def test_adaptive_ascan(RE, hw):
scan1 = bp.adaptive_scan([hw.det], 'det', hw.motor, 0, 5, 0.1, 1, 0.1, True)
scan2 = bp.adaptive_scan([hw.det], 'det', hw.motor, 0, 5, 0.1, 1, 0.2, True)
scan3 = bp.adaptive_scan([hw.det], 'det', hw.motor, 0, 5, 0.1, 1, 0.1, False)
scan4 = bp.adaptive_scan([hw.det], 'det', hw.motor, 5, 0, 0.1, 1, 0.1, False)
actual_traj = []
col = collector('motor', actual_traj)
counter1 = CallbackCounter()
counter2 = CallbackCounter()
RE(scan1, {'event': [col, counter1]})
RE(scan2, {'event': counter2})
assert counter1.value > counter2.value
assert actual_traj[0] == 0
actual_traj = []
col = collector('motor', actual_traj)
RE(scan3, {'event': col})
monotonic_increasing = np.all(np.diff(actual_traj) > 0)
assert monotonic_increasing
actual_traj = []
col = collector('motor', actual_traj)
RE(scan4, {'event': col})
monotonic_decreasing = np.all(np.diff(actual_traj) < 0)
assert monotonic_decreasing
with pytest.raises(ValueError): # min step < 0
scan5 = bp.adaptive_scan([hw.det], 'det', hw.motor,
5, 0, -0.1, 1.0, 0.1, False)
RE(scan5)
def test_adaptive_ascan(RE, hw):
scan1 = bp.adaptive_scan([hw.det], 'det', hw.motor, 0, 5, 0.1, 1, 0.1, True)
scan2 = bp.adaptive_scan([hw.det], 'det', hw.motor, 0, 5, 0.1, 1, 0.2, True)
scan3 = bp.adaptive_scan([hw.det], 'det', hw.motor, 0, 5, 0.1, 1, 0.1, False)
scan4 = bp.adaptive_scan([hw.det], 'det', hw.motor, 5, 0, 0.1, 1, 0.1, False)
actual_traj = []
col = collector('motor', actual_traj)
counter1 = CallbackCounter()
counter2 = CallbackCounter()
RE(scan1, {'event': [col, counter1]})
RE(scan2, {'event': counter2})
assert counter1.value > counter2.value
assert actual_traj[0] == 0
actual_traj = []
col = collector('motor', actual_traj)
RE(scan3, {'event': col})
monotonic_increasing = np.all(np.diff(actual_traj) > 0)
assert monotonic_increasing
actual_traj = []
col = collector('motor', actual_traj)
RE(scan4, {'event': col})
monotonic_decreasing = np.all(np.diff(actual_traj) < 0)
assert monotonic_decreasing
with pytest.raises(ValueError): # min step < 0
scan5 = bp.adaptive_scan([hw.det], 'det', hw.motor,
5, 0, -0.1, 1.0, 0.1, False)
RE(scan5)
def test_table(RE, hw):
with _print_redirect() as fout:
hw.det.precision = 2
hw.motor.precision = 2
hw.motor.setpoint.put(0.0) # Make dtype 'number' not 'integer'.
hw.det.put(0.0) # Make dtype 'number' not 'integer'.
assert hw.det.describe()["det"]["precision"] == 2
assert hw.motor.describe()["motor"]["precision"] == 2
assert hw.det.describe()["det"]["dtype"] == "number"
assert hw.motor.describe()["motor"]["dtype"] == "number"
table = LiveTable(["det", "motor"], min_width=16, extra_pad=2)
ad_scan = bp.adaptive_scan([hw.det], "det", hw.motor, -15.0, 5., .01,
1, .05, True)
# use lossless sub here because rows can get dropped
token = RE.subscribe(table)
RE(ad_scan)
RE.unsubscribe_lossless(token)
fout.seek(0)
for ln, kn in zip(fout, KNOWN_TABLE.split("\n")):
# this is to strip the `\n` from the print output
ln = ln.rstrip()
if ln[0] == "+":
# test the full line on the divider lines
assert ln == kn
else:
# skip the 'time' column on data rows
# this is easier than faking up times in the scan!
assert ln[:16] == kn[:16]
assert ln[26:] == kn[26:]
def test_table(RE, hw):
with _print_redirect() as fout:
hw.det.precision = 2
hw.motor.precision = 2
hw.motor.setpoint.put(0.0) # Make dtype 'number' not 'integer'.
hw.det.trigger()
assert hw.det.describe()['det']['precision'] == 2
assert hw.motor.describe()['motor']['precision'] == 2
assert hw.det.describe()['det']['dtype'] == 'number'
assert hw.motor.describe()['motor']['dtype'] == 'number'
table = LiveTable(['det', 'motor'],
min_width=16,
extra_pad=2,
separator_lines=False)
ad_scan = bp.adaptive_scan([hw.det], 'det', hw.motor, -15.0, 5., .01,
1, .05, True)
# use lossless sub here because rows can get dropped
token = RE.subscribe(table)
RE(ad_scan)
RE.unsubscribe_lossless(token)
fout.seek(0)
_compare_tables(fout, KNOWN_TABLE)
def test_table(RE, hw):
with _print_redirect() as fout:
hw.det.precision = 2
hw.motor.precision = 2
hw.motor.setpoint.put(0.0) # Make dtype 'number' not 'integer'.
