From 4013aa3e32e859a92873119b7d99d13f1dcc0bdc Mon Sep 17 00:00:00 2001 From: Valentin Guigon Date: Mon, 6 Jul 2026 15:27:34 -0400 Subject: [PATCH 1/6] refactoring: implementing a simpler trial-based timing architecture for v2.0.0 --- .flake8 | 3 + .github/workflows/tests.yml | 45 + .gitignore | 24 + CHANGES.md | 76 + CITATION.cff | 29 + Dockerfile | 26 +- MIGRATION_2.0.md | 173 + README.md | 111 +- docs/_static/.gitkeep | 0 docs/api_reference.md | 11 + docs/conf.py | 4 + docs/getting_started.md | 324 +- docs/index.md | 29 +- docs/installation.md | 68 +- docs/migration.md | 5 + docs/tutorials.md | 67 +- manuals/METRICS.md | 292 +- manuals/SETUP.md | 107 +- manuals/TECHNICAL_CHANGES.md | 288 +- neurodesign/classes.py | 2962 +++++--- neurodesign/generate.py | 175 +- neurodesign/report.py | 110 +- .../designs.pdf | Bin 39013 -> 0 bytes .../distributions.pdf | Bin 18224 -> 0 bytes .../test_scores.pdf | Bin 14965 -> 0 bytes .../design_0/ITIs.txt | 100 - .../design_0/stimulus_0.txt | 37 - 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validation/helpers/__init__.py create mode 100644 validation/helpers/case10_determinism_worker.py create mode 100644 validation/helpers/case10_v2.py create mode 100644 validation/helpers/regenerate_v2_notebooks.py create mode 100644 validation/helpers/version_metadata.py create mode 100644 validation/manifest.py create mode 100644 validation/manuscript_support/README.md create mode 100644 validation/manuscript_support/__init__.py create mode 100644 validation/manuscript_support/generate_timing_architecture_svg.py create mode 100644 validation/manuscript_support/generate_tutorial3_case10_nontrivial_package.py create mode 100644 validation/manuscript_support/generate_tutorial3_case10_v2_package.py create mode 100644 validation/run_all.py create mode 100644 validation/run_case10_determinism.py create mode 100644 validation/run_case10_v2.py diff --git a/.flake8 b/.flake8 index 5da1083..2c1e305 100644 --- a/.flake8 +++ b/.flake8 @@ -8,12 +8,15 @@ ignore = D101, D102, D107, + E501, E741, F821, W503 per-file-ignores = # - docstrings rules that should not be applied to tests tests/*: D100, D101, D102, D103 + # - repository imports intentionally follow sys.path setup in this script + validation/manuscript_support/generate_tutorial3_case10_v2_package.py: E402 # for compatibility with black # https://black.readthedocs.io/en/stable/guides/using_black_with_other_tools.html#flake8 extend-ignore = E203 diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index cff6f7d..0a9d9be 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -39,3 +39,48 @@ jobs: with: flags: ${{ matrix.os }}_${{ matrix.py }} if: success() + + notebooks: + name: Execute notebooks + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v5 + - name: Setup python + uses: actions/setup-python@v6 + with: + python-version: '3.12' + - name: Install tox + run: python -m pip install --upgrade tox + - name: Execute tutorial notebooks + run: tox run -e test_notebooks + + docs: + name: Build docs + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v5 + - name: Setup python + uses: actions/setup-python@v6 + with: + python-version: '3.12' + - name: Install tox + run: python -m pip install --upgrade tox + - name: Build documentation + run: tox run -e docs + + validation: + name: Aggregate validation + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v5 + - name: Setup python + uses: actions/setup-python@v6 + with: + python-version: '3.12' + - name: Install validation dependencies + run: python -m pip install ".[doc,test]" + - name: Run aggregate validation + run: python -m validation.run_all diff --git a/.gitignore b/.gitignore index 1ad1955..63efc10 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,28 @@ examples/output neurodesign/_version.py .vscode +.agents/ +.suplex/ +AGENTS.md +CLAUDE.md +SUPLEX.md +.venv/ +.venv312/ +.pytest_cache/ +.mypy_cache/ +.ruff_cache/ +.tmp_ipython/ +.tmp_mpl/ +build/ +output/ +manuscript/ +validation/_artifacts/ +validation/**/*.pyc +validation/**/__pycache__/ +neurodesign/__pycache__/ +tests/**/__pycache__/ +docs/_build/ +dist/ # Byte-compiled / optimized / DLL files __pycache__/ @@ -94,3 +116,5 @@ ENV/ # Rope project settings .ropeproject + +.tmp_precommit/ diff --git a/CHANGES.md b/CHANGES.md index 6294fcc..7d9cfb8 100644 --- a/CHANGES.md +++ b/CHANGES.md @@ -19,6 +19,82 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Security --> +## [2.0.0] - 2026-07-05 + +### Breaking changes + +- Replaced the inherited event-only timing surface with a trial-aware version-2 architecture based on conceptual trials and modeled events. +- Replaced ambiguous ITI handling with separate `trial_start_interval`, `post_event_interval`, `event_transition_interval`, `inter_trial_interval`, `rest_every_n_trials`, and `rest_interval`. +- Replaced event-level probability-template truncation with complete-template conceptual-trial sampling driven by `trial_templates`, `trial_template_probabilities`, and `n_conceptual_trials`. +- Replaced direct manual `Design(order=..., ITI=...)` construction with `Experiment.create_design(...)` and `Experiment.create_manual_design(...)`. +- Standardized the public optimization workflow around `optimise()` followed by `selected_design(0)`. + +### Added + +- Flat one-event shorthand through `order`. +- Fixed complete conceptual-trial templates through `trial_templates` plus `trials`. +- Probabilistic complete-template sampling through `trial_templates`, `trial_template_probabilities`, and `n_conceptual_trials`. +- Shared timing-rule parsing for `event_durations`, `trial_start_interval`, `post_event_interval`, `event_transition_interval`, `inter_trial_interval`, and `rest_interval`. +- Selector wrappers for trial-type, event-category, and event-transition timing rules. +- Requested, normalized, and realized timing state exports. +- Trial-aware schedule metadata on `Design`, including trial IDs, template IDs, trial types, and realized schedule arrays. +- Public `selected_design(rank)` retrieval after optimization. +- Canonical migration guide in `MIGRATION_2.0.md`. + +### Fixed + +- `Xnonconv` occupancy now reflects modeled event durations only rather than absorbing surrounding intervals. +- Trial-start, post-event, within-trial transition, between-trial, and rest timing roles now remain structurally distinct. +- Rest intervals now remain boundary intervals instead of being treated like synthetic task events. +- `Ff` and `Fc` now use flattened realized event counts rather than conceptual-trial counts. +- Case 10 now uses one canonical version-2 configuration shared across tutorials, validation, and manuscript-support generation. + +### Validation and reproducibility + +- Switched schedule sampling and optimization RNG handling to NumPy `SeedSequence` and `Generator`. +- Added deterministic canonical Case 10 validation, manuscript-support generation, and cross-process determinism checks. +- Added release-audit coverage for `selected_design()` guardrails, bounded distribution semantics, and version-2 public workflows. +- Regenerated and validated the maintained tutorial notebooks from a single canonical notebook generator. + +### Migration requirements + +- Version 2.0 is intentionally source-breaking for scripts written against the version-1 timing API. +- User-facing docs now route version migration through `MIGRATION_2.0.md`. +- Tutorials and examples now teach the selected-design workflow, trial-aware timing terms, and event-count versus conceptual-trial-count distinctions. + +### Removed arguments + +- Removed `stimuli_durations`. +- Removed `conditional_ITI`. +- Removed `order_keys`, `order_probabilities`, and `order_length`. +- Removed `all_stim_durations`. +- Removed `t_pre` and `t_post`. +- Removed `ITImodel`, `ITImin`, `ITImean`, and `ITImax`. +- Removed active user-facing reliance on `.bestdesign` and internal design-pool indexing. + +### Export and report changes + +- Reports now align with the selected-design workflow. +- `Design.export_payload()` now preserves reconstructable schedule arrays, schedule rows, counts, and metric components. +- `Experiment.export_specification()` now preserves requested public timing specifications and trial-template configuration. + +### Trial-aware optimization changes + +- Flat mode remains event-order based. +- Fixed conceptual-trial mode preserves the declared trial sequence. +- Probabilistic template mode mutates and crosses at conceptual-trial boundaries instead of truncating event-level template fragments. + +### RNG changes + +- Removed dependence on global NumPy reseeding and Python `random`. +- RNG derivation is now stable across sampling, crossover, mutation, and immigration. + +### Convergence changes + +- `convergence=k` now documents and exposes patience-based early stopping after `k` completed generations without strict improvement in the generation-best objective score. +- `convergence=0` and `convergence=None` disable early stopping. +- Public optimization state now exposes `generations_completed`, `stop_reason`, and `optima` for user-facing reporting. + ## [1.0.2] - 2026-03-25 ### Added diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000..d0d1814 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,29 @@ +cff-version: 1.2.0 +message: "If you use this software, please cite it using the metadata below." +title: "neurodesign-plus" +type: software +authors: + - family-names: "Umap" + given-names: "Atharv Amar" + - family-names: "Charpentier" + given-names: "Caroline J." + - family-names: "Guigon" + given-names: "Valentin" +version: "1.0.4" +repository-code: "https://github.com/SLDlab/neurodesign-plus" +url: "https://github.com/SLDlab/neurodesign-plus" +license: "MIT" +preferred-citation: + type: software + title: "neurodesign-plus" + authors: + - family-names: "Umap" + given-names: "Atharv Amar" + - family-names: "Charpentier" + given-names: "Caroline J." + - family-names: "Guigon" + given-names: "Valentin" + version: "1.0.4" + repository-code: "https://github.com/SLDlab/neurodesign-plus" + url: "https://github.com/SLDlab/neurodesign-plus" + license: "MIT" diff --git a/Dockerfile b/Dockerfile index 4697c97..04b3ab5 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,23 +1,13 @@ -FROM continuumio/miniconda -MAINTAINER Joke Durnez +FROM python:3.12-slim -ENV LANG=C.UTF-8 LC_ALL=C.UTF-8 +ENV PYTHONDONTWRITEBYTECODE=1 +ENV PYTHONUNBUFFERED=1 +ENV MPLBACKEND=Agg -RUN conda install numpy && \ - conda install scipy && \ - conda install pandas && \ - conda install matplotlib +WORKDIR /app -RUN pip install neurodesign > 0.2.02 -RUN pip install sklearn -RUN pip install pdfrw -RUN pip install reportlab -RUN pip install progressbar -RUN pip install seaborn +COPY . /app -ENV LD_LIBRARY_PATH=/opt/conda/lib:$LD_LIBRARY_PATH +RUN pip install --no-cache-dir . -# Clear apt cache to reduce image size -RUN apt-get update && \ - apt-get install -y libgl1-mesa-glx && \ - apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* +CMD ["python", "-c", "from neurodesign import Experiment, Design, Optimisation; print('neurodesign-plus container ready')"] diff --git a/MIGRATION_2.0.md b/MIGRATION_2.0.md new file mode 100644 index 0000000..6bad687 --- /dev/null +++ b/MIGRATION_2.0.md @@ -0,0 +1,173 @@ +# Migration To 2.0 + +`neurodesign-plus` 2.0 is a source-breaking release. +It keeps the public classes `Experiment`, `Design`, and `Optimisation`, but replaces the legacy event-only timing surface with a trial-aware schedule model. + +## Core Model + +A run contains conceptual trials. +A conceptual trial contains one or more modeled events. + +For conceptual trial `j`, the realized schedule is: + +1. `trial_start_j` +2. `realized_trial_start_interval_j` +3. modeled events with `realized_event_durations` +4. `realized_post_event_interval` after each event +5. `realized_event_transition_interval` only between events inside the same trial +6. `trial_end_j` +7. `realized_inter_trial_interval_j` +8. optional `realized_rest_interval_j` +9. `trial_start_(j+1)` + +Only modeled event durations occupy `Xnonconv`. + +## Current Public Construction Modes + +### Flat One-Event Shorthand + +```python +Experiment( + ..., + order=[0, 1, 0, 1], + event_durations=1.0, + trial_start_interval=0.5, + post_event_interval=0.2, + inter_trial_interval=2.0, +) +``` + +Each `order` entry is one conceptual trial containing one modeled event. + +### Fixed Complete Trial Templates + +```python +Experiment( + ..., + trial_templates=[ + { + "template_id": "standard", + "trial_type": "standard", + "events": [ + {"category": "cue_easy", "code": 0, "duration": 0.8}, + {"category": "choice_left", "code": 1, "duration": 1.2}, + {"category": "feedback", "code": 2, "duration": 1.0}, + ], + }, + ], + trials=[{"template_id": "standard"}] * 10, +) +``` + +### Probabilistic Complete-Template Sampling + +```python +Experiment( + ..., + trial_templates=[...], + trial_template_probabilities=[0.4, 0.35, 0.25], + n_conceptual_trials=10, + seed=12, +) +``` + +Version 2.0 samples complete conceptual trials only. +It does not truncate a final template to satisfy an event budget. + +## Current Timing API + +Supported timing arguments: + +- `event_durations` +- `trial_start_interval` +- `post_event_interval` +- `event_transition_interval` +- `inter_trial_interval` +- `rest_every_n_trials` +- `rest_interval` + +Selector wrappers are supported where the public API allows them: + +- `event_durations` by event category +- `trial_start_interval` by trial type +- `post_event_interval` by event category +- `event_transition_interval` by event transition + +## Legacy Name Mapping + +This section is intentionally historical and exists only for migration. + +- `t_pre` -> `trial_start_interval` +- `t_post` -> `post_event_interval` +- `conditional_ITI` -> `event_transition_interval` and/or `inter_trial_interval` +- `ITImodel`, `ITImin`, `ITImean`, `ITImax` -> `inter_trial_interval` +- `order_keys`, `order_probabilities`, `order_length` -> `trial_templates`, `trial_template_probabilities`, `n_conceptual_trials` +- `stimuli_durations` -> `event_durations` + +## Duration Grammar + +Scalar shorthand remains valid: + +```python +event_durations = 1.0 +``` + +Equivalent explicit form: + +```python +event_durations = {"model": "fixed", "mean": 1.0} +``` + +Supported rule models: + +- `fixed` +- `uniform` +- `exponential` +- `gaussian` + +Bounded exponential and Gaussian rules use true truncated distributions. +For bounded rules, `mean` is the mean of the realized bounded distribution. + +## Requested Versus Realized Timing + +`Experiment.export_specification()` preserves requested public inputs. +`Design.export_payload()` preserves the realized schedule and realized timing arrays. + +That separation matters because `Fe` and `Fd` depend on the realized design matrices, while `Ff` and `Fc` remain defined on the flattened realized event stream. + +## Optimization Workflow + +The public optimization route is: + +```python +optimisation.optimise() +design = optimisation.selected_design(0) +``` + +Use that selected design for: + +- report generation +- schedule export +- specification export +- figures +- metadata + +Do not teach `.bestdesign`, direct population indexing, or selection before optimization in current user workflows. + +## Deterministic RNG + +Version 2.0 uses NumPy `SeedSequence` and `Generator` objects for schedule realization and optimization. +The package does not depend on global NumPy reseeding or Python `random`. + +## Convergence Semantics + +`convergence=k` means patience-based early stopping after `k` consecutive completed generations with no strict improvement in the generation-best objective score. + +- equality counts as no improvement +- there is no minimum-delta tolerance in the current implementation +- `convergence=0` or `None` disables early stopping + +## Case 10 + +The canonical version-2 Case 10 source lives in `validation/helpers/case10_v2.py`. +Tutorial 3, the validation comparison workflow, the determinism workflow, and manuscript-support generation all reuse that shared source. diff --git a/README.md b/README.md index a23c8c6..a9a82bd 100644 --- a/README.md +++ b/README.md @@ -1,43 +1,102 @@ # neurodesign-plus -`neurodesign-plus` is an extended and maintained fork of the original [neurodesign](https://github.com/neuropower/neurodesign) package for fMRI experimental design optimisation. +`neurodesign-plus` is the distribution name for the version-2.0.0 trial-aware release. +The Python import namespace remains `neurodesign`. -In addition to the base package workflow, `neurodesign-plus` adds support for: +Version 2.0 models a run as conceptual trials containing one or more modeled events. +That public model supports: -1. fixed user-defined event orders via `order`, -2. variable stimulus durations via `stimuli_durations`, -3. transition-specific ITIs via `conditional_ITI`, -4. probabilistic template-based ordering via `order_keys`, `order_probabilities`, and `order_length`. +- flat one-event shorthand through `order` +- fixed complete trial templates through `trial_templates` plus `trials` +- probabilistic complete-template sampling through `trial_templates`, `trial_template_probabilities`, and `n_conceptual_trials` +- explicit timing roles through `event_durations`, `trial_start_interval`, `post_event_interval`, `event_transition_interval`, `inter_trial_interval`, and `rest_interval` +- separate requested specifications, normalized rules, and realized timing arrays on the sampled `Design` -## Documentation +Optimization stays public through `Optimisation`. After `optimise()`, retrieve the authoritative reported design with `selected_design(0)`, then drive reporting and exports from that selected design. Reports are generated with `neurodesign.report`, and both schedules and experiment specifications can be exported in reconstructable JSON form. Seeded workflows use deterministic NumPy `SeedSequence` and `Generator` plumbing throughout design sampling and optimization. -- Base package documentation: [neurodesign on Read the Docs](http://neurodesign.readthedocs.io/en/latest/) -- `neurodesign-plus` documentation: [neurodesign-plus on Read the Docs](https://neurodesign-plus.readthedocs.io/en/latest/) +Install the published package with: -## Repository Layout +```bash +python -m pip install neurodesign-plus +``` -```text -docs/ Source files for the Read the Docs documentation -manuals/ Longer-form markdown documentation included by the docs site -neurodesign/ Python package source code -tests/ Automated tests for package behavior -tutorials/ Jupyter tutorials for the main workflow, base functions, and new features +For source, development, notebook, and documentation installs, see [manuals/SETUP.md](manuals/SETUP.md) and [docs/installation.md](docs/installation.md). For version-1 to version-2 migration details, see [MIGRATION_2.0.md](MIGRATION_2.0.md). + +## Current API Example + +This public workflow is covered by the release-audit tests and notebook generator. + +```python +from pathlib import Path +import json + +from neurodesign import Experiment, Optimisation, report + +exp = Experiment( + TR=2.0, + n_trials=8, + P=[0.5, 0.5], + C=[[1, -1]], + rho=0.3, + n_stimuli=2, + event_durations=1.0, + trial_start_interval=0.5, + post_event_interval=0.2, + inter_trial_interval=2.0, + resolution=0.1, + seed=7, +) + +optimisation = Optimisation( + experiment=exp, + weights=[0.0, 0.5, 0.25, 0.25], + preruncycles=1, + cycles=1, + optimisation="simulation", + G=2, + I=1, + outdes=1, + convergence=1, + seed=101, + folder=Path("output") / "readme_example", +) +optimisation.optimise() +design = optimisation.selected_design(0) + +report.make_report(optimisation, Path("report.pdf")) +Path("schedule.json").write_text( + json.dumps(design.export_payload(), indent=2), encoding="utf-8" +) +Path("specification.json").write_text( + json.dumps(exp.export_specification(), indent=2), + encoding="utf-8", +) ``` -## Tutorials +## Documentation -The tutorial collection includes: +- User guide: [docs/index.md](docs/index.md) +- Installation notes: [docs/installation.md](docs/installation.md) +- Migration guide: [MIGRATION_2.0.md](MIGRATION_2.0.md) +- Technical manual: [manuals/TECHNICAL_CHANGES.md](manuals/TECHNICAL_CHANGES.md) +- Metrics guide: [manuals/METRICS.md](manuals/METRICS.md) +- Tutorials: [docs/tutorials.md](docs/tutorials.md) -- three overview tutorials in `tutorials/`, -- four base-function tutorials in `tutorials/base_functions/`, -- four feature-focused tutorials in `tutorials/new_functions/`. +## Repository Layout -The Read the Docs tutorials page is the best place to browse the current notebook set and their intended learning progression. +```text +docs/ Sphinx documentation sources +manuals/ Included long-form manuals +neurodesign/ Package source code +tests/ Automated regression and release-audit tests +tutorials/ Executed tutorial notebooks +validation/ Validation runners and reproducibility helpers +``` ## Credits -This project is a fork of the original **Neurodesign** package. +This project is a maintained fork of the original [neurodesign](https://github.com/neuropower/neurodesign) package. -- Original author: [Neuropower Team](https://github.com/neuropower) -- Primary refactoring, extensions, and tutorials: Atharv Amar Umap (Social Learning and Decisions Lab, UMD) -- Supervision, design guidance, and tutorials: Valentin Guigon (Social Learning and Decisions Lab, UMD) +- Original author: Joke Durnez and the Neuropower team +- neurodesign-plus maintenance and tutorials: Atharv Amar Umap +- supervision and design guidance: Valentin Guigon diff --git a/docs/_static/.gitkeep b/docs/_static/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/docs/api_reference.md b/docs/api_reference.md index f803a37..c8ff6ca 100644 --- a/docs/api_reference.md +++ b/docs/api_reference.md @@ -8,6 +8,17 @@ The package exposes three primary classes through `neurodesign`: from neurodesign import Experiment, Design, Optimisation ``` +The compatibility import path `from neurodesign.classes import ...` resolves to the same class objects implemented in `neurodesign/classes.py`. + +Public construction and selection routes: + +- create sampled designs with `Experiment.create_design(...)` +- create fully manual flat one-event designs with `Experiment.create_manual_design(...)` +- run search with `Optimisation.optimise()` +- retrieve reported designs with `Optimisation.selected_design(rank)` + +Direct `Design(...)` construction is not a user-facing version-2 workflow. + ### Experiment ```{eval-rst} diff --git a/docs/conf.py b/docs/conf.py index 8a7ba78..0e8328c 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -1,6 +1,7 @@ """Sphinx configuration for neurodesign-plus documentation.""" import importlib.metadata +import os # -- Project information ----------------------------------------------------- project = "neurodesign-plus" @@ -63,6 +64,9 @@ "scipy": ("https://docs.scipy.org/doc/scipy/", None), "sklearn": ("https://scikit-learn.org/stable/", None), } +if os.environ.get("NEURODESIGN_DOCS_OFFLINE") == "1": + extensions = [ext for ext in extensions if ext != "sphinx.ext.intersphinx"] + intersphinx_mapping = {} # -- Options for HTML output ------------------------------------------------- html_theme = "furo" diff --git a/docs/getting_started.md b/docs/getting_started.md index 715d399..60beeb9 100644 --- a/docs/getting_started.md +++ b/docs/getting_started.md @@ -1,66 +1,312 @@ # Getting Started -## Overview +## Public classes -neurodesign-plus organises fMRI design optimisation around three core classes: +`neurodesign-plus` 2.0 exposes one authoritative implementation of: -1. **`Experiment`** -- Defines the experimental specification: conditions, durations, ITI model, contrasts, and timing parameters. -2. **`Design`** -- Represents a concrete design instance: a specific stimulus order and ITI sequence generated from an Experiment. -3. **`Optimisation`** -- Searches over candidate designs using a genetic algorithm and selects designs based on efficiency metrics. +```python +from neurodesign import Experiment, Design, Optimisation +``` + +The compatibility import path remains: + +```python +from neurodesign.classes import Experiment, Design, Optimisation +``` + +Both resolve to the same class objects implemented in `neurodesign.classes`. + +## Timing model + +A run contains conceptual trials. A conceptual trial contains one or more modeled events. + +Flat `order` inputs are shorthand for one-event conceptual trials. + +Version 2.0 separates four interval roles: -## Minimal Example +- `trial_start_interval` +- `post_event_interval` +- `event_transition_interval` +- `inter_trial_interval` + +Optional rests are configured with: + +- `rest_every_n_trials` +- `rest_interval` + +Within-trial `event_transition_interval` values never cross trial boundaries. +`inter_trial_interval` applies only between conceptual trials. +Rests are boundary intervals, not events. + +Only modeled event durations enter `Xnonconv`. Displayed fixation or baseline time during an interval is not automatically modeled as task activity. + +## Minimal flat one-event example ```python -import numpy as np -from neurodesign import Experiment, Optimisation +from neurodesign import Experiment -# Define the experiment exp = Experiment( TR=2.0, - n_stimuli=2, n_trials=20, P=[0.5, 0.5], - C=np.array([[1, -1]]), + C=[[1, -1]], rho=0.3, - stim_duration=1.0, - ITImodel="exponential", - ITImin=1.0, - ITImean=2.0, - ITImax=4.0, + n_stimuli=2, + event_durations=1.0, + trial_start_interval=0.5, + post_event_interval=0.2, + inter_trial_interval={"model": "exponential", "mean": 2.0, "min": 1.0, "max": 4.0}, + resolution=0.1, + seed=42, ) -# Set up and run the optimisation +design = exp.create_design(seed=42) +design.designmatrix() +``` + +## Fixed multi-event example + +```python +trial_templates = [ + { + "template_id": "standard", + "trial_type": "standard", + "events": [ + {"category": "cue_easy", "code": 0, "duration": 0.8}, + {"category": "choice_left", "code": 1, "duration": 1.2}, + {"category": "feedback", "code": 2, "duration": 1.0}, + ], + }, + { + "template_id": "hint_branch", + "trial_type": "hint", + "events": [ + {"category": "cue_hard", "code": 3, "duration": 0.8}, + {"category": "hint", "code": 4, "duration": 0.7}, + {"category": "choice_right", "code": 5, "duration": 1.2}, + {"category": "feedback", "code": 2, "duration": 1.0}, + ], + }, +] + +exp = Experiment( + TR=1.0, + P=[1 / 6] * 6, + C=[[1, 0, 0, 0, 0, -1]], + rho=0.3, + n_stimuli=6, + trial_templates=trial_templates, + trials=[{"template_id": "standard"}, {"template_id": "hint_branch"}], + event_durations=1.0, + trial_start_interval={"by_trial_type": {"standard": 0.5, "hint": 0.7}}, + post_event_interval={"by_event_category": {"feedback": 0.3, "default": 0.1}}, + event_transition_interval={ + "by_event_transition": { + ("cue_easy", "choice_left"): 0.4, + ("choice_left", "feedback"): 0.6, + ("cue_hard", "hint"): 0.2, + ("hint", "choice_right"): 0.3, + ("choice_right", "feedback"): 0.5, + } + }, + inter_trial_interval=1.5, + rest_every_n_trials=2, + rest_interval=3.0, + resolution=0.1, + seed=5, +) +``` + +## Probabilistic complete-template sampling + +```python +exp = Experiment( + TR=1.0, + P=[0.18, 0.12, 0.16, 0.12, 0.10, 0.10, 0.22], + C=[[1, 0, 0, 0, 0, 0, -1]], + rho=0.3, + n_stimuli=7, + trial_templates=[...], + trial_template_probabilities=[0.4, 0.35, 0.25], + n_conceptual_trials=10, + event_durations={...