def __init__(self, camera):
super(Sensor, self).__init__()
self.image_width_px = ccfg.image_width_px
self.image_height_px = ccfg.image_height_px
self.lenslet_pitch_m = ccfg.lenslet_pitch_m
self.lenslet_focal_length_m = ccfg.lenslet_focal_length_m
self.pixel_size_m = ccfg.pixel_size_m
self.beam_diameter_m = ccfg.beam_diameter_m
self.wavelength_m = ccfg.wavelength_m
self.background_correction = ccfg.background_correction
self.centroiding_iterations = ccfg.centroiding_iterations
self.iterative_centroiding_step = ccfg.iterative_centroiding_step
self.filter_lenslets = ccfg.sensor_filter_lenslets
self.estimate_background = ccfg.estimate_background
self.reconstruct_wavefront = ccfg.sensor_reconstruct_wavefront
self.remove_tip_tilt = ccfg.sensor_remove_tip_tilt
try:
xy = np.loadtxt(ccfg.reference_coordinates_filename)
except Exception as e:
xy = np.loadtxt(ccfg.reference_coordinates_bootstrap_filename)
print 'Bootstrapping with %s' % ccfg.reference_coordinates_bootstrap_filename
self.search_boxes = SearchBoxes(xy[:, 0], xy[:, 1],
ccfg.search_box_half_width)
self.sensor_mask = np.loadtxt(ccfg.reference_mask_filename)
self.x0 = np.zeros(self.search_boxes.x.shape)
self.y0 = np.zeros(self.search_boxes.y.shape)
self.x0[:] = self.search_boxes.x[:]
self.y0[:] = self.search_boxes.y[:]
self.n_lenslets = self.search_boxes.n
n_lenslets = self.n_lenslets
self.image = np.zeros((ccfg.image_height_px, ccfg.image_width_px))
self.x_slopes = np.zeros(n_lenslets)
self.y_slopes = np.zeros(n_lenslets)
self.x_centroids = np.zeros(n_lenslets)
self.y_centroids = np.zeros(n_lenslets)
self.box_maxes = np.zeros(n_lenslets)
self.box_mins = np.zeros(n_lenslets)
self.box_means = np.zeros(n_lenslets)
self.box_backgrounds = np.zeros(n_lenslets)
self.error = 0.0
self.tip = 0.0
self.tilt = 0.0
self.zernikes = None
self.wavefront = np.zeros(self.sensor_mask.shape)
self.cam = camera
self.frame_timer = FrameTimer('Sensor', verbose=False)
self.reconstructor = Reconstructor(self.search_boxes.x,
self.search_boxes.y,
self.sensor_mask)
self.logging = False
self.paused = False
def __init__(self,camera):
super(Sensor,self).__init__()
self.image_width_px = kcfg.image_width_px
self.image_height_px = kcfg.image_height_px
self.lenslet_pitch_m = kcfg.lenslet_pitch_m
self.lenslet_focal_length_m = kcfg.lenslet_focal_length_m
self.pixel_size_m = kcfg.pixel_size_m
self.beam_diameter_m = kcfg.beam_diameter_m
self.wavelength_m = kcfg.wavelength_m
self.background_correction = kcfg.background_correction
self.centroiding_iterations = kcfg.centroiding_iterations
self.iterative_centroiding_step = kcfg.iterative_centroiding_step
self.filter_lenslets = kcfg.sensor_filter_lenslets
self.estimate_background = kcfg.estimate_background
self.reconstruct_wavefront = kcfg.sensor_reconstruct_wavefront
self.remove_tip_tilt = kcfg.sensor_remove_tip_tilt
try:
# check to see if the camera object produced its own
# search boxes, and if not, use reference coordinates
self.search_boxes = camera.search_boxes
self.mask = camera.lenslet_mask
except Exception as e:
xy = np.loadtxt(kcfg.reference_coordinates_filename)
self.search_boxes = SearchBoxes(xy[:,0],xy[:,1],kcfg.search_box_half_width)
self.mask = np.loadtxt(kcfg.reference_mask_filename)
self.x0 = np.zeros(self.search_boxes.x.shape)
self.y0 = np.zeros(self.search_boxes.y.shape)
self.x0[:] = self.search_boxes.x[:]
self.y0[:] = self.search_boxes.y[:]
self.n_lenslets = self.search_boxes.n
n_lenslets = self.n_lenslets
self.image = np.zeros((kcfg.image_height_px,kcfg.image_width_px))
self.x_slopes = np.zeros(n_lenslets)
self.y_slopes = np.zeros(n_lenslets)
self.x_centroids = np.zeros(n_lenslets)
self.y_centroids = np.zeros(n_lenslets)
self.box_maxes = np.zeros(n_lenslets)
self.box_mins = np.zeros(n_lenslets)
self.box_means = np.zeros(n_lenslets)
self.box_backgrounds = np.zeros(n_lenslets)
self.error = 0.0
self.tip = 0.0
self.tilt = 0.0
self.zernikes = None
self.wavefront = None
self.cam = camera
self.frame_timer = FrameTimer('Sensor',verbose=False)
self.reconstructor = Reconstructor(self.search_boxes.x,
self.search_boxes.y,self.mask)
self.logging = False
self.paused = False
def __init__(self,camera):
self.image_width_px = ccfg.image_width_px
self.image_height_px = ccfg.image_height_px
self.dark_image = np.zeros((ccfg.image_height_px,ccfg.image_width_px),dtype=np.int16)
self.dark_subtract = False
self.n_dark = 10
self.lenslet_pitch_m = ccfg.lenslet_pitch_m
self.lenslet_focal_length_m = ccfg.lenslet_focal_length_m
self.pixel_size_m = ccfg.pixel_size_m
self.beam_diameter_m = ccfg.beam_diameter_m
self.wavelength_m = ccfg.wavelength_m
self.background_correction = ccfg.background_correction
self.centroiding_iterations = ccfg.centroiding_iterations
self.iterative_centroiding_step = ccfg.iterative_centroiding_step
self.filter_lenslets = ccfg.sensor_filter_lenslets
