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gkanwargkanwar
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Adds preliminary ising/potts code
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Diff for: ‎ising/ising_action.py

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"""
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Compute the Ising actions on cfgs in the given ensemble.
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"""
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import argparse
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import numpy as np
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def action(beta, cfg):
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assert(len(cfg.shape) == 2)
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return (beta * np.roll(cfg, -1, axis=0) * cfg +
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beta * np.roll(cfg, -1, axis=1) * cfg)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description='Compute Ising actions on cfgs in the given ensemble.')
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parser.add_argument('--tag', type=str, required=True)
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parser.add_argument('--Ncfg', type=int, required=True)
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parser.add_argument('--Lx', type=int, required=True)
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parser.add_argument('--Lt', type=int, required=True)
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parser.add_argument('--beta', type=float, required=True)
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args = parser.parse_args()
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fname = args.tag + '.dat'
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cfgs = np.fromfile(fname, dtype=np.float64).reshape(
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args.Ncfg, args.Lx, args.Lt)
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actions = []
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for c in cfgs:
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S = np.sum(action(args.beta, c))
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print('S = {}'.format(S))
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actions.append(S)
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fname = args.tag + '.S.dat'
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np.array(actions).tofile(fname)
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print('Wrote actions to {}.'.format(fname))

Diff for: ‎ising/ising_corr.py

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"""
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Compute correlators for 2D Ising model.
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"""
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import argparse
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import numpy as np
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import time
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import tqdm
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def compute_twopt_vev(c, alpha, beta):
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alpha_term = alpha * np.sum(c, axis=0)
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beta_term = beta * np.sum(
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c * np.roll(c, -1, axis=0) * np.roll(c, -2, axis=0), axis=0)
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op = alpha_term + beta_term
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assert(len(op.shape) == 1) # projected to just time
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Lt = op.shape[0]
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twopt = np.zeros((Lt,), dtype=np.float64)
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for dt in range(Lt):
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twopt[dt] = np.sum(op * np.roll(op, -dt, axis=0)) / Lt
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return twopt, op
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description='Compute 2D Ising corrs on ensemble.')
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parser.add_argument('--tag', type=str, required=True)
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parser.add_argument('--Ncfg', type=int, required=True)
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parser.add_argument('--Lx', type=int, required=True)
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parser.add_argument('--Lt', type=int, required=True)
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parser.add_argument('--alpha', type=float, required=True,
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help='One sigma coeff.')
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parser.add_argument('--beta', type=float, required=True,
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help='Three sigma coeff.')
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args = parser.parse_args()
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print('Running with args = {}'.format(args))
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fname = args.tag + '.dat'
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print('Reading ensemble from {}'.format(fname))
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cfgs = np.fromfile(fname, dtype=np.float64).reshape(
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args.Ncfg, args.Lx, args.Lt)
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twopts = []
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vevs = []
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start = time.time()
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for c in tqdm.tqdm(cfgs):
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twopt, vev = compute_twopt_vev(c, args.alpha, args.beta)
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twopts.append(twopt)
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vevs.append(vev)
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twopts = np.array(twopts)
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vevs = np.array(vevs)
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print('Done all corrs in {:.1f}s'.format(time.time() - start))
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print('twopts shape = {}'.format(twopts.shape))
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print('vevs shape = {}'.format(vevs.shape))
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fname = args.tag + '.twopt_a{:.2f}_b{:.2f}.dat'.format(args.alpha, args.beta)
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twopts.tofile(fname)
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print('Wrote twopts to {}.'.format(fname))
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fname = args.tag + '.vev_a{:.2f}_b{:.2f}.dat'.format(args.alpha, args.beta)
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vevs.tofile(fname)
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print('Wrote vevs to {}.'.format(fname))

