diff --git a/dhi/plots/exclusion.py b/dhi/plots/exclusion.py
index 0df034c61949d7007af354f640d1d3d086a317e8..1a1d02f420017286ed4b3cc747ac50df305c807d 100644
--- a/dhi/plots/exclusion.py
+++ b/dhi/plots/exclusion.py
@@ -7,12 +7,10 @@ Exclusion result plots using ROOT.
import math
import numpy as np
-import scipy.interpolate
from dhi.config import poi_data, campaign_labels, colors, br_hh_names
-from dhi.util import (
- import_ROOT, DotDict, to_root_latex, create_tgraph, minimize_1d, try_int,
-)
+from dhi.util import import_ROOT, to_root_latex, create_tgraph, try_int
+from dhi.plots.limits import evaluate_limit_scan_1d
from dhi.plots.likelihoods import evaluate_likelihood_scan_1d, evaluate_likelihood_scan_2d
from dhi.plots.util import (
use_style, draw_model_parameters, invert_graph, get_graph_points, get_contours, get_text_extent,
@@ -533,63 +531,6 @@ def plot_exclusion_and_bestfit_2d(
canvas.SaveAs(path)
-def evaluate_limit_scan_1d(scan_values, limit_values):
- """
- Takes the results of an upper limit scan given by the *scan_values* and the corresponding *limit*
- values, performs an interpolation and returns certain results of the scan in a dict.
-
- The returned fields are:
-
- - ``interp``: The generated interpolation function.
- - ``excluded_ranges``: A list of 2-tuples denoting ranges in units of the poi where limits are
- below one.
- """
- scan_values = np.array(scan_values)
- limit_values = np.array(limit_values)
-
- # first, obtain an interpolation function
- mask = ~np.isnan(limit_values)
- scan_values = scan_values[mask]
- limit_values = limit_values[mask]
- # interp = scipy.interpolate.interp1d(scan_values, limit_values, kind="cubic")
- interp = scipy.interpolate.interp1d(scan_values, limit_values, kind="linear")
-
- # interpolation bounds
- bounds = (scan_values.min() + 1e-4, scan_values.max() - 1e-4)
-
- # helper to find intersections with one given a starting point
- def get_intersection(start):
- objective = lambda x: (interp(x) - 1) ** 2.0
- res = minimize_1d(objective, bounds, start=start)
- return res.x[0] if res.status == 0 and (bounds[0] < res.x[0] < bounds[1]) else None
-
- # get exclusion range edges from intersections and remove duplicates
- rnd = lambda v: round(v, 3)
- edges = [rnd(scan_values.min()), rnd(scan_values.max())]
- for start in np.linspace(bounds[0], bounds[1], 100):
- edge = get_intersection(start)
- if edge is None:
- continue
- edge = rnd(edge)
- if edge not in edges:
- edges.append(edge)
- edges.sort()
-
- # create ranges consisting of two adjacent edges
- ranges = [(edges[i - 1], edges[i]) for i in range(1, len(edges))]
-
- # select those ranges whose central value is below 1
- excluded_ranges = [
- r for r in ranges
- if interp(0.5 * (r[1] + r[0])) < 1
- ]
-
- return DotDict(
- interp=interp,
- excluded_ranges=excluded_ranges,
- )
-
-
def get_auto_contour_levels(values, steps=(1,)):
min_value = min(values)
max_value = max(values)
diff --git a/dhi/plots/likelihoods.py b/dhi/plots/likelihoods.py
index 2f838567257ef9794450e94a058ed8cbc26a5bdf..ed141eadc1f35c4d1249045d685f5f31b0702ae6 100644
--- a/dhi/plots/likelihoods.py
+++ b/dhi/plots/likelihoods.py
@@ -285,26 +285,9 @@ def plot_likelihood_scans_1d(
r.setup_line(line, props={"LineColor": colors.black, "LineStyle": 2, "NDC": False})
draw_objs.append(line)
- # theory prediction with uncertainties
- if theory_value:
- has_thy_err = len(theory_value) == 3
- if has_thy_err:
- # theory graph