self.estimate_background = ccfg.estimate_background
self.reconstruct_wavefront = ccfg.sensor_reconstruct_wavefront
self.remove_tip_tilt = ccfg.sensor_remove_tip_tilt
# calculate diffraction limited spot size on sensor, to determine
# the centroiding window size
lenslet_dlss = 1.22*self.wavelength_m*self.lenslet_focal_length_m/self.lenslet_pitch_m
lenslet_dlss_px = lenslet_dlss/self.pixel_size_m
# now we need to account for smearing of spots due to axial displacement of retinal layers
extent = 500e-6 # retinal thickness
smear = extent*6.75/16.67 # pupil diameter and focal length; use diameter in case beacon is at edge of field
try:
magnification = ccfg.retina_sensor_magnification
except Exception as e:
magnification = 1.0
total_size = lenslet_dlss+smear*magnification
total_size_px = total_size/self.pixel_size_m
self.centroiding_half_width = int(np.floor(total_size_px/2.0))*2
try:
xy = np.loadtxt(ccfg.reference_coordinates_filename)
except Exception as e:
xy = np.loadtxt(ccfg.reference_coordinates_bootstrap_filename)
print 'Bootstrapping with %s'%ccfg.reference_coordinates_bootstrap_filename
self.search_boxes = SearchBoxes(xy[:,0],xy[:,1],ccfg.search_box_half_width)
self.sensor_mask = np.loadtxt(ccfg.reference_mask_filename)
self.x0 = np.zeros(self.search_boxes.x.shape)
self.y0 = np.zeros(self.search_boxes.y.shape)
self.x0[:] = self.search_boxes.x[:]
self.y0[:] = self.search_boxes.y[:]
self.n_lenslets = self.search_boxes.n
n_lenslets = self.n_lenslets
self.image = np.zeros((ccfg.image_height_px,ccfg.image_width_px))
self.x_slopes = np.zeros(n_lenslets)
self.y_slopes = np.zeros(n_lenslets)
self.x_centroids = np.zeros(n_lenslets)
self.y_centroids = np.zeros(n_lenslets)
self.box_maxes = np.zeros(n_lenslets)
self.box_mins = np.zeros(n_lenslets)
self.box_means = np.zeros(n_lenslets)
self.valid_centroids = np.zeros(n_lenslets,dtype=np.int16)
try:
self.fast_centroiding = ccfg.fast_centroiding
except Exception as e:
self.fast_centroiding = False
self.box_backgrounds = np.zeros(n_lenslets)
self.error = 0.0
self.tip = 0.0
self.tilt = 0.0
self.zernikes = np.zeros(ccfg.n_zernike_terms)
self.wavefront = np.zeros(self.sensor_mask.shape)
self.image_max = -1
self.image_min = -1
self.image_mean = -1
self.cam = camera
self.frame_timer = FrameTimer('Sensor',verbose=False)
self.reconstructor = Reconstructor(self.search_boxes.x,
self.search_boxes.y,self.sensor_mask)
self.n_zernike_orders_corrected=self.reconstructor.N_orders
self.centroiding_time = -1.0
self.beeper = Beeper()
self.logging = False
self.paused = False
self.sense_timer = BlockTimer('Sensor sense method')
try:
self.profile_update_method = ccfg.profile_sensor_update_method
except:
self.profile_update_method = False
class Sensor:
def __init__(self,camera):
self.image_width_px = ccfg.image_width_px
self.image_height_px = ccfg.image_height_px
self.dark_image = np.zeros((ccfg.image_height_px,ccfg.image_width_px),dtype=np.int16)
self.dark_subtract = False
self.n_dark = 10
self.lenslet_pitch_m = ccfg.lenslet_pitch_m
self.lenslet_focal_length_m = ccfg.lenslet_focal_length_m
self.pixel_size_m = ccfg.pixel_size_m
self.beam_diameter_m = ccfg.beam_diameter_m
self.wavelength_m = ccfg.wavelength_m
self.background_correction = ccfg.background_correction
self.centroiding_iterations = ccfg.centroiding_iterations
self.iterative_centroiding_step = ccfg.iterative_centroiding_step
self.filter_lenslets = ccfg.sensor_filter_lenslets
self.estimate_background = ccfg.estimate_background
self.reconstruct_wavefront = ccfg.sensor_reconstruct_wavefront
self.remove_tip_tilt = ccfg.sensor_remove_tip_tilt
# calculate diffraction limited spot size on sensor, to determine
# the centroiding window size
lenslet_dlss = 1.22*self.wavelength_m*self.lenslet_focal_length_m/self.lenslet_pitch_m
lenslet_dlss_px = lenslet_dlss/self.pixel_size_m
# now we need to account for smearing of spots due to axial displacement of retinal layers
extent = 500e-6 # retinal thickness
smear = extent*6.75/16.67 # pupil diameter and focal length; use diameter in case beacon is at edge of field
try:
magnification = ccfg.retina_sensor_magnification
except Exception as e:
magnification = 1.0
total_size = lenslet_dlss+smear*magnification
total_size_px = total_size/self.pixel_size_m
self.centroiding_half_width = int(np.floor(total_size_px/2.0))*2
try:
xy = np.loadtxt(ccfg.reference_coordinates_filename)
except Exception as e:
xy = np.loadtxt(ccfg.reference_coordinates_bootstrap_filename)
print 'Bootstrapping with %s'%ccfg.reference_coordinates_bootstrap_filename
self.search_boxes = SearchBoxes(xy[:,0],xy[:,1],ccfg.search_box_half_width)
self.sensor_mask = np.loadtxt(ccfg.reference_mask_filename)
self.x0 = np.zeros(self.search_boxes.x.shape)
self.y0 = np.zeros(self.search_boxes.y.shape)
self.x0[:] = self.search_boxes.x[:]
self.y0[:] = self.search_boxes.y[:]
