Merge pull request #334 from Zeitsperre/typo-adjustments

Typo adjustments, black formatting, drop Python 3.9, support Python 3.13

Author: thouska

.github/workflows/main.yml

@@ -18,37 +18,40 @@ env:
 
 jobs:
   source_check:
-    name: source check
+    name: Linting (Python ${{ matrix.python-version }})
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
+      matrix:
+        python-version: [ '3.12' ]
 
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python 3.12
+      - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v5
         with:
-          python-version: 3.12
+          python-version: ${{ matrix.python-version }}
 
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install black 'isort[colors]<6'
-          pip install --editable .
+          python -m pip install black 'isort[colors]<6'
+          python -m pip install --editable .
 
       - name: isort check
         run: |
+          python -m black --check --diff --color .
           python -m isort --check --diff --color .
 
   build_sdist:
-    name: sdist on ${{ matrix.os }} with py ${{ matrix.python-version }}
+    name: sdist on ${{ matrix.os }} with Python ${{ matrix.python-version }}
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
       matrix:
-        os: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: ['3.12']
+        os: [ 'ubuntu-latest', 'windows-latest', 'macos-latest' ]
+        python-version: [ '3.10', '3.13' ]
 
     steps:
       - uses: actions/checkout@v4
@@ -63,28 +66,36 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install build coveralls>=3.0.0
-          pip install --editable .[test]
+          python -m pip install build pytest-cov
+          python -m pip install --editable .[test]
 
       - name: Run tests
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         run: |
           python -m pytest --cov spotpy --cov-report term-missing -v tests/
+
+      - name: Upload coverage to Coveralls
+        if: matrix.python-version != '3.13'
+        run: |
+          python -m pip install coveralls
           python -m coveralls --service=github
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
 
       - name: Build sdist
+        if: matrix.python-version == '3.13'
         run: |
           python -m build
 
       - uses: actions/upload-artifact@v4
-        if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.12'
+        if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.13'
         with:
           name: dist-sdist
           path: dist/
 
   upload_to_pypi:
-    needs: [build_sdist]
+    name: Upload to PyPI/Test PyPI
+    needs:
+      - build_sdist
     runs-on: ubuntu-latest
     steps:
       - name: Download all the dists

README.md

@@ -16,17 +16,13 @@ A Statistical Parameter Optimization Tool for Python
 [license-image]: https://img.shields.io/badge/license-MIT-blue.png
 [license-link]: http://opensource.org/licenses/MIT
 
-
-
 Purpose
-=================
-
+=======
 
 https://github.com/user-attachments/assets/4df71cb2-dcf9-4a24-bb78-6e4e1c43356a
 
-
 SPOTPY is a Python framework that enables the use of Computational optimization techniques for calibration, uncertainty
-and sensitivity analysis techniques of almost every (environmental-) model. The package is puplished in the open source journal PLoS One:
+and sensitivity analysis techniques of almost every (environmental-) model. The package is published in the open source journal PLoS One:
 
 Houska, T., Kraft, P., Chamorro-Chavez, A. and Breuer, L.: SPOTting Model Parameters Using a Ready-Made Python Package, PLoS ONE,
 10(12), e0145180, doi:[10.1371/journal.pone.0145180](http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0145180 "SPOTting Model Parameters Using a Ready-Made Python Package"), 2015
@@ -39,10 +35,8 @@ algorithms of almost any model, without the need of complex codes::
 	results = sampler.getdata()                          # Load the results
 	spotpy.analyser.plot_parametertrace(results)         # Show the results
 
-
-
 Features
-=================
+========
 
 Complex algorithms bring complex tasks to link them with a model.
 We want to make this task as easy as possible.
@@ -65,7 +59,7 @@ Some features you can use with the SPOTPY package are:
   * Artificial Bee Colony (`ABC`)
   * Fitness Scaled Chaotic Artificial Bee Colony (`FSCABC`)
   * Dynamically Dimensioned Search algorithm (`DDS`)
-  * Pareto Archived - Dynamicallly Dimensioned Search algorithm (`PA-DDS`)
+  * Pareto Archived - Dynamically Dimensioned Search algorithm (`PA-DDS`)
   * Fast and Elitist Multiobjective Genetic Algorithm (`NSGA-II`)
 
