def initialize_camera(self):
p = self.parameters.IonsOnCamera
from lmfit import Parameters as lmfit_Parameters
self.fitter = ion_state_detector(int(p.ion_number))
self.image_region = [
int(p.horizontal_bin),
int(p.vertical_bin),
int(p.horizontal_min),
int(p.horizontal_max),
int(p.vertical_min),
int(p.vertical_max),
]
self.fit_parameters = lmfit_Parameters()
self.fit_parameters.add('ion_number', value=int(p.ion_number))
self.fit_parameters.add('background_level',
value=p.fit_background_level)
self.fit_parameters.add('amplitude', value=p.fit_amplitude)
self.fit_parameters.add('rotation_angle', p.fit_rotation_angle)
self.fit_parameters.add('center_x', value=p.fit_center_horizontal)
self.fit_parameters.add('center_y', value=p.fit_center_vertical)
self.fit_parameters.add('spacing', value=p.fit_spacing)
self.fit_parameters.add('sigma', value=p.fit_sigma)
x_axis = np.arange(self.image_region[2], self.image_region[3] + 1,
self.image_region[0])
y_axis = np.arange(self.image_region[4], self.image_region[5] + 1,
self.image_region[1])
xx, yy = np.meshgrid(x_axis, y_axis)
self.fitter.set_fitted_parameters(self.fit_parameters, xx, yy)
def initialize_camera(self):
p = self.parameters.IonsOnCamera
from lmfit import Parameters as lmfit_Parameters
self.fitter = ion_state_detector(int(p.ion_number))
self.image_region = [
int(p.horizontal_bin),
int(p.vertical_bin),
int(p.horizontal_min),
int(p.horizontal_max),
int(p.vertical_min),
int(p.vertical_max),
]
self.fit_parameters = lmfit_Parameters()
self.fit_parameters.add('ion_number', value = int(p.ion_number))
self.fit_parameters.add('background_level', value = p.fit_background_level)
self.fit_parameters.add('amplitude', value = p.fit_amplitude)
self.fit_parameters.add('rotation_angle', p.fit_rotation_angle)
self.fit_parameters.add('center_x', value = p.fit_center_horizontal)
self.fit_parameters.add('center_y', value = p.fit_center_vertical)
self.fit_parameters.add('spacing', value = p.fit_spacing)
self.fit_parameters.add('sigma', value = p.fit_sigma)
x_axis = np.arange(self.image_region[2], self.image_region[3] + 1, self.image_region[0])
y_axis = np.arange(self.image_region[4], self.image_region[5] + 1, self.image_region[1])
xx,yy = np.meshgrid(x_axis, y_axis)
self.fitter.set_fitted_parameters(self.fit_parameters, xx, yy)
def initialize_camera(self, cxn):
self.total_camera_confidences = []
p = self.parameters.IonsOnCamera
from lmfit import Parameters as lmfit_Parameters
self.camera = cxn.andor_server
self.fitter = ion_state_detector(int(p.ion_number))
self.camera_initially_live_display = self.camera.is_live_display_running(
)
self.camera.abort_acquisition()
self.initial_exposure = self.camera.get_exposure_time()
exposure = self.parameters.StateReadout.state_readout_duration
self.camera.set_exposure_time(exposure)
self.initial_region = self.camera.get_image_region()
self.image_region = [
int(p.horizontal_bin),
int(p.vertical_bin),
int(p.horizontal_min),
int(p.horizontal_max),
int(p.vertical_min),
int(p.vertical_max),
]
self.fit_parameters = lmfit_Parameters()
