Pot_NLO = np.empty((Nch, Nch, Ndata, Nconf))
for iconf in range(Nconf):
tmpPot__LO, tmpPot_NLO = calc_potential_NLO(Pot[:,:,:,:,iconf],
Rcr[:,:,:,:,iconf],
LpR[:,:,:,:,iconf])
if (tmpPot__LO is tmpPot_NLO is None):
quit()
for idata in range(Ndata):
for i in range(Nch):
for j in range(Nch):
Pot__LO[i, j, idata, iconf] = tmpPot__LO[i, j, idata]
Pot_NLO[i, j, idata, iconf] = tmpPot_NLO[i, j, idata]
output_bin_data(oFbase+"/LN-LN_CCP__LO_t%d"%(time), Pot__LO[0, 0, :, :], xData)
output_bin_data(oFbase+"/LN-LN_CCP_NLO_t%d"%(time), Pot_NLO[0, 0, :, :], xData)
output_bin_data(oFbase+"/LN-SN_CCP__LO_t%d"%(time), Pot__LO[0, 1, :, :], xData)
output_bin_data(oFbase+"/LN-SN_CCP_NLO_t%d"%(time), Pot_NLO[0, 1, :, :], xData)
output_bin_data(oFbase+"/SN-LN_CCP__LO_t%d"%(time), Pot__LO[1, 0, :, :], xData)
output_bin_data(oFbase+"/SN-LN_CCP_NLO_t%d"%(time), Pot_NLO[1, 0, :, :], xData)
output_bin_data(oFbase+"/SN-SN_CCP__LO_t%d"%(time), Pot__LO[1, 1, :, :], xData)
output_bin_data(oFbase+"/SN-SN_CCP_NLO_t%d"%(time), Pot_NLO[1, 1, :, :], xData)
for i in range(Nch):
for j in range(Nch):
for idata in range(Ndata):
mean0, err0 = make_mean_err(Pot__LO[i, j, idata, :])
mean1, err1 = make_mean_err(Pot_NLO[i, j, idata, :])
print("%lf %e %e %e %e" % (xData[idata], mean0, err0, mean1, err1))
print("\n")
Params_perV[0, i] = integrand_1D_PotOpt_DensWS
Params_perV[1, i] = tmpFunc_perV
Params_perV[2, i] = tmpParams_perV[:, i]
Params_perV[3, i] = Params_dens
#print factor_perV; print Func_perV; print Params_perV; quit()
### Calculation & Output
if (iFname_orgV is not None):
Energy_0 = np.empty(Nconf)
print("#")
for i in range(Nconf):
Energy_0[i] = solve_sch_0th_pert(Range, Coeff[:, i], mass,
Func_orgV, Params_orgV[:, i])
print("# conf = %d: End" % i)
Mean, Err = make_mean_err(Energy_0)
print("#\n# E_0 = %1.16f +/- %1.16f" % (Mean, Err))
Energy_1 = np.empty(Nconf)
print("#")
for i in range(Nconf):
Energy_1[i] = solve_sch_1st_pert(Range, Coeff[:, i], Func_perV,
Params_perV[:, i])
print("# conf = %d: End" % i)
Mean, Err = make_mean_err(Energy_1)
print("#\n# E_1 = %1.16f +/- %1.16f" % (Mean, Err))
print("#\n# JOB END: elapsed_time = %lf [sec]" % (time.time() - JOB_start))
print("#.data = %d" % Ndata)
xData = np.empty(Ndata)
eData = np.empty(Ndata)
yData = np.empty((Ndata, Nfile))
for idata in range(Ndata):
xData[idata] = tmpData[idata, 0]
yData[idata, 0] = tmpData[idata, 1]
for iarg in range(1, Nfile):
print("Reading: %s" % iFname[iarg])
tmpData = input_text_data(iFname[iarg])
if (len(tmpData[0, :]) != 2):
print("ERROR: ONLY 2-column text data can be read, exit.")
quit()
for idata in range(Ndata):
yData[idata, iarg] = tmpData[idata, 1]
if (xData[idata] != tmpData[idata, 0]):
print("ERROR: x-data are different, exit.")
