def get_ft_2d(res, obs_key, tmn, tmx):
if obs_key in ['s']:
dat = res[obs_key][tmn:tmx, ::]
if obs_key in ['z', 'x', 'y']:
dat = ms.get_diag_vecs(res[(obs_key + obs_key)][::])[tmn:tmx, ::]
ws, ks, amps, iboard, board = make_ft_2d(dat)
return ws, ks, amps, iboard, board
def get_ft_1d(res, obs_key):
if obs_key in ['ND', 'CC', 'Y']:
dat = res[obs_key][::]
if obs_key in ['s']:
dat = res[obs_key][::]
dat = np.mean(dat, axis=1)
if obs_key in ['z', 'x']:
dat = ms.get_diag_vecs(res[(obs_key + obs_key)][::])
dat = np.mean(dat, axis=1)
freqs, FT, rn = make_ft_1d(dat)
return freqs, FT, rn
params_list_list = io.make_params_list_list(fixed_params_dict, var_params_dict)
for params_list in params_list_list:
for params in params_list:
res = h5py.File(io.default_file_name(params, 'data', '.hdf5'))
mode = params['mode']
S = params['S']
V = params['V']
T = params['T']
L = params['L']
th = get_th(V)
n = 2
sds_frac = 0.6
grid = ms.get_diag_vecs(res['zz'][::])
grid = 0.5 * (1.0 - grid)
impact_t = np.argmax(grid[:,L-1])
M1_0, M1_1, M2_0, M2_1, intersection = \
fit_gmix(grid, sds_frac=sds_frac, n=n)
im = plt.imshow(grid, origin='lower', aspect=1, interpolation='none')
#plt.errorbar(M1_0, range(T+1), xerr=M2_0, c='k', lw=1.5)
plt.errorbar(M1_1, range(T+1), xerr=M2_1, c='r', lw=1.5)
Bs, chi2, Bs_sd = ft.f_fits(ft.flin, [1.0, 0.0], range(4, cross_t),
M1_0[4:cross_t], xerr=M2_0[4:cross_t])
def transport_calc(params,
tmax_1,
span=[0, 61],
n=2,
tmin=2,
sds_frac=0.6,
speed_f=ft.flin,
speed_Bs_est=[1.0, 0.0],
diff_f=ft.flin,
diff_Bs_est=[1.0, 0.0],
dirr='R-L',
g_fignum=1000):
transport = {}
res = h5py.File(io.default_file_name(params, 'data', '.hdf5'))
print(io.default_file_name(params, 'data', '.hdf5'))
print([key for key in res.keys()])
exp = ms.get_diag_vecs(res['zz'][::])
Ptj = ((1.0 - exp) / 2.0)[span[0]:span[1]]
if dirr == 'L-R':
reflect_t = np.argmax(Ptj[:, params['L'] - 1])
if dirr == 'R-L':
reflect_t = np.argmax(Ptj[:, 0])
transport['grid'] = Ptj
M1_0, M1_1, M2_0, M2_1, intersection = fit_gmix(Ptj,
params,
n=n,
sds_frac=sds_frac,
g_fignum=g_fignum)
keys = ['Bs', 'chi2', 'dBs']
for moment in [1, 2]:
for peak in [0, 1]:
moment_name = 'M' + str(moment) + '_' + str(peak)
mom = eval(moment_name)
tmax = reflect_t
if moment == 2:
tmin = 1
tmax = np.argmax(M2_0[0:params['L'] - 2])
if moment == 1 and peak == 1:
if tmax_1 is None:
tmax_1 = tmax
transport['tmax_1'] = tmax_1
tmax = tmax_1
if moment == 1 and peak == 0:
transport['intersection'] = intersection[tmin:tmax]
times = np.array(range(tmin, tmax))
Bs_chi2_dBs = \
list(ft.f_fits(speed_f, speed_Bs_est, times, mom[tmin:tmax]))
transport[moment_name] = eval(moment_name)
transport['times_' + moment_name] = times
for j, key in enumerate(keys):
transport_key = '_'.join([moment_name, key])
transport[transport_key] = Bs_chi2_dBs[j]
return transport
def plot(params, corrj=None):
print('Plotting results...')
