def test_match_condition_success_no_stop(RE, mot_and_sig):
logger.debug("test_match_condition_success_no_stop")
mot, sig = mot_and_sig
mot.delay = 0
# Delay has no purpose if we aren't going to stop
def condition(x):
if 5 < x < 7:
return True
elif 10 < x < 16:
return True
return False
RE(run_wrapper(match_condition(sig, condition, mot, 20, has_stop=False)))
assert 12 < mot.position < 14
# Motor should end in the middle of the largest True region
mot.move(0, wait=True)
def condition(x):
return 10 < x < 16
RE(run_wrapper(match_condition(sig, condition, mot, 20, has_stop=False)))
assert 12 < mot.position < 14
mot.move(0, wait=True)
def condition(x):
return x > 10
RE(run_wrapper(match_condition(sig, condition, mot, 20, has_stop=False)))
assert 14 < mot.position < 16
def test_fiducialize(RE, fiducialized_yag):
logger.debug('test_fiducialize')
fake_slits, fake_yag = fiducialized_yag
center = []
measuredcenter = collector("det", center)
# Run plan with sufficiently large max_width
RE(
run_wrapper(
fiducialize(fake_slits,
fake_yag,
start=0.1,
step_size=1.0,
centroid='det',
samples=1)), {'event': [measuredcenter]})
# First shot is blocked second is not
assert center == [0.0, 0.3]
# Run plan with insufficiently large max_width
with pytest.raises(BeamNotFoundError):
RE(
run_wrapper(
fiducialize(fake_slits,
fake_yag,
start=0.1,
step_size=1.0,
max_width=0.25,
centroid='det',
samples=1)))
def test_slit_scan_area_compare(RE):
pre = 'fakeslits'
fake_slits = FakeSlits(name=pre)
class FakeYag(Device):
xwidth = Cmp(SynSignal,
func=lambda: fake_slits.read()[pre + '_xwidth']['value'])
ywidth = Cmp(SynSignal,
func=lambda: fake_slits.read()[pre + '_ywidth']['value'])
def trigger(self):
xstat = self.xwidth.trigger()
ystat = self.ywidth.trigger()
return xstat & ystat
fake_yag = FakeYag(name='fakeyag')
# collector callbacks aggregate data from 'yield from' in the given lists
xwidths = []
ywidths = []
measuredxwidths = collector("fakeyag_xwidth", xwidths)
measuredywidths = collector("fakeyag_ywidth", ywidths)
# test two basic positions
RE(run_wrapper(slit_scan_area_comp(fake_slits, fake_yag, 1.0, 1.0, 2)),
{'event': [measuredxwidths, measuredywidths]})
RE(run_wrapper(slit_scan_area_comp(fake_slits, fake_yag, 1.1, 1.5, 2)),
{'event': [measuredxwidths, measuredywidths]})
# excpect error if both measurements <= 0
with pytest.raises(ValueError):
RE(run_wrapper(slit_scan_area_comp(fake_slits, fake_yag, 0.0, 0.0, 2)),
{'event': [measuredxwidths, measuredywidths]})
# expect error if one measurement <= 0
with pytest.raises(ValueError):
RE(run_wrapper(slit_scan_area_comp(fake_slits, fake_yag, 1.1, 0.0, 2)),
{'event': [measuredxwidths, measuredywidths]})
logger.debug(xwidths)
logger.debug(ywidths)
assert xwidths == [
1.0,
1.0,
1.1,
1.1,
0.0,
0.0,
1.1,
1.1,
]
assert ywidths == [
1.0,
1.0,
1.5,
1.5,
0.0,
0.0,
0.0,
0.0,
]
def test_slit_scan_fiducialize(RE, fiducialized_yag):
logger.debug('test_slit_scan_fiducialize')
fake_slits, fake_yag = fiducialized_yag
center = []
measuredcenter = collector("det", center)
# Run plan with wide slits
RE(
run_wrapper(
slit_scan_fiducialize(fake_slits,
fake_yag,
x_width=1.0,
y_width=1.0,
centroid='det',
samples=1)), {'event': [measuredcenter]})
assert center == [0.3]
center = []
measuredcenter = collector("det", center)
# Run plan with narrow slits
RE(
run_wrapper(
slit_scan_fiducialize(fake_slits,
fake_yag,
centroid='det',
samples=1)), {'event': [measuredcenter]})
assert center == [0.0]
def test_measure_average(RE, one_bounce_system):
logger.debug("test_measure_average")
_, mot, det = one_bounce_system
centroids = []
readbacks = []
col_c = collector(det.name + '_detector_stats2_centroid_x', centroids)
col_r = collector(mot.name + '_sim_alpha', readbacks)
RE(run_wrapper(measure_average([det, mot], delay=0.1, num=5)),
{'event': [col_c, col_r]})
assert centroids == [250., 250., 250., 250., 250.]
