jnwb

Dataset-agnostic Python library for Neurodata Without Borders (NWB 2.0+) electrophysiology: addressing, spikes, LFP, spectral analysis, statistics, population methods, decoding, connectivity, laminar depth (CSD, the published vFLIP2), filtering, QC, and visualization.

Documentation: https://jnwb.readthedocs.io/ | Source: https://github.com/HNXJ/jnwb

jnwb is a toolbox, not a pipeline. It supplies small operations over NWB files, arrays, and metadata tables. Task structure, condition codes, and experimental hypotheses stay in project code.

• Dataset-agnostic. No experiment condition names or manuscript results live in the library.
• Explicit nulls. Label permutation requires an exchangeability scheme (global or within_group).
• Preserved signal semantics. Units, sampling rates, coordinate frames, and 0- vs 1-indexing do not change across a function boundary.

Capabilities

Installation

Requires Python 3.12 or newer. Tested in CI on 3.12, 3.13, 3.14 and 3.15 on Linux, and on Windows for all but the last.

pip install jnwb                  # latest published release
pip install "jnwb[torch,gpu]"     # optional backends

This checkout is 0.2.12. To install it from a clone instead of from PyPI:

pip install .                     # or: pip install -e ".[test,docs,vis]" for development

The vis extra exports SVG and PNG through kaleido, which needs a Chrome or Chromium: see Install.

Core dependencies: numpy, scipy, pandas, h5py, pynwb, hdmf, matplotlib, scikit-learn, statsmodels, joblib.

NWB workflow

jnwb.inspect lists acquisitions, electrodes, units, and every interval table with column samples. It does not pick a default event table. Event codes are opaque labels in a named column (default codes). Onsets from jnwb.event_onsets are in seconds. When several interval tables exist, pass table= explicitly; jnwb raises rather than guessing.

import jnwb

info = jnwb.inspect("session.nwb")

# Read the layout off the inspection rather than assuming it.
for table in info["interval_tables"]:
    print(table["name"], [column["name"] for column in table["columns"]])
# trials ['id', 'start_time', 'stimulus', 'stop_time']

table = jnwb.events("session.nwb", table="trials", code_column="stimulus")
onsets = jnwb.event_onsets(
    "session.nwb", table="trials", code_column="stimulus", codes=["grating"],
)

The codes default, and the errors for a missing column, are on the Errors page. On a table with no codes column, events returns the onsets and warns; event_onsets without codes= returns them silently.

Executable walkthroughs: Read the Docs tutorials or examples/tutorials/.

Quickstart (arrays)

import numpy as np
import jnwb

rng = np.random.default_rng(42)
events = np.arange(1.0, 21.0, 0.5)                          # 40 trials, 0.5 s apart
lags = 0.060 + rng.uniform(0.0, 0.34, (events.size, 25))    # each responds from t0 = 60 ms
spikes = np.sort(np.concatenate([rng.uniform(0.0, 21.5, 110),   # homogeneous background
                                 (events[:, None] + lags).ravel()]))

time_bins, rate_hz, _ = jnwb.raster_psth(spikes, events, win_ms=(-100.0, 400.0), bin_ms=10.0)
smooth_hz = jnwb.causal_exp_smooth(rate_hz, bin_ms=10.0, tau_ms=25.0)
fit = jnwb.fit_exponential_onset(time_bins, smooth_hz, t0_bounds_ms=(0.0, 200.0))
print(f"Onset t0: {fit['t0']:.1f} ms of a true 60.0 "
      f"(R2={fit['r2']:.2f}, {fit['bound_status'] or 'interior'})")

fs = 1000.0
lfp = rng.normal(size=1000)
tfr = jnwb.complex_tfr(lfp, fs=fs, freqs=np.linspace(10.0, 60.0, 10))
beta = jnwb.band_power(lfp, fs=fs, freq_range=jnwb.CANONICAL_BANDS["beta"], normalize=False)
print(f"TFR shape: {tfr.shape}, beta power: {beta:.4f}")