Diff for: ‎ising/potts_action.py

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"""
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Compute the Potts actions on cfgs in the given ensemble.
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"""
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import argparse
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import numpy as np
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# Action is normalized/shifted to equal Ising for identical beta.
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def action(beta, cfg):
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assert(len(cfg.shape) == 2)
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return (beta * (2*(np.roll(cfg, -1, axis=0) == cfg)-1) +
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beta * (2*(np.roll(cfg, -1, axis=1) == cfg)-1))
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description='Compute Potts actions on cfgs in the given ensemble.')
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parser.add_argument('--tag', type=str, required=True)
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parser.add_argument('--Ncfg', type=int, required=True)
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parser.add_argument('--Lx', type=int, required=True)
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parser.add_argument('--Lt', type=int, required=True)
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parser.add_argument('--beta', type=float, required=True)
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args = parser.parse_args()
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fname = args.tag + '.dat'
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cfgs = np.fromfile(fname, dtype=np.float64).reshape(
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args.Ncfg, args.Lx, args.Lt)
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actions = []
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for c in cfgs:
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S = np.sum(action(args.beta, c))
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print('S = {}'.format(S))
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actions.append(S)
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fname = args.tag + '.S.dat'
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np.array(actions).tofile(fname)
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print('Wrote actions to {}.'.format(fname))

Diff for: ‎ising/potts_corr.py

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"""
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Compute correlators for 2D Potts model (either directly or random cluster).
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"""
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from cftp import *
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import argparse
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import math
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import networkx as nx
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import numpy as np
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import time
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import tqdm
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def compute_twopt(c, use_extra_ops):
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op = np.sum(c, axis=0)
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assert(len(op.shape) == 1) # projected to just time
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Lt = op.shape[0]
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ops = [np.ones((Lt,), dtype=np.complex128), op]
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# twopt = np.zeros((Lt,), dtype=np.complex128)
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# for dt in range(Lt):
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# twopt[dt] = np.sum(np.conj(op) * np.roll(op, -dt, axis=0)) / Lt
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# return twopt, op
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if use_extra_ops:
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# (sigma_z^x)^2 = (sigma_z^x)^*
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ops.append(np.sum(np.conj(c), axis=0))
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# sigma_z^x (sigma_z^(x+1))^*
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ops.append(np.sum(c * np.roll(np.conj(c), -1, axis=0), axis=0))
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# (sigma_z^x)^* sigma_z^(x+1)
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ops.append(np.sum(np.conj(c) * np.roll(c, -1, axis=0), axis=0))
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# sigma_z^x (sigma_z^(x+2))^*
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ops.append(np.sum(c * np.roll(np.conj(c), -2, axis=0), axis=0))
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# (sigma_z^x)^* sigma_z^(x+2)
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ops.append(np.sum(np.conj(c) * np.roll(c, -2, axis=0), axis=0))
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# sigma_z^x sigma_z^(x+1)
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ops.append(np.sum(c * np.roll(c, -1, axis=0), axis=0))
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# (sigma_z^x)^* (sigma_z^(x+1))^*
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ops.append(np.sum(np.conj(c * np.roll(c, -1, axis=0)), axis=0))
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N_ops = len(ops)
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twopt = np.zeros((Lt,N_ops,N_ops), dtype=np.complex128)
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for dt in range(Lt):
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for src,src_op in enumerate(ops):
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for snk,snk_op in enumerate(ops):
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twopt[dt,src,snk] = np.sum(np.conj(src_op) * np.roll(snk_op, -dt, axis=0)) / Lt
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return twopt
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def compute_twopt_cluster(c_and_mag):
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c = c_and_mag[:2]
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mag = c_and_mag[2]
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Lt = c.shape[-1]
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g = make_cluster_graph(c)
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g = extend_graph_with_ghost(g, mag)
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comps = nx.connected_components(g)
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twopt = np.zeros((Lt,), dtype=np.complex128)
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mag_count = 0
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for comp in comps:
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counts = np.zeros((Lt,), dtype=np.complex128)
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ghost_comp = 'ghost' in comp
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for x in comp:
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if x == 'ghost': continue
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counts[x[-1]] += 1
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if ghost_comp: mag_count += 1
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for dt in range(Lt):
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twopt[dt] += np.sum(counts * np.roll(counts, -dt, axis=0)) / Lt
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vev = mag_count/Lt
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return twopt, vev
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description='Compute 2D Potts corrs on ensemble.')
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parser.add_argument('--tag', type=str, required=True)
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parser.add_argument('--Ncfg', type=int, required=True)
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parser.add_argument('--n_state', type=int, required=True)
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parser.add_argument('--Lx', type=int, required=True)
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parser.add_argument('--Lt', type=int, required=True)
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parser.add_argument('--use_clusters', action='store_true')
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parser.add_argument('--use_extra_ops', action='store_true')
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args = parser.parse_args()
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print('Running with args = {}'.format(args))
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if args.use_clusters:
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fname = args.tag + '.cluster.dat'
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print('Reading cluster ensemble from {}'.format(fname))
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cfgs = np.fromfile(fname, dtype=np.float64).reshape(
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args.Ncfg, 2+1, args.Lx, args.Lt)
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else:
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fname = args.tag + '.dat'
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print('Reading ensemble from {}'.format(fname))
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cfgs = np.fromfile(fname, dtype=np.float64).reshape(
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args.Ncfg, args.Lx, args.Lt)
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cfgs = np.exp(2j * math.pi * cfgs / args.n_state)
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twopts = []
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vevs = []
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start = time.time()
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for c in tqdm.tqdm(cfgs):
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if args.use_clusters:
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twopt,vev = compute_twopt_cluster(c)
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vevs.append(vev)
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else:
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twopt = compute_twopt(c, args.use_extra_ops)
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twopts.append(twopt)
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twopts = np.array(twopts)
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vevs = np.array(vevs)
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print('Done all corrs in {:.1f}s'.format(time.time() - start))
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print('twopts shape = {}'.format(twopts.shape))
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print('vevs shape = {}'.format(vevs.shape))
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if args.use_clusters:
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fname = args.tag + '.twopt_clusters.dat'
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else:
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fname = args.tag + '.twopt.dat'
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twopts.tofile(fname)
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print('Wrote twopts to {}.'.format(fname))
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if args.use_clusters:
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fname = args.tag + '.vev_clusters.dat'
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else:
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fname = args.tag + '.vev.dat'
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vevs.tofile(fname)
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print('Wrote vevs to {}.'.format(fname))

