Merge branch 'master' into fix-(p)dds-parameter-bounds

.gitignore

@@ -8,11 +8,10 @@ __pycache__/
 
 #other files
 *.out
-*.in
 *.png
 *.csv
 site/*
-mkdocs.yml
+#mkdocs.yml
 
 # Distribution / packaging
 .Python

.travis.yml

@@ -1,9 +1,11 @@
 language: python
 python:
   #- "2.7"
-  #- "3.5"
-  - "3.6"
   - "3.7"
+#  - "3.8" #Errors on Travis, build times out because no output was received...
+
+# Uses cache to build faster
+cache: pip
 
 # this ubuntu distribution has sure the libopenmpi-dev packages available
 dist: bionic
@@ -31,8 +33,26 @@ install:
 
 
 script:
-  - py.test  tests/test_* --cov spotpy --cov-report term-missing -v
+  - py.test tests/test_* --cov spotpy --cov-report term-missing -v
 #  - mpirun -c 4 python spotpy/examples/tutorial_parallel_computing_hymod.py 100
   - pip uninstall spotpy -y
-after_success:
-  - coveralls
+
+jobs:
+  include:
+    - stage: Coveralls
+      name: Report Coveralls
+      after_success: python3 -m coveralls
+
+# Publish spotpy on PyPi if taged
+deploy:
+  provider: pypi
+  distributions:  "sdist bdist_wheel"
+  on:
+    # In this case I want to deploy only when a tag is present...
+    tags: true
+    # ... and when tag is on master and respects the form "v0.0.0"
+    branch:
+      - master
+      - /v?(\d+\.)?(\d+\.)?(\*|\d+)$/
+  user: thouska
+  password : ${deploying}

MANIFEST.in

@@ -0,0 +1,9 @@
+include README.md
+include MANIFEST.in
+include setup.py
+include setup.cfg
+recursive-include spotpy/examples/cmf_data *
+recursive-include spotpy/examples/hymod_exe *
+recursive-include spotpy/examples/hymod_unix *
+recursive-include spotpy/examples/hymod_python *
+include LICENSE

README.md

@@ -8,6 +8,7 @@ A Statistical Parameter Optimization Tool for Python
 [![Build Status][travis-image]][travis-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)
+[![Documentation Status](https://readthedocs.org/projects/spotpy/badge/?version=latest)](https://readthedocs.org/projects/spotpy/badge/?version=latest)
 
 [pypi-v-image]: https://img.shields.io/pypi/v/spotpy.png
 [pypi-v-link]: https://pypi.python.org/pypi/spotpy
@@ -147,7 +148,7 @@ Or, after downloading the [source code](https://pypi.python.org/pypi/spotpy "sou
 Support
 =================
 
-* Documentation: http://www.uni-giessen.de/cms/faculties/f09/institutes/ilr/hydro/download/spotpy
+* Documentation: https://spotpy.readthedocs.io/en/latest/
 
 * Feel free to contact the authors of this tool for any support questions.
 
@@ -160,7 +161,7 @@ Support
 
 Getting started
 =================
-Have a look at https://github.com/thouska/spotpy/tree/master/spotpy/examples and http://fb09-pasig.umwelt.uni-giessen.de/spotpy/Tutorial/2-Rosenbrock/
+Have a look at https://github.com/thouska/spotpy/tree/master/spotpy/examples and https://spotpy.readthedocs.io/en/latest/getting_started/
 
 
 Contributing

README.rst

@@ -130,7 +130,7 @@ We want to make this task as easy as possible. Some features you can use with th
 Documentation
 -------------
 
-Documentation is available at `<http://fb09-pasig.umwelt.uni-giessen.de/spotpy>`__
+Documentation is available at `<https://spotpy.readthedocs.io/en/latest>`__
 
 
 Install
@@ -178,4 +178,4 @@ Patches/enhancements/new algorithms and any other contributions to this package
 Getting started
 ---------------
 
