Modernize packaging

.github/workflows/main.yml

@@ -17,31 +17,36 @@ env:
   GITHUB_TOKEN: ${{ secrets.COVERALLS_TOKEN }}
 
 jobs:
-  source_check:
+  lint:
     name: Linting (Python ${{ matrix.python-version }})
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
       matrix:
-        python-version: [ '3.12' ]
+        python-version: [ '3.13' ]
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
+        with:
+          persist-credentials: false
 
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
 
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          python -m pip install black 'isort[colors]<6'
-          python -m pip install --editable .
+          python -m pip install --group lint
 
+      - name: Linting checks
+        run: |
+          black --check src/spotpy
+          isort --check src/spotpy
 
-  build_sdist:
-    name: sdist on ${{ matrix.os }} with Python ${{ matrix.python-version }}
+  test_build:
+    name: Test and build (${{ matrix.os }}, Python ${{ matrix.python-version }})
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
@@ -50,20 +55,24 @@ jobs:
         python-version: [ '3.10', '3.13' ]
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           fetch-depth: '0'
+          persist-credentials: false
 
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
 
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          python -m pip install build pytest-cov
-          python -m pip install --editable .[test]
+          python -m pip install --group build --group test
+
+      - name: Install spotpy with optional dependencies
+        run: |
+          python -m pip install --editable ".[database,describe,mpi,plotting]"
 
       - name: Run tests
         run: |
@@ -82,20 +91,21 @@ jobs:
         run: |
           python -m build
 
-      - uses: actions/upload-artifact@v4
-        if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.13'
+      - uses: actions/upload-artifact@v7
+        if: (matrix.os == 'ubuntu-latest') && (matrix.python-version == '3.13')
         with:
           name: dist-sdist
           path: dist/
 
   upload_to_pypi:
     name: Upload to PyPI/Test PyPI
     needs:
-      - build_sdist
+      - test_build
+    if: (github.ref == 'refs/heads/master') || (startsWith(github.ref, 'refs/tags'))
     runs-on: ubuntu-latest
     steps:
       - name: Download all the dists
-        uses: actions/download-artifact@v4
+        uses: actions/download-artifact@v8
         with:
           name: dist-sdist
           path: dist/

MANIFEST.in

@@ -2,5 +2,5 @@ prune **
 graft src/spotpy
 graft tests
 graft tutorials
-include LICENSE README.md pyproject.toml setup.py
+include LICENSE README.md pyproject.toml
 global-exclude __pycache__ *.py[co]

README.md

@@ -7,7 +7,7 @@ A Statistical Parameter Optimization Tool for Python
 [![Python Versions][pypi-pyv-image]][pypi-pyv-link]
 [![License][license-image]][license-link]
 [![Coverage Status](https://coveralls.io/repos/github/thouska/spotpy/badge.svg?branch=master)](https://coveralls.io/github/thouska/spotpy?branch=master)
-[![DOI](https://zenodo.org/badge/47562322.svg)](https://zenodo.org/badge/latestdoi/47562322)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4075177.svg)](https://doi.org/10.5281/zenodo.4075177)
 
 [pypi-v-image]: https://img.shields.io/pypi/v/spotpy.png
 [pypi-v-link]: https://pypi.python.org/pypi/spotpy

pyproject.toml

@@ -1,10 +1,23 @@
 [build-system]
 requires = [
-    "setuptools>=64",
-    "setuptools_scm[toml]>=6.4",
+    "setuptools>=80",
+    "setuptools_scm[toml]>=9.2",
 ]
 build-backend = "setuptools.build_meta"
 
+[dependency-groups]
+build = [
+    "build"
+]
+lint = [
+    "black >=26.3.0",
+    "isort >=8.0.1"
+]
+test = [
+    "pytest >=9.0",
+    "pytest-cov >=7.1.0",
+]
+
 [project]
 requires-python = ">=3.10"
 name = "spotpy"
@@ -21,7 +34,8 @@ maintainers = [
     {name = "Tobias Houska", email = "tobias.houska@umwelt.uni-giessen.de"},
 ]
 readme = "README.md"
-license = {file = "LICENSE"}
+license = "MIT"
+license-files = ["LICENSE"]
 dynamic = ["version"]
 keywords = [
     "Monte Carlo",
@@ -47,7 +61,6 @@ classifiers = [
     "Intended Audience :: End Users/Desktop",
     "Intended Audience :: Science/Research",
     "Intended Audience :: Education",
-    "License :: OSI Approved :: MIT License",
     "Natural Language :: English",
     "Operating System :: Unix",
     "Operating System :: Microsoft",
@@ -66,25 +79,29 @@ classifiers = [
     "Topic :: Utilities",
 ]
 dependencies = [
-    "numpy>=2.0",
-    "scipy>=1.5.0",
+    "click >=8.1",
+    "numpy >=2.0",
+    "scipy >=1.12.0",
 ]
 