self.fit_parameters.add('ion_number', value=int(p.ion_number))
self.fit_parameters.add('background_level',
value=p.fit_background_level)
self.fit_parameters.add('amplitude', value=p.fit_amplitude)
self.fit_parameters.add('rotation_angle', p.fit_rotation_angle)
self.fit_parameters.add('center_x', value=p.fit_center_horizontal)
self.fit_parameters.add('center_y', value=p.fit_center_vertical)
self.fit_parameters.add('spacing', value=p.fit_spacing)
self.fit_parameters.add('sigma', value=p.fit_sigma)
x_axis = numpy.arange(self.image_region[2], self.image_region[3] + 1,
self.image_region[0])
y_axis = numpy.arange(self.image_region[4], self.image_region[5] + 1,
self.image_region[1])
xx, yy = numpy.meshgrid(x_axis, y_axis)
self.fitter.set_fitted_parameters(self.fit_parameters, xx, yy)
self.camera.set_image_region(*self.image_region)
self.camera.set_acquisition_mode('Kinetics')
self.initial_trigger_mode = self.camera.get_trigger_mode()
self.camera.set_trigger_mode('External')
def initialize_camera(self, cxn):
self.total_camera_confidences = []
p = self.parameters.IonsOnCamera
from lmfit import Parameters as lmfit_Parameters
self.camera = cxn.andor_server
self.fitter = ion_state_detector(int(p.ion_number))
self.camera_initially_live_display = self.camera.is_live_display_running()
self.camera.abort_acquisition()
self.initial_exposure = self.camera.get_exposure_time()
exposure = self.parameters.StateReadout.state_readout_duration
self.camera.set_exposure_time(exposure)
self.initial_region = self.camera.get_image_region()
self.image_region = [
int(p.horizontal_bin),
int(p.vertical_bin),
int(p.horizontal_min),
int(p.horizontal_max),
int(p.vertical_min),
int(p.vertical_max),
]
self.fit_parameters = lmfit_Parameters()
self.fit_parameters.add('ion_number', value = int(p.ion_number))
self.fit_parameters.add('background_level', value = p.fit_background_level)
self.fit_parameters.add('amplitude', value = p.fit_amplitude)
self.fit_parameters.add('rotation_angle', p.fit_rotation_angle)
self.fit_parameters.add('center_x', value = p.fit_center_horizontal)
self.fit_parameters.add('center_y', value = p.fit_center_vertical)
self.fit_parameters.add('spacing', value = p.fit_spacing)
self.fit_parameters.add('sigma', value = p.fit_sigma)
#self.fit_parameters.add('sigma', value = 3.5, vary=False)
x_axis = numpy.arange(self.image_region[2], self.image_region[3] + 1, self.image_region[0])
y_axis = numpy.arange(self.image_region[4], self.image_region[5] + 1, self.image_region[1])
xx,yy = numpy.meshgrid(x_axis, y_axis)
self.fitter.set_fitted_parameters(self.fit_parameters, xx, yy)
self.camera.set_image_region(*self.image_region)
self.camera.set_acquisition_mode('Kinetics')
self.initial_trigger_mode = self.camera.get_trigger_mode()
self.camera.set_trigger_mode('External')
def camera_ion_probabilities(images, repetitions, p, readout_mode = 'camera'):
"""
Method for analyzing camera images. For an
N-ion chain, returns an N element list
indicating the probability that each
ion is excited.