assert readbacks == [0., 0., 0., 0., 0.]
centroids.clear()
readbacks.clear()
# Run with array of delays
RE(run_wrapper(measure_average([det, mot], delay=[0.1], num=2)),
{'event': [col_c, col_r]})
assert centroids == [250., 250.]
assert readbacks == [0., 0.]
# Invalid delay settings
with pytest.raises(ValueError):
RE(run_wrapper(measure_average([det, mot], delay=[0.1], num=3)))
def test_iterwalk_raises_RuntimeError_on_motion_timeout(
RE, lcls_two_bounce_system):
logger.debug("test_iterwalk_raises_RuntimeError_on_motion_timeout")
s, m1, m2, y1, y2 = lcls_two_bounce_system
# Center pixels of yag
center_pix = [y1.size[0] / 2] * 2
goal = [p + 300 for p in center_pix]
# Define a bad set command
def bad_set(yag, cmd=None, **kwargs):
logger.info("{0}Setting Attributes. (BAD)".format(yag.log_pref))
logger.debug("{0}Setting: CMD:{1}, {2} (BAD)".format(
yag.log_pref, cmd, kwargs))
return Status(done=True, success=False)
# Patch yag set command
y1.set = lambda cmd, **kwargs: bad_set(y1, cmd, **kwargs)
plan = run_wrapper(
iterwalk([y1, y2], [m1, m2],
goal,
starts=None,
first_steps=1,
gradients=None,
detector_fields='detector_stats2_centroid_x',
motor_fields='sim_alpha',
tolerances=TOL,
system=None,
averages=1,
overshoot=0,
max_walks=5,
timeout=None))
# Check a RunTimError is raised
with pytest.raises(RuntimeError):
RE(plan)
# Reload system
s, m1, m2, y1, y2 = lcls_two_bounce_system
# Patch yag set command
y2.set = lambda cmd, **kwargs: bad_set(y2, cmd, **kwargs)
plan = run_wrapper(
iterwalk([y1, y2], [m1, m2],
goal,
starts=None,
first_steps=1e-6,
gradients=None,
detector_fields='detector_stats2_centroid_x',
motor_fields='sim_alpha',
tolerances=TOL,
system=None,
averages=1,
overshoot=0,
max_walks=5,
timeout=None))
# Check a RunTimError is raised
with pytest.raises(RuntimeError):
RE(plan)
def test_walk_to_pixel(RE, one_bounce_system):
logger.debug("test_walk_to_pixel")
_, mot, det = one_bounce_system
##########################
# Test on simple devices #
##########################
simple_motor = SynAxis(name='motor')
simple_det = SynSignal(name='det',
func=lambda: 5*simple_motor.read()['motor']['value']
+ 2)
# Naive step
plan = run_wrapper(walk_to_pixel(simple_det, simple_motor, 200, 0,
first_step=1e-6,
tolerance=10, average=None,
target_fields=['det', 'motor'],
max_steps=3))
RE(plan)
assert np.isclose(simple_det.read()['det']['value'], 200, atol=1)
simple_motor.set(0.)
# Gradient
simple_motor = SynAxis(name='motor')
simple_det = SynSignal(name='det',
func=lambda: 5*simple_motor.read()['motor']['value']
+ 2)
plan = run_wrapper(walk_to_pixel(simple_det, simple_motor, 200, 0,
gradient=1.6842e+06,
tolerance=10, average=None,
target_fields=['det', 'motor'],
max_steps=3))
RE(plan)
assert np.isclose(simple_det.read()['det']['value'], 200, atol=1)
##########################
# Test on full model #
##########################
# Naive step
cent = 'detector_stats2_centroid_x'
plan = run_wrapper(walk_to_pixel(det, mot, 200, 0, first_step=1e-6,
tolerance=10, average=None,
target_fields=[cent, 'sim_alpha'],
max_steps=3))
RE(plan)
assert np.isclose(det.read()[det.name + "_" + cent]['value'], 200, atol=1)
mot.set(0.)