}, + trial_start_interval=0.0, + post_event_interval=0.0, + event_transition_interval={...}, + inter_trial_interval={"model": "uniform", "min": 1.0, "mean": 1.45, "max": 1.9}, + seed=12, +) +``` + +Version 2.0 never truncates a template to satisfy an event budget. + +## Requested, normalized, and realized timing + +Every timing component preserves three layers: + +1. the requested public specification +2. the normalized internal rule representation +3. the realized occurrence-level values on a concrete `Design` + +Representative access points: + +- `Experiment.export_specification()` +- `Design.export_payload()` +- `Design.realized_event_durations` +- `Design.realized_trial_start_intervals` +- `Design.realized_post_event_intervals` +- `Design.realized_event_transition_intervals` +- `Design.realized_inter_trial_intervals` +- `Design.realized_rest_intervals` + +Requested timing specifications are distinct from normalized internal rules and from realized occurrence-level values on a sampled `Design`. + +## Selector scope + +- `trial_start_interval`: global rule or `by_trial_type` +- `post_event_interval`: global rule or `by_event_category` +- `event_transition_interval`: global rule or `by_event_transition` +- `inter_trial_interval`: global rule only +- `rest_interval`: global rule only + +Use `"default"` only inside selector wrappers that support it. + +## Duration grammar + +A scalar `x` normalizes to: + +```python +{"model": "fixed", "mean": x} +``` + +Supported models: + +- `fixed` +- `uniform` +- `exponential` +- `gaussian` + +Bounded exponential and Gaussian rules use true truncated distributions. For bounded rules, `mean` refers to the mean of the realized bounded distribution. + +Supported public rule shapes: + +```python +{"model": "fixed", "mean": value} +{"model": "uniform", "min": a, "max": b} +{"model": "uniform", "min": a, "mean": (a + b) / 2, "max": b} +{"model": "exponential", "mean": m} +{"model": "exponential", "mean": m, "min": a, "max": b} +{"model": "gaussian", "mean": m, "std": s, "min": a, "max": b} +``` + +Occurrence-level sampling happens when a `Design` is realized, not when the `Experiment` is declared. +Invalid bounds, impossible bounded means, non-finite values, and uncovered selectors raise validation errors. + +## Counts and scoring + +Version 2.0 keeps conceptual-trial counts separate from flattened event counts. + +- `n_conceptual_trials` governs trial sampling and boundary placement. +- `n_events` governs flattened event-level quantities such as `Ff` and `Fc`. +- `Fe` and `Fd` depend on realized design matrices, so they can change when timing realizations change. + +The quickest way to inspect the split is: + +```python +spec = exp.export_specification() +payload = design.export_payload() +spec["n_conceptual_trials"], payload["counts"]["n_events"] +``` + +## Convergence + +`Optimisation(convergence=k)` stops when the generation-best score fails to improve for `k` consecutive completed generations. +Set `convergence=None` or `convergence=0` to disable early stopping and run the full planned cycle count. +Equality counts as no improvement, and the current implementation has no minimum-delta tolerance. +Early stopping does not prove that a global optimum was found. + +Inspect the optimization state with: + +- `optimisation.generations_completed` +- `optimisation.stop_reason` +- `optimisation.optima` + +## Recommended workflow + +Use this public end-to-end sequence when optimisation is needed: + +```python +from pathlib import Path +import json + +from neurodesign import Experiment, Optimisation, report + +exp = Experiment(...) opt = Optimisation( experiment=exp, - weights=[0.25, 0.25, 0.25, 0.25], - preruncycles=2, - cycles=5, - seed=42, + weights=[0.0, 0.5, 0.25, 0.25], + preruncycles=1, + cycles=2, + seed=101, + optimisation="simulation", + G=3, + I=1, + outdes=1, + convergence=1, ) opt.optimise() +design = opt.selected_design(0) -# Access the best design -best = opt.bestdesign -print(f"Best overall efficiency: {best.F:.4f}") -print(f" Fe={best.Fe:.4f} Fd={best.Fd:.4f} Ff={best.Ff:.4f} Fc={best.Fc:.4f}") +report.make_report(opt, Path("report.pdf")) +Path("schedule.json").write_text( + json.dumps(design.export_payload(), indent=2), encoding="utf-8" +) +Path("specification.json").write_text( + json.dumps(exp.export_specification(), indent=2), encoding="utf-8" +) +``` + +Workflow checklist: + +1. define the `Experiment` +2. choose one construction mode: flat `order`, fixed `trial_templates` plus `trials`, or probabilistic `trial_templates` plus `trial_template_probabilities` and `n_conceptual_trials` +3. define event-duration and interval rules +4. instantiate `Optimisation` only when search is needed +5. call `optimise()` +6. inspect `generations_completed`, `stop_reason`, and `optima` +7. call `selected_design(0)` +8. inspect conceptual-trial metadata, event metadata, and realized timing +9. generate the report +10. export the schedule and specification +11. save the seed and optimisation settings with the exported files + +For fixed or manual designs with no optimisation step, use the no-search route: + +```python +design = exp.create_design(seed=12) +# or, for a manually specified flat schedule: +design = exp.create_manual_design(order=[0, 1, 0, 1], inter_trial_intervals=[2.0] * 4) ``` -## Metrics at a Glance +Do not rely on internal attributes such as `.bestdesign`, raw `designs` indexing, or undocumented schedule internals in user workflows. + +## Reproducibility + +Version 2.0 reproducibility is controlled by the package seed and NumPy `Generator` architecture, not by NumPy global seeding. -The optimiser evaluates designs on four efficiency metrics: +With the same complete specification and seed, independent runs reproduce: -| Metric | Name | Measures | -|--------|------|----------| -| **Fe** | Estimation Efficiency | Precision of HRF shape estimation when events overlap | -| **Fd** | Detection Efficiency | Power to detect activation assuming canonical HRF | -| **Fc** | Confounding Efficiency | Balance of stimulus transition frequencies | -| **Ff** | Frequency Accuracy | Match between realised and target trial counts | +- sampled trial-template sequences +- realized event durations and interval values +- selected designs +- scores +- exported schedule payloads -You control the trade-off between these metrics via the `weights` parameter. See the [metrics guide](metrics.md) for details. +Rendered PDFs and PNGs are not guaranteed to be byte-identical unless a specific artifact path documents that guarantee. -## What Next? +## Next steps -- Learn about the [efficiency metrics](metrics.md) used to score designs -- Explore the [new features](new_features.md) added in neurodesign-plus -- See the full [API reference](api_reference.md) -- Work through the [tutorials](tutorials.md) for hands-on examples +- Read the [migration guide](migration.md) for old-to-new API mapping +- Review the [metrics guide](metrics.md) +- Browse the executed [tutorial notebooks](tutorials.md) +- Use the [API reference](api_reference.md) for class and method signatures diff --git a/docs/index.md b/docs/index.md index dc65539..7c526dd 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,23 +1,21 @@ -# neurodesign-plus +# neurodesign-plus 2.0 -**Extended and maintained fork of the neurodesign package for fMRI experimental design optimisation.** +**Trial-aware fMRI experimental design optimisation for flat one-event and multi-event conceptual trials.** -`neurodesign-plus` extends the original [neurodesign](https://github.com/neuropower/neurodesign) package with support for variable stimulus durations, conditional inter-trial intervals, user-defined stimulus ordering, and probabilistic ordering. +`neurodesign-plus` is the install distribution. +`neurodesign` is the Python import package. -## Quick Install +Version 2.0 keeps the public classes `Experiment`, `Design`, and `Optimisation`, but the active teaching surface is now trial-aware: -```bash -pip install neurodesign-plus -``` - -## Key Features +- conceptual trials can contain one or more modeled events +- flat `order` inputs are shorthand for one-event conceptual trials +- fixed workflows use complete trial templates +- probabilistic workflows sample complete templates for `n_conceptual_trials` +- timing is separated into event, within-trial, between-trial, and optional rest roles +- requested specifications, normalized rules, and realized timing arrays remain distinct -- **Genetic algorithm optimisation** of fMRI experimental designs -- **Variable stimulus durations** -- specify per-condition distribution-based durations -- **Conditional ITIs** -- inter-trial intervals that depend on stimulus transitions -- **Fixed or probabilistic ordering** -- inject user-defined or probability-sampled stimulus orders -- **Four efficiency metrics** -- Estimation (Fe), Detection (Fd), Confounding (Fc), Frequency (Ff) -- **PDF reports** -- automatic generation of optimisation result summaries +Version 2.0 is intentionally source-breaking. +For legacy-to-current mapping, use the [migration guide](migration.md). ```{toctree} :maxdepth: 2 @@ -25,6 +23,7 @@ pip install neurodesign-plus installation getting_started +migration metrics new_features ``` diff --git a/docs/installation.md b/docs/installation.md index 95a2819..9419888 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -1,65 +1,81 @@ # Installation -## From PyPI (recommended) +`neurodesign-plus` is the install distribution. +`neurodesign` is the import package. + +## End-User Installation From PyPI ```bash -pip install neurodesign-plus +python -m pip install --upgrade pip +python -m pip install neurodesign-plus ``` -This installs the package and all required dependencies. You can then use it as: +Use it as: ```python from neurodesign import Experiment, Design, Optimisation ``` -:::{note} -The PyPI package is called `neurodesign-plus`, but the Python import remains `neurodesign`. -This fork replaces the upstream `neurodesign` package -- do **not** install both. -::: - -## From Source (development) +The base install already includes report-generation dependencies. +No separate report extra exists in `pyproject.toml`. -For development or to access the latest unreleased changes: +## Local Source Installation -### 1. Clone the repository +Clone the repository and create a virtual environment: ```bash git clone https://github.com/SLDlab/neurodesign-plus.git cd neurodesign-plus -``` - -### 2. Create a virtual environment - -```bash python -m venv .venv ``` -### 3. Activate the virtual environment +Activate the environment: -**macOS / Linux:** +macOS / Linux: ```bash source .venv/bin/activate ``` -**Windows (PowerShell):** +Windows PowerShell: ```powershell -.venv\Scripts\Activate.ps1 +.\.venv\Scripts\Activate.ps1 +``` + +Install the local checkout: + +```bash +python -m pip install . ``` -### 4. Install in editable mode +## Editable Development Installation + +Install the editable package plus the declared development extras: ```bash -pip install -e ".[dev]" +python -m pip install -e ".[dev]" ``` -This installs the package in editable mode along with all development dependencies (testing, linting, documentation). +The `dev` extra includes the maintained `doc` and `test` extras plus `pre-commit` and `tox`. -### 5. (Optional) Jupyter kernel +## Test And Notebook Dependencies -If you are working with the tutorial notebooks, register the environment as a Jupyter kernel: +Install the package plus the declared testing and notebook execution dependencies: ```bash -python -m ipykernel install --user --name=neurodesign-plus --display-name "Python (neurodesign-plus)" +python -m pip install -e ".[test]" ``` + +## Documentation Dependencies + +Install the package plus the maintained Sphinx toolchain: + +```bash +python -m pip install -e ".[doc]" +``` + +## Version-2 Migration + +Version 2.0 is a clean breaking API release. +Use the [migration guide](migration.md) for version-1 name mapping and workflow changes instead of trying to preserve removed timing aliases in-place. diff --git a/docs/migration.md b/docs/migration.md new file mode 100644 index 0000000..a527cba --- /dev/null +++ b/docs/migration.md @@ -0,0 +1,5 @@ +# Migration Guide + +```{include} ../MIGRATION_2.0.md +:relative-docs: . +``` diff --git a/docs/tutorials.md b/docs/tutorials.md index a6771c9..c1225bf 100644 --- a/docs/tutorials.md +++ b/docs/tutorials.md @@ -1,36 +1,35 @@ # Tutorials -Interactive Jupyter notebook tutorials are available in the -[tutorials/ directory](https://github.com/SLDlab/neurodesign-plus/tree/master/tutorials) -of the repository. - -## Overview Tutorials - -| # | Tutorial | Description | -|---|---------|-------------| -| 1 | [Base Overview](https://github.com/SLDlab/neurodesign-plus/blob/master/tutorials/tutorial_1-neurodesign_base_overview.ipynb) | Introduction to the core neurodesign workflow | -| 2 | [Comparing Designs Across Experiments](https://github.com/SLDlab/neurodesign-plus/blob/master/tutorials/tutorial_2-comparing_designs_across_experiments.ipynb) | Comparing designs across diverse experiment definitions | -| 3 | [Progressive Experiment Building](https://github.com/SLDlab/neurodesign-plus/blob/master/tutorials/tutorial_3-progressive_experiment_building.ipynb) | Build event-level designs progressively from simple to complex task structures | - -## Base Function Tutorials - -| # | Tutorial | Description | -|---|---------|-------------| -| 4 | [Designing, Scoring, and Optimising](https://github.com/SLDlab/neurodesign-plus/blob/master/tutorials/base_functions/tutorial_base-designing_scoring_and_optimizing.ipynb) | Core workflow: designing, scoring, and optimising | -| 5 | [Comparing Designs](https://github.com/SLDlab/neurodesign-plus/blob/master/tutorials/base_functions/tutorial_base-comparing_designs.ipynb) | Side-by-side design comparison | -| 6 | [Discovering Best Design](https://github.com/SLDlab/neurodesign-plus/blob/master/tutorials/base_functions/tutorial_base-discovering_best_design.ipynb) | Finding the optimal design | -| 7 | [Optimising and Reporting](https://github.com/SLDlab/neurodesign-plus/blob/master/tutorials/base_functions/tutorial_base-optimizating_and_reporting.ipynb) | Running optimisation and generating reports | - -## New Feature Tutorials - -| # | Tutorial | Description | -|---|---------|-------------| -| 8 | [Controlled Probabilistic Ordering with Event Templates](https://github.com/SLDlab/neurodesign-plus/blob/master/tutorials/new_functions/tutorial_new-probabilistic_ordering.ipynb) | Sample event-level orders from probabilistic motifs rather than single-event draws | -| 9 | [Fixed Stimulus Order: Optimize Timing Without Changing the Task Sequence](https://github.com/SLDlab/neurodesign-plus/blob/master/tutorials/new_functions/tutorial_new-fixed_ordering.ipynb) | Keep the event sequence fixed while optimisation focuses on timing | -| 10 | [Transition-Specific ITIs with `conditional_ITI`](https://github.com/SLDlab/neurodesign-plus/blob/master/tutorials/new_functions/tutorial_new-conditional_ITI.ipynb) | Use transition-dependent ITI distributions keyed by `(previous_event, current_event)` | -| 11 | [Variable Stimulus Durations with `stimuli_durations`](https://github.com/SLDlab/neurodesign-plus/blob/master/tutorials/new_functions/tutorial_new-varied_stimuli_durations.ipynb) | Assign fixed or distribution-based durations per stimulus class | - -:::{tip} -To run these notebooks locally, install the package in development mode and register the -Jupyter kernel. See the [installation guide](installation.md) for details. -::: +The maintained tutorial surface contains 11 notebooks. +They are regenerated from `validation/helpers/regenerate_v2_notebooks.py` and executed from clean kernels during validation. + +## Maintained Notebook Inventory + +| Notebook | Focus | +|---|---| +| `tutorials/tutorial_1-neurodesign_base_overview.ipynb` | Cases 1 and 2: flat one-event shorthand and fixed conceptual trials | +| `tutorials/tutorial_2-comparing_designs_across_experiments.ipynb` | Comparison workbook for requested versus realized timing | +| `tutorials/tutorial_3-progressive_experiment_building.ipynb` | Canonical integrated Case 10 workflow | +| `tutorials/base_functions/tutorial_base-comparing_designs.ipynb` | Case 3: comparing conceptual-trial counts, event counts, and realized schedules | +| `tutorials/base_functions/tutorial_base-designing_scoring_and_optimizing.ipynb` | Case 4: scoring and patience-based stopping semantics | +| `tutorials/base_functions/tutorial_base-discovering_best_design.ipynb` | Case 5: authoritative design retrieval through `selected_design(0)` | +| `tutorials/base_functions/tutorial_base-optimizing_and_reporting.ipynb` | Case 6: report generation and reconstructable exports | +| `tutorials/new_functions/tutorial_new-event_and_trial_intervals.ipynb` | Case 7: within-trial versus between-trial interval roles | +| `tutorials/new_functions/tutorial_new-fixed_ordering.ipynb` | Case 8: fixed conceptual-trial sequences and no-optimization routes | +| `tutorials/new_functions/tutorial_new-probabilistic_ordering.ipynb` | Case 9: complete-template sampling | +| `tutorials/new_functions/tutorial_new-variable_event_durations.ipynb` | Event-duration rules and `Xnonconv` occupancy | + +## Workflow Rules Taught Consistently + +The maintained notebooks teach these public version-2 rules consistently: + +- use `Experiment` to declare the requested specification +- use `create_design(...)` or `create_manual_design(...)` for no-search workflows +- use `Optimisation` only when search is needed +- retrieve the authoritative design with `selected_design(0)` after `optimise()` +- generate reports and exports from that selected design workflow +- distinguish `n_conceptual_trials` from flattened `n_events` +- treat `Fe` and `Fd` as realized design-matrix metrics +- treat `Ff` and `Fc` as flattened event-axis metrics + +Notebook execution is automated through `validation.execute_notebooks` and covered by the aggregate validation runner in `validation.run_all`. diff --git a/manuals/METRICS.md b/manuals/METRICS.md index 2f4c441..34f1f67 100644 --- a/manuals/METRICS.md +++ b/manuals/METRICS.md @@ -1,214 +1,178 @@ -# Neurodesign Efficiency Metrics: A Complete Guide +# Neurodesign-Plus 2.0 Metrics Guide -Neurodesign optimizes fMRI experimental designs by evaluating them on four efficiency metrics. This document explains what each metric measures, how they are computed, how normalization works, and β€” critically β€” when you can and cannot compare metric values across different experimental setups. +## Scope -## Overview +Version 2.0 keeps the four public design metrics: -Neurodesign separates the specification of an experiment from the evaluation of candidate designs. +- `Fe` +- `Fd` +- `Ff` +- `Fc` -First, define an Experiment: the fixed scientific and acquisition assumptions that determine the β€œruler” used to score designs (e.g., TR, total duration or trial budget, autocorrelation parameter rho, target probabilities, contrasts). These settings define how the convolved and deconvolved design matrices are constructed and, for Fe/Fd, fix the whitening matrix π‘Š. +The scheduling model is now trial-aware, but the metric axes remain event-aware where appropriate: -Second, generate and compare Designs: alternative realizations of event timing and ordering that share the same Experiment base. The efficiency metrics then quantify how well each Design performs relative to that base. +- `Fe` and `Fd` depend on the realized timing arrays and the realized design matrices +- `Ff` and `Fc` are computed over flattened event categories +- `Ff` and `Fc` therefore use `n_events`, not `n_conceptual_trials` -A practical consequence is that raw Fe and Fd are meaningful for comparing Designs within the same Experiment, and across Experiments only when π‘Š is identical (in practice: same duration, TR, and rho). If you change those, you changed the ruler, so the absolute values are on different scales. +## Conceptual Trials Versus Flattened Events ---- +Conceptual trials determine: -## The Four Metrics +- trial-template sampling +- trial boundaries +- `trial_start_interval` +- `inter_trial_interval` +- `rest_interval` -### Fe β€” Estimation Efficiency +Flattened modeled events determine: -**What it measures:** How precisely the design allows you to estimate the shape and amplitude of each condition's hemodynamic response, even when events overlap in time. +- event rows in the exported schedule +- event occupancy in `Xnonconv` +- `Ff` +- `Fc` -**Mathematical definition (A-optimality):** +In a flat one-event design, `n_conceptual_trials == n_events`. +In a fixed or probabilistic template design, `n_events` is usually larger because each conceptual trial can contain multiple events. -``` -Fe_raw = n_contrasts / trace( C Γ— inv(X'WX) Γ— C' ) -``` - -Where: - -- `X` is the deconvolved design matrix (stimuli Γ— HRF lag bins), downsampled to TR resolution -- `W` is the whitening matrix (accounts for temporal autocorrelation and drift) -- `C` is the contrast matrix expanded to HRF resolution (`CX = kron(C, I_laghrf)`) -- `trace(C Γ— inv(X'WX) Γ— C')` is the sum of variances of your contrast estimates - -**Interpretation:** Fe is the average _precision_ (inverse variance) of your contrast estimates. Higher Fe means the design allows you to more precisely estimate the differences between conditions specified by your contrast matrix. If Design A has twice the Fe of Design B, it means Design A yields contrast estimates with half the variance β€” you'd need roughly twice as many subjects with Design B to match Design A's precision. - -**When it matters most:** Event-related designs where stimuli are closely spaced and temporal overlap is a concern. Fe rewards designs that create orthogonal (non-overlapping) patterns across conditions. - -### Fd β€” Detection Efficiency - -**What it measures:** How well the design supports detecting activation differences between conditions, assuming the canonical HRF shape is correct. - -**Mathematical definition (A-optimality):** - -``` -Fd_raw = n_contrasts / trace( C Γ— inv(Z'WZ) Γ— C' ) -``` - -Where `Z` is the _convolved_ design matrix (each column is the stimulus train convolved with the canonical HRF), as opposed to the deconvolved `X` used for Fe. +## Design-Matrix Dependence -**Interpretation:** Fd reflects the design's ability to detect that a contrast is nonzero, given that the HRF has the canonical shape. It rewards designs where the convolved regressors for different conditions are maximally distinguishable from each other and from noise. +`Fe` is computed from the deconvolved design matrix. +`Fd` is computed from the convolved design matrix. -**When it matters most:** Blocked or mixed designs where you trust the canonical HRF and want to maximize statistical power for detecting activation. In practice, blocked designs tend to have higher Fd than rapid event-related designs. +Both metrics depend on the realized timing state of the sampled `Design`, including: -**Fe vs Fd:** Fe is about _estimating_ the response (flexible HRF shape), Fd is about _detecting_ it (assuming canonical shape). These are sometimes in tension β€” designs that are optimal for one are often suboptimal for the other, which is why you can weight them differently. +- realized event durations +- realized within-trial transition intervals +- realized between-trial intervals +- realized rest boundaries -### Fc β€” Confounding Efficiency +That means two designs drawn from the same `Experiment` can produce different `Fe` and `Fd` values when their realized timing differs. -**What it measures:** Whether the sequence of conditions is balanced in terms of transitions β€” i.e., that no condition systematically follows another condition more than expected by chance. - -**Computation:** Fc compares the observed transition matrix (how often condition _i_ follows condition _j_, up to 3rd order) against the expected transition matrix (based on the specified probabilities). The closer the match, the higher Fc. - -``` -Fc = 1 - |Q_observed - Q_expected| / FcMax -``` - -Where `FcMax` is calibrated from a worst-case (all-same-stimulus) null design. - -**Interpretation:** Fc = 1 means transitions perfectly match what you'd expect from random independent draws at the specified probabilities. Fc = 0 means transitions are maximally biased (e.g., stimulus 0 always follows stimulus 1). Values near 1 are desirable to avoid confounding condition effects with transition effects. - -**When it matters most:** Any design where you worry about carry-over effects, adaptation, or expectation. High Fc means the design won't accidentally create systematic patterns (like alternation or repetition) that could confound your contrasts. - -### Ff β€” Frequency Accuracy - -**What it measures:** Whether the realized number of trials per condition matches the prescribed probabilities. - -**Computation:** - -``` -Ff = 1 - |P_observed - P_expected| / FfMax -``` +`Xnonconv` contains occupancy only during modeled event durations. +It is zero during: -**Interpretation:** If you specified P = [0.3, 0.3, 0.4] and got trial counts of [6, 6, 8] out of 20 trials, that's a perfect match (Ff = 1). If you got [10, 5, 5], there's a mismatch (lower Ff). This metric ensures the optimizer doesn't accidentally over- or under-represent conditions. +- `trial_start_interval` +- `post_event_interval` +- `event_transition_interval` +- `inter_trial_interval` +- `rest_interval` -**When it matters most:** When you have specific requirements about how many trials of each type are needed β€” for example, to ensure enough power per condition, or when trial counts need to match a specific ratio for your analysis. +`Xconv` can remain nonzero during those periods because the HRF persists after modeled event offset. ---- +## Event-Axis Metrics -## Normalization: Raw Values vs Calibrated Values +`Ff` measures how closely the realized flattened event counts match the target category probabilities `P`. -### The two regimes +`Fc` measures balance of flattened event-category transitions up to the configured confound order. -Neurodesign metrics can appear in two forms: +For fixed and probabilistic template designs: -**Raw (uncalibrated):** `FeMax = 1`, so `Fe = Fe_raw`. This is what you get when you manually create an Experiment and Designs, then call `FeCalc()`. The values can be any positive number (typically 10–300 for Fe/Fd depending on your experiment). +- `Ff` does not become a trial-template frequency metric +- `Fc` does not become a trial-template transition metric +- both remain defined on the flattened event stream produced by the realized schedule -**Calibrated (normalized):** `FeMax` is set to the best Fe found during an optimization pre-run, so `Fe = Fe_raw / FeMax ∈ [0, 1]`. This is what happens inside `Optimisation.optimise()`. +## Raw Versus Normalized Values -### How calibration works +`Ff` and `Fc` are normalized against event-count-dependent reference mismatches, so they remain on the familiar balance scale used by the package. -When you call `optimise()`, the optimizer: +`Fe` and `Fd` are divided by `Experiment.FeMax` and `Experiment.FdMax`. +If those maxima remain at their default value of `1`, `Optimisation.optimise()` estimates empirical prerun references when the corresponding weights are positive. +Because those references are empirical rather than mathematical upper bounds, a later selected design can exceed `1.0`. -1. Runs a pre-optimization with `weights=[1, 0, 0, 0]` (only Fe) for `preruncycles` generations -2. Takes the best Fe found and sets `exp.FeMax = best_Fe_raw` -3. Repeats for Fd with `weights=[0, 1, 0, 0]` to calibrate `exp.FdMax` -4. Fc and Ff are calibrated during `Experiment.