90 lines
3.1 KiB
Python
90 lines
3.1 KiB
Python
import numpy as np
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import argparse
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import time
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import h5py
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starttime = time.asctime()
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rng = np.random.default_rng()
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def potential_v(x,lamb):
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'''Compute the potential function V(x).'''
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return lamb*(x*x-1)*(x*x-1)+x*x
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def neighbor_sum(phi,s):
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'''Compute the sum of the state phi on all 8 neighbors of the site s.'''
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w = len(phi)
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return (phi[(s[0]+1)%w,s[1],s[2],s[3]] + phi[(s[0]-1)%w,s[1],s[2],s[3]] +
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phi[s[0],(s[1]+1)%w,s[2],s[3]] + phi[s[0],(s[1]-1)%w,s[2],s[3]] +
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phi[s[0],s[1],(s[2]+1)%w,s[3]] + phi[s[0],s[1],(s[2]-1)%w,s[3]] +
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phi[s[0],s[1],s[2],(s[3]+1)%w] + phi[s[0],s[1],s[2],(s[3]-1)%w] )
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def scalar_action_diff(phi,site,newphi,lamb,kappa):
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'''Compute the change in the action when phi[site] is changed to newphi.'''
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return (2 * kappa * neighbor_sum(phi,site) * (phi[site] - newphi) +
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potential_v(newphi,lamb) - potential_v(phi[site],lamb) )
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def scalar_MH_step(phi,lamb,kappa,delta):
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'''Perform Metropolis-Hastings update on state phi with range delta.'''
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site = tuple(rng.integers(0,len(phi),4))
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newphi = phi[site] + rng.uniform(-delta,delta)
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deltaS = scalar_action_diff(phi,site,newphi,lamb,kappa)
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if deltaS < 0 or rng.uniform() < np.exp(-deltaS):
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phi[site] = newphi
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return True
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return False
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def run_scalar_MH(phi,lamb,kappa,delta,n):
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'''Perform n Metropolis-Hastings updates on state phi and return number of accepted transtions.'''
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total_accept = 0
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for _ in range(n):
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total_accept += scalar_MH_step(phi,lamb,kappa,delta)
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return total_accept
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def batch_estimate(data,observable,k):
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'''Devide data into k batches and apply the function observable to each.
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Returns the mean and standard error.'''
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batches = np.reshape(data,(k,-1))
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values = np.apply_along_axis(observable, 1, batches)
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return np.mean(values), np.std(values)/np.sqrt(k-1)
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def main():
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lamb = 1.5
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kappas = np.linspace(0.08,0.18,11)
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width = 3
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num_sites = width**4
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delta = 1.5 # chosen to have ~ 50% acceptance
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equil_sweeps = 10
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measure_sweeps = 2
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measurements = 20
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mean_magn = []
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for kappa in kappas:
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phi_state = np.zeros((width,width,width,width))
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run_scalar_MH(phi_state,lamb,kappa,delta,equil_sweeps * num_sites)
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magnetizations = np.empty(measurements)
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for i in range(measurements):
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run_scalar_MH(phi_state,lamb,kappa,delta,measure_sweeps * num_sites)
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magnetizations[i] = np.mean(phi_state)
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mean, err = batch_estimate(np.abs(magnetizations),lambda x:np.mean(x),10)
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mean_magn.append([mean,err])
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output_filename = 'preliminary_simulation.h5'
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with h5py.File(output_filename,'a') as f:
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if not "mean-magn" in f:
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dataset = f.create_dataset("mean-magn", chunks=True, data=mean_magn)
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# store some information as metadata for the data set
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dataset.attrs["lamb"] = lamb
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dataset.attrs["kappas"] = kappas
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dataset.attrs["width"] = width
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dataset.attrs["num_sites"] = num_sites
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dataset.attrs["delta"] = delta
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dataset.attrs["equil_sweeps"] = equil_sweeps
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dataset.attrs["measure_sweeps"] = measure_sweeps
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dataset.attrs["measurements"] = measurements
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dataset.attrs["start time"] = starttime
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dataset.attrs["stop time"] = time.asctime()
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if __name__ == "__main__":
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main()
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