# Gradient
plan = run_wrapper(walk_to_pixel(det, mot, 200, 0, gradient=1.6842e+06,
tolerance=10, average=None,
target_fields=[cent, 'sim_alpha'],
max_steps=3))
RE(plan)
assert np.isclose(det.read()[det.name + "_" + cent]['value'], 200, atol=10)
def test_x348_scan(fresh_RE):
#m1 = SynAxis(name="m1")
#m2 = SynAxis(name="m2")
#seq = SynSequencer('', name='sequencer')
pal = McgrainPalette(name='test_palette',
N=10,
M=5,
chip_spacing=0,
chip_dims=[10])
pal._accept_calibration(start_pt=np.array([0, 0, 0]),
n_pt=np.array([pal.N - 1, 0, 0]),
m_pt=np.array([0, pal.M - 1, 0]))
seq = SimSequencer('', name='sequencer')
seq._cb_sleep = 0
def test_plan():
yield from x348_scan(pal, seq, 0, 7, .1)
assert pal.x_motor.position == 1.0
assert pal.y_motor.position == 3.0
assert seq.play_control.get() == 1
# Send all metadata/data captured to the BestEffortCallback.
fresh_RE(run_wrapper(test_plan()))
def test_homs_fiducialize(RE, fiducialized_yag_set):
fset = fiducialized_yag_set
slit_set, yag_set = list(zip(*fset))
yag_set = [
PIM("TEST"),
PIM("TEST"),
PIM("TEST"),
]
center = []
measuredcenter = collector("TST:TEST_detector_stats2_centroid_y", center)
state = []
measuredstate = collector("TST:TEST_states", state)
RE(
run_wrapper(
homs_fiducialize(slit_set,
yag_set,
x_width=.6,
y_width=.6,
samples=2,
centroid='detector_stats2_centroid_y')),
{'event': [measuredcenter, measuredstate]})
for x in yag_set:
assert x.read()['TST:TEST_states']['value'] == 'OUT', "yag not removed"
for index, _ in enumerate(center):
assert center[index] == 0.0, 'measurment incorrect'
def test_return_to_start_returns_motors_correctly(fresh_RE, return_devices):
# Grab the initial positions
initial_positions = [mot.position for mot in motors]
final_positions = []
# Create the plan
@return_to_start(*return_devices)
def test_plan():
for mot in motors:
yield from rel_set(mot, 1)
# Grab the final positions
final_positions.append(mot.position)
# Run the plan
fresh_RE(run_wrapper(test_plan()))
# get the expected and current positions
expected_positions = [
ipos if m in return_devices else fpos
for m, ipos, fpos in zip(motors, initial_positions, final_positions)
]
current_positions = [mot.position for mot in motors]
# Assert they are the same
assert current_positions == expected_positions
def test_centroid_scan_returns_correct_columns(fresh_RE, mot_fields,
det_fields, system_attrs):
# Simulated camera
camera = SynCamera(m1, m2, delay, name="camera")
# Create the plan
def test_plan():
steps = 2 # Start and stop position
delay_scan = (yield from centroid_scan(camera,
delay,
-1,
1,
steps,
detector_fields=det_fields,
motor_fields=mot_fields,
system=system_attrs[0],
system_fields=system_attrs[1]))
expected_length = (len(as_list(mot_fields)) or 1) \
+ len(as_list(det_fields)) \
+ len(as_list(system_attrs[1]))
# Check the number of columns based in the inputs
assert delay_scan.shape[1] == expected_length
expected_columns = as_list(mot_fields or delay.name) \
+ as_list(system_attrs[1]) \
+ ["_".join([camera.name, fld]) for fld in as_list(
det_fields)]
# Check the columns are all what we expect them to be
assert (delay_scan.columns == expected_columns).all()
# Run the plan
fresh_RE(run_wrapper(test_plan()))
def test_match_condition_fail_no_stop(RE, mot_and_sig):
logger.debug("test_match_condition_fail_no_stop")
mot, sig = mot_and_sig
mot.delay = 0
RE(run_wrapper(match_condition(sig, lambda x: x > 50, mot, 40,
has_stop=False)))
assert mot.position == 40
def test_scan_vars(RE, daq):
logger.debug('test_scan_vars')
daq.configure(events=120)
scan_vars = ScanVars('TST', name='tst', RE=RE)
scan_vars.enable()
check = CheckVals(scan_vars)
RE.subscribe(check)
check.plan = 'scan'
RE(