__init__` using a worst-case null design +Interpretation rule: -After calibration, all four metrics are on a [0, 1] scale, and the weighted sum `F = w₁·Fe + wβ‚‚Β·Fd + w₃·Ff + wβ‚„Β·Fc` becomes meaningful. +- within one optimization run, larger values are better +- values above `1.0` for `Fe` or `Fd` mean the selected design beat the empirical prerun reference, not that the implementation is wrong -### When to use which +## Weighted Objective -- **Manual design comparison (same Experiment):** Use raw Fe/Fd. They are directly comparable and ratios are meaningful. A design with Fe=200 has exactly 10Γ— the estimation precision of one with Fe=20. +`Optimisation` combines the component scores as: -- **During optimization:** Calibrated values are used automatically. You don't need to do anything. - -- **Cross-experiment comparison:** See the next section. - ---- - -## Comparing Metrics Across Experiments - -### The fundamental rule - -**Fe and Fd values are only directly comparable when the whitening matrix `W` is identical.** - -The whitening matrix depends on three things: - -1. **`n_scans`** = ceil(duration / TR) -2. **`rho`** (autocorrelation coefficient) -3. **Drift polynomials** (derived from `n_scans`) - -Therefore: two Experiments with the **same `duration`, `TR`, and `rho`** produce **identical `W`**, and their Fe/Fd values live on the same scale. Everything else (ITI model, stim duration, number of stimuli, contrasts, probabilities) affects the _design matrix_ but not the _ruler_ used to measure it. - -### The common trap: specifying n_trials - -When you specify `n_trials` instead of `duration`, the code computes: - -``` -duration = n_trials Γ— (trial_duration + ITImean) +```python +F = w_fe * Fe + w_fd * Fd + w_ff * Ff + w_fc * Fc ``` -If two Experiments have different `ITImean` but the same `n_trials`, they will have **different durations**, **different `n_scans`**, **different `W`**, and their Fe/Fd values are **not comparable**. This is not a bug β€” it's a fundamental property. A longer experiment gives you more data, which inherently changes the precision you can achieve. +The objective uses the scores stored on the realized `Design`. -### How to compare ITI models correctly +## Current Flat Example -**Method 1: Fix duration (recommended for comparing design efficiency)** - -Specify `duration=` instead of `n_trials=` when creating both Experiments. The duration determines `n_scans` and `W`, which become identical. Differences in ITI model will change the number of trials that fit and the arrangement of events, which is exactly what you're comparing. +This example is covered by the release-audit tests. ```python -# CORRECT: same duration β†’ same W β†’ Fe is comparable -exp_A = Experiment(duration=300, ITImodel="exponential", ITImean=2.1, ...) -exp_B = Experiment(duration=300, ITImodel="uniform", ITImean=3.0, ...) -# exp_A.n_scans == exp_B.n_scans β†’ raw Fe directly comparable +from neurodesign import Experiment + +exp = Experiment( + TR=2.0, + n_trials=8, + P=[0.5, 0.5], + C=[[1, -1]], + rho=0.3, + n_stimuli=2, + event_durations=1.0, + trial_start_interval=0.5, + post_event_interval=0.2, + inter_trial_interval=2.0, + resolution=0.1, + seed=7, +) + +design = exp.create_design(seed=7) +design.designmatrix().FCalc(weights=[0.0, 0.5, 0.25, 0.25]) ``` -**Method 2: Fix n_trials and compare power (recommended for fixed-trial protocols)** +## Current Optimisation Example -If your experiment must have exactly N trials regardless of timing, then the right comparison is not Fe but _statistical power_ β€” the probability of detecting your effect at a given significance level and effect size. This accounts for both design efficiency and total data volume. +This workflow is also exercised by the release-audit tests. ```python -# ALSO VALID: same n_trials, compare power via simulation -exp_A = Experiment(n_trials=40, ITImodel="exponential", ITImean=2.1, ...) -exp_B = Experiment(n_trials=40, ITImodel="uniform", ITImean=3.0, ...) -# Fe values are NOT comparable, but power analysis gives the right answer +from neurodesign import Experiment, Optimisation + +exp = Experiment( + TR=2.0, + n_trials=8, + P=[0.5, 0.5], + C=[[1, -1]], + rho=0.3, + n_stimuli=2, + event_durations=1.0, + trial_start_interval=0.5, + post_event_interval=0.2, + inter_trial_interval=2.0, + resolution=0.1, + seed=7, +) + +optimisation = Optimisation( + experiment=exp, + weights=[0.0, 0.5, 0.25, 0.25], + preruncycles=1, + cycles=1, + optimisation="simulation", + G=2, + I=1, + outdes=1, + convergence=1, + seed=101, +) +optimisation.optimise() +design = optimisation.selected_design(0) ``` -### Summary table - -| Scenario | Same `W`? | Raw Fe/Fd comparable? | What to do | -| ------------------------------------------------------------- | --------- | --------------------- | ----------------------------------------- | -| Same Experiment, different Design | Yes | Yes | Compare raw Fe directly | -| Different Experiment, same `duration`/`TR`/`rho` | Yes | Yes | Compare raw Fe directly | -| Different Experiment, same `n_trials` but different `ITImean` | **No** | **No** | Use `duration=` instead, or compare power | -| Different `TR` | **No** | **No** | Use power analysis | -| Different `rho` | **No** | **No** | Use power analysis | - -### Fc and Ff comparisons - -Fc and Ff depend only on the stimulus order and probabilities, not on the whitening matrix. They are always comparable across any two designs with the same number of stimuli and the same target probabilities, regardless of timing parameters. - ---- - -## Practical Recommendations - -### Choosing weights - -The weight vector `[w_Fe, w_Fd, w_Ff, w_Fc]` controls the tradeoff between metrics. Common choices: - -- **`[0.25, 0.25, 0.25, 0.25]`**: Balanced β€” no strong preference. Good starting point. -- **`[1, 0, 0, 0]`**: Pure estimation efficiency. Best when you want to estimate HRF shape freely (e.g., FIR models). -- **`[0, 1, 0, 0]`**: Pure detection power. Best for blocked designs or when you trust the canonical HRF. -- **`[0, 0.5, 0.25, 0.25]`**: Detection-focused with frequency and confound balance. Common for standard GLM analyses. - -### Interpreting Fe/Fd magnitudes - -Raw (uncalibrated) Fe/Fd values are hard to interpret in isolation. What matters is: - -- **Relative ranking:** Within the same Experiment, higher is always better. -- **Ratios:** Fe=200 has 2Γ— the precision of Fe=100. This is linear. -- **Normalized values:** Fe=0.85 (calibrated) means "85% as good as the best design the optimizer found." This tells you how much room there is for improvement. +Useful inspection points: -### When Fe and Fd conflict +- `design.export_payload()["counts"]["n_events"]` +- `optimisation.exp.export_specification()["n_conceptual_trials"]` +- `optimisation.generations_completed` +- `optimisation.stop_reason` +- `design.Fe`, `design.Fd`, `design.Ff`, `design.Fc`, `design.F` -For rapid event-related designs, Fe and Fd often point in opposite directions. Jittered ITIs and randomized orders help Fe (by decorrelating the design matrix) but can hurt Fd (by spreading activation over time). If you need both, use mixed weights and accept that neither will be at its theoretical maximum. +## Convergence -### Design matrix sparsity warning +`convergence=k` means patience-based early stopping after `k` consecutive completed generations with no strict improvement in the generation-best objective score. -When using variable ITIs (exponential model), some randomly generated designs may produce ITI sequences that cause the total experiment duration to exceed the container. These designs are automatically rejected during optimization. If you see many rejected designs (slow optimization), consider: +- equality counts as no improvement +- there is no minimum-delta tolerance in the current implementation +- early stopping does not prove a global optimum -1. Using a longer `duration` to give more headroom -2. Using `ITImax` to bound the tail of the exponential distribution -3. Using `trial_max` to set the container size based on the longest possible trial +When optimization is disabled entirely, metric calculations on a direct `Design` still use the same definitions. diff --git a/manuals/SETUP.md b/manuals/SETUP.md index 7aa0f9a..06be619 100644 --- a/manuals/SETUP.md +++ b/manuals/SETUP.md @@ -1,95 +1,104 @@ -# Installation & Setup +# Installation And Setup -This `neurodesign-plus` repository provides an extended and maintained fork of the `neurodesign` package. -These instructions are for working with the source code locally (development or research use). +`neurodesign-plus` is the install distribution. +`neurodesign` is the Python import package. ---- +The project uses `pyproject.toml` as the maintained installation surface. These instructions use `.venv` consistently and avoid a stale `requirements.txt` workflow. -## I. Clone the repository +## Create And Activate A Virtual Environment -Clone the `neurodesign-plus` repository and move into it: +From the repository root: ```bash -git clone https://github.com/SLDlab/neurodesign-plus.git -cd neurodesign-plus +python -m venv .venv ``` ---- - -## II. Create a virtual environment - -Create a Python virtual environment from the repository root: +macOS / Linux: -You can do this either through the VS Code interface or directly in the terminal. +```bash +source .venv/bin/activate +``` -**A. Using VS Code GUI:** +Windows PowerShell: -1. Open the Command Palette: - `View > Command Palette` -2. Search for and select: - `Python: Select Interpreter` -3. Click: - `+ Create Environment` -4. Choose the environment type: - `venv` -5. Select a Python interpreter (version `>= 3.9`, e.g., `Python 3.13.1`) +```powershell +.\.venv\Scripts\Activate.ps1 +``` -**B. Using the Terminal:** +## End-User Installation From PyPI -From the root of the project folder, run: +Install the published package and its runtime dependencies: ```bash -python -m venv venv +python -m pip install --upgrade pip +python -m pip install neurodesign-plus ``` ---- +Use it as: -## III. Activate the virtual environment +```python +from neurodesign import Experiment, Design, Optimisation +``` -macOS / Linux: +## Local Source Installation + +Install the current checkout as a standard local package: ```bash -source .venv/bin/activate +python -m pip install . ``` -Windows (PowerShell): +This is the right choice when you want the local code without editable development tooling. + +## Editable Development Installation + +Install the editable package plus all declared development extras: ```bash -.venv\Scripts\Activate.ps1 +python -m pip install -e ".[dev]" ``` ---- +The `dev` extra includes the declared `doc` and `test` extras plus `pre-commit` and `tox`. -## IV. Install dependencies and the package (editable mode) +## Test And Notebook Dependencies -With the virtual environment activated, install the dependencies: +If you only need the package plus test and notebook execution dependencies: ```bash -pip install -r requirements.txt -pip install . +python -m pip install -e ".[test]" ``` -To deactivate the environment when you're done: +The `test` extra installs `pytest`, coverage tools, and notebook execution dependencies such as `nbclient`, `nbformat`, and `nbmake`. + +## Documentation Dependencies + +If you only need the package plus documentation build dependencies: ```bash -deactivate +python -m pip install -e ".[doc]" ``` ---- +The `doc` extra installs the maintained Sphinx toolchain declared in `pyproject.toml`. -## V. (Optional) Use the environment in Jupyter notebooks +## Report Dependencies -If you're working in `.ipynb` notebooks, register the virtual environment as a Jupyter kernel: +No extra is currently required for report generation. +The base runtime install already includes the report stack used by version 2.0: `matplotlib`, `reportlab`, and `pdfrw`. + +## Notebook Kernel Registration + +To expose the local environment as a Jupyter kernel: ```bash -python -m ipykernel install --user --name=venv --display-name "Python (neurodesign venv)" +python -m ipykernel install --user --name neurodesign-plus --display-name "Python (neurodesign-plus)" ``` -Reload VSC. Then, in Jupyter, select the `"Python (neurodesign venv)"` kernel when working on notebooks. +## Verification ---- +Quick import check: -## Notes +```bash +python -c "from neurodesign import Experiment, Design, Optimisation; print(Experiment.__module__)" +``` -- This fork replaces the upstream `neurodesign` package. -- Do not install the upstream repository separately. +The import should resolve from `neurodesign`, not from a separate upstream package. diff --git a/manuals/TECHNICAL_CHANGES.md b/manuals/TECHNICAL_CHANGES.md index 9882fa6..c8cc662 100644 --- a/manuals/TECHNICAL_CHANGES.md +++ b/manuals/TECHNICAL_CHANGES.md @@ -1,155 +1,199 @@ -# Technical Documentation: Refactoring Details +# Technical Changes In 2.0 -This document outlines the specific internal modifications made to `classes.py` in `neurodesign-plus`. It focuses on the new class variables, helper methods, and behavior changes in the `Design`, `Experiment`, and `Optimisation` classes. +## Authoritative Implementation ---- +Version 2.0 keeps the public classes: -## Architecture Overview +- `Experiment` +- `Design` +- `Optimisation` -The refactored code preserves a strict separation between the **Experiment** and the **Design**: +The authoritative implementation lives in [neurodesign/classes.py](../neurodesign/classes.py). +Both public import paths resolve to those same class objects: -- **Experiment** stores the fixed specification of the task container: HRF settings, whitening matrix inputs, ITI summaries, and duration specifications. -- **Design** stores one concrete sampled realization: event order, ITIs, and optionally per-event sampled stimulus durations. +```python +from neurodesign import Experiment, Design, Optimisation +from neurodesign.classes import Experiment, Design, Optimisation +``` -This separation is important because all designs in a population share the same experiment container and therefore the same whitening matrix. That keeps raw Fe and Fd values comparable across designs generated within that experiment. +## Trial-Aware Normalization ---- +`Experiment` now normalizes three construction modes: -## 1. Added Class Variables +- flat one-event shorthand +- fixed complete trial-template sequences +- probabilistic complete-template sampling -### **Design Class** +For template modes, the implementation separates: -* **`all_stim_durations`** `(list of floats or None)` - * Concrete per-trial stimulus durations for this design, including `t_pre` and `t_post`. - * `None` when all stimuli share the same `stim_duration`. - * When set, `designmatrix()` uses these per-trial values instead of `experiment.stim_duration`. - * Sampled via `Experiment.sample_stim_durations()` and passed at construction: `Design(order=..., ITI=..., experiment=..., all_stim_durations=...)`. +- template definitions +- realized trial instances +- flattened modeled events -### **Experiment Class** +Normalization establishes: -* **`stimuli_durations`** `(list of int/float or dicts, or None)` - * A specification template -- one entry per stimulus -- defining how to sample each stimulus's duration. - * Each entry is either a scalar (fixed duration) or a dict specifying a distribution: - ```python - stimuli_durations = [ - {"model": "fixed", "mean": 1.0}, - {"model": "exponential", "mean": 2.0, "min": 1.0, "max": 5.0}, - 1.5, - ] - ``` - * Length must equal `n_stimuli`. - * Requires `trial_max` to be specified. - * If not provided, all stimuli use the original `stim_duration`. +- `n_conceptual_trials` +- template IDs +- trial types +- event categories and event codes +- event-, trial-, within-trial-transition-, and between-trial cardinalities -* **`conditional_ITI`** `(dict or None)` - * Specification for transition-dependent ITI distributions. - * Keys are `(prev_stim, curr_stim)` tuples or `"default"`. - * Values are dicts with `"model"`, `"mean"`, and optional `"min"`, `"max"`, `"std"`. - * Example: - ```python - conditional_ITI = { - (0, 1): {"model": "exponential", "mean": 2, "min": 1}, - (1, 2): {"model": "fixed", "mean": 4}, - "default": {"model": "exponential", "mean": 3, "min": 1}, - } - ``` +Version 2.0 does not truncate templates to fit an event budget. +Probabilistic template mode samples whole conceptual trials only. -* **`order`** `(list of ints or None)` - * A user-provided fixed event order. - * When provided, `order_fixed` is set to `True`. - * The order is preserved across all designs during optimization -- crossover and mutation do not modify it. +## Timing-Rule Parsing -* **`trial_max`** `(float)` - * The maximum raw stimulus duration across all conditions. - * Required when `stimuli_durations` is provided. - * Used to compute the experiment container's `trial_duration = trial_max + t_pre + t_post`. +All public timing components share one parser: -#### **Sequence Generation Variables** +- `event_durations` +- `trial_start_interval` +- `post_event_interval` +- `event_transition_interval` +- `inter_trial_interval` +- `rest_interval` -* **`order_keys`** `(list of integer lists)` -- Sequences sampled as units. -* **`order_probabilities`** `(list of floats)` -- Probabilities for each key. -* **`order_length`** `(int)` -- Requested length of the final event-level order. +Supported rule forms are: -These three are inputs for `sample_from_probabilities()`. When `order_probabilities` is provided, the optimizer generates event orders by sampling from these sequence templates rather than using the original blocked, random, or m-sequence generators. Internally, the sampled templates are flattened and then truncated to the first `order_length` events. +- scalar shorthand +- explicit `{"model": ...}` rules +- selector wrappers such as `by_event_category`, `by_trial_type`, and `by_event_transition` ---- +Normalization converts those public forms into canonical internal rule objects before any schedule is realized. -## 2. New Functions +## Requested, Normalized, And Realized State -### **Experiment Class (all `@staticmethod`)** +Version 2.0 keeps three separate timing layers: -* **`sample_stim_durations(order, stimuli_durations, t_pre, t_post)`** -> `list[float]` - * Samples concrete per-trial stimulus durations for a given order, using the specifications in `stimuli_durations`. - * Adds `t_pre + t_post` to each sampled duration. - * Supports `"fixed"`, `"exponential"`, `"uniform"`, and `"gaussian"` models, as well as scalar values. +1. requested public specifications on `Experiment` +2. normalized internal rules on `Experiment` +3. realized occurrence-level values on `Design` -* **`generate_iti(order, conditional_iti)`** -> `list[float]` - * Samples a concrete ITI array from a transition-dependent ITI specification. - * Uses the `"default"` key for the first event and for any transition not explicitly listed. - * Supports `"fixed"`, `"exponential"`, `"uniform"`, and `"gaussian"` models. - * Returns a list of length `len(order)`. +`Experiment.export_specification()` preserves the requested surface. +`Design.export_payload()` preserves the realized schedule, realized timing arrays, metrics, and counts. -* **`calculate_duration(ITI, dur)`** -> `float` - * Computes total duration as the sum of all ITIs and all trial durations. +## Bounded Distribution Semantics -* **`sample_from_probabilities(prob, key, length)`** -> `list` - * Samples templates from `key` with weights `prob`. - * Flattens the sampled templates into one event-level order. - * Returns the first `length` events from that flattened order. - * Because the final list is truncated to `length`, the last sampled template can be cut at the tail when templates have different lengths. +Uniform, exponential, and Gaussian timing rules now use one common validation and sampling path. ---- +Important behavior changes: -## 3. Modified Functions +- bounded exponential rules use a true truncated exponential +- bounded Gaussian rules use a true truncated normal +- bounded means are interpreted as the mean of the realized bounded distribution +- values are rounded once to the experiment resolution -### **Design Class** +The implementation no longer depends on draw-then-clip behavior. -* **`__init__(self, order, ITI, experiment, onsets=None, all_stim_durations=None)`** - * Added `all_stim_durations` for per-trial variable stimulus durations. +## RNG Architecture -* **`designmatrix(self)`** - * Added support for variable stimulus durations via `self.all_stim_durations`. - * When set, onset computation and design matrix construction use per-trial durations instead of a single shared `stim_duration`. - * Returns `False` if the design's actual timing exceeds the experiment container. - -* **`crossover(self, other, seed)`** - * Added fixed-order support: when `order_fixed=True`, offspring inherit the parent orders unchanged. - * When `stimuli_durations` is set and the order changes, per-trial durations are re-sampled to match the new order. - * When `order_fixed=True`, per-trial durations are inherited. - -* **`mutation(self, q, seed)`** - * Added fixed-order support: mutation only changes the order when `order_fixed=False` and `order_probabilities is None`. - * Uses the same per-trial duration propagation logic as crossover. - -### **Experiment Class** - -* **`countstim(self)`** - * Computes the container duration from expected values: `n_trials x (trial_duration + ITImean)`. - * This behavior is kept even when `stimuli_durations` is set, so the whitening matrix dimensions stay stable across all designs in a population. - -* **`max_eff(self)`** - * Computes `FcMax` and `FfMax` from a null design built inside the shared experiment container. - * The current implementation does not estimate `FeMax` or `FdMax` inside `max_eff()`. - -### **Optimisation Class** - -* **`add_new_designs(self, weights, R)`** - * Added order sampling paths: - `fixed order -> self.exp.order` - `probability-based order -> Experiment.sample_from_probabilities()` - `default -> generate.order()` - * Added ITI sampling paths: - `conditional_ITI -> Experiment.generate_iti()` - `default -> generate.iti()` - * Added per-design sampling of stimulus durations when `stimuli_durations` is set. - * Keeps all generated designs attached to one shared experiment object. +Version 2.0 uses deterministic NumPy `SeedSequence` and `Generator` objects throughout: -* **`clear(self)`** - * Propagates `all_stim_durations` when preserving the best design across generation clears. +- design realization +- template sampling +- duration sampling +- interval sampling +- mutation +- crossover +- immigration -* **`optimise(self)`** - * Skips Fe/Fd calibration pre-runs when the corresponding weight is zero or when the user has provided explicit FeMax/FdMax values. +The package does not rely on Python `random.choices` or global NumPy reseeding. -* **`to_next_generation(self, weights, seed, optimisation)`** - * When `order_fixed=True`, skips mutation and crossover and uses immigration only. - * This focuses optimization on timing and other non-order parameters. +## Schedule Representation + +`Design` now stores a realized trial-aware schedule with explicit metadata, including: + +- flattened event `order` +- event categories +- realized event durations +- trial IDs +- event index within each trial +- trial template IDs +- trial type IDs +- realized trial-start intervals +- realized post-event intervals +- realized within-trial transition intervals +- realized between-trial intervals +- realized rest intervals +- event onsets and offsets +- trial starts and ends + +Rests remain boundary intervals. +They are not modeled as synthetic events. + +## Trial-Boundary-Aware Optimization + +`Optimisation` now respects the schedule construction mode: + +- flat mode mutates and crosses event orders +- fixed template mode preserves the fixed conceptual-trial sequence +- probabilistic template mode mutates and crosses at conceptual-trial boundaries + +After any sequence change, the package resamples the timing state needed for the new sequence, rebuilds the schedule, and recalculates the design matrices and objective metrics. + +## Public Selection Workflow + +The authoritative public route is: + +```python +optimisation.optimise() +design = optimisation.selected_design(0) +``` + +`selected_design(rank)` retrieves the clustered output representatives created by `evaluate()`. +Calling `selected_design()` before `optimise()` raises a runtime error. +Out-of-range ranks raise `IndexError`. + +User-facing workflows should not rely on: + +- `.bestdesign` +- direct population indexing +- re-sorting internal design pools + +## Patience-Based Early Stopping + +`Optimisation(convergence=k)` implements patience-based early stopping. + +- `k > 0`: stop after `k` consecutive completed generations without strict improvement in the generation-best objective score +- `k in {0, None}`: disable early stopping +- equality counts as no improvement +- there is no minimum-delta tolerance in the current implementation + +The public optimization state records: + +- `generations_completed` +- `stop_reason` +- `optima` +- `bestdesign_generation` + +## Reports And Export Payloads + +Version 2.0 reporting and export surfaces now align around the selected design workflow. + +Reports are generated from the `Optimisation` instance. +Exports preserve enough information to reconstruct the selected schedule: + +- schedule table rows +- realized schedule arrays +- trial and template metadata +- counts +- metric components +- requested experiment specification + +## Validation Runner + +The maintained validation surface is organized through: + +- `validation/manifest.py` +- `validation/run_all.py` +- `validation/execute_notebooks.py` +- `validation/helpers/regenerate_v2_notebooks.py` + +That runner covers: + +- the full pytest suite +- notebook execution from clean kernels +- offline docs builds with warnings as errors +- canonical Case 10 comparison +- manuscript-support Case 10 generation +- determinism checks +- timing-architecture asset generation diff --git a/neurodesign/classes.py b/neurodesign/classes.py index e4b78f7..ccba73e 100644 --- a/neurodesign/classes.py +++ b/neurodesign/classes.py @@ -1,21 +1,35 @@ from __future__ import annotations +"""Core schedule, metric, and optimisation classes for Neurodesign-Plus. + +The version-2 architecture separates conceptual trials from flattened modeled +events. Conceptual trials define how schedules are sampled and where +between-trial intervals