HDF5

HDF5 files have no fixed layout, so you read the arrays you need with h5py, shape them into columns and upload them as tables. Reading in slices keeps files larger than memory out of RAM.

The examples also need pip install h5py.

Connect the client

This guide assumes you have installed the client and setup your API keys.

from nominal.core import NominalClient

client = NominalClient.from_profile("default")

Inspect the file

This example writes a small sample file with one group of three sensor arrays:

import h5py
import numpy as np

with h5py.File("sample.h5", "w") as f:
    group = f.create_group("sensors/imu")
    group["time_s"] = np.arange(10_000) / 100  # 100 Hz
    group["accel_x"] = np.random.randn(10_000)
    group["accel_y"] = np.random.randn(10_000)

List the file’s groups and datasets to find the arrays to upload:

with h5py.File("sample.h5") as f:
    f.visititems(lambda name, obj: print(name, getattr(obj, "shape", "")))

sensors
sensors/imu
sensors/imu/accel_x (10000,)
sensors/imu/accel_y (10000,)
sensors/imu/time_s (10000,)

Upload in batches

Read a slice of each array into a pandas DataFrame and upload it with upload_dataframe_to_dataset(). Each batch adds to the same dataset.

from datetime import datetime, timezone

import pandas as pd

from nominal import ts
from nominal.thirdparty.pandas import upload_dataframe_to_dataset

dataset = client.create_dataset("IMU from HDF5")
recording_start = datetime(2026, 9, 10, 14, 38, tzinfo=timezone.utc)
batch_size = 2_500

with h5py.File("sample.h5") as f:
    imu = f["sensors/imu"]
    for i in range(0, imu["time_s"].shape[0], batch_size):
        batch = pd.DataFrame({name: imu[name][i : i + batch_size] for name in ["time_s", "accel_x", "accel_y"]})
        upload_dataframe_to_dataset(
            dataset,
            batch,
            timestamp_column="time_s",
            timestamp_type=ts.Relative("seconds", start=recording_start),
        )

upload_dataframe_to_dataset waits for each batch to finish ingesting. Multidimensional arrays need flattening into one column per element first, for example with array[:, j]. For timestamp options, see Timestamp formats.