def test_synaxis_subcribe():
hits = dict.fromkeys(['r', 's', 'a'], 0)
vals = dict.fromkeys(['r', 's', 'a'], None)
def p1(tar, value):
hits[tar] += 1
vals[tar] = value
motor = SynAxis(name='motor1')
# prime the cb cache so these run an subscription
motor.set(0)
motor.subscribe(lambda *, value, _tar='a', **kwargs:
p1(_tar, value))
motor.readback.subscribe(lambda *, value, _tar='r', **kwargs:
p1(_tar, value))
motor.setpoint.subscribe(lambda *, value, _tar='s', **kwargs:
p1(_tar, value))
assert vals['r'] == motor.readback.get()
assert vals['a'] == motor.readback.get()
assert vals['s'] == motor.setpoint.get()
assert all(v == 1 for v in hits.values())
motor.set(1)
assert vals['r'] == motor.readback.get()
assert vals['a'] == motor.readback.get()
assert vals['s'] == motor.setpoint.get()
assert all(v == 2 for v in hits.values())
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_synaxis_timestamps():
from ophyd.status import wait
import time
def time_getter(m):
return {k: v['timestamp']
for k, v in m.read().items()}
def tester(m, orig_time):
new_time = time_getter(m)
assert orig_time != new_time
return new_time
motor = SynAxis(name='motor1')
motor.delay = .01
orig_time = time_getter(motor)
wait(motor.set(3))
orig_time = tester(motor, orig_time)
wait(motor.setpoint.set(4))
orig_time = tester(motor, orig_time)
motor.setpoint.put(3)
time.sleep(2*motor.delay)
orig_time = tester(motor, orig_time)
from glob import glob
from pathlib import Path
import numpy
from ophyd.sim import SynSignal, SynAxis
import pandas
for filename in glob(str(Path(__file__).parent / "data" / "pitch_vs_I0" / "*.csv")):
# Grab the first file. Maybe later take a random one.
# Each one is at a different energy, so it would not make sense to concat them.
data = pandas.read_csv(filename)
break
def compute_I0():
return numpy.interp(pitch.readback.get(), data["dcm_pitch"], data["I0"])
pitch = SynAxis(name="pitch")
pitch.set(4).wait() # initialize at a reasonable value
pitch.delay = 0.05 # to simulate movement time
I0 = SynSignal(name="I0", func=compute_I0)