Diff for: ‎ising/potts_sample_cluster.py

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"""
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Sample clusters for 2D Potts model.
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"""
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from cftp import *
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import argparse
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import networkx as nx
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import numpy as np
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import time
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import tqdm
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description='Sample potts cfgs from clusters.')
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parser.add_argument('--tag', type=str, required=True)
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parser.add_argument('--Ncfg', type=int, required=True)
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parser.add_argument('--n_state', type=int, required=True)
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parser.add_argument('--Lx', type=int, required=True)
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parser.add_argument('--Lt', type=int, required=True)
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args = parser.parse_args()
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print('Running with args = {}'.format(args))
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fname = args.tag + '.cluster.dat'
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print('Reading cluster ensemble from {}'.format(fname))
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cfgs = np.fromfile(fname, dtype=np.float64).reshape(
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args.Ncfg, 2+1, args.Lx, args.Lt)
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L = (args.Lx, args.Lt)
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spin_cfgs = []
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start = time.time()
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for c_and_mag in tqdm.tqdm(cfgs):
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c = c_and_mag[:2]
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mag = c_and_mag[2]
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g = make_cluster_graph(c)
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g = extend_graph_with_ghost(g, mag)
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comps = nx.connected_components(g)
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spin_cfg = np.zeros(L)
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for comp in comps:
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if 'ghost' in comp:
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val = 0
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else:
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val = np.random.choice(np.arange(args.n_state))
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for x in comp:
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if x == 'ghost': continue
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spin_cfg[x] = val
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spin_cfgs.append(spin_cfg)
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print('Potts resampling complete!')
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print('Total time = {:.1f}s'.format(time.time() - start))
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fname = args.tag + '.dat'
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np.array(spin_cfgs).tofile(fname)
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print('Wrote Potts spin cfgs to file {}'.format(fname))

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