def plot_response(data, plate_name, save_folder = 'Figures/'):
"""
"""
if not os.path.isdir(save_folder):
os.makedirs(save_folder)
for block in data:
#
group = group_similar(data[block].keys())
names = data[block].keys()
names.sort()
#
plt.figure(figsize=(16, 4 + len(names)/8), dpi=300)
#
for i, name in enumerate(names):
a, b, c = get_index(group, name)
color, pattern = color_shade_pattern(a, b, c, group)
mean = data[block][name]['mean'][0]
std = data[block][name]['std'][0]
plt.barh([i], [mean], height=1.0, color=color, hatch=pattern)
plt.errorbar([mean], [i+0.5], xerr=[std], ecolor = [0,0,0], linestyle = '')
plt.yticks([i+0.5 for i in xrange(len(names))], names, size = 8)
plt.title(plate_name)
plt.ylim(0, len(names))
plt.xlabel('change')
plt.tight_layout()
plt.savefig(save_folder + 'response_' + str(block + 1))
#
return None
def _plot(self):
(binsX, binsY) = (self.bins, self.bins) if isinstance(self.bins, int) else self.bins
X, Y = self.data
H, ex, ey = numpy.histogram2d(X, Y, bins=(binsX, binsY))
x_center = numpy.diff(ex) / 2 + ex[0:-1]
x_digit = numpy.digitize(X, ex)
y_center = numpy.empty(binsY)
y_std = numpy.empty(binsY)
for i in range(binsX):
y_pop = Y[numpy.where(x_digit == i + 1)[0]]
y_center[i] = numpy.mean(y_pop)
y_std[i] = numpy.std(y_pop)
plt.errorbar(x_center, y_center, y_std)
plt.xlim(ex[0], ex[-1])
def _plot(self):
for name, val in self.errors.items():
x, y, yerr, xerr = val
yerr_mod = yerr
if self.log:
y_mod = numpy.log(y)
if yerr is not None:
yerr_mod = numpy.log(y + yerr) - y_mod
else:
y_mod = y
err_bar = plt.errorbar(x, y_mod, yerr=yerr_mod, xerr=xerr, label=name, fmt='o', ms=self.size)
err_bar[0].set_label('_nolegend_')
def mkplots(self):
# run to make plots of the resulting posteriors. Modified from marginal_plots.py
# from pymultinest. Produces basename+marg.pdf and basename+marge.png files
prefix = self.basename
parameters = json.load(file(prefix + 'params.json'))
n_params = len(parameters)
a = pymultinest.Analyzer(n_params=n_params,
outputfiles_basename=prefix)
s = a.get_stats()
p = pymultinest.PlotMarginal(a)
try:
values = a.get_equal_weighted_posterior()
except IOError as e:
print 'Unable to open: %s' % e
return
assert n_params == len(s['marginals'])
modes = s['modes']
dim2 = os.environ.get('D', '1' if n_params > 20 else '2') == '2'
nbins = 100 if n_params < 3 else 20
if dim2:
plt.figure(figsize=(5.1 * n_params, 5 * n_params))
for i in range(n_params):
plt.subplot(n_params, n_params, i + 1)
plt.xlabel(parameters[i])
m = s['marginals'][i]
plt.xlim(m['5sigma'])
oldax = plt.gca()
x, w, patches = oldax.hist(values[:, i],
bins=nbins,
edgecolor='grey',
color='grey',
histtype='stepfilled',
alpha=0.2)
oldax.set_ylim(0, x.max())
newax = plt.gcf().add_axes(oldax.get_position(),
sharex=oldax,
frameon=False)
p.plot_marginal(i, ls='-', color='blue', linewidth=3)
newax.set_ylim(0, 1)
ylim = newax.get_ylim()
y = ylim[0] + 0.05 * (ylim[1] - ylim[0])
center = m['median']
low1, high1 = m['1sigma']
print center, low1, high1
newax.errorbar(x=center,
y=y,
xerr=np.transpose(
[[center - low1, high1 - center]]),
color='blue',
linewidth=2,
marker='s')
oldax.set_yticks([])
#newax.set_yticks([])
newax.set_ylabel("Probability")
ylim = oldax.get_ylim()
newax.set_xlim(m['5sigma'])
oldax.set_xlim(m['5sigma'])
#plt.close()
for j in range(i):
plt.subplot(n_params, n_params, n_params * (j + 1) + i + 1)
p.plot_conditional(i, j, bins=20, cmap=plt.cm.gray_r)
for m in modes:
plt.errorbar(x=m['mean'][i],
y=m['mean'][j],
xerr=m['sigma'][i],
yerr=m['sigma'][j])
ax = plt.gca()
if j == i - 1:
plt.xlabel(parameters[i])
plt.ylabel(parameters[j])
[l.set_rotation(45) for l in ax.get_xticklabels()]
else:
ax.set_xticklabels([])
ax.set_yticklabels([])
plt.xlim([
m['mean'][i] - 5 * m['sigma'][i],
m['mean'][i] + 5 * m['sigma'][i]
])
plt.ylim([
m['mean'][j] - 5 * m['sigma'][j],
m['mean'][j] + 5 * m['sigma'][j]
])
#plt.savefig('cond_%s_%s.pdf' % (params[i], params[j]), bbox_tight=True)
#plt.close()
plt.tight_layout()
plt.savefig(prefix + 'marg.pdf')
plt.savefig(prefix + 'marg.png')
plt.close()
else:
from matplotlib.backends.backend_pdf import PdfPages
print '1dimensional only. Set the D environment variable D=2 to force'
print '2d marginal plots.'
