def fitter(prob, steps, num_steps, burn, num_burn, key):
problem = FitProblem(prob)
opts = Opts(DreamFit, '/tmp/bland/store_' + key, [burn, steps])
setup_logging()
problem.path = '/mnt/hgfs/Ubuntu_Shared/mysite/bland/views.py'
mapper = SerialMapper
monitor = CustomMonitor(problem, key)
#monitor = StepMonitor(problem, fp)
extra_opts = {
'burn': num_burn,
'pop': 10,
'init': 'eps',
'steps': num_steps,
'thin': 1,
'samples': 10000
}
fitdriver = FitDriver(DreamFit,
problem=problem,
monitors=[monitor],
abort_test=lambda: False,
**extra_opts)
make_store(problem, opts, exists_handler=store_overwrite_query)
resume_path = None
fitdriver.mapper = mapper.start_mapper(problem, opts.args)
best, fbest = fitdriver.fit(resume=resume_path)
save_best(fitdriver, problem, best)
mapper.stop_mapper(fitdriver.mapper)
problem.model_update()
#chisq = "\n" + fitdriver.problem.chisq_str()
with open("/tmp/bland/store_" + key + "/out.txt", 'a') as txt_file:
#txt_file.write(chisq)
txt_file.write("\nComplete!")
return
def calcScatteringPlane(self, h1, h2, UBmatrix, ei, stars):
"Returns the chi and phi for the scattering plane defined by h1 and h2. Used with calcIdealAngles2."
#Accepts two scattering plane vectors, h1 and h2, the UB matrix, and the wavelength
#Should we allow the scattering plane to be defined by two vectors at different energies?
#For now, I say NoN
h1p = np.dot(UBmatrix, h1)
h2p = np.dot(UBmatrix, h2)
x0 = [0.0, 0.0, 0.0, 0.0]
wavelength = e_to_wavelength(ei)
q1 = calcq(h1[0], h1[1], h1[2], stars)
twotheta1 = np.degrees(2.0 * np.arcsin(wavelength * q1 / 4.0 / np.pi))
q2 = calcq(h2[0], h2[1], h2[2], stars)
twotheta2 = np.degrees(2.0 * np.arcsin(wavelength * q2 / 4.0 / np.pi))
if 1:
#original openopt
#outp=self.scatteringEquations([45,90,-90,0],h1p, h2p, q1, q2,wavelength,twotheta1/2,twotheta2/2)
p0 = SNLE(self.scatteringEquations,
x0,
args=(h1p, h2p, q1, q2),
contol=1e-15,
ftol=1e-15,
maxFunEvals=1e8,
maxIter=1e5)
r0 = p0.solve('nlp:ralg')
#outp=self.scatteringEquations(r0.xf,h1p, h2p, q1, q2,wavelength,twotheta1/2,twotheta2/2)
chi = r0.xf[0] #xf is the final array, xf[0] = chi
phi = r0.xf[1] # xf[1] = phi
if 0:
import bumps
from bumps.fitters import FIT_OPTIONS, FitDriver, DreamFit, StepMonitor, ConsoleMonitor
import bumps.modelfn, bumps.fitproblem
fn = lambda chi, phi, omega1, omega2: np.linalg.norm(
self.scatteringEquations([chi, phi, omega1, omega2], h1p, h2p,
q1, q2))
print fn(chi=45, phi=72.4, omega1=-90, omega2=0)
M = bumps.modelfn.ModelFunction(fn,
chi=0.0,
phi=0.0,
omega1=0.0,
omega2=0.0)
M._parameters['chi'].range(30, 60.)
M._parameters['phi'].range(0, 90.)
M._parameters['omega1'].range(-90, 90.)
M._parameters['omega2'].range(-90, 90.)
problem = bumps.fitproblem.FitProblem(M)
fitdriver = FitDriver(DreamFit, problem=problem, burn=1000)
best, fbest = fitdriver.fit()
print best, fbest
print 'done'
print 'chi, phi', chi, phi
return chi, phi
def calcScatteringPlane(self,h1, h2, UBmatrix, ei,stars):
"Returns the chi and phi for the scattering plane defined by h1 and h2. Used with calcIdealAngles2."
#Accepts two scattering plane vectors, h1 and h2, the UB matrix, and the wavelength
#Should we allow the scattering plane to be defined by two vectors at different energies?
#For now, I say NoN
h1p = np.dot(UBmatrix, h1)
h2p = np.dot(UBmatrix, h2)
x0 = [0.0, 0.0, 0.0, 0.0]
wavelength=e_to_wavelength(ei)
q1 = calcq (h1[0], h1[1], h1[2], stars)
twotheta1 = np.degrees(2.0 * np.arcsin(wavelength * q1 / 4.0 / np.pi))
q2 = calcq (h2[0], h2[1], h2[2], stars)
twotheta2 = np.degrees(2.0 * np.arcsin(wavelength * q2 / 4.0 / np.pi))
if 1:
#original openopt
#outp=self.scatteringEquations([45,90,-90,0],h1p, h2p, q1, q2,wavelength,twotheta1/2,twotheta2/2)
p0 = SNLE(self.scatteringEquations, x0, args=(h1p, h2p, q1, q2),contol=1e-15, ftol=1e-15,maxFunEvals=1e8,maxIter=1e5)
r0 = p0.solve('nlp:ralg')
#outp=self.scatteringEquations(r0.xf,h1p, h2p, q1, q2,wavelength,twotheta1/2,twotheta2/2)
chi = r0.xf[0] #xf is the final array, xf[0] = chi
phi = r0.xf[1] # xf[1] = phi
if 0:
import bumps
from bumps.fitters import FIT_OPTIONS, FitDriver, DreamFit, StepMonitor, ConsoleMonitor
import bumps.modelfn, bumps.fitproblem
fn=lambda chi,phi,omega1,omega2: np.linalg.norm(self.scatteringEquations([chi,phi,omega1,omega2], h1p, h2p,q1,q2))
print fn(chi=45, phi=72.4, omega1=-90, omega2=0)
M=bumps.modelfn.ModelFunction(fn,chi=0.0,phi=0.0,omega1=0.0,omega2=0.0)
M._parameters['chi'].range(30,60.)
