def plot_nmVsL(L_list, IC_list, typical_nm, fignum=2, saveQ=False):
fig = plt.figure(fignum, figsize=(7,8.5))
#obs_list_flat = list(set([obs for obs_list in obs_list_list
# for obs in obs_list]))
obs_list_flat = ['ND', 'CC', 'Y']
letters = {0:'a', 1:'b', 2:'c', 3:'d'}
M = len(IC_list)
N = len(obs_list_flat)
for m, IC in enumerate(IC_list):
if IC[0] == 'R':
IC = 'R'
for n, obs in enumerate(obs_list_flat):
i = 1+m*(M-1) + n
ax = fig.add_subplot(M,N,i)
x = typical_nm[IC]['L']
xs = np.linspace(min(x), max(x), 100)
y = typical_nm[IC][obs]
ys = nm_exact(IC, obs, xs)
#error = max(np.abs(y - nm_exact(IC, obs, x[:])))
if m != M-1:
pass
#plt.setp([ax.get_xticklabels()], visible=False)
ax.set_ylim(0, max(y)*5/4)
if np.std(y)<1e-10:
ax.set_ylim(np.mean(y)-0.1, np.mean(y)+0.1)
if IC == 'W':
ax.set_ylim(top=0.4)
if IC == 'c2_B0-1_3':
if obs == 'Y':
ax.set_ylim(top=0.6)
#plot_loglog_fit(x, y, ax)
#plot_semilogy(x, y, ax)
ax.plot(x,y, 'ok', alpha=0.6, markersize=4)
plt.setp([ax.get_xticklabels()], visible=True)
ax.xaxis.major.locator.set_params(nbins=6)
ax.yaxis.major.locator.set_params(nbins=3)
ax.plot(xs, ys, '-k', alpha=0.7)
#ax.set_title('$\mathrm{error}_\mathrm{max} = 0$' , fontsize=12)
#if error != 0:
# ax.set_title('$\mathrm{error}_\mathrm{max} = ' +
# pt.as_sci(error,1)+'$', fontsize=12)
if n == 0:
ax.text(-0.5, 1.1, '('+letters[m]+') '+pretty_names_dict(IC),
transform=ax.transAxes, fontsize=13)
ax.set_ylabel(pretty_names_dict(obs))
if m == M-1:
ax.set_xlabel(r'$L$')
ax.margins(0.1)
fig.subplots_adjust(hspace=0.4, wspace=0.7, left=0.13, right=.98,
top=.94, bottom=.05)
if saveQ:
bnp = io.base_name('typical_nm', 'plots')
fnamep = 'nmVsL.pdf'
io.multipage(bnp + fnamep, clip=False)
else:
plt.show()
def make_typical_nm(L_list, IC_list, importQ=False, nsamp=300):
bnd = io.base_name('typical_nm', 'data')
fnamed = 'typical_net_meas_rand.hdf5'
if importQ:
typical_nm = h5py.File(bnd+fnamed)
return typical_nm
else:
L_list_cp = L_list.copy()
typical_nm = dict()
print('IC', 'L', 'ND', 'CC', 'Y')
for IC in IC_list:
typical_nm[IC] = dict()
ND_list, CC_list, Y_list = [], [], []
for i, L in enumerate(L_list):
if IC == 'R':
ND, CC, Y = 0, 0 ,0
mjk = np.zeros((L,L))
for s in range(nsamp):
IC_tmp = IC +str(s)
state = get_state(L, IC_tmp)
mjk = mutual_info_calc(state)
ND += nm.density(mjk)/nsamp
CC += nm.clustering(mjk)/nsamp
Y += nm.disparity(mjk)/nsamp
elif IC == 'N':
ND, CC, Y = 0, 0 ,0
for s in range(nsamp):
mjk = random_network(L)
ND += nm.density(mjk)/nsamp
CC += nm.clustering(mjk)/nsamp
Y += nm.disparity(mjk)/nsamp
else:
state = get_state(L, IC)
if state is not None:
mjk = mutual_info_calc(state)
ND = nm.density(mjk)
CC = nm.clustering(mjk)
Y = nm.disparity(mjk)
if state is not None:
Y_list.append(Y)
CC_list.append(CC)
ND_list.append(ND)
print(IC, L, ND, CC, Y)
typical_nm[IC]['ND'] = np.array(ND_list)
typical_nm[IC]['CC'] = np.array(CC_list)
typical_nm[IC]['Y'] = np.array(Y_list)
typical_nm[IC]['L'] = np.array(L_list_cp)
typ_copy = typical_nm.copy()
io.write_hdf5(bnd + fnamed, typical_nm, force_rewrite=True)
return typ_copy
def plot_scatters(L_list, IC_list, typical_nm, fignum=1, saveQ=True):
fig = plt.figure(fignum, figsize=(6.5, 2.1))
letters = ['(a)', '(b)', '(c)']
for IC in IC_list:
c, c_full = make_c_list(IC, L_list, typical_nm)
for i, obs_list in enumerate(obs_list_list):
ax = scatter_plot(fig, typical_nm, obs_list, IC, c, c_full, i,
