ess-streaming-data-types 0.26.1

Python utilities for handling ESS streamed data

Python Streaming Data Types

Utilities for working with the FlatBuffers schemas used at the European Spallation Source ERIC for data transport.

https://github.com/ess-dmsc/streaming-data-types

FlatBuffer Schemas

name	description
hs00	Histogram schema (deprecated in favour of hs01)
hs01	Histogram schema
ns10	NICOS cache entry schema
pl72	Run start
6s4t	Run stop
f142	Log data (deprecated in favour of f144)
f144	Log data
ev42	Event data (deprecated in favour of ev44)
ev43	Event data from multiple pulses
ev44	Event data with signed data types
x5f2	Status messages
tdct	Timestamps
ep00	EPICS connection info (deprecated in favour of ep01)
ep01	EPICS connection info
rf5k	Forwarder configuration update (deprecated in favour of fc00)
fc00	Forwarder configuration update
answ	File-writer command response
wrdn	File-writer finished writing
NDAr	Deprecated
ADAr	EPICS areaDetector data
al00	Alarm/status messages used by the Forwarder and NICOS
senv	Deprecated
json	Generic JSON data
se00	Arrays with optional timestamps, for example waveform data. Replaces senv.
da00	Scipp-like data arrays, for histograms, etc.

hs00 and hs01

Schema for histogram data. It is one of the more complicated to use schemas. It takes a Python dictionary as its input; this dictionary needs to have correctly named fields.

The input histogram data for serialisation and the output deserialisation data have the same dictionary "layout". Example for a 2-D histogram:

hist = {
    "source": "some_source",
    "timestamp": 123456,
    "current_shape": [2, 5],
    "dim_metadata": [
        {
            "length": 2,
            "unit": "a",
            "label": "x",
            "bin_boundaries": np.array([10, 11, 12]),
        },
        {
            "length": 5,
            "unit": "b",
            "label": "y",
            "bin_boundaries": np.array([0, 1, 2, 3, 4, 5]),
        },
    ],
    "last_metadata_timestamp": 123456,
    "data": np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]),
    "errors": np.array([[5, 4, 3, 2, 1], [10, 9, 8, 7, 6]]),
    "info": "info_string",
}

The arrays passed in for data, errors and bin_boundaries can be NumPy arrays or regular lists, but on deserialisation they will be NumPy arrays.

Developer documentation

See README_DEV.md