self.n_lenslets = self.search_boxes.n
n_lenslets = self.n_lenslets
self.image = np.zeros((ccfg.image_height_px,ccfg.image_width_px))
self.x_slopes = np.zeros(n_lenslets)
self.y_slopes = np.zeros(n_lenslets)
self.x_centroids = np.zeros(n_lenslets)
self.y_centroids = np.zeros(n_lenslets)
self.box_maxes = np.zeros(n_lenslets)
self.box_mins = np.zeros(n_lenslets)
self.box_means = np.zeros(n_lenslets)
self.valid_centroids = np.zeros(n_lenslets,dtype=np.int16)
try:
self.fast_centroiding = ccfg.fast_centroiding
except Exception as e:
self.fast_centroiding = False
self.box_backgrounds = np.zeros(n_lenslets)
self.error = 0.0
self.tip = 0.0
self.tilt = 0.0
self.zernikes = np.zeros(ccfg.n_zernike_terms)
self.wavefront = np.zeros(self.sensor_mask.shape)
self.image_max = -1
self.image_min = -1
self.image_mean = -1
self.cam = camera
self.frame_timer = FrameTimer('Sensor',verbose=False)
self.reconstructor = Reconstructor(self.search_boxes.x,
self.search_boxes.y,self.sensor_mask)
self.n_zernike_orders_corrected=self.reconstructor.N_orders
self.centroiding_time = -1.0
self.beeper = Beeper()
self.logging = False
self.paused = False
self.sense_timer = BlockTimer('Sensor sense method')
try:
self.profile_update_method = ccfg.profile_sensor_update_method
except:
self.profile_update_method = False
def update(self):
if not self.paused:
try:
self.sense()
except Exception as e:
print 'Sensor update exception:',e
if self.logging:
self.log()
self.frame_timer.tick()
def pause(self):
print 'sensor paused'
self.paused = True
def unpause(self):
print 'sensor unpaused'
self.paused = False
def get_average_background(self):
return np.mean(self.box_backgrounds)
def get_n_zernike_orders_corrected(self):
return self.n_zernike_orders_corrected
def set_n_zernike_orders_corrected(self,n):
self.n_zernike_orders_corrected = n
def set_dark_subtraction(self,val):
self.dark_subtract = val
def set_n_dark(self,val):
self.n_dark = val
def set_dark(self):
self.pause()
temp = np.zeros(self.dark_image.shape)
for k in range(self.n_dark):
temp = temp + self.cam.get_image()
temp = np.round(temp/float(self.n_dark)).astype(np.int16)
self.dark_image[...] = temp[...]
self.unpause()
def log0(self):
t_string = now_string(True)
#seconds = float(datetime.datetime.strptime(t_string,'%Y%m%d%H%M%S.%f').strftime('%s.%f'))
outfn = os.path.join(ccfg.logging_directory,'sensor_%s.mat'%(t_string))
d = {}
#d['time_seconds'] = seconds
d['x_slopes'] = self.x_slopes
d['y_slopes'] = self.y_slopes
d['x_centroids'] = self.x_centroids
d['y_centroids'] = self.y_centroids
d['search_box_x1'] = self.search_boxes.x1
d['search_box_x2'] = self.search_boxes.x2
d['search_box_y1'] = self.search_boxes.y1
d['search_box_y2'] = self.search_boxes.y2
d['ref_x'] = self.search_boxes.x
d['ref_y'] = self.search_boxes.y
d['error'] = np.squeeze(self.error)
d['tip'] = self.tip
d['tilt'] = self.tilt
d['wavefront'] = self.wavefront
d['zernikes'] = np.squeeze(self.zernikes)
#d['spots_image'] = self.image
sio.savemat(outfn,d)
def log(self):
t_string = now_string(True)
#seconds = float(datetime.datetime.strptime(t_string,'%Y%m%d%H%M%S.%f').strftime('%s.%f'))
d = {}
#d['time_seconds'] = np.array([seconds])
d['x_slopes'] = self.x_slopes
d['y_slopes'] = self.y_slopes
d['x_centroids'] = self.x_centroids
d['y_centroids'] = self.y_centroids
d['search_box_x1'] = self.search_boxes.x1
d['search_box_x2'] = self.search_boxes.x2
d['search_box_y1'] = self.search_boxes.y1
d['search_box_y2'] = self.search_boxes.y2
d['ref_x'] = self.search_boxes.x
d['ref_y'] = self.search_boxes.y
d['error'] = np.array([np.squeeze(self.error)])
#d['tip'] = self.tip
#d['tilt'] = self.tilt
d['wavefront'] = self.wavefront
d['zernikes'] = np.squeeze(self.zernikes)
d['spots_image'] = self.image.astype(np.uint16)
for k in d.keys():
outfn = os.path.join(ccfg.logging_directory,'sensor_%s_%s.txt'%(k,t_string))
if k=='spots_image':
np.savetxt(outfn,d[k],fmt='%d')
else:
np.savetxt(outfn,d[k])
#sio.savemat(outfn,d)
def set_background_correction(self,val):
#sensor_mutex.lock()
self.background_correction = val
#sensor_mutex.unlock()
def set_fast_centroiding(self,val):
self.fast_centroiding = val
def set_logging(self,val):
self.logging = val
def set_defocus(self,val):
self.pause()
newx = self.search_boxes.x0 + self.reconstructor.defocus_dx*val*ccfg.zernike_dioptric_equivalent
newy = self.search_boxes.y0 + self.reconstructor.defocus_dy*val*ccfg.zernike_dioptric_equivalent
self.search_boxes.move(newx,newy)
self.unpause()
def set_astig0(self,val):
self.pause()
newx = self.search_boxes.x + self.reconstructor.astig0_dx*val*ccfg.zernike_dioptric_equivalent
newy = self.search_boxes.y + self.reconstructor.astig0_dy*val*ccfg.zernike_dioptric_equivalent
self.search_boxes.move(newx,newy)
self.unpause()
def set_astig1(self,val):
self.pause()