pp = PdfPages(prefix + 'marg1d.pdf')
for i in range(n_params):
plt.figure(figsize=(5, 5))
plt.xlabel(parameters[i])
m = s['marginals'][i]
plt.xlim(m['5sigma'])
oldax = plt.gca()
x, w, patches = oldax.hist(values[:, i],
bins=20,
edgecolor='grey',
color='grey',
histtype='stepfilled',
alpha=0.2)
oldax.set_ylim(0, x.max())
newax = plt.gcf().add_axes(oldax.get_position(),
sharex=oldax,
frameon=False)
p.plot_marginal(i, ls='-', color='blue', linewidth=3)
newax.set_ylim(0, 1)
ylim = newax.get_ylim()
y = ylim[0] + 0.05 * (ylim[1] - ylim[0])
center = m['median']
low1, high1 = m['1sigma']
print center, low1, high1
newax.errorbar(x=center,
y=y,
xerr=np.transpose(
[[center - low1, high1 - center]]),
color='blue',
linewidth=2,
marker='s')
oldax.set_yticks([])
newax.set_ylabel("Probability")
ylim = oldax.get_ylim()
newax.set_xlim(m['5sigma'])
oldax.set_xlim(m['5sigma'])
plt.savefig(pp, format='pdf', bbox_inches='tight')
plt.close()
pp.close()
N = np.load('./results/Node_contrib_' + mode + '_' + direction + '_70128.npy')
error = abs(S - N) / N * 100
weightedError = abs(S - N) / N * quantiles / np.mean(quantiles) * 100
means = np.mean(error, axis=1)
weightedMeans = np.mean(weightedError, axis=1)
stds = np.std(error, axis=1)
nodeMean = np.mean(means)
weightedNodeMean = np.mean(weightedMeans)
x = np.linspace(.5, 29.5, 30)
if mode == 'linear': title = 'localised'
if mode == 'square': title = 'synchronised'
plt.figure()
ax = plt.subplot()
plt.errorbar(x, means[loadOrder], yerr=stds * 0, marker='s', lw=0, elinewidth=1)
plt.plot([0, 30], [nodeMean, nodeMean], '--k', lw=2)
plt.title(title + ' ' + direction + ', sum of colors vs. total network usage')
plt.ylabel('Mean link deviation in %')
ax.set_xticks(np.linspace(1, 30, 30))
ax.set_xticklabels(loadNames, rotation=60, ha="right", va="top", fontsize=9)
plt.axis([0, 30, min(means) - (.1 * min(means)), max(means) + (.1 * max(means))])
plt.legend(('individual country', 'mean of countries'), loc=2, ncol=2)
plt.savefig(figPath + 'error/' + title + '_' + direction + '.pdf', bbox_inches='tight')
plt.figure()
ax = plt.subplot()
plt.errorbar(x, weightedMeans[loadOrder], yerr=stds * 0, marker='s', lw=0, elinewidth=1)
plt.plot([0, 30], [weightedNodeMean, weightedNodeMean], '--k', lw=2)
plt.title(title + ' ' + direction + ', sum of colors vs. total network usage')
plt.ylabel(r'Weighed mean link deviation in % normalised to $\left\langle \mathcal{K}^T \right\rangle$')
def mkplots(self):
# run to make plots of the resulting posteriors. Modified from marginal_plots.py
# from pymultinest. Produces basename+marg.pdf and basename+marge.png files
prefix = self.basename
parameters = json.load(file(prefix + 'params.json'))
n_params = len(parameters)
a = pymultinest.Analyzer(n_params = n_params, outputfiles_basename = prefix)
s = a.get_stats()
p = pymultinest.PlotMarginal(a)
try:
values = a.get_equal_weighted_posterior()
except IOError as e:
print 'Unable to open: %s' % e
