lemke_handClaude Sonnet 5
e876c1af4f
Three follow-ups to the status_server namespace component and recording feature: 1. StatusServer's recording settings are now AdjustableFS, not AdjustableMemory - each a small JSON file under the same shared eco_cnf_bernina/configuration tree channels_JF/config_JFs already use. The point: a setting changed in one process is visible to any other process reading the same filesystem (another session, or the status server itself) with no REST call or other new communication - confirmed with a test constructing two independent StatusServer instances against the same config_dir. 2. RecordingSession.size_report(): per-channel projected bandwidth - rate (this channel's own point count over elapsed time) times a sample value's size (element count * dtype itemsize, "frequency * data shape * bitdepth") - to judge storage/bandwidth cost from a short trial recording before committing to a long one. Works on a still-running recording. Exposed via GET /recording/<id>?size=1 (optional &size_top_n=N) and StatusServerClient.recording(..., size=True). 3. failed_required for recordings: a channel that fails to attach and belongs to a required namespace component (namespace.required_names()) is now reported separately in a recording's report() - the same "everyone else failing is expected, this failing is not" distinction /health's failed_required already makes for init failures. New client-side warn_recording_failed_required (red, but does not suggest reinit() the way the health one does - a demoted/ disconnected channel isn't fixed by rebuilding the namespace), wired into Daq.start_scan_monitoring. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
eco — Experiment Control
___ _______
/ -_) __/ _ \
Experiment Control \__/\__/\___/
eco is a Python-based control environment for experiments, developed and used at SwissFEL, PSI. It is used both as:
- a library of experimental devices for higher-level Python applications or GUIs, and
- an interactive command-line interface, e.g. from an IPython/Jupyter shell or notebook.
eco follows an object-oriented approach: every device is represented as a Python object with a small, predictable interface, so devices can be freely combined in generic control/acquisition routines and analysed with the scientific Python ecosystem. For a general introduction to object-oriented Python, see e.g. this short introduction.
Documentation
The full documentation — installation, core concepts, and worked examples (listening monitors, archiver data and strip charts, pipeline offload, motor configuration) — lives in docs/ and is built with Sphinx, configured to build on Read the Docs via .readthedocs.yaml.
Build it locally:
pip install -r docs/requirements.txt
sphinx-build -b html docs docs/_build/html
Installation
conda install -c paulscherrerinstitute eco
or, for development, in editable mode from a checkout:
git clone https://github.com/paulscherrerinstitute/eco.git
cd eco
pip install -e .
See Installation for beamline-specific setup (the
eco launcher, .ecorc defaults) and the full dependency picture.
Creating a new device
New devices are implemented as a subclass of Assembly, which provides
naming, aliasing, and shell representation:
from eco.elements.assembly import Assembly
class MyDevice(Assembly):
def __init__(self, name=None):
super().__init__(name=name)
self._append(MySubObject, name="my_sub_object", is_setting=True, is_status=True)
is_setting=True marks the child as a setting of the assembly (shown by
.settings() and captured when settings are saved); is_status=True marks it
as contributing to the assembly's .status(). See
Representing real devices — the Assembly in the full docs
for the rest of the model (Adjustable, Detector, Namespace) and a
from-scratch, runnable example of each.