def __call__(self, raw_image, return_maxima_locations=False):
"""
Takes a raw_image and produces an optimized CSPad geometry.
"""
self.optimize_geometry(raw_image)
if return_maxima_locations:
if self.use_filter:
raw_image = utils.preprocess_image(raw_image,
threshold=self.threshold,
sigma=self.sigma,
minf_size=self.minf_size,
rank_size=self.rank_size,
sobel=self.sobel)
assembled_image = self.cspad(raw_image)
bc, bv = self._bin_intensities_by_radius(self.beam_location,
assembled_image)
maxima_locations = self._maxima_indices(bv)
return self.cspad, maxima_locations
else:
return self.cspad
def __call__(self, raw_image, return_maxima_locations=False):
"""
Takes a raw_image and produces an optimized CSPad geometry.
"""
self.optimize_geometry(raw_image)
if return_maxima_locations:
if self.use_filter:
raw_image = utils.preprocess_image(raw_image,
threshold=self.threshold,
sigma=self.sigma,
minf_size=self.minf_size,
rank_size=self.rank_size,
sobel=self.sobel)
assembled_image = self.cspad(raw_image)
bc, bv = self._bin_intensities_by_radius(self.beam_location,
assembled_image)
maxima_locations = self._maxima_indices(bv)
return self.cspad, maxima_locations
else:
return self.cspad
def optimize_geometry(self, raw_image):
"""
Find the center of `image`, which contains one or more concentric rings.
Parameters
----------
raw_image : ndarray
The image, in pyana/psana's raw format.
use_edge_mask : bool
Whether or not to apply an edge mask noise filter before finding
the center. Highly recommended.
Returns
-------
params_dict : dict
Dict of the pyana parameters used to optimize the geometry.
"""
print ""
print "Beginning optimization..."
print "Optimizing:", self.params_to_optimize
print "Objective function: %s" % self.objective_type
print ""
initial_guesses = np.concatenate([ self.cspad.get_param(p).flatten() \
for p in self.params_to_optimize ])
# check to make sure the parameters are more or less on the same abs
# scale -- this can help ensure the optimizer works as advertised
if ((np.abs(initial_guesses).std() / np.abs(initial_guesses).mean()) >
5.0):
print "\nWARNING:"
print " There is a large variation in the magnitudes of the intial"
print " parameters passed to the optimization routine. Consider an"
print " appropriate scaling to equilize the scale of each."
print "Parameters:", initial_guesses
print ""
# turn on interactive plotting -- this is the only way I've gotten it
# to work
if self.plot_each_iteration:
plt.ion()
self._fig = plt.figure(figsize=(12, 6))
self._axL = self._fig.add_subplot(121)
self._axL.set_aspect('equal')
self._axR = self._fig.add_subplot(122)
if self.use_filter:
image = utils.preprocess_image(raw_image,
threshold=self.threshold,
sigma=self.sigma,
minf_size=self.minf_size,
rank_size=self.rank_size,
sobel=self.sobel)
else:
image = raw_image
# benchmark our starting condition
init_objective = float(self._objective(initial_guesses, image))
time0 = time.clock()
# run minimization -- downhill simplex
opt_params = optimize.fmin_powell(self._objective,
initial_guesses,
args=(image, ),
xtol=1e-3,
ftol=1e-3,
disp=0)
# un-ravel & inject the param values in the CSPad object
param_dict = self._unravel_params(opt_params)
self.cspad.set_many_params(param_dict.keys(), param_dict.values())
# print some diagnostics
final_objective = self._objective(opt_params, image)
print ""
print "Optimization terminated normally"
print "--------------------------------"
print "Final objective value: %.4e" % final_objective
print "Objective function change: %.4e" % (final_objective -
init_objective)
print "Time to reach solution: %.2g min" % (
(time.clock() - time0) / 60.0)
#print "Final parameters:"
#pprint(param_dict)
print ""
# turn off interactive plotting
if self.plot_each_iteration: plt.ioff()
return
def optimize_geometry(self, raw_image):
"""
Find the center of `image`, which contains one or more concentric rings.
Parameters
----------
raw_image : ndarray
The image, in pyana/psana's raw format.
use_edge_mask : bool
Whether or not to apply an edge mask noise filter before finding
the center. Highly recommended.
Returns
-------
params_dict : dict
Dict of the pyana parameters used to optimize the geometry.
"""
print ""
print "Beginning optimization..."
print "Optimizing:", self.params_to_optimize
print "Objective function: %s" % self.objective_type
print ""
initial_guesses = np.concatenate([ self.cspad.get_param(p).flatten() \
for p in self.params_to_optimize ])
# check to make sure the parameters are more or less on the same abs
# scale -- this can help ensure the optimizer works as advertised
if ( ( np.abs(initial_guesses).std() / np.abs(initial_guesses).mean()) > 5.0):
print "\nWARNING:"
print " There is a large variation in the magnitudes of the intial"
print " parameters passed to the optimization routine. Consider an"
print " appropriate scaling to equilize the scale of each."
print "Parameters:", initial_guesses
print ""
# turn on interactive plotting -- this is the only way I've gotten it
# to work
if self.plot_each_iteration:
plt.ion()
self._fig = plt.figure(figsize=(12,6))
self._axL = self._fig.add_subplot(121)
self._axL.set_aspect('equal')
self._axR = self._fig.add_subplot(122)
if self.use_filter:
image = utils.preprocess_image(raw_image, threshold=self.threshold,
sigma=self.sigma,
minf_size=self.minf_size,
rank_size=self.rank_size,
sobel=self.sobel)
else:
image = raw_image
# benchmark our starting condition
init_objective = float( self._objective(initial_guesses, image) )
time0 = time.clock()
# run minimization -- downhill simplex
opt_params = optimize.fmin_powell(self._objective, initial_guesses,
args=(image,), xtol=1e-3, ftol=1e-3,
disp=0)
# un-ravel & inject the param values in the CSPad object
param_dict = self._unravel_params(opt_params)
self.cspad.set_many_params(param_dict.keys(), param_dict.values())
# print some diagnostics
final_objective = self._objective(opt_params, image)
print ""
print "Optimization terminated normally"
print "--------------------------------"
print "Final objective value: %.4e" % final_objective
print "Objective function change: %.4e" % (final_objective - init_objective)
print "Time to reach solution: %.2g min" % ((time.clock()-time0) / 60.0)
#print "Final parameters:"
#pprint(param_dict)
print ""
# turn off interactive plotting
if self.plot_each_iteration: plt.ioff()
return