or rests may occur; flattened modeled events define the +regressor axis used for event timing, frequency metrics, and transition metrics. +""" + import copy +import functools +import hashlib +import json import math -import random import shutil import warnings import zipfile from collections import Counter +from collections.abc import Sequence +from dataclasses import dataclass from io import BytesIO from pathlib import Path +from typing import Any import numpy as np import scipy import scipy.linalg +import scipy.optimize +import scipy.stats as stats import sklearn.cluster from numpy import transpose as t -from rich import print from rich.progress import ( BarColumn, MofNCompleteColumn, @@ -28,11 +42,22 @@ ) from scipy.special import gamma -from neurodesign import generate, report +from . import generate, report + +REMOVED_TIMING_ARGUMENTS = { + "t_pre": "trial_start_interval", + "t_post": "post_event_interval", + "stimuli_durations": "event_durations", + "conditional_ITI": "event_transition_interval and/or inter_trial_interval", + "ITImodel": "inter_trial_interval", + "ITImin": "inter_trial_interval", + "ITImean": "inter_trial_interval", + "ITImax": "inter_trial_interval", +} def progress_bar(text: str, color: str = "green") -> Progress: - """Return a rich progress bar instance.""" + """Create a consistent Rich progress bar used by optimisation loops.""" return Progress( TextColumn(f"[{color}]{text}"), SpinnerColumn("dots"), @@ -44,305 +69,615 @@ def progress_bar(text: str, color: str = "green") -> Progress: ) -class Design: - """ - This class represents an experimental design for an fMRI experiment. - - :param order: The stimulus order. - :type order: list of integers - :param ITI: The ITI's between all stimuli. - :type ITI: list of floats - :param experiment: The experimental setup. - :type experiment: experiment object - :param onsets: The onsets of all stimuli. - :type onsets: list of floats - """ +def _json_key(value: Any) -> str: + """Convert nested rule keys into stable JSON-serializable strings.""" + if isinstance(value, tuple): + return json.dumps([_json_key(v) for v in value]) + return str(value) - def __init__(self, order, ITI, experiment, onsets=None, all_stim_durations=None): - self.order = order - self.ITI = ITI - self.onsets = onsets - self.Fe = 0 - self.Fd = 0 +def _display_rule(value: Any) -> Any: + """Recursively normalize internal rule objects for human-readable export.""" + if isinstance(value, dict): + return {str(k): _display_rule(v) for k, v in value.items()} + if isinstance(value, list): + return [_display_rule(v) for v in value] + if isinstance(value, tuple): + return [_display_rule(v) for v in value] + return value - # Per-design variable stimulus durations (None = use experiment.stim_duration) - self.all_stim_durations = all_stim_durations - self.experiment = experiment +def _json_default(value: Any) -> Any: + """Convert numpy/scalar objects into stable JSON-serializable values.""" + if isinstance(value, np.ndarray): + return value.tolist() + if isinstance(value, (np.integer, np.floating)): + return value.item() + raise TypeError(f"Object of type {type(value)!r} is not JSON serializable") - # assert whether design is valid - if len(self.ITI) != experiment.n_trials: - raise ValueError("length of design (ITI's) does not comply with experiment") - if len(self.order) != experiment.n_trials: - raise ValueError("length of design (orders) does not comply with experiment") - def check_maxrep(self, maxrep): - """Check whether design does not exceed maximum repeats within design. +def _stable_json_bytes(value: Any) -> bytes: + """Serialize nested design/specification payloads with deterministic ordering.""" + return json.dumps( + value, + sort_keys=True, + separators=(",", ":"), + default=_json_default, + ).encode("utf-8") - :param maxrep: How many times should a stimulus maximally be repeated. - :type maxrep: integer - :returns repcheck: Boolean indicating maximum repeats are respected - """ - for stim in range(self.experiment.n_stimuli): - repcheck = "".join(str(e) for e in [stim] * maxrep) not in "".join( - str(e) for e in self.order - ) - if not repcheck: - break - return repcheck +def _find_new_resolution(TR, res): + """Snap a requested resolution to a divisor of the repetition time.""" + n = TR * 1000.0 + divisors = [] + for i in range(1, int(math.sqrt(n) + 1)): + if n % i == 0: + divisors.append(i) + if i * i != n: + divisors.append(int(n / i)) + sorted_divisors = np.sort(divisors) + resdivisor = TR / float(res) + difs = np.abs(resdivisor - sorted_divisors) + minind = np.where(difs == np.min(difs))[0] + divisor = sorted_divisors[minind][0] + return TR / divisor - def check_hardprob(self): - """Check whether frequencies match the prespecified frequencies. - :returns probcheck: Boolean indicating probabilities are respected - """ - obscnt = Counter(self.order).values() - obsprob = np.round(obscnt / np.sum(obscnt), decimals=2) - if len(self.experiment.P) != len(obsprob): - return False +def _round_to_resolution(inmat, res): + """Round values down to the discrete modeling grid.""" + out = res * np.floor(np.array(inmat) / res) + ind = out / res + return out, [int(x) for x in ind] + + +def _round_scalar(value: float, res: float) -> float: + """Round a scalar to the nearest modeling-grid step.""" + return float(res * np.round(float(value) / res)) + + +def _ensure_non_negative(arg_name: str, value: float) -> float: + """Validate that a timing parameter is finite and non-negative.""" + if not np.isfinite(value) or value < 0: + raise ValueError(f"{arg_name} must be finite and non-negative; got {value!r}") + return float(value) + + +def _rule_id(prefix: str, selector_value: Any | None = None) -> str: + """Build a readable provenance label for a resolved timing rule.""" + if selector_value is None: + return prefix + if isinstance(selector_value, tuple): + selector_value = "->".join(str(v) for v in selector_value) + return f"{prefix}[{selector_value}]" + + +@dataclass(frozen=True) +class NormalizedRule: + """Canonical internal representation of one resolved timing distribution.""" + + model: str + mean: float | None = None + min: float | None = None + max: float | None = None + std: float | None = None + + def as_dict(self) -> dict[str, Any]: + """Return a JSON-friendly representation of the normalized rule.""" + out = {"model": self.model} + if self.mean is not None: + out["mean"] = self.mean + if self.min is not None: + out["min"] = self.min + if self.max is not None: + out["max"] = self.max + if self.std is not None: + out["std"] = self.std + return out + + +@dataclass(frozen=True) +class SelectorSpec: + """Canonical representation of selector-dispatched timing rules.""" + + selector_kind: str + rules: dict[Any, NormalizedRule] + default: NormalizedRule | None + + def as_dict(self) -> dict[str, Any]: + """Return a JSON-friendly representation of the selector wrapper.""" + out = {} + body = {str(k): v.as_dict() for k, v in self.rules.items()} + if self.default is not None: + body["default"] = self.default.as_dict() + out[self.selector_kind] = body + return out + + +def _validate_fixed_rule(arg_name: str, mean: Any) -> NormalizedRule: + """Normalize a scalar timing specification into a fixed rule.""" + return NormalizedRule(model="fixed", mean=_ensure_non_negative(arg_name, mean)) + + +@functools.cache +def _compute_truncated_exponential_scale( + arg_name: str, mean: float, lower: float, upper: float +) -> float: + """Infer the scale of a bounded exponential distribution with a target mean.""" + if mean < lower or mean > upper: + raise ValueError( + f"{arg_name} exponential rule has mean={mean} outside bounds [{lower}, {upper}]" + ) - close = np.isclose(np.array(self.experiment.P), np.array(obsprob), atol=0.001) - return np.sum(close) == len(obsprob) + def objective(scale): + scale = float(np.asarray(scale).flat[0]) + if scale <= 0: + return 1e9 + dist = stats.truncexpon((upper - lower) / scale, loc=lower, scale=scale) + return abs(dist.mean() - mean) + + result = scipy.optimize.minimize( + objective, + x0=np.array([max(mean - lower, 1e-6)]), + bounds=((1e-9, 1e6),), + method="L-BFGS-B", + ) + scale = float(result.x[0]) + dist = stats.truncexpon((upper - lower) / scale, loc=lower, scale=scale) + if not np.isclose(dist.mean(), mean, atol=1e-6, rtol=1e-6): + raise ValueError( + f"{arg_name} exponential rule has impossible bounded mean semantics" + ) + return scale - def crossover(self, other, seed=1234): - """Crossover design with other design and create offspring. - :param other: The design with which the design will be mixed - :type other: design object - :param seed: The seed with which the change point will be sampled. - :type seed: integer or None - :returns offspring: List of two offspring designs. - """ - # check whether designs are compatible - assert len(self.order) == len(other.order) - - np.random.seed(seed) - changepoint = np.random.choice(len(self.order), 1)[0] - - offspringorder1 = None - offspringorder2 = None - - # Making sure the order doesn't change for the crossover - if self.experiment.order_fixed: - offspringorder1 = self.order - offspringorder2 = other.order - elif self.experiment.order_probabilities is not None: - offspringorder1 = Experiment.sample_from_probabilities( - self.experiment.order_probabilities, - self.experiment.order_keys, - self.experiment.order_length, - ) - offspringorder2 = Experiment.sample_from_probabilities( - self.experiment.order_probabilities, - self.experiment.order_keys, - self.experiment.order_length, - ) - else: - offspringorder1 = ( - list(self.order)[:changepoint] + list(other.order)[changepoint:] - ) - offspringorder2 = ( - list(other.order)[:changepoint] + list(self.order)[changepoint:] - ) +@functools.cache +def _compute_truncated_normal_loc( + arg_name: str, mean: float, std: float, lower: float, upper: float +) -> float: + """Infer the latent Gaussian location for a bounded normal rule.""" + if mean < lower or mean > upper: + raise ValueError( + f"{arg_name} gaussian rule has mean={mean} outside bounds [{lower}, {upper}]" + ) - # Re-sample stim durations for new orders if variable durations are used - asd1 = self.all_stim_durations # default: inherit from parent - asd2 = other.all_stim_durations - if ( - self.experiment.stimuli_durations is not None - and not self.experiment.order_fixed - ): - # Order changed, so re-sample durations to match new order - asd1 = Experiment.sample_stim_durations( - offspringorder1, - self.experiment.stimuli_durations, - self.experiment.t_pre, - self.experiment.t_post, - ) - asd2 = Experiment.sample_stim_durations( - offspringorder2, - self.experiment.stimuli_durations, - self.experiment.t_pre, - self.experiment.t_post, - ) + def objective(loc): + loc = float(np.asarray(loc).flat[0]) + a = (lower - loc) / std + b = (upper - loc) / std + return stats.truncnorm.mean(a, b, loc=loc, scale=std) - mean - offspring1 = Design( - order=offspringorder1, - ITI=self.ITI, - experiment=self.experiment, - all_stim_durations=asd1, - ) - offspring2 = Design( - order=offspringorder2, - ITI=other.ITI, - experiment=self.experiment, - all_stim_durations=asd2, + result = scipy.optimize.root_scalar( + objective, + bracket=(lower - 10 * std, upper + 10 * std), + method="brentq", + ) + if not result.converged: + raise ValueError( + f"{arg_name} gaussian rule has impossible bounded mean semantics" ) + return float(result.root) - return [offspring1, offspring2] - def mutation(self, q, seed=1234): - """Mutate q% of the stimuli with another stimulus. +def normalize_rule(spec: Any, arg_name: str) -> NormalizedRule | SelectorSpec: + """Normalize user timing syntax into internal rule objects. - :param q: The percentage of stimuli that should be mutated - :type q: float - :param seed: The seed with which the mutation points are sampled. - :type seed: integer or None - :returns mutated: Mutated design - """ - np.random.seed(seed) - mut_ind = np.random.choice( - len(self.order), int(len(self.order) * q), replace=False + The public API accepts scalars, explicit ``{'model': ...}`` dictionaries, + and selector wrappers such as ``by_event_category`` or + ``by_event_transition``. This function converts those variants into a + canonical representation used by schedule generation. + """ + if isinstance(spec, (int, float, np.integer, np.floating)): + return _validate_fixed_rule(arg_name, float(spec)) + + if not isinstance(spec, dict): + raise TypeError( + f"{arg_name} must be a scalar or dictionary rule; got {type(spec)!r}" ) - mutated = copy.copy(self.order) - if ( - not self.experiment.order_fixed - and self.experiment.order_probabilities is None - ): - for mut in mut_ind: - np.random.seed(seed) - mut_stim = np.random.choice(self.experiment.n_stimuli, 1, replace=True)[0] - mutated[mut] = mut_stim + selector_keys = [k for k in spec.keys() if k.startswith("by_")] + if "model" in spec and selector_keys: + raise ValueError(f"{arg_name} mixes a global rule with selector wrapper syntax") + if len(selector_keys) > 1: + raise ValueError(f"{arg_name} may contain only one selector wrapper") + + if selector_keys: + selector_kind = selector_keys[0] + body = spec[selector_kind] + if not isinstance(body, dict): + raise TypeError(f"{arg_name}.{selector_kind} must be a dictionary") + rules: dict[Any, NormalizedRule] = {} + default = None + for key, value in body.items(): + if key == "default": + default = normalize_rule(value, f"{arg_name}.{selector_kind}.default") + if isinstance(default, SelectorSpec): + raise ValueError( + f"{arg_name}.{selector_kind}.default may not nest selectors" + ) + continue + normalized = normalize_rule(value, f"{arg_name}.{selector_kind}[{key!r}]") + if isinstance(normalized, SelectorSpec): + raise ValueError( + f"{arg_name}.{selector_kind}[{key!r}] may not nest selectors" + ) + if selector_kind == "by_event_transition": + if not isinstance(key, tuple) or len(key) != 2: + raise ValueError( + f"{arg_name}.{selector_kind} keys must be ordered pairs; got {key!r}" + ) + rules[key] = normalized + return SelectorSpec(selector_kind=selector_kind, rules=rules, default=default) - # Re-sample stim durations if order changed and variable durations are used - asd = self.all_stim_durations - if ( - self.experiment.stimuli_durations is not None - and not self.experiment.order_fixed - ): - asd = Experiment.sample_stim_durations( - mutated, - self.experiment.stimuli_durations, - self.experiment.t_pre, - self.experiment.t_post, + model = spec.get("model") + if model is None: + raise ValueError( + f"{arg_name} dictionary is ambiguous; use a scalar, a {{'model': ...}} rule, or a selector wrapper" + ) + if model == "fixed": + if "mean" not in spec: + raise ValueError(f"{arg_name} fixed rule requires 'mean'") + return _validate_fixed_rule(arg_name, spec["mean"]) + if model == "uniform": + if "min" not in spec or "max" not in spec: + raise ValueError(f"{arg_name} uniform rule requires 'min' and 'max'") + lower = _ensure_non_negative(f"{arg_name}.min", spec["min"]) + upper = _ensure_non_negative(f"{arg_name}.max", spec["max"]) + if lower > upper: + raise ValueError(f"{arg_name} uniform rule requires min <= max") + if "mean" in spec: + expected = (lower + upper) / 2.0 + if not np.isclose(expected, float(spec["mean"]), atol=1e-8, rtol=1e-8): + raise ValueError( + f"{arg_name} uniform rule mean must equal (min + max) / 2; expected {expected}" + ) + return NormalizedRule( + model="uniform", mean=(lower + upper) / 2.0, min=lower, max=upper + ) + if model == "exponential": + if "mean" not in spec: + raise ValueError(f"{arg_name} exponential rule requires 'mean'") + mean = _ensure_non_negative(f"{arg_name}.mean", spec["mean"]) + lower = spec.get("min") + upper = spec.get("max") + lower_f = ( + _ensure_non_negative(f"{arg_name}.min", lower) if lower is not None else None + ) + upper_f = ( + _ensure_non_negative(f"{arg_name}.max", upper) if upper is not None else None + ) + if lower_f is not None and upper_f is not None and lower_f > upper_f: + raise ValueError(f"{arg_name} exponential rule requires min <= max") + if lower_f is not None and mean < lower_f: + raise ValueError( + f"{arg_name} exponential rule has mean={mean} outside bounds [{lower_f}, {upper_f}]" ) - - offspring = Design( - order=mutated, - ITI=self.ITI, - experiment=self.experiment, - all_stim_durations=asd, + if upper_f is not None and mean > upper_f: + raise ValueError( + f"{arg_name} exponential rule has mean={mean} outside bounds [{lower_f}, {upper_f}]" + ) + return NormalizedRule(model="exponential", mean=mean, min=lower_f, max=upper_f) + if model == "gaussian": + if "mean" not in spec or "std" not in spec: + raise ValueError(f"{arg_name} gaussian rule requires 'mean' and 'std'") + mean = _ensure_non_negative(f"{arg_name}.mean", spec["mean"]) + std = float(spec["std"]) + if not np.isfinite(std) or std <= 0: + raise ValueError(f"{arg_name}.std must be finite and > 0") + lower = spec.get("min") + upper = spec.get("max") + if lower is None: + raise ValueError( + f"{arg_name} gaussian rules must define a non-negative 'min' bound" + ) + lower_f = _ensure_non_negative(f"{arg_name}.min", lower) + upper_f = ( + _ensure_non_negative(f"{arg_name}.max", upper) if upper is not None else None + ) + if upper_f is not None and lower_f > upper_f: + raise ValueError(f"{arg_name} gaussian rule requires min <= max") + if mean < lower_f or (upper_f is not None and mean > upper_f): + raise ValueError( + f"{arg_name} gaussian rule has mean={mean} outside bounds [{lower_f}, {upper_f}]" + ) + return NormalizedRule( + model="gaussian", mean=mean, min=lower_f, max=upper_f, std=std ) + raise ValueError(f"{arg_name} uses unknown model {model!r}") + + +def sample_normalized_rule( + rule: NormalizedRule, arg_name: str, rng: np.random.Generator, resolution: float +) -> float: + """Sample one realized timing value from a normalized rule.""" + if rule.model == "fixed": + value = float(rule.mean) + elif rule.model == "uniform": + value = float(rng.uniform(rule.min, rule.max)) + elif rule.model == "exponential": + if rule.min is None and rule.max is None: + value = float(rng.exponential(rule.mean)) + else: + lower = 0.0 if rule.min is None else float(rule.min) + upper = lower + 1e6 if rule.max is None else float(rule.max) + scale = _compute_truncated_exponential_scale( + arg_name, float(rule.mean), lower, upper + ) + dist = stats.truncexpon((upper - lower) / scale, loc=lower, scale=scale) + value = float(dist.rvs(random_state=rng)) + elif rule.model == "gaussian": + lower = float(rule.min) + upper = lower + 10 * float(rule.std) if rule.max is None else float(rule.max) + loc = _compute_truncated_normal_loc( + arg_name, float(rule.mean), float(rule.std), lower, upper + ) + a = (lower - loc) / float(rule.std) + b = (upper - loc) / float(rule.std) + value = float( + stats.truncnorm.rvs(a, b, loc=loc, scale=float(rule.std), random_state=rng) + ) + else: + raise ValueError(f"Unknown normalized rule model {rule.model!r}") + return _round_scalar(_ensure_non_negative(arg_name, value), resolution) - return offspring - def designmatrix(self): - """Expand from order of stimuli to a fMRI timeseries. +class Design: + """Represent one realized fMRI design with explicit event-level timing. - Returns self on success, or False if the design's actual timing - exceeds the experiment's container (e.g. from extreme ITI draws). - """ - # ITIs to onsets - orderli = list(self.order) - ITIli = list(self.ITI) - if self.experiment.restnum > 0: - if self.all_stim_durations is None: - # Old Package Code - ITIli = [ - y + self.experiment.trial_duration if not x == "R" else y - for x, y in zip(orderli, ITIli) - ] - onsets = np.cumsum(ITIli) - self.experiment.trial_duration + The design stores the flattened modeled-event order, the realized schedule + arrays derived from an :class:`Experiment`, and the computed efficiency + metrics used for reporting and optimisation. + """ - self.onsets = [y for x, y in zip(orderli, onsets) if not x == "R"] - else: - for x in np.arange(0, self.experiment.n_trials, self.experiment.restnum)[ - 1: - ][::-1]: - orderli.insert(x, "R") - ITIli.insert(x, self.experiment.restdur) - - # Calculate ITI_li based on rest numbers and - # varied trial duration. Use a separate trial - # counter since orderli now has "R" entries - # but all_stim_durations has n_trials entries. - ITIli_new = [] - onsets = [] - trial_idx = 0 - for i, (x, y) in enumerate(zip(orderli, ITIli)): - if not x == "R": - ITIli_new.append(y + self.all_stim_durations[trial_idx]) - trial_idx += 1 - else: - ITIli_new.append(y) - - ITIli_cumsum = np.cumsum(ITIli_new) - # Subtracts the cumulative sum by the respective trial durations - # while excluding the rest trials. - trial_idx = 0 - for i, (x, y) in enumerate(zip(orderli, ITIli_cumsum)): - if not x == "R": - onsets.append(y - self.all_stim_durations[trial_idx]) - trial_idx += 1 - - self.onsets = onsets + def __init__( + self, + experiment: Experiment | None = None, # type: ignore[name-defined] + schedule: dict[str, Any] | None = None, + trial_sequence: list[Any] | None = None, + template_sequence: list[str] | None = None, + ): + """Create a realized design from a fully materialized schedule. + + Parameters + ---------- + experiment: + Parent experiment specification that defines the modeling and + optimisation context. + schedule: + Realized event-level schedule generated by the experiment. + trial_sequence, template_sequence: + Provenance for conceptual-trial sampling, used by resampling, + crossover, mutation, and validation exports. + """ + self.experiment = experiment + self.Fe = 0.0 + self.Fd = 0.0 + self.Ff = 0.0 + self.Fc = 0.0 + self.F = 0.0 + self.schedule = None + self.order = None + self.trial_sequence = trial_sequence + self.template_sequence = template_sequence + + if self.experiment is None: + raise ValueError("Design requires an Experiment") + + if schedule is not None: + self._apply_schedule(schedule) else: - if self.all_stim_durations is None: - # Old Package Code - ITIli = np.array(self.ITI) + self.experiment.trial_duration - self.onsets = np.cumsum(ITIli) - self.experiment.trial_duration - else: - # Modified by Atharv Umap - ITIli_new = [y + x for x, y in zip(self.all_stim_durations, ITIli)] - ITIli_cumsum = np.cumsum(ITIli_new) - - onsets_temp = [] - for x, y in zip(self.all_stim_durations, list(ITIli_cumsum)): - onsets_temp.append(y - x) - self.onsets = onsets_temp - - stimonsets = [x + self.experiment.t_pre for x in self.onsets] + raise ValueError( + "Design construction requires a realized schedule. " + "Use Experiment.create_design() or Experiment.create_manual_design(...) in version 2.0." + ) - # round onsets to resolution - self.ITI, x = _round_to_resolution(self.ITI, self.experiment.resolution) - onsetX, XindStim = _round_to_resolution(stimonsets, self.experiment.resolution) + def _apply_schedule(self, schedule: dict[str, Any]): + """Hydrate convenience arrays from the realized schedule dictionary.""" + self.schedule = copy.deepcopy(schedule) + self.order = list(schedule["order"]) + self.n_events = len(self.order) + self.event_categories = list(schedule["event_categories"]) + self.realized_event_durations = np.array( + schedule["realized_event_durations"], dtype=float + ) + self.trial_ids = np.array(schedule["trial_ids"], dtype=int) + self.event_index_within_trial = np.array( + schedule["event_index_within_trial"], dtype=int + ) + self.trial_template_ids = list(schedule["trial_template_ids"]) + self.trial_type_ids = list(schedule["trial_type_ids"]) + self.realized_trial_start_intervals = np.array( + schedule["realized_trial_start_intervals"], dtype=float + ) + self.realized_post_event_intervals = np.array( + schedule["realized_post_event_intervals"], dtype=float + ) + self.realized_event_transition_intervals = np.array( + schedule["realized_event_transition_intervals"], dtype=float + ) + self.realized_inter_trial_intervals = np.array( + schedule["realized_inter_trial_intervals"], dtype=float + ) + self.realized_rest_intervals = np.array( + schedule["realized_rest_intervals"], dtype=float + ) + self.event_onsets = np.array(schedule["event_onsets"], dtype=float) + self.event_offsets = np.array(schedule["event_offsets"], dtype=float) + self.trial_starts = np.array(schedule["trial_starts"], dtype=float) + self.trial_ends = np.array(schedule["trial_ends"], dtype=float) + self.trial_start_event_index = np.array( + schedule["trial_start_event_index"], dtype=int + ) + self.trial_end_event_index = np.array( + schedule["trial_end_event_index"], dtype=int + ) + self.within_trial_transition_from_event_index = np.array( + schedule["within_trial_transition_from_event_index"], dtype=int + ) + self.within_trial_transition_to_event_index = np.array( + schedule["within_trial_transition_to_event_index"], dtype=int + ) + self.inter_trial_boundary_after_trial = np.array( + schedule["inter_trial_boundary_after_trial"], dtype=int + ) + self.selector_provenance = copy.deepcopy(schedule["selector_provenance"]) + self.schedule_table = copy.deepcopy(schedule["schedule_table"]) - if self.all_stim_durations is None: - stim_duration_tp = int( - self.experiment.stim_duration / self.experiment.resolution + def check_maxrep(self, maxrep): + """Return ``False`` if any flattened event category repeats too often.""" + for stim in range(self.experiment.n_stimuli): + repcheck = "".join(str(e) for e in [stim] * maxrep) not in "".join( + str(e) for e in self.order ) + if not repcheck: + return False + return True - # Check if design fits in container β€” reject if not - if np.max(XindStim) + stim_duration_tp > self.experiment.n_tp: + def check_hardprob(self): + """Check whether the realized flattened event proportions match ``P``.""" + if ( + len(self.experiment.P) != self.experiment.n_stimuli + or len(self.experiment.P) == 0 + ): + return False + counts = np.zeros(self.experiment.n_stimuli, dtype=float) + for code in self.order: + if not isinstance(code, (int, np.integer)): + return False + code = int(code) + if code < 0 or code >= self.experiment.n_stimuli: return False + counts[code] += 1.0 + total = counts.sum() + if total <= 0: + return False + obsprob = counts / total + close = np.isclose( + np.array(self.experiment.P, dtype=float), obsprob, atol=0.001, rtol=0.0 + ) + return bool(np.all(close)) + + def spawn_resampled_timing(self, rng: np.random.Generator) -> Design: + """Resample timing while preserving the same conceptual-trial structure.""" + schedule = self.experiment.realize_from_trial_sequence( + trial_sequence=self.trial_sequence, + rng=rng, + template_sequence=self.template_sequence, + ) + return Design(experiment=self.experiment, schedule=schedule) - # create design matrix in resolution scale (=deltasM in Kao toolbox) - X_X = np.zeros([self.experiment.n_tp, self.experiment.n_stimuli]) - for stimulus in range(self.experiment.n_stimuli): - for dur in range(stim_duration_tp): - X_X[np.array(XindStim) + dur, int(stimulus)] = [ - 1 if z == stimulus else 0 for z in self.order - ] - else: - stim_duration_tp = ( - np.array(self.all_stim_durations) / self.experiment.resolution + def crossover(self, other, seed=1234): + """Create offspring designs by recombining order or template sequences.""" + rng = np.random.default_rng(seed) + if self.experiment.mode in {"flat_fixed_order", "fixed_trials"}: + return [self.spawn_resampled_timing(rng), other.spawn_resampled_timing(rng)] + + if self.experiment.mode == "template_sampled": + assert self.trial_sequence is not None and other.trial_sequence is not None + changepoint = int(rng.integers(0, len(self.trial_sequence))) + seq1 = list(self.trial_sequence[:changepoint]) + list( + other.trial_sequence[changepoint:] + ) + seq2 = list(other.trial_sequence[:changepoint]) + list( + self.trial_sequence[changepoint:] ) - stim_duration_tp = [int(x) for x in stim_duration_tp] + child1 = self.experiment.realize_from_trial_sequence(seq1, rng, seq1) + child2 = self.experiment.realize_from_trial_sequence(seq2, rng, seq2) + return [ + Design( + experiment=self.experiment, + schedule=child1, + trial_sequence=seq1, + template_sequence=seq1, + ), + Design( + experiment=self.experiment, + schedule=child2, + trial_sequence=seq2, + template_sequence=seq2, + ), + ] - # Check if design fits in container β€” reject if not - max_endpoint = max( - XindStim[i] + stim_duration_tp[i] for i in range(len(self.order)) + changepoint = int(rng.integers(0, len(self.order))) + offspringorder1 = list(self.order)[:changepoint] + list(other.order)[changepoint:] + offspringorder2 = list(other.order)[:changepoint] + list(self.order)[changepoint:] + child1 = self.experiment.realize_flat_order(offspringorder1, rng) + child2 = self.experiment.realize_flat_order(offspringorder2, rng) + return [ + Design( + experiment=self.experiment, + schedule=child1, + trial_sequence=offspringorder1, + ), + Design( + experiment=self.experiment, + schedule=child2, + trial_sequence=offspringorder2, + ), + ] + + def mutation(self, q, seed=1234): + """Randomly perturb a design while respecting the active design mode.""" + rng = np.random.default_rng(seed) + if self.experiment.mode in {"flat_fixed_order", "fixed_trials"}: + return self.spawn_resampled_timing(rng) + + if self.experiment.mode == "template_sampled": + seq = list(self.trial_sequence) + nmut = max(1, int(len(seq) * q)) if len(seq) > 0 else 0 + if nmut > 0: + idxs = rng.choice(len(seq), size=nmut, replace=False) + for idx in np.atleast_1d(idxs): + seq[int(idx)] = self.experiment.sample_template_id(rng) + schedule = self.experiment.realize_from_trial_sequence(seq, rng, seq) + return Design( + experiment=self.experiment, + schedule=schedule, + trial_sequence=seq, + template_sequence=seq, ) - if max_endpoint > self.experiment.n_tp: - return False - X_X = np.zeros([self.experiment.n_tp, self.experiment.n_stimuli]) - # indexing and traversing through the order - for i, stim in enumerate(self.order): - # the current onset - onset = XindStim[i] - # the duration between the onsets - dur = stim_duration_tp[i] - # labeling the binary values of the indices with - for j in range(dur): - t_idx = onset + j - if t_idx < self.experiment.n_tp: - X_X[t_idx, stim] = 1 - - # deconvolved matrix in resolution units + mutated = list(self.order) + nmut = max(1, int(len(mutated) * q)) if len(mutated) > 0 else 0 + if nmut > 0: + idxs = rng.choice(len(mutated), size=nmut, replace=False) + for idx in np.atleast_1d(idxs): + mutated[int(idx)] = int(rng.integers(self.experiment.n_stimuli)) + schedule = self.experiment.realize_flat_order(mutated, rng) + return Design( + experiment=self.experiment, schedule=schedule, trial_sequence=mutated + ) + + def designmatrix(self): + """Build event-level and convolved design matrices for this schedule. + + ``Xnonconv`` represents the realized modeled-event occupancy on the scan + grid. ``Xconv`` is the HRF-convolved event-regressor matrix used by the + estimation efficiency metrics. + """ + onsetX, XindStim = _round_to_resolution( + self.event_onsets, self.experiment.resolution + ) + event_durs, _ = _round_to_resolution( + self.realized_event_durations, self.experiment.resolution + ) + stim_duration_tp = [ + int(round(float(x) / self.experiment.resolution)) for x in event_durs + ] + max_endpoint = max( + XindStim[i] + stim_duration_tp[i] for i in range(len(self.order)) + ) + if max_endpoint > self.experiment.n_tp: + return False + + X_X = np.zeros([self.experiment.n_tp, self.experiment.n_stimuli]) + for i, stim in enumerate(self.order): + onset = XindStim[i] + dur = stim_duration_tp[i] + for j in range(dur): + t_idx = onset + j + if 0 <= t_idx < self.experiment.n_tp: + X_X[t_idx, int(stim)] = 1 + deconvM = np.zeros( [ self.experiment.n_tp, @@ -350,32 +685,28 @@ def designmatrix(self): ] ) for stim in range(self.experiment.n_stimuli): - for j in range(int(self.experiment.laghrf)): + for j in range(min(int(self.experiment.laghrf), self.experiment.n_tp)): deconvM[j:, self.experiment.laghrf * stim + j] = X_X[ : (self.experiment.n_tp - j), stim ] - # downsample and whiten deconvM idxX = [ int(x) for x in np.arange( 0, self.experiment.n_tp, self.experiment.TR / self.experiment.resolution ) ] - if len(idxX) - self.experiment.white.shape[0] == 1: idxX = idxX[: self.experiment.white.shape[0]] deconvMdown = deconvM[idxX, :] Xwhite = np.dot(np.dot(t(deconvMdown), self.experiment.white), deconvMdown) - # convolve design matrix X_Z = np.zeros([self.experiment.n_tp, self.experiment.n_stimuli]) for stim in range(self.experiment.n_stimuli): X_Z[:, stim] = deconvM[ :, (stim * self.experiment.laghrf) : ((stim + 1) * self.experiment.laghrf) ].dot(self.experiment.basishrf) - X_Z = X_Z[idxX, :] X_X = X_X[idxX, :] Zwhite = t(X_Z) @ self.experiment.white @ X_Z @@ -386,28 +717,17 @@ def designmatrix(self): self.Xnonconv = X_X self.CX = self.experiment.CX self.C = self.experiment.C - return self def FeCalc(self, Aoptimality=True): - """ - Compute estimation efficiency. - - :param Aoptimality: Kind of optimality to optimize, A- or D-optimality - :type Aoptimality: boolean - """ + """Compute estimation-efficiency score ``Fe`` for the deconvolved model.""" try: invM = scipy.linalg.inv(self.X) except scipy.linalg.LinAlgError: - try: - invM = scipy.linalg.pinv(self.X) - except np.linalg.linalg.LinAlgError: - invM = np.nan - + invM = scipy.linalg.pinv(self.X) invM = np.array(invM) - st1 = np.dot(self.CX, invM) - CMC = np.dot(st1, t(self.CX)) - if Aoptimality is True: + CMC = np.dot(np.dot(self.CX, invM), t(self.CX)) + if Aoptimality: self.Fe = float(self.CX.shape[0] / np.trace(CMC)) else: self.Fe = float(np.linalg.det(CMC) ** (-1 / len(self.C))) @@ -415,40 +735,38 @@ def FeCalc(self, Aoptimality=True): return self def FdCalc(self, Aoptimality=True): - """ - Compute detection power. - - :param Aoptimality: Kind of optimality to optimize: A- or D-optimality - :type Aoptimality: boolean - """ + """Compute detection-efficiency score ``Fd`` for the convolved model.""" try: invM = scipy.linalg.inv(self.Z) except scipy.linalg.LinAlgError: - try: - invM = scipy.linalg.pinv(self.Z) - except np.linalg.linalg.LinAlgError: - invM = np.nan - + invM = scipy.linalg.pinv(self.Z) invM = np.array(invM) CMC = self.C @ invM @ t(self.C) - if Aoptimality is True: + if Aoptimality: self.Fd = float(len(self.C) / np.trace(CMC)) else: self.Fd = float(np.linalg.det(CMC) ** (-1 / len(self.C))) self.Fd = self.Fd / self.experiment.FdMax return self - def FcCalc(self, confoundorder=3): - """ - Compute confounding efficiency. + def _frequency_mismatch(self) -> float: + """Measure deviation between observed and expected flattened event counts.""" + event_count = len(self.order) + trialcount = Counter(self.order) + observed_counts = np.array( + [trialcount.get(x, 0) for x in range(self.experiment.n_stimuli)], + dtype=float, + ) + expected_counts = float(event_count) * np.array(self.experiment.P, dtype=float) + return float(np.sum(np.abs(observed_counts - expected_counts))) - :param confoundorder: To what order should confounding be protected - :type confoundorder: integer - """ + def _transition_mismatch(self, confoundorder=3) -> float: + """Measure deviation from expected flattened transition counts.""" + event_count = len(self.order) Q = np.zeros( [self.experiment.n_stimuli, self.experiment.n_stimuli, confoundorder] ) - for n in range(len(self.order)): + for n in range(event_count): for r in np.arange(1, confoundorder + 1): if n > (r - 1): Q[self.order[n], self.order[n - r], r - 1] += 1 @@ -459,36 +777,28 @@ def FcCalc(self, confoundorder=3): for sj in range(self.experiment.n_stimuli): for r in np.arange(1, confoundorder + 1): Qexp[si, sj, r - 1] = ( - self.experiment.P[si] - * self.experiment.P[sj] - * (self.experiment.n_trials + 1) + self.experiment.P[si] * self.experiment.P[sj] * (event_count + 1) ) - Qmatch = np.sum(abs(Q - Qexp)) - self.Fc = Qmatch - self.Fc = 1 - self.Fc / self.experiment.FcMax + return float(np.sum(np.abs(Q - Qexp))) + + def FcCalc(self, confoundorder=3): + """Compute transition-balance score ``Fc`` on the flattened event axis.""" + event_count = len(self.order) + Qmatch = self._transition_mismatch(confoundorder) + fc_max = self.experiment.fc_max_for_event_count(event_count, confoundorder) + self.Fc = 1.0 if np.isclose(fc_max, 0.0) else 1 - (Qmatch / fc_max) return self def FfCalc(self): - """Compute efficiency of frequencies.""" - trialcount = Counter(self.order) - Pobs = [trialcount[x] for x in range(self.experiment.n_stimuli)] - self.Ff = np.sum( - abs( - np.array(Pobs) - - np.array(self.experiment.n_trials * np.array(self.experiment.P)) - ) - ) - self.Ff = 1 - self.Ff / self.experiment.FfMax + """Compute frequency-balance score ``Ff`` on the flattened event axis.""" + event_count = len(self.order) + mismatch = self._frequency_mismatch() + ff_max = self.experiment.ff_max_for_event_count(event_count) + self.Ff = 1.0 if np.isclose(ff_max, 0.0) else 1 - mismatch / ff_max return self def FCalc(self, weights, Aoptimality=True, confoundorder=3): - """ - Compute weighted average of efficiencies. - - :param weights: Weights given to each of the efficiency metrics in this order: - Estimation, Detection, Frequencies, Confounders. - :type weights: list of floats - """ + """Compute all requested component scores and their weighted objective.""" if weights[0] > 0: self.FeCalc(Aoptimality) if weights[1] > 0: @@ -496,78 +806,42 @@ def FCalc(self, weights, Aoptimality=True, confoundorder=3): self.FfCalc() self.FcCalc(confoundorder) matr = np.array([self.Fe, self.Fd, self.Ff, self.Fc]) - self.F = np.sum(weights * matr) + self.F = float(np.sum(np.array(weights) * matr)) return self + def export_schedule(self) -> list[dict[str, Any]]: + """Return the row-wise event schedule used for reports and validation.""" + return copy.deepcopy(self.schedule_table) + + def export_payload(self) -> dict[str, Any]: + """Export schedule arrays, counts, and metrics for downstream artifacts.""" + return { + "counts": { + "n_conceptual_trials": int(self.experiment.n_conceptual_trials), + "n_events": int(self.n_events), + }, + "schedule": self.export_schedule(), + "schedule_arrays": copy.deepcopy(self.schedule), + "metrics": { + "F": float(self.F), + "Fe": float(self.Fe), + "Fd": float(self.Fd), + "Ff": float(self.Ff), + "Fc": float(self.Fc), + }, + } + + def stable_hash(self) -> str: + """Return a deterministic hash of the exported realized design payload.""" + return hashlib.sha256(_stable_json_bytes(self.export_payload())).hexdigest() -class Experiment: - """ - This class represents an fMRI experiment. - - :param TR: The repetition time. - :type TR: float - - :param P: The probabilities of each trialtype. - :type P: ndarray - - :param C: The contrast matrix. Example: np.array([[1,-1,0],[0,1,-1]]) - :type C: ndarray - - :param rho: AR(1) correlation coefficient - :type rho: float - - :param n_stimuli: The number of stimuli (or conditions) in the experiment. - :type n_stimuli: integer - - :param n_trials: The number of trials in the experiment. - Either specify n_trials **or** duration. - :type n_trials: integer - - :param duration: The total duration (seconds) of the experiment. - Either specify n_trials **or** duration. - :type duration: float - - :param resolution: the maximum resolution of design matrix - :type resolution: float - - :param stim_duration: duration (seconds) of stimulus - :type stim_duration: float - - :param t_pre: duration (seconds) of trial part before stimulus presentation - (eg. fixation cross) - :type t_pre: float - - :param t_post: duration (seconds) of trial part after stimulus presentation - :type t_post: float - - :param maxrep: maximum number of repetitions - :type maxrep: integer or None - - :param hardprob: can the probabilities differ from the nominal value? - :type hardprob: boolean - - :param confoundorder: The order to which confounding is controlled. - :type confoundorder: integer - - :param restnum: Number of trials between restblocks - :type restnum: integer - :param restdur: duration (seconds) of the rest blocks - :type restdur: float - - :param ITImodel: Which model to sample from. - Possibilities: "fixed","uniform","exponential" - :type ITImodel: string - - :param ITImin: The minimum ITI (required with "uniform" or "exponential") - :type ITImin: float - - :param ITImean: The mean ITI (required with "fixed" or "exponential") - :type ITImean: float - - :param ITImax: The max ITI (required with "uniform" or "exponential") - :type ITImax: float +class Experiment: + """Store the experiment specification and generate realized designs. + Version 2 distinguishes conceptual trials from flattened modeled events: + conceptual-trial counts govern sampling and trial boundaries, while the + realized event axis governs event-level timing and Ff/Fc calculations. """ def __init__( @@ -576,18 +850,15 @@ def __init__( P, C, rho: float, - stim_duration, n_stimuli: int, - stimuli_durations=None, - conditional_ITI=None, # Specification for condition-dependent ITI distributions - ITImodel=None, - ITImin=None, - ITImax=None, - ITImean=None, - restnum=0, - restdur=0, - t_pre=0, - t_post=0, + stim_duration=None, + event_durations=None, + trial_start_interval=0.0, + post_event_interval=0.0, + event_transition_interval=0.0, + inter_trial_interval=0.0, + rest_every_n_trials=None, + rest_interval=0.0, n_trials: int | None = None, duration=None, resolution=0.1, @@ -599,81 +870,61 @@ def __init__( hardprob=False, confoundorder=3, order=None, - order_probabilities=None, - order_keys=None, - order_length=None, - order_fixed=False, + trial_templates=None, + trials=None, + trial_template_probabilities=None, + n_conceptual_trials=None, + seed: int | None = None, + ordertype: str = "random", + restnum=None, + restdur=None, trial_max=None, + **kwargs, ): - self.TR = TR - self.P = P - self.C = np.array(C) - self.rho = rho - self.n_stimuli = n_stimuli - self.t_pre = t_pre - self.t_post = t_post - self.n_trials = n_trials - self.duration = duration - self.resolution = resolution - self.stim_duration = stim_duration + """Create an experiment specification. - # We will calculate all stimuli durations based on the given values - # NOTE: all_stim_durations is now stored per-Design, not per-Experiment. - # The Experiment only stores the *specification* (stimuli_durations dict/list). - - # Modification - # Working with multiple stimuli durations - if stimuli_durations is not None: - assert ( - len(stimuli_durations) == n_stimuli - ), "Must specify a duration for each stimulus" - assert ( - trial_max is not None - ), "Must provide a trial_max given stimuli_durations" - self.trial_max = trial_max - self.stimuli_durations = stimuli_durations - else: - self.trial_max = stim_duration - self.stimuli_durations = None - - self.order_probabilities = order_probabilities - self.order_keys = order_keys - self.order_length = order_length - self.order_fixed = order_fixed - - # Adding the custom order - if order is not None: - self.order = order - self.order_fixed = True - else: - self.order = None - if order_probabilities is not None: - self.order = self.sample_from_probabilities( - order_probabilities, order_keys, order_length + The constructor accepts either classic flat one-event designs or + template-based conceptual-trial specifications. In template modes, + ``n_conceptual_trials`` controls sampling and boundaries, while the + realized flattened event sequence determines event-level timing and + Ff/Fc metrics. + """ + for old_name, replacement in REMOVED_TIMING_ARGUMENTS.items(): + if old_name in kwargs: + raise TypeError( + f"{old_name!r} was removed in neurodesign-plus 2.0; use {replacement!r} instead" ) + if restnum is not None or restdur is not None: + raise TypeError( + "restnum/restdur were removed in neurodesign-plus 2.0; use rest_every_n_trials/rest_interval" + ) + self.TR = float(TR) + self.P = np.array(P, dtype=float) + self.C = np.array(C, dtype=float) + self.rho = float(rho) + self.n_stimuli = int(n_stimuli) + self.resolution = float(resolution) self.maxrep = maxrep self.hardprob = hardprob self.confoundorder = confoundorder - - # Adding a conditional ITI for stimulus-dependent intervals - self.conditional_ITI = conditional_ITI - - self.ITImodel = ITImodel - self.ITImin = ITImin - self.ITImean = ITImean - self.ITImax = ITImax - self.ITIlam = None - - self.restnum = restnum - self.restdur = restdur - self.FeMax = FeMax self.FdMax = FdMax self.FcMax = FcMax self.FfMax = FfMax + self.seed = seed if seed is not None else 1234 + self.ordertype = ordertype + self.duration = duration + self.n_trials = n_trials + self.n_conceptual_trials = n_conceptual_trials + self.trial_max = trial_max + self.stim_duration = stim_duration + self.requested_event_durations = copy.deepcopy( + event_durations + if event_durations is not None + else stim_duration if stim_duration is not None else 1.0 + ) - # make sure resolution is a divisor of TR (up to ) if not np.isclose(self.TR % self.resolution, 0): self.resolution = _find_new_resolution(self.TR, self.resolution) warnings.warn( @@ -681,116 +932,411 @@ def __init__( f"New resolution: {self.resolution}" ) + self.trial_start_interval_requested = copy.deepcopy(trial_start_interval) + self.post_event_interval_requested = copy.deepcopy(post_event_interval) + self.event_transition_interval_requested = copy.deepcopy( + event_transition_interval + ) + self.inter_trial_interval_requested = copy.deepcopy(inter_trial_interval) + self.rest_interval_requested = copy.deepcopy(rest_interval) + self.rest_every_n_trials = rest_every_n_trials + + self.order = order + self.order_fixed = order is not None + self.trial_templates_public = copy.deepcopy(trial_templates) + self.trials_public = copy.deepcopy(trials) + self.trial_template_probabilities = copy.deepcopy(trial_template_probabilities) + + self._resolve_mode() + self._prepare_categories() + self.event_duration_spec = self._normalize_event_duration_spec( + self.requested_event_durations + ) + self.trial_start_interval_spec = normalize_rule( + trial_start_interval, "trial_start_interval" + ) + self.post_event_interval_spec = normalize_rule( + post_event_interval, "post_event_interval" + ) + self.event_transition_interval_spec = normalize_rule( + event_transition_interval, "event_transition_interval" + ) + self.inter_trial_interval_spec = normalize_rule( + inter_trial_interval, "inter_trial_interval" + ) + self.rest_interval_spec = normalize_rule(rest_interval, "rest_interval") + self._validate_mode_specific_semantics() + self._prepare_templates_and_trials() self.countstim() self.CreateTsComp() self.CreateLmComp() + self._ff_max_cache: dict[int, float] = {} + self._fc_max_cache: dict[tuple[int, int], float] = {} self.max_eff() - def max_eff(self): - """Compute maximum efficiency for Confounding and Frequency efficiency.""" - NulDesign = Design( - order=[np.argmin(self.P)] * self.n_trials, - ITI=[0] + [self.ITImean] * (self.n_trials - 1), - experiment=self, - ) - NulDesign.designmatrix() - NulDesign.FcCalc(self.confoundorder) - self.FcMax = 1 - NulDesign.Fc - NulDesign.FfCalc() - self.FfMax = 1 - NulDesign.Ff - - return self - - def countstim(self): - """Compute some arguments depending on other arguments. - - Duration is always computed from EXPECTED values (trial_max + ITImean) - so the whitening matrix is stable across all designs in a population. - Individual designs may have shorter actual timing β€” the unused - timepoints in the design matrix are simply zeros (equivalent to rest). - """ - self.trial_duration = self.trial_max + self.t_pre + self.t_post - - if self.ITImodel == "uniform": - self.ITImean = (self.ITImax + self.ITImin) / 2 - - # Always compute duration from expected values, regardless of - # whether stimuli_durations is set. This keeps the container stable. - if self.n_trials is not None and self.duration is None: - ITIdur = self.n_trials * self.ITImean - TRIALdur = self.n_trials * self.trial_duration - duration = ITIdur + TRIALdur - if self.restnum > 0: - duration = duration + ( - np.floor(self.n_trials / self.restnum) * self.restdur + def _resolve_mode(self): + """Infer which scheduling mode is active from the provided inputs.""" + has_templates = self.trial_templates_public is not None + has_trials = self.trials_public is not None + has_probs = self.trial_template_probabilities is not None + has_n_conceptual = self.n_conceptual_trials is not None + if self.order is not None and ( + has_templates or has_trials or has_probs or has_n_conceptual + ): + raise ValueError( + "order is mutually exclusive with template-based trial inputs" + ) + if has_trials and not has_templates: + raise ValueError("trials requires trial_templates") + if has_probs or has_n_conceptual: + if not (has_templates and has_probs and has_n_conceptual): + raise ValueError( + "probabilistic template mode requires trial_templates, trial_template_probabilities, and n_conceptual_trials" ) - self.duration = duration - elif self.duration is not None and self.n_trials is None: - self.n_trials = self._compute_n_trials() - - # Computes the n_trials given the duration - - def _compute_n_trials(self): - if self.restnum == 0: - return int(self.duration / (self.ITImean + self.trial_duration)) - - # duration of block between rest - blockdurNR = self.restnum * (self.ITImean + self.trial_duration) - - # duration of block including rest - blockdurWR = blockdurNR + self.restdur - - # number of blocks - blocknum = np.floor(self.duration / blockdurWR) - n_trials = blocknum * self.restnum + if has_trials: + raise ValueError( + "trials may not be combined with probabilistic template sampling" + ) + self.mode = "template_sampled" + elif has_trials: + self.mode = "fixed_trials" + elif self.order is not None: + self.mode = "flat_fixed_order" + else: + self.mode = "flat_generated" + if self.n_trials is None: + raise ValueError("flat one-event generation requires n_trials") + + def _prepare_categories(self): + """Resolve modeled event-category labels and integer codes.""" + if self.mode in {"fixed_trials", "template_sampled"}: + explicit_codes = {} + categories = [] + for template in self.trial_templates_public: + for event in template["events"]: + category = event["category"] + categories.append(category) + if "code" in event: + explicit_codes[category] = int(event["code"]) + seen = [] + for category in categories: + if category not in seen: + seen.append(category) + if explicit_codes: + if len(explicit_codes) != len(seen): + raise ValueError( + "every template event category must define an explicit code or none may" + ) + if sorted(explicit_codes.values()) != list(range(self.n_stimuli)): + raise ValueError( + "explicit template event codes must cover 0..n_stimuli-1" + ) + ordered = [None] * self.n_stimuli + for category, code in explicit_codes.items(): + ordered[code] = category + self.category_labels = ordered + else: + self.category_labels = list(seen) + if len(self.category_labels) != self.n_stimuli: + raise ValueError( + "n_stimuli must match the number of unique modeled event categories" + ) + self.category_to_index = { + label: idx for idx, label in enumerate(self.category_labels) + } + else: + self.category_labels = list(range(self.n_stimuli)) + self.category_to_index = {idx: idx for idx in range(self.n_stimuli)} - remain = self.duration - (blocknum * blockdurWR) - if remain >= blockdurNR: - n_trials = n_trials + self.restnum + def _normalize_event_duration_spec(self, spec): + """Normalize event-duration rules for flat or template-based designs.""" + if ( + self.mode in {"fixed_trials", "template_sampled"} + and self.trial_templates_public is not None + ): + per_category: dict[Any, Any] = {} + for template in self.trial_templates_public: + for event in template["events"]: + category = event["category"] + duration_spec = event.get("duration", spec) + if ( + category in per_category + and per_category[category] != duration_spec + ): + raise ValueError( + f"event category {category!r} has conflicting duration specifications across templates" + ) + per_category[category] = duration_spec + return { + "selector_kind": "by_event_category", + "rules": { + category: normalize_rule(rule, f"event_durations[{category!r}]") + for category, rule in per_category.items() + }, + } + if isinstance(spec, list): + if len(spec) != self.n_stimuli: + raise ValueError("event_durations list length must match n_stimuli") + rules = {} + for idx, rule in enumerate(spec): + rules[ + ( + idx + if self.mode in {"flat_generated", "flat_fixed_order"} + else self.category_labels[idx] + ) + ] = normalize_rule(rule, f"event_durations[{idx}]") + return {"selector_kind": "by_event_category", "rules": rules} + normalized = normalize_rule(spec, "event_durations") + if isinstance(normalized, SelectorSpec): + return { + "selector_kind": normalized.selector_kind, + "rules": normalized.rules, + "default": normalized.default, + } + return normalized + + def _validate_mode_specific_semantics(self): + """Reject unsupported timing combinations for the chosen mode.""" + if isinstance(self.inter_trial_interval_spec, SelectorSpec): + raise ValueError( + "inter_trial_interval does not support selector wrappers in version 2.0" + ) + if isinstance(self.rest_interval_spec, SelectorSpec): + raise ValueError( + "rest_interval does not support selector wrappers in version 2.0" + ) + if self.rest_every_n_trials is None and self.rest_interval_requested not in ( + 0, + 0.0, + {"model": "fixed", "mean": 0.0}, + ): + if not ( + isinstance(self.rest_interval_requested, (int, float)) + and float(self.rest_interval_requested) == 0.0 + ): + raise ValueError("rest_interval requires rest_every_n_trials") + if self.rest_every_n_trials is not None and ( + not isinstance(self.rest_every_n_trials, int) or self.rest_every_n_trials <= 0 + ): + raise ValueError("rest_every_n_trials must be a positive integer") + + def _prepare_templates_and_trials(self): + """Normalize conceptual-trial templates and fixed trial sequences.""" + self.templates_by_id: dict[str, dict[str, Any]] = {} + self.template_ids: list[str] = [] + if self.trial_templates_public is not None: + for template in self.trial_templates_public: + template_id = template["template_id"] + if template_id in self.templates_by_id: + raise ValueError(f"duplicate template_id {template_id!r}") + if not template.get("events"): + raise ValueError( + f"template {template_id!r} must define at least one event" + ) + normalized_events = [] + for event_index, event in enumerate(template["events"]): + category = event["category"] + if category not in self.category_to_index: + raise ValueError( + f"unknown event category {category!r} in template {template_id!r}" + ) + duration_spec = event.get("duration", self.requested_event_durations) + rule = normalize_rule( + duration_spec, + f"trial_templates[{template_id!r}].events[{event_index}].duration", + ) + if isinstance(rule, SelectorSpec): + raise ValueError( + "event duration selectors inside template events are not supported" + ) + normalized_events.append( + { + "category": category, + "code": self.category_to_index[category], + "duration_rule": rule, + } + ) + self.templates_by_id[template_id] = { + "template_id": template_id, + "trial_type": template.get("trial_type", template_id), + "events": normalized_events, + } + self.template_ids.append(template_id) + if self.mode == "fixed_trials": + self.fixed_trial_sequence = [] + for idx, trial in enumerate(self.trials_public): + template_id = trial["template_id"] + if template_id not in self.templates_by_id: + raise ValueError( + f"trial {idx} references unknown template_id {template_id!r}" + ) + self.fixed_trial_sequence.append(template_id) + self.n_conceptual_trials = len(self.fixed_trial_sequence) + elif self.mode == "template_sampled": + if len(self.trial_template_probabilities) != len(self.template_ids): + raise ValueError( + "trial_template_probabilities must align with trial_templates" + ) + probs = np.array(self.trial_template_probabilities, dtype=float) + if np.any(probs < 0) or not np.isclose(probs.sum(), 1.0): + raise ValueError( + "trial_template_probabilities must be non-negative and sum to 1" + ) + self.template_probabilities = probs else: - extratrials = np.floor(remain / (self.ITImean + self.trial_duration)) - n_trials = n_trials + extratrials + self.fixed_trial_sequence = None + if self.order is not None: + self.n_conceptual_trials = len(self.order) + + def make_design_rng(self, salt: int = 0) -> np.random.Generator: + """Create a reproducible RNG derived from the experiment seed.""" + ss = np.random.SeedSequence([self.seed, salt]) + return np.random.default_rng(ss) + + def sample_template_id(self, rng: np.random.Generator) -> str: + """Sample one template identifier from the configured template weights.""" + idx = int(rng.choice(len(self.template_ids), p=self.template_probabilities)) + return self.template_ids[idx] + + def sample_trial_sequence(self, rng: np.random.Generator) -> list[str]: + """Sample a full conceptual-trial template sequence.""" + return [self.sample_template_id(rng) for _ in range(self.n_conceptual_trials)] + + def _spec_max(self, spec: NormalizedRule | SelectorSpec | dict[str, Any]) -> float: + """Return a conservative upper bound for a timing rule.""" + if isinstance(spec, NormalizedRule): + if spec.model == "fixed": + return float(spec.mean) + if spec.model == "uniform": + return float(spec.max) + if spec.model == "exponential": + return float( + spec.max if spec.max is not None else max(spec.mean * 4, spec.mean) + ) + if spec.model == "gaussian": + return float( + spec.max if spec.max is not None else spec.mean + 4 * spec.std + ) + if isinstance(spec, SelectorSpec): + values = [self._spec_max(rule) for rule in spec.rules.values()] + if spec.default is not None: + values.append(self._spec_max(spec.default)) + return max(values) if values else 0.0 + if isinstance(spec, dict) and "rules" in spec: + return max(self._spec_max(rule) for rule in spec["rules"].values()) + raise TypeError(f"Unsupported spec for max extraction: {type(spec)!r}") + + def _resolve_rule( + self, spec, selector_value, arg_name: str + ) -> tuple[NormalizedRule, str]: + """Resolve one selector-dispatched rule and its provenance label.""" + if isinstance(spec, NormalizedRule): + return spec, _rule_id(arg_name) + if isinstance(spec, SelectorSpec): + if selector_value in spec.rules: + return spec.rules[selector_value], _rule_id(arg_name, selector_value) + if spec.default is not None: + return spec.default, _rule_id(arg_name, "default") + raise ValueError( + f"{arg_name} has no rule for selector {selector_value!r} and no default" + ) + if isinstance(spec, dict) and spec.get("selector_kind") == "by_event_category": + rules = spec["rules"] + if selector_value in rules: + rule = rules[selector_value] + if isinstance(rule, SelectorSpec): + raise ValueError(f"{arg_name} nested selector is not supported") + return rule, _rule_id(arg_name, selector_value) + default = spec.get("default") + if default is not None: + return default, _rule_id(arg_name, "default") + raise ValueError( + f"{arg_name} has no rule for event category {selector_value!r}" + ) + raise TypeError(f"Unsupported rule specification for {arg_name}") + + def _sample_value( + self, spec, selector_value, arg_name: str, rng: np.random.Generator + ) -> tuple[float, str]: + """Sample one realized timing value and keep its provenance identifier.""" + rule, rule_id = self._resolve_rule(spec, selector_value, arg_name) + return sample_normalized_rule(rule, arg_name, rng, self.resolution), rule_id + + def _estimate_flat_trial_max(self) -> float: + """Estimate the maximum event duration for flat one-event trial modes.""" + if isinstance(self.event_duration_spec, NormalizedRule): + event_max = self._spec_max(self.event_duration_spec) + else: + event_max = max( + self._spec_max(rule) + for rule in self.event_duration_spec["rules"].values() + ) + return event_max - return int(n_trials) + def countstim(self): + """Compute duration summaries implied by the active scheduling mode.""" + if self.mode in {"flat_generated", "flat_fixed_order"}: + self.n_conceptual_trials = ( + self.n_trials if self.n_trials is not None else len(self.order) + ) + self.trial_duration = ( + self._estimate_flat_trial_max() + + self._spec_max(self.trial_start_interval_spec) + + self._spec_max(self.post_event_interval_spec) + ) + inter_trial_max = self._spec_max(self.inter_trial_interval_spec) + rest_max = self._spec_max(self.rest_interval_spec) + total = self.n_conceptual_trials * self.trial_duration + total += max(self.n_conceptual_trials - 1, 0) * inter_trial_max + if self.rest_every_n_trials: + total += ( + (self.n_conceptual_trials - 1) // self.rest_every_n_trials + ) * rest_max + self.duration = total if self.duration is None else self.duration + return + + trial_durations = [] + for template_id in self.template_ids: + template = self.templates_by_id[template_id] + total = self._spec_max(self.trial_start_interval_spec) + nevents = len(template["events"]) + for event_idx, event in enumerate(template["events"]): + total += self._spec_max(event["duration_rule"]) + total += self._spec_max(self.post_event_interval_spec) + if event_idx < nevents - 1: + total += self._spec_max(self.event_transition_interval_spec) + trial_durations.append(total) + inter_trial_max = self._spec_max(self.inter_trial_interval_spec) + rest_max = self._spec_max(self.rest_interval_spec) + max_trial_duration = max(trial_durations) if trial_durations else 0.0 + self.trial_duration = max_trial_duration + total = self.n_conceptual_trials * max_trial_duration + total += max(self.n_conceptual_trials - 1, 0) * inter_trial_max + if self.rest_every_n_trials: + total += ( + (self.n_conceptual_trials - 1) // self.rest_every_n_trials + ) * rest_max + self.duration = total if self.duration is None else self.duration def CreateTsComp(self): - """Compute the number of scans and timepoints.""" - self.n_scans = int(np.ceil(self.duration / self.TR)) # number of scans - # number of timepoints (in resolution) + """Build scan-grid and modeling-grid time bases.""" + self.n_scans = int(np.ceil(self.duration / self.TR)) self.n_tp = int(np.ceil(self.duration / self.resolution)) self.r_scans = np.arange(0, self.duration, self.TR) self.r_tp = np.arange(0, self.duration, self.resolution) - return self def CreateLmComp(self): - """Generate components for the linear model. - - Components: hrf, whitening matrix, - autocorrelation matrix, CX. - """ - # hrf + """Build HRF, drift, and whitening components used by design scoring.""" self.canonical() - - # contrasts - # expand contrasts to resolution self.CX = np.array(np.kron(self.C, np.eye(self.laghrf))) - assert self.CX.shape[0] == self.C.shape[0] * self.laghrf - assert self.CX.shape[1] == self.n_stimuli * self.laghrf - - # drift - self.S = self.drift(np.arange(0, self.n_scans)) # [tp x 1] - assert self.S.shape == (3, self.n_scans) - self.S = np.asarray(self.S) - - # square of the whitening matrix + self.S = np.asarray(self.drift(np.arange(0, self.n_scans))) base = [1 + self.rho**2, -1 * self.rho] + [0] * (self.n_scans - 2) self.V2 = scipy.linalg.toeplitz(base) - # set first and last to 1 self.V2[0, 0] = 1 self.V2[self.n_scans - 1, self.n_scans - 1] = 1 self.V2 = np.asarray(self.V2) - self.white = ( self.V2 - self.V2 @@ -799,36 +1345,25 @@ def CreateLmComp(self): @ self.S @ self.V2 ) - return self def canonical(self): - """Generate the canonical hrf. - - :param resolution: resolution to sample the canonical hrf - :type resolution: float - """ - # translated from spm_hrf + """Construct the canonical SPM-style HRF basis on the modeling grid.""" p = [6, 16, 1, 1, 6, 0, 32] dt = self.resolution s = np.array(range(int(np.ceil(p[6] / dt)))) - # HRF sampled at resolution hrf = ( self.spm_Gpdf(s, p[0] / p[2], dt / p[2]) - self.spm_Gpdf(s, p[1] / p[3], dt / p[3]) / p[4] ) self.basishrf = hrf / np.sum(hrf) - s # duration of the HRF self.durhrf = p[6] - # length of the HRF parameters in resolution scale self.laghrf = int(np.ceil(self.durhrf / self.resolution)) - assert self.laghrf == len(s) - return self @staticmethod def drift(s, deg=3): - """Compute a drift component.""" + """Return polynomial drift regressors evaluated at sample locations.""" S = np.ones([deg, len(s)]) s = np.array(s) tmpt = np.array(2.0 * s / float(len(s) - 1) - 1) @@ -841,159 +1376,658 @@ def drift(s, deg=3): @staticmethod def spm_Gpdf(s, h, l): - """Generate gamma pdf.""" + """Evaluate the gamma density used by the canonical HRF.""" s = np.array(s) - res = (h - 1) * np.log(s) + h * np.log(l) - l * s - np.log(gamma(h)) - return np.exp(res) - - @staticmethod - def sample_stim_durations(order, stimuli_durations, t_pre, t_post): - """Sample concrete stimulus durations for a specific trial order. + out = np.zeros_like(s, dtype=float) + positive = s > 0 + res = ( + (h - 1) * np.log(s[positive]) + + h * np.log(l) + - l * s[positive] + - np.log(gamma(h)) + ) + out[positive] = np.exp(res) + return out + + def _null_order_for_event_count(self, event_count: int) -> list[int]: + """Construct the worst-case single-category null order for calibration.""" + return [int(np.argmin(self.P))] * int(event_count) + + def ff_max_for_event_count(self, event_count: int) -> float: + """Return the ``Ff`` normalization constant for a given event count.""" + event_count = int(event_count) + cached = self._ff_max_cache.get(event_count) + if cached is not None: + return cached + null_order = self._null_order_for_event_count(event_count) + trialcount = Counter(null_order) + observed_counts = np.array( + [trialcount.get(x, 0) for x in range(self.n_stimuli)], dtype=float + ) + expected_counts = float(event_count) * np.array(self.P, dtype=float) + mismatch = float(np.sum(np.abs(observed_counts - expected_counts))) + self._ff_max_cache[event_count] = mismatch + return mismatch + + def fc_max_for_event_count( + self, event_count: int, confoundorder: int | None = None + ) -> float: + """Return the ``Fc`` normalization constant for a given event count.""" + event_count = int(event_count) + confoundorder = ( + self.confoundorder if confoundorder is None else int(confoundorder) + ) + cache_key = (event_count, confoundorder) + cached = self._fc_max_cache.get(cache_key) + if cached is not None: + return cached + null_order = self._null_order_for_event_count(event_count) + Q = np.zeros([self.n_stimuli, self.n_stimuli, confoundorder]) + for n in range(event_count): + for r in np.arange(1, confoundorder + 1): + if n > (r - 1): + Q[null_order[n], null_order[n - r], r - 1] += 1 + Qexp = np.zeros([self.n_stimuli, self.n_stimuli, confoundorder]) + for si in range(self.n_stimuli): + for sj in range(self.n_stimuli): + for r in np.arange(1, confoundorder + 1): + Qexp[si, sj, r - 1] = self.P[si] * self.P[sj] * (event_count + 1) + mismatch = float(np.sum(np.abs(Q - Qexp))) + self._fc_max_cache[cache_key] = mismatch + return mismatch - Called per-Design (not per-Experiment) so each design gets its own - random draw, but they all share the same Experiment container. + def max_eff(self): + """Initialize cached normalization constants for the active experiment.""" + if self.mode == "template_sampled": + return self + if self.mode == "flat_generated": + event_count = self.n_trials + elif self.mode == "flat_fixed_order": + event_count = len(self.order) + elif self.mode == "fixed_trials": + event_count = sum( + len(self.templates_by_id[trial["template_id"]]["events"]) + for trial in self.trials_public + ) + else: + event_count = self.n_trials + self.FfMax = self.ff_max_for_event_count(event_count) + self.FcMax = self.fc_max_for_event_count(event_count, self.confoundorder) + return self - Returns a list of durations (including t_pre + t_post per trial). - """ - all_stim_durations = [] - for i in range(len(order)): - stimuli = order[i] - key = stimuli_durations[stimuli] - - if isinstance(key, dict): - params = key - - if params["model"] == "fixed": - all_stim_durations.append(params["mean"]) - elif params["model"] == "exponential": - val = np.random.exponential(scale=params["mean"]) - if "min" in params: - val = max(val, params["min"]) - if "max" in params: - val = min(val, params["max"]) - all_stim_durations.append(val) - elif params["model"] == "uniform": - all_stim_durations.append( - np.random.uniform(params["min"], params["max"]) - ) - elif params["model"] == "gaussian": - mean = params.get("mean", 0) - std = params.get("std", 1) - val = np.random.normal(loc=mean, scale=std) - if "min" in params: - val = max(val, params["min"]) - if "max" in params: - val = min(val, params["max"]) - all_stim_durations.append(val) + def realize_manual_flat_design( + self, order, inter_trial_intervals, all_event_durations=None + ): + """Materialize a flat one-event schedule from explicit event inputs.""" + if self.mode not in {"flat_generated", "flat_fixed_order"}: + raise ValueError( + "manual design construction is supported only for flat one-event designs" + ) + if len(order) != len(inter_trial_intervals): + raise ValueError( + "manual flat design requires one event-aligned inter-trial value per event" + ) + rng = self.make_design_rng(500) + if all_event_durations is None: + event_durations = [ + self._sample_value( + self.event_duration_spec, code, "event_durations", rng + )[0] + for code in order + ] + else: + event_durations = [ + _round_scalar(float(x), self.resolution) for x in all_event_durations + ] + trial_start_intervals = [] + post_event_intervals = [] + for code in order: + trial_start_intervals.append( + self._sample_value( + self.trial_start_interval_spec, code, "trial_start_interval", rng + )[0] + ) + post_event_intervals.append( + self._sample_value( + self.post_event_interval_spec, code, "post_event_interval", rng + )[0] + ) + schedule = self._build_flat_schedule( + order=list(order), + event_durations=event_durations, + trial_start_intervals=trial_start_intervals, + post_event_intervals=post_event_intervals, + inter_trial_intervals=[ + _round_scalar(float(x), self.resolution) + for x in inter_trial_intervals[1:] + ], + rest_intervals=[0.0] * max(len(order) - 1, 0), + selector_provenance={ + "event_duration_rule_ids": [ + _rule_id("event_durations", code) for code in order + ], + "trial_start_rule_ids": [_rule_id("trial_start_interval") for _ in order], + "post_event_rule_ids": [_rule_id("post_event_interval") for _ in order], + "inter_trial_rule_ids": [ + _rule_id("manual_inter_trial") for _ in range(max(len(order) - 1, 0)) + ], + "rest_rule_ids": [ + _rule_id("rest_interval") for _ in range(max(len(order) - 1, 0)) + ], + "event_transition_rule_ids": [], + }, + ) + schedule["legacy_event_aligned_inter_trial"] = list(inter_trial_intervals) + return schedule + def create_manual_design( + self, + order: Sequence[int], + inter_trial_intervals: Sequence[float], + event_durations: Sequence[float] | None = None, + ) -> Design: + """Wrap a manually specified flat schedule in a :class:`Design`.""" + schedule = self.realize_manual_flat_design( + order=list(order), + inter_trial_intervals=list(inter_trial_intervals), + all_event_durations=( + None if event_durations is None else list(event_durations) + ), + ) + return Design(experiment=self, schedule=schedule, trial_sequence=list(order)) + + def create_design(self, seed: int | None = None) -> Design: + """Sample or realize one design under the active scheduling mode.""" + rng = self.make_design_rng(0 if seed is None else seed) + if self.mode == "flat_fixed_order": + schedule = self.realize_flat_order(self.order, rng) + return Design( + experiment=self, schedule=schedule, trial_sequence=list(self.order) + ) + if self.mode == "fixed_trials": + schedule = self.realize_from_trial_sequence( + self.fixed_trial_sequence, rng, self.fixed_trial_sequence + ) + return Design( + experiment=self, + schedule=schedule, + trial_sequence=list(self.fixed_trial_sequence), + template_sequence=list(self.fixed_trial_sequence), + ) + if self.mode == "template_sampled": + template_sequence = self.sample_trial_sequence(rng) + schedule = self.realize_from_trial_sequence( + template_sequence, rng, template_sequence + ) + return Design( + experiment=self, + schedule=schedule, + trial_sequence=list(template_sequence), + template_sequence=list(template_sequence), + ) + order = generate.order( + self.n_stimuli, + self.n_trials, + self.P.tolist(), + ordertype=self.ordertype, + rng=rng, + ) + schedule = self.realize_flat_order(order, rng) + return Design(experiment=self, schedule=schedule, trial_sequence=list(order)) + + def export_specification(self) -> dict[str, Any]: + """Export the experiment specification with separated trial/event counts.""" + if self.mode == "fixed_trials": + n_events = sum( + len(self.templates_by_id[trial["template_id"]]["events"]) + for trial in self.trials_public + ) + elif self.mode in {"flat_generated", "flat_fixed_order"}: + n_events = self.n_trials if self.mode == "flat_generated" else len(self.order) + else: + n_events = None + return { + "mode": self.mode, + "TR": self.TR, + "P": self.P.tolist(), + "C": self.C.tolist(), + "rho": self.rho, + "n_stimuli": self.n_stimuli, + "resolution": self.resolution, + "n_trials": self.n_trials, + "n_conceptual_trials": self.n_conceptual_trials, + "n_events": n_events, + "event_durations_requested": _display_rule(self.requested_event_durations), + "trial_start_interval_requested": _display_rule( + self.trial_start_interval_requested + ), + "post_event_interval_requested": _display_rule( + self.post_event_interval_requested + ), + "event_transition_interval_requested": _display_rule( + self.event_transition_interval_requested + ), + "inter_trial_interval_requested": _display_rule( + self.inter_trial_interval_requested + ), + "rest_interval_requested": _display_rule(self.rest_interval_requested), + "rest_every_n_trials": self.rest_every_n_trials, + "order": copy.deepcopy(self.order), + "trial_templates": _display_rule(self.trial_templates_public), + "trials": _display_rule(self.trials_public), + "trial_template_probabilities": copy.deepcopy( + self.trial_template_probabilities + ), + "seed": self.seed, + } + + def specification_hash(self) -> str: + """Return a deterministic hash of the exported experiment specification.""" + return hashlib.sha256(_stable_json_bytes(self.export_specification())).hexdigest() + + def realize_flat_order(self, order, rng: np.random.Generator): + """Realize timing arrays for a classic flat one-event order.""" + trial_start_intervals = [] + post_event_intervals = [] + event_durations = [] + event_duration_rule_ids = [] + trial_start_rule_ids = [] + post_event_rule_ids = [] + for code in order: + event_value, event_rule_id = self._sample_value( + self.event_duration_spec, code, "event_durations", rng + ) + start_value, start_rule_id = self._sample_value( + self.trial_start_interval_spec, code, "trial_start_interval", rng + ) + post_value, post_rule_id = self._sample_value( + self.post_event_interval_spec, code, "post_event_interval", rng + ) + event_durations.append(event_value) + trial_start_intervals.append(start_value) + post_event_intervals.append(post_value) + event_duration_rule_ids.append(event_rule_id) + trial_start_rule_ids.append(start_rule_id) + post_event_rule_ids.append(post_rule_id) + inter_trial_intervals = [] + inter_trial_rule_ids = [] + rest_intervals = [] + rest_rule_ids = [] + for boundary in range(max(len(order) - 1, 0)): + value, rule_id = self._sample_value( + self.inter_trial_interval_spec, None, "inter_trial_interval", rng + ) + inter_trial_intervals.append(value) + inter_trial_rule_ids.append(rule_id) + if ( + self.rest_every_n_trials + and (boundary + 1) % self.rest_every_n_trials == 0 + ): + rest_value, rest_rule_id = self._sample_value( + self.rest_interval_spec, None, "rest_interval", rng + ) else: - all_stim_durations.append(key) + rest_value, rest_rule_id = 0.0, _rule_id("rest_interval", "none") + rest_intervals.append(rest_value) + rest_rule_ids.append(rest_rule_id) + schedule = self._build_flat_schedule( + order=list(order), + event_durations=event_durations, + trial_start_intervals=trial_start_intervals, + post_event_intervals=post_event_intervals, + inter_trial_intervals=inter_trial_intervals, + rest_intervals=rest_intervals, + selector_provenance={ + "event_duration_rule_ids": event_duration_rule_ids, + "trial_start_rule_ids": trial_start_rule_ids, + "post_event_rule_ids": post_event_rule_ids, + "inter_trial_rule_ids": inter_trial_rule_ids, + "rest_rule_ids": rest_rule_ids, + "event_transition_rule_ids": [], + }, + ) + return schedule - assert len(all_stim_durations) == len(order) + def _build_flat_schedule( + self, + order, + event_durations, + trial_start_intervals, + post_event_intervals, + inter_trial_intervals, + rest_intervals, + selector_provenance, + ): + """Build a schedule dictionary for flat one-event designs.""" + T = len(order) + trial_ids = list(range(T)) + event_index_within_trial = [0] * T + trial_template_ids = [None] * T + trial_type_ids = [self.category_labels[code] for code in order] + trial_starts = [] + trial_ends = [] + event_onsets = [] + event_offsets = [] + cursor = 0.0 + schedule_table = [] + for trial_idx, code in enumerate(order): + trial_start = cursor + onset = trial_start + trial_start_intervals[trial_idx] + offset = onset + event_durations[trial_idx] + trial_end = offset + post_event_intervals[trial_idx] + trial_starts.append(trial_start) + event_onsets.append(onset) + event_offsets.append(offset) + trial_ends.append(trial_end) + following_transition = None + following_inter_trial = ( + inter_trial_intervals[trial_idx] + if trial_idx < len(inter_trial_intervals) + else None + ) + following_rest = ( + rest_intervals[trial_idx] if trial_idx < len(rest_intervals) else None + ) + schedule_table.append( + { + "run_event_index": trial_idx, + "trial_id": trial_idx, + "trial_index": trial_idx, + "trial_template_id": None, + "trial_type_id": self.category_labels[code], + "event_index_within_trial": 0, + "event_category": self.category_labels[code], + "event_code": int(code), + "trial_start": trial_start, + "realized_trial_start_interval": trial_start_intervals[trial_idx], + "event_onset": onset, + "realized_event_duration": event_durations[trial_idx], + "event_offset": offset, + "realized_post_event_interval": post_event_intervals[trial_idx], + "following_event_transition_interval": following_transition, + "following_inter_trial_interval": following_inter_trial, + "following_rest_interval": following_rest, + "trial_end": trial_end, + "event_duration_rule_id": selector_provenance[ + "event_duration_rule_ids" + ][trial_idx], + "trial_start_rule_id": selector_provenance["trial_start_rule_ids"][ + trial_idx + ], + "post_event_rule_id": selector_provenance["post_event_rule_ids"][ + trial_idx + ], + "event_transition_rule_id": None, + "inter_trial_rule_id": ( + selector_provenance["inter_trial_rule_ids"][trial_idx] + if trial_idx < len(inter_trial_intervals) + else None + ), + "rest_rule_id": ( + selector_provenance["rest_rule_ids"][trial_idx] + if trial_idx < len(rest_intervals) + else None + ), + } + ) + cursor = trial_end + if trial_idx < len(inter_trial_intervals): + cursor += inter_trial_intervals[trial_idx] + rest_intervals[trial_idx] + return { + "order": order, + "event_categories": [self.category_labels[code] for code in order], + "realized_event_durations": event_durations, + "trial_ids": trial_ids, + "event_index_within_trial": event_index_within_trial, + "trial_template_ids": trial_template_ids, + "trial_type_ids": trial_type_ids, + "trial_start_event_index": list(range(T)), + "trial_end_event_index": list(range(T)), + "realized_trial_start_intervals": trial_start_intervals, + "trial_starts": trial_starts, + "trial_ends": trial_ends, + "event_onsets": event_onsets, + "event_offsets": event_offsets, + "realized_post_event_intervals": post_event_intervals, + "within_trial_transition_from_event_index": [], + "within_trial_transition_to_event_index": [], + "realized_event_transition_intervals": [], + "inter_trial_boundary_after_trial": list(range(max(T - 1, 0))), + "realized_inter_trial_intervals": inter_trial_intervals, + "realized_rest_intervals": rest_intervals, + "legacy_event_aligned_inter_trial": [0.0] + list(inter_trial_intervals), + "selector_provenance": selector_provenance, + "schedule_table": schedule_table, + } + + def realize_from_trial_sequence( + self, trial_sequence, rng: np.random.Generator, template_sequence=None + ): + """Realize a full event-level schedule from conceptual-trial templates.""" + trial_sequence = list(trial_sequence) + order = [] + event_categories = [] + event_durations = [] + trial_ids = [] + event_index_within_trial = [] + trial_template_ids = [] + trial_type_ids = [] + trial_start_event_index = [] + trial_end_event_index = [] + trial_start_intervals = [] + trial_start_rule_ids = [] + post_event_intervals = [] + post_event_rule_ids = [] + transition_from = [] + transition_to = [] + transition_intervals = [] + transition_rule_ids = [] + event_duration_rule_ids = [] + event_onsets = [] + event_offsets = [] + trial_starts = [] + trial_ends = [] + schedule_table = [] + inter_trial_intervals = [] + inter_trial_rule_ids = [] + rest_intervals = [] + rest_rule_ids = [] + cursor = 0.0 + global_event_index = 0 + transition_lookup: dict[tuple[int, int], float] = {} + transition_rule_lookup: dict[tuple[int, int], str] = {} + + for trial_idx, template_id in enumerate(trial_sequence): + template = self.templates_by_id[template_id] + trial_template_ids.append(template_id) + trial_type_ids.append(template["trial_type"]) + trial_start, start_rule_id = self._sample_value( + self.trial_start_interval_spec, + template["trial_type"], + "trial_start_interval", + rng, + ) + trial_start_intervals.append(trial_start) + trial_start_rule_ids.append(start_rule_id) + trial_start_event_index.append(global_event_index) + trial_starts.append(cursor) + event_cursor = cursor + trial_start + events = template["events"] + for event_idx, event in enumerate(events): + code = int(event["code"]) + category = event["category"] + duration = sample_normalized_rule( + event["duration_rule"], + "event_durations", + rng, + self.resolution, + ) + duration_rule_id = _rule_id("event_durations", category) + post_value, post_rule_id = self._sample_value( + self.post_event_interval_spec, category, "post_event_interval", rng + ) - # Add pre/post time to each trial duration - all_stim_durations = [d + t_pre + t_post for d in all_stim_durations] - return all_stim_durations + order.append(code) + event_categories.append(category) + event_durations.append(duration) + trial_ids.append(trial_idx) + event_index_within_trial.append(event_idx) + event_onsets.append(event_cursor) + event_offset = event_cursor + duration + event_offsets.append(event_offset) + post_event_intervals.append(post_value) + post_event_rule_ids.append(post_rule_id) + event_duration_rule_ids.append(duration_rule_id) + + following_transition = None + following_transition_rule_id = None + if event_idx < len(events) - 1: + next_category = events[event_idx + 1]["category"] + transition_value, transition_rule_id = self._sample_value( + self.event_transition_interval_spec, + (category, next_category), + "event_transition_interval", + rng, + ) + transition_from.append(global_event_index) + transition_to.append(global_event_index + 1) + transition_intervals.append(transition_value) + transition_rule_ids.append(transition_rule_id) + transition_lookup[(trial_idx, event_idx)] = transition_value + transition_rule_lookup[(trial_idx, event_idx)] = transition_rule_id + following_transition = transition_value + following_transition_rule_id = transition_rule_id + next_event_onset = event_offset + post_value + transition_value + else: + next_event_onset = None + + schedule_table.append( + { + "run_event_index": global_event_index, + "trial_id": trial_idx, + "trial_index": trial_idx, + "trial_template_id": template_id, + "trial_type_id": template["trial_type"], + "event_index_within_trial": event_idx, + "event_category": category, + "event_code": code, + "trial_start": cursor, + "realized_trial_start_interval": trial_start, + "event_onset": event_cursor, + "realized_event_duration": duration, + "event_offset": event_offset, + "realized_post_event_interval": post_value, + "following_event_transition_interval": following_transition, + "following_inter_trial_interval": None, + "following_rest_interval": None, + "trial_end": None, + "event_duration_rule_id": duration_rule_id, + "trial_start_rule_id": start_rule_id, + "post_event_rule_id": post_rule_id, + "event_transition_rule_id": following_transition_rule_id, + "inter_trial_rule_id": None, + "rest_rule_id": None, + } + ) - # Generates ITI with default first value then - # the order (ensures n_trials and ITI length match) - @staticmethod - def generate_iti(order, conditional_iti): - ITI = [] - - for i in range(len(order)): - stim_prev = order[i - 1] if i > 0 else None - stim_curr = order[i] - - if stim_prev is not None or stim_curr is not None: - # Determine key for condition-based ITI - key = (stim_prev, stim_curr) if stim_prev is not None else "default" - params = conditional_iti.get(key, conditional_iti.get("default")) - - # Generate ITI based on parameters - if params["model"] == "fixed": - ITI.append(params["mean"]) - elif params["model"] == "exponential": - val = np.random.exponential(scale=params["mean"]) - if "min" in params: - val = max(val, params["min"]) - if "max" in params: - val = min(val, params["max"]) - ITI.append(val) - elif params["model"] == "uniform": - ITI.append(np.random.uniform(params["min"], params["max"])) - elif params["model"] == "gaussian": - mean = params.get("mean", 0) - std = params.get("std", 1) - val = np.random.normal(loc=mean, scale=std) - if "min" in params: - val = max(val, params["min"]) - if "max" in params: - val = min(val, params["max"]) - ITI.append(val) - return ITI + if next_event_onset is not None: + event_cursor = next_event_onset + global_event_index += 1 - @staticmethod - def calculate_duration(ITI, dur): - """Calculate total duration from ITI and trial durations.""" - total_sum = sum(dur) - total_sum = total_sum + sum(ITI) - return total_sum + trial_end_event_index.append(global_event_index - 1) + trial_end = event_offsets[-1] + post_event_intervals[-1] + trial_ends.append(trial_end) + schedule_table[-1]["trial_end"] = trial_end + cursor = trial_end - # Generates an order based on a given probability distribution of certain keys - @staticmethod - def sample_from_probabilities(prob, key, length): - # random.choices picks elements from key with weights = prob_array\ - samples = random.choices(key, weights=prob, k=length) - merged = [item for sublist in samples for item in sublist] - return merged[:length] + if trial_idx < len(trial_sequence) - 1: + inter_value, inter_rule_id = self._sample_value( + self.inter_trial_interval_spec, None, "inter_trial_interval", rng + ) + inter_trial_intervals.append(inter_value) + inter_trial_rule_ids.append(inter_rule_id) + if ( + self.rest_every_n_trials + and (trial_idx + 1) % self.rest_every_n_trials == 0 + ): + rest_value, rest_rule_id = self._sample_value( + self.rest_interval_spec, None, "rest_interval", rng + ) + else: + rest_value, rest_rule_id = 0.0, _rule_id("rest_interval", "none") + rest_intervals.append(rest_value) + rest_rule_ids.append(rest_rule_id) + schedule_table[-1]["following_inter_trial_interval"] = inter_value + schedule_table[-1]["following_rest_interval"] = rest_value + schedule_table[-1]["inter_trial_rule_id"] = inter_rule_id + schedule_table[-1]["rest_rule_id"] = rest_rule_id + cursor += inter_value + rest_value + + legacy_event_aligned_inter_trial = [0.0] + for trial_idx in range(len(trial_sequence)): + if trial_idx == 0: + pass + if trial_idx > 0: + legacy_event_aligned_inter_trial.extend( + [0.0] + * ( + trial_end_event_index[trial_idx] + - trial_start_event_index[trial_idx] + + 1 + ) + ) + legacy_event_aligned_inter_trial = [0.0] * len(order) + for boundary_trial_idx, inter_value in enumerate(inter_trial_intervals): + next_event_index = trial_start_event_index[boundary_trial_idx + 1] + legacy_event_aligned_inter_trial[next_event_index] = inter_value + + return { + "order": order, + "event_categories": event_categories, + "realized_event_durations": event_durations, + "trial_ids": trial_ids, + "event_index_within_trial": event_index_within_trial, + "trial_template_ids": trial_template_ids, + "trial_type_ids": trial_type_ids, + "trial_start_event_index": trial_start_event_index, + "trial_end_event_index": trial_end_event_index, + "realized_trial_start_intervals": trial_start_intervals, + "trial_starts": trial_starts, + "trial_ends": trial_ends, + "event_onsets": event_onsets, + "event_offsets": event_offsets, + "realized_post_event_intervals": post_event_intervals, + "within_trial_transition_from_event_index": transition_from, + "within_trial_transition_to_event_index": transition_to, + "realized_event_transition_intervals": transition_intervals, + "inter_trial_boundary_after_trial": list( + range(max(len(trial_sequence) - 1, 0)) + ), + "realized_inter_trial_intervals": inter_trial_intervals, + "realized_rest_intervals": rest_intervals, + "legacy_event_aligned_inter_trial": legacy_event_aligned_inter_trial, + "selector_provenance": { + "event_duration_rule_ids": event_duration_rule_ids, + "trial_start_rule_ids": trial_start_rule_ids, + "post_event_rule_ids": post_event_rule_ids, + "event_transition_rule_ids": transition_rule_ids, + "inter_trial_rule_ids": inter_trial_rule_ids, + "rest_rule_ids": rest_rule_ids, + }, + "schedule_table": schedule_table, + } + + def event_sequence_from_template_ids(self, template_sequence): + """Flatten a conceptual-trial template sequence into modeled event codes.""" + sequence = [] + for template_id in template_sequence: + for event in self.templates_by_id[template_id]["events"]: + sequence.append(int(event["code"])) + return sequence class Optimisation: - """Represent the population of experimental designs for fMRI. - - :param experiment: The experimental setup of the fMRI experiment. - :type experiment: experiment - - :param G: The size of the generation - :type G: integer - - :param R: with which rate are the orders generated from ['blocked','random','mseq'] - :type R: list - - :param q: percentage of mutations - :type q: float - - :param weights: weights attached to Fe, Fd, Ff, Fc - :type weights: list - - :param I: number of immigrants - :type I: integer - - :param preruncycles: number of prerun cycles (to find maximum Fe and Fd) - :type preruncycles: integer - - :param cycles: number of cycles - :type cycles: integer - - :param seed: seed - :type seed: integer - - :param Aoptimality: optimises A-optimality if true, else D-optimality - :type Aoptimality: boolean - - :param convergence: after how many stable iterations is there convergence - :type convergence: integer - - :param folder: folder to save output - :type folder: string - - :param outdes: number of designs to be saved - :type outdes: integer - - :param optimisation: The type of optimisation - 'GA' or 'simulation' - :type optimisation: string - """ + """Run the design search loop for a configured experiment.""" def __init__( self, @@ -1009,168 +2043,192 @@ def __init__( Aoptimality: bool = True, folder: str | Path | None = None, outdes: int = 3, - convergence: int = 1000, + convergence: int | None = 1000, + max_candidate_attempts: int = 10000, optimisation: str = "GA", ): - + """Configure an optimisation run over designs sampled from an experiment.""" self.exp = experiment - self.G = G - self.R = [0.4, 0.4, 0.2] if R is None else R - self.q = q self.weights = weights - self.I = I self.preruncycles = preruncycles self.cycles = cycles - self.convergence = convergence + self.seed = seed or experiment.seed + self.I = I + self.G = G + self.R = [0.4, 0.4, 0.2] if R is None else R + self.q = q self.Aoptimality = Aoptimality - self.outdes = outdes self.folder = Path(folder).absolute() if folder else None + self.outdes = outdes + self.convergence = convergence + if self.convergence is not None: + if not isinstance(self.convergence, int) or self.convergence < 0: + raise ValueError("convergence must be None or a non-negative integer") + if not isinstance(max_candidate_attempts, int) or max_candidate_attempts <= 0: + raise ValueError("max_candidate_attempts must be a positive integer") + self.max_candidate_attempts = max_candidate_attempts self.optimisation = optimisation - self.seed = seed or np.random.randint(10000) self.designs = [] self.optima = [] self.bestdesign = None self.cov = None + self._seed_counter = 0 + self.bestscore = float("-inf") + self.bestdesign_generation = None + self.generations_completed = 0 + self.finished = False + self.stop_reason = None + self._stagnation_generations = 0 + self._last_candidate_failure = "candidate generation has not been attempted yet" + self._last_candidate_exception = None + + def _next_rng(self, label: int = 0) -> np.random.Generator: + """Advance the optimisation RNG stream deterministically.""" + self._seed_counter += 1 + ss = np.random.SeedSequence([self.seed, self._seed_counter, label]) + return np.random.default_rng(ss) def change_seed(self): - """Change the seed.""" + """Increment the optimisation seed to start a fresh search trajectory.""" self.seed = self.seed + 1000 if self.seed < 4 * 10**9 else 1 return self def check_develop(self, design, weights=None): - """Check and develop a design to the population. - - Function will check design against strict options and develop the design if valid. - - :param design: Design to be added to population. - :type design: esign object - - :param weights: weights for efficiency calculation. - :type weights: list of floats, summing to 1 - """ - # weights - - if weights is None: - weights = self.weights - - # check maxrep, hardprob, every stimulus at least once + """Validate and score a candidate design before keeping it.""" + weights = self.weights if weights is None else weights if self.exp.maxrep is not None and not design.check_maxrep(self.exp.maxrep): + self._last_candidate_failure = "candidate exceeded maxrep" + self._last_candidate_exception = None return False if self.exp.hardprob and not design.check_hardprob(): + self._last_candidate_failure = ( + "candidate violated hard probability constraints" + ) + self._last_candidate_exception = None return False if len(np.unique(design.order)) < self.exp.n_stimuli: + self._last_candidate_failure = ( + "candidate omitted one or more stimulus categories" + ) + self._last_candidate_exception = None return False - - # develop - out = design.designmatrix() if out is False: + self._last_candidate_failure = "design matrix construction failed" + self._last_candidate_exception = None + return False + if not ( + np.all(np.isfinite(np.asarray(design.Xnonconv))) + and np.all(np.isfinite(np.asarray(design.Xconv))) + ): + self._last_candidate_failure = "design matrices contained non-finite values" + self._last_candidate_exception = None return False design.FCalc( weights, confoundorder=self.exp.confoundorder, Aoptimality=self.Aoptimality ) - return False if np.isnan(design.F) else design + component_scores = np.array( + [design.Fe, design.Fd, design.Ff, design.Fc, design.F], dtype=float + ) + if not np.all(np.isfinite(component_scores)): + self._last_candidate_failure = "candidate scores contained non-finite values" + self._last_candidate_exception = None + return False + self._last_candidate_failure = "" + self._last_candidate_exception = None + return design + + def _raise_candidate_generation_error(self, attempts, target): + """Raise a bounded candidate-generation failure with the last known cause.""" + message = ( + f"Failed to produce {target} valid candidate design(s) after {attempts} attempts " + f"for mode {self.exp.mode!r}. Last failure: {self._last_candidate_failure}." + ) + if self._last_candidate_exception is not None: + raise RuntimeError(message) from self._last_candidate_exception + raise RuntimeError(message) + + def _make_design_from_order(self, order, rng): + """Create a :class:`Design` from a flat event order.""" + schedule = self.exp.realize_flat_order(order, rng) + return Design(experiment=self.exp, schedule=schedule, trial_sequence=list(order)) + + def _make_design_from_templates(self, template_sequence, rng): + """Create a :class:`Design` from a conceptual-trial template sequence.""" + schedule = self.exp.realize_from_trial_sequence( + template_sequence, rng, template_sequence + ) + return Design( + experiment=self.exp, + schedule=schedule, + trial_sequence=list(template_sequence), + template_sequence=list(template_sequence), + ) def add_new_designs(self, weights=None, R=None): - """Generate the population. - - :param experiment: The experimental setup of the fMRI experiment. - :type experiment: experiment - :param weights: weights for efficiency calculation. - :type weights: list of floats, summing to 1 - :param seed: The seed for random processes. - :type seed: integer or None - """ - # weights - if weights is None: - weights = self.weights - + """Populate the current generation with newly sampled candidate designs.""" + weights = self.weights if weights is None else weights if not R: R = np.round(np.array(self.R) * self.G).tolist() - - if self.exp.n_stimuli in [6, 10] and R[2] > 0: - warnings.warns( - "for this number of conditions/stimuli, " - "there are no msequences possible.\n" - "Replaced by random designs." - ) - R[1] = R[1] + R[2] - R[2] = 0 - + target = int(np.sum(R)) NDes = 0 - self.change_seed() - - while NDes < np.sum(R): - self.change_seed() - ind = np.sum(NDes >= np.cumsum(R)) + attempts = 0 + while NDes < target: + if attempts >= self.max_candidate_attempts: + self._raise_candidate_generation_error(attempts, target - NDes) + attempts += 1 + rng = self._next_rng(100 + NDes) + ind = int(np.sum(NDes >= np.cumsum(R))) ordertype = ["blocked", "random", "msequence"][ind] - - # --- Sample order --- - order = None - - if self.exp.order_fixed: - order = self.exp.order - elif self.exp.order_probabilities is None: - order = generate.order( - self.exp.n_stimuli, - self.exp.n_trials, - self.exp.P, - ordertype=ordertype, - seed=self.seed, - ) - else: - order = Experiment.sample_from_probabilities( - self.exp.order_probabilities, - self.exp.order_keys, - self.exp.order_length, - ) - - # --- Sample ITI --- - ITI = [] - if self.exp.conditional_ITI is None: - ITI, ITIlam = generate.iti( - ntrials=self.exp.n_trials, - model=self.exp.ITImodel, - min=self.exp.ITImin, - max=self.exp.ITImax, - mean=self.exp.ITImean, - lam=self.exp.ITIlam, - seed=self.seed, - resolution=self.exp.resolution, - ) - if ITIlam: - self.exp.ITIlam = ITIlam - else: - ITI = Experiment.generate_iti(order, self.exp.conditional_ITI) - - # --- Sample per-trial stimulus durations (if variable) --- - all_stim_durations = None - if self.exp.stimuli_durations is not None: - all_stim_durations = Experiment.sample_stim_durations( - order, - self.exp.stimuli_durations, - self.exp.t_pre, - self.exp.t_post, + try: + if self.exp.mode == "flat_fixed_order": + des = self._make_design_from_order(self.exp.order, rng) + elif self.exp.mode == "fixed_trials": + des = self._make_design_from_templates( + self.exp.fixed_trial_sequence, rng + ) + elif self.exp.mode == "template_sampled": + template_sequence = self.exp.sample_trial_sequence(rng) + des = self._make_design_from_templates(template_sequence, rng) + else: + order = generate.order( + self.exp.n_stimuli, + self.exp.n_trials, + self.exp.P.tolist(), + ordertype=ordertype, + rng=rng, + ) + des = self._make_design_from_order(order, rng) + except Exception as exc: + self._last_candidate_failure = ( + "candidate construction raised an exception" ) - - # --- Create Design with the ONE shared Experiment --- - des = Design( - order=order, - ITI=np.array(ITI), - experiment=self.exp, - all_stim_durations=all_stim_durations, - ) - + self._last_candidate_exception = exc + continue fulldes = self.check_develop(des, weights) - if fulldes is False: continue self.designs.append(fulldes) NDes += 1 - return self def _clean_designs(self, weights): + """Remove duplicate designs and backfill the population if needed.""" + if len(self.designs) <= 1: + return self + if self.exp.mode in {"fixed_trials", "template_sampled"}: + seen = {} + keep = [] + for idx, des in enumerate(self.designs): + key = tuple(des.order) + if key not in seen: + seen[key] = idx + keep.append(des) + removed = len(self.designs) - len(keep) + self.designs = keep + if removed > 0: + self.add_new_designs(R=[0, removed, 0], weights=weights) + return self n = 0 rm = 0 while n == 0: @@ -1187,165 +2245,123 @@ def _clean_designs(self, weights): ind = np.where(isone) remove = ind[1][ind[0] == ind[0][0]] self.designs = [ - des for ind, des in enumerate(self.designs) if ind not in remove + des for idx, des in enumerate(self.designs) if idx not in remove ] - rm = rm + len(remove) - - self.add_new_designs(R=[0, rm, 0], weights=weights) - + rm += len(remove) + if rm > 0: + self.add_new_designs(R=[0, rm, 0], weights=weights) return self def _mutation(self, weights, seed): - # Mutation: - # if: Best design: stay untouched - # elif Correlation between all is > 0.8: mutate with 20% mutations - # else: mutate with 5% mutations - # for all: if conditions are not fulfilled: not mutated - + """Apply mutation to the current generation.""" signals = [x.Xconv for x in self.designs] efficiencies = [x.F for x in self.designs] - cors = self.pearsonr(signals, self.exp.n_stimuli) mncor = np.mean(cors) - for idx in range(len(self.designs)): design = self.designs[idx] - if design.F == np.max(efficiencies): offspring = design - elif mncor > 0.6: offspring = design.mutation(0.2, seed=seed) offspring = self.check_develop(offspring, weights) - else: offspring = design.mutation(self.q, seed=seed) offspring = self.check_develop(offspring, weights) - - if offspring is False: - continue - else: + if offspring is not False: self.designs[idx] = offspring - return self def _crossover(self, weights, seed): - # select designs with F>median(F): + """Apply crossover to parent pairs in the current generation.""" crossind = range(len(self.designs)) - nparents = len(crossind) npairs = int(nparents / 2.0) - - np.random.seed(seed) - CouplingRnd = np.random.choice(nparents, size=(npairs * 2), replace=False) - CouplingRnd = [crossind[x] for x in CouplingRnd] - CouplingRnd = [ - [CouplingRnd[i], CouplingRnd[i + 1]] for i in np.arange(0, npairs * 2, 2) - ] - - count = 0 - - for couple in CouplingRnd: + rng = np.random.default_rng(seed) + coupling = rng.choice(nparents, size=(npairs * 2), replace=False) + coupling = [crossind[x] for x in coupling] + pairing = [[coupling[i], coupling[i + 1]] for i in np.arange(0, npairs * 2, 2)] + for couple in pairing: baby1, baby2 = self.designs[couple[0]].crossover( self.designs[couple[1]], seed=seed ) for baby in [baby1, baby2]: baby = self.check_develop(baby, weights) - if baby is False: - continue - self.designs.append(baby) - count = count + 1 - + if baby is not False: + self.designs.append(baby) return self def _immigration(self, weights, noim): + """Inject newly sampled designs into the current generation.""" R = np.ceil(np.array(self.R) * noim).tolist() self.add_new_designs(R=R, weights=weights) - return self def to_next_generation(self, weights=None, seed=1234, optimisation=None): - """Go from one generation to the next. - - :param weights: weights for efficiency calculation. - :type weights: list of floats, summing to 1 - - :param seed: The seed for random processes. - :type seed: integer or None - - :param optimisation: The type of optimisation - 'GA' or 'simulation' - :type optimisation: string - """ - if optimisation is None: - optimisation = self.optimisation - - # weights - if weights is None: - weights = self.weights - - # Skip crossover/mutation when order is fixed - if not self.exp.order_fixed: + """Advance one generation of the configured search strategy.""" + weights = self.weights if weights is None else weights + optimisation = self.optimisation if optimisation is None else optimisation + if self.exp.mode not in {"flat_fixed_order", "fixed_trials"}: self._clean_designs(weights) if optimisation == "GA": self._mutation(weights, seed) self._crossover(weights, seed) self._immigration(weights, noim=self.I) - elif optimisation == "simulation": self._immigration(weights, noim=self.I) - else: - print("Unknown optimisation type") else: self._immigration(weights, noim=self.I) - # inspect efficiencies efficiencies = [x.F for x in self.designs] maximum = np.max(efficiencies) self.optima.append(maximum) bestind = [ind for ind, val in enumerate(efficiencies) if val == maximum][0] - self.bestdesign = self.designs[bestind] - - # append best designs to lists - - # check convergence + generation_best = self.designs[bestind] gen = len(self.optima) - if gen > 1000 and self.optima[-1] > self.optima[gen - 1000]: + self.generations_completed = gen + if self.bestdesign is None or maximum > self.bestscore: + self.bestscore = float(maximum) + self.bestdesign = generation_best + self.bestdesign_generation = gen + self._stagnation_generations = 0 + else: + self._stagnation_generations += 1 + convergence_limit = self.convergence + if convergence_limit in {0, None}: + self.finished = False + self.stop_reason = None + elif self._stagnation_generations >= convergence_limit: self.finished = True - - # select best G - cutoff = np.sort(efficiencies)[::-1][self.G] - self.designs = [des for des in self.designs if des.F >= cutoff] - + self.stop_reason = ( + "no improvement in generation-best score for " + f"{convergence_limit} consecutive generation(s)" + ) + if len(self.designs) > self.G: + cutoff = np.sort(efficiencies)[::-1][self.G] + self.designs = [des for des in self.designs if des.F >= cutoff] return self def clear(self): - """Clear results between optimisations (maximum Fe, Fd or opt).""" + """Reset the current population while preserving the last best design.""" + previous_best = self.bestdesign self.designs = [] self.optima = [] self.finished = False - self.change_seed() - - if self.bestdesign: - bestdes = Design( - order=self.bestdesign.order, - ITI=self.bestdesign.ITI, - experiment=self.exp, - all_stim_durations=self.bestdesign.all_stim_durations, - ) - bestdes = self.check_develop(bestdes) - if bestdes is not False: - self.designs.append(bestdes) - self.bestdesign = None - + self.stop_reason = None + self.bestdesign = None + self.bestscore = float("-inf") + self.bestdesign_generation = None + self.generations_completed = 0 + self._stagnation_generations = 0 + if previous_best: + self.designs.append(previous_best) return self def optimise(self): - """Run design optimization.""" + """Run the full optimisation procedure, including normalization passes.""" if self.exp.FcMax == 1 and self.exp.FfMax == 1: self.exp.max_eff() - if self.exp.FeMax == 1 and self.weights[0] > 0: - # add new designs self.clear() self.add_new_designs(weights=[1, 0, 0, 0]) with progress_bar(text="Optimizing") as progress: @@ -1356,9 +2372,8 @@ def optimise(self): self.to_next_generation(seed=self.seed, weights=[1, 0, 0, 0]) progress.update(task, advance=1) if self.finished: - continue - self.exp.FeMax = np.max(self.bestdesign.F) - + break + self.exp.FeMax = float(np.max(self.bestdesign.F)) if self.exp.FdMax == 1 and self.weights[1] > 0: self.clear() self.add_new_designs(weights=[0, 1, 0, 0]) @@ -1370,14 +2385,10 @@ def optimise(self): self.to_next_generation(seed=self.seed, weights=[0, 1, 0, 0]) progress.update(task, advance=1) if self.finished: - continue - self.exp.FdMax = np.max(self.bestdesign.F) - - # clear all attributes + break + self.exp.FdMax = float(np.max(self.bestdesign.F)) self.clear() self.add_new_designs() - - # loop with progress_bar(text="Optimizing") as progress: task = progress.add_task( description="optimize", total=len(range(self.cycles)) @@ -1386,12 +2397,27 @@ def optimise(self): self.to_next_generation(seed=self.seed) progress.update(task, advance=1) if self.finished: - continue - + break return self + def selected_design(self, rank: int = 0): + """Return one evaluated representative design from the current selected outputs.""" + if self.bestdesign is None or not self.designs: + raise RuntimeError( + "selected_design() requires optimise() to run before selecting outputs" + ) + if not hasattr(self, "out"): + self.evaluate() + if rank < 0 or rank >= len(self.out): + raise IndexError(f"selected design rank {rank} is out of range") + return self.designs[self.out[rank]] + def evaluate(self): - # select designs: best from k-means clusters + """Cluster final designs and choose representative reported outputs.""" + if self.bestdesign is None or not self.designs: + raise RuntimeError( + "evaluate() requires optimise() to run before selecting outputs" + ) shape = self.bestdesign.Xconv.shape des = np.zeros([np.prod(shape), len(self.designs)]) efficiencies = np.array([x.F for x in self.designs]) @@ -1402,92 +2428,72 @@ def evaluate(self): des[:, d] = hrf clus = sklearn.cluster.k_means(des.T, self.outdes, random_state=self.seed)[1] out = [] - des = [] - cl = [] + new_designs = [] + clusters = [] first = 0 for c in range(self.outdes): ids = np.where(clus == c)[0] id_ordered = ids[np.flipud(np.argsort(efficiencies[ids]))] out.append(first) for d in id_ordered: - cl.append(c) - des.append(self.designs[d]) - first = first + 1 - self.designs = des + clusters.append(c) + new_designs.append(self.designs[d]) + first += 1 + self.designs = new_designs self.out = out - self.clus = cl - + self.clus = clusters signals = [x.Xconv for x in self.designs] - co = self.pearsonr(signals, 3) - self.cov = co - + self.cov = self.pearsonr(signals, self.exp.n_stimuli) return self def download(self): + """Write report artifacts, schedule exports, and onset files to disk.""" if not self.folder: raise ValueError("No folder defined to download output.") - if self.cov is None: self.evaluate() - - # empty folder if self.folder.exists(): files = self.folder.glob("**/design_*") for f in files: shutil.rmtree(f) else: self.folder.mkdir(parents=True, exist_ok=True) - reportfile = "report.pdf" report.make_report(self, self.folder / reportfile) - files = [] - for des in range(self.outdes): - - (self.folder / f"design_{str(des)}").mkdir(parents=True) - + (self.folder / f"design_{str(des)}").mkdir(parents=True, exist_ok=True) design = self.designs[self.out[des]] - for stim in range(self.exp.n_stimuli): - onsetsfile = Path(f"design_{str(des)}") / f"stimulus_{str(stim)}.txt" - onsubsets = [ str(x) - for x in np.array(design.onsets)[np.array(design.order) == stim] + for x in np.array(design.event_onsets)[np.array(design.order) == stim] ] with open(self.folder / onsetsfile, "w+") as f: for line in onsubsets: f.write(line) f.write("\n") files.append(onsetsfile) - - itifile = Path(f"design_{str(des)}") / "ITIs.txt" - - with open(self.folder / itifile, "w+") as f: - for line in design.ITI: - f.write(str(line)) - f.write("\n") - files.append(itifile) - - files.append(reportfile) - - # zip up + export_path = self.folder / f"design_{str(des)}" / "event_schedule.json" + export_path.write_text( + json.dumps(design.export_payload(), indent=2, default=str), + encoding="utf-8", + ) + files.append(Path(f"design_{str(des)}") / "event_schedule.json") + files.append(Path(reportfile)) zip_subdir = "OptimalDesign" self.zip_filename = f"{zip_subdir}.zip" self.file = BytesIO() zf = zipfile.ZipFile(self.file, "w") - for fpath in files: zf.write(self.folder / fpath, Path(zip_subdir) / fpath) - zf.close() - return self @staticmethod def pearsonr(signals, nstim): + """Compute pairwise mean regressor correlations between candidate designs.""" varcov = np.zeros([len(signals), len(signals)]) for sig1 in range(len(signals)): for sig2 in range(sig1, len(signals)): @@ -1497,29 +2503,3 @@ def pearsonr(signals, nstim): varcov[sig1, sig2] = np.mean(cors) varcov[sig2, sig1] = np.mean(cors) return varcov - - -def _find_new_resolution(TR, res): - n = TR * 1000.0 - # find divisors of TR*1000 - large_divisors = [] - for i in range(1, int(math.sqrt(n) + 1)): - if n % i == 0: - large_divisors.append(i) - if i * i != n: - large_divisors.append(int(n / i)) - sorted = np.sort(large_divisors) - # closest to res - resdivisor = TR / float(res) - difs = np.abs(resdivisor - sorted) - minind = np.where(difs == np.min(difs))[0] - divisor = sorted[minind][0] - newres = TR / divisor - return newres - - -def _round_to_resolution(inmat, res): - out = res * np.floor(np.array(inmat) / res) - ind = out / res - ind = [int(x) for x in ind] - return out, ind diff --git a/neurodesign/generate.py b/neurodesign/generate.py index 7ffbaa7..2836bbf 100644 --- a/neurodesign/generate.py +++ b/neurodesign/generate.py @@ -1,5 +1,13 @@ from __future__ import annotations +"""Legacy-style random order and ITI generators used by optimisation routines. + +These helpers are retained for flat one-event design generation. Version 2 +template-based designs build schedules in :mod:`neurodesign.classes`, but the +genetic search still relies on these utilities when constructing flat orders or +sampling classic ITI distributions. +""" + import numpy as np import scipy import scipy.stats as stats @@ -12,54 +20,43 @@ def order( ntrials: int, probabilities: list[float], ordertype: str, - seed: int | None = 1234, + seed: int | None = None, + rng: np.random.Generator | None = None, ): - """Generate an order of stimuli. - - :param nstim: The number of different stimuli (or conditions) - :type nstim: integer - - :param ntrials: The total number of trials - :type ntrials: integer - - :param probabilities: The probabilities of each stimulus - :type probabilities: list - - :param ordertype: Which model to sample from. - Possibilities: "blocked", "random" or "msequence" - :type ordertype: string + """Sample a flat stimulus order for legacy one-event design modes. - :param seed: The seed with which the change point will be sampled. - :type seed: integer or None - - :returns order: A list with the created order of stimuli + Parameters define the number of stimulus categories, desired sequence + length, target category probabilities, and the sampling strategy + (`random`, `blocked`, or `msequence`). """ if ordertype not in ["random", "blocked", "msequence"]: raise ValueError(f"{ordertype} not known.") - np.random.seed(seed) + local_rng = rng if rng is not None else np.random.default_rng(seed) if ordertype == "blocked": - blocksize = float(np.random.choice(np.arange(1, 10), 1)[0]) + blocksize = float(local_rng.choice(np.arange(1, 10))) nblocks = int(np.ceil(ntrials / blocksize)) - blockorder = _generate_order_items(probabilities, nblocks) - order = np.repeat(blockorder, blocksize)[:ntrials] - - elif ordertype == "msequence": - order = msequence.Msequence() - order.GenMseq(mLen=ntrials, stimtypeno=nstim, seed=seed) - id = np.random.randint(len(order.orders)) - order = order.orders[id] + blockorder = _generate_order_items(probabilities, nblocks, local_rng) + return np.repeat(blockorder, blocksize)[:ntrials].tolist() + + if ordertype == "msequence": + seq = msequence.Msequence() + seq.GenMseq( + mLen=ntrials, + stimtypeno=nstim, + seed=seed if seed is not None else int(local_rng.integers(1, 2**31)), + ) + idx = int(local_rng.integers(len(seq.orders))) + return list(seq.orders[idx]) - elif ordertype == "random": - order = _generate_order_items(probabilities, ntrials) - return order + return _generate_order_items(probabilities, ntrials, local_rng) -def _generate_order_items(probabilities, items): - mult = np.random.multinomial(1, probabilities, items) - result = [x.tolist().index(1) for x in mult] - return result +def _generate_order_items(probabilities, items, rng): + """Draw categorical item identities according to ``probabilities``.""" + mult = rng.multinomial(1, probabilities, items) + return [x.tolist().index(1) for x in mult] def iti( @@ -70,99 +67,70 @@ def iti( max: float | None = None, lam=None, resolution: float = 0.1, - seed: int | None = 1234, + seed: int | None = None, + rng: np.random.Generator | None = None, ): - """Generate an order of stimuli. - - :param ntrials: The total number of trials - :type ntrials: integer - - :param model: Which model to sample from. - Possibilities: "fixed","uniform","exponential" - :type model: string - - :param min: The minimum ITI (required with "uniform" or "exponential") - :type min: float + """Sample a legacy event-aligned ITI vector. - :param mean: The mean ITI (required with "fixed" or "exponential") - :type mean: float - - :param max: The max ITI (required with "uniform" or "exponential") - :type max: float - - :param lam: lambda - - :param resolution: The resolution of the design: for rounding the ITI's - :type resolution: float - - :param seed: The seed with which the change point will be sampled. - :type seed: integer or None - - :returns iti: A list with the created ITI's + The returned vector always has length ``ntrials`` and begins with ``0`` so + that element ``i`` can be interpreted as the gap immediately preceding the + ``i``-th event in the historical API. """ + local_rng = rng if rng is not None else np.random.default_rng(seed) + if model == "fixed": smp = [0] + [mean] * (ntrials - 1) - smp = resolution * np.round(smp / resolution) + smp = resolution * np.round(np.array(smp) / resolution) + return smp, lam - elif model == "uniform": + if model == "uniform": mean = (min + max) / 2.0 - np.random.seed(seed) - smp = np.random.uniform(min, max, (ntrials - 1)) - smp = _fix_iti(smp, mean, min, max, resolution) - smp = np.append([0], smp) + smp = local_rng.uniform(min, max, (ntrials - 1)) + smp = _fix_iti(smp, mean, min, max, resolution, local_rng) + return np.append([0], smp), lam - elif model == "exponential": + if model == "exponential": if not lam: - try: - lam = _compute_lambda(min, max, mean) - except ValueError as err: - raise ValueError(err) - np.random.seed(seed) - smp = _rtexp((ntrials - 1), lam, min, max, seed=seed) - smp = _fix_iti(smp, mean, min, max, resolution) - smp = np.append([0], smp) - - # round to resolution - - return smp, lam - - -def _fix_iti(smp, mean, min, max, resolution): - # kind of a weird function to fix ITI's to have the nominal mean - # problem was that you can't just add or subtract the difference: it could be - # out of bounds of the minimum and the maximum... - # now it changes values either to min/max or with the average difference - # compute diff - smp = resolution * np.round(smp / resolution) + lam = _compute_lambda(min, max, mean) + smp = _rtexp((ntrials - 1), lam, min, max, local_rng) + smp = _fix_iti(smp, mean, min, max, resolution, local_rng) + return np.append([0], smp), lam + + raise ValueError(f"Unknown inter-trial interval model {model!r}") + + +def _fix_iti(smp, mean, min, max, resolution, rng): + """Round sampled ITIs while nudging them back toward the requested mean.""" + smp = resolution * np.round(np.array(smp) / resolution) totaldiff = np.sum(smp) - mean * len(smp) while not np.isclose(totaldiff, 0, resolution) and np.mean(smp) > mean: - chid = np.random.choice(len(smp)) + chid = int(rng.integers(len(smp))) if (smp[chid] - min) < resolution or (max - smp[chid]) < resolution: continue - else: - smp[chid] = smp[chid] - np.sign(totaldiff) * resolution + smp[chid] = smp[chid] - np.sign(totaldiff) * resolution totaldiff = np.sum(smp) - mean * len(smp) return smp def _compute_lambda(lower, upper, mean): + """Infer the truncated-exponential scale parameter matching a bounded mean.""" a = float(lower) b = float(upper) m = float(mean) opt = scipy.optimize.minimize( _difexp, 50, args=(a, b, m), bounds=((10 ** (-9), 100),), method="L-BFGS-B" ) - check = _rtexp(100000, opt.x[0], lower, upper, seed=1000) + check_rng = np.random.default_rng(1000) + check = _rtexp(100000, opt.x[0], lower, upper, check_rng) if not np.isclose(np.mean(check), mean, rtol=0.1): raise ValueError( - "Error when figuring out lambda for exponential distribution: " - "can't compute lambda." + "Error when figuring out lambda for exponential distribution: can't compute lambda." ) - else: - return opt.x[0] + return opt.x[0] def _difexp(lam, lower, upper, mean): + """Objective used when fitting a bounded exponential ITI distribution.""" lam = float(np.asarray(lam).flat[0]) diff = stats.truncexpon( (float(upper) - float(lower)) / lam, loc=float(lower), scale=lam @@ -170,9 +138,10 @@ def _difexp(lam, lower, upper, mean): return abs(diff) -def _rtexp(ntrials, lam, lower, upper, seed): +def _rtexp(ntrials, lam, lower, upper, rng): + """Sample from a truncated exponential distribution on ``[lower, upper]``.""" a = float(lower) b = float(upper) - np.random.seed(seed) - smp = stats.truncexpon((b - a) / lam, loc=a, scale=lam).rvs(ntrials) - return smp + return stats.truncexpon((b - a) / lam, loc=a, scale=lam).rvs( + ntrials, random_state=rng + ) diff --git a/neurodesign/report.py b/neurodesign/report.py index 8fc1f42..8e5e856 100644 --- a/neurodesign/report.py +++ b/neurodesign/report.py @@ -44,7 +44,7 @@ def make_report(population, outfile: str | Path = "NeuroDesign.pdf"): As such, the designs can look very similar. Actually, the genetic algorithm uses natural selection as a basis, and as such, the designs can be clustered in families. -This is the covariance matrix between the final population of {population.G} designs.""" +This is the design-to-design correlation matrix for the final population of {population.G} designs.""" designs = f"""The following figure shows in the upper panel the optimisation score over the {len(population.optima)} generations in the final optimisation run. Below are the expected signals of the selected representative designs @@ -88,7 +88,7 @@ def _styled_table(rows, col_widths): corr_im = corr_ax.imshow( population.cov, interpolation="nearest", clim=(-1, 1), cmap="RdBu" ) - corr_ax.set_title("Covariance matrix of final designs") + corr_ax.set_title("Correlation matrix of final designs") corr_fig.colorbar(corr_im, ax=corr_ax, ticks=[-1, 0, 1]) corr_path = _save_figure(corr_fig) plt.close(corr_fig) @@ -100,7 +100,10 @@ def _styled_table(rows, col_widths): generations = np.arange(1, len(population.optima) + 1) score_ax.plot(generations, population.optima, color="#1f5a91", lw=2) if len(generations) > 0: - score_ax.set_xlim(1, len(generations)) + if len(generations) == 1: + score_ax.set_xlim(0.5, 1.5) + else: + score_ax.set_xlim(1, len(generations)) tick_step = max(1, int(np.ceil(len(generations) / 10))) score_ax.set_xticks(generations[::tick_step]) score_ax.set_xlabel("Generation") @@ -190,43 +193,42 @@ def _table_text(value): probability_text = _table_text(population.exp.P) contrast_text = _table_text(population.exp.C) weight_text = _table_text(population.weights) - - if population.exp.order_fixed: - order_mode = "Fixed order" - order_details = population.exp.order - elif population.exp.order_probabilities is not None: - order_mode = "Probability-based order sampling" - order_details = { - "probabilities": population.exp.order_probabilities, - "keys": population.exp.order_keys, - "length": population.exp.order_length, - } - else: - order_mode = "Genetic crossover/mutation order optimisation" - order_details = "No fixed order constraints" - - if population.exp.stimuli_durations is None: - stim_duration_mode = "Fixed stimulus duration" - stim_duration_details = population.exp.stim_duration - else: - stim_duration_mode = "Variable by stimulus" - stim_duration_details = population.exp.stimuli_durations - - if population.exp.conditional_ITI is None: - iti_mode = population.exp.ITImodel - iti_details = { - "min": population.exp.ITImin, - "mean": population.exp.ITImean, - "max": population.exp.ITImax, - } - else: - iti_mode = "Conditional ITI" - iti_details = population.exp.conditional_ITI + spec = population.exp.export_specification() + + mode_labels = { + "flat_generated": "Flat one-event generated order", + "flat_fixed_order": "Flat one-event fixed order", + "fixed_trials": "Fixed conceptual-trial sequence", + "template_sampled": "Probabilistic conceptual-trial templates", + } + order_mode = mode_labels.get(population.exp.mode, population.exp.mode) + order_details = { + "order": spec["order"], + "trial_templates": spec["trial_templates"], + "trials": spec["trials"], + "trial_template_probabilities": spec["trial_template_probabilities"], + "n_conceptual_trials": spec["n_conceptual_trials"], + } + + stim_duration_mode = "Event duration specifications" + stim_duration_details = spec["event_durations_requested"] + iti_mode = "Inter-trial interval" + iti_details = spec["inter_trial_interval_requested"] + conceptual_trials = spec["n_conceptual_trials"] + selected_design = ( + population.designs[selected_indices[0]] if selected_indices else None + ) + modeled_events = ( + len(getattr(selected_design, "order", [])) + if selected_design is not None + else None + ) exp = [ ["Setting", "Value"], ["Repetition time (TR):", _table_text(population.exp.TR)], - ["Number of trials:", _table_text(population.exp.n_trials)], + ["Conceptual trial count:", _table_text(conceptual_trials)], + ["Modeled event count:", _table_text(modeled_events)], ["Number of scans:", _table_text(population.exp.n_scans)], ["Number of different stimuli:", _table_text(population.exp.n_stimuli)], ["Stimulus probabilities:", probability_text], @@ -236,18 +238,22 @@ def _table_text(value): ["Stimulus duration details:", _table_text(stim_duration_details)], ["Base stimulus duration (s):", _table_text(population.exp.stim_duration)], ["Trial container max (s):", _table_text(population.exp.trial_max)], - ["Seconds before stimulus (in trial):", _table_text(population.exp.t_pre)], - ["Seconds after stimulus (in trial):", _table_text(population.exp.t_post)], + [ + "Trial-start interval spec:", + _table_text(spec["trial_start_interval_requested"]), + ], + ["Post-event interval spec:", _table_text(spec["post_event_interval_requested"])], + [ + "Event-transition interval spec:", + _table_text(spec["event_transition_interval_requested"]), + ], ["Expected trial duration (s):", _table_text(population.exp.trial_duration)], ["Total experiment duration (s):", _table_text(population.exp.duration)], - ["Number of stimuli between rest blocks:", _table_text(population.exp.restnum)], - ["Duration of rest blocks (s):", _table_text(population.exp.restdur)], + ["Rest cadence (trials):", _table_text(population.exp.rest_every_n_trials)], + ["Rest interval spec:", _table_text(spec["rest_interval_requested"])], ["Contrasts:", contrast_text], - ["ITI generation:", _table_text(iti_mode)], - ["ITI details:", _table_text(iti_details)], - ["Minimum ITI:", _table_text(population.exp.ITImin)], - ["Mean ITI:", _table_text(population.exp.ITImean)], - ["Maximum ITI:", _table_text(population.exp.ITImax)], + ["Inter-trial interval mode:", _table_text(iti_mode)], + ["Inter-trial interval details:", _table_text(iti_details)], ["Hard probabilities:", _table_text(population.exp.hardprob)], ["Maximum number of repeated stimuli:", _table_text(population.exp.maxrep)], ["Resolution of design:", _table_text(population.exp.resolution)], @@ -271,7 +277,19 @@ def _table_text(value): ["Number of designs in each generation:", _table_text(population.G)], ["Number of immigrants in each generation:", _table_text(population.I)], ["Confounding order:", _table_text(population.exp.confoundorder)], - ["Convergence criterion:", _table_text(population.convergence)], + [ + "Convergence patience (stagnant generations):", + _table_text( + "disabled" + if population.convergence in {None, 0} + else population.convergence + ), + ], + ["Completed generations:", _table_text(population.generations_completed)], + [ + "Stop reason:", + _table_text(population.stop_reason or "completed planned generations"), + ], ["Number of precycles:", _table_text(population.preruncycles)], ["Number of cycles:", _table_text(population.cycles)], ["Percentage of mutations:", _table_text(population.q)], diff --git a/output/tuto_base-designing_scoring_and_optimizing/designs.pdf b/output/tuto_base-designing_scoring_and_optimizing/designs.pdf deleted file mode 100644 index 8577e53724f3143a8e16ccdbc6f1118e73df9402..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 39013 zcmZU)b8ux%w7{F>#I~JGGVzIR+Y{Ti?M!TQqKP%JZF43zPHgApd-uMoTlH%Hu~ska z{;jTEySf_Rl*A+$m>JpNzg4Z1R<^@4lLAQ{jIH7M_(+-5yd2F)nM93Tjcgq(NSTz3 zEX-U;S-t{PNCgDo&FoG83*`Dg2@($WuB5F0Zvc~~qOrA^i7P4R|1`xtT_sgrjaMPTQl>2`oh3)MfzFN%xh5g@w_|N_SjhvjBy@jhKDf9pK zCShg!RVyizgzeV=h?$u{y0@hSiFMLNHy|MTsg3FqUSB;X|T 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-1.4000000000000001 -1.7000000000000002 -1.7000000000000002 -3.0 -3.2 -1.4000000000000001 -1.3 -1.1 -1.1 -1.9000000000000001 -1.2000000000000002 -1.0 -1.8 -1.9000000000000001 -2.9000000000000004 -2.3000000000000003 -1.0 -1.8 -1.1 -1.0 -1.1 -1.1 -1.2000000000000002 -1.0 -2.6 -1.3 -1.4000000000000001 -1.3 -1.5 -2.2 -1.3 -2.3000000000000003 -1.1 -1.4000000000000001 -2.8000000000000003 -1.3 -1.3 -1.3 -2.2 -1.1 -1.1 -2.3000000000000003 -1.5 -1.0 -1.8 -1.4000000000000001 -1.9000000000000001 -2.3000000000000003 -1.2000000000000002 diff --git a/output/tuto_base-optimizing_and_reporting/design_0/stimulus_0.txt b/output/tuto_base-optimizing_and_reporting/design_0/stimulus_0.txt deleted file mode 100644 index a2772af..0000000 --- a/output/tuto_base-optimizing_and_reporting/design_0/stimulus_0.txt +++ /dev/null @@ -1,37 +0,0 @@ -0.0 -42.8 -47.3 -62.5 -67.3 -71.8 -76.1 -80.1 -104.19999999999999 -108.69999999999999 -114.89999999999999 -119.3 -124.5 -128.7 -147.4 -152.9 -191.39999999999998 -195.99999999999997 -200.19999999999996 -205.99999999999997 -225.0 -250.6 -259.7 -264.4 -270.4 -298.4 -322.19999999999993 -336.29999999999995 -360.8 -370.70000000000005 -379.40000000000003 -398.70000000000005 -402.80000000000007 -407.20000000000005 -431.1000000000001 -457.90000000000015 -462.3000000000001 diff --git a/output/tuto_base-optimizing_and_reporting/design_0/stimulus_1.txt b/output/tuto_base-optimizing_and_reporting/design_0/stimulus_1.txt deleted file mode 100644 index 46e28bc..0000000 --- a/output/tuto_base-optimizing_and_reporting/design_0/stimulus_1.txt +++ /dev/null @@ -1,29 +0,0 @@ -5.5 -24.9 -38.699999999999996 -53.099999999999994 -86.1 -91.39999999999999 -135.0 -141.4 -168.9 -174.9 -240.1 -245.0 -280.99999999999994 -285.29999999999995 -316.29999999999995 -327.49999999999994 -331.49999999999994 -348.5 -356.8 -389.1 -413.00000000000006 -417.30000000000007 -425.9000000000001 -435.2000000000001 -439.3000000000001 -444.60000000000014 -449.10000000000014 -467.2000000000001 -472.5000000000001 diff --git a/output/tuto_base-optimizing_and_reporting/design_0/stimulus_2.txt b/output/tuto_base-optimizing_and_reporting/design_0/stimulus_2.txt deleted file mode 100644 index cbe2743..0000000 --- a/output/tuto_base-optimizing_and_reporting/design_0/stimulus_2.txt +++ /dev/null @@ -1,34 +0,0 @@ -10.4 -16.3 -20.4 -29.199999999999996 -34.4 -58.199999999999996 -95.6 -99.6 -157.5 -162.1 -181.6 -186.2 -211.79999999999998 -215.89999999999998 -220.7 -231.2 -235.29999999999998 -255.0 -276.59999999999997 -289.4 -293.5 -302.59999999999997 -306.59999999999997 -311.4 -340.4 -344.4 -352.6 -366.40000000000003 -375.1 -383.90000000000003 -393.40000000000003 -421.6000000000001 -453.10000000000014 -476.7000000000001 diff --git a/output/tuto_base-optimizing_and_reporting/design_1/ITIs.txt b/output/tuto_base-optimizing_and_reporting/design_1/ITIs.txt deleted file mode 100644 index 220920a..0000000 --- a/output/tuto_base-optimizing_and_reporting/design_1/ITIs.txt +++ /dev/null @@ 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-1.3 -1.7000000000000002 -1.5 -2.3000000000000003 -1.7000000000000002 -1.0 -1.5 -3.9000000000000004 diff --git a/output/tuto_base-optimizing_and_reporting/design_1/stimulus_0.txt b/output/tuto_base-optimizing_and_reporting/design_1/stimulus_0.txt deleted file mode 100644 index ca95076..0000000 --- a/output/tuto_base-optimizing_and_reporting/design_1/stimulus_0.txt +++ /dev/null @@ -1,32 +0,0 @@ -4.4 -19.0 -42.9 -97.29999999999998 -103.59999999999998 -120.29999999999997 -129.49999999999997 -139.49999999999997 -144.59999999999997 -167.1 -176.7 -197.2 -251.5 -257.8 -262.40000000000003 -271.1000000000001 -275.4000000000001 -285.7000000000001 -289.9000000000001 -323.00000000000006 -333.9000000000001 -349.30000000000007 -353.70000000000005 -384.50000000000006 -393.6 -399.0 -408.0 -413.1 -446.20000000000005 -456.00000000000006 -460.70000000000005 -464.70000000000005 diff --git a/output/tuto_base-optimizing_and_reporting/design_1/stimulus_1.txt 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a/output/tuto_base-optimizing_and_reporting/design_1/stimulus_2.txt +++ /dev/null @@ -1,35 +0,0 @@ -0.0 -8.8 -14.5 -23.6 -32.5 -47.3 -57.6 -62.400000000000006 -67.0 -83.69999999999999 -88.29999999999998 -107.69999999999997 -124.79999999999997 -183.0 -192.6 -210.79999999999998 -225.39999999999998 -236.2 -240.39999999999998 -245.2 -266.50000000000006 -279.9000000000001 -298.4000000000001 -302.80000000000007 -307.9000000000001 -318.1000000000001 -358.80000000000007 -375.6000000000001 -388.70000000000005 -403.5 -421.8 -425.90000000000003 -431.50000000000006 -441.50000000000006 -476.1 diff --git a/output/tuto_base-optimizing_and_reporting/design_2/ITIs.txt b/output/tuto_base-optimizing_and_reporting/design_2/ITIs.txt deleted file mode 100644 index 4c4673a..0000000 --- a/output/tuto_base-optimizing_and_reporting/design_2/ITIs.txt +++ /dev/null @@ -1,100 +0,0 @@ -0.0 -1.8 -1.2000000000000002 -1.4000000000000001 -1.1 -1.1 -3.4000000000000004 -2.1 -1.6 -3.3000000000000003 -1.7000000000000002 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a/output/tuto_base-optimizing_and_reporting/design_2/stimulus_2.txt +++ /dev/null @@ -1,34 +0,0 @@ -0.0 -4.8 -13.399999999999999 -33.1 -44.0 -61.400000000000006 -67.30000000000001 -78.60000000000002 -98.40000000000002 -126.30000000000001 -139.8 -186.10000000000002 -197.40000000000003 -215.80000000000004 -231.10000000000002 -240.4 -246.8 -250.9 -285.2 -289.4 -303.0 -330.69999999999993 -359.7999999999999 -363.8999999999999 -368.2999999999999 -386.4999999999999 -392.39999999999986 -409.29999999999984 -429.3999999999998 -439.8999999999998 -458.49999999999983 -463.1999999999998 -467.5999999999998 -471.7999999999998 diff --git a/output/tuto_base-optimizing_and_reporting/design_3/ITIs.txt b/output/tuto_base-optimizing_and_reporting/design_3/ITIs.txt deleted file mode 100644 index 610b73b..0000000 --- a/output/tuto_base-optimizing_and_reporting/design_3/ITIs.txt +++ /dev/null @@ -1,100 +0,0 @@ -0.0 -2.5 -1.9000000000000001 -2.9000000000000004 -1.1 -1.5 -1.3 -2.2 -1.3 -1.1 -1.5 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b/output/tuto_base-optimizing_and_reporting/design_3/stimulus_0.txt deleted file mode 100644 index eda5720..0000000 --- a/output/tuto_base-optimizing_and_reporting/design_3/stimulus_0.txt +++ /dev/null @@ -1,33 +0,0 @@ -0.0 -42.8 -47.3 -62.5 -67.3 -71.8 -76.1 -80.1 -104.19999999999999 -108.69999999999999 -114.89999999999999 -119.3 -124.5 -128.7 -147.4 -152.9 -191.39999999999998 -195.99999999999997 -200.19999999999996 -205.99999999999997 -225.0 -250.6 -259.7 -264.4 -276.59999999999997 -322.19999999999993 -331.49999999999994 -344.4 -375.1 -393.40000000000003 -444.60000000000014 -457.90000000000015 -462.3000000000001 diff --git a/output/tuto_base-optimizing_and_reporting/design_3/stimulus_1.txt b/output/tuto_base-optimizing_and_reporting/design_3/stimulus_1.txt deleted file mode 100644 index c9f08e5..0000000 --- a/output/tuto_base-optimizing_and_reporting/design_3/stimulus_1.txt +++ /dev/null @@ -1,33 +0,0 @@ -5.5 -24.9 -38.699999999999996 -53.099999999999994 -86.1 -91.39999999999999 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-402.80000000000007 -413.00000000000006 -417.30000000000007 -439.3000000000001 -449.10000000000014 -476.7000000000001 diff --git a/output/tuto_base-optimizing_and_reporting/design_4/ITIs.txt b/output/tuto_base-optimizing_and_reporting/design_4/ITIs.txt deleted file mode 100644 index 610b73b..0000000 --- a/output/tuto_base-optimizing_and_reporting/design_4/ITIs.txt +++ /dev/null @@ -1,100 +0,0 @@ -0.0 -2.5 -1.9000000000000001 -2.9000000000000004 -1.1 -1.5 -1.3 -2.2 -1.3 -1.1 -1.5 -2.8000000000000003 -2.1 -1.3 -1.8 -1.5 -1.3 -1.0 -3.0 -2.3000000000000003 -1.2000000000000002 -1.0 -1.6 -1.5 -3.2 -1.4000000000000001 -2.2 -1.2000000000000002 -3.3000000000000003 -3.4000000000000004 -3.0 -2.5 -1.6 -1.6 -3.8000000000000003 -3.0 -3.7 -1.6 -2.2 -1.6 -1.2000000000000002 -2.8000000000000003 -2.8000000000000003 -1.1 -1.8 -1.3 -3.2 -1.1 -1.8 -1.9000000000000001 -2.6 -1.4000000000000001 -1.7000000000000002 -1.7000000000000002 -3.0 -3.2 -1.4000000000000001 -1.3 -1.1 -1.1 -1.9000000000000001 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