newx = self.search_boxes.x + self.reconstructor.astig1_dx*val*ccfg.zernike_dioptric_equivalent
newy = self.search_boxes.y + self.reconstructor.astig1_dy*val*ccfg.zernike_dioptric_equivalent
self.search_boxes.move(newx,newy)
self.unpause()
def aberration_reset(self):
self.pause()
self.search_boxes.move(self.x0,self.y0)
self.unpause()
def set_centroiding_half_width(self,val):
self.centroiding_half_width = val
def sense(self,debug=False):
if self.profile_update_method:
self.sense_timer.tick('start')
self.image = self.cam.get_image()
if self.profile_update_method:
self.sense_timer.tick('cam.get_image')
if self.dark_subtract:
self.image = self.image - self.dark_image
self.image_min = self.image.min()
self.image_mean = self.image.mean()
self.image_max = self.image.max()
if self.profile_update_method:
self.sense_timer.tick('image stats')
t0 = time.time()
if not self.fast_centroiding:
if self.estimate_background:
centroid.estimate_backgrounds(spots_image=self.image,
sb_x_vec = self.search_boxes.x,
sb_y_vec = self.search_boxes.y,
sb_bg_vec = self.box_backgrounds,
sb_half_width_p = self.search_boxes.half_width)
self.box_backgrounds = self.box_backgrounds + self.background_correction
if self.profile_update_method:
self.sense_timer.tick('estimate background')
centroid.compute_centroids(spots_image=self.image,
sb_x_vec = self.search_boxes.x,
sb_y_vec = self.search_boxes.y,
sb_bg_vec = self.box_backgrounds,
sb_half_width_p = self.search_boxes.half_width,
iterations_p = self.centroiding_iterations,
iteration_step_px_p = self.iterative_centroiding_step,
x_out = self.x_centroids,
y_out = self.y_centroids,
mean_intensity = self.box_means,
maximum_intensity = self.box_maxes,
minimum_intensity = self.box_mins,
num_threads_p = 1)
if self.profile_update_method:
self.sense_timer.tick('centroid')
else:
centroid.fast_centroids(spots_image=self.image,
sb_x_vec = self.search_boxes.x,
sb_y_vec = self.search_boxes.y,
sb_half_width_p = self.search_boxes.half_width,
centroiding_half_width_p = self.centroiding_half_width,
x_out = self.x_centroids,
y_out = self.y_centroids,
sb_max_vec = self.box_maxes,
valid_vec = self.valid_centroids,
verbose_p = 0,
num_threads_p = 1)
self.centroiding_time = time.time()-t0
self.x_slopes = (self.x_centroids-self.search_boxes.x)*self.pixel_size_m/self.lenslet_focal_length_m
self.y_slopes = (self.y_centroids-self.search_boxes.y)*self.pixel_size_m/self.lenslet_focal_length_m
self.tilt = np.mean(self.x_slopes)
self.tip = np.mean(self.y_slopes)
if self.remove_tip_tilt:
self.x_slopes-=self.tilt
self.y_slopes-=self.tip
if self.reconstruct_wavefront:
self.zernikes,self.wavefront,self.error = self.reconstructor.get_wavefront(self.x_slopes,self.y_slopes)
if self.profile_update_method:
self.sense_timer.tick('reconstruct wavefront')
self.filter_slopes = self.n_zernike_orders_corrected<self.reconstructor.N_orders
if self.filter_slopes:
# Outline of approach: the basic idea is to filter the residual error
# slopes by Zernike mode before multiplying by the mirror command
# matrix.
# 1. multiply the slopes by a wavefront reconstructor matrix
# to get Zernike coefficients; these are already output by
# the call to self.reconstructor.get_wavefront above
# 2. zero the desired modes
# 3. multiply the modes by the inverse of that matrix, which is stored
# in the Reconstructor object as reconstructor.slope_matrix
# convert the order into a number of terms:
n_terms = self.reconstructor.Z.nm2j(self.n_zernike_orders_corrected,self.n_zernike_orders_corrected)
# get the slope matrix (inverse of zernike matrix, which maps slopes onto zernikes)
slope_matrix = self.reconstructor.slope_matrix
# create a filtered set of zernike terms
# not sure if we should zero piston here
z_filt = np.zeros(len(self.zernikes))
z_filt[:n_terms+1] = self.zernikes[:n_terms+1]
zero_piston = True
if zero_piston:
z_filt[0] = 0.0
# filter the slopes, and assign them to this sensor object:
filtered_slopes = np.dot(slope_matrix,z_filt)
self.x_slopes = filtered_slopes[:self.n_lenslets]
self.y_slopes = filtered_slopes[self.n_lenslets:]
if self.profile_update_method:
self.sense_timer.tick('end sense')
self.sense_timer.tock()
try:
self.beeper.beep(self.error)
except Exception as e:
print e
print self.error
sys.exit()
def record_reference(self):
print 'recording reference'
self.pause()
xcent = []
ycent = []
for k in range(ccfg.reference_n_measurements):
print 'measurement %d of %d'%(k+1,ccfg.reference_n_measurements),
self.sense()
print '...done'
xcent.append(self.x_centroids)
ycent.append(self.y_centroids)
xcent = np.array(xcent)
ycent = np.array(ycent)
x_ref = xcent.mean(0)
y_ref = ycent.mean(0)
x_var = xcent.var(0)
y_var = ycent.var(0)
err = np.sqrt(np.mean([x_var,y_var]))
print 'reference coordinate error %0.3e px RMS'%err
self.search_boxes = SearchBoxes(x_ref,y_ref,self.search_boxes.half_width)
