Download data#

nom download asks for an asset, a dataset, channels and a time range, then saves the channel data as CSV or Parquet files. It exports samples, not the files that were uploaded.

You need a profile from API keys setup. To export from Python, or download the uploaded files, see Export data.

Run the wizard#

nom download --profile default  # Parquet
nom download --profile default --format csv

The wizard asks for:

  1. An asset, newest first.

  2. A dataset on that asset, listed with its data scope name. An asset with one dataset skips this step.

  3. Channels, as comma-separated text to match, such as RPM, TEMP, or * for all. Matching is case-insensitive and finds the text anywhere in a name. You can then remove channels in an editor, but not add them. To add channels, decline the selection and search again.

  4. An output folder, created if needed. Use a new folder for each export, because files from an earlier export can be overwritten or left behind.

  5. A time range from an event, a run or timestamps you enter. Use UTC timestamps ending in Z, such as 2026-09-10T14:00:00Z. For a run that’s still going, the suggested end is your local time read as UTC, so replace it unless your machine uses UTC.

Choosing a run or event sets only the time range, not the asset, dataset or channels. The export applies the data scope’s tags, but not its time offset.

The output#

Files are named after the end of the dataset’s RID, such as abc123-part_0.parquet. Each covers a slice of time.

  • The timestamp column holds epoch seconds as floats, so it can’t represent every nanosecond.

  • Channels sampled at different times have nulls where they don’t line up.

  • DOUBLE, INT and STRING channels are exported. Others are skipped with a warning.

  • Batches that fail are logged and skipped, so check the warnings and the time coverage.

To combine the parts in Python:

from pathlib import Path

import polars as pl

parts = sorted(Path("flight_data").glob("abc123-part_*.parquet"))
data = pl.concat([pl.read_parquet(p) for p in parts], how="diagonal_relaxed").sort("timestamp")

diagonal_relaxed fills columns missing from some parts with nulls. In MATLAB, read Parquet parts with parquetread and CSV parts with readtable, and convert timestamps with datetime(t, "ConvertFrom", "posixtime", "TimeZone", "UTC").

Tune large downloads#

Option

Default

Effect

--channels-per-request

10

Channels per request. Raise it for many low-rate channels, lower it to spread a few channels across workers.

--points-per-file

25000000

Target points per file. Lower it to use less memory per part.

Change one option at a time on a short range, into a new folder each time. Selecting fewer channels or a shorter range helps most.

If the wizard finds no assets, check the profile’s workspace with nom config profile list. For a dataset that isn’t on an asset, export it from Python.