hw.det.put(0.0) # Make dtype 'number' not 'integer'.
assert hw.det.describe()['det']['precision'] == 2
assert hw.motor.describe()['motor']['precision'] == 2
assert hw.det.describe()['det']['dtype'] == 'number'
assert hw.motor.describe()['motor']['dtype'] == 'number'
table = LiveTable(['det', 'motor'], min_width=16, extra_pad=2)
ad_scan = bp.adaptive_scan([hw.det], 'det', hw.motor,
-15.0, 5., .01, 1, .05,
True)
# use lossless sub here because rows can get dropped
token = RE.subscribe(table)
RE(ad_scan)
RE.unsubscribe_lossless(token)
fout.seek(0)
for ln, kn in zip(fout, KNOWN_TABLE.split('\n')):
# this is to strip the `\n` from the print output
ln = ln.rstrip()
if ln[0] == '+':
# test the full line on the divider lines
assert ln == kn
else:
# skip the 'time' column on data rows
# this is easier than faking up times in the scan!
assert ln[:16] == kn[:16]
assert ln[26:] == kn[26:]
def preFitScan(detectors, motor, alignRange, stepRange, targetDelta): yield from adaptive_scan([detectors], det_name, motor, start = alignRange[0], stop = alignRange[1], min_step = stepRange[0], max_step = stepRange[1], target_delta = targetDelta, backstep = True)
def test_table(RE, hw):
with _print_redirect() as fout:
hw.det.precision = 2
hw.motor.precision = 2
assert hw.det.describe()['det']['precision'] == 2
assert hw.motor.describe()['motor']['precision'] == 2
table = LiveTable(['det', 'motor'], min_width=16, extra_pad=2)
ad_scan = bp.adaptive_scan([hw.det], 'det', hw.motor, -15, 5, .01, 1,
.05, True)
# use lossless sub here because rows can get dropped
token = RE.subscribe(table)
RE(ad_scan)
RE.unsubscribe_lossless(token)
fout.seek(0)
for ln, kn in zip(fout, KNOWN_TABLE.split('\n')):
# this is to strip the `\n` from the print output
ln = ln.rstrip()
if ln[0] == '+':
# test the full line on the divider lines
assert ln == kn
else:
# skip the 'time' column on data rows
# this is easier than faking up times in the scan!
assert ln[:16] == kn[:16]
assert ln[26:] == kn[26:]
def describe_plans(self):
from bluesky import RunEngine
import bluesky.plans as bp
from bluesky.callbacks.fitting import PeakStats
from bact2.ophyd.utils.preprocessors.CounterSink import CounterSink
cs = CounterSink(name = "cnt_b_cur", delay = .1)
dep = self.DEVICES['booster_current'].cur1.current
indep = self.DEVICES['inj_kicker'].offset
md = {
'nickname' : 'injection_kicker_scan_adaptive'
}
start, end = 10.5, 12
min_step = 0.01
max_step = 0.05
min_change = 1
dets = [indep, dep, self.DEVICES['booster_current']]
plan = bp.adaptive_scan(dets, dep, indep, start, end, min_step, max_step, min_change, False, md=md)
self.PLANS.append(plan)
start, end = 11, 11.5
steps = 101
plan2 = bp.scan(dets, indep, start, end, steps)
self.PLANS.append(plan2)
def fineScan(detectors, motor, fRange, pts = 50): #Adaptive scan on region localized near peak yield from adaptive_scan([detector], detector_name, motor, start = fRange[0], stop = fRange[1], min_step = fStepRange[0], max_step = fStepRange[1], target_delta = thTargetDelta, backstep = False)
def _gen(self):
return adaptive_scan(self.detectors,
self.target_field,
self.motor,
self.start,
self.stop,
self.min_step,
self.max_step,
self.target_delta,
self.backstep,
self.threshold,
md=self.md)
from bluesky.callbacks import LivePlot
from ophyd.sim import motor, det
# Do this if running the example interactively;
# skip it when building the documentation.
import os
if 'BUILDING_DOCS' not in os.environ:
from bluesky.utils import install_qt_kicker # for notebooks, qt -> nb
install_qt_kicker()
plt.ion()
det.exposure_time = 0.5 # simulate slow exposures
RE = RunEngine({})
RE(
adaptive_scan([det],
'det',
motor,
start=-15,
stop=10,
min_step=0.01,
max_step=5,
target_delta=.05,
backstep=True),
LivePlot('det', 'motor', markersize=10, marker='o'))
###############################################################################
# Observe how the scan lengthens its stride through the flat regions, oversteps
# through the peak, moves back, samples it with smaller steps, and gradually
# adopts a larger stride as the peak flattens out again.
from bluesky.plans import adaptive_scan
from bluesky.callbacks import LivePlot
from bluesky.examples import motor, det
# Adaptive Rocking curve scan using just the neutron counts
# (either total or ROI). This will not trigger the ADARA
# system - therefore no NeXus file will be created.
## TODO : Update with realistic values for the steps, etc....
RE(
adaptive_scan([bs_neutrons_roi],
'bs_neutrons_roi',
bs_axis1,
start=-15,
stop=10,
min_step=0.01,
max_step=5,
target_delta=.05,
backstep=True),
LivePlot('bs_neutrons_roi', 'bs_axis1', markersize=5, marker='o'))