notata 0.2.0

Structured scientific logging using Logbook

notata

notata is a minimal library for structured filesystem logging of scientific runs.

Each Logbook creates a run directory with parameters, arrays, plots, artifacts, metadata, and a timestamped log. Explicit. Reproducible. Grep-friendly.

Unlike ML logging tools (e.g. W&B, TensorBoard, MLflow), notata is built for scientific workflows such as simulations, solvers, and numerical experiments, where file-based logging matters more than dashboards. No servers, no setup, no reinventing the wheel for each new project.

Installation

pip install notata

Quick Start

Context Manager

Logs a single simulation run to log_<run_id>/, including arrays, metadata, and messages.

from notata import Logbook
import numpy as np

with Logbook("oscillator_dt1e-3", params={"omega": 2.0, "dt": 1e-3, "steps": 10_000}) as log:
    omega = 2.0
    dt = 1e-3
    steps = 10_000
    x, v = 1.0, 0.0

    xs = np.empty(steps)
    vs = np.empty(steps)
    E  = np.empty(steps)

    for n in range(steps):
        a = -omega**2 * x
        x += v*dt + 0.5*a*dt*dt
        a_new = -omega**2 * x
        v += 0.5*(a + a_new)*dt
        xs[n], vs[n] = x, v
        E[n] = 0.5*(v**2 + (omega*x)**2)
        if (n+1) % 2000 == 0:
            log.info(f"step={n+1} x={x:.4f} v={v:.4f} E={E[n]:.6f}")

    log.arrays("trajectory", x=xs, v=vs)
    log.array("energy", E)
    log.json("final_state", {"x": float(x), "v": float(v), "E": float(E[-1])})

This creates a structured folder with logs, parameters, and output arrays for reproducibility.

Manual Lifecycle

For full control, create a Logbook manually and mark it complete when you're done.

from notata import Logbook
import numpy as np

Nx = Ny = 64
kappa = 0.01
dx = 1.0
dt = 0.2 * dx*dx / kappa

X, Y = np.meshgrid(np.linspace(-1,1,Nx), np.linspace(-1,1,Ny), indexing="ij")
T = np.exp(-6*(X**2 + Y**2))

log = Logbook("heat_eq")
log.params(Nx=Nx, Ny=Ny, kappa=kappa, steps=steps)

for step in range(500):
    lap = (np.roll(T,1,0)+np.roll(T,-1,0)+np.roll(T,1,1)+np.roll(T,-1,1)-4*T)
    T += kappa * dt * lap
    if (step+1) % 100 == 0:
        log.array(f"states/T_step{step+1}", T)
        log.info(f"step={step+1} maxT={T.max():.4f}")
log.json("final_stats", {"max": float(T.max()), "mean": float(T.mean())})
log.mark_complete()

Parameter sweeps with Experiment

Automatically log multiple runs, each in its own directory, with structured metadata and failure tracking:

from notata import Experiment
import numpy as np

exp = Experiment("falling_ball")

for dt in [0.01, 0.5]:  # stable vs unstable
    log = exp.add(dt=dt, skip_existing=True)
    if log is None:
        continue
    with log:
        v, h = 0.0, 100.0
        for _ in range(100):
            v += 9.81 * dt
            h -= v * dt
            if h < 0:
                raise RuntimeError(f"Object hit the ground (h={h:.2f})")
        log.json("metrics", {"final_height": h, "final_speed": v})

Each run creates a log_<run_id>/ folder and appends a row to index.csv with parameters, status, and final metrics:

run_id	dt	status	final_height	final_speed
falling_ball_dt_0.01	0.01	complete	95.04595	9.81
falling_ball_dt_0.5	0.5	missing

Using ExperimentReader

Since notata log dirs are somewhat verbose, we also provided a utility wrapper to load and read the data. This makes it more intuitive when you need to compare runs (e.g. in experiments), or you just don't want to deal with the files paths. For single runs you can directly use notata.LogReader.

from notata import ExperimentReader
import matplotlib.pyplot as plt

# Load the experiment
exp = ExperimentReader("outputs/oscillator_sweep")

# Plot energy vs time for each run
fig, ax = plt.subplots()
for run in exp:
    omega, dt = run.params['omega'], run.params['dt']
    energy = run.load_array("energy")
    label = f"omega={omega}, dt={dt}"
    ax.plot(energy, label=label)
ax.set(xlabel="Time step", ylabel="Energy", title="Energy vs Time")
ax.legend()
plt.show()

# Print summary of runs
for run in exp:
    status = run.meta.get('status', 'unkown')
    duration = run.meta.get('runtime_sec', 'unknown')
    print(f"Run ID: {run.run_id}, {status=}, {duration=}")

Output format

Data is stored as following in order to be intuitive to explore:

log_<run_id>/
  log.txt
  metadata.json
  params.yaml
  data/
  plots/
  artifacts/

where the files follow:

Path / Pattern	Purpose / Format
log.txt	Plain text log; lines: [YYYY-MM-DDTHH:MM:SS] LEVEL message
metadata.json	Run metadata: status, start_time, optional end_time, runtime_sec, optional failure_reason, run_id
params.yaml / params.json	Parameter snapshot (latest saved form)
data/*.npy / data/*.npz	.npy for single arrays (array()); .npz for multi-array bundles (arrays(...))
plots/*.(png|pdf|svg)	Saved figures (save_plot)
artifacts/*.txt	Text artifacts (save_text)
artifacts/*.json	JSON artifacts (save_json)
artifacts/*.pkl	Pickled objects (save_pickle)
artifacts/* (other)	Raw bytes (save_bytes)
artifacts/**/	Nested artifact categories

Documentation

Full documentation, tutorials, and examples are available at: https://notata.readthedocs.io/en/latest/

Citation

You don't have to, but if you use notata in your research and need to reference it, please cite it as follows:

@software{notata_2025,
  author  = {Albert Alonso},
  title   = {notata: Structured Filesystem Logging for Scientific Runs},
  url     = {https://github.com/alonfnt/notata},
  version = {0.2.0},
  year    = {2025}
}

License

MIT License