def plot_model(filename):
from matplotlib import pyplot as plt
#import sys; print >>sys.stderr, "in plot with",filename, example_dir()
np.random.seed(SEED)
p = load_model(os.path.join(example_dir(), filename))
p.plot()
plt.show()
def plot_model(filename):
from matplotlib import pyplot as plt
#import sys; print >>sys.stderr, "in plot with",filename, example_dir()
np.random.seed(SEED)
p = load_model(os.path.join(example_dir(), filename))
p.plot()
plt.show()
def fit_model(filename):
#import sys; print >>sys.stderr, "in plot with",filename, example_dir()
numpy.random.seed(SEED)
p =load_model([os.path.join(example_dir(),filename)])
#x.fx = RLFit(p).solve(steps=1000, burn=99)
x,fx = DEFit(p).solve(steps=200, pop=10)
#x,fx = PTFit(p).solve(steps=100,burn=400)
#x.fx = BFGSFit(p).solve(steps=200)
chisq = p(x)
print("chisq=%g"%chisq)
if chisq>2:
raise RuntimeError("Fit did not converge")
p.plot()
pylab.show()
def fit_model(filename):
#import sys; print >>sys.stderr, "in plot with",filename, example_dir()
numpy.random.seed(SEED)
p = load_model(os.path.join(example_dir(), filename))
#x.fx = fit.RLFit(p).solve(steps=1000, burn=99)
#x,fx = fit.DEFit(p).solve(steps=200, pop=10)
#x,fx = fit.PTFit(p).solve(steps=100,burn=400)
#x.fx = fit.BFGSFit(p).solve(steps=200)
x, fx = fit.SimplexFit(p).solve(steps=200)
chisq = p(x)
print("chisq=%g" % chisq)
if chisq > 2:
raise RuntimeError("Fit did not converge")
p.plot()
pylab.show()
def fit_model(filename):
from matplotlib import pyplot as plt
#import sys; print >>sys.stderr, "in plot with",filename, example_dir()
np.random.seed(SEED)
p =load_model(os.path.join(example_dir(),filename))
#x.fx = fit.RLFit(p).solve(steps=1000, burn=99)
#x,fx = fit.DEFit(p).solve(steps=200, pop=10)
#x,fx = fit.PTFit(p).solve(steps=100,burn=400)
#x.fx = fit.BFGSFit(p).solve(steps=200)
x,fx = fit.SimplexFit(p).solve(steps=1000)
chisq = p(x)
print("chisq=%g"%chisq)
if chisq>2:
raise RuntimeError("Fit did not converge")
p.plot()
plt.show()
def main():
parser = argparse.ArgumentParser(
description="run bumps model through emcee",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument('-b', '--burn', type=int, default=100, help='Number of burn iterations')
parser.add_argument('-n', '--steps', type=int, default=400, help='Number of collection iterations')
parser.add_argument('-N', '--samples', type=int, default=None, help='Number of samples to keep [default is steps*dim*npop]')
parser.add_argument('-i', '--init', choices='eps lhs cov random'.split(), default='eps', help='Population initialization method')
parser.add_argument('-k', '--npop', type=int, default=2, help='Population multiplier (must be even)')
parser.add_argument('-p', '--pars', type=str, default="", help='retrieve starting point from .par file')
parser.add_argument('-t', '--nT', type=int, default=20, help='Number of temperatures')
parser.add_argument('-m', '--Tmax', type=float, default=None, help='Max temperature for exponential ladder [default is dT^(nT-1)]')
parser.add_argument('-d', '--dT', type=float, default=np.sqrt(2.0), help='Temperature steps for exponential ladder if Tmax is not provided')
parser.add_argument('-r', '--resume', type=str, default=None, help='Resume from file')
parser.add_argument('-s', '--store', type=str, default='mc.out', help='Save to file')
