def getPressIsotherm(thisDir):
from math import sqrt
from gcmc_toolbox import read_gcmc_log
from gcmc_toolbox import logFileList
from gcmc_toolbox import plotEqTimeSeries
from gcmc_toolbox import plotRunTimeSeries
log_files=logFileList(thisDir, searchString='log.nvt')
rho=[]
p=[]
dp=[]
s_eff=10
plot_id=765
col_descr = np.dtype([('step', 'int32'), ('T', 'float64'), ('P', 'float64'), \
('potEng', 'float64'), ('kinEng', 'float64'), ('rho', 'float64'), ('N', 'float64')])
for i in range(len(log_files)):
eq, md, empty = read_gcmc_log(thisDir+log_files[i], col_descr)
# plotEqTimeSeries(plot_id, eq.step, eq.P, ylabel=r'Pressure $P$ / $\epsilon\sigma^{-3}$')
# plotRunTimeSeries(plot_id, md.step, md.P, ylabel=r'Pressure $P$ / $\epsilon\sigma^{-3}$')
rho.append(md.rho.mean())
p.append(md.P.mean())
dp.append(md.P.std()/sqrt(len(md.P)/s_eff))
return ( np.array(rho, dtype='float64'), np.array(p, dtype='float64'), np.array(dp, dtype='float64'))
def getBulkIsotherm(thisDir):
from math import sqrt
from gcmc_toolbox import read_gcmc_log
from gcmc_toolbox import logFileList
from gcmc_toolbox import plotEqTimeSeries
from gcmc_toolbox import plotRunTimeSeries
log_files=logFileList(thisDir)
log_files.reverse()
mu=[]
rho=[]
drho=[]
p=[]
dp=[]
s_eff=10
mu_rho_id=123
plot_id=124
col_descr = np.dtype([('step', 'int32'), ('T', 'float64'), ('P', 'float64'), ('rho', 'float64'), \
('N', 'float64'), ('potEng', 'float64'), ('kinEng', 'float64')])
for i in range(len(log_files)):
eq, mc, md = read_gcmc_log(thisDir+log_files[i], col_descr)
# plotEqTimeSeries(mu_rho_id, eq.step, eq.rho)
# plotRunTimeSeries(mu_rho_id, mc.step, mc.rho)
# plotRunTimeSeries(mu_rho_id, md.step, md.rho, color='c')
# plotEqTimeSeries(p_rho_id, eq.step, eq.P, ylabel=r'Pressure $P$ / $\epsilon\sigma^{-3}$')
# plotRunTimeSeries(p_rho_id, mc.step, mc.P, ylabel=r'Pressure $P$ / $\epsilon\sigma^{-3}$')
# plotRunTimeSeries(p_rho_id, md.step, md.P, color='c', ylabel=r'Pressure $P$ / $\epsilon\sigma^{-3}$')
#######################################################
# !!!! CAREFUL With the mu values here str->float !!!!
#######################################################
mu.append(log_files[i].split('mu')[1])
rho.append(mc.rho.mean())
drho.append(mc.rho.std()/sqrt(len(mc.rho)/s_eff))
p.append(mc.P.mean())
dp.append(mc.P.std()/sqrt(len(mc.P)/s_eff))
return ( np.array(mu, dtype='float64'), np.array(rho, dtype='float64'), np.array(drho, dtype='float64') ,\
np.array(p, dtype='float64'), np.array(dp, dtype='float64'))
def getPoreIsotherm(thisDir):
from math import sqrt
from gcmc_toolbox import read_gcmc_log
from gcmc_toolbox import logFileList
from gcmc_toolbox import plotEqTimeSeries
from gcmc_toolbox import plotRunTimeSeries
log_files=logFileList(thisDir)
log_files.reverse()
mu=[]
Np=[]
dNp=[]
rho_nom=[]
drho_nom=[]
s_eff=10 # statitical inefficiency
N_mu_id=234
col_descr = np.dtype([('step', 'int32'), ('T', 'float64'), ('P', 'float64'), ('rho', 'float64'), ('N_f', 'float64'), \
('N_t', 'float64'), ('T_f', 'float64'), ('T_s', 'float64'), ('potEng', 'float64'), ('kinEng', 'float64')])
for i in range(len(log_files)):
eq, mc, md = read_gcmc_log(thisDir+log_files[i], col_descr)
plotEqTimeSeries(N_mu_id, eq.step, eq.N_f, ylabel=r'Particles in pore / $<N>$')
plotRunTimeSeries(N_mu_id, mc.step, mc.N_f, ylabel=r'Particles in pore / $<N>$')
plotRunTimeSeries(N_mu_id, md.step, md.N_f, color='c', ylabel=r'Particles in pore / $<N>$')
#######################################################
# !!!! CAREFUL With the mu values here str->float !!!!
#######################################################
mu.append(log_files[i].split('mu')[1])
Np.append(mc.N_f.mean())
dNp.append(mc.N_f.std()/sqrt(len(mc.N_f)/s_eff))
rho_nom.append(mc.rho.mean())
drho_nom.append(mc.rho.std()/sqrt(len(mc.rho)/s_eff))
return np.array(mu, dtype='float64'), np.array(Np, dtype='float64'), np.array(dNp, dtype='float64'),\
np.array(rho_nom, dtype='float64'), np.array(drho_nom, dtype='float64')