refxy = np.array((x_ref,y_ref)).T
# Record the new reference set to two locations, the
# filename specified by reference_coordinates_filename
# in ciao config, and also an archival filename to keep
# track of the history.
archive_fn = os.path.join(ccfg.reference_directory,prepend('reference.txt',now_string()))
np.savetxt(archive_fn,refxy,fmt='%0.3f')
np.savetxt(ccfg.reference_coordinates_filename,refxy,fmt='%0.3f')
self.unpause()
time.sleep(1)
class Sensor(QObject):
finished = pyqtSignal()
def __init__(self, camera):
super(Sensor, self).__init__()
self.image_width_px = ccfg.image_width_px
self.image_height_px = ccfg.image_height_px
self.lenslet_pitch_m = ccfg.lenslet_pitch_m
self.lenslet_focal_length_m = ccfg.lenslet_focal_length_m
self.pixel_size_m = ccfg.pixel_size_m
self.beam_diameter_m = ccfg.beam_diameter_m
self.wavelength_m = ccfg.wavelength_m
self.background_correction = ccfg.background_correction
self.centroiding_iterations = ccfg.centroiding_iterations
self.iterative_centroiding_step = ccfg.iterative_centroiding_step
self.filter_lenslets = ccfg.sensor_filter_lenslets
self.estimate_background = ccfg.estimate_background
self.reconstruct_wavefront = ccfg.sensor_reconstruct_wavefront
self.remove_tip_tilt = ccfg.sensor_remove_tip_tilt
try:
xy = np.loadtxt(ccfg.reference_coordinates_filename)
except Exception as e:
xy = np.loadtxt(ccfg.reference_coordinates_bootstrap_filename)
print 'Bootstrapping with %s' % ccfg.reference_coordinates_bootstrap_filename
self.search_boxes = SearchBoxes(xy[:, 0], xy[:, 1],
ccfg.search_box_half_width)
self.sensor_mask = np.loadtxt(ccfg.reference_mask_filename)
self.x0 = np.zeros(self.search_boxes.x.shape)
self.y0 = np.zeros(self.search_boxes.y.shape)
self.x0[:] = self.search_boxes.x[:]
self.y0[:] = self.search_boxes.y[:]
self.n_lenslets = self.search_boxes.n
n_lenslets = self.n_lenslets
self.image = np.zeros((ccfg.image_height_px, ccfg.image_width_px))
self.x_slopes = np.zeros(n_lenslets)
self.y_slopes = np.zeros(n_lenslets)
self.x_centroids = np.zeros(n_lenslets)
self.y_centroids = np.zeros(n_lenslets)
self.box_maxes = np.zeros(n_lenslets)
self.box_mins = np.zeros(n_lenslets)
self.box_means = np.zeros(n_lenslets)
self.box_backgrounds = np.zeros(n_lenslets)
self.error = 0.0
self.tip = 0.0
self.tilt = 0.0
self.zernikes = None
self.wavefront = np.zeros(self.sensor_mask.shape)
self.cam = camera
self.frame_timer = FrameTimer('Sensor', verbose=False)
self.reconstructor = Reconstructor(self.search_boxes.x,
self.search_boxes.y,
self.sensor_mask)
self.logging = False
self.paused = False
@pyqtSlot()
def update(self):
if not self.paused:
try:
self.sense()
except Exception as e:
print 'Sensor update exception:', e
if self.logging:
self.log()
self.finished.emit()
self.frame_timer.tick()
@pyqtSlot()
def pause(self):
print 'sensor paused'
self.paused = True
@pyqtSlot()
def unpause(self):
print 'sensor unpaused'
self.paused = False
#self.sense()
def log(self):
outfn = os.path.join(ccfg.logging_directory,
'sensor_%s.mat' % (now_string(True)))
d = {}
d['x_slopes'] = self.x_slopes
d['y_slopes'] = self.y_slopes
d['x_centroids'] = self.x_centroids
d['y_centroids'] = self.y_centroids
d['search_box_x1'] = self.search_boxes.x1
d['search_box_x2'] = self.search_boxes.x2
d['search_box_y1'] = self.search_boxes.y1
d['search_box_y2'] = self.search_boxes.y2
d['ref_x'] = self.search_boxes.x
d['ref_y'] = self.search_boxes.y
d['error'] = self.error
d['tip'] = self.tip
d['tilt'] = self.tilt
d['wavefront'] = self.wavefront
d['zernikes'] = self.zernikes
sio.savemat(outfn, d)
def set_background_correction(self, val):
#sensor_mutex.lock()
self.background_correction = val
#sensor_mutex.unlock()
def set_logging(self, val):
self.logging = val
def set_defocus(self, val):
self.pause()
newx = self.x0 + self.reconstructor.defocus_dx * val * ccfg.zernike_dioptric_equivalent
newy = self.y0 + self.reconstructor.defocus_dy * val * ccfg.zernike_dioptric_equivalent
self.search_boxes.move(newx, newy)
self.unpause()
def sense(self, debug=False):
self.image = self.cam.get_image()
centroid.estimate_backgrounds(
spots_image=self.image,
sb_x_vec=self.search_boxes.x,
sb_y_vec=self.search_boxes.y,
sb_bg_vec=self.box_backgrounds,
sb_half_width_p=self.search_boxes.half_width)
centroid.compute_centroids(
spots_image=self.image,
sb_x_vec=self.search_boxes.x,
sb_y_vec=self.search_boxes.y,
sb_bg_vec=self.box_backgrounds,
sb_half_width_p=self.search_boxes.half_width,
iterations_p=self.centroiding_iterations,
iteration_step_px_p=self.iterative_centroiding_step,
x_out=self.x_centroids,
y_out=self.y_centroids,
mean_intensity=self.box_means,
maximum_intensity=self.box_maxes,
minimum_intensity=self.box_mins,
num_threads_p=1)
self.x_slopes = (self.x_centroids - self.search_boxes.x
