jaxfne
jaxfne is a compact JAX package for Tensor-Field Neural Equations (TFNE): a typed computational chain from neural emitters to source tensors, field-proxy operators, probe readouts, objective reports, optimizers, and run manifests.
Install
pip install jaxfne
Visualization extras:
pip install "jaxfne[viz]"
Development checkout:
git clone https://github.com/HNXJ/jaxfne.git
cd jaxfne
pip install -e ".[dev,viz]"
Canonical import:
import jaxfne as jtfne
Objective grammar form
Config
-> Runtime(seed, dtype, backend, jit, vmap, duration_ms, dt_ms)
-> Identity(area, layer, cell_type, unit_id, position)
-> Emitter(theta_e, state_0, drive, noise, key)
-> SourceMap(source_mode, source_calibration_status, support, normalization)
-> FieldProxy(kernel, geometry_metadata, field_solver_status, field_claim_level)
-> Probe(kind, selector, channel_geometry, units_status, method)
-> Signals(spk, vm, source, lfp_proxy, csd_proxy, eeg_proxy, meg_proxy, spectrolaminar_proxy, emm_proxy)
-> Objective(metrics, targets, gates, nulls, rejection_reasons)
-> Optimizer(search_space, budget, key, constraints)
-> Manifest(run_id, version, repo_sha, runtime_report, artifact_paths, asset_hashes, truth_gates)
-> Validation(finite_outputs, strict_json, png_assets, notebook_receipts, optional_dependency_laziness)
Minimal workflow
import jaxfne as jtfne
cfg = jtfne.suite2_four_celltype_config(seed=0, duration_ms=1000.0, dt_ms=0.1)
model = jtfne.construct(cfg)
signals = jtfne.simulate(model, duration_ms=1000.0, dt_ms=0.1, seed=0)
vm_e = signals.get("vm", cell_type="E")
spk = signals.get("spk")
Checkout validation
python -m compileall -q jaxfne tests examples scripts
PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 PYTHONPATH=. python -m pytest tests -q --tb=short
PYTHONPATH=. python scripts/audit_notebooks_and_assets.py --check
PYTHONPATH=. python scripts/audit_notebook_grammar.py --check
mkdocs build --strict
1 maintainer
Release files for jaxfne 0.4.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| jaxfne-0.4.0.tar.gz | 5.0 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| jaxfne-0.4.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 5.2 MB
Release files / jaxfne-0.4.0.tar.gz
| Download URL | jaxfne-0.4.0.tar.gz |
|---|---|
| Size | 5.0 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / jaxfne-0.4.0-py3-none-any.whl
| Download URL | jaxfne-0.4.0-py3-none-any.whl |
|---|---|
| Size | 250.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
d6d2f58da548be1f8fca3fa8b0954c26cc87f00e5a78b87495e93a9ad5dc7b01
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
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PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
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