def save_redist_pattern_figure(level=0):
name = "redist_figure"
entropy_redist_potential = [(
(70, -110),
ippg.GradientParabolaMinusGauss_GaussDipFloor(
#parab=[600, 900], amplitude=1,
parab=[600 / 2., 900 / 2.],
amplitude=1,
gausscomp=[120, 180],
gausscomp_height=0.2,
level=level,
# gauss=[60*.75*1.0, 60*.75*1.0], gauss_height=1,
gauss=[60 * 1.0, 60 * 1.0],
gauss_height=1,
#gradient=[0.0000, 0.0002]# [++ -> upper right on atoms, ++ -> upper left]
gradient=[0.0006, -0.0008
] # [++ -> upper right on atoms, ++ -> upper left]
))]
pattern_settings = {
'zero_pixel': (702, 816),
'sites': entropy_redist_potential,
'binarization_algorithm': 'error_diffusion'
}
potential = ippg.ProjectedPotential(**pattern_settings)
binarized, real_valued = potential.binarized_potential, potential.real_valued_potential
return binarized, real_valued
def get_boxes(cls):
box_dict = {
cls.center1():ippg.BoxFunction(radii = [cls.l_NE, cls.w_short], x_angle=cls.angle_long,
amplitude=cls.amp1),
cls.center2():ippg.BoxFunction(radii = [cls.w_long, cls.l_long], x_angle=cls.angle_long,
amplitude=cls.amp2),
cls.center3():ippg.BoxFunction(radii = [cls.l_NE, cls.w_short], x_angle=cls.angle_long,
amplitude=cls.amp3),
cls.center4():ippg.BoxFunction(radii = [cls.w_long, cls.l_long], x_angle=cls.angle_long,
amplitude=cls.amp4),
}
return box_dict
def real_pattern_print():
#Prepare Pattern
lc_dmd = lc.LC6500Device()
settings = {
'compression':'rle',
'exposure_time':5000000 # in us
}
dmd_pattern = pc.DMDPattern(**settings)
is_debug = False
#TEST STUFF
sites_dict = [((0, 0), ippg.GradientParabolaMinusGauss_GaussDip(parab=[600*1.5, 900*1.5], amplitude=1,
gausscomp=[120, 180], gausscomp_height=0.2,
gauss=[60, 90], gauss_height=1.0,
#gradient=[0.0, 0.0]
gradient=[-0.0003, 0.0002] # [++ -> upper right on atoms, ++ -> upper left]
))
]
# settings = {'zero_pixel': (824, 429),
settings = {'zero_pixel': (762, 586),
'sites': sites_dict,
'binarization_algorithm': 'threshold'}#''error_diffusion'}
potential = ippg.ProjectedPotential(**settings)
# uncomment this to get a saved copy of the potential
# imsave('out.png', evaluated_matrix)
# uncomment this to use the painted potential
# potential.real_valued_potential = np.array(imread('out.png', mode='L')).astype('float')
# potential.real_valued_potential /= float(np.max(potential.real_valued_potential))
binary_matrix =potential.binarized_potential
if is_debug:
plt.gray()
plt.imshow(binary_matrix,interpolation='none')
# plt.imshow(potential.real_valued_potential,interpolation='none')
plt.figaspect(1)
pkl.dump(binary_matrix, open('diffused.pkl', 'wb'), protocol=2)
plt.show()
dmd_pattern.pattern = binary_matrix
dmd_patterns = {'patterns':[dmd_pattern]}
lc_dmd.upload_image_sequence(dmd_patterns)
def real_pattern_series_test():
lc_dmd = lc.LC6500Device()
print lc_dmd.dev
#Prepare Pattern
#settings = {'function':pc.stripes_2,#pc.circle_fun_2,
settings = {'function':pc.circle_fun_2,
'compression':'rle',
'exposure_time':500000 # in us
