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 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