def optimize_despot1_protocol(params, start_range, t1, m0, L, wrt_in, protocol_frameworks, cost_types, **kwargs):
"""
Optimize the DESPOT1 protocol of collecting SPGR images to estimate a subset of (t1, t2, m0, off_resonance_phase).
Arguments:
params -- the parameters that are free to vary for each sequence, sequences may be removed to fit with the protocol framework
start_range -- a list of tuples containing the solver's initial point start range for each of the params.
L -- the lambda weights in the cost function, in corresponding order with "wrt_in"
wrt_in -- the parameters being estimated, the strings must match the actual signal equation function input names exactly
protocol_frameworks -- a list of dictionaries for example [{'spgr': 2, 'ssfp': 2}], note that params and start_range will be properly filtered if the sequence is missing from a framework
cost_types -- a dictionary containing a 'combine' function for the
"""
store_solutions = OrderedDict()
for n_images in protocol_frameworks:
print '\n\n========== Solving %s ==========' % (n_images, )
p = {k: v for k, v in params.items() if k in n_images}
sr = {k: v for k, v in start_range.items() if k in n_images}
DESPOT1_Cost_Function = construct_despot1_cost_function(n_images=n_images, params=p, start_range=sr, **kwargs)
print 'Constraints:', len(DESPOT1_Cost_Function.constraints), DESPOT1_Cost_Function.constraints
partial_cost_func = partial(
DESPOT1_Cost_Function,
t1=t1,
m0=m0,
L=L,
wrt_in=wrt_in,
)
# Call this first with arbitrary input to cache the compiled function and avoid MultiStart compiling many times.
print 'Compile Theano for floats'
try:
partial_cost_func(np.random.random(len(DESPOT1_Cost_Function.start_range)))
except spla.LinAlgError:
pass
# Only SLSQP can handle equality and inequality constraints.
M = MultiStart(
100,
DESPOT1_Cost_Function.start_range,
constraints=DESPOT1_Cost_Function.constraints,
method='SLSQP',
)
for i, (cost_type_name, cost_type) in enumerate(cost_types.iteritems()):
print 'Cost Type', cost_type_name, cost_type
res = M.solve(
parallel_pool=0,
fun=partial(partial_cost_func, **cost_type),
label=str(n_images)
)
if res:
print ' Top Solution: %s\n' % DESPOT1_Cost_Function._parameter_values(res.x)
store_solutions['%s %s' % (n_images, cost_type_name)] = M.candidates
return store_solutions
def optimize_diffusion_protocol(params, start_range, D, m0, L, wrt_in, protocol_frameworks, cost_types, **kwargs):
"""
Optimize the diffusion protocol of collecting images at different b-values to estimate a m0 and D.
Arguments:
params -- the parameters that are free to vary for each sequence, sequences may be removed to fit with the protocol framework
start_range --
L --
wrt_in --
wrt_out_ssfp --
protocol_frameworks -- a list of dictionaries for example [{'spgr': 2, 'ssfp': 2}], note that params and start_range will be properly filtered if the sequence is missing from a framework
cost_types --
"""
store_solutions = OrderedDict()
for n_images in protocol_frameworks:
print "\n\n========== Solving %s ==========" % (n_images,)
p = {k: v for k, v in params.items() if k in n_images}
sr = {k: v for k, v in start_range.items() if k in n_images}
Cost_Function = construct_diffusion_cost_function(n_images=n_images, params=p, start_range=sr, **kwargs)
print "Constraints:", len(Cost_Function.constraints), Cost_Function.constraints
partial_cost_func = partial(Cost_Function, D=D, m0=m0, L=L, wrt_in=wrt_in, regularization=0.0)
print "Compile Theano for floats"
try:
partial_cost_func(np.random.random(len(Cost_Function.start_range)))
except spla.LinAlgError:
pass
M = MultiStart(
100,
Cost_Function.start_range,
# constraints=Cost_Function.constraints,
method="L-BFGS-B",
)
for i, (cost_type_name, cost_type) in enumerate(cost_types.iteritems()):
print "Cost Type", cost_type_name, cost_type
res = M.solve(parallel_pool=0, fun=partial(partial_cost_func, **cost_type), label=str(n_images))
if res:
top_solution = Cost_Function._parameter_values(res.x)
print " Top Solution: %s %s\n" % (res.fun, top_solution)
store_solutions["%s %s" % (n_images, cost_type_name)] = M.candidates
return store_solutions