Merge pull request #15 from SpeysideHEP/simplify-bugfix

Bugfix in Simplify

.zenodo.json

@@ -1,8 +1,8 @@
 {
     "description": "pyhf plug-in for spey package",
     "license": "MIT",
-    "title": "SpeysideHEP/spey-pyhf: v0.1.7",
-    "version": "v0.1.7",
+    "title": "SpeysideHEP/spey-pyhf: v0.1.8",
+    "version": "v0.1.8",
     "upload_type": "software",
     "creators": [
         {
@@ -29,7 +29,7 @@
         },
         {
             "scheme": "url",
-            "identifier": "https://github.com/SpeysideHEP/spey-pyhf/tree/v0.1.7",
+            "identifier": "https://github.com/SpeysideHEP/spey-pyhf/tree/v0.1.8",
             "relation": "isSupplementTo"
         },
         {

docs/releases/changelog-v0.1.md

@@ -42,6 +42,9 @@
   [#5](https://github.com/SpeysideHEP/spey-pyhf/issues/5).
   ([#2](https://github.com/SpeysideHEP/spey-pyhf/pull/2))
 
+* Bugfix in uncertainty quantification for full statistical model mapping on effective sigma
+  ([#15](https://github.com/SpeysideHEP/spey-pyhf/pull/15))
+
 ## Contributors
 
 This release contains contributions from (in alphabetical order):

setup.py

@@ -37,7 +37,7 @@
     },
     download_url=f"https://github.com/SpeysideHEP/spey-pyhf/archive/refs/tags/v{version}.tar.gz",
     author="Jack Y. Araz",
-    author_email=("[email protected]"),
+    author_email=("[email protected]"),
     license="MIT",
     package_dir={"": "src"},
     packages=find_packages(where="src"),

src/spey_pyhf/_version.py

@@ -1,3 +1,3 @@
 """Version of the spey - pyhf plugin"""
 
-__version__ = "0.1.7"
+__version__ = "0.1.8"

src/spey_pyhf/simplify.py

@@ -428,8 +428,12 @@ def __call__(
         elif convert_to == "default_pdf.effective_sigma":
             # Get 68% quantiles
             q = (1.0 - (norm.cdf(1.0) - norm.cdf(-1.0))) / 2.0
+            # upper and lower uncertainties
             absolute_uncertainty_envelops = np.stack(
-                [np.quantile(samples, q, axis=0), np.quantile(samples, 1 - q, axis=0)],
+                [
+                    np.abs(np.quantile(samples, 1 - q, axis=0) - background_yields),
+                    np.abs(background_yields - np.quantile(samples, q, axis=0)),
+                ],
                 axis=1,
             )
             save_kwargs.update(