return
assert n_params == len(s['marginals'])
modes = s['modes']
dim2 = os.environ.get('D', '1' if n_params > 20 else '2') == '2'
nbins = 100 if n_params < 3 else 20
if dim2:
plt.figure(figsize=(5.1*n_params, 5*n_params))
for i in range(n_params):
plt.subplot(n_params, n_params, i + 1)
plt.xlabel(parameters[i])
m = s['marginals'][i]
plt.xlim(m['5sigma'])
oldax = plt.gca()
x,w,patches = oldax.hist(values[:,i], bins=nbins, edgecolor='grey', color='grey', histtype='stepfilled', alpha=0.2)
oldax.set_ylim(0, x.max())
newax = plt.gcf().add_axes(oldax.get_position(), sharex=oldax, frameon=False)
p.plot_marginal(i, ls='-', color='blue', linewidth=3)
newax.set_ylim(0, 1)
ylim = newax.get_ylim()
y = ylim[0] + 0.05*(ylim[1] - ylim[0])
center = m['median']
low1, high1 = m['1sigma']
print center, low1, high1
newax.errorbar(x=center, y=y,
xerr=np.transpose([[center - low1, high1 - center]]),
color='blue', linewidth=2, marker='s')
oldax.set_yticks([])
#newax.set_yticks([])
newax.set_ylabel("Probability")
ylim = oldax.get_ylim()
newax.set_xlim(m['5sigma'])
oldax.set_xlim(m['5sigma'])
#plt.close()
for j in range(i):
plt.subplot(n_params, n_params, n_params * (j + 1) + i + 1)
p.plot_conditional(i, j, bins=20, cmap = plt.cm.gray_r)
for m in modes:
plt.errorbar(x=m['mean'][i], y=m['mean'][j], xerr=m['sigma'][i], yerr=m['sigma'][j])
ax = plt.gca()
if j == i-1:
plt.xlabel(parameters[i])
plt.ylabel(parameters[j])
[l.set_rotation(45) for l in ax.get_xticklabels()]
else:
ax.set_xticklabels([])
ax.set_yticklabels([])
plt.xlim([m['mean'][i]-5*m['sigma'][i],m['mean'][i]+5*m['sigma'][i]])
plt.ylim([m['mean'][j]-5*m['sigma'][j],m['mean'][j]+5*m['sigma'][j]])
#plt.savefig('cond_%s_%s.pdf' % (params[i], params[j]), bbox_tight=True)
#plt.close()
plt.tight_layout()
plt.savefig(prefix + 'marg.pdf')
plt.savefig(prefix + 'marg.png')
plt.close()
else:
from matplotlib.backends.backend_pdf import PdfPages
print '1dimensional only. Set the D environment variable D=2 to force'
print '2d marginal plots.'
pp = PdfPages(prefix + 'marg1d.pdf')
for i in range(n_params):
plt.figure(figsize=(5, 5))
plt.xlabel(parameters[i])
m = s['marginals'][i]
plt.xlim(m['5sigma'])
oldax = plt.gca()
x,w,patches = oldax.hist(values[:,i], bins=20, edgecolor='grey', color='grey', histtype='stepfilled', alpha=0.2)
oldax.set_ylim(0, x.max())
newax = plt.gcf().add_axes(oldax.get_position(), sharex=oldax, frameon=False)
p.plot_marginal(i, ls='-', color='blue', linewidth=3)
newax.set_ylim(0, 1)
ylim = newax.get_ylim()
y = ylim[0] + 0.05*(ylim[1] - ylim[0])
center = m['median']
low1, high1 = m['1sigma']
print center, low1, high1
newax.errorbar(x=center, y=y,
xerr=np.transpose([[center - low1, high1 - center]]),
color='blue', linewidth=2, marker='s')
oldax.set_yticks([])
newax.set_ylabel("Probability")
ylim = oldax.get_ylim()
newax.set_xlim(m['5sigma'])
oldax.set_xlim(m['5sigma'])
plt.savefig(pp, format='pdf', bbox_inches='tight')
plt.close()
pp.close()