[feat] Implementation of two-sided tests with lower and upper bounds on the POI

.github/workflows/check+build+deploy.yaml

@@ -0,0 +1,77 @@
+name: Check/Build/Deploy
+
+on:
+  release:
+    types: [published]
+  push:
+    branches: ["releases/**"]
+  workflow_dispatch:
+
+jobs:
+    check:
+        name: OS ${{ matrix.os }}, Python ${{ matrix.python-version }}
+        runs-on: ${{ matrix.os }}
+
+        strategy:
+          fail-fast: false
+          matrix:
+            python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+            os: [ubuntu-latest, macos-latest]
+
+        steps:
+        - uses: actions/checkout@v3
+        - name: Set up Python ${{ matrix.python-version }}
+          uses: actions/setup-python@v3
+          with:
+            python-version: ${{ matrix.python-version }}
+        - name: Install dependencies
+          run: |
+            python -m pip install --upgrade pip
+            python -m pip install pytest pytest-cov
+            python -m pip install -e .
+        - name: Test with pytest
+          run: |
+            python -c 'import spey;spey.about()'
+            pytest --cov=spey tests/*py #--cov-fail-under 99
+
+    build:
+        name: Build wheel
+        runs-on: ubuntu-latest
+        needs: check
+        steps:
+          - name: Checkout git repository
+            uses: actions/checkout@v4
+
+          - name: Build Spey wheel
+            run: |
+              python -m pip install --upgrade pip
+              python -m pip install wheel
+              python setup.py bdist_wheel
+
+          - name: Upload wheels as artifacts
+            uses: actions/upload-artifact@v4
+            with:
+              name: wheel
+              path: ./dist/*.whl
+
+    deploy:
+        if:  ${{ !github.event.release.prerelease && !contains(github.ref, '-rc') && !contains(github.ref, '-beta') && !contains(github.ref, '-alpha') }}
+        runs-on: ubuntu-latest
+        needs: build
+        permissions:
+          id-token: write
+        steps:
+          - uses: actions/download-artifact@v4
+            with:
+              pattern: wheel
+              merge-multiple: true
+              path: dist
+
+          - name: Debug
+            run: ls -l dist
+
+          - uses: pypa/[email protected]
+            with:
+              user: ${{ secrets.TWINE_USERNAME }}
+              password: ${{ secrets.TWINE_PSSWRD }}
+              repository-url: https://pypi.org/project/spey/

.github/workflows/tests.yml

@@ -18,7 +18,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12.6"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
         os: [ubuntu-latest, macos-latest]
 
     steps:

.zenodo.json

@@ -1,8 +1,8 @@
 {
     "description": "smooth inference for reinterpretation studies",
     "license": "MIT",
-    "title": "SpeysideHEP/spey: v0.1.11",
-    "version": "v0.1.11",
+    "title": "SpeysideHEP/spey: v0.2.0",
+    "version": "v0.2.0",
     "upload_type": "software",
     "creators": [
         {
@@ -31,7 +31,7 @@
         },
         {
             "scheme": "url",
-            "identifier": "https://github.com/SpeysideHEP/spey/tree/v0.1.11",
+            "identifier": "https://github.com/SpeysideHEP/spey/tree/v0.2.0",
             "relation": "isSupplementTo"
         },
         {

README.md

@@ -55,13 +55,13 @@ Finally, the name "Spey" originally comes from the Spey River, a river in the mi
 
 | Accessor                                | Description                                                                  |
 | --------------------------------------- | ---------------------------------------------------------------------------- |
-| ``"default_pdf.uncorrelated_background"`` | Constructs uncorrelated multi-bin statistical model.                         |
-| ``"default_pdf.correlated_background"``   | Constructs correlated multi-bin statistical model with Gaussian nuisances.   |
-| ``"default_pdf.third_moment_expansion"``  | Implements the skewness of the likelihood by using third moments.            |
-| ``"default_pdf.effective_sigma"``         | Implements the skewness of the likelihood by using asymmetric uncertainties. |
-| ``"default_pdf.poisson"``                 | Implements simple Poisson-based likelihood without uncertainties. |
-| ``"default_pdf.normal"``                  | Implements Normal distribution. |
-| ``"default_pdf.multivariate_normal"``     | Implements Multivariate normal distribution. |
+| ``"default.uncorrelated_background"`` | Constructs uncorrelated multi-bin statistical model.                         |
+| ``"default.correlated_background"``   | Constructs correlated multi-bin statistical model with Gaussian nuisances.   |
+| ``"default.third_moment_expansion"``  | Implements the skewness of the likelihood by using third moments.            |
+| ``"default.effective_sigma"``         | Implements the skewness of the likelihood by using asymmetric uncertainties. |
+| ``"default.poisson"``                 | Implements simple Poisson-based likelihood without uncertainties. |
+| ``"default.normal"``                  | Implements Normal distribution. |
+| ``"default.multivariate_normal"``     | Implements Multivariate normal distribution. |
 | ``"pyhf.uncorrelated_background"``        | Uses uncorrelated background functionality of pyhf (see [``spey-phyf`` plugin](https://github.com/SpeysideHEP/spey-pyhf)).                          |
 | ``"pyhf"``                                | Uses generic likelihood structure of pyhf (see [``spey-phyf`` plugin](https://github.com/SpeysideHEP/spey-pyhf))                                   |
 
