'subconf': 01,
'periodicBC': True,
'loadlattice_z': '001',
'alph': 1.0,
'origin': np.array([0., 0.]),
'Omk': -1.0,
'Omg': -1.0,
'V0_pin_gauss': 0.,
'V0_spring_gauss': 0.,
'dcdisorder': False,
'percolation_density': 0.5,
}
cp = {
'ksize_frac_arr':
le.string_sequence_to_numpy_array('0.0:0.01:1.10', dtype=float),
'omegac':
np.array([2.25]),
'regalph':
np.pi * (11. / 6.),
'regbeta':
np.pi * (7. / 6.),
'reggamma':
np.pi * 0.5,
'shape':
'hexagon',
'polyT':
False,
'poly_offset':
'none',
'basis':
args = parser.parse_args()
maindir = '/Users/npmitchell/Dropbox/Soft_Matter/GPU/experiments/'
# Get timestamp for this calculation
print '\n args.datehourmin = ', args.datehourmin
timestr, newdatadir, LOADPARAMS, datadir = le.determine_simulation_timestamp(
args.datehourmin, maindir)
datadir = maindir + args.seriesdir + '/' + args.LatticeTop
if args.delta_lattice != '0.667':
datadir += '_delta' + args.delta_lattice.replace('.', 'p')
datadir += '/'
# Series of sizes to run is N array (Narr) or series of other values
if args.NP_load == 0:
Narr = le.string_sequence_to_numpy_array(args.N, dtype=int)
doNP = False
else:
Narr = le.string_sequence_to_numpy_array(args.NP_load, dtype=int)
doNP = True
ksize_arr = le.string_sequence_to_numpy_array(args.ksize_frac, dtype=float)
print 'Narr = ', Narr
print 'type(Narr) = ', type(Narr)
sysV = np.zeros(len(Narr) * len(ksize_arr), dtype=int)
N_V = np.zeros(len(Narr) * len(ksize_arr), dtype=int)
nuV = np.zeros(len(Narr) * len(ksize_arr), dtype=complex)
ksize_V = np.zeros(len(Narr) * len(ksize_arr), dtype=float)
ksys_sizeV = np.zeros_like(ksize_V)
# parameters
####################################################################################
cprootdir = '/Users/npmitchell/Desktop/test/chern/'
# Grab and prepare directories for both input (datadir) and output (outdir)
if args.seriesdir is not 'none':
outdir = le.prepdir(cprootdir + args.seriesdir)
datadir = le.prepdir(args.rootdir + args.seriesdir)
else:
outdir = le.prepdir(cprootdir)
datadir = args.rootdir
le.ensure_dir(outdir)
cp = {
'ksize_frac_arr':
le.string_sequence_to_numpy_array(args.ksize_frac_array, dtype=float),
'omegac':
args.omegac,
'regalph':
args.regalph,
'regbeta':
args.regbeta,
'reggamma':
args.reggamma,
'shape':
args.shape,
'polyT':
args.polyT,
'poly_offset':
args.poly_offset,
'basis':
[ax, axcb] = lat.plot_numbered(axis_off=False)
plt.show()
hlat = haldane_lattice_class.HaldaneLattice(lat, lp)
hlat.load()
print '\n\n\nhaldane_chern_class: ', hlat.lattice.lp, '\n'
chern = HaldaneChern(hlat, cp=cp)
proj = chern.get_projector()
dists, magproj, evxymag = haldane_chern_functions.characterize_projector(
proj, chern.haldane_lattice, check=True)
# plot and save characterization of locality
network_str = leplt.lt2description(hlat.lp)
# If args.proj_ind is not specified (negative), then use args.proj_XY to find nearest
if args.proj_ind < 0:
xyloc = le.string_sequence_to_numpy_array(args.proj_XY,
dtype=float)
print 'xyloc = ', xyloc
proj_ind = ((lat.xy[:, 0] - xyloc[0])**2 +
(lat.xy[:, 1] - xyloc[1])**2).argmin()
print 'proj_ind = ', proj_ind
else:
proj_ind = args.proj_ind
kpfns.plot_projector_locality_singlept(hlat,
proj_ind,
dists,
magproj,
outdir=None,
network_str=network_str)
# Save evxyproj as pkl
evxyproj = np.dstack(
'subconf': args.sub_realization_number,
'periodicBC': args.periodicBC,
'loadlattice_z': args.loadlattice_z,
'alph': args.alph,
'origin': np.array([0., 0.]),
'Omk': -1.0,
'Omg': -1.0,
'V0_pin_gauss': args.V0_pin_gauss,
'V0_spring_gauss': args.V0_spring_gauss,
'dcdisorder': dcdisorder,
'percolation_density': args.percolation_density,
}
cp = {
'ksize_frac_arr':
le.string_sequence_to_numpy_array(args.ksize_frac_array, dtype=float),
'omegac':
args.omegac,
'regalph':
args.regalph,
'regbeta':
args.regbeta,
'reggamma':
args.reggamma,
'shape':
args.shape,
'polyT':
args.polyT,
'poly_offset':
args.poly_offset,
'basis':