scan([det1, det2], motor1, 0, 10, motor2, 20, 0, motor3, 0, 1, motor,
0, 1, 11))
check.plan = 'count'
RE(count([det1, det2], 11))
def custom(detector):
for i in range(3):
yield from create()
yield from read(detector)
yield from save()
check.plan = 'custom'
daq.configure(duration=4)
RE(stage_wrapper(run_wrapper(custom(det1)), [det1]))
scan_vars.disable()
# Last, let's force an otherwise uncaught error to cover the catch-all
# try-except block to make sure the log message doesn't error
scan_vars.start({'motors': 4})
def test_calibration_scan(fresh_RE, weights):
camera = SynCamera(m1, m2, delay, name="camera")
centroids = [camera.centroid_x, camera.centroid_y]
calib_motors = [m1, m2]
for cent, weight in zip(centroids, weights):
cent.weights = [weight, cent.weights[1]]
def test_plan():
# Perform the scan
df_calib, df_scan, _, _ = yield from calib.calibration_scan(
camera, ['camera_centroid_x', 'camera_centroid_y'],
delay,
None, [m1, m2],
None,
-1,
1,
5,
tolerance=0)
# Expected positions of the centroids are the first positions
expected_centroids = df_scan[[c.name for c in centroids]].iloc[0]
for i in range(len(df_scan)):
# Move to the scan position for the main motor
delay.set(df_calib[delay.name].iloc[i])
for cmotor, exp_cent, cent in zip(calib_motors, expected_centroids,
centroids):
# Move to the abosolute corrected position
cmotor.set(df_calib[cmotor.name + "_post"].iloc[i])
# Check the centroids are where they should be
assert np.isclose(cent.get(), exp_cent, rtol=rtol)
# Run the plan
fresh_RE(run_wrapper(test_plan()))
def test_CalibMotor_move_compensation(fresh_RE, get_calib_motor):
# Define all the needed variables
motor = get_calib_motor
calib_motors = motor.calib_motors
camera = SynCamera(*motor.calib_motors, motor, name="camera")
centroids = [camera.centroid_x, camera.centroid_y]
start, stop, steps = -1, 1, 5
# Create the plan
def test_plan():
# Perform the calibration
df_scan = (yield from
centroid_scan(camera,
motor,
start,
stop,
steps,
motor_fields=motor.motor_fields,
detector_fields=motor.detector_fields))
# Expected positions of the centroids are the first positions
df_centroids = df_scan[[c.name for c in centroids]]
assert (df_centroids == df_centroids.iloc[0]).all().all()
# Calibrate the motors
motor.calibrate(start,
stop,
steps,
RE=fresh_RE,
detector=camera,
average=1,
tolerance=0,
confirm_overwrite=False)
# Run the plan
fresh_RE(run_wrapper(test_plan()))
def test_detector_scaling_walk_start_positions_are_valid(fresh_RE):
camera = SynCamera(m1, m2, delay, name="camera")
m1.set(0)
m2.set(0)
delay.set(0)
calib_motors = [m1, m2]
def test_plan():
# Get the current positions of the calib motors
expected_positions = [
test_df_scan[m.name + "_pre"][-1] for m in calib_motors
]
# Perform the walk
_, start = yield from calib.detector_scaling_walk(test_df_scan,
camera,
calib_motors,
tolerance=0,
system=delay)
# Make sure we dont have any bad values
assert np.nan not in start and np.inf not in start
# Make sure we are get the expected positions of the motors
assert np.isclose(start, expected_positions, rtol=rtol).all()
# Run the plan
fresh_RE(run_wrapper(test_plan()))
def test_detector_scaling_walk_scale_values_are_valid(fresh_RE, weights):
camera = SynCamera(m1, m2, delay, name="camera")
centroids = [camera.centroid_x, camera.centroid_y]
calib_motors = [m1, m2]
for cent, weight in zip(centroids, weights):
cent.weights = [weight, cent.weights[1]]
def test_plan():
df_scan = yield from calib.calibration_centroid_scan(
camera,
delay, [m1, m2],
-1,
1,
5,
detector_fields=['camera_centroid_x', 'camera_centroid_y'])
# Get the expected scales if we do the walk
expected_scales = [1 / cent.weights[0] for cent in centroids]
