braven

Python SDK for Braven, the experiment tracker for hardware and photonics R&D teams.

pip install braven

Install and login

pip install braven
python -m braven login

login asks for your backend URL and an SDK API key (braven_..., from your Braven account's Settings → Watcher Keys) and saves them to ~/.braven/config.json. Every script on the machine picks the credentials up automatically — no need to pass them around.

Direct logging (wandb-style)

The common case: a script you run yourself, logging config, metrics, and plots straight to Braven as it runs.

import braven

run = braven.init(name="My Experiment", company="My Company", project="My Project")

braven.config("radius_um", 55.0)
braven.config("gap_nm", 200.0)

braven.summary("q_factor", 18_400)
braven.summary("extinction_ratio_dB", 22.1)

braven.upload("spectrum.png")
braven.finish()

company/project are only required if your API key can see more than one project — pass them to disambiguate.

Matplotlib figures

Pass a live matplotlib.figure.Figure to upload() instead of a file path and the SDK saves the PNG for you (same as fig.savefig() would) and extracts an interactive companion plot from the figure's line/scatter data, so the experiment page shows a zoomable, hoverable chart alongside the static image — no extra API to learn:

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot(wavelengths_nm, through_db, label="Through port")
ax.set_xlabel("Wavelength (nm)")
ax.set_ylabel("Transmission (dB)")

braven.upload(fig, name="spectrum.png")

This works the same way whether the figure came from a script you run yourself or a script run through Braven's Pipelines tab — one code path, one behavior, no separate API for "give me a nice list thumbnail."

Prefer to log a plain array without a matplotlib figure at all? plot_series() does that directly:

braven.plot_series("snr_vs_temp", y=snr_values, x=temperatures, x_label="Temp (C)", y_label="SNR (dB)")

Devices — multiple sensors/units in one experiment

Tag a value with a stable per-device key and the KPI name stays the same across devices (no SNR_dev1, SNR_dev2) — the device becomes a separate coordinate instead of a suffix:

for sensor_id, snr in results.items():
    braven.device(sensor_id).log_summary("SNR", snr)

braven.log_summary("max_device_mismatch", spread)  # experiment-level, all devices

A device is auto-created on first sight and its history accumulates across experiments — pass an optional type on first use (braven.device("SENSOR-4471", "Photodiode")) to name what kind of device it is; it's ignored once the device already exists.

Pipeline scripts (run by the Braven worker)

If your script runs through Braven's Pipelines tab (server-side, executed by the Braven worker against uploaded files) rather than on your own machine, the SDK dispatches to the same functions automatically — no braven.init() call needed, and no credentials to manage (the worker supplies the experiment context):

import braven

def process(braven=None):
    braven.log_config("lr", "0.001")
    braven.log_summary("acc", "0.94")
    braven.log_artifact("plot.png")

    # Devices and matplotlib figures work exactly as in direct logging:
    braven.device("SENSOR-1").log_summary("SNR", 14.2)
    braven.upload(fig, name="spectrum.png")

braven.files (uploaded file → local temp path) and braven.params (extracted parameters) are populated by the worker before process() runs.

Querying experiments

Read-only access to experiments already logged in Braven — no API key required beyond what you already set up with python -m braven login, or construct a client directly:

from braven import Braven

b = Braven("https://your-backend.example.com", api_key="braven_...")
exp = b.get("high temp run")          # partial, case-insensitive name match
df = exp.file("data.csv").as_dataframe()  # requires: pip install "braven[dataframe]"

print(exp.metadata)   # {key: value} config/summary metadata
for f in exp.files:
    print(f.filename)

b.get() also accepts a list of names and returns a list of Experiment objects. b.experiments() returns lightweight summaries of everything visible to the API key.

Cross-experiment analysis

Pull an explicit, frozen set of experiments out of Braven for local work (training a model, building a comparison plot — anything outside Braven), then log results back onto the same object so the record of what was analyzed can never drift from what was actually read:

analysis = braven.collect(
    experiment_ids=["exp_1", "exp_2", "exp_3"],
    name="Cross-lot yield comparison",
)

for exp in analysis.experiments():
    df = exp.file("data.csv").as_dataframe()
    # ... do local analysis ...

analysis.log_summary("mean_yield", 0.94)
analysis.upload(fig, name="comparison.png")

filter_token (copied from Braven's webapp "Copy as code" action) works as an alternative to an explicit experiment_ids list — it's re-resolved to concrete experiment IDs at call time, never stored as a live query.

Optional dependencies

requests is the only hard dependency. Everything else is optional and imported lazily:

Contributing / issues

Source: github.com/bravenlab/braven-python. Found a bug or have a feature request? File it on GitHub Issues — that's the right place for anything specific to the SDK itself.

License

MIT — see LICENSE.