def __init__(self, image, pos, imparams):
cx=imparams['centerx']
cy=imparams['centery']
fwhm = int(imparams['lambdaoverd'])
angle = imparams['angle']
speckle_rad = fwhm * 2.5 *3.
# !!!!! remove factor * 2.5 * 3. for a full aperture !!!!!!!
aperture_mask = detect_speckles.create_speckle_mask(image, pos, cx, cy, speckle_rad)
# modified attribute
self.none = aperture_mask
self.speckleim=np.zeros(image.shape)
self.moments = cv2.moments(image*aperture_mask)
self.intensity = self.moments['m00']
self.intensity_scan = np.zeros(4)
self.xcentroid = self.moments['m10']/self.intensity
self.ycentroid = self.moments['m01']/self.intensity
self.kvec = DM.convert_pixels_kvecs(self.xcentroid,
self.ycentroid,
**imparams)
self.kvecx = -self.kvec[0]
self.kvecy = self.kvec[1]
self.krad = np.linalg.norm((self.kvecx, self.kvecy))
# new attributes
self.amplitude = detect_speckles.get_speckle_photometry(image, aperture_mask)
#self.kxy = detect_speckles.get_speckle_spatial_freq(image, pos, cx, cy, fwhm, angle)
self.kxy = DM.convert_pixels_kvecs(pos[1],pos[0],**imparams)
self.kx = -self.kxy[0]
self.ky = self.kxy[1]
self.kmod = np.linalg.norm((self.kx, self.ky))
def __init__(self, image, xp, yp, config):
self.imparams = config['IM_PARAMS']
self.abc = config['INTENSITY_CAL']['abc']
self.xcentroid = xp
self.ycentroid = yp
self.kvec = DM.convert_pixels_kvecs(self.xcentroid, self.ycentroid,
**self.imparams)
self.kvecx = self.kvec[0]
self.kvecy = self.kvec[1]
self.krad = np.linalg.norm((self.kvecx, self.kvecy))
self.aperture = detect_speckles.create_speckle_aperture(
image, self.xcentroid, self.ycentroid,
config['INTENSITY_CAL']['aperture_radius'])
#Edit to take into account aperture on other side
#self.aperture = self.aperture + detect_speckles.create_speckle_aperture(
# image, 2*self.imparams['centerx']-self.xcentroid,
# 2*self.imparams['centery']-self.ycentroid,
# config['INTENSITY_CAL']['aperture_radius'])
#Edit to take into account aperture on other side
self.exclusionzone = detect_speckles.create_speckle_aperture(
image, self.xcentroid, self.ycentroid,
1.5 * config['INTENSITY_CAL']['aperture_radius'])
#self.exclusionzone = self.exclusionzone + detect_speckles.create_speckle_aperture(
# image, 2*self.imparams['centerx']-self.xcentroid,
# 2*self.imparams['centery']-self.ycentroid,
# 1.5*config['INTENSITY_CAL']['aperture_radius'])
self.intensity = detect_speckles.get_speckle_photometry(
image, self.aperture)
self.phase_intensities = [None, None, None, None]
self.phases = config['NULLING']['phases']
self.null_phase = None
def __init__(self, image,xp, yp, config):
self.imparams = config['IM_PARAMS']
self.abc = config['INTENSITY_CAL']['abc']
self.xcentroid = xp
self.ycentroid = yp
self.kvec = DM.convert_pixels_kvecs(self.xcentroid,
self.ycentroid,
**self.imparams)
self.kvecx = self.kvec[0]
self.kvecy = self.kvec[1]
self.krad = np.linalg.norm((self.kvecx, self.kvecy))
self.aperture = detect_speckles.create_speckle_aperture(
image, self.xcentroid, self.ycentroid, config['INTENSITY_CAL']['aperture_radius'])
self.intensity = detect_speckles.get_speckle_photometry(image, self.aperture)
self.intensities = [None, None, None, None]
def recompute_intensity(self, phaseimage):
return detect_speckles.get_speckle_photometry(phaseimage,
self.aperture)
subim = pre.subimage(im, xy, window=10)
subx = pre.subimage(ximcoords, xy, window=10)
suby = pre.subimage(yimcoords, xy, window=10)
gauss_params = snm.fitgaussian((subx, suby), subim)
spotx, spoty = gauss_params[1], gauss_params[2]
#convert pixels returns an xy pair.
# in theory all the kvecs should be (x, 0) or (0, y)
# in which case the norm will be equal to x or y
kvec = np.linalg.norm(
DM.convert_pixels_kvecs(spotx, spoty, **im_params))
print "Gaussian centroid", spotx, spoty
print "corr. k-vector", kvec
meankvec = (meankvec * float(idx) / float(idx + 1) +
kvec / float(idx + 1))
aperture = create_speckle_aperture(im, spotx, spoty, ap_rad)
photometry = get_speckle_photometry(im, aperture)
w1.set_data(np.log(np.abs(im * aperture)))
plt.draw()
plt.pause(0.01)
w1.set_data(np.log(np.abs(im)))
plt.draw()
plt.pause(0.01)
#w1.autoscale_view()
meanphotom = (meanphotom * float(idx) / float(idx + 1) +
photometry / float(idx + 1))
print("\nIntended radial k-vector: " + str(kvecr) +
"\nMean k-vector: " + str(meankvec))
intensity_array.append(meanphotom)