 [project.optional-dependencies]
-plotting = [
-    "pandas>=1",
-    "matplotlib>=3",
+database = [
+    "tables"
 ]
-test = [
-    "pytest-cov>=3",
-    "numba",
-    "pathos",
-    "matplotlib",
-    "click",
-    "pandas",
-    "tables",
-    "docutils",
+describe = [
+    "docutils"
+]
+mpi = [
+    "mpi4py",
+    "pathos"
 ]
+plotting = [
+    "pandas >=2.2.0",
+    "matplotlib >=3.8.0",
+]
+
+[project.scripts]
+spotpy = "spotpy.cli:cli"
 
 [project.urls]
 Changelog = "https://github.com/thouska/spotpy/blob/master/CHANGELOG.md"
@@ -108,11 +125,10 @@ multi_line_output = 3
 [tool.black]
 exclude = "_version.py"
 target-version = [
-    "py39",
     "py310",
     "py311",
 	"py312",
-    "py313",
+    "py313"
 ]
 
 [tool.coverage]

src/spotpy/__init__.py

@@ -31,6 +31,7 @@
 
 Please cite our paper, if you are using SPOTPY.
 """
+
 from . import algorithms  # Contains all the different algorithms implemented in SPOTPY
 from . import (
     analyser,  # Contains some examples to analyse the results of the different algorithms

src/spotpy/algorithms/__init__.py

@@ -14,7 +14,6 @@
 To reduce dependencies, one may select here just the needed algorithm.
 """
 
-
 from ._algorithm import _algorithm
 from .abc import abc  # Artificial Bee Colony
 from .dds import dds  # Dynamically Dimensioned Search algorithm
@@ -28,11 +27,11 @@
 from .mc import mc  # Monte Carlo
 from .mcmc import mcmc  # Metropolis Markov Chain Monte Carlo
 from .mle import mle  # Maximum Likelihood Estimation
+from .morris import morris  # Morris Screening Sensitivity Test
 from .nsgaii import (
     NSGAII,  # A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II
 )
 from .padds import padds  # Pareto Archived - Dynamicallly Dimensioned Search algorithm
 from .rope import rope  # RObust Parameter Estimation
 from .sa import sa  # Simulated annealing
 from .sceua import sceua  # Shuffled Complex Evolution
-from .morris import morris # Morris Screening Sensitivity Test

src/spotpy/algorithms/efast.py

@@ -564,4 +564,4 @@ def calc_sensitivity(self, results, dbname, freq="cukier"):
             np.savetxt(f, [sens_data], delimiter=",", fmt="%1.5f")
 
         f.close()
-        return sens_data
+        return sens_data

src/spotpy/algorithms/fast.py

@@ -149,12 +149,10 @@ def analyze(self, problem, Y, D, parnames, M=4, print_to_console=False):
                 """
             )
         else:
-            print(
-                """
+            print("""
                 Error: Number of samples in model output file must be a multiple of D,
                 where D is the number of parameters in your parameter file.
-              """
-            )
+              """)
             exit()
 
         # Recreate the vector omega used in the sampling

src/spotpy/algorithms/list_sampler.py

@@ -4,6 +4,7 @@
 This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY).
 :author: Tobias Houska
 """
+
 from .. import analyser
 from . import _algorithm
 

src/spotpy/algorithms/morris.py

@@ -19,13 +19,13 @@ class morris(_algorithm):
     This class holds the Morris Screening Sensitivity Test (MORRIS) based on Morris (1991), Campolongo et al (2007) and Ruano et al. (2012):
 
     Morris, M.D., 1991, Factorial Sampling Plans for Preliminary Computational Experiments. Technometrics 33, 161-174.
-       
+
     Campolongo, F., Cariboni, J., & Saltelli, A. 2007. An effective screening design for sensitivity analysis of large
     models. Environmental Modelling & Software, 22(10), 1509-1518.
 