}
dmd_pattern = pc.DMDPattern(**settings)
dmd_pattern_2 = pc.DMDPattern(**settings)
#dmd_pattern.compute_pattern()
site_rad = 6
site_radii = [site_rad, site_rad]
sites_dict = {(-25,-15):ippg.GaussFunction(radii = [1000,200])}
settings = {'sites':sites_dict,
'binarization_algorithm':'randomize'}
potential = ippg.ProjectedPotential(**settings)
evaluated_matrix = potential.evaluate_potential()
binary_matrix = potential.binarize()
dmd_pattern.pattern = binary_matrix
sites_dict = {(-25,-15):ippg.GaussFunction(radii = [200,1000])}
settings = {'sites':sites_dict,
'binarization_algorithm':'threshold'}
potential = ippg.ProjectedPotential(**settings)
evaluated_matrix = potential.evaluate_potential()
binary_matrix = potential.binarize()
dmd_pattern_2.pattern = binary_matrix
dmd_patterns = {'patterns':[dmd_pattern, dmd_pattern_2, dmd_pattern]}
lc_dmd.upload_image_sequence(dmd_patterns)
#lc_dmd.upload_image(dmd_pattern)
#command_sequence = dmd_pattern.compress_pattern()
#lc_dmd.upload_image(command_sequence)
lc_dmd.release()
def save_redist_pattern():
name = "redist"
entropy_redist_potential = [(
(70, -110),
ippg.GradientParabolaMinusGauss_GaussDipFloor(
#parab=[600, 900], amplitude=1,
parab=[600 / 2., 900 / 2.],
amplitude=1,
gausscomp=[120, 180],
gausscomp_height=0.2,
level=0.00,
# gauss=[60*.75*1.0, 60*.75*1.0], gauss_height=1,
gauss=[60 * 1.0, 60 * 1.0],
gauss_height=1,
#gradient=[0.0000, 0.0002]# [++ -> upper right on atoms, ++ -> upper left]
gradient=[0.0006, -0.0008
] # [++ -> upper right on atoms, ++ -> upper left]
))]
# gradient=[0.0036, 0.0012]# [++ -> upper right on atoms, ++ -> upper left]
# entropy_redist_potential = [((30, -60), ippg.GradientParabolaMinusGauss_GaussDipFloor(
# #parab=[600, 900], amplitude=1,
# parab=[600/2., 900/2.], amplitude=1,
# gausscomp=[120, 180],
# gausscomp_height=0.2,
# level=0.0, #0.42, #0,
# gauss=[60*.75*1.0, 60*.75*1.0], gauss_height=1,
# #gradient=[0.0000, 0.0002]# [++ -> upper right on atoms, ++ -> upper left]
# gradient=[0.0014, -0.0006]# [++ -> upper right on atoms, ++ -> upper left]
# ))]
pattern_settings = {
'zero_pixel': (702, 816),
'sites': entropy_redist_potential,
'binarization_algorithm': 'error_diffusion'
}
potential = ippg.ProjectedPotential(**pattern_settings)
SavedPattern(potential.binarized_potential,
potential.real_valued_potential,
name,
metadata=pattern_settings)
def save_focusing_pattern():
name = "focusing"
entropy_redist_potential = [((30, -60), ippg.BoxFunction(radii=[20, 10])),
((80, 60), ippg.BoxFunction(radii=[20, 10])),
((30, 60), ippg.BoxFunction(radii=[20, 10])),
((80, -60), ippg.BoxFunction(radii=[20, 10]))]
pattern_settings = {
'zero_pixel': (702, 816),
'sites': entropy_redist_potential,
'binarization_algorithm': 'threshold '
}
potential = ippg.ProjectedPotential(**pattern_settings)
SavedPattern(potential.binarized_potential,
potential.real_valued_potential,
name,
metadata=pattern_settings)
def upload_video_pattern(dmd_idx):
