMatrix class

• Docs apply same API page improvements to Adjacency class

• Docs remove unused paired .py files for rewritten tutorials

• Docs apply same API page improvements to BrainCollection class

• Docs rewrite BrainCollection tutorial with execution support

• Docs suppress progress bars in BrainCollection tutorial

• Docs docs updates

• Docs add gallery index page for API Classes sidebar section

• Docs include tutorial pages as hidden TOC entries

• Docs docs updates

• Docs capture v0.6.0 API conventions and breaking-commit format

• Docs sync migration guide with last month of breaking commits

• Docs fix tutorial kwarg names and ridge-regression xref

• Docs regenerate from current source

• Docs use load_haxby_example in first-level GLM

• Docs merge group analysis into the GLM tutorial

• Docs add explicit decompose mentions

• Docs PPI worked example using v0.6.0 idioms

• Docs loading canonical brain images section

• Docs regenerate api docs to pick up docstring drift

• Docs switch tutorial atlas from k200 to k50 for faster builds

• Docs refresh Multivariate_Prediction migration guidance

• Docs mark Multivariate_Prediction migration done

• Docs enable RSA tutorial and rewrite for current API

• Docs rewrite RSA tutorial for trial-level RSA in MNI space

• Docs add marimo→myst pipeline and GLM tutorial template

• Docs consolidate workflows into 4 standardized notebooks

• Docs remove broken BrainCollection basics card

• Docs align API doc generation with the uv-cleanup module layout

• Docs standardize docstrings to Google/Markdown, automate changelog, fix migration guide

• Docs fix broken cross-reference links in generated API docs

• Docs document iplot() colorbar + threshold slider; ipywidgets in JupyterLite

• Docs explicit page-scoped MyST targets to silence heading-ref warnings

• Docs silence remaining mystmd build warnings (frontmatter, grid, docstrings)

• Docs regenerate API reference (fetch_pain signature + docstring fixes)

• Docs update SPEC status header to reflect implemented state

• Docs regenerate API reference after BrainCollection bring-over

• Docs regenerate collection_core after seq-tiebreak

• Docs regenerate API reference for statistic rename + permutation removal

• Docs fix docstring/RST-leakage bucket + regenerate API reference

• Docs regenerate API reference (F068/F021/F182 + owed drift)

• Docs wire encoding + isc notebooks for in-browser WASM data (#3673)

• Docs commit the 0.6.0 pre-release hygiene audit record

• Docs fix stale Adjacency.similarity/regress/isc claims

• ⚠ Breaking Docs reorder summary tables to Parameters/Attributes/Classes/Methods

• Docs reconcile Args/Returns docstrings with actual signatures

• Docs tidy module docstrings (dedupe, drop leftover headings)

• Docs tidy first-line docstring summaries for griffe tables

• Docs add BrainCollection basics notebook

• Docs normalize docstring style (Note: header, typos, models blank line)

• Docs add interactive design tour + wire standalone-page build

• Docs add static-markdown tutorial build mode (default) + simplify docs poe tasks

• Docs trim CLAUDE.md to load-bearing guidance

• Docs clean up and fix stale references

• Docs remind to use vendored nilearn/marimo skills

• Docs delete superseded SPEC.md and ridge design docs; repoint to docs/development

• Docs reconcile docstrings/comments with implementation across data/stats

• Docs fix similarity result-key docs to ‘correlation’; add semgrep guard

• Docs reconcile remaining algorithm-layer docstrings with implementation

• Docs generate canonical-kwarg vocab from a single source

• Docs regenerate API docs (griffe2md)

• Docs integrate pikachu CUDA run, make perf doc host-aware

• Docs correct the nilearn cluster-forming threshold scale

• Docs batched regeneration — API sources, changelog, tail-docstring cleanup

• Docs per-subject predict + sklearn cv names — migration guide, execution model, vocabulary

• ⚠ Breaking Docs plain marimo notebooks, executed previews; defer browser support to 0.6.1

• Docs regenerate changelog for the commits since the batched docs pass

• Docs close the gaps found by the breaking-commit audit

• Docs regenerate API sources and changelog for the review-fix commits

• Docs refresh pikachu CUDA baseline at 55e44f06

0.5.0 (2023-10-31)¶

Bug Fixes¶

• Bug Fix fix documentation build errors

• Bug Fix fix testing bug

• Bug Fix fix #413

• Bug Fix fix up test bugs, support pandas 2.0, pin numpy until we replace deepdish, use only pip for GA

• Bug Fix fix #409

• Bug Fix fix #392

• Bug Fix fix up docs testing. merge cron and push GA files

• Bug Fix fix bug in downloading data

0.4.6 (2022-08-15)¶

Improvements¶

• Improvement refactored trim and winsorize to single subfunction

• Improvement refactored correlation_permutation with combined case testing.