quit()
for idata in range(Ndata):
eData[idata] = make_mean_err(yData[idata, :])[1]
print("Writing: %s" % oFname)
output_bin_data(oFname, yData, xData, eData)
for i in range(Ndata):
if (xData1[i] != xData2[i] or
xData1[i] != xData3[i] or
xData1[i] != xData4[i] or
xData2[i] != xData3[i] or
xData2[i] != xData4[i] or
xData3[i] != xData4[i]):
print("\nERROR: x-data is differ, exit.\n"); quit()
for iconf in range(Nconf):
V_NLO[i, iconf] = FitFunc(xData1[i], *Params[:, iconf]) / (yData3[i, iconf] - yData4[i, iconf])
tmpV_LO1[iconf] = yData1[i, iconf] - yData3[i, iconf] * V_NLO[i, iconf]
tmpV_LO2[iconf] = yData2[i, iconf] - yData4[i, iconf] * V_NLO[i, iconf]
# Selection:
V__LO[i, iconf] = tmpV_LO1[iconf]
#V__LO[i, iconf] = tmpV_LO2[iconf]
#V__LO[i, iconf] = (tmpV_LO1[iconf] + tmpV_LO2[iconf]) / 2.0
m__LO, e__LO = make_mean_err(V__LO[i, :])
m_NLO, e_NLO = make_mean_err(V_NLO[i, :])
# For Debug:
#m__LO, e__LO = make_mean_err(tmpV_LO1)
#m_NLO, e_NLO = make_mean_err(tmpV_LO2)
print("%lf %1.16e %1.16e %1.16e %1.16e" % (xData1[i], m__LO, e__LO, m_NLO, e_NLO))
output_bin_data(oFname__LO, V__LO, xData1)
output_bin_data(oFname_NLO, V_NLO, xData1)
yData[:, iconf],
sigma=eData,
p0=in_Params,
bounds=LU_bounds)
#, maxfev=Max_iter)
#, max_nfev=Max_iter)
Chisq_dof[iconf] = np.sum(
((fit_func(xData, *tmpParams) - yData[:, iconf]) / eData)**
2) / (Ndata - Nparam)
for iparam in range(Nparam):
ResParams[iparam, iconf] = tmpParams[iparam]
print("# === Fitting Results (%d) ===" % (ires + 1))
tmp_mean, tmp_err = make_mean_err(Chisq_dof)
print("# Chisq/dof = %lf +/- %lf" % (tmp_mean, tmp_err))
print("# Parameters:"),
for iparam in range(Nparam):
tmp_mean, tmp_err = make_mean_err(ResParams[iparam, :])
in_Params[iparam] = tmp_mean
print("%e" % tmp_mean),
print
### Print Results ###
print("#\n# === Fitting Results (Final) ===")
tmp_mean, tmp_err = make_mean_err(Chisq_dof)
print("# Chisq/dof = %lf +/- %lf" % (tmp_mean, tmp_err))
tmpParams = np.empty(Nparam)
for iparam in range(Nparam):
tmpParams[iparam], tmp_err = make_mean_err(ResParams[iparam, :])
(1.0, 1.0)), Params_dens)
else:
Results_Coul_woF = Fpot_C(r_fpot, Integrand_1D,
fitfunc_Coulomb,
np.array(
(1.0, 1.0)), Params_dens)
Results_Coul_w_F = Fpot_C(r_fpot, Integrand_1D,
fitfunc_Coulomb_wF,
np.array(
(1.0, 1.0)), Params_dens)
#print("%lf %1.16lf %1.16lf" % (r_fpot, Results_Coul_woF, Results_Coul_w_F))
#Results += Results_Coul_woF
Results += Results_Coul_w_F
Mean, Err = make_mean_err(Results)
print("%lf %1.16e %1.16e" % (r_fpot, Mean, Err))
#print("%lf" % r_fpot), # For all configurations
#for iconf in range(Nconf):
# print(" %e" % Results[iconf]),
#print
else:
if (dens_type != 'Gaussian'):
print(
"\nERROR: The density-type '%s' cannot be converted, exit.\n" %
dens_type)
quit()
if (re.match('^[1-9]G$', FuncName_pot) is None):
yData2, xData2, Dummy = input_bin_data(iFname2)
if (xData2 is yData2 is Dummy is None):
quit()
if (len(xData1) != len(xData2)):
print("ERROR: The lengths of each file are differ, exit.\n")
quit()
Ndata = len(xData1)
for i in range(Ndata):
if (xData1[i] != xData2[i]):
print("ERROR: The x-data of each file are differ, exit.\n")
quit()
Nconf = len(yData1[0, :])
tmp_d = empty(Nconf)
for iconf in range(Nconf):
tmp_d[iconf] = 0.0
for idata in range(Ndata):
for iconf in range(Nconf):
tmp_d[iconf] = yData1[idata, iconf] - yData2[idata, iconf]
#tmp_d[iconf] += abs(yData1[idata, iconf] - yData2[idata, iconf])
mean, err = make_mean_err(tmp_d)
print("%lf %1.16lf %1.16lf" % (xData1[idata], mean, err))
mean, err = make_mean_err(tmp_d)
print("#Dif: %1.16lf +/- %1.16lf" % (mean, err))
print
quit()
def set_args(ARGC, ARGV):
global iFname
iFname = ARGV[1].strip()
###### Main part
if __name__ == "__main__":
argv = sys.argv
argc = len(argv)
if (argc != 2):
usage(argv)
set_args(argc, argv)
yData, xData, eData = input_bin_data(iFname)
if (xData is yData is eData is None):
quit()
for idata in range(len(xData)):
mean, err = make_mean_err(yData[idata, :])
print("%lf %1.16e %1.16e" % (xData[idata], mean, err)),
for iconf in range(len(yData[idata, :])):
print(" %1.16e" % yData[idata, iconf]),
print
###### Main part
if __name__ == "__main__":
from numpy import empty
argv = sys.argv
argc = len(argv)
if (argc == 1 or argc > 3):
usage(argv)
set_args(argc, argv)
print("# r_max = %lf" % r_max)
yData, xData, Dummy = input_bin_data(iFname)
if (xData is yData is Dummy is None):
quit()
Nconf = len(yData[0, :])
Ndata = len(yData[:, 0])
r2V = empty(Nconf)
for iconf in range(Nconf):
r2V[iconf] = 0.0
for idata in range(Ndata):
if (r_max > xData[idata]):
r2V[iconf] += yData[idata, iconf] * xData[idata]**2
mean, err = make_mean_err(r2V)
print("r2V: %1.16lf +/- %1.16lf" % (mean, err))