def to_event_model(data, output_info, md=None):
"""Take an iterable of data and put it into the event model
Parameters
----------
data: iterable
The data to be inserted
output_info: list of tuple
The name of the data and information to put into the descriptor
md : iterable of dicts
an iterable of dictionaries to use as metadata for the start documents
Yields
-------
name: str
Name of doc
document: dict
Document of data
Notes
-----
This is only for demonstration/example use, do not use for production.
"""
if md is None:
md = {}
else:
md = md.copy()
# add some metadata
md.update({"source": "to_event_model"})
source = Stream()
fes = SimpleFromEventStream("start", (), source, principle=True)
tes = SimpleToEventStream(fes, output_info, **md)
start = None
for d in data:
if not start:
yield tes.start(d)
yield tes.descriptor(d)
yield tes.event(d)
yield "stop", tes._create_stop(d)
def run_exp(delay): # pragma: no cover
time.sleep(delay)
print("running exp")
p = Publisher(proxy[0], prefix=b"raw")
RE.subscribe(p)
# Tiny fake pipeline
pp = Publisher(proxy[0], prefix=b"an")
raw_source = Stream()
SimpleFromEventStream(
"event",
("data", "img"),
raw_source.starmap(Retrieve({"NPY_SEQ": NumpySeqHandler})),
principle=True,
).map(lambda x: x * 2).SimpleToEventStream(
("img2", ), analysis_stage="pdf").starsink(pp)
RE.subscribe(lambda *x: raw_source.emit(x))
RE(bp.count([hw.img], md=dict(analysis_stage="raw")))
print("finished exp")
p.close()
motors = step.keys()
yield from move()
plt.pause(.001)
yield from trigger_and_read(list(detectors) + list(motors))
install_kicker()
bec = BestEffortCallback()
bec.enable_plots()
hw = hw()
RE = RunEngine()
# build the pipeline
raw_source = Stream()
raw_output = SimpleFromEventStream('event', ('data', 'det_a'),
raw_source,
principle=True)
raw_output2 = SimpleFromEventStream('event', ('data', 'noisy_det'), raw_source)
pipeline = raw_output.union(raw_output2).map(lambda x: 1).accumulate(
lambda x, y: x + y)
res = SimpleToEventStream(pipeline, ('result', ))
merge = AlignEventStreams(res, raw_source)
merge.starsink(bec)
RE.subscribe(lambda *x: raw_source.emit(x))
RE(
pchain(
bp.scan([hw.noisy_det], hw.motor, 0, 10, 10),
install_kicker()
p = Publisher(glbl_dict["inbound_proxy_address"])
hw = hw()
import numpy as np
rand_img = SynSignal(
func=lambda: np.array(np.random.random((10, 10))),
name="img",
labels={"detectors"},
)
RE = RunEngine()
# build the pipeline
raw_source = Stream()
raw_output = SimpleFromEventStream("event", ("data", "det_a"),
raw_source,
principle=True)
raw_output2 = SimpleFromEventStream("event", ("data", "noisy_det"), raw_source)
raw_output3 = SimpleFromEventStream("event", ("data", "img"), raw_source)
pipeline = (raw_output.union(raw_output2, raw_output3.map(
np.sum)).map(lambda x: x**2).accumulate(lambda x, y: x + y))
res = SimpleToEventStream(pipeline, ("result", ))
merge = AlignEventStreams(raw_source.starmap(StripDepVar()), res)
merge.sink(pprint)
# send to viz server
merge.starsink(p)
RE.subscribe(lambda *x: raw_source.emit(x))
def amorphsivity_fem(source, **kwargs):
pdf = SimpleFromEventStream(
"event", ("data", "gr"), source, principle=True
)
return locals()