-Have a look at https://github.com/thouska/spotpy/tree/master/spotpy/examples and http://fb09-pasig.umwelt.uni-giessen.de/spotpy/Tutorial/2-Rosenbrock/
+Have a look at https://github.com/thouska/spotpy/tree/master/spotpy/examples and https://spotpy.readthedocs.io/en/latest/getting_started/

docs/Tutorial/7-Advanced_hints.md → docs/Advanced_hints.md

@@ -48,7 +48,7 @@ IMPLEMENTATION OF TWO COMPLEMENTING VARIANCE-BASED ALGORITHMS, 2012] (https://ww
 
 $$N = (1+4M^2(1+(k-2)d))k$$ 
 
-with N = needed parameter iterations, M= inference factor (SPOTPY default M=4) and d= frequenzy step (SPOTPY default d=2) and k as the number of parameters of your model.
+with N = needed parameter iterations, M= inference factor (SPOTPY default M=4) and d= frequency step (SPOTPY default d=2) and k as the number of parameters of your model.
 
 You can start the simulation with
 
@@ -99,9 +99,9 @@ Just note that this works not for 'MLE', 'MCMC', 'SCE-UA', 'SA', 'DE-MCz' and 'R
 
 ## Sampling from a given parameter list
 
-SPOTPY enables you to sample directly from a given parameter list. This can be useful if you want to check specific parameter combinations or if you want to resample
+SPOTPY enables you to sample directly from a given parameter list. This can be useful if you want to check specific parameter combinations or if you want to re-sample
 calibrated parameter set, in order to test different model setups or to save further model outputs. To use this functionality you just have to rewrite your paramters function
-in your spotpy setup. We will show you a example how to test parameters of the rosenbrock tutorial. Just give the values you want to test as a list into the spotpy.parameter.List function:
+in your spotpy setup. We will show you a example how to test parameters of the Rosenbrock tutorial. Just give the values you want to test as a list into the spotpy.parameter.List function:
 
     def __init__(self):
         self.params = [spotpy.parameter.List('x',[1,2,3,4,6,7,8,9,0]), #Give possible x values as a List
@@ -128,13 +128,13 @@ After that you will be able to sample the parameter combinations with the normal
 	sampler=spotpy.algorithms.mc(spot_setup(),dbname='Iterator_example',  dbformat='csv') #Parameter lists can be sampled with MC
 	sampler.sample(10) #Choose equaly (or less) repetitions as you have parameters in your List
 
-This will also run with MPI parallelzation.
+This will also run with MPI parallelization.
 
 ## Create a own database
 
-SPOTPY enables you to save results of the sampling in a own database. Users may request different sorts of databases like SQL, hdf5 files, tab separated txt files, xls timeseries.
+SPOTPY enables you to save results of the sampling in a own database. Users may request different sorts of databases like SQL, hdf5 files, tab separated txt files, xls time-series.
 SPOTPY does not provide all these databases yet, BUT any sort of database can be connected to SPOTPY. Therefore one just has to write his one interface. We provide a simple example how this can be done:
-We use the above created example and add a selfmade txt database into a new save function:
+We use the above created example and add a self-made txt database into a new save function:
 
 	class spot_setup(object):
 		slow = 1000
@@ -172,10 +172,10 @@ out the keywords 'dbname' and 'dbformat' when you initialize the algorithm:
 	spot_setup=spot_setup()
 	'Leave out dbformat and dbname and spotpy will return results in spot_setup.save function'
 	sampler=spotpy.algorithms.mc(spot_setup) 
-	sampler.sample(10) #Choose equaly or less repetitions as you have parameters in your List
+	sampler.sample(10) #Choose equally or less repetitions as you have parameters in your List
 	spot_setup.database.close() # Close the created txt file
 