results = h5py.File(params['fname'], 'r+')
# get spin projections along x, y, and z
x_grid, y_grid, z_grid = [
measures.get_diag_vecs(results[ab][::]) for ab in ('xx', 'yy', 'zz')
]
proj_grids_stats = results['stats']
# get g2 correlators at constant row j
if corrj is None:
corrj = results['gstats']['corrj'][0]
x_g2grid, y_g2grid, z_g2grid = [
measures.get_row_vecs(results[ab][::], j=corrj)
for ab in ['gxx', 'gyy', 'gzz']
]
g2grids_stats = results['gstats']
# get mi measure results and place in ordered dict for plotting
meas_keys = ['ND', 'CC', 'Y']
meas_list = [results[meas_key][::] for meas_key in meas_keys]
Fmeas_list = [{
'amps': results['F' + meas_key][::],
'RN': results['RN' + meas_key][::]
} for meas_key in meas_keys]
meas_dict = OrderedDict(
(key, data) for key, data in zip(meas_keys, meas_list))
Fmeas_dict = OrderedDict(
(key, Fdata) for key, Fdata in zip(meas_keys, Fmeas_list))
# get local and entropies
stj = results['s'][::]
# get mutual information adjacency matrices
mtjk = results['m'][::]
# plot spin projections
proj_titles = [
r'$\langle \sigma^x_j \rangle$', r'$\langle \sigma^y_j \rangle$',
r'$\langle \sigma^z_j \rangle$'
]
prob_titles = [
r'$\frac{\sum_j j P_j(x-)}{\sum_j P_j(x-)}$',
r'$\frac{\sum_j j P_j(y-)}{\sum_j P_j(y-)}$',
r'$\frac{\sum_j j P_j(z-)}{\sum_j P_j(z-)}$'
]
plot_grids([x_grid, y_grid, z_grid],
fignum=0,
titles=proj_titles,
suptitle='Spin Projections',
xlabels=['site', 'site', 'site'],
wspace=.05)
plot_grid_time_avg_stats(proj_grids_stats, fignum=1, titles=proj_titles)
# this makes three figures
#plot_grid_center_stats(proj_grids_stats, fignum=2, titles=prob_titles)
# plot two-point correlator w.r.t site corrj
g2_titles = [
'$g_2(\sigma^x_{%i},\sigma^x_k;t)$' % corrj,
'$g_2(\sigma^y_{%i},\sigma^y_k;t)$' % corrj,
'$g_2(\sigma^z_{%i},\sigma^z_k;t)$' % corrj
]
plot_grids([x_g2grid, y_g2grid, z_g2grid],
fignum=5,
titles=g2_titles,
suptitle='Two Point Correlator',
xlabels=['site', 'site', 'site'],
wspace=0.05)
plot_grid_time_avg_stats(g2grids_stats, fignum=6, titles=g2_titles)
#plot_grid_space_center_stats(g2grids_stats, fignum=7, titles=g2_titles)
# plot local and bond entropies
entropies = [stj]
if 'sbond' in results:
stc = results['sbond'][::]
entropies.append(stc)
if len(entropies) == 2:
wspace = 0.088
elif len(entropies) == 1:
wspace = -0.23
plot_grids(entropies,
titles=[r'$S(j,t)$', r'$S_c(j,t)$'],
xlabels=['site', 'cut'],
suptitle='von Neumann entropies',
wspace=wspace,
fignum=10)
# plot probabilities of spin down and space/time averages
plot_grid_with_avgs(z_grid,
fignum=11,
suptitle='average probability of measuring 1')
# plot mi measures
plot_measures(meas_dict, fignum=12)
# plot measure Fourier transforms
plot_measure_fts(Fmeas_dict, fignum=13)
# plot distribution of mutual information over time
#plot_edge_strength_contour(mtjk,
# bins=60, rng=(0,.1), emax=150, fignum=14)
# create the full path to where plots will be saved
fname = params['fname']
io.base_name(params['output_dir'], 'plots')
path_list = fname.split('/')
sub_dir_ind = path_list.index('data')
path_list[sub_dir_ind] = 'plots'
path_ext_list = '/'.join(path_list).split('.')
path_ext_list[-1] = '.pdf'
out_fname = ''.join(path_ext_list)
# save all figures to one pdf
io.multipage(out_fname)
results.close()
plt.close('all')
return out_fname