parser.add_argument('-x', '--thin', type=int, default=1, help='Number of iterations between collected points')
parser.add_argument('modelfile', type=str, nargs=1, help='bumps model file')
parser.add_argument('modelopts', type=str, nargs='*', help='options passed to the model')
opts = parser.parse_args()
problem = load_model(opts.modelfile[0], model_options=opts.modelopts)
if opts.pars:
load_best(problem, opts.pars)
dim = len(problem.getp())
steps = (opts.steps if opts.samples is None
else (opts.samples+dim*opts.npop-1)//(dim*opts.npop))
preserved, state, tail = load_state(opts, dim, steps)
sampler = walk(problem,
init=opts.init, state=state,
burn=opts.burn if not preserved else 0,
steps=steps-preserved, nthin=opts.thin,
ntemps=opts.nT, maxtemp=opts.Tmax, dtemp=opts.dT,
npop=opts.npop)
save_state(opts.store, sampler, tail, labels=problem.labels())
plot_results(problem, sampler, tail, tempstats=False)
plt.show()
for entry in arr:
if entry not in uniques:
uniques.append(entry)
else:
lyrs = [ param for param in uniques if entry in param ]
layer_number = len(lyrs)
temp_entry = entry + "_" + str(layer_number)
uniques.append(temp_entry)
return uniques
#model = r"superlattice\Triple5data\T5unpol\T5fit.py"
store = r"C:\Users\oliver\OneDrive - UNSW\code\jackson_superlattice\superlattice\Triple5data\1 Tesla\de_mag_interface"
cwd = os.getcwd()
# load model, then from this model load best fit
model = load_model(os.path.join(store, "Triple_five_1T_v2.py"))
p_fit = load_best(model, store)
p = p_fit.getp()
STO = SLD (name = 'SrTiO3', rho = 3.54)
LSMO = SLD(name="LSMO", rho=p[1])
LNO = SLD(name="LNO", rho=p[4])
thetaM = 90
LSMO_mag = FreeMagnetism( z = np.linspace(0,1,9), rhoM = np.full(9,5), thetaM = np.full(9,90) )
LNO_mag = FreeMagnetism( z = np.linspace(0,1,3), rhoM = np.full(3,0.2), thetaM = np.full(3,90) )
#LSMO_lyrs_mag = [ LSMO_mag for i in range(1,16) ]
LNO_lyrs_mag = [ LNO_mag for i in range(1,16) ]
LSMO_lyrs_mag = [ Magnetism( rhoM=1, thetaM=90, interface_above=2, name="LSMO_mag_" + str(i) ) for i in range(1,16)]
if entry not in uniques:
uniques.append(entry)
else:
lyrs = [param for param in uniques if entry in param]
layer_number = len(lyrs)
temp_entry = entry + "_" + str(layer_number)
uniques.append(temp_entry)
return uniques
#model = r"superlattice\Triple5data\T5unpol\T5fit.py"
store = r"C:\Users\oliver\OneDrive - UNSW\code\jackson_superlattice\superlattice\Triple5data\T5unpol\OP_diff-ev1"
cwd = os.getcwd()
# load model, then from this model load best fit
model = load_model(os.path.join(store, "T5fit.py"))
p_fit = load_best(model, store)
p = p_fit.getp()
STO = SLD(name='SrTiO3', rho=3.54)
LSMO = SLD(name="LSMO", rho=p[1])
LNO = SLD(name="LNO", rho=p[4])
thetaM = 90
LSMO_mag = FreeMagnetism(z=np.linspace(0, 1, 9),
rhoM=np.full(9, 5),
thetaM=np.full(9, 90))
LNO_mag = FreeMagnetism(z=np.linspace(0, 1, 3),
rhoM=np.full(3, 0.2),
thetaM=np.full(3, 90))
def plot_model(filename):
#import sys; print >>sys.stderr, "in plot with",filename, example_dir()
numpy.random.seed(SEED)
p = load_model(os.path.join(example_dir(), filename))
p.plot()
pylab.show()
def plot_model(filename):
#import sys; print >>sys.stderr, "in plot with",filename, example_dir()
numpy.random.seed(SEED)
p = load_model(os.path.join(example_dir(), filename))
p.plot()
pylab.show()