- g_thy = create_tgraph(1, theory_value[0], y_min, theory_value[2], theory_value[1],
- 0, y_max_line)
- r.setup_graph(g_thy, props={"LineColor": colors.red, "FillStyle": 1001,
- "FillColor": colors.red_trans_50})
- draw_objs.append((g_thy, "SAME,02"))
- legend_entries.append((g_thy, "Theory prediction", "LF"))
- # theory line
- line_thy = ROOT.TLine(theory_value[0], y_min, theory_value[0], y_max_line)
- r.setup_line(line_thy, props={"NDC": False}, color=colors.red)
- draw_objs.append(line_thy)
- if not has_thy_err:
- legend_entries.append((line_thy, "Theory prediction", "L"))
-
# perform scans and draw nll curves
- for d, col, ms in zip(data, color_sequence[:len(data)], marker_sequence[:len(data)]):
+ for d, col, ms in zip(data[::-1], color_sequence[:len(data)][::-1],
+ marker_sequence[:len(data)][::-1]):
# evaluate the scan, run interpolation and error estimation
scan = evaluate_likelihood_scan_1d(d["values"][poi], d["values"]["dnll2"],
poi_min=d["poi_min"])
@@ -315,7 +298,7 @@ def plot_likelihood_scans_1d(
r.setup_graph(g_nll, props={"LineWidth": 2, "MarkerStyle": ms, "MarkerSize": 1.2},
color=colors[col])
draw_objs.append((g_nll, "SAME,CP" if show_points else "SAME,C"))
- legend_entries.append((g_nll, to_root_latex(br_hh_names.get(d["name"], d["name"])),
+ legend_entries.insert(-1, (g_nll, to_root_latex(br_hh_names.get(d["name"], d["name"])),
"LP" if show_points else "L"))
# line for best fit value
@@ -323,6 +306,24 @@ def plot_likelihood_scans_1d(
r.setup_line(line_fit, props={"LineWidth": 2, "NDC": False}, color=colors[col])
draw_objs.append(line_fit)
+ # theory prediction with uncertainties
+ if theory_value:
+ has_thy_err = len(theory_value) == 3
+ if has_thy_err:
+ # theory graph
+ g_thy = create_tgraph(1, theory_value[0], y_min, theory_value[2], theory_value[1],
+ 0, y_max_line)
+ r.setup_graph(g_thy, props={"LineColor": colors.red, "FillStyle": 1001,
+ "FillColor": colors.red_trans_50})
+ draw_objs.insert(-len(data), (g_thy, "SAME,02"))
+ legend_entries.append((g_thy, "Theory prediction", "LF"))
+ # theory line
+ line_thy = ROOT.TLine(theory_value[0], y_min, theory_value[0], y_max_line)
+ r.setup_line(line_thy, props={"NDC": False}, color=colors.red)
+ draw_objs.append(line_thy)
+ if not has_thy_err:
+ legend_entries.insert(-len(data), (line_thy, "Theory prediction", "L"))
+
# legend
legend_cols = min(int(math.ceil(len(legend_entries) / 4.)), 3)
legend_rows = int(math.ceil(len(legend_entries) / float(legend_cols)))
@@ -590,7 +591,7 @@ def plot_likelihood_scans_2d(
draw_objs.append((h_dummy, "HIST"))
# loop through data entries
- for d, (cont1, cont2), col in zip(data, contours, color_sequence[:len(data)]):
+ for d, (cont1, cont2), col in zip(data[::-1], contours[::-1], color_sequence[:len(data)][::-1]):
# evaluate the scan
scan = evaluate_likelihood_scan_2d(
d["values"][poi1], d["values"][poi2], d["values"]["dnll2"],
@@ -605,7 +606,7 @@ def plot_likelihood_scans_2d(
r.setup_graph(g2, props={"LineWidth": 2, "LineStyle": 2, "LineColor": colors[col]})
draw_objs.append((g2, "SAME,C"))
name = to_root_latex(br_hh_names.get(d["name"], d["name"]))
- legend_entries.append((g1, name, "L"))
+ legend_entries.insert(-1, (g1, name, "L"))
# best fit point
g_fit = create_tgraph(1, scan.num1_min(), scan.num2_min())
diff --git a/dhi/plots/limits.py b/dhi/plots/limits.py
index abfe18b489cd25ba386bebc9d795d3a2faefc979..3c25f8156bc0e3d9cbf06989b6a2ce454e27c9c1 100644
--- a/dhi/plots/limits.py
+++ b/dhi/plots/limits.py
@@ -7,11 +7,14 @@ Limit plots using ROOT.
