def calc_coefs(itarget, tcommon, drst, pathvars, ltemp, plotfile=None):
# initialize dcfs
dcfs = {}
# sorted points
points = sd.sorted_points(drst, hess=True)
# Calculate adiabatic potential
dMols, Vadi, pathvars = obtain_adipot(tcommon, drst, pathvars)
# Calculate CVT correction factor
if pathvars._cvt == "yes":
dcfs, lscvt, gibbs, lnew = obtain_cvt(dMols,
points,
Vadi,
ltemp,
pathvars,
dcfs=dcfs)
else:
lscvt = None
# Calculate CAG correction factor
dcfs = obtain_cag(Vadi, ltemp, lscvt, dcfs=dcfs)
# Calculate SCT correction factor
if pathvars._sct == "yes":
# v1 vector along MEP
if pathvars._v1mode == "grad": dv1 = sct.get_numv1(drst)
elif pathvars._v1mode == "hess": dv1 = {}
# calculate SCT coefficient
dcfs, tplot_sct = obtain_sct(dMols,
points,
Vadi,
ltemp,
dv1,
pathvars,
dcfs=dcfs)
# save data for plotting
if plotfile is not None:
plotdata = {}
plotdata.update(
manage_data_for_plot_mep(itarget, drst, pathvars._eref, Vadi))
if "cvt" in dcfs.keys():
plotdata.update(
manage_data_for_plot_cvt(itarget, ltemp, Vadi.xx(),
(gibbs, lnew), lscvt, dcfs["cvt"]))
if "sct" in dcfs.keys():
plotdata.update(
manage_data_for_plot_sct(itarget, dcfs["zct"], dcfs["sct"],
*tplot_sct))
write_plotfile(plotfile, plotdata)
# Delete variables
del dMols
del Vadi
# Return data
return dcfs, pathvars
rcons = dall["rcons"][key]
# the fitting
khu, dfit = fit2analytic(ltemp, rcons, num)
string, lines = PS.sfit_anafit(dfit, key)
print_string(string, 9)
# save lines for KMC
LINES.update({(anatype, rctype, case, rcname, direc): line
for anatype, line in lines.items()})
# save data for plotting
if case == 0: d4plot[rctype] = (khu, dfit)
else: d4plot["anh" + rctype] = (khu, dfit)
plotdata.update(
manage_data_for_plot_rcons(rcname, direc, ltemp,
d4plot, units))
if plotfile is not None and plotdata != {}:
write_plotfile(plotfile, plotdata)
# create loop list
loop = [(rctype,case) \
for case in [0,1] \
for rctype in "tst,cvt,tstzct,cvtzct,tstsct,cvtsct".split(",") \
]
# Print lines for KMC
string = ""
string += "=================\n"
string += " FITTING SUMMARY \n"
string += "=================\n"
string += "\n"
for rctype, case in loop:
lines = ""
for anatype in [1, 2, 3, 4]:
def main(idata, status, case):
stat2check = [2, 5, 6]
mustexist = []
tocreate = [PN.DIR3, PN.DIR6]
#-------------------------------------------------------#
# Read Pilgrim input files, check file/folder status #
# and expand tuple 'case' #
#-------------------------------------------------------#
# expand data
(dctc, dimasses), ltemp, dpath, (dtesLL,
dtesHL), dchem, tkmc, ddlevel = idata
# status ok?
fstatus = status_check(status, stat2check)
if fstatus == -1: exit()
# existency of folders
fstatus = ffchecking(mustexist, tocreate)
if fstatus == -1: exit()
# expand case
(dof, hlf, plotfile), dlevel, software = case
#-------------------------------------------------------#
#---------------------------------#
# files with data and output file #
#---------------------------------#
pof = PN.get_pof(dlevel, "kmc")
print " Pilgrim output file: %s" % pof
print
sys.stdout = Logger(pof, "w", True)
# expand KMC tuple
ipops, rcs, psteps, volume, timeunits, excess = tkmc
valid_tu = "fs,ps,mcs,ms,s,min,hr".split()
if timeunits not in valid_tu: valid_tu = "ps"
# reactivo limitante
try:
POP0 = min(
[ipop for species, ipop in sorted(ipops.items()) if ipop != 0.0])
except:
POP0 = 0
print_string(
PS.skmc_init(ipops, POP0, excess, rcs, psteps, volume, timeunits, dof),
5)
# continue?
if POP0 == 0:
print " All initial populations are zero..."
print
return
if rcs == {}:
print " No reactions considered in %s" % PN.IFILE6
print
return
# read dof
dall = RW.read_alldata(dof, ltemp)[0]
# perform KMC for each temperature
data = []
fratios = {}
ftimes = []
for idx, temp in enumerate(ltemp):
# title
title = " T = %.3f Kelvin " % temp
division = "-" * len(title)
string = " " + division + "\n"
string += " " + title + "\n"
string += " " + division + "\n"
print string
# get rate constants
processes = get_ratecons(rcs, dchem, dall, idx, temp)
# print initial information before simulation
print_string(PS.skmc_processes(temp, processes), 9)
# perform kmc simulation
xvalues, yvalues = kmc(ipops, processes, excess, volume, psteps)
fratios["%7.2f" % temp] = {}
for species in yvalues.keys():
fratios["%7.2f" % temp][species] = yvalues[species][-1] / POP0
# print data from simulation
print_string(PS.skmc_results(xvalues, yvalues, timeunits), 9)
# save data needed for txt and pdf files
data += [("%.2f" % temp, xvalues, yvalues)]
ftimes.append(xvalues[-1])
# print final ratios
species = sorted(yvalues.keys())
print_string(PS.skmc_finaltimes(ltemp, ftimes, timeunits), 5)
print_string(PS.skmc_finalratio(ltemp, species, fratios), 5)
# save data for plotting
if plotfile is not None:
plotdata = {}
plotdata.update(
manage_data_for_plot_kmc(data, fratios, volume, timeunits))
write_plotfile(plotfile, plotdata)