fig = plt.figure(figsize=(12, 5))
ax = plt.subplot( 121 )
ax.set_title( 'Plot title (left)' )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( 'x label' )
ax.set_ylabel( 'y label' )
plt.ticklabel_format(style='sci', axis='y', scilimits=(-2,2), useMathText=True)
hist.hist.hist.plot_hist(label='histogram 1')
ax.legend()
ax = plt.subplot( 122 )
ax.set_title( 'Plot title (right)' )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( 'x label' )
ax.set_ylabel( 'y label' )
plt.ticklabel_format(style='sci', axis='y', scilimits=(-2,2), useMathText=True)
hist.hist.hist.plot_bar(label='histogram 1 (bar)', alpha=0.6)
ax.legend()
from mpl_tools.helpers import savefig
savefig(tutorial_image_name('png'))
plt.show()
narray = np.exp(-0.5 * (X - 15.0)**2 / 10.0**2 - 0.5 * (Y - 30.0)**2 / 3.0**2)
# Create a histogram instance with data, stored in `narray`
# and edges, stored in `edges`
hist = C.Histogram2d(edgesx, edgesy, narray)
fig = plt.figure()
ax = plt.subplot(111)
ax.set_title('pcolorfast')
ax.minorticks_on()
ax.set_xlabel('x label')
ax.set_ylabel('y label')
hist.hist.hist.plot_pcolorfast(colorbar=True)
savefig(tutorial_image_name('png', suffix='pcolorfast'))
fig = plt.figure()
ax = plt.subplot(111)
ax.set_title('imshow')
ax.minorticks_on()
ax.set_xlabel('x label')
ax.set_ylabel('y label')
hist.hist.hist.plot_imshow(colorbar=True)
savefig(tutorial_image_name('png', suffix='imshow'))
fig = plt.figure()
ax = plt.subplot(111)
ax.set_title('matshow')
narray = np.exp(-0.5 * (X - cx[15])**2 / 150.0**2 - 0.5 *
(Y - cy[20])**2 / 0.10**2)
# Create a histogram instance with data, stored in `narray`
# and edges, stored in `edges`
hist = C.Histogram2d(edgesx, edgesy, narray)
fig = plt.figure()
ax = plt.subplot(111)
ax.set_title('pcolormesh')
ax.minorticks_on()
ax.set_xlabel('x label')
ax.set_ylabel('y label')
hist.hist.hist.plot_pcolormesh(colorbar=True)
savefig(tutorial_image_name('png', suffix='pcolormesh'))
fig = plt.figure()
ax = plt.subplot(111)
ax.set_title('pcolor')
ax.minorticks_on()
ax.set_xlabel('x label')
ax.set_ylabel('y label')
hist.hist.hist.plot_pcolor(colorbar=True)
savefig(tutorial_image_name('png', suffix='pcolor'))
plt.show()
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()
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
#
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()
# Create a histogram instance with data, stored in `narray`
# and edges, stored in `edges`
hist = C.Histogram2d(edgesx, edgesy, narray)
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = plt.subplot(111, projection='3d')
ax.set_title('surface')
ax.minorticks_on()
ax.set_xlabel('x label')
ax.set_ylabel('y label')
hist.hist.hist.plot_surface(cmap='viridis', colorbar=True)
savefig(tutorial_image_name('png', suffix='surface'))
fig = plt.figure()
ax = plt.subplot(111, projection='3d')
ax.set_title('bar3d')
ax.minorticks_on()
ax.set_xlabel('x label')
ax.set_ylabel('y label')
hist.hist.hist.plot_bar3d(cmap=True, colorbar=True)
savefig(tutorial_image_name('png', suffix='bar3d'))
fig = plt.figure()
ax = plt.subplot(111, projection='3d')
ax.set_title('wireframe')
fig = plt.figure()
ax = plt.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('X axis')
ax.set_ylabel('Entries')
ax.set_title('Points and TH1 comparison')
roothist1d.plot(alpha=0.6, linestyle='dashdot', label='TH1')
h1d.hist.hist.plot_hist(alpha=0.6, linestyle='dashed', label='Histogram')
p1.points.points.plot_hist(label='Points')
ax.legend(loc='upper center', ncol=3)
ax.set_ylim(top=ax.get_ylim()[1] * 1.1)
savefig(tutorial_image_name('png', suffix='1d'))
# Check p2
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2,
ncols=2,
figsize=(8, 8),
gridspec_kw=dict(hspace=0.3,
wspace=0.35,
left=0.10,
right=0.90))
ax1.minorticks_on()
ax1.grid()
ax1.set_xlabel('X axis')
ax1.set_ylabel('Y axis')
ax1.set_title('TH2')
integrator.points.setLabel('Sampler\n(Gauss-Legendre)')
integrator.hist.setLabel('Integrator\n(convolution)')
sin_t.sin.setLabel('sin(ax+by)')
arg_t.sum.setLabel('ax+by')
# Make 2d color plot
fig = plt.figure()
ax = plt.subplot(111, xlabel='x', ylabel='y', title=r'$\int\int\sin(ax+by)$')
ax.minorticks_on()
ax.set_aspect('equal')
# Draw the function and integrals
integrator.hist.hist.plot_pcolormesh(colorbar=True)
# 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')
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')
ax.set_title('Plot title (left)')
ax.minorticks_on()
ax.grid()
ax.set_xlabel('x label')
ax.set_ylabel('y label')
plt.ticklabel_format(style='sci',
axis='y',
scilimits=(-2, 2),
useMathText=True)
hist1.hist.hist.plot_hist(label='exp(+)')
hist2.hist.hist.plot_hist(label='exp(-)')
hist3.hist.hist.plot_hist(label='gauss')
ax.legend()
savefig(tutorial_image_name('png', suffix='hist'))
fig = plt.figure()
ax = plt.subplot(111)
ax.set_title('Plot title (left)')
ax.minorticks_on()
ax.grid()
ax.set_xlabel('x label')
ax.set_ylabel('y label')
plt.ticklabel_format(style='sci',
axis='y',
scilimits=(-2, 2),
useMathText=True)
hist1.hist.hist.plot_bar(label='exp(+)', alpha=0.4)
hist2.hist.hist.plot_bar(label='exp(-)', alpha=0.4)
