def get_text(cid):
return TB.make("snd-value(text(<str>))",
"Hello World, my first ToolBus tool in Python!\n")
ALL['stats'] = {'pre':'allplanes_','filee':'.stats'}
ALL['qMDDmet'] = ['coMDD','CS','MDD','FT'] # ['coMDD','CS','MDD','FT']
############## Normal #################
load_data = True #True False None
save_data = True # True False
BBL = {}
BBL.update(ALL)
BBL['desc'] = {'name':'bbl-f75-p33','comp_to':'FT','CS_niter':10,'NITER':50,'random_seed':'y'}
BBL['path'] = os.path.join(dn,'data','bblM_20130104_pH6_5C_0Murea_normal','analysis_FT','int_corr_ft_method_all_awk_full')
#BBL['path'] = os.path.join('/','home','tlinnet','kte','t1rho','bblM_20130104_pH6_5C_0Murea_normal','analysis_FT','int_corr_ft_method_all_awk_full')
logout = os.path.join(outdir,"%s.log"%(BBL['desc']['name']))
tlog=logging.FileHandler(logout); tlog.setFormatter(logformatter); logger.addHandler(tlog); logger.setLevel(logging.INFO)
if load_data==False:
TB.getstat(BBL,BBL['qMDDmet'])
TB.getser(BBL,BBL['qMDDmet'])
#TB.plotstats([BBL],BBL['qMDDmet'])
TB.sortInt(BBL,BBL['qMDDmet'])
#l = range(2,10,2); BBL['NIarr']['CS'] = l[::-1]; BBL['NIarr']['MDD'] = l[::-1]; BBL['NIarr']['FT'] = l[::-1]
TB.getdecay(BBL,BBL['qMDDmet'])
#TB.plotdecays([BBL],BBL['qMDDmet'],peaks=[3],fss=range(0,5,5))
TB.getrates(BBL,BBL['qMDDmet'])
#TB.plotrates([BBL],BBL['qMDDmet'], peaks=[1,2,3,4])
psel = [3, 5, 6, 7, 8, 9, 11, 14, 15, 16, 18, 19, 21, 23, 24, 26, 27, 28, 30, 31, 32, 33]
TB.getglobfit(BBL,BBL['qMDDmet'],psel)
#TB.plot_kEX([BBL],BBL['qMDDmet'])
TB.get_glob_props(BBL,BBL['qMDDmet'])
#TB.plot_glob_props([BBL],['coMDD','CS'])
# hello.py -- hello tool in Python
import TB
import sys
import __main__ # Don't forget this one!
def rec_terminate(cid, A):
sys.exit(0)
def get_text(cid):
return TB.make("snd-value(text(<str>))",
"Hello World, my first ToolBus tool in Python!\n")
cid = TB.parseArgs(sys.argv, __main__)
TB.connect(cid)
TB.eventloop()
def mag_corr_loop(U_array, J_array, dJ_array, jobdef, jobdef_file, model, temp_modelfile, orb_type, number_decimals):
"""
Function mag_corr_loop is designed to run over the U, J and dJ values to
fill up the mag_corr dictionary.
INPUTS TYPE DESCRIPTION
U_array nparray All the U values in a numpy array.
J_array nparray All the J values in a numpy array.
dJ_array nparray All the dJ values in a numpy array.
jobdef dict The dictionary that defines the tight binding
job to be run.
jobdef_file str The name of the jobdef file.
model dict The dictionary that contains the model system
for the tight binding job to be run.
temp_modelfile str The name of the model file.
orb_type str On-site orbital symmetry. either s, p or d.
number_decimals int The number of decimal places to report values
of U, J and dJ.
OUTPUTS TYPE DESCRIPTION
SuccessFlag bool If all the tight binding simulations are
successful then this is returned as True, if
any are not successful the loop is exited and
this is returned as False.
mag_corr dict A dictionary containing value of the magnetic
correlation at each value of U, J and dJ.