     Ruano, M.V., Ribes, J., Seco, A., Ferrer, J., 2012. An improved sampling strategy based on trajectory design for
     application of the Morris method to systems with many input factors. Environmental Modelling & Software 37, 103-109.
-    
+
     The presented code is based on SALib
     Copyright (C) 2013-2015 Jon Herman and others. Licensed under the GNU Lesser General Public License.
     The Sensitivity Analysis Library is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
@@ -98,18 +98,29 @@ def scale_samples(self, params, bounds):
     def matrix(self, bounds, problem, N, M=4):
         from SALib.sample import morris as morris_sample
 
-        X = morris_sample.sample(problem, N=N, num_levels=M,
-                                 optimal_trajectories=None,  # set int to use Campolongo opt design
-                                 local_optimization=True,    # Ruano-style distance improvement
-                                 seed=123)
+        X = morris_sample.sample(
+            problem,
+            N=N,
+            num_levels=M,
+            optimal_trajectories=None,  # set int to use Campolongo opt design
+            local_optimization=True,  # Ruano-style distance improvement
+            seed=123,
+        )
 
         self.scale_samples(X, bounds)
         return X
 
     def analyze(self, problem, X, Y, num_levels=4, print_to_console=False):
         from SALib.analyze import morris as morris_analyze
-        Si = morris_analyze.analyze(problem, X, Y, conf_level=0.95, num_levels=num_levels,
-                                    print_to_console=print_to_console)
+
+        Si = morris_analyze.analyze(
+            problem,
+            X,
+            Y,
+            conf_level=0.95,
+            num_levels=num_levels,
+            print_to_console=print_to_console,
+        )
         return Si
 
     def sample(self, repetitions, num_levels=4):
@@ -132,7 +143,6 @@ def sample(self, repetitions, num_levels=4):
         # distribution
         parmin, parmax = self.parameter()["minbound"], self.parameter()["maxbound"]
 
-
         bounds = []
         for i in range(len(parmin)):
             bounds.append([parmin[i], parmax[i]])
@@ -142,14 +152,14 @@ def sample(self, repetitions, num_levels=4):
         # Assume df_params from your existing script
         # columns: name, change_type, lower_bound, upper_bound, subbasins
         problem = {
-            'num_vars': len(names),
-            'names': names.tolist(),  # or name_spotpy if you expanded names
-            'bounds':  bounds
+            "num_vars": len(names),
+            "names": names.tolist(),  # or name_spotpy if you expanded names
+            "bounds": bounds,
             #'bounds': self.parameter()[['minbound','maxbound']].values.tolist()
         }
 
-        k = problem['num_vars']
-        N = 15          # number of trajectories (adjust for robustness vs runtime)
+        k = problem["num_vars"]
+        N = 15  # number of trajectories (adjust for robustness vs runtime)
         num_levels = num_levels  # classic Morris grid
 
         # Create an Matrix to store the parameter sets
@@ -179,10 +189,16 @@ def sample(self, repetitions, num_levels=4):
         try:
             data = self.datawriter.getdata()
             # this is likely to crash if database does not assign name 'like1'
-            Si = self.analyze(problem, Matrix, data["like1"], num_levels=num_levels, print_to_console=False)
+            Si = self.analyze(
+                problem,
+                Matrix,
+                data["like1"],
+                num_levels=num_levels,
+                print_to_console=False,
+            )
             # Si = self.analyze(
             #     bounds, data["like1"], len(bounds), names, M=num_levels, print_to_console=True
             # )
-            print(Si['mu_star'])
+            print(Si["mu_star"])
         except AttributeError:  # Happens if no database was assigned
             pass

src/spotpy/algorithms/padds.py

@@ -382,7 +382,7 @@ def calc_initial_pareto_front(self, its):
                     ]
                 )
             else:
-                (self.pareto_front, dominance_flag) = nd_check(
+                self.pareto_front, dominance_flag = nd_check(
                     self.pareto_front,
                     self.obj_func_current,
                     self.parameter_current.copy(),

src/spotpy/algorithms/rope.py

@@ -4,6 +4,7 @@
 This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY).
 :author: Tobias Houska and Alejandro Chamorro-Chavez
 """
+
 import random
 import time