def figure_style1(pylab):
f = pylab.gcf()
size = 1.57 * 3
ratio = 1
f.set_size_inches((size, size * ratio))
ieee_spines(pylab)
yield pylab
y_axis_extra_space(pylab)
x_axis_extra_space(pylab)
def report_predstats(id_discdds, id_subset, id_distances, records):
r = Report('predistats-%s-%s' % (id_discdds, id_subset))
print records.dtype
r.data('records', records)
f = r.figure()
colors = list(islice(cycle(['r', 'g', 'b', 'k', 'y', 'm']), 50))
delta = records['delta']
W = 0.2
# pdb.set_trace()
# Save the raw values
for i, id_d in enumerate(id_distances):
r.data(id_d, records[id_d])
with f.plot('values_order', **dp_predstats_fig) as pylab:
ax = pylab.subplot(111)
for i, id_d in enumerate(id_distances):
distance = records[id_d]
distance_order = scale_score(distance) / (float(distance.size) - 1)
step = float(i) / max(len(id_distances) - 1, 1)
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax, delta + xstep, distance_order,
colors[i], perc=10, label=id_d)
ieee_spines(pylab)
ticks = sorted(list(set(list(delta))))
pylab.xlabel('interval length')
pylab.ylabel('normalized distance')
pylab.xticks(ticks, ticks)
pylab.yticks((0, 1), (0, 1))
pylab.axis((0.5, 0.5 + np.max(delta), -0.024, 1.2))
legend_put_below(ax)
with f.plot('values', **dp_predstats_fig) as pylab:
ax = pylab.subplot(111)
for i, id_d in enumerate(id_distances):
distance = records[id_d]
step = float(i) / max(len(id_distances) - 1, 1)
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax, delta + xstep, distance,
colors[i], perc=10, label=id_d)
ieee_spines(pylab)
ticks = sorted(list(set(list(delta))))
pylab.xlabel('interval length')
pylab.ylabel('distance')
pylab.xticks(ticks, ticks)
# pylab.yticks((0, 1), (0, 1))
a = pylab.axis()
pylab.axis((0.5, 0.5 + np.max(delta), -0.024, a[3]))
legend_put_below(ax)
return r
def figure_style1(pylab):
f = pylab.gcf()
size = 1.57 * 3
ratio = 1
f.set_size_inches((size, size * ratio))
ieee_spines(pylab)
yield pylab
y_axis_extra_space(pylab)
x_axis_extra_space(pylab)
def report_statistics_all(id_sub, stats, perc=10, W=0.2):
records = stats['records']
r = Report('statsall-%s' % id_sub)
r.data('records', records)
f = r.figure()
id_distances = sorted(set(records['id_distance']))
logger.info('%s: %s %s reo %s' % (id_sub, len(stats), id_distances,
len(records)))
colors = list(islice(cycle(['r', 'g', 'b', 'k', 'y', 'm']), 50))
with f.plot('distance_order', **dp_predstats_fig) as pylab:
ax = pylab.subplot(111)
for i, id_d in enumerate(id_distances):
which = records['id_distance'] == id_d
delta = records[which]['delta']
distance = records[which]['distance']
order = scale_score(distance)
order = order / float(order.size)
step = float(i) / (max(len(id_distances) - 1, 1))
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax, delta + xstep, order,
colors[i], perc=perc, label=id_d)
ieee_spines(pylab)
ticks = sorted(list(set(list(delta))))
pylab.xlabel('plan length')
pylab.ylabel('normalized distance')
pylab.xticks(ticks, ticks)
pylab.yticks((0, 1), (0, 1))
pylab.axis((0.5, 0.5 + np.max(delta), -0.024, 1.2))
legend_put_below(ax)
with f.plot('distance', **dp_predstats_fig) as pylab:
ax = pylab.subplot(111)
for i, id_d in enumerate(id_distances):
which = records['id_distance'] == id_d
delta = records[which]['delta']
distance = records[which]['distance']
step = float(i) / max(len(id_distances) - 1, 1)
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax, delta + xstep, distance,
colors[i], perc=perc, label=id_d)
ieee_spines(pylab)
ticks = sorted(list(set(list(delta))))