) * self.pixel_size_m / self.lenslet_focal_length_m
self.y_slopes = (self.y_centroids - self.search_boxes.y
) * self.pixel_size_m / self.lenslet_focal_length_m
self.tilt = np.mean(self.x_slopes)
self.tip = np.mean(self.y_slopes)
if self.remove_tip_tilt:
self.x_slopes -= self.tilt
self.y_slopes -= self.tip
if self.reconstruct_wavefront:
self.zernikes, self.wavefront, self.error = self.reconstructor.get_wavefront(
self.x_slopes, self.y_slopes)
def sense0(self, debug=False):
image = self.cam.get_image()
sb = self.search_boxes
xr = np.zeros(self.search_boxes.x.shape)
xr[:] = self.search_boxes.x[:]
yr = np.zeros(self.search_boxes.y.shape)
yr[:] = self.search_boxes.y[:]
half_width = sb.half_width
for iteration in range(self.centroiding_iterations):
#QApplication.processEvents()
msi = iteration == self.centroiding_iterations - 1
if debug:
plt.figure()
plt.imshow(image)
plt.title('iteration %d' % iteration)
centroid.compute_centroids(
spots_image=image,
sb_x1_vec=sb.x1,
sb_x2_vec=sb.x2,
sb_y1_vec=sb.y1,
sb_y2_vec=sb.y2,
x_out=xr,
y_out=yr,
mean_intensity=self.box_means,
maximum_intensity=self.box_maxes,
minimum_intensity=self.box_mins,
background_intensity=self.box_backgrounds,
estimate_background=self.estimate_background,
background_correction=self.background_correction,
num_threads=1,
modify_spots_image=msi)
half_width -= self.iterative_centroiding_step
sb = SearchBoxes(xr, yr, half_width)
if debug:
plt.figure()
plt.imshow(image)
plt.show()
sys.exit()
self.x_centroids[:] = xr[:]
self.y_centroids[:] = yr[:]
self.x_slopes = (self.x_centroids - self.search_boxes.x
) * self.pixel_size_m / self.lenslet_focal_length_m
self.y_slopes = (self.y_centroids - self.search_boxes.y
) * self.pixel_size_m / self.lenslet_focal_length_m
self.tilt = np.mean(self.x_slopes)
self.tip = np.mean(self.y_slopes)
if self.remove_tip_tilt:
self.x_slopes -= self.tilt
self.y_slopes -= self.tip
self.image = image
if self.reconstruct_wavefront:
self.zernikes, self.wavefront, self.error = self.reconstructor.get_wavefront(
self.x_slopes, self.y_slopes)
def record_reference(self):
print 'recording reference'
self.pause()
xcent = []
ycent = []
for k in range(ccfg.reference_n_measurements):
print 'measurement %d of %d' % (k + 1,
ccfg.reference_n_measurements),
self.sense()
print '...done'
xcent.append(self.x_centroids)
ycent.append(self.y_centroids)
xcent = np.array(xcent)
ycent = np.array(ycent)
x_ref = xcent.mean(0)
y_ref = ycent.mean(0)
x_var = xcent.var(0)
y_var = ycent.var(0)
err = np.sqrt(np.mean([x_var, y_var]))
print 'reference coordinate error %0.3e px RMS' % err
self.search_boxes = SearchBoxes(x_ref, y_ref,
self.search_boxes.half_width)
refxy = np.array((x_ref, y_ref)).T
# Record the new reference set to two locations, the
# filename specified by reference_coordinates_filename
# in ciao config, and also an archival filename to keep
# track of the history.
archive_fn = os.path.join(ccfg.reference_directory,
prepend('reference.txt', now_string()))
np.savetxt(archive_fn, refxy, fmt='%0.3f')
np.savetxt(ccfg.reference_coordinates_filename, refxy, fmt='%0.3f')
self.unpause()
time.sleep(1)
class Sensor(QObject):
finished = pyqtSignal()
def __init__(self, camera):
super(Sensor, self).__init__()
self.image_width_px = ccfg.image_width_px
self.image_height_px = ccfg.image_height_px
self.lenslet_pitch_m = ccfg.lenslet_pitch_m
self.lenslet_focal_length_m = ccfg.lenslet_focal_length_m
self.pixel_size_m = ccfg.pixel_size_m
self.beam_diameter_m = ccfg.beam_diameter_m
self.wavelength_m = ccfg.wavelength_m
self.background_correction = ccfg.background_correction
self.centroiding_iterations = ccfg.centroiding_iterations
self.iterative_centroiding_step = ccfg.iterative_centroiding_step
self.filter_lenslets = ccfg.sensor_filter_lenslets
self.estimate_background = ccfg.estimate_background
self.reconstruct_wavefront = ccfg.sensor_reconstruct_wavefront
self.remove_tip_tilt = ccfg.sensor_remove_tip_tilt
try:
# check to see if the camera object produced its own
# search boxes, and if not, use reference coordinates
self.search_boxes = camera.search_boxes
self.mask = camera.lenslet_mask
except Exception as e:
xy = np.loadtxt(ccfg.reference_coordinates_filename)
self.search_boxes = SearchBoxes(xy[:, 0], xy[:, 1],
ccfg.search_box_half_width)
self.mask = np.loadtxt(ccfg.reference_mask_filename)
self.x0 = np.zeros(self.search_boxes.x.shape)
self.y0 = np.zeros(self.search_boxes.y.shape)
self.x0[:] = self.search_boxes.x[:]
self.y0[:] = self.search_boxes.y[:]
self.n_lenslets = self.search_boxes.n
n_lenslets = self.n_lenslets
self.image = np.zeros((ccfg.image_height_px, ccfg.image_width_px))
self.x_slopes = np.zeros(n_lenslets)
self.y_slopes = np.zeros(n_lenslets)
self.x_centroids = np.zeros(n_lenslets)
