Drop support for Python 3.11

.github/workflows/ci.yaml

@@ -15,7 +15,7 @@ jobs:
     strategy:
       matrix:
         os: [macos-latest, ubuntu-latest]
-        python-version: ["3.11", "3.12", "3.13"]
+        python-version: ["3.12", "3.13", "3.14"]
         backend: [numpy, numba, jax, torch]
 
     steps:

README.md

@@ -11,7 +11,7 @@
 [![pypi](https://img.shields.io/pypi/v/scoringrules.svg?colorB=<brightgreen>)](https://pypi.python.org/pypi/scoringrules/)
 
 `scoringrules` is a python library that provides scoring rules to evaluate probabilistic forecasts.
-It's original goal was to reproduce the functionality of the R package
+Its original goal was to reproduce the functionality of the R package
 [`scoringRules`](https://cran.r-project.org/web/packages/scoringRules/index.html) in python,
 thereby allowing forecasting practitioners working in python to enjoy the same tools as those
 working in R. The methods implemented in `scoringrules` are therefore based around those
@@ -37,7 +37,7 @@ The scoring rules available in `scoringrules` include, but are not limited to, t
 - **Didactic approach** to probabilistic forecast evaluation through clear code and documentation
 
 ## Installation
-Requires python `>=3.11`!
+Requires python `>=3.12`.
 
 ```
 pip install scoringrules

pyproject.toml

@@ -3,10 +3,10 @@ name = "scoringrules"
 version = "0.10.0"
 description = "Scoring rules for probabilistic forecast evaluation."
 readme = "README.md"
-requires-python = ">=3.11"
+requires-python = ">=3.12"
 dependencies = [
-    "numpy>=1.25.0",
-    "scipy>=1.10.0",
+    "numpy>=2.0.0",
+    "scipy>=1.14.0",
 ]
 authors = [
     {name = "Francesco Zanetta", email = "zanetta.francesco@gmail.com"},
@@ -18,12 +18,11 @@ classifiers = [
     "Development Status :: 4 - Beta",
     "Intended Audience :: Developers",
     "Intended Audience :: Science/Research",
-    "License :: OSI Approved :: MIT License",
     "License :: OSI Approved :: Apache Software License",
     "Topic :: Scientific/Engineering :: Mathematics",
-    "Programming Language :: Python :: 3.10",
-    "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+    "Programming Language :: Python :: 3.14",
 ]
 
 [project.urls]

scoringrules/visualization/reliability.py

@@ -12,7 +12,6 @@
 except ImportError:
     IR_AVAILABLE = False
 
-from scipy.interpolate import interp1d
 from scipy.stats import bernoulli
 
 
@@ -126,15 +125,7 @@ def _uncertainty_band(x, cep, n_bootstrap=100, bandtype="consistency", alpha=0.0
         elif bandtype == "confidence":
             _y = bernoulli.rvs(cep[_idx_resample])
         _x, _y, _cep = corp_reliability(_y, _x)
-        res.append(
-            interp1d(
-                _x,
-                _cep,
-                fill_value="nan",
-                bounds_error=False,
-                assume_sorted=True,
-            )(x)
-        )
+        res.append(np.interp(x, _x, _cep, left=np.nan, right=np.nan))
     res = np.array(res)
     ql = np.nanpercentile(res, alpha * 100, axis=0)
     qu = np.nanpercentile(res, (1 - alpha) * 100, axis=0)