# Perform the walk
scales, _ = yield from calib.detector_scaling_walk(df_scan,
camera,
calib_motors,
tolerance=0,
system=delay)
# Make sure we dont have any bad values
assert np.nan not in scales and np.inf not in scales
# Make sure we are get the expected positions of the motors
assert np.isclose(scales, expected_scales, rtol=rtol).all()
# Run the plan
fresh_RE(run_wrapper(test_plan()))
def test_iterwalk(RE, lcls_two_bounce_system, goal1, goal2, first_steps,
gradients, tolerances, overshoot, max_walks, tol_scaling):
logger.debug(
"test_iterwalk with goal1=%s, goal2=%s, first_steps=%s, " +
"gradients=%s, tolerances=%s, overshoot=%.2f, max_walks=%s", goal1,
goal2, first_steps, gradients, tolerances, overshoot, max_walks)
s, m1, m2, y1, y2 = lcls_two_bounce_system
goal1 += y1.size[0] / 2
goal2 += y2.size[0] / 2
goal = [goal1, goal2]
plan = run_wrapper(
iterwalk([y1, y2], [m1, m2],
goal,
starts=None,
first_steps=first_steps,
gradients=gradients,
detector_fields='detector_stats2_centroid_x',
motor_fields='sim_alpha',
tolerances=tolerances,
system=[m1, m2, y1, y2],
averages=1,
overshoot=overshoot,
max_walks=max_walks,
timeout=None,
tol_scaling=tol_scaling))
RE(plan)
assert np.isclose(y1.read()[y1.name +
'_detector_stats2_centroid_x']['value'],
goal[0],
atol=tolerances)
assert np.isclose(y2.read()[y2.name +
'_detector_stats2_centroid_x']['value'],
goal[1],
atol=tolerances)
# Make sure we actually read all the groups as we went
m1_reads = 0
m2_reads = 0
y1_reads = 0
y2_reads = 0
saves = 0
for msg in RE.msg_hook.msgs:
if msg.command == 'read':
if msg.obj == m1:
m1_reads += 1
if msg.obj == m2:
m2_reads += 1
if msg.obj == y1:
y1_reads += 1
if msg.obj == y2:
y2_reads += 1
if msg.command == 'save':
saves += 1
assert saves > 0
assert all(
map(lambda x: x == saves, [m1_reads, m2_reads, y1_reads, y2_reads]))
def macro_sweep_test(target):
logging.info(
'macro_sweep_test initiated with target {:0.4f}'.format(target))
RE = RunEngine({})
bec = BestEffortCallback()
RE.subscribe(bec)
RE.waiting_hook = ProgressBarManager()
RE(run_wrapper(rel_smooth_sweep_test(tst_23, target)))
def test_recover_threshold_timeout_failure(RE, mot_and_sig):
logger.debug("test_recover_threshold_timeout_failure")
mot, sig = mot_and_sig
# Make the motor slower to guarantee a timeout
mot.n_steps = 5000
RE(run_wrapper(recover_threshold(sig, 50, mot, +1, timeout=0.1)))
pos = mot.position
assert not 49 < pos < 51
assert mot.position not in (100, -100)
def test_measure(RE):
# Simplest implementation
plan = run_wrapper(measure([det, motor], num=5, delay=0.01))
# Fake callback storage
shots = list()
cb = collector('det', shots)
# Run simple
RE(plan, {'event': cb})
assert shots == [1.0, 1.0, 1.0, 1.0, 1.0]
# Create counting detector
index = -1
def count():
nonlocal index
index += 1
return index
counter = SynSignal(name='intensity',
func=count)
# Filtered implementation
plan = run_wrapper(measure([counter],
filters={'intensity': lambda x: x > 2},
num=5))
# Fake callback storage
shots = list()
cb = collector('intensity', shots)
# Run filtered
RE(plan, {'event': cb})
assert shots == [1, 2, 3, 4, 5, 6, 7]
# Make sure an exception is raised when we fail too many filter checks
plan = run_wrapper(measure([counter],
filters={'intensity': lambda x: False},
num=500))
with pytest.raises(FilterCountError):
RE(plan)
def test_measure_centroid(RE, one_bounce_system):
logger.debug("test_measure_centroid")
_, mot, det = one_bounce_system
centroids = []
key_ext = 'detector_stats2_centroid_x'
col_c = collector(det.name + "_" + key_ext, centroids)
RE(run_wrapper(measure_centroid(det, average=5,
target_field=key_ext)),
{'event': [col_c]})
assert centroids == [250., 250., 250., 250., 250.]
