#!/usr/bin/env python
import numpy as np
import supervillain
from supervillain.generator import Generator
from supervillain.h5 import ReadWriteable
import logging
logger = logging.getLogger(__name__)
[docs]class VortexUpdate(ReadWriteable, Generator):
r'''
This performs the same update to $v$ as :class:`PlaquetteUpdate <supervillain.generator.worldline.PlaquetteUpdate>` but leaves $m$ untouched.
Proposals are drawn according to
.. math ::
\begin{align}
\Delta v_p &\sim [-\texttt{interval_v}, +\texttt{interval_v}] \setminus \{0\} &&(W<\infty)
\\
\Delta v_p &\sim \text{uniform}(-\texttt{interval_v}, +\texttt{interval_v}) &&(W=\infty)
\end{align}
on each plaquette $p$ independently.
.. warning ::
This update is not ergodic on its own, since it does not change $m$ at all.
'''
def __init__(self, action, interval_v = 1):
if not isinstance(action, supervillain.action.Worldline):
raise ValueError('Need a Worldline action')
self.Action = action
self.interval_v = interval_v
self.vs = tuple(v for v in range(-interval_v, 0)) + tuple(v for v in range(1, interval_v+1))
self.rng = np.random.default_rng()
self.accepted = 0
self.proposed = 0
self.acceptance = 0.
self.sweeps = 0
def __str__(self):
return 'VortexUpdate'
[docs] def step(self, cfg):
r'''
Make a volume's worth of changes to v.
Parameters
----------
cfg: dict
A dictionary with m and v field variables.
Returns
-------
dict
Another configuration of fields.
'''
self.sweeps += 1
total_acceptance = 0
accepted = 0
m = cfg['m'].copy()
v = cfg['v'].copy()
L = self.Action.Lattice
W = self.Action._W
metropolis = self.rng.uniform(0, 1, v.shape)
total_accepted = 0
total_acceptance = 0
# Each v only talks to the m on the immediately surrounding links (through δv). So if we freeze m
# and only change v one checkerboarding color at a time then the change in action on each link
# comes from the v of that color.
for color in L.checkerboarding:
# We need to compute delta_v every time through the loop because v will get updated on each pass.
delta_v = L.delta(2, v)
# Randomly bump v
if self.Action.W < float('inf'):
change_v = L.form(0, dtype=int)
change_v[color] = self.rng.choice(self.vs, len(color[0]))
else:
change_v = L.form(0, dtype=float)
change_v[color] = self.rng.uniform(-self.interval_v, +self.interval_v, len(color[0]))
# and compute the change of action on each link.
change_delta_v = L.delta(2, change_v)
dS_link = 0.5 / self.Action.kappa * (-change_delta_v / W) * (2*(m - delta_v / W) - change_delta_v / W)
# The change in action originating from the plaquette on the color under consideration
# is just the sum of all the changes from the boundary links. So we sum them up.
dS = dS_link[0] + dS_link[1] + L.roll(dS_link[0], (0, -1)) + L.roll(dS_link[1], (-1, 0))
# Now dS is a 2-form encoding the change in action from the changes in v. But we should be careful:
# dS is not 0 on the off-color plaquettes---those plaquettes still have links touching the current color.
# We only want to accept/reject updates on the current color, so we restrict our attention when computing the acceptance.
acceptance = np.clip( np.exp(-dS[color]), a_min=0, a_max=1)
accepted = (metropolis[color] < acceptance)
total_accepted += accepted.sum()
total_acceptance += acceptance.sum()
# Finally, we update the v where the change is accepted.
v[color] += change_v[color] * accepted
self.proposed += L.plaquettes
self.acceptance += total_acceptance / L.plaquettes
self.accepted += total_accepted
logger.debug(f'Average proposal acceptance {total_acceptance / L.plaquettes:.6f}; Actually accepted {total_accepted} / {L.plaquettes} = {total_accepted / L.plaquettes}')
return {'m': m, 'v': v}
[docs] def report(self):
return (
f'There were {self.accepted} vortex proposals accepted of {self.proposed} proposed updates.'
+'\n'+
f' {self.accepted/self.proposed:.6f} acceptance rate'
+'\n'+
f' {self.acceptance / self.sweeps:.6f} average Metropolis acceptance probability.'
)