Source code for mastermsm.fewsm.fewsm_lib
"""
This file is part of the MasterMSM package.
"""
import copy, itertools
import numpy as np
[docs]def map_micro2macro(cmic, mac, states):
""" maps microstates into macrostates """
m = len(mac)
cmac = np.zeros((m, m), int)
for i in range(m):
for j in range(m):
if i == j:
cmac[j,i] = reduce(lambda x, y: x + y, \
[cmic[states[x],states[y]] for (x,y) in \
itertools.product(mac[j],mac[i])])
else:
cmac[j,i] = reduce(lambda x, y: x + y, \
[cmic[states[x],states[y]] for (x,y) in \
itertools.product(mac[j],mac[i])])
return cmac
[docs]def test_sign(v):
"""check whether positive and negative signs are present in vector"""
test = False
if any(v > 0.) and any(v<0):
test = True
return test
[docs]def split_sign(macro, lvec):
""" split based on sign structure """
# calculate spread in eigenvector
nt = len(macro)
spread = []
vals = lvec
for _, v in macro.items():
# check that there are positive and negative values in evec
if test_sign(vals[v]):
#spread.append(np.sum(vals**2))
spread.append(np.mean(vals[v]**2))
else:
spread.append(0.)
isplit = np.argsort(-np.array(spread))[0]
# print " macrostate to split: %i"%isplit,np.array(spread)
# split
lvec_split = lvec[macro[isplit]]
# print lvec_split
elems = []
for i in filter(lambda x: lvec_split[x] < 0.,\
range(len(macro[isplit]))):
elems.append(macro[isplit][i])
macro_new = copy.deepcopy(macro)
macro_new[nt] = elems
# update old macrostate
for i in elems:
macro_new[isplit].remove(i)
return macro_new, vals
[docs]def split_sigma(macro, lvec):
""" split based on distribution """
nt = len(macro)
spread = []
for i in macro.keys():
spread.append(np.std(lvec[macro[i]]))
# split macrostates with maximum spread
isplit = np.argsort(-np.array(spread))[0]
#print " macrostate to split: %i"%isplit,spread[isplit]
# split based on distribution
elems = []
keep = []
val_max = np.max(lvec[macro[isplit]])
val_min = np.min(lvec[macro[isplit]])
vals = (lvec[macro[isplit]] - val_min)/(val_max - val_min)
for i in filter(lambda x: vals[x] < 0.5,range(len(macro[isplit]))):
elems.append(macro[isplit][i])
for i in filter(lambda x: vals[x] >= 0.5,range(len(macro[isplit]))):
keep.append(macro[isplit][i])
macro_new = copy.deepcopy(macro)
macro_new[nt] = elems
#print macro_new
# update old macrostate
for i in elems:
macro_new[isplit].remove(i)
macro = copy.deepcopy(macro_new)
return macro, vals
[docs]def beta(imc,mcsteps):
# inverse temperature for MCSA
x = imc - 1
a = 4./mcsteps
temp = (1 + (np.exp(-a*x)-1.)/(1.- np.exp(-a*mcsteps))) # MCSA temperature
try:
beta = 1./temp
except ZeroDivisionError:
beta = 1e20
return beta
[docs]def metropolis(delta):
if delta < 0:
return True
else:
accept = False
p = min(1.0,np.exp(-delta))
rand = np.random.random()
if (rand < p):
accept = True
return accept