cmap=cmap, letter=letters[i])
plot_config(fig, ax, L_list, IC_list)
if saveQ:
bnp = io.base_name('typical_nm', 'plots')
fnamep = 'typical_net_meas.pdf'
io.multipage(bnp + fnamep, clip=False)
else:
plt.show()
def rule_plot(params_list_list, show_S=[1, 6, 9, 14], show_L=[15]):
fignum = 0
c_list = ['c', 'orange', 'crimson', 'limegreen']
c_list = cycle(c_list)
m_list = ['s', 'o', '^', 'd']
m_list = cycle(m_list)
fig = plt.figure(fignum, figsize=(3, 3))
ax = fig.add_subplot(1, 1, 1)
for m, params_list in enumerate(params_list_list):
avg_list = []
for n, params in enumerate(params_list):
if data_repo is not None:
sname = io.sim_name(params)
data_path = data_repo + sname + '_v0.hdf5'
else:
sname = io.sim_name(params)
data_path = io.default_file_name(params, 'data', '.hdf5')
data_set = h5py.File(data_path)
L = params['L']
S = params['S']
T = params['T']
sb = data_set['sbond'][::]
print(sb[0:3, int(L / 2)])
sb[::, int(L / 2)] = np.zeros(params['T'] + 1)
sb /= [min(c + 1, L - c - 1) for c in range(L - 1)]
center_sb = sb[::, int(L / 2)]
avg_sb = np.mean(sb, axis=1)
avg_avg_sb = np.mean(avg_sb[500::])
avg_center_sb = np.mean(center_sb[500::])
avg_list.append([L, S, avg_avg_sb])
#S_plot(ax, c_list, center_sb, r'$L={}$'.format(L), r'$S={}$'.format(S))
L_avg_plot(ax, c_list, m_list, avg_list, r'$S={}$'.format(S))
bn = io.base_name(output_dir, 'plots/sbond/')
#fn = 'S1_sbond_center_growth'+'.pdf'
fn = 'S1-6-9-14_th0_sbond_center_tavg_Lscale' + '.pdf'
io.multipage(bn + fn)
def make_speeds(fixed_params_dict, var_params_dict, **kwargs):
params_list_list = io.make_params_list_list(fixed_params_dict,
var_params_dict)
fignum = 3
for c, params_list in zip(['b', 'g', 'r'], params_list_list):
speeds_0, dspeeds_0, diffs_0, ddiffs_0 = [], [], [], []
speeds_1, dspeeds_1, diffs_1, ddiffs_1 = [], [], [], []
ths = []
transport = {'tmax_1': None}
for params in params_list:
transport, fignum = add_speed(params, transport, fignum, speeds_0,
dspeeds_0, diffs_0, ddiffs_0,
speeds_1, dspeeds_1, diffs_1,
ddiffs_1, ths, **kwargs)
plot_speed_diffs(params, transport, fignum, speeds_0, dspeeds_0,
diffs_0, ddiffs_0, speeds_1, dspeeds_1, diffs_1,
ddiffs_1, ths, c)
io.multipage(
io.base_name(fixed_params_dict['output_dir'][0], 'plots') + 'L' +
str(params['L']) + '_S6_speeds_' + kwargs['dirr'] + '_90_gaussian.pdf')
def init_params_1d():
output_dir = 'fock_IC'
data_repo = '/mnt/ext0/qca_output/' + output_dir + '/data/'
#data_repo = None
#uID = '_somethin_'
uID = '_ICc3_f1_'
IC_label = 'fock'
modes = ['alt']
Ss = [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14]
degs = [90]
Vs = ['HP_' + str(deg) for deg in degs]
Ls = [17]
Ts = [1000]
ICs = ['c3_f1']
#ICs = ['G', 'W', 'c2_B0-1_0', 'c2_B0-1_1', 'c2_B0-1_2', 'c2_B0-1_3' ]
#ICs = ['c3_f1', 'c3_f0-1', 'c3_f0-2', 'c3_f0-1-2']
#ICs = ['r5-10', 'r5-20', 'r5-30']
obs_list = ['ND', 'CC', 'Y', 'z']
# outer params
fixed_params_dict = {
'output_dir': [output_dir],
'L': Ls,
'T': Ts,
'BC': ['1_00'],
}
#inner params
var_params_dict = {'V': Vs, 'IC': ICs, 'S': Ss, 'mode': modes}
params_list_list = io.make_params_list_list(fixed_params_dict,
var_params_dict)
bn = io.base_name(fixed_params_dict['output_dir'][0], 'plots/fourier')
def plots_out_pathf(L):
return bn + IC_label + '_'+'-'.join(modes) + '_L'+str(L)+ uID +\
'DCYZ.pdf'
return params_list_list, obs_list, data_repo, plots_out_pathf
obs[np.isnan(obs)] = 0