def test_iterwalk_raises_RuntimeError_on_failed_walk_to_pixel(
RE, lcls_two_bounce_system):
logger.debug("test_iterwalk_raises_RuntimeError_on_failed_walk_to_pixel")
s, m1, m2, y1, y2 = lcls_two_bounce_system
# Center pixels of yag
center_pix = [y1.size[0] / 2] * 2
goal = [p + 300 for p in center_pix]
# Define a bad set command
def bad_set(mirror, cmd=None, **kwargs):
logger.info("{0}Setting Attributes. (BAD)".format(mirror.log_pref))
logger.debug("{0}Setting: CMD:{1}, {2} (BAD)".format(
mirror.log_pref, cmd, kwargs))
err = 0.1
if cmd in ("IN", "OUT"):
pass # If these were removable we'd implement it here
elif cmd is not None:
# Here is where we move the pitch motor if a value is set
cmd += err
mirror.sim_pitch = cmd
return mirror.pitch.set(cmd)
mirror.sim_x = kwargs.get('x', mirror.sim_x)
mirror.sim_z = kwargs.get('z', mirror.sim_z)
mirror.sim_pitch = kwargs.get('pitch', mirror.sim_pitch)
for motor in mirror.motors:
motor_params = motor.read()
for key in kwargs.keys():
if key in motor_params:
# Add error term to sets
motor.set(kwargs[key] + err)
return Status(done=True, success=True)
# Patch yag set command
m1.set = lambda cmd, **kwargs: bad_set(m1, cmd, **kwargs)
plan = run_wrapper(
iterwalk([y1, y2], [m1, m2],
goal,
starts=None,
first_steps=1e-6,
gradients=None,
detector_fields='sim_x',
motor_fields='sim_alpha',
tolerances=TOL,
system=None,
averages=1,
overshoot=0,
max_walks=5,
timeout=None))
# Check a RunTimError is raised
with pytest.raises(RuntimeError):
RE(plan)
def test_fitwalk(RE):
# Create simulated devices
motor = SynAxis(name='motor')
det = SynSignal(name='centroid',
func=lambda: 5*motor.read()['motor']['value'] + 2)
linear = LinearFit('centroid', 'motor', average=1)
parabola = ParabolicFit('centroid', 'motor', average=1)
walk = fitwalk([det], motor, [parabola, linear], 89.4,
average=1, tolerance=0.5)
RE(run_wrapper(walk))
assert np.isclose(det.read()['centroid']['value'], 89.4, 0.5)
def test_mcgrain_scan(fresh_RE):
m1 = SynAxis(name="m1")
m2 = SynAxis(name="m2")
seq = SynSequencer('', name='sequencer')
def test_plan():
yield from mcgrain_scan(m1, m2, seq, 0, 5, 6, 5)
assert m1.position == 5.0
assert m2.position == 30
assert seq.state_control.get() == 1
# Send all metadata/data captured to the BestEffortCallback.
fresh_RE(run_wrapper(test_plan()))
def macro_RSXS_smooth_sweep(stroke_height,
stroke_spacing,
n_strokes,
both_directions=True):
"""
macro_RSXS_smooth_sweep
This method wraps up the bluesky/ophyd codeand allows users to drive
the LU20 experiment with minimal code overhead. It contains the following
bluesky plan.
