The Databroker project is now in maintenance mode, and it is not recommended for new users. It will be maintained until for years to come to support existing user code. New users should use Bluesky Tiled Plugins.
| PyPI | pip install databroker |
| Conda | conda install -c conda-forge databroker |
| Source code | https://github.com/bluesky/databroker |
| Documentation | https://blueskyproject.io/databroker |
The bundle of metadata and data looks like this, for example.
>>> run
BlueskyRun
uid='4a794c63-8223-4893-895e-d16e763188a8'
exit_status='success'
2020-03-07 09:17:40.436 -- 2020-03-07 09:28:53.173
Streams:
* primary
* baselineAdditional user metadata beyond what is shown is stored in run.metadata.
The bundle contains some number of logical tables of data ("streams"). They can
be accessed by name and read into a standard data structure from xarray.
>>> run.primary.read()
<xarray.Dataset>
Dimensions: (time: 411)
Coordinates:
* time (time) float64 1.584e+09 1.584e+09 ... 1.584e+09
Data variables:
I0 (time) float64 13.07 13.01 12.95 ... 9.862 9.845
It (time) float64 11.52 11.47 11.44 ... 4.971 4.968
Ir (time) float64 10.96 10.92 10.88 ... 4.761 4.763
dwti_dwell_time (time) float64 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
dwti_dwell_time_setpoint (time) float64 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
dcm_energy (time) float64 1.697e+04 1.698e+04 ... 1.791e+04
dcm_energy_setpoint (time) float64 1.697e+04 1.698e+04 ... 1.791e+04Common search queries can be done with a high-level Python interface.
>>> from databroker.queries import TimeRange
>>> catalog.search(TimeRange(since="2020"))Custom queries can be done with the MongoDB query language.
>>> query = {
... "motors": {"$in": ["x", "y"]}, # scanning either x or y
... "temperature" {"$lt": 300}, # temperature less than 300
... "sample.element": "Ni",
... }
>>> catalog.search(query)See the tutorials for more.