obs_mean = np.nanmean(obs)
obs_means[m, j, n] = obs_mean
colors[m, j, n] = val
if typ == 'PCA':
plot_PCA(obs_means, fignum)
clip = False
if typ == 'scatter':
ncplus = False
if color_by == 'S':
ncplus = True
plot_scatter(fig, ax, m, obs_means, colors, val_min,
val_max, cbar_label, obs_list, cmap, ncplus,
cb_tick_labels)
ax.set_title(letter)
clip = True
if typ == 'hist3D':
plot_3D_hist(fignum, m, colors, val_min, val_max,
obs_means, cbar_label, obs_list, letter)
clip = False
bn = io.base_name(fixed_params_dict['output_dir'][0], 'plots/scatters')
fname = IC_label + '_' + '-'.join(modes) + '_L' + '-'.join(map(
str, Ls)) + uID + typ + '.pdf'
print(fname)
io.multipage(bn + fname, clip=clip)
def mem(L):
return 2**(L + 4)
nb_list = []
L_list = []
for L in range(Lmin, Lmax):
state = ss.make_state(L, 'f0')
nb = state.nbytes
del state
nb_list.append(nb)
L_list.append(L)
fig = plt.figure(1, (7, 2.5))
ax = fig.add_subplot(111)
ax.plot(Ls, mem(Ls), '--c', label='theoretical', lw=2)
ax.plot(L_list, nb_list, 'ks', label='measured', ms=5)
ax.set_ylabel(r'$N_{\mathrm{bytes}}$')
ax.set_xlabel(r'system size [$L$]')
ax.set_yscale('log', basey=2)
ax.set_yticks([2**n for n in range(6, 29, 4)])
#ax.get_yaxis().set_major_formatter(mpl.ticker.ScalarFormatter())
ax.legend(loc='lower right')
ax.grid('on')
ax.set_ylim([2**(Lmin + 3), 2**(Lmax + 4)])
ax.set_xlim([Lmin - 0.5, Lmax - 0.5])
#plt.show()
plots_fname = io.base_name('timing', 'plots') + 'state_memory_use.pdf'
io.multipage(plots_fname)
delta = max(1, int(len(x_tick_labels)/n_xticks))
ax.set_xticks(range(0, len(x_tick_labels), delta ))
ax.set_xticklabels(x_tick_labels[::delta])
n_yticks = 3
delta = max(1, int(len(y_tick_labels)/n_yticks))
ax.set_yticks(range(0, len(y_tick_labels), delta ))
ax.set_yticklabels(y_tick_labels[::delta])
ax.spines['bottom'].set_position(('data',0.1))
ax.spines['left'].set_bounds(0.1, 60)
ax.spines['right'].set_bounds(0.1, 60)
im_ext = im.get_extent()
box = ax.get_position()
# cax = plt.axes([box.x1+0.01, box.y0, 0.02, box.height - 0.03])
cax = plt.axes([box.x1-0.03, box.y0, 0.02, box.height - 0.01])
cb = plt.colorbar(im, cax = cax, ticks = [0.0,0.5, 1.0])
# cb.ax.tick_params(labelsize=9)
# cb.set_label(r'$s_j$', rotation=0, labelpad = -24, y=1.12)
cb.set_label(r'$s^{\mathrm{bond}}$', rotation=0, labelpad = -24,
y=1.1, fontsize=14)
# cb.set_label(r'$P_1(j,t)$', rotation=0, labelpad = -22, y=1.10)
#cax = plt.axes([box.x1-0.05, box.y0+0.2, 0.02, box.height + 0.02])
#cb = plt.colorbar(im, cax = cax)
fig.subplots_adjust(wspace=-0.5, hspace=0.2)
bn = io.base_name(output_dir,'plots')
#plt.savefig(bn+params['mode']+'_sbond_comp.pdf',
# dpi=300, clip=True)
io.multipage(bn+params['mode']+'_sbond_comp.pdf')
#plt.show()
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
plot_time_series(ys, plt.subplot(gs[0]))
plot_ft(freqs, amps, plt.subplot(gs[1]), RN=rn, dt=dt)
plt.subplot(gs[1]).scatter(max_freq, max_amp)
print('freq found: ', max_freq, ' given freq: ', f)
plt.show()
if __name__ == '__main__':
import time_evolve
params = {
'output_dir': 'L27',
'L': 17,
'T': 100,
'mode': 'alt',
'S': 6,
'V': 'HP_0',
'IC': 'f0-16',
'BC': '1_00'
}
import numpy
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
#fft_check()
params['fname'] = io.base_name(params['output_dir'], 'data') +\
io.sim_name(params)+'_v0.hdf5'
print(params['fname'])
plot(params, corrj=13)