 * Wide range of objective functions (also known as loss function, fitness function or energy function) to validate the sampled results. Available functions are
@@ -84,7 +78,7 @@ Some features you can use with the SPOTPY package are:
   * Agreement Index (`AI`)
   * Covariance, Decomposed MSE (`dMSE`)
   * Kling-Gupta Efficiency (`KGE`)
-  * Non parametric Kling-Gupta Efficiency (`KGE_non_parametric`)
+  * Non-parametric Kling-Gupta Efficiency (`KGE_non_parametric`)
 
 * Wide range of hydrological signatures functions to validate the sampled results:
 
@@ -102,12 +96,12 @@ Some features you can use with the SPOTPY package are:
 
   * Uniform
   * Normal
-  * logNormal
-  * Chisquare
+  * log-normal
+  * Chi-Square
   * Exponential
   * Gamma
   * Wald
-  * Weilbull
+  * Weibull
 
 * Wide range to adapt algorithms to perform uncertainty-, sensitivity analysis or calibration
   of a model.
@@ -137,7 +131,7 @@ Some features you can use with the SPOTPY package are:
 
 
 Install
-=================
+=======
 
 Classical Python options exist to install SPOTPY:
 
@@ -157,7 +151,7 @@ From [Source](https://pypi.python.org/pypi/spotpy):
 
 
 Support
-=================
+=======
 
 * Documentation: https://spotpy.readthedocs.io/en/latest/
 
@@ -171,12 +165,12 @@ Support
 
 
 Getting started
-=================
+===============
 Have a look at https://github.com/thouska/spotpy/tree/master/spotpy/examples and https://spotpy.readthedocs.io/en/latest/getting_started/
 
 
 Contributing
-=================
+============
 Patches/enhancements/new algorithms and any other contributions to this package are very welcome!
 
 1. Fork it ( http://github.com/thouska/spotpy/fork )
@@ -188,5 +182,5 @@ Patches/enhancements/new algorithms and any other contributions to this package
 7. Create new Pull Request
 
 Papers citing SPOTPY
-=====================
+====================
 See [Google Scholar](https://scholar.google.de/scholar?cites=17155001516727704728&as_sdt=2005&sciodt=0,5&hl=de) for a continuously updated list.

pyproject.toml

@@ -6,7 +6,7 @@ requires = [
 build-backend = "setuptools.build_meta"
 
 [project]
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 name = "spotpy"
 description = "A Statistical Parameter Optimization Tool."
 authors = [
@@ -55,8 +55,10 @@ classifiers = [
     "Programming Language :: Python",
     "Programming Language :: Python :: 3",
     "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
-
+    "Programming Language :: Python :: 3.13",
     "Topic :: Scientific/Engineering",
     "Topic :: Scientific/Engineering :: Hydrology",
     "Topic :: Scientific/Engineering :: Mathematics",
@@ -106,7 +108,11 @@ multi_line_output = 3
 [tool.black]
 exclude = "_version.py"
 target-version = [
+    "py39",
+    "py310",
+    "py311",
 	"py312",
+    "py313",
 ]
 
 [tool.coverage]

src/spotpy/algorithms/__init__.py

@@ -6,8 +6,8 @@
 
 :author: Tobias Houska
 
-:paper: Houska, T., Kraft, P., Chamorro-Chavez, A. and Breuer, L.: 
-SPOTting Model Parameters Using a Ready-Made Python Package, 
+:paper: Houska, T., Kraft, P., Chamorro-Chavez, A. and Breuer, L.:
+SPOTting Model Parameters Using a Ready-Made Python Package,
 PLoS ONE, 10(12), e0145180, doi:10.1371/journal.pone.0145180, 2015.
 
 Imports the different algorithms from this package.

src/spotpy/algorithms/_algorithm.py

@@ -5,6 +5,7 @@
 
 This file holds the standards for every algorithm.
 """
+
 import random
 import threading
 import time
@@ -516,9 +517,9 @@ def simulate(self, id_params_tuple):
         can mix up the ordering of runs
         """
         id, params = id_params_tuple
-        self.all_params[
-            self.non_constant_positions
-        ] = params  # TODO: List parameters are not updated if not accepted for the algorithm, we may have to warn/error if list is given
+        self.all_params[self.non_constant_positions] = (
+            params  # TODO: List parameters are not updated if not accepted for the algorithm, we may have to warn/error if list is given
+        )
         all_params = self.all_params
 
         if self.sim_timeout:
@@ -528,7 +529,7 @@ def model_layer(q, all_params):
                 q.put(self.setup.simulation(self.partype(*all_params)))
 