import math
import numpy as np
+import scipy.interpolate
from dhi.config import (
poi_data, br_hh_names, campaign_labels, colors, color_sequence, marker_sequence,
)
-from dhi.util import import_ROOT, to_root_latex, create_tgraph, try_int, colored
+from dhi.util import (
+ import_ROOT, DotDict, to_root_latex, create_tgraph, try_int, colored, minimize_1d,
+)
from dhi.plots.util import (
use_style, draw_model_parameters, create_hh_process_label, determine_limit_digits,
get_graph_points,
@@ -342,7 +345,7 @@ def plot_limit_scans(
color=colors[col])
draw_objs.append((g_exp, "SAME,CP" if show_points else "SAME,C"))
name = names[n_graphs - i - 1]
- legend_entries.append((g_exp, to_root_latex(br_hh_names.get(name, name)),
+ legend_entries.insert(0, (g_exp, to_root_latex(br_hh_names.get(name, name)),
"LP" if show_points else "L"))
y_max_value = max(y_max_value, max(ev["limit"]))
y_min_value = min(y_min_value, min(ev["limit"]))
@@ -863,3 +866,60 @@ def plot_benchmark_limits(
# save
r.update_canvas(canvas)
canvas.SaveAs(path)
+
+
+def evaluate_limit_scan_1d(scan_values, limit_values):
+ """
+ Takes the results of an upper limit scan given by the *scan_values* and the corresponding *limit*
+ values, performs an interpolation and returns certain results of the scan in a dict.
+
+ The returned fields are:
+
+ - ``interp``: The generated interpolation function.
+ - ``excluded_ranges``: A list of 2-tuples denoting ranges in units of the poi where limits are
+ below one.
+ """
+ scan_values = np.array(scan_values)
+ limit_values = np.array(limit_values)
+
+ # first, obtain an interpolation function
+ mask = ~np.isnan(limit_values)
+ scan_values = scan_values[mask]
+ limit_values = limit_values[mask]
+ # interp = scipy.interpolate.interp1d(scan_values, limit_values, kind="cubic")
+ interp = scipy.interpolate.interp1d(scan_values, limit_values, kind="linear")
+
+ # interpolation bounds
+ bounds = (scan_values.min() + 1e-4, scan_values.max() - 1e-4)
+
+ # helper to find intersections with one given a starting point
+ def get_intersection(start):
+ objective = lambda x: (interp(x) - 1) ** 2.0
+ res = minimize_1d(objective, bounds, start=start)
+ return res.x[0] if res.status == 0 and (bounds[0] < res.x[0] < bounds[1]) else None
+
+ # get exclusion range edges from intersections and remove duplicates
+ rnd = lambda v: round(v, 3)
+ edges = [rnd(scan_values.min()), rnd(scan_values.max())]
+ for start in np.linspace(bounds[0], bounds[1], 100):
+ edge = get_intersection(start)
+ if edge is None:
+ continue
+ edge = rnd(edge)
+ if edge not in edges:
+ edges.append(edge)
+ edges.sort()
+
+ # create ranges consisting of two adjacent edges
+ ranges = [(edges[i - 1], edges[i]) for i in range(1, len(edges))]
+
+ # select those ranges whose central value is below 1
+ excluded_ranges = [
+ r for r in ranges
+ if interp(0.5 * (r[1] + r[0])) < 1
+ ]
+
+ return DotDict(
+ interp=interp,
+ excluded_ranges=excluded_ranges,
+ )
diff --git a/dhi/plots/significances.py b/dhi/plots/significances.py
index fd0e79d63e535aa1b5779e48bb730db4272d63f9..b6a3a3918c91f5f40d3dbd7cb5c56d09fd5146b6 100644
--- a/dhi/plots/significances.py
+++ b/dhi/plots/significances.py
@@ -246,7 +246,7 @@ def plot_significance_scans(
"MarkerSize": 1.2}, color=colors[col])
draw_objs.append((g_exp, "SAME,CP" if show_points else "SAME,C"))
name = names[n_graphs - i - 1]
- legend_entries.append((g_exp, to_root_latex(br_hh_names.get(name, name)),
+ legend_entries.insert(0, (g_exp, to_root_latex(br_hh_names.get(name, name)),
"LP" if show_points else "L"))
y_max_value = max(y_max_value, max(ev["significance"]))
y_min_value = min(y_min_value, min(ev["significance"]))
diff --git a/dhi/tasks/limits.py b/dhi/tasks/limits.py
index 5903b1064bb0db78a8a7ebcd3cc1db69583dd39f..589447f761d3cc664412caa77bad08a8dfd49247 100644
--- a/dhi/tasks/limits.py
+++ b/dhi/tasks/limits.py
@@ -415,6 +415,8 @@ class PlotMultipleUpperLimitsByModel(PlotUpperLimits, MultiHHModelTask):
unblinded = None
+ allow_empty_hh_model = True
+
def requires(self):
return [
[