@@ -75,17 +75,17 @@ likelihood prescriptions which can be checked via `AvailableBackends` function
 ```python
 import spey
 print(spey.AvailableBackends())
-# ['default_pdf.correlated_background',
-#  'default_pdf.effective_sigma',
-#  'default_pdf.third_moment_expansion',
-#  'default_pdf.uncorrelated_background']
+# ['default.correlated_background',
+#  'default.effective_sigma',
+#  'default.third_moment_expansion',
+#  'default.uncorrelated_background']
 ```
 
-Using ``'default_pdf.uncorrelated_background'`` one can simply create single or multi-bin
+Using ``'default.uncorrelated_background'`` one can simply create single or multi-bin
 statistical models:
 
 ```python
-pdf_wrapper = spey.get_backend('default_pdf.uncorrelated_background')
+pdf_wrapper = spey.get_backend('default.uncorrelated_background')
 
 data = [1]
 signal_yields = [0.5]
@@ -147,7 +147,7 @@ collaborations to estimate the difference from the Standard Model rather than th
 One can play the same game using the same backend for multi-bin histograms as follows;
 
 ```python
-pdf_wrapper = spey.get_backend('default_pdf.uncorrelated_background')
+pdf_wrapper = spey.get_backend('default.uncorrelated_background')
 
 data = [36, 33]
 signal = [12.0, 15.0]

docs/bib/cited.bib

@@ -1,3 +1,15 @@
+@article{Ghosh:2024puc,
+    author        = {Ghosh, Kirtiman and Huitu, Katri and Sahu, Rameswar},
+    title         = {{Revisiting Universal Extra-Dimension Model with Gravity Mediated Decays}},
+    eprint        = {2412.09344},
+    archiveprefix = {arXiv},
+    primaryclass  = {hep-ph},
+    month         = {12},
+    year          = {2024},
+    journal       = {}
+}
+
+
 @article{Goodsell:2024aig,
     author        = {Goodsell, Mark D.},
     title         = {{HackAnalysis 2: A powerful and hackable recasting tool}},
@@ -15,25 +27,29 @@ @article{Agin:2024yfs
     eprint        = {2404.12423},
     archiveprefix = {arXiv},
     primaryclass  = {hep-ph},
-    month         = {4},
-    year          = {2024},
-    journal       = {}
+    doi           = {10.1140/epjc/s10052-024-13594-9},
+    journal       = {Eur. Phys. J. C},
+    volume        = {84},
+    number        = {11},
+    pages         = {1218},
+    year          = {2024}
 }
 
 
 @article{Feike:2024zfz,
-    archiveprefix = {arXiv},
     author        = {Feike, Alexander and Fiaschi, Juri and Fuks, Benjamin and Klasen, Michael and Puck Neuwirth, Alexander},
+    title         = {{Combination and reinterpretation of LHC SUSY searches}},
     eprint        = {2403.11715},
-    month         = {3},
+    archiveprefix = {arXiv},
     primaryclass  = {hep-ph},
     reportnumber  = {MS-TP-23-49},
-    title         = {{Combination and Reinterpretation of LHC SUSY Searches}},
-    year          = {2024},
-    journal       = {}
+    doi           = {10.1007/JHEP07(2024)122},
+    journal       = {JHEP},
+    volume        = {07},
+    pages         = {122},
+    year          = {2024}
 }
 
-
 @article{Elmer:2023wtr,
     author        = {Elmer, Nina and Madigan, Maeve and Plehn, Tilman and Schmal, Nikita},
     title         = {{Staying on Top of SMEFT-Likelihood Analyses}},

docs/comb.rst

@@ -39,7 +39,7 @@ Let us first import all the necessary packages and construct the data (please ad
     >>> models = {}
     >>> # loop overall data
     >>> for data in example_data["data"]:
-    >>>     pdf_wrapper = spey.get_backend("default_pdf.uncorrelated_background")
+    >>>     pdf_wrapper = spey.get_backend("default.uncorrelated_background")
 
     >>>     stat_model = pdf_wrapper(
     ...         signal_yields=data["signal_yields"],

docs/exclusion.md

@@ -73,7 +73,7 @@ The `allow_negative_signal` keyword controls which test statistic is used and re
 
 For complex statistical models, optimizing the likelihood can be challenging and depends on the choice of optimizer. Spey uses SciPy for optimization and fitting tasks. Any additional keyword arguments not explicitly covered in the {func}`~spey.StatisticalModel.exclusion_confidence_level` function description are passed directly to the optimizer, allowing users to customize its behavior through the interface.
 
-Below we compare the exclusion limits computed with each approach. This comparisson uses normal distribution for the likelihood ([`default_pdf.normal`](#normal)) background yields are set to $n_b$, uncertainties are shown with $\sigma$ and observations are given with $n$.
+Below we compare the exclusion limits computed with each approach. This comparisson uses normal distribution for the likelihood ([`default.normal`](#normal)) background yields are set to $n_b$, uncertainties are shown with $\sigma$ and observations are given with $n$.
 
 ```{figure} ./figs/comparisson_observed.png
 ---
@@ -90,7 +90,7 @@ exclusion limit calculator comparisson for observed p-values
 ```{code-cell} ipython3
 import spey
 
-stat_wrapper = spey.get_backend("default_pdf.normal")
+stat_wrapper = spey.get_backend("default.normal")
 statistical_model = stat_wrapper(
     signal_yields=[3.0],
     background_yields=[2.0],