pylab.xlabel('plan length')
pylab.ylabel('distance')
pylab.xticks(ticks, ticks)
# pylab.yticks((0, 1), (0, 1))
a = pylab.axis()
pylab.axis((0.5, 0.5 + np.max(delta), -0.024, a[3]))
legend_put_below(ax)
return r
def report_predstats(id_discdds, id_subset, id_distances, records):
r = Report('predistats-%s-%s' % (id_discdds, id_subset))
r.data('records', records)
f = r.figure()
colors = list(islice(cycle(['r', 'g', 'b', 'k', 'y', 'm']), 50))
delta = records['delta']
W = 0.2
# Save the raw values
for i, id_d in enumerate(id_distances):
r.data(id_d, records[id_d])
with f.plot('values_order', **dp_predstats_fig) as pylab:
ax = pylab.subplot(111)
for i, id_d in enumerate(id_distances):
distance = records[id_d]
distance_order = scale_score(distance) / (float(distance.size) - 1)
step = float(i) / max(len(id_distances) - 1, 1)
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax,
delta + xstep,
distance_order,
colors[i],
perc=10,
label=id_d)
ieee_spines(pylab)
ticks = sorted(list(set(list(delta))))
pylab.xlabel('interval length')
pylab.ylabel('normalized distance')
pylab.xticks(ticks, ticks)
pylab.yticks((0, 1), (0, 1))
pylab.axis((0.5, 0.5 + np.max(delta), -0.024, 1.2))
legend_put_below(ax)
with f.plot('values', **dp_predstats_fig) as pylab:
ax = pylab.subplot(111)
for i, id_d in enumerate(id_distances):
distance = records[id_d]
step = float(i) / max(len(id_distances) - 1, 1)
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax,
delta + xstep,
distance,
colors[i],
perc=10,
label=id_d)
ieee_spines(pylab)
ticks = sorted(list(set(list(delta))))
pylab.xlabel('interval length')
pylab.ylabel('distance')
pylab.xticks(ticks, ticks)
# pylab.yticks((0, 1), (0, 1))
a = pylab.axis()
pylab.axis((0.5, 0.5 + np.max(delta), -0.024, a[3]))
legend_put_below(ax)
return r
def report_statistics_all(id_sub, stats, perc=10, W=0.2):
records = stats['records']
r = Report('statsall-%s' % id_sub)
r.data('records', records)
f = r.figure()
id_distances = sorted(set(records['id_distance']))
logger.info('%s: %s %s reo %s' %
(id_sub, len(stats), id_distances, len(records)))
colors = list(islice(cycle(['r', 'g', 'b', 'k', 'y', 'm']), 50))
with f.plot('distance_order', **dp_predstats_fig) as pylab:
ax = pylab.subplot(111)
for i, id_d in enumerate(id_distances):
which = records['id_distance'] == id_d
delta = records[which]['delta']
distance = records[which]['distance']
order = scale_score(distance)
order = order / float(order.size)
step = float(i) / (max(len(id_distances) - 1, 1))
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax,
delta + xstep,
order,
colors[i],
perc=perc,
label=id_d)
ieee_spines(pylab)
ticks = sorted(list(set(list(delta))))
pylab.xlabel('plan length')
pylab.ylabel('normalized distance')
pylab.xticks(ticks, ticks)
pylab.yticks((0, 1), (0, 1))
pylab.axis((0.5, 0.5 + np.max(delta), -0.024, 1.2))
legend_put_below(ax)
with f.plot('distance', **dp_predstats_fig) as pylab:
ax = pylab.subplot(111)
for i, id_d in enumerate(id_distances):
which = records['id_distance'] == id_d
delta = records[which]['delta']
distance = records[which]['distance']
step = float(i) / max(len(id_distances) - 1, 1)
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax,
delta + xstep,
distance,
colors[i],
perc=perc,
label=id_d)
ieee_spines(pylab)
ticks = sorted(list(set(list(delta))))
pylab.xlabel('plan length')
pylab.ylabel('distance')
pylab.xticks(ticks, ticks)
# pylab.yticks((0, 1), (0, 1))
a = pylab.axis()
pylab.axis((0.5, 0.5 + np.max(delta), -0.024, a[3]))
legend_put_below(ax)
return r