"""
from lmfit import Parameters as lmfit_Parameters
fitter = ion_state_detector(int(p.ion_number))
image_region = [
int(p.horizontal_bin),
int(p.vertical_bin),
int(p.horizontal_min),
int(p.horizontal_max),
int(p.vertical_min),
int(p.vertical_max),
]
fit_parameters = lmfit_Parameters()
fit_parameters.add('ion_number', value = int(p.ion_number))
fit_parameters.add('background_level', value = p.fit_background_level)
fit_parameters.add('amplitude', value = p.fit_amplitude)
fit_parameters.add('rotation_angle', p.fit_rotation_angle)
fit_parameters.add('center_x', value = p.fit_center_horizontal)
fit_parameters.add('center_y', value = p.fit_center_vertical)
fit_parameters.add('spacing', value = p.fit_spacing)
fit_parameters.add('sigma', value = p.fit_sigma)
x_axis = np.arange(image_region[2], image_region[3] + 1, image_region[0])
y_axis = np.arange(image_region[4], image_region[5] + 1, image_region[1])
xx,yy = np.meshgrid(x_axis, y_axis)
fitter.set_fitted_parameters(fit_parameters, xx, yy)
x_pixels = int( (image_region[3] - image_region[2] + 1.) / (image_region[0]) )
y_pixels = int( (image_region[5] - image_region[4] + 1.) / (image_region[1]) )
#print "inside the camera readout"
#print "x_pixels {}".format(x_pixels)
#print "y_pixels {}".format(y_pixels)
#print images
#np.save('37ions_global', images)
#print "repetitions{}".format(repetitions)
images = np.reshape(images, (repetitions, y_pixels, x_pixels))
readouts, confidences = fitter.state_detection(images)
ion_state = 1 - readouts.mean(axis = 0)
#np.save('temp_camera_ion_state', ion_state)
#np.save('temp_camera_readout', readouts)
if readout_mode == 'camera_states':
ion_state=get_states_camera(readouts,int(p.ion_number))
if readout_mode == 'camera_parity':
ion_state=get_states_camera(readouts,int(p.ion_number))
parity= Calc_parity(ion_state)
ion_state=np.append(ion_state,[parity])
# print "555"
# print "readout_mode", readout_mode
# print "ion_stste", ion_state
#
return ion_state, readouts, confidences
def camera_ion_probabilities(images, repetitions, p, readout_mode = 'camera'):
"""
Method for analyzing camera images. For an
N-ion chain, returns an N element list
indicating the probability that each
ion is excited.
"""
from lmfit import Parameters as lmfit_Parameters
fitter = ion_state_detector(int(p.ion_number))
image_region = [
int(p.horizontal_bin),
int(p.vertical_bin),
int(p.horizontal_min),
int(p.horizontal_max),
int(p.vertical_min),
int(p.vertical_max),
]
fit_parameters = lmfit_Parameters()
fit_parameters.add('ion_number', value = int(p.ion_number))
fit_parameters.add('background_level', value = p.fit_background_level)
fit_parameters.add('amplitude', value = p.fit_amplitude)
fit_parameters.add('rotation_angle', p.fit_rotation_angle)
fit_parameters.add('center_x', value = p.fit_center_horizontal)
fit_parameters.add('center_y', value = p.fit_center_vertical)
fit_parameters.add('spacing', value = p.fit_spacing)
fit_parameters.add('sigma', value = p.fit_sigma)
x_axis = np.arange(image_region[2], image_region[3] + 1, image_region[0])
y_axis = np.arange(image_region[4], image_region[5] + 1, image_region[1])
xx,yy = np.meshgrid(x_axis, y_axis)
fitter.set_fitted_parameters(fit_parameters, xx, yy)
x_pixels = int( (image_region[3] - image_region[2] + 1.) / (image_region[0]) )
y_pixels = int( (image_region[5] - image_region[4] + 1.) / (image_region[1]) )
#print "inside the camera readout"
#print "x_pixels {}".format(x_pixels)
#print "y_pixels {}".format(y_pixels)
#print images
#np.save('37ions_global', images)
#print "repetitions{}".format(repetitions)
images = np.reshape(images, (repetitions, y_pixels, x_pixels))
readouts, confidences = fitter.state_detection(images)
ion_state = 1 - readouts.mean(axis = 0)
#np.save('temp_camera_ion_state', ion_state)
#np.save('temp_camera_readout', readouts)
if readout_mode == 'camera_states':
ion_state=get_states_camera(readouts,int(p.ion_number))
if readout_mode == 'camera_parity':
ion_state=get_states_camera(readouts,int(p.ion_number))
parity= Calc_parity(ion_state)
ion_state=np.append(ion_state,[parity])
# print "555"
# print "readout_mode", readout_mode
# print "ion_stste", ion_state
#
return ion_state, readouts, confidences