-Apart from that, some users might be interested not to save there simulation results and only the objective functions and the paramter values,
+Apart from that, some users might be interested not to save there simulation results and only the objective functions and the parameter values,
 e.g. to save memory space. This is supported during the initialization of the algorithm, by setting save_sim to False (default=True):
 
 	sampler = spotpy.algorithms.mc(spotpy_setup() dbname='no_simulations', dbformat='csv', save_sim=False)

docs/Tutorial/5-Hydrological_model.md → docs/Hydrological_model.md

@@ -142,7 +142,9 @@ Keep in mind, that the \__init\__ function is only called once during the sampli
 The more you can separate from you model into the \__init\__ function, the faster you sampling will be.
 
 	class spotpy_setup(object):
-		def __init__(self):
+		def __init__(self, obj_func=None):
+			#Just a way to keep this example flexible and applicable to various examples
+			self.obj_func = obj_func 
 			datastart     = datetime(1998,6,1)
 			dataend       = datetime(2000,1,1)
 			analysestart  = datetime(1999,1,1)
@@ -171,21 +173,29 @@ To define the VanGenuchten parameter boundaries we use a normal distribution.
 				return self.cmfmodel.observations
 
 		def objectivefunction(self,simulation,evaluation):
-			objectivefunction= -spotpy.objectivefunctions.rmse(evaluation,simulation)
-			return objectivefunction
+		        #SPOTPY expects to get one or multiple values back, 
+				#that define the performence of the model run
+				if not self.obj_func:
+					# This is used if not overwritten by user
+					# RMSE (root mean squared error) works good for the SCE-UA algorithm, 
+					# as it minimizes the objective function.
+					# All other implemented algorithm maximize the objectivefunction
+					model_performance = spotpy.objectivefunctions.rmse(evaluation,simulation)
+				else:
+					#Way to ensure flexible spot setup class
+					model_performance = self.obj_func(evaluation,simulation) 
+			return model_performance
 
 
 ## Sampling
 
-	spotpy_setup=spotpy_setup()
-
-	sampler = spotpy.algorithms.mc(spotpy_setup,dbname='MC_CMF',dbformat='csv')
-	sampler = spotpy.algorithms.mle(spotpy_setup,dbname='MLE_CMF',dbformat='csv')
-	sampler = spotpy.algorithms.lhs(spotpy_setup,dbname='LHS_CMF',dbformat='csv')
-	sampler = spotpy.algorithms.sceua(spotpy_setup,dbname='SCEUA_CMF',dbformat='csv')
-	sampler = spotpy.algorithms.demcz(spotpy_setup,dbname='DE-MCz_CMF',dbformat='csv')
-	sampler = spotpy.algorithms.sa(spotpy_setup,dbname='SA_CMF',dbformat='csv')
-	sampler = spotpy.algorithms.rope(spotpy_setup,dbname='ROPE_CMF',dbformat='csv')
+	sampler = spotpy.algorithms.mc(spotpy_setup(),dbname='MC_CMF',dbformat='csv')
+	sampler = spotpy.algorithms.mle(spotpy_setup(),dbname='MLE_CMF',dbformat='csv')
+	sampler = spotpy.algorithms.lhs(spotpy_setup(),dbname='LHS_CMF',dbformat='csv')
+	sampler = spotpy.algorithms.sceua(spotpy_setup(used_algorithm='sceua'),dbname='SCEUA_CMF',dbformat='csv')
+	sampler = spotpy.algorithms.demcz(spotpy_setup(),dbname='DE-MCz_CMF',dbformat='csv')
+	sampler = spotpy.algorithms.sa(spotpy_setup(),dbname='SA_CMF',dbformat='csv')
+	sampler = spotpy.algorithms.rope(spotpy_setup(),dbname='ROPE_CMF',dbformat='csv')
 
 	algorithms=['MC','LHS','MLE','MCMC','SCE-UA','SA','DE-MCz','ROPE']
 	results=[]

docs/Tutorial/1-Introduction.md → docs/Introduction.md

@@ -4,6 +4,6 @@ Before you start to use SPOTPY you should be familiar with Python. Otherwise I r
 
 Basic knowledge about NumPy is also needed. You can get this [here](http://www.engr.ucsb.edu/~shell/che210d/numpy.pdf "NumPY tutorial").
 