sites = [((0, 0), ippg.GaussFunction(radii=[100, 500], x_angle=1))]
pattern_settings = [{
'zero_pixel': (762, 429),
'sites': sites,
'binarization_algorithm': 'randomize'
}]
dmd_client = DmdClient(dmd_idx, pattern_settings)
#dmd_client.ping_dmd_channel(1)
dmd_client.upload_to_dmd_video_definition()
def real_pattern_test():
lc_dmd = lc.LC6500Device()
#Prepare Pattern
#settings = {'function':pc.stripes_2,#pc.circle_fun_2,
settings = {'function':pc.circle_fun_2,
'compression':'rle',
'exposure_time':500000
}
dmd_pattern = pc.DMDPattern(**settings)
#dmd_pattern.compute_pattern()
site_rad = 6
site_radii = [site_rad, site_rad]
sites_dict = {#(0,0):ippg.CircFunction(radii = site_radii),
#(1,0):ippg.CircFunction(radii = site_radii),
#(3,1):ippg.CircFunction(radii = site_radii),
#(0,0):ippg.GaussFunction(radii = [1000,1000])
(0,0):ippg.GaussFunction(radii = [500,500])
}
#settings = {'sites':sites_dict,
# 'binarization_algorithm':'error_diffusion'}
# sites_dict = {(-100,-158):ippg.CircFunction(radii = [4,4], x_angle=1, amplitude=1)}
#sites_dict = {(-188,-264):ippg.CircFunction(radii = [4,4], x_angle=1, amplitude=1)}
settings = {'sites':sites_dict,
'binarization_algorithm':'threshold'}
#'binarization_algorithm':'error_diffusion'}
potential = ippg.ProjectedPotential(**settings)
evaluated_matrix = potential.evaluate_potential()
binary_matrix = potential.binarize()
dmd_pattern.pattern = binary_matrix
#dmd_pattern.show_pattern()
lc_dmd.upload_image(dmd_pattern)
def save_manual_pattern(dmd_settings={}):
"""
Manually save a pickle and a png showing the pattern
:param dmd_settings: optional dict of dmd settings. required if you want to use pattern on the fly mode
:return:
"""
name = "TEST"
sites = [((0, 0), ippg.BoxFunction(radii=[20, 10], x_angle=1)),
((0, 0), ippg.GaussFunction(radii=[100, 500], x_angle=1))]
pattern_settings = {
'zero_pixel': (762, 429),
'sites': sites,
'binarization_algorithm': 'error_diffusion'
}
potential = ippg.ProjectedPotential(**pattern_settings)
SavedPattern(potential.binarized_potential,
potential.real_valued_potential,
name,
metadata=pattern_settings)
def upload_manual_pattern_on_the_fly(dmd_idx):
dmd_settings = [{
'compression': 'rle',
'exposure_time': 5000000 # in us
}]
sites_dict = {((0, 0), ippg.BoxFunction(radii=[100, 100], x_angle=1))}
pattern_settings = [{
'zero_pixel': (762, 429),
'sites': sites_dict,
'binarization_algorithm': 'error_diffusion'
}]
dmd_client = DmdClient(dmd_idx,
pattern_settings,
dmd_settings_list=dmd_settings)
dmd_client.upload_to_dmd()
def upload_img_video_definition(self, potential_settings_list, **kwargs):
"""
Uploads the definition of the image (computed on dmd server)
:param potential_settings_list: list of length 1, to be computed on the server
:param kwargs:
:return: 0
"""
if not self.mode == 'normal_video':
self._set_dmd_to_video_mode()
if not len(potential_settings_list) == 1:
raise Exception(
"The video mode cannot play movies (ironic), since the device is not synced to the experiment"