• Improvement refactored tests

• Improvement refactored code using sourcery

• Improvement refactored code using sourcery

• Improvement refactored roi_to_brain to be much faster

Bug Fixes¶

• Bug Fix fixed bug

• Bug Fix fixed bug

• Bug Fix fixed predict problems

• Bug Fix fixed bug with plotting with no Xval

• Bug Fix fixed bug

• Bug Fix fix error related to email alert

• Bug Fix fix an undefined variable; format the code layout

• Bug Fix fix undefined attribute

• Bug Fix fix numpy’s ValueError: The truth value of an array with more...

• Bug Fix fixed dist_from_hyperplane_plot bug

• Bug Fix fixed tests

• Bug Fix fixed simulator test

• Bug Fix fixed pytests

• Bug Fix fixed cross_validation bug from updating to sklearn 0.17

• Bug Fix fixed bug in analysis.Predict and updated tutorials

• Bug Fix fixed bug when self.Y is empty

• Bug Fix fixed bug in similarity metric

• Bug Fix fixed small bug

• Bug Fix fixed bug in Brain_Data.bootstrap() method related to indexing

• Bug Fix fixed bug with Brain_Data.similarity

• Bug Fix fixed roc figure return type

• Bug Fix fixed icc bugs

• Bug Fix fixed bug on Brain_Data list import

• Bug Fix fixed extract_roi bug

• Bug Fix fixed bugs with permutation ttest

• Bug Fix fixed cross-val bug and added test

• Bug Fix fixed typo

• Bug Fix fixed bug

• Bug Fix fixed bug with upload naming

• Bug Fix fixed bug in Adjacency import function

• Bug Fix fixed bugs

• Bug Fix fixed downsample bug

• Bug Fix fixed bugs in tutorials

• Bug Fix fixed bugs in cross-validation

• Bug Fix fixed bug with apply_mask

• Bug Fix fixed bug with apply mask

• Bug Fix fixed test tolerance

• Bug Fix fixed test

• Bug Fix fixed bug with Adjacency.append()

• Bug Fix fix auto-rounding bug in extract_roi

• Bug Fix fixed bug with reading directed flat adjacency data

• Bug Fix fix mn_score bug in Roc.calculate for forced choice

• Bug Fix fix forced_choice_idx in Roc.calculate function

• Bug Fix fix the line up issue in analysis.py line 95

• Bug Fix fixed roc forced choice plotting bug

• Bug Fix fixed bug in roc forced choice plotting

• Bug Fix fixed bug with create sphere and mask

• Bug Fix fixed neurovault_upload method

• Bug Fix fixed tests

• Bug Fix fixed tests

• Bug Fix fixed bug with cross-validation in predict

• Bug Fix fixed bug in simulator

• Bug Fix fixed bug in test_analysis

• Bug Fix fixed typo

• Bug Fix fixed roc forced choice accuracy

• Bug Fix fixed kwargs bug

• Bug Fix fix issue #152, rename Stimulus to Stim to keep the naming consistent

• Bug Fix fixed bug that requires creating a copy of input data on align function.

• Bug Fix fixed Brain_Data.threshold bug

• Bug Fix fixed random seed issue with permutations/bootstraps and joblib

• Bug Fix fixed check_random_state imports

• Bug Fix fixed transform pairwise and added tests

• Bug Fix fixed bug in test and crucial missing line in function

• Bug Fix fixed nilearn version dependency

• Bug Fix fixed bug regression bug with 2d arrays

• Bug Fix fixed bugs in regression

• Bug Fix fixed missing plot call

• Bug Fix fixed check_brain_data bug

• Bug Fix fixed typos

• Bug Fix fixed bug with social relations model

• Bug Fix fixed bug in fetch_localizer

• Bug Fix fixed predict Y warnings.

• Bug Fix fixed pearsonr bug

• Bug Fix fixed bug with glover_hrf function

• Bug Fix fixed bug with labels in Adjacency.plot_silhouette

• Bug Fix fixed issues with labels in plot_mds

• Bug Fix fixed silent errors to roi_to_brain

• Bug Fix fixed doc string for roi_to_brain

• Bug Fix fixed extract_roi bug and added new functionality

• Bug Fix fixed import error with ipywidgets

• Bug Fix fixed isc bugs

• Bug Fix fixed smooth bug

• Bug Fix fixed codacy recs.

• Bug Fix fixed new smooth test.

• Bug Fix fixed align check.

• Bug Fix fixed spacing.

• Bug Fix fix ga. fix bug in design matrix repr. pin pandas version until deepdish updates.

• Bug Fix fixes #364

• Bug Fix fixed broken tests

• Bug Fix fixed cluster_summary in adjacency tutorial

• Bug Fix fix ci badge. try to fix failing gallery build on ga

• Bug Fix fix #396, fix #398, remove uneccesary files for doc build

• Bug Fix fix nilearn warnings and onsets_to_dm warnings