             # starting a queue, where in python2.7 this is a multiprocessing class and can cause errors because of
-            # incompability which the main thread. Therefore only for older Python version a workaround follows
+            # incompability which the main thread. Therefore, only for older Python version a workaround follows
             que = Queue()
 
             sim_thread = threading.Thread(target=model_layer, args=(que, all_params))

src/spotpy/algorithms/abc.py

@@ -78,9 +78,7 @@ def sample(
             sets the limit
         """
         self.set_repetiton(repetitions)
-        print(
-            "Starting the ABC algotrithm with " + str(repetitions) + " repetitions..."
-        )
+        print(f"Starting the ABC algorithm with {repetitions} repetitions...")
         # Initialize ABC parameters:
         randompar = self.parameter()["random"]
         self.nopt = randompar.size

src/spotpy/algorithms/dds.py

@@ -281,9 +281,7 @@ def sample(self, repetitions, trials=1, x_initial=np.array([])):
             self.parameter()["minbound"],
             self.parameter()["maxbound"],
         )
-        print(
-            "Starting the DDS algotrithm with " + str(repetitions) + " repetitions..."
-        )
+        print(f"Starting the DDS algorithm with {repetitions} repetitions...")
 
         number_of_parameters = (
             self.status.parameters
@@ -389,12 +387,14 @@ def calc_initial_para_configuration(
                 (
                     rep,
                     [
-                        self.np_random.randint(
-                            int(self.min_bound[j]), int(self.max_bound[j]) + 1
+                        (
+                            self.np_random.randint(
+                                int(self.min_bound[j]), int(self.max_bound[j]) + 1
+                            )
+                            if discrete_flag[j]
+                            else self.min_bound[j]
+                            + parameter_bound_range[j] * self.np_random.rand()
                         )
-                        if discrete_flag[j]
-                        else self.min_bound[j]
-                        + parameter_bound_range[j] * self.np_random.rand()
                         for j in range(number_of_parameters)
                     ],
                 )
@@ -471,8 +471,8 @@ def calculate_next_s_test(self, previous_x_curr, rep, rep_limit, r):
                 self.min_bound[dec_var],
                 self.max_bound[dec_var],
             )
-            new_x_curr[
-                dec_var
-            ] = new_value  # change relevant decision variable value in s_test
+            new_x_curr[dec_var] = (
+                new_value  # change relevant decision variable value in s_test
+            )
 
         return new_x_curr

src/spotpy/algorithms/demcz.py

@@ -91,9 +91,9 @@ def __init__(self, *args, **kwargs):
             * False: Simulationt results will not be saved
         """
         kwargs["optimization_direction"] = "maximize"
-        kwargs[
-            "algorithm_name"
-        ] = "Differential Evolution Markov Chain (DE-MC) algorithm"
+        kwargs["algorithm_name"] = (
+            "Differential Evolution Markov Chain (DE-MC) algorithm"
+        )
         super(demcz, self).__init__(*args, **kwargs)
 
     def check_par_validity(self, par):
@@ -144,7 +144,7 @@ def sample(
 
         self.set_repetiton(repetitions)
         print(
-            "Starting the DEMCz algotrithm with " + str(repetitions) + " repetitions..."
+            "Starting the DEMCz algorithm with " + str(repetitions) + " repetitions..."
         )
 
         self.min_bound, self.max_bound = (

src/spotpy/algorithms/dream.py

@@ -54,9 +54,9 @@ def __init__(self, *args, **kwargs):
         """
 
         kwargs["optimization_direction"] = "maximize"
-        kwargs[
-            "algorithm_name"
-        ] = "DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm"
+        kwargs["algorithm_name"] = (
+            "DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm"
+        )
         super(dream, self).__init__(*args, **kwargs)
 
     def check_par_validity_bound(self, par):
@@ -270,7 +270,7 @@ def sample(
     ):
         self.set_repetiton(repetitions)
         print(
-            "Starting the DREAM algotrithm with " + str(repetitions) + " repetitions..."
+            "Starting the DREAM algorithm with " + str(repetitions) + " repetitions..."
         )
         if nChains < 2 * delta + 1:
             print("Please use at least n=2*delta+1 chains!")