)
potential = ippg.ProjectedPotential(**potential_settings_list[0])
self.video_image = VideoImage(potential.binarized_potential, **kwargs)
print "uploaded image with UUID %s" % self.video_image.img_uuid
return 0
def send_pattern_to_dmd(self, dmd_settings_list, potential_settings_list):
'''
:param dmd_settings: list of dict of settings for dmd
:param potential_settings_list: list of dict of settings for various patterns
:return:
'''
self.dmd_settings_list = dmd_settings_list
self.potential_settings_list = potential_settings_list
dmd_patterns = []
for idx, pot_set in enumerate(potential_settings_list):
dmd_pattern = pc.DMDPattern(**dmd_settings_list[idx])
potential = ippg.ProjectedPotential(**pot_set)
#potential.evaluate_potential()
#binary_matrix = potential.binarize()
dmd_pattern.pattern = potential.binarized_potential
dmd_patterns.append(dmd_pattern)
dmd_patterns = {'patterns': dmd_patterns}
self.lc_dmd = lc.LC6500Device()
self.lc_dmd.upload_image_sequence(dmd_patterns)
self.mode = 'pattern_on_the_fly'
def real_pattern_show():
lc_dmd = lc.LC6500Device()
print lc_dmd.dev
#Prepare Pattern
settings = {
'compression':'rle',
'exposure_time':5000000 # in us
}
dmd_pattern = pc.DMDPattern(**settings)
is_debug = False
# sites_dict={}
# sites_dict = {(-31,4):ippg.BoxFunction(radii = [30,10], x_angle=1),
# (120, 72):ippg.
# BoxFunction(radii=[30,3], x_angle=2.5),
# (-31, 4):ippg.BoxFunction(radii=[30,10], x_angle=1)
# }
# sites_dict = {(0, 0):ippg.CircFunction(radii = [50,50], x_angle=1, amplitude=1)}
# # sites_dict = BoxCancellation.get_boxes()
# sites_dict = {(0,-0):ippg.GaussFunction(radii = [100,100])}
# sites_dict = {(0, 0): ippg.Parabola_BoxDip(parab=[600, 900], amplitude=1,
# box=[1, 1], box_height=0.0,
# )}
#sites_dict = {(0, 0): ippg.Parabola_EllipseDip(parab=[600, 900], amplitude=1,
# ell=[40, 60], ell_height=0.0,
# )}
# sites_dict = {(0, 0): ippg.Parabola_GaussDip(parab=[600, 900], amplitude=1,
# gauss=[60, 90], gauss_height=1.,
# )}
#LAB STUFF
'''
sites_dict = {(0, 0): ippg.GradientParabolaMinusGauss_GaussDip(parab=[600*1.5, 900*1.5], amplitude=1,
gausscomp=[120, 180], gausscomp_height=0.2,
gauss=[60, 90], gauss_height=1.0,
#gradient=[0.0, 0.0]
gradient=[-0.0003, 0.0002] # [++ -> upper right on atoms, ++ -> upper left]
)}
'''
#TEST STUFF
sites_dict = {(0, 0): ippg.GradientParabolaMinusGauss_GaussDip(parab=[600*1.5, 900*1.5], amplitude=1,
gausscomp=[120, 180], gausscomp_height=0.2,
gauss=[60, 90], gauss_height=1.0,
#gradient=[0.0, 0.0]
gradient=[-0.0003, 0.0002] # [++ -> upper right on atoms, ++ -> upper left]
)}
# settings = {'zero_pixel': (824, 429),
settings = {'zero_pixel': (762, 586),
'sites': sites_dict,
'binarization_algorithm': 'threshold'}
potential = ippg.ProjectedPotential(**settings)
evaluated_matrix = potential.evaluate_potential()
