alpha=0.4,
linewidth=0.5)
ax.legend(loc='lower right')
# Add difference
ax = plt.subplot(gs[3, 0], sharex=ax)
ax.set_xlabel('x')
diff_factor = 1.e-7
ax.set_ylabel(r'diff., $\times10^{-7}$')
diff = integral_n - integral_a
ax.bar(x_edges[:-1], diff / diff_factor, bar_width * 2.0, align='edge')
# Freeze axis limits and draw bin edges
ax.autoscale(enable=True, axis='y')
ymin, ymax = ax.get_ylim()
ax.vlines(integrator.points.xedges.data(),
ymin,
ymax,
linestyle='--',
alpha=0.4,
linewidth=0.5)
# Save figure and graph as images
savefig(tutorial_image_name('png'))
savegraph(fcn.sum, tutorial_image_name('png', suffix='graph'), rankdir='TB')
plt.show()
print(' <no functions executed>')
print()
print('Done')
integrator.print()
print()
print(dbg1b.debug.target.data().sum(), dbg2b.debug.target.data().sum())
# # Label transformations
hist.hist.setLabel('Input histogram\n(bins definition)')
integrator.points.setLabel('Sampler\n(Gauss-Legendre)')
sin_t.sin.setLabel('sin(x)')
cos1_arg.sum.setLabel('k1*x')
cos2_arg.sum.setLabel('k2*x')
cos1_t.cos.setLabel('cos(k1*x)')
cos2_t.cos.setLabel('cos(k2*x)')
int1.setLabel('Integrator 1\n(convolution)')
int2.setLabel('Integrator 2\n(convolution)')
fcn1.sum.setLabel('a1*sin(x) + b1*cos(k1*x)')
fcn2.sum.setLabel('a2*sin(x) + b2*cos(k2*x)')
dbg1a.debug.setLabel('Debug 1\n(before integral 1)')
dbg2a.debug.setLabel('Debug 2\n(before integral 2)')
dbg1b.debug.setLabel('Debug 1\n(after integral 1)')
dbg2b.debug.setLabel('Debug 2\n(after integral 2)')
savegraph(int_points, tutorial_image_name('png', suffix='graph'), rankdir='TB')
plt.show()
# Save figure
savefig(tutorial_image_name('png'))
# Add integration points and save
ax.scatter(X, Y, c='red', marker='.', s=0.2)
ax.set_xlim(-0.5, 0.5)
ax.set_ylim(0.0, 1.0)
savefig(tutorial_image_name('png', suffix='zoom'))
# Plot 3d function and a histogram
fig = plt.figure()
ax = plt.subplot(111,
xlabel='x',
ylabel='y',
title=r'$\sin(ax+by)$',
projection='3d')
ax.minorticks_on()
ax.view_init(elev=17., azim=-33)
# Draw the function and integrals
integrator.hist.hist.plot_surface(cmap='viridis', colorbar=True)
sin_t.sin.result.plot_wireframe_vs(X, Y, rstride=8, cstride=8)
# Save the figure and the graph
savefig(tutorial_image_name('png', suffix='3d'))
savegraph(sin_t.sin, tutorial_image_name('png', suffix='graph'), rankdir='TB')
plt.show()
hist1 = C.Histogram(edges, data1)
hist1.hist.setLabel('Input histogram 1')
hist2 = C.Histogram(edges, data2)
hist2.hist.setLabel('Input histogram 2')
hist3 = C.Histogram(edges, data3)
hist3.hist.setLabel('Input histogram 3')
#
# Bind outputs
#
suffix = '' if cfg.split_transformations else 'merged_'
savegraph(b.context.outputs.smearing_matrix.values(),
tutorial_image_name('png', suffix=suffix + 'graph0'),
rankdir='TB')
hist1 >> b.context.inputs.smearing_matrix.values(nested=True)
hist1 >> b.context.inputs.eres.D1.values(nested=True)
hist2 >> b.context.inputs.eres.D2.values(nested=True)
hist3 >> b.context.inputs.eres.D3.values(nested=True)
print(b.context)
savegraph(hist1,
tutorial_image_name('png', suffix=suffix + 'graph1'),
rankdir='TB')
#
# Plot
#
for i, p in enumerate(points_list):
p.points.setLabel('Sum input:\nP{:d}'.format(i))
tfactor.points.setLabel('Scale S')
tsum.sum.setLabel('Sum of matrices')
tprod.product.setLabel('Scaled matrix')
tprod.product.product.setLabel('result')
tsum.print()
print()
tprod.print()
print()
print('The sum:')
print(tsum.transformations[0].outputs[0].data())
print()
print('The scale:')
print(tfactor.transformations[0].outputs[0].data())
print()
print('The scaled sum:')
print(tprod.transformations[0].outputs[0].data())
print()
from gna.graphviz import savegraph
savegraph(tprod.transformations[0], tutorial_image_name('dot'), rankdir='TB')
savegraph(tprod.transformations[0], tutorial_image_name('pdf'), rankdir='TB')
savegraph(tprod.transformations[0], tutorial_image_name('png'), rankdir='TB')