self.y_centroids = np.zeros(n_lenslets)
self.box_maxes = np.zeros(n_lenslets)
self.box_mins = np.zeros(n_lenslets)
self.box_means = np.zeros(n_lenslets)
self.box_backgrounds = np.zeros(n_lenslets)
self.error = 0.0
self.tip = 0.0
self.tilt = 0.0
self.zernikes = None
self.wavefront = None
self.cam = camera
self.frame_timer = FrameTimer('Sensor', verbose=False)
self.reconstructor = Reconstructor(self.search_boxes.x,
self.search_boxes.y, self.mask)
self.logging = False
self.paused = False
@pyqtSlot()
def update(self):
if not self.paused:
try:
self.sense()
except Exception as e:
print e
if self.logging:
self.log()
self.finished.emit()
self.frame_timer.tick()
@pyqtSlot()
def pause(self):
print 'sensor paused'
self.paused = True
@pyqtSlot()
def unpause(self):
print 'sensor unpaused'
self.paused = False
#self.sense()
def log(self):
outfn = os.path.join(ccfg.logging_directory,
'sensor_%s.mat' % (now_string(True)))
d = {}
d['x_slopes'] = self.x_slopes
d['y_slopes'] = self.y_slopes
d['x_centroids'] = self.x_centroids
d['y_centroids'] = self.y_centroids
d['search_box_x1'] = self.search_boxes.x1
d['search_box_x2'] = self.search_boxes.x2
d['search_box_y1'] = self.search_boxes.y1
d['search_box_y2'] = self.search_boxes.y2
d['ref_x'] = self.search_boxes.x
d['ref_y'] = self.search_boxes.y
d['error'] = self.error
d['tip'] = self.tip
d['tilt'] = self.tilt
d['wavefront'] = self.wavefront
d['zernikes'] = self.zernikes
sio.savemat(outfn, d)
def set_background_correction(self, val):
#sensor_mutex.lock()
self.background_correction = val
#sensor_mutex.unlock()
def set_logging(self, val):
self.logging = val
def set_defocus(self, val):
self.pause()
newx = self.x0 + self.reconstructor.defocus_dx * val * ccfg.zernike_dioptric_equivalent
newy = self.y0 + self.reconstructor.defocus_dy * val * ccfg.zernike_dioptric_equivalent
self.search_boxes.move(newx, newy)
self.unpause()
def sense(self):
image = self.cam.get_image()
sb = self.search_boxes
xr = np.zeros(self.search_boxes.x.shape)
xr[:] = self.search_boxes.x[:]
yr = np.zeros(self.search_boxes.y.shape)
yr[:] = self.search_boxes.y[:]
half_width = sb.half_width
for iteration in range(self.centroiding_iterations):
#QApplication.processEvents()
msi = iteration == self.centroiding_iterations - 1
centroid.compute_centroids(
spots_image=image,
sb_x1_vec=sb.x1,
sb_x2_vec=sb.x2,
sb_y1_vec=sb.y1,
sb_y2_vec=sb.y2,
x_out=xr,
y_out=yr,
mean_intensity=self.box_means,
maximum_intensity=self.box_maxes,
minimum_intensity=self.box_mins,
background_intensity=self.box_backgrounds,
estimate_background=self.estimate_background,
background_correction=self.background_correction,
num_threads=1,
modify_spots_image=msi)
half_width -= self.iterative_centroiding_step
sb = SearchBoxes(xr, yr, half_width)
self.x_centroids[:] = xr[:]
self.y_centroids[:] = yr[:]
self.x_slopes = (self.x_centroids - self.search_boxes.x
) * self.pixel_size_m / self.lenslet_focal_length_m
self.y_slopes = (self.y_centroids - self.search_boxes.y
) * self.pixel_size_m / self.lenslet_focal_length_m
self.tilt = np.mean(self.x_slopes)
self.tip = np.mean(self.y_slopes)
if self.remove_tip_tilt:
self.x_slopes -= self.tilt
self.y_slopes -= self.tip
self.image = image
if self.reconstruct_wavefront:
self.zernikes, self.wavefront, self.error = self.reconstructor.get_wavefront(
self.x_slopes, self.y_slopes)
def record_reference(self):
print 'recording reference'
self.pause()
xcent = []
ycent = []
for k in range(ccfg.reference_n_measurements):
print 'measurement %d of %d' % (k + 1,
ccfg.reference_n_measurements),
self.sense()
print '...done'
xcent.append(self.x_centroids)
ycent.append(self.y_centroids)
x_ref = np.array(xcent).mean(0)
y_ref = np.array(ycent).mean(0)
self.search_boxes = SearchBoxes(x_ref, y_ref,
self.search_boxes.half_width)
outfn = os.path.join(ccfg.reference_directory,
prepend('coords.txt', now_string()))
refxy = np.array((x_ref, y_ref)).T
np.savetxt(outfn, refxy, fmt='%0.2f')
self.unpause()
time.sleep(1)
class Sensor(QObject):
finished = pyqtSignal()
def __init__(self,camera):
super(Sensor,self).__init__()
self.image_width_px = kcfg.image_width_px
self.image_height_px = kcfg.image_height_px
self.lenslet_pitch_m = kcfg.lenslet_pitch_m
self.lenslet_focal_length_m = kcfg.lenslet_focal_length_m
self.pixel_size_m = kcfg.pixel_size_m
self.beam_diameter_m = kcfg.beam_diameter_m
self.wavelength_m = kcfg.wavelength_m
self.background_correction = kcfg.background_correction
self.centroiding_iterations = kcfg.centroiding_iterations
self.iterative_centroiding_step = kcfg.iterative_centroiding_step
self.filter_lenslets = kcfg.sensor_filter_lenslets
self.estimate_background = kcfg.estimate_background
self.reconstruct_wavefront = kcfg.sensor_reconstruct_wavefront
self.remove_tip_tilt = kcfg.sensor_remove_tip_tilt