# uncomment this to get a saved copy of the potential
# imsave('out.png', evaluated_matrix)
# uncomment this to use the painted potential
# potential.real_valued_potential = np.array(imread('out.png', mode='L')).astype('float')
# potential.real_valued_potential /= float(np.max(potential.real_valued_potential))
binary_matrix = potential.binarize()
if is_debug:
plt.gray()
plt.imshow(binary_matrix,interpolation='none')
# plt.imshow(potential.real_valued_potential,interpolation='none')
plt.figaspect(1)
plt.show()
dmd_pattern.pattern = binary_matrix
dmd_patterns = {'patterns':[dmd_pattern]}
lc_dmd.upload_image_sequence(dmd_patterns)
def real_pattern_show():
lc_dmd = lc.LC6500Device()
#Prepare Pattern
settings = {
'function': pc.circle_fun_2,
'compression': 'rle',
'exposure_time': 100000000 # in us
}
dmd_pattern = pc.DMDPattern(**settings)
is_debug = False
sites_dict = {}
circle_dict = {
(0, 0): ippg.BoxFunction(radii=[200, 200], x_angle=0, amplitude=1),
(0, 0): ippg.CircFunction(radii=[500, 500], x_angle=0, amplitude=1),
}
entire_beam_dict = {
(0, 0): ippg.CircFunction(radii=[5000, 5000], x_angle=0, amplitude=1),
}
multipoint_dict = {
(-100, -100): ippg.CircFunction(radii=[5, 5], x_angle=0, amplitude=1),
(100, -100): ippg.CircFunction(radii=[5, 5], x_angle=0, amplitude=1),
(-100, 100): ippg.CircFunction(radii=[5, 5], x_angle=0, amplitude=1),
(100, 100): ippg.CircFunction(radii=[5, 5], x_angle=0, amplitude=1),
(-150, -150): ippg.CircFunction(radii=[5, 5], x_angle=0, amplitude=1),
(150, -150): ippg.CircFunction(radii=[5, 5], x_angle=0, amplitude=1),
(50, -50): ippg.CircFunction(radii=[5, 5], x_angle=0, amplitude=1),
}
sites_dict = {
(0, 0):
ippg.Parabola_EllipseDip(
parab=[400, 600],
amplitude=1,
ell=[80, 120],
ell_height=0.0,
)
}
#93
# settings = {'zero_pixel': (824, 429),
#'zero_pixel': (762, 429),
settings = {
'zero_pixel': (762, 586),
'sites': sites_dict,
'binarization_algorithm': 'randomize'
}
potential = ippg.ProjectedPotential(**settings)
evaluated_matrix = potential.evaluate_potential()
binary_matrix = potential.binarize()
if True:
corrector = DMDBeamCorrector('points.pgm',
'profile.pgm',
center=(772, 576))
arr = np.zeros((1080, 1920))
y, x = np.ogrid[0:1080, 0:1920]
arr = corrector.homogenize(evaluated_matrix,
w_center=(762, 586),
w_size=(300, 300))
if is_debug:
plt.gray()
plt.imshow(arr, interpolation='none')
plt.show()
for y in range(arr.shape[1]):
print y
for x in range(arr.shape[0]):
# custom floyd-steinberg
op = arr[x][y]
# arr[x][y] = 1 if random() < op else 0
if random() < 0:
newp = 1 if random() < op else 0
else:
newp = 1 if 0.5 < op else 0
arr[x][y] = newp
err = op - newp
if x < arr.shape[0] - 1:
arr[x + 1][y] = arr[x + 1][y] + err * 7 / 16
if y < arr.shape[1] - 1:
arr[x + 1][y + 1] = arr[x + 1][y + 1] + err * 1 / 16
if y < arr.shape[1] - 1:
arr[x - 1][y + 1] = arr[x - 1][y + 1] + err * 3 / 16
arr[x][y + 1] = arr[x][y + 1] + err * 5 / 16
binary_matrix = arr
if is_debug:
plt.gray()
plt.imshow(binary_matrix, interpolation='none')
plt.show()
dmd_pattern.pattern = binary_matrix
dmd_patterns = {'patterns': [dmd_pattern]}
lc_dmd.upload_image_sequence(dmd_patterns)