def RE(request):
loop = asyncio.new_event_loop()
loop.set_debug(True)
RE = RunEngine({}, loop=loop)
bec = best_effort.BestEffortCallback()
RE.subscribe(bec)
return RE
def bluesky_utils():
# setup run engine
RE = RunEngine({})
bec = best_effort.BestEffortCallback()
bec.disable_plots()
RE.subscribe(bec)
# setup databroker and mongo
db = Broker.named("local")
try:
databroker.assets.utils.install_sentinels(db.reg.config, version=1)
except Exception:
pass
# setup file handler
RE.subscribe(db.insert)
db.reg.register_handler("srw", SRWFileHandler, overwrite=True)
db.reg.register_handler("SIREPO_FLYER", SRWFileHandler, overwrite=True)
return RE, db
def utils():
# bluesky RunEngine
RE = RunEngine({})
bec = best_effort.BestEffortCallback()
RE.subscribe(bec)
# Mongo Backend
db = Broker.named("local")
try:
databroker.assets.utils.install_sentinels(db.reg.config, version=1)
except Exception:
pass
# update database info
RE.subscribe(db.insert)
db.reg.register_handler("srw", SRWFileHandler, overwrite=True)
# store data
root_dir = "/tmp/sirepo_flyer_data"
_ = make_dir_tree(datetime.datetime.now().year, base_path=root_dir)
return RE, db
# Register bluesky IPython magics. #from bluesky.magics import BlueskyMagics #get_ipython().register_magics(BlueskyMagics) #nslsii.configure_base(get_ipython().user_ns, 'amx') import bluesky.plans as bp from bluesky.run_engine import RunEngine from bluesky.utils import get_history RE = RunEngine(get_history()) beamline = os.environ["BEAMLINE_ID"] from databroker import Broker db = Broker.named(beamline) RE.subscribe(db.insert) # from bluesky.callbacks.best_effort import BestEffortCallback # bec = BestEffortCallback() # RE.subscribe(bec) # convenience imports # from ophyd.commands import * from bluesky.callbacks import * # from bluesky.spec_api import * # from bluesky.global_state import gs, abort, stop, resume # from databroker import (DataBroker as db, get_events, get_images, # get_table, get_fields, restream, process) from time import sleep import numpy as np
return np.squeeze(out)
f = FullField()
# det = SynSignal(f, name="img", labels={"detectors"})
det = SynSignalWithRegistry(f, name="img", labels={"detectors"},)
det.kind = "hinted"
#g = Pencil()
#det2 = SynSignal(g, name="img", labels={"detectors"})
#det2.kind = "hinted"
RE = RunEngine()
RE.md['analysis_stage'] = 'raw'
p = Publisher(glbl_dict["inbound_proxy_address"], prefix=b"raw")
t = RE.subscribe(p)
# RE.subscribe(print)
# Build scan
l = [0, 90]
for i in range(8):
ll = l.copy()
interval = sorted(set(ll))[1] / 2
for lll in ll:
j = lll + interval
j = round(j, 0)
if j not in l and j < 180:
l.append(j)
# Run Full Field Scans, each scan has more slices, showing how we can minimize
# the number of slices by interleaving them by half
for i in [180]:
import json # noqa F401
import databroker
import matplotlib.pyplot as plt
import numpy as np # noqa F401
from bluesky.callbacks import best_effort
from bluesky.run_engine import RunEngine
from databroker import Broker
from ophyd.utils import make_dir_tree
from sirepo_bluesky.shadow_handler import ShadowFileHandler
from sirepo_bluesky.srw_handler import SRWFileHandler
RE = RunEngine({})
bec = best_effort.BestEffortCallback()
RE.subscribe(bec)
# MongoDB backend:
db = Broker.named('local') # mongodb backend
try:
databroker.assets.utils.install_sentinels(db.reg.config, version=1)
except Exception:
pass
RE.subscribe(db.insert)
db.reg.register_handler('srw', SRWFileHandler, overwrite=True)
# db.reg.register_handler('shadow', ShadowFileHandler, overwrite=True)
db.reg.register_handler('SIREPO_FLYER', SRWFileHandler, overwrite=True)
plt.ion()
# det = SynSignal(f, name="img", labels={"detectors"})
det = SynSignalWithRegistry(
f,
name="img",
labels={"detectors"},
)
det.kind = "hinted"
#g = Pencil()
#det2 = SynSignal(g, name="img", labels={"detectors"})
#det2.kind = "hinted"
RE = RunEngine()
RE.md['analysis_stage'] = 'raw'
p = Publisher(glbl_dict["inbound_proxy_address"], prefix=b"raw")
t = RE.subscribe(p)
# RE.subscribe(print)
# Build scan
l = [0, 90]
for i in range(8):
ll = l.copy()
interval = sorted(set(ll))[1] / 2
for lll in ll:
j = lll + interval
j = round(j, 0)
if j not in l and j < 180:
l.append(j)
# Run Full Field Scans, each scan has more slices, showing how we can minimize
# the number of slices by interleaving them by half
for i in [180]:
# Create a graph
source = Stream()
# Convert from raw event model to data
fes = FromEventStream('event', ('data', 'noisy_det'), source, principle=True)
# Averageing graph
adder = fes.accumulate(lambda x, y: x + y)
counter = fes.accumulate(lambda s, x: s + 1, start=0)
averager = adder.zip(counter).map(lambda x: x[0] / x[1])
# Binned averaging
sw = fes.sliding_window(2).map(sum).map(lambda x: x / 2)
# Convert back to Event Model
tes1 = ToEventStream(averager, ('average', ))
tes2 = ToEventStream(sw, ('binned', ))
# sink to plotting
tes1.sink(lambda x: lp(*x))
tes2.sink(lambda x: lp2(*x))
# Run the scan
RE = RunEngine()
t = RE.subscribe(lambda *x: source.emit(x))
# RE.subscribe(lp3)
# RE.subscribe(print)
source.visualize(source_node=True)
RE(count([hw().noisy_det], 100))
plt.show()
