bluesky / data-samples

Samples of "real" data acquired using bluesky for use in development, testing, and teaching

Bluesky — An Experiment Specification & Orchestration Engine

Source	https://github.com/bluesky/bluesky
PyPI	pip install bluesky
Documentation	https://bluesky.github.io/bluesky
Releases	https://github.com/bluesky/bluesky/releases

Bluesky is a library for experiment control and collection of scientific data and metadata. It emphasizes the following virtues:

• Live, Streaming Data: Available for inline visualization and processing.
• Rich Metadata: Captured and organized to facilitate reproducibility and searchability.
• Experiment Generality: Seamlessly reuse a procedure on completely different hardware.
• Interruption Recovery: Experiments are "rewindable," recovering cleanly from interruptions.
• Automated Suspend/Resume: Experiments can be run unattended, automatically suspending and resuming if needed.
• Pluggable I/O: Export data (live) into any desired format or database.
• Customizability: Integrate custom experimental procedures and commands, and get the I/O and interruption features for free.
• Integration with Scientific Python: Interface naturally with numpy and Python scientific stack.

Bluesky Documentation.

The Bluesky Project enables experimental science at the lab-bench or facility scale. It is a collection of Python libraries that are co-developed but independently useful and may be adopted a la carte.

Bluesky Project Documentation.

See https://bluesky.github.io/bluesky for more detailed documentation.

data-samples's Issues

Add IOS samples

From an email from @irawaluyo:

---

Energy scans (99% of our important data) covering various energy ranges. These are very old data from real samples. Either they’ve been published or will never be, so it should be OK to share publicly.

2108, 2139, 2146, 2156, 2187, 2243, 2255

Sample position scan:

19425

EPU scans:

19860, 19868

Photodiode scans:

19824, 19861

Add CSX samples

Quoted from an email from @ambarb:

---

ROI from no-image save. Iterative for aligning beamline and then the putting the sample in the beam

137787 – 137797

Bragg alignment with full dynamic range of FastCCD

137567 – 137570, 137700-137703

Diffuse peaks, CSX really measures

137587 - 137591

Flatfield – like XPCS scan, could be confused with some XPCS scans

137495 – 137502

Fixed Q E scan – fastCCD, and msc for appropriate normalization. Need to often compare two polarizations as done here

137587 - 137805

Setting up beamline energy and epu gap

137488 – 137494

Example grid scan for nanodiffraction or ptycho or just to find a “good spot” on the sample

137475

--- problably need to get more scan numbers on this one . some scans could be 100 images for each point

Add PDF LaB6 sample

From @DanOlds, slightly paraphrased:

a6e9d767-3bdd-42f0-bc37-24260b640aa7

LaB6 at wavelength 0.16635 Angstroms (~74 keV)

You will need to subtract a dark off (named in start as ‘sc_dk_field_uid’), whose UID is: 32dca9b5-91a3-44a3-856c-c7841d80c810

Data samples from RSOXS

Action items:

• Export this data with databroker-pack / rube into a tarball.
• Add it to this repo using Git LFS to manage the binary artifacts.

From @EliotGann at RSOXS:

uid	info
uid  -  9f033a6a-79e2-4449-a716-4a9766299b15	spectral data - interesting thing here is I'm collecting monitors as the primary measurement, which are going really fast, so I go back through and average the monitors for the timestamps between primary steps to get mean and stdev values for nice plotting
uid  -  e96bf63c-16b3-4415-b3e3-26a5e4ae72fa	different channels have data here, otherwise the same style as above
uid  -  6fc30d13-f5a3-49e4-a030-b231b203be7c	Usual basic energy scan dataset, interested in both the 1D monitor data(as above) but also the image data at each step.  Being able to select certain images (by clicking on a list of energies that are available) to tile and compare, and then reduce and analyze further is the important user interaction needed, as well as switching to the 1D data view as above (maybe selecting interesting image from the tile and highlighting those values on the ID scan or visa versa).
uid  -  68d58334-a49a-45f9-a716-a28f6e9625a8	spiral search dataset, everything as above, but here I want a very easy way to show all of the images (as they are coming in) and allow users to tag the "good" and "bad" ones, and record the relevant positions quickly of the "best position" (here the x and y motor positions) this will be used for machine learning to automatically tag bad ones in the future, and allow automatic spiral searching - this is the most time intensive part of the data collection process for users
uid  -  777b44ae-4576-4a15-b044-d5fc6e88797b	SAXS and WAXS images simultaneously, sometimes people will want to analyze both, sometimes only one.
uid  -  f9bca3d9-74e2-4523-9d0d-5294829eb3dc	same as above
uid  -  745e3f30-3b6a-4ec6-b36d-419cc05a6923	snapshot, interested in seeing the single image (here it is actually two cameras at once) as quick as possible, for live adjustment
uid  -  0c7df870-8a9b-4d18-94be-aab606b36b07	single image snapshot

Data samples from BMM

Text file of uids from BMM: forDan.txt

Add FXI samples

UIDs from Mingyuan Ge at FXI

8b4b1963-67d6-4c4b-815c-df15ec3432ce
a89eaa42-2f3f-46bf-a76f-0f67ccdaae8a
f77c7ecd-1370-482c-8945-1f7f5ab3487d
f378a32f-63da-4ca6-a23c-8025d1d3311e
1b0b4d73-6d87-43ab-8d62-ed035c51b9b4
d106586f-44e6-4045-8bf6-985cfdef3574
105f7a3d-c795-4821-9be2-b07b9abfb935
7bcb93db-39a4-4258-98b6-f201d0dce992
7aa5a71e-e892-4812-871c-687eb5c29d37
294a8fa6-2aee-46de-828f-1556f79e493b