M._parameters['phi'].range(0,90.)
M._parameters['omega1'].range(-90,90.)
M._parameters['omega2'].range(-90,90.)
problem=bumps.fitproblem.FitProblem(M)
fitdriver = FitDriver(DreamFit, problem=problem, burn=1000)
best, fbest = fitdriver.fit()
print best,fbest
print 'done'
print 'chi, phi', chi, phi
return chi, phi
def bumps_fit(self,
method='dream',
pop=15,
samples=1e5,
burn=100,
steps=0,
thin=1,
alpha=0,
outliers='none',
trim=False,
monitors=[],
problem=None,
**options):
# create a fitter similar to the bumps.fitters.fit function but with option for GUI monitoring
from scipy.optimize import OptimizeResult
from bumps.fitters import FitDriver, FIT_AVAILABLE_IDS, FITTERS, FIT_ACTIVE_IDS
options['pop'] = pop
options['samples'] = samples
options['burn'] = burn
options['steps'] = steps
options['thin'] = thin
options['alpha'] = alpha
options['outliers'] = outliers
options['trim'] = trim
if problem is None:
problem = self.bumps_problem()
# verbose = True
if method not in FIT_AVAILABLE_IDS:
raise ValueError("unknown method %r not one of %s" %
(method, ", ".join(sorted(FIT_ACTIVE_IDS))))
for fitclass in FITTERS:
if fitclass.id == method:
break
driver = FitDriver(fitclass=fitclass,
problem=problem,
monitors=monitors,
**options)
driver.clip() # make sure fit starts within domain
x0 = problem.getp()
x, fx = driver.fit()
problem.setp(x)
dx = driver.stderr()
result = OptimizeResult(x=x,
dx=driver.stderr(),
fun=fx,
cov=driver.cov(),
success=True,
status=0,
message="successful termination")
if hasattr(driver.fitter, 'state'):
result.state = driver.fitter.state
return result
def fitter(prob, steps, num_steps, burn, num_burn, key):
problem = FitProblem(prob)
opts = Opts(DreamFit, '/tmp/bland/store_' + key, [burn, steps])
setup_logging()
problem.path = '/mnt/hgfs/Ubuntu_Shared/mysite/bland/views.py'
mapper = SerialMapper
monitor = CustomMonitor(problem, key)
#monitor = StepMonitor(problem, fp)
extra_opts = {'burn': num_burn, 'pop': 10, 'init': 'eps', 'steps': num_steps, 'thin': 1, 'samples': 10000}
fitdriver = FitDriver(
DreamFit, problem=problem, monitors=[monitor], abort_test=lambda: False,
**extra_opts)
make_store(problem, opts, exists_handler=store_overwrite_query)
resume_path = None
fitdriver.mapper = mapper.start_mapper(problem, opts.args)
best, fbest = fitdriver.fit(resume=resume_path)
save_best(fitdriver, problem, best)
mapper.stop_mapper(fitdriver.mapper)
problem.model_update()
#chisq = "\n" + fitdriver.problem.chisq_str()
with open("/tmp/bland/store_" + key + "/out.txt", 'a') as txt_file:
#txt_file.write(chisq)
txt_file.write("\nComplete!")
return
if 1:
from bumps.mapper import MPMapper
from bumps.cli import remember_best
import pylab
#mydirectory=r'D:\BiFeO3film\Mar27_2011'
mydirectory = r'/net/charlotte/var/ftp/pub/ncnrdata/bt9/201102/ylem/BiFeO3film/Mar27_2011'
myend = 'bt9'
dataset = "mesh" + (sys.argv[1] if len(sys.argv) > 1 else "g")
cost = sys.argv[2] if len(sys.argv) > 2 else "poisson"
problem = build_problem(mydirectory, dataset, myend, cost)
peak1 = problem.fitness.parts[0]
if dataset[4] in 'tu':
peak1.xc.value = -0.49
peak1.xc.range(-0.55, -0.4)
if dataset[4] in 't':
peak1.yc.value = -0.49
peak1.yc.range(-0.55, -0.4)
if __name__ == "__main__":
fitdriver = FitDriver(DreamFit, problem=problem, burn=50000)
#mapper = MPMapper
#fitdriver.mapper = mapper.start_mapper(problem, ())
#make_store(problem,opts,exists_handler=store_overwrite_query)
problem.output_path = r'/tmp/TestBumpDir'
best, fbest = fitdriver.fit()
print best, fbest
remember_best(fitdriver, problem, best)
pylab.show()
print 'done'