"""
# initialise the mag_corr dictionary
mag_corr = {}
SuccessFlag = True
for U in U_array:
for J in J_array:
print "U = ", U, "\t J = ", J
for dJ in dJ_array:
# if J > U:
# mag_corr[U, J, dJ] = 0.0
# else:
model['species'][0]["U"] = round(U, number_decimals)
if orb_type == "p":
model['species'][0]["I"] = round(J, number_decimals)
elif orb_type == "d":
model['species'][0]["I"] = round(J, number_decimals)
model['species'][0]["dJ"] = round(dJ, number_decimals)
# write out the new modelfile
with open(temp_modelfile, 'w') as f:
commentjson.dump(model, f, sort_keys=True, indent=4, separators=(',', ': '))
try:
SCFflag, mag_corr[round(U, number_decimals), round(J, number_decimals), round(dJ, number_decimals)] = TB.main()
# if we end up with a linear algebra error then we can re-run with a different optimisation scheme.
except numpy.linalg.linalg.LinAlgError:
# store original optimisation routine choice
old_routine = jobdef['optimisation_routine']
if old_routine == 1:
# then set it to routine 2
jobdef['optimisation_routine'] = 2
else:
# then set it to routine 1
jobdef['optimisation_routine'] = 1
# write jobdef back to file
with open(jobdef_file, 'w') as f:
commentjson.dump(jobdef, f, sort_keys=True, indent=4, separators=(',', ': '))
# and run again
print("SCF did not converge. Re-running simulation with different optimisation routine. ")
SCFflag, mag_corr[round(U, number_decimals), round(J, number_decimals), round(dJ, number_decimals)] = TB.main()
# reset optimisation routine
jobdef['optimisation_routine'] = old_routine
# write jobdef back to file
with open(jobdef_file, 'w') as f:
commentjson.dump(jobdef, f, sort_keys=True, indent=4, separators=(',', ': '))
# If the SCF has converged then we can trust the result
if SCFflag == True:
pass
# If the SCF flag is False and this was an SCF calculation then rerun
elif jobdef["scf_on"] == 1:
# Use a smaller value of A (divide by 10)
jobdef["A"] = jobdef["A"]/10.0
# Increase number of steps by a factor of 10
jobdef["scf_max_loops"] = int(jobdef["scf_max_loops"]*10)
# write jobdef back to file
with open(jobdef_file, 'w') as f:
commentjson.dump(jobdef, f, sort_keys=True, indent=4, separators=(',', ': '))
# and run again
print("SCF did not converge. Re-running simulation with smaller mixing value. ")
SCFflag, mag_corr[round(U, number_decimals), round(J, number_decimals), round(dJ, number_decimals)] = TB.main()
# Re-set the jobdef variables:
jobdef["A"] = jobdef["A"]*10.0
jobdef["scf_max_loops"] = int(jobdef["scf_max_loops"]/10)
with open(jobdef_file, 'w') as f:
commentjson.dump(jobdef, f, sort_keys=True, indent=4, separators=(',', ': '))
# If that still hasn't worked, exit gracefully...
if SCFflag == False:
SuccessFlag = False
print "SCF did not converge for U = ", round(U, number_decimals), "; J = ", round(J, number_decimals), "; dJ = ", round(dJ, number_decimals)
print "Exiting."
return SuccessFlag, mag_corr
return SuccessFlag, mag_corr
from scipy import *
from scipy import integrate
import TB
W=2.0
rom=201
nom=6000
T=0.01
t = 1.
tp = 0.
e0 = 0.
mu = 0.
nk = 300
ndos = 101
SL = TB.square_lattice(nk,mu,hopping=(t,tp,e0))
SL.Create_irr_klist()
SL.Fourier_Transform()
SL.Compute_eigvals()
ommesh, DOS = TB.Compute_DOS(ndos,SL.eigvals,SL.wklist)
iom=(2*arange(nom)+1)*pi*T
Delta=zeros(nom,dtype=complex)
for i in range(nom):
denom=iom[i]**2+(mu-ommesh)**2
Del_re=DOS*(mu-ommesh)/denom
Del_im=DOS*iom[i]/denom
Delta[i]=integrate.simps(Del_re,ommesh)-1j*integrate.simps(Del_im,ommesh)
with open('Delta.inp', 'w') as f:
def get_text(cid):
return TB.make("snd-value(text(<str>))",
"Hello World, my first ToolBus tool in Python!\n")
# hello.py -- hello tool in Python
import TB
import sys
import __main__ # Don't forget this one!
def rec_terminate(cid, A):
sys.exit(0)
def get_text(cid):
return TB.make("snd-value(text(<str>))",
"Hello World, my first ToolBus tool in Python!\n")
cid = TB.parseArgs(sys.argv, __main__)
TB.connect(cid)
TB.eventloop()
