Ingest data

There are two live paths into Braven today. Pick based on whether your instrument/software already drops files into a folder, or you already have analysis code.

A third path — syncing a OneDrive folder without installing anything locally — is visible in the app under Pipelines → Watch Folders, but is marked “coming soon” and not yet connectable. Use the desktop watcher below for folder-based ingestion for now.

Path A — Desktop Watcher (no code)

Best when your instrument or software already writes files to a folder.

Install and connect

From Settings, download Braven Watcher and run it. Paste your Watcher Key when prompted (see Quickstart if you don’t have one yet).

Add a folder to watch

Add a folder in the desktop agent, or from the app: Pipelines → Watch Folders. Each watcher (machine) can watch multiple folders; assign each one to a Project.

Drop files in

Each direct subfolder of a watched folder becomes one Experiment. Deeper nesting rolls up into that parent experiment:

WatchedFolder/
  ├── run_25C_chipA/        →  Experiment "run_25C_chipA"
  │     ├── data.h5
  │     └── notes.txt
  └── run_65C_chipA/        →  Experiment "run_65C_chipA"
        └── data.h5

The agent waits until every file in a subfolder finishes uploading before the experiment is considered ready — so a pipeline never runs against a half-copied dataset.

Attach a pipeline (optional but recommended)

From Pipelines → Watch Folders, find the folder and assign a pipeline (write one first — see Write a pipeline script). Every new experiment in that folder then runs it automatically. Watch progress in the Activity tab.

The watcher keys off filename. A duplicated source file (e.g. a Windows/OneDrive ”— Copy” artifact) currently becomes a duplicate Experiment — there’s no content-based dedup yet.

Worked example — temperature characterization

TemperatureRuns/
  ├── 2026_07_09 Temperature characterization TCS3448/
  │     └── GLINT4_20260709.ffm
  └── 2026_07_09 Temperature characterization TCS3448 - Copy (3)/
        └── GLINT1_20260714.ffm

Pipeline assigned to TemperatureRuns:

import glob
import h5py
import numpy as np
import matplotlib.pyplot as plt
import braven

# Files are in the working directory — just read them.
for path in glob.glob("**/*.ffm", recursive=True):
    with h5py.File(path, "r") as f:
        temp = f["temperature"][:]      # adjust to your dataset keys
        signal = f["signal"][:]

    slope = float(np.polyfit(temp, signal, 1)[0])
    name = path.split("/")[-1].replace(".ffm", "")
    braven.log_summary(f"{name}_mean_slope", f"{slope:.4f}")
    braven.log_series(f"{name}_signal_vs_temp", signal.tolist())

    fig, ax = plt.subplots()
    ax.plot(temp, signal, ".")
    ax.set_xlabel("Temperature (°C)"); ax.set_ylabel("Signal")
    braven.upload(fig, f"{name}_fit.png")

Every run becomes an experiment with a mean_slope summary value, a plottable series, and a figure — no manual work per run.

Path B — Python SDK (direct logging)

Best when you already have analysis code and want to push results directly, wandb-style — no folder or pipeline required.

import braven

braven.init(name="High-temp run 1", project="TCS3448")
braven.config("temperature_c", 65)
braven.summary("mean_slope", -37.71)
braven.plot_series("signal_vs_temp", y=signal, x=temp, y_label="chipA")
braven.upload("fit.png")
braven.finish()

Each braven.init(...) call creates a new experiment, visible immediately in the app. Full reference: Direct logging.

Either way, you land in the same place

Both paths produce ordinary Experiments — same tabs, same ability to attach devices, same eligibility for Analysis and Reports. Neither path is “more first-class” than the other.

Get started

Concepts

Navigating Braven

Guides

Worked example

Path A — Desktop Watcher (no code)

Worked example — temperature characterization

Path B — Python SDK (direct logging)

Either way, you land in the same place

​Path A — Desktop Watcher (no code)

​Worked example — temperature characterization

​Path B — Python SDK (direct logging)

​Either way, you land in the same place

Path A — Desktop Watcher (no code)

Worked example — temperature characterization

Path B — Python SDK (direct logging)

Either way, you land in the same place