def test_maximum_out_of_soft_limits_left(self):
feedback = DummyGradientMeasure(self.motor['position'], 20 * q.mm)
optimize_parameter(self.motor["position"],
lambda: -feedback(),
self.motor.position,
optimization.halver,
alg_kwargs=self.halver_kwargs).wait()
assert_almost_equal(self.motor.position, 25 * q.mm)
def run_optimization(self, initial_position=1 * q.mm):
optimize_parameter(
self.motor["position"],
lambda: -self.feedback(),
initial_position,
optimization.halver,
alg_kwargs=self.halver_kwargs,
).join()
def test_maximum_out_of_soft_limits_left(self):
feedback = DummyGradientMeasure(self.motor["position"], 20 * q.mm)
optimize_parameter(
self.motor["position"],
lambda: -feedback(),
self.motor.position,
optimization.halver,
alg_kwargs=self.halver_kwargs,
).join()
assert_almost_equal(self.motor.position, 25 * q.mm)
def focus(camera,
motor,
measure=np.std,
opt_kwargs=None,
plot_consumer=None,
frame_consumer=None):
"""
Focus *camera* by moving *motor*. *measure* is a callable that computes a
scalar that has to be maximized from an image taken with *camera*.
*opt_kwargs* are keyword arguments sent to the optimization algorithm.
*plot_consumer* is fed with y values from the optimization and
*frame_consumer* is fed with the incoming frames.
This function is returning a future encapsulating the focusing event. Note,
that the camera is stopped from recording as soon as the optimal position
is found.
"""
if opt_kwargs is None:
opt_kwargs = {'initial_step': 0.5 * q.mm, 'epsilon': 1e-2 * q.mm}
def get_measure():
camera.trigger()
frame = camera.grab()
if frame_consumer:
frame_consumer.send(frame)
return -measure(frame)
@coroutine
def filter_optimization():
"""
Filter the optimization's (x, y) subresults to only the y part,
otherwise the the plot update is not lucid.
"""
while True:
tup = yield
if plot_consumer:
plot_consumer.send(tup[1])
camera.trigger_mode = camera.trigger_modes.SOFTWARE
camera.start_recording()
f = optimize_parameter(motor['position'],
get_measure,
motor.position,
halver,
alg_kwargs=opt_kwargs,
consumer=filter_optimization())
f.add_done_callback(lambda unused: camera.stop_recording())
return f
def focus(camera, motor, measure=np.std, opt_kwargs=None,
plot_consumer=None, frame_consumer=None):
"""
Focus *camera* by moving *motor*. *measure* is a callable that computes a
scalar that has to be maximized from an image taken with *camera*.
*opt_kwargs* are keyword arguments sent to the optimization algorithm.
*plot_consumer* is fed with y values from the optimization and
*frame_consumer* is fed with the incoming frames.
This function is returning a future encapsulating the focusing event. Note,
that the camera is stopped from recording as soon as the optimal position
is found.
"""
if opt_kwargs is None:
opt_kwargs = {'initial_step': 0.5 * q.mm,
'epsilon': 1e-2 * q.mm}
def get_measure():
camera.trigger()
frame = camera.grab()
if frame_consumer:
frame_consumer.send(frame)
return - measure(frame)
@coroutine
def filter_optimization():
"""
Filter the optimization's (x, y) subresults to only the y part,
otherwise the the plot update is not lucid.
"""
while True:
tup = yield
if plot_consumer:
plot_consumer.send(tup[1])
camera.trigger_source = camera.trigger_sources.SOFTWARE
camera.start_recording()
f = optimize_parameter(motor['position'], get_measure, motor.position,
halver, alg_kwargs=opt_kwargs,
consumer=filter_optimization())
f.add_done_callback(lambda unused: camera.stop_recording())
return f
def focus(camera, motor, measure=np.std):
"""
Focus *camera* by moving *motor*. *measure* is a callable that computes a
scalar that has to be maximized from an image taken with *camera*.
This function is returning a future encapsulating the focusing event. Note,
that the camera is stopped from recording as soon as the optimal position
is found.
"""
# we should guess this from motor limits
opts = {'initial_step': 10 * q.mm,
'epsilon': 5e-3 * q.mm}
def get_measure():
frame = camera.grab()
return - measure(frame)
camera.start_recording()
f = optimize_parameter(motor['position'], get_measure, motor.position,
halver, alg_kwargs=opts)
f.add_done_callback(lambda unused: camera.stop_recording())
return f
def run_optimization(self, initial_position=1 * q.mm):
optimize_parameter(self.motor["position"],
lambda: -self.feedback(),
initial_position,
optimization.halver,
alg_kwargs=self.halver_kwargs).wait()
def optimize(self, algorithm):
optimize_parameter(self.motor["position"], self.feedback,
self.motor.position, algorithm).join()
def optimize(self, algorithm):
optimize_parameter(self.motor["position"], self.feedback,
self.motor.position, algorithm).wait()