from correlations.errors import uWerr, acorrnErr, getW
from theory.clibs.autocorrelations.exponential import hmc
from theory.operators import magnetisation_sq
from correlations.acorr import acorrMapped
from results.common.utils import prll_map
from results.data.store import load
from plotter.pretty_plotting import Pretty_Plotter
min_sep = 0.
max_sep = 50
max_x_view = 10.
max_y_view = 2.
file_desc = 'acorr_mag2_hmc'
acs = load('results/data/numpy_objs/{}_acs.json'.format(file_desc))
t = load('results/data/numpy_objs/{}_trajs.json'.format(file_desc))
p = load('results/data/other_objs/{}_probs.pkl'.format(file_desc))
s = load('results/data/numpy_objs/{}_samples.json'.format(file_desc))
op_samples = magnetisation_sq(s)
av_op = op_samples.mean()
tsum = np.cumsum(t)
n, dim = s.shape[1], 1
spacing = 1.
n_samples, n_burn_in = s.shape[0], 50
c_len = 10000
m = 1.0
n_steps = 20
step_size = 1. / ((3. * np.sqrt(3) - np.sqrt(15)) * m / 2.) / float(n_steps)
break
if plateau_plot == True:
fig_plateau = plt.figure()
ax_plateau = fig_plateau.add_subplot(111)
ax_plateau.plot(tplot)
ax_plateau.plot((W, W), (0,tplot[W]))
ax_plateau.set_xlabel(r'$W$')
ax_plateau.set_ylabel(r'$\tau_{int}$')
ax_plateau.set_title(r'$\tau_{int}$ in dependence of $W$')
dtint = np.sqrt(4*(W + 0.5-tint)/float(self.N)) * tint
return tint,dtint,W
# load data from christian and use his data
comparison_xx = load(comparison_loc)
comparison_xx = np.asarray(comparison_xx)
print 'Comparing autocorrelation calculations...'
# assert that the autocorrelation routine is the same
av_xx = comparison_xx.mean()
norm = ((comparison_xx-av_xx)**2).mean()
my_acorr = np.asarray(map(lambda s: myAcorr(comparison_xx, av_xx, s), np.asarray(separations)))
christian_class = Christian_Autocorrelation(comparison_xx)
christian_acorr = christian_class.acf()[:c_len]
christian_acorr = np.asarray(christian_acorr)
diffs = christian_acorr[:my_acorr.size] - my_acorr
print " > av. difference: {}".format(diffs.mean())
#!/usr/bin/env python
import numpy as np
from results.data.store import load
# from theory.autocorrelations import M2_Exp as Theory
from common.acorr import plot
from common.utils import saveOrDisplay
save = False
file_name = 'acorr_mag2_khmc'
dest = 'results/data/other_objs/{}_allPlot.pkl'.format(file_name)
a = load(dest)
pa = load('results/data/other_objs/{}_probs.pkl'.format(file_name))
# m = 0.1
# n_steps = 1000
# step_size = 1./((3.*np.sqrt(3)-np.sqrt(15))*m/2.)/float(n_steps)
# tau = step_size*n_steps
#
# n, dim = 10, 1
# x0 = np.random.random((n,)*dim)
# spacing = 1.0
#
# n_samples, n_burn_in = 1000000, 50
# c_len = 100000
#
# th = Theory(tau=tau, m=m)
# vFn = lambda x: th.eval(t=x, pa=pa, theta=np.pi/2)/th.eval(t=0, pa=pa, theta=np.pi/2.)
#
# l = a['lines'].keys()[0]
# x, f0 = a['lines'][l]
ax[0].legend(bbox_to_anchor=(0., -0.3, 1., .102), loc=9,
ncol=6, mode="expand", borderaxespad=0.)
fig.subplots_adjust(bottom=0.3)
pp.save_or_show(save, PLOT_LOC)
pass
if __name__ == '__main__':
from results.common.utils import saveOrDisplay
file_name = __file__
save = True
# load the existing parameters
dest = "results/optimalParams_minimiseCost_xx_hmc_params_coarse.pkl"
output = store.load(dest)
# in this case best_params: [best_theta, best_]
best_params, best_fn, arg_grid, fn_grid = output
print '\n\n__Model details__'
print 'Potential:\tFree-Field'
print 'Lattice:\t(100,1)'
print 'Mass:\t\t0.01'
print '\nBest parameters:\n\tstep_size: {:10.5f}\n\tn_steps: {:10.5f}\n\ttau: {:10.5f}'.format(
best_params[0], best_params[1], np.prod(best_params))
x,y = arg_grid
z = fn_grid
l = z.shape[0] # this is the grid length (square grid)
try:
from theory.clibs.autocorrelations.exponential import hmc
cpp = True
except:
cpp = False
print 'failed to import c++'
from theory.operators import magnetisation_sq
from correlations.acorr import acorrMapped, acorrMapped_noDups
from results.common.utils import prll_map
from results.data.store import load
from plotter import Pretty_Plotter, PLOT_LOC
from common.utils import saveOrDisplay, multiprocessing
import ctypes
fname = 'acorr_mag2_hmc'
acs = load('results/data/numpy_objs/{}_acs.json'.format(fname))
seps = load('results/data/numpy_objs/{}_trajs.json'.format(fname))
p = load('results/data/other_objs/{}_probs.pkl'.format(fname))
s = load('results/data/numpy_objs/{}_samples.json'.format(fname))
save = True
op = magnetisation_sq(s)
av_op = op.mean()
n, dim = 10, 1
spacing = 1.
n_samples, n_burn_in = 100000, 50
c_len = 1000
m = 1.0
n_steps = 20
step_size = 1. / ((3. * np.sqrt(3) - np.sqrt(15)) * m / 2.) / float(n_steps)
#!/usr/bin/env python
import numpy as np
from results.data.store import load
from correlations.acorr import acorr as getAcorr
from correlations.errors import uWerr, gW
from common.uWerrWind import plot, preparePlot
from common.utils import saveOrDisplay
save = False
file_name = 'uWerrWind_mag2_hmc.py'
dest = 'results/data/other_objs/uWerrWind_mag2_hmc_allPlot.pkl'
a = load(dest)
m = 1.0
n_steps = 10
step_size = 1. / ((3. * np.sqrt(3) - np.sqrt(15)) * m / 2.) / float(n_steps)
tau = step_size * n_steps
n, dim = 100, 1
x0 = np.random.random((n, ) * dim)
spacing = 0.05
op_name = r'$\hat{O} = \phi_0^2 :\phi_0 = \mathcal{F}^{-1}\tilde{\phi}_0 = '\
+ r' \sum_{x\in\mathbb{Z}^d_\text{L}}\tilde{\phi}_0$'
subtitle = r"Potential: {}; Lattice Shape: ".format('Klein Gordon') \
+ r"${}$; $a={:.1f}; \delta\tau={:.1f}; n={}$".format(
x0.shape, spacing, step_size, n_steps)
plot(save=saveOrDisplay(save, file_name), **a)
