Merge pull request #330 from Herzog-A/pr-316

Pr 316

.github/workflows/main.yml

@@ -24,12 +24,12 @@ jobs:
       fail-fast: false
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
 
-      - name: Set up Python 3.8
-        uses: actions/setup-python@v2
+      - name: Set up Python 3.9
+        uses: actions/setup-python@v5
         with:
-          python-version: 3.8
+          python-version: 3.9
 
       - name: Install dependencies
         run: |
@@ -48,15 +48,15 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: ['3.7', '3.8', '3.9', '3.10']
+        python-version: ['3.10', '3.11', '3.12']
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
         with:
           fetch-depth: '0'
 
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
 
@@ -77,7 +77,7 @@ jobs:
         run: |
           python -m build
 
-      - uses: actions/upload-artifact@v2
+      - uses: actions/upload-artifact@v4
         if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.9'
         with:
           path: dist
@@ -87,11 +87,6 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/download-artifact@v2
-        with:
-          name: artifact
-          path: dist
-
       - name: Publish to Test PyPI
         # only if working on master
         if: github.ref == 'refs/heads/master'

README.md

@@ -61,6 +61,7 @@ Some features you can use with the SPOTPY package are:
   * Differential Evolution Adaptive Metropolis Algorithm (`DREAM`)
   * RObust Parameter Estimation (`ROPE`)
   * Fourier Amplitude Sensitivity Test (`FAST`)
+  * extended Fourier Amplitude Sensitivity Test (`eFAST`)  
   * Artificial Bee Colony (`ABC`)
   * Fitness Scaled Chaotic Artificial Bee Colony (`FSCABC`)
   * Dynamically Dimensioned Search algorithm (`DDS`)

docs/index.md

@@ -35,7 +35,7 @@ Simulated Annealing (`SA`), Shuffled Complex Evolution Algorithm (`SCE-UA`),
 Differential Evolution Adaptive Metropolis Algorithm (`DE-MCz`), 
 RObust Parameter Estimation (`ROPE`), Artificial Bee Colony (`ABC`),
 Dynamicallly Dimensioned Search algorithm (`DDS`), Pareto Archived Dynamicallly Dimensioned Search algorithm (`PA-DDS`)
-Fitness Scaled Chaotic Artificial Bee Colony (`FSCABC`) and Fourier Amplitude Sensitivity Test (`FAST`).
+Fitness Scaled Chaotic Artificial Bee Colony (`FSCABC`), Fourier Amplitude Sensitivity Test (`FAST`) and extended Fourier Amplitude Sensitivity Test (`eFAST`).
 
 * Wide range of objective functions, likelihood functions and hydroligcal signatures to validate the sampled results. 
 Available objective functions are:
@@ -153,6 +153,7 @@ The results can be analysed with some pre-build statistics and plotting features
 		sa.py         # Simulated annealing
 		rope.py       # RObust Parameter Estimation
 		fast.py       # Fourier Amplitude Sensitivity Testing
+		efast.py      # extended Fourier Amplitude Sensitivity Testing
 		abc.py        # Artificial Bee Colony
 		fscabc.py     # Fitness Scaled Chaotic Artificial Bee Colony
 		dream.py      # Differential Evolution Adaptive Metropolis
@@ -172,7 +173,17 @@ The results can be analysed with some pre-build statistics and plotting features
 		getting_started.py          # Recommended test file for starters
 
 	tutorials/
-		tutorial_rosenbrock.py      # Source code for the Rosenbrock example the Tutorial
-		tutorial_griewank.py        # Source code for the Rosenbrock example the Tutorial
-		tutorial_ackley.py          # Source code for the Rosenbrock example the Tutorial
-		tutorial_Parameterlist_iterator.py        # Example how to sample given parameter combinations
+		tutorial_rosenbrock.py               # Source code for the Rosenbrock example the Tutorial
+		tutorial_griewank.py                 # Source code for the Griewank example the Tutorial
+		tutorial_ackley.py                   # Source code for the Ackley example the Tutorial
+		tutorial_Parameterlist_iterator.py   # Example how to sample given parameter combinations
+		tutorial_own_database.py             # Example how to implement your own database
+		tutorial_parallel_computing_hymod.py # Example how to set up spotpy on a cluster computer
+		tutorial_signatures.py               # Example on usage on hydrological signatures
+		tutorial_nsgaii.py				 	 # Example on how to use the nsgaii algorithm
+		tutorial_padds_hymod.py              # Example on how to use the padds algorithm
+		tutorial_dds_hymod.py                # Example on how to use the dds algorithm
+		tutorial_sceua_hymod.py              # Example on how to use the sceua algorithm
+		tutorial_fast_hymod.py               # Example on how to use the fast algorithm
+		tutorial_efast_hymod.py              # Example on how to use the efast algorithm
+		tutorial_dream_hymod.py              # Example on how to use the dream algorithm

src/spotpy/algorithms/__init__.py

@@ -20,6 +20,7 @@
 from .dds import dds  # Dynamically Dimensioned Search algorithm
 from .demcz import demcz  # Differential Evolution Markov Chain
 from .dream import dream  # DiffeRential Evolution Adaptive Metropolis
+from .efast import efast  # eFAST algorithm adapted from FAST R package
 from .fast import fast  # Fourier Amplitude Sensitivity Test
 from .fscabc import fscabc  # Fitness Scaling Artificial Bee Colony
 from .lhs import lhs  # Latin Hypercube Sampling

src/spotpy/algorithms/demcz.py

@@ -400,7 +400,7 @@ def __init__(self, maxChainDraws, nChains, dimensions):
         self._logPSequences = np.zeros((nChains, maxChainDraws))
         self._logPHistory = np.zeros(nChains * maxChainDraws)
         self.r_hat = [] * dimensions
-        self._sampling_start = np.Inf
+        self._sampling_start = np.inf
 
         self._nChains = nChains
         self._dimensions = dimensions
@@ -483,7 +483,7 @@ def samples(self):
         return self.group_samples("all")
 
     def group_samples(self, name):
-        if self._sampling_start < np.Inf:
+        if self._sampling_start < np.inf:
             start = int(
                 max(np.ceil(self.relevantHistoryStart), self._sampling_start)
                 * self._nChains
@@ -627,11 +627,11 @@ class _GRConvergence:
     """
 
     def __init__(self):
-        self._W = np.Inf
-        self._R = np.Inf
-        self._V = np.Inf
-        self._VChange = np.Inf
-        self._WChange = np.Inf
+        self._W = np.inf
+        self._R = np.inf
+        self._V = np.inf
+        self._VChange = np.inf
+        self._WChange = np.inf
 
     def _get_R(self):
         return self._R