try:
# check to see if the camera object produced its own
# search boxes, and if not, use reference coordinates
self.search_boxes = camera.search_boxes
self.mask = camera.lenslet_mask
except Exception as e:
xy = np.loadtxt(kcfg.reference_coordinates_filename)
self.search_boxes = SearchBoxes(xy[:,0],xy[:,1],kcfg.search_box_half_width)
self.mask = np.loadtxt(kcfg.reference_mask_filename)
self.x0 = np.zeros(self.search_boxes.x.shape)
self.y0 = np.zeros(self.search_boxes.y.shape)
self.x0[:] = self.search_boxes.x[:]
self.y0[:] = self.search_boxes.y[:]
self.n_lenslets = self.search_boxes.n
n_lenslets = self.n_lenslets
self.image = np.zeros((kcfg.image_height_px,kcfg.image_width_px))
self.x_slopes = np.zeros(n_lenslets)
self.y_slopes = np.zeros(n_lenslets)
self.x_centroids = np.zeros(n_lenslets)
self.y_centroids = np.zeros(n_lenslets)
self.box_maxes = np.zeros(n_lenslets)
self.box_mins = np.zeros(n_lenslets)
self.box_means = np.zeros(n_lenslets)
self.box_backgrounds = np.zeros(n_lenslets)
self.error = 0.0
self.tip = 0.0
self.tilt = 0.0
self.zernikes = None
self.wavefront = None
self.cam = camera
self.frame_timer = FrameTimer('Sensor',verbose=False)
self.reconstructor = Reconstructor(self.search_boxes.x,
self.search_boxes.y,self.mask)
self.logging = False
self.paused = False
@pyqtSlot()
def update(self):
if not self.paused:
try:
self.sense()
except Exception as e:
print e
if self.logging:
self.log()
self.finished.emit()
self.frame_timer.tick()
@pyqtSlot()
def pause(self):
print 'sensor paused'
self.paused = True
@pyqtSlot()
def unpause(self):
print 'sensor unpaused'
self.paused = False
#self.sense()
def log(self):
outfn = os.path.join(kcfg.logging_directory,'sensor_%s.mat'%(now_string(True)))
d = {}
d['x_slopes'] = self.x_slopes
d['y_slopes'] = self.y_slopes
d['x_centroids'] = self.x_centroids
d['y_centroids'] = self.y_centroids
d['search_box_x1'] = self.search_boxes.x1
d['search_box_x2'] = self.search_boxes.x2
d['search_box_y1'] = self.search_boxes.y1
d['search_box_y2'] = self.search_boxes.y2
d['ref_x'] = self.search_boxes.x
d['ref_y'] = self.search_boxes.y
d['error'] = self.error
d['tip'] = self.tip
d['tilt'] = self.tilt
d['wavefront'] = self.wavefront
d['zernikes'] = self.zernikes
sio.savemat(outfn,d)
def set_background_correction(self,val):
sensor_mutex.lock()
self.background_correction = val
sensor_mutex.unlock()
def set_logging(self,val):
self.logging = val
def set_defocus(self,val):
self.pause()
newx = self.x0 + self.reconstructor.defocus_dx*val*kcfg.zernike_dioptric_equivalent
newy = self.y0 + self.reconstructor.defocus_dy*val*kcfg.zernike_dioptric_equivalent
self.search_boxes.move(newx,newy)
self.unpause()
def sense(self):
image = cam.get_image()
sensor_mutex.lock()
sb = self.search_boxes
xr = np.zeros(self.search_boxes.x.shape)
xr[:] = self.search_boxes.x[:]
yr = np.zeros(self.search_boxes.y.shape)
yr[:] = self.search_boxes.y[:]
half_width = sb.half_width
for iteration in range(self.centroiding_iterations):
#QApplication.processEvents()
msi = iteration==self.centroiding_iterations-1
centroid.compute_centroids(spots_image=image,
sb_x1_vec=sb.x1,
sb_x2_vec=sb.x2,
sb_y1_vec=sb.y1,
sb_y2_vec=sb.y2,
x_out=xr,
y_out=yr,
mean_intensity = self.box_means,
maximum_intensity = self.box_maxes,
minimum_intensity = self.box_mins,
background_intensity = self.box_backgrounds,
estimate_background = self.estimate_background,
background_correction = self.background_correction,
num_threads = 1,
modify_spots_image = msi)
half_width-=self.iterative_centroiding_step
sb = SearchBoxes(xr,yr,half_width)
self.x_centroids[:] = xr[:]
self.y_centroids[:] = yr[:]
self.x_slopes = (self.x_centroids-self.search_boxes.x)*self.pixel_size_m/self.lenslet_focal_length_m
self.y_slopes = (self.y_centroids-self.search_boxes.y)*self.pixel_size_m/self.lenslet_focal_length_m
self.tilt = np.mean(self.x_slopes)
self.tip = np.mean(self.y_slopes)
if self.remove_tip_tilt:
self.x_slopes-=self.tilt
self.y_slopes-=self.tip
self.image = image
if self.reconstruct_wavefront:
self.zernikes,self.wavefront,self.error = self.reconstructor.get_wavefront(self.x_slopes,self.y_slopes)
sensor_mutex.unlock()
def record_reference(self):
print 'recording reference'
self.pause()
xcent = []
ycent = []
for k in range(kcfg.reference_n_measurements):
print 'measurement %d of %d'%(k+1,kcfg.reference_n_measurements),
self.sense()
print '...done'
xcent.append(self.x_centroids)
ycent.append(self.y_centroids)
x_ref = np.array(xcent).mean(0)
y_ref = np.array(ycent).mean(0)
self.search_boxes = SearchBoxes(x_ref,y_ref,self.search_boxes.half_width)
outfn = os.path.join(kcfg.reference_directory,prepend('coords.txt',now_string()))
refxy = np.array((x_ref,y_ref)).T
np.savetxt(outfn,refxy,fmt='%0.2f')
self.unpause()
time.sleep(1)