lemke_handClaude Sonnet 5
74cd091970
Found by running a real scan against a real bs stream on
saresb-vcons-03 (a test eco.status_server + a real BsStreamCounter),
then reading last_arrays back afterward, mirroring exactly what a user
would do interactively - arr._scan_parameter raised deep inside h5py
("invalid identifier type to function").
_store_arrays() copied CounterValue.store_arrays's reload-then-close
pattern verbatim ("re-open read-back handles so last_arrays keeps
working after the write file closes"), but escape.Array's own dataset
wrapper (ArrayH5Dataset) is lazy for at least some attributes even
past load_from_result_file (default lazy_loading=False notwithstanding)
- so the *reloaded* handles this assigned into self.last_arrays were
themselves tied to a second h5py.File that gets closed two lines later,
just as unusable as what it was replacing.
There was never a need to reload in the first place: the arrays
already in self.last_arrays (from _build_arrays) are plain in-memory,
numpy-backed Array objects with no file dependency at all. Dropped the
reload entirely; verified against the real recorded data (real scan
parameter values, correct shape) both immediately and via a fresh
DataSet.load_from_result_file() in a separate process.
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.