Read spikes and LFP for alignment after you have onsets. Onsets are session time; sample 0 of the LFP is at its starting_time, or at its first timestamp when the series stores timestamps (starting_time is None and acquisition_channel raises, so the samples are read from the file):

import numpy as np
from pynwb import NWBHDF5IO

spikes = jnwb.unit_spike_times("session.nwb", unit_index=0)
entry = next(a for a in info["acquisitions"] if a["name"] == "probe_0_lfp")
if entry["starting_time"] is not None:       # stored with starting_time and rate
    lfp, fs_hz = jnwb.acquisition_channel("session.nwb", name="probe_0_lfp", channel=0)
    start_s = entry["starting_time"]
else:                                        # stored with timestamps, sampled regularly
    with NWBHDF5IO("session.nwb", "r") as io:
        series = io.read().acquisition["probe_0_lfp"]
        t, lfp = series.timestamps[:], series.data[:, 0]   # entry["layout"], raw samples
    fs_hz, start_s = 1.0 / np.median(np.diff(t)), float(t[0])
epochs, t_axis_s = jnwb.epoch_continuous(lfp, onsets - start_s, win_s=(-0.1, 0.4), fs=fs_hz)

Unit and electrode census:

units = jnwb.get_all_units_metadata("session.nwb")
electrodes = jnwb.electrode_inventory("session.nwb")

More examples

Each block plants a known effect in synthetic data and prints what jnwb recovers.

Direction between two signals (x leads y by 3 samples):

import numpy as np
import jnwb

rng = np.random.default_rng(0)
x = rng.normal(size=5000)
y = 0.6 * np.roll(x, 3) + rng.normal(size=5000)
gc = jnwb.granger(x, y, order=5)
print(f"x->y {gc.x_to_y:.3f} (p={gc.p_x_to_y:.3f}), y->x {gc.y_to_x:.3f} (p={gc.p_y_to_x:.3f})")
# x->y 0.311 (p=0.000), y->x 0.001 (p=0.452)

A condition difference in time, tested with a cluster permutation (Maris & Oostenveld, 2007):

import numpy as np
import jnwb

rng = np.random.default_rng(1)
a = rng.normal(size=(20, 100))               # trials x time points
b = rng.normal(size=(20, 100))
b[:, 40:60] += 1.5                           # effect at points 40-59
res = jnwb.cluster_permutation_test(a, b, n_permutations=500, rng=1)
best = min(res["clusters"], key=lambda c: c["p_value"])
span = np.flatnonzero(best["mask"])
print(f"cluster {span.min()}-{span.max()}, p = {best['p_value']:.3f}")
# cluster 39-59, p = 0.002

Decoding against a label-permutation null with a named scheme:

import numpy as np
import jnwb

rng = np.random.default_rng(2)
labels = np.repeat([0, 1], 40)
X = rng.normal(size=(80, 30))                # trials x units
X[labels == 1, :5] += 1.2                    # 5 informative units
dec = jnwb.nested_cv_linear_svm(X, labels, n_splits=5, rng=2)
shuffled = jnwb.permute_labels(labels, scheme="global", rng=3)
null = jnwb.nested_cv_linear_svm(X, shuffled, n_splits=5, rng=2)
print(f"accuracy {dec['accuracy']:.2f}, shuffled {null['accuracy']:.2f}, "
      f"majority {dec['majority_baseline_accuracy']:.2f}")
# accuracy 0.88, shuffled 0.42, majority 0.50

Documentation

Guides, the public API (every symbol in jnwb.__all__), and common mistakes are on Read the Docs.

Contributing

Setup, the checks to run, the branch model and the release procedure are in CONTRIBUTING.md. Work lands on dev; main holds releases.

For AI agents: see artifacts/agents.md.

License

MIT. See LICENSE.