Skip to content

implement POI scaling for 2D scan #142

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
davidwalter2 merged 1 commit into from
Jun 22, 2026
Merged

implement POI scaling for 2D scan #142

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
47 changes: 42 additions & 5 deletions bin/rabbit_plot_likelihood_scan2D.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@

import os

import hist
import matplotlib.pyplot as plt
import mplhep as hep
import numpy as np
Expand All @@ -23,6 +24,14 @@ def make_parser():
action="append",
help="Parameters to plot the likelihood scan",
)
parser.add_argument(
"--scales",
type=float,
nargs=2,
action="append",
default=[],
help="Scaling factors for parameter values",
)
parser.add_argument(
"--legPos", type=str, default="upper right", help="Set legend position"
)
Expand Down Expand Up @@ -305,6 +314,16 @@ def plot_scan(

def main():
args = make_parser().parse_args()

if len(args.scales) > len(args.params):
raise Exception("""
option "--scales" has more pairs than "--params". It can only have as many or less.
If less, pairs of 1.0 will be automatically patched.
""")
elif len(args.scales) < len(args.params):
for i in range(len(args.params) - len(args.scales)):
args.scales.append([1.0, 1.0])

fitresult, meta = io_tools.get_fitresult(args.infile, args.result, meta=True)
config = plot_tools.load_config(args.config)

Expand All @@ -322,13 +341,22 @@ def main():
if "contour_scans2D" in fitresult.keys():
h_contour = fitresult["contour_scans2D"].get()

for px, py in args.params:
px_value = h_params[{"parms": px}].value
py_value = h_params[{"parms": py}].value
for ip, (px, py) in enumerate(args.params):
px_scale, py_scale = args.scales[ip]
px_value = px_scale * h_params[{"parms": px}].value
py_value = py_scale * h_params[{"parms": py}].value

cov = None
if h_cov is not None and not args.noHessian:
cov = h_cov[{"parms_x": [px, py], "parms_y": [px, py]}].values()
# given the scalings w1 and w2, compute
# [ [c11 * w1 * w1, c12 * w1 * w2],
# [c21 * w1 * w2, c22 * w2 * w2],
# ]
w = np.array([px_scale, py_scale])
cov = (
np.outer(w, w)
* h_cov[{"parms_x": [px, py], "parms_y": [px, py]}].values()
)

h_contour_params = None
if (
Expand All @@ -340,7 +368,16 @@ def main():

h_scan = None
if f"nll_scan2D_{px}_{py}" in fitresult.keys() and not args.noScan:
h_scan = fitresult[f"nll_scan2D_{px}_{py}"].get()
h_scan_in = fitresult[f"nll_scan2D_{px}_{py}"].get()
# scale categorical axis value
cats0 = [str(float(x) * px_scale) for x in h_scan_in.axes[0]]
cats1 = [str(float(x) * py_scale) for x in h_scan_in.axes[1]]
h_scan = hist.Hist(
hist.axis.StrCategory(cats0, name=h_scan_in.axes[0].name),
hist.axis.StrCategory(cats1, name=h_scan_in.axes[1].name),
storage=h_scan_in.storage_type(),
)
h_scan.view(flow=True)[...] = h_scan_in.view(flow=True)

fig = plot_scan(
args,
Expand Down