class ComputePi(object):
def __init__(self, ndim=2, nsamples=1e4):
#
self.ndim = ndim
self.nsamples = nsamples
#
self.radius = 44
self.potential = nullpotential #NullPotential()
self.mc = MC(self.potential, np.ones(ndim), 1)
self.step = UniformRectangularSampling(42, self.radius)
self.mc.set_take_step(self.step)
self.mc.set_report_steps(0)
self.conftest_check_spherical_container = CheckSphericalContainerConfig(
self.radius)
#no problem till here
self.mc.add_conf_test(self.conftest_check_spherical_container)
self.mc.set_print_progress(False)
self.mc.run(nsamples)
self.p = self.mc.get_accepted_fraction()
self.pi = get_pi(self.p, self.ndim)
class IsingMetropolisMonteCarlo(MC):
""" Single flip montecarlo runner for the ising model
"""
def __init__(self,
potential,
initial_coords,
temperature,
niter,
hEmin=0,
hEmax=100,
hbinsize=0.01,
radius=2.5,
acceptance=0.5,
adjustf=0.9,
adjustf_niter=1e4,
adjustf_navg=100,
bdim=3,
single=False,
seeds=None):
#calling the base class
hEmin = 0
print("initial", hEmin)
super(IsingMetropolisMonteCarlo,
self).__init__(potential, initial_coords, temperature)
random.seed(10)
#initial_coords = random.randint(2,
# size=(N, N),
# dtype=bool) # we're starting
# with a random initial configuration
# define the monte carlo runner with
# the potential function , initial coordinates and temperature
#ising_model = IsingModel(N, N)
self.mc = MC(potential, initial_coords, temperature)
# add the step type
#self.take_step = IsingFlip(10, N, N)
seeds = range(np.iinfo(np.int32).max)
# accept test
self.accepttest = MetropolisTest(seeds)
self.mc.take_steps = SimpleGaussian(43, 4)
self.conftest = CheckSphericalContainer(radius, bdim)
print("Here", self.mc.take_steps)
# action
self.binsize = hbinsize
# record_energy
print("min1", hEmin, "max1", hEmax)
self.histogram = RecordEnergyHistogram(hEmin, hEmax, self.binsize,
adjustf_niter)
#self.action = RecordEnergyTimeSeries(record_every, stepskip)
self.config = 0
self.mc.set_take_step(self.mc.take_steps)
self.mc.add_accept_test(self.accepttest)
self.mc.add_action(self.histogram)
self.mc.add_conf_test(self.conftest)
self.mc.set_report_steps(adjustf_niter)
self.nsteps = niter
def run(self):
""" Runs the Monte Carlo simulation
"""
#print(self.nsteps)
#print(self.mc.trial_coords)
self.mc.run(self.nsteps)
def get_et_series(self):
""" gets the array of energies at every iteration
"""
return np.array(self.action.get_energy_time_series())
def set_control(self, c):
"""
sets the temperature or whichever control parameter that takes
the role of the temperature, such as the stifness of an harmonic
"""
self.temperature = c
def get_stepsize(self):
return self.step.get_stepsize()
def dump_histogram(self, fname):
"""write histogram to fname"""
Emin, Emax = self.histogram.get_bounds_val()
histl = self.histogram.get_histogram()
hist = np.array(histl)
Energies, step = np.linspace(Emin,
Emax,
num=len(hist),
endpoint=False,
retstep=True)
assert (abs(step - self.binsize) < self.binsize / 100)
np.savetxt(fname, np.column_stack((Energies, hist)), delimiter='\t')
mean, variance = self.histogram.get_mean_variance()
return mean, variance
def get_histogram(self):
"""returns a energy list and a histogram list"""
Emin, Emax = self.histogram.get_bounds_val()
histl = self.histogram.get_histogram()
hist = np.array(histl)
Energies, step = np.linspace(Emin,
Emax,
num=len(hist),
endpoint=False,
retstep=True)
mean, variance = self.histogram.get_mean_variance()
assert (abs(step - self.binsize) < self.binsize / 100)
return Energies, hist, mean, variance
def show_histogram(self):
"""shows the histogram"""
hist = self.histogram.get_histogram()
val = [i * self.binsize for i in xrange(len(hist))]
plt.hist(val, weights=hist, bins=len(hist))
plt.show()