This bluesky plan moves a 2-axis actuator across multiple traversals of a
sample. The plan traverses the entirety of the stroke_height (y-axis) and
after each traversal, steps in the x-axis by the stroke_spacing.It may be
configured to scan in only a single direction and shutter the beam for the
opposite direction. This removes the shutter at the beginning of the plan
and reinserts it at the end. At the end of the plan, the sample is moved to
its original y-axis position but with an x-axis posiiton ready for the next
run. For more details about the path, see the documentation of the
xy_sequencer, the method that generates the sample's path.
Parameters
----------
stroke_height : float
Vertical distance (y-axs) of each stroke.
stroke_spacing : float
Horizontal distance between individual strokes.
n_strokes : int
Number of strokes to complete.
both_directions : bool, optional
Defaults to True. If this value is true the beam will be scanned across
the sample while moving in both vertical directions. If false, the beam
is only scanned in a single direction.
"""
RE = RunEngine({})
bec = BestEffortCallback()
RE.subscribe(bec)
RE.waiting_hook = ProgressBarManager()
RE(
run_wrapper(
rel_smooth_sweep(mot_x=rsxs_sample_x,
mot_y=rsxs_sample_y,
shutter=shutter,
stroke_height=stroke_height,
stroke_spacing=stroke_spacing,
n_strokes=n_strokes,
both_directions=both_directions)))
def test_euclidean_distance(fresh_RE):
camera = SynCamera(m1, m2, delay, name="camera")
# Define the distance plan that makes the assertion
def test_plan():
distance = yield from euclidean_distance(camera,
['centroid_x', 'centroid_y'],
[1, 1])
assert np.isclose(distance, math.sqrt(2))
# Wrap the plan
plan = run_wrapper(test_plan())
# And now run it
fresh_RE(plan)
def test_nonrewindable_detector(RE, hw, start_state, msg_seq):
class FakeSig:
def get(self):
return False
hw.det.rewindable = FakeSig()
RE.rewindable = start_state
m_col = MsgCollector()
RE.msg_hook = m_col
RE(run_wrapper(trigger_and_read([hw.motor, hw.det])))
assert [m.command for m in m_col.msgs] == msg_seq
def test_nonrewindable_detector(RE, hw, start_state, msg_seq):
class FakeSig:
def get(self):
return False
hw.det.rewindable = FakeSig()
RE.rewindable = start_state
m_col = MsgCollector()
RE.msg_hook = m_col
RE(run_wrapper(trigger_and_read([hw.motor, hw.det])))
assert [m.command for m in m_col.msgs] == msg_seq
def test_lorentz_maximize(fresh_RE):
# Simulated diode readout
diode = Diode('intensity', crystal, 'angle', 10.0, noise_multiplier=None)
# Create plan to maximize the signal
plan = run_wrapper(maximize_lorentz(diode, crystal, 'intensity',
step_size=0.2, bounds=(9., 11.),
position_field='angle',
initial_guess = {'center' : 8.}))
# Run the plan
fresh_RE(plan)
# Trigger an update
diode.trigger()
#Check that we were within 10%
assert np.isclose(diode.read()['intensity']['value'], 1.0, 0.1)
def test_calibration_centroid_scan_df_is_valid(fresh_RE):
camera = SynCamera(m1, m2, delay, name="camera")
def test_plan():
df = yield from calib.calibration_centroid_scan(
camera,
delay, [m1, m2],
-1,
1,
5,
detector_fields=['camera_centroid_x', 'camera_centroid_y'])
assert True not in df.isnull().values
# Run the plan
fresh_RE(run_wrapper(test_plan()))
def test_interrupted_with_callbacks(RE, int_meth, stop_num, msg_num):
docs = defaultdict(list)
def collector_cb(name, doc):
nonlocal docs
docs[name].append(doc)
RE.msg_hook = MsgCollector()
with pytest.raises(RunEngineInterrupted):
RE(subs_wrapper(run_wrapper(pause()), {"all": collector_cb}))
getattr(RE, int_meth)()
assert len(docs["start"]) == 1
assert len(docs["event"]) == 0
assert len(docs["descriptor"]) == 0
assert len(docs["stop"]) == stop_num
assert len(RE.msg_hook.msgs) == msg_num