-Last but not least you will need to have knowledge about [Bayesian techniques](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/tree/master/ "Bayesian techniques").
+Last but not least you might want to have some knowledge about [Bayesian techniques](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/tree/master/ "Bayesian techniques").
 
 You will find all the needed code to reproduce the following examples in the `PYTHONPATH/Lib/site-packages/spotpy/examples` folder. 

docs/Tutorial/2-Rosenbrock.md → docs/Rosenbrock.md

@@ -13,7 +13,7 @@ where we defined control variables as  *-10 < x < 10* and *-10 < y < 10*, with *
 
 *Figure 1: Response surface of the two dimensional Rosenbrock function. Check out `/examples/3dplot.py`to produce such plots.*
 
-We want to find the global minimum of this function. To do so, one could test every possible parameter setting for *x* and *y* OR one can use SPOTPYPY.
+We want to find the global minimum of this function. To do so, one could test every possible parameter setting for *x* and *y* OR one can use SPOTPY.
 
 ## Creating the setup file
 
@@ -34,10 +34,12 @@ We start to have a look at the parameters *x* and *y*:
 We assume to have no prior knowledge about the parameters *x* and *y*, which is why we select a uniform prior distribution from them using NumPy.
 Numpy offers a wide range of distributions such as uniform, normal and chi-squared. 
 As a minimum (`low`) of the distribution we select *-10* and as maximum (`high`) *10*. The next information which is needed is the name of each parameter (`x` and `y`).
+
 The `stepsize` is biggest possible jump, which a algorithm can make to the next sampling point (used in MCMC, MLE and SA). 
 It is used as the standard derivation within a normal distribution around the last sampled point.
 The `optguess` value is the initial parameter setting. It will be your first sample in the results and should be selected to a good as possible setting, 
 because it influences how the algorithms evolove from this position.
+
 Further settings can be made by minbound and maxbound, which set the boundaries for the algorithms to sample parameter combination inside the prior given range. If they are not given,
 bound are determined by sampling 1,000 times from the prior distribution and selecting the minimum and maximum of the sampled values as bounds.
 
@@ -55,7 +57,7 @@ In this case, we know, that *f(x=1,y=1) = 0*. Accordingly, our evaluation functi
 			observations=[0]
 			return observations
 
-Finally, we have to create a function to evaluate our samples. To do so, we select a objective function out of the SPOTPYPY objective function package. 
+Finally, we have to create a function to evaluate our samples. To do so, we select a objective function out of the SPOTPY objective function package. 
 In this case we select the Root Mean Squared Error, giving the simulations and the evaluations. 
 As we want to minimize our function, we select a negative objective function at this point.
 

mkdocs.yml

@@ -0,0 +1,34 @@
+site_name: SPOTPY Documentation
+site_url: http://fb09-pasig.umwelt.uni-giessen.de/spotpy/
+site_description: The documentation of the Statistical Parameter Optimization Framework
+site_author: Tobias Houska
+
+repo_url: https://github.com/thouska/spotpy/
+edit_url: ""
+
+theme:
+    name: readthedocs
+
+#markdown_extensions: [mathjax]
+
+
+nav:
+    - Home: index.md
+    - Tutorial:
+        - Introduction: Introduction.md
+        - Getting Started: getting_started.md
+        - Rosenbrock: Rosenbrock.md
+        - Griewank: Griewank.md
+        - Ackley: Ackley.md
+        - Hydrological_model: Hydrological_model.md
+        - Working with DREAM and Hymod: Working with DREAM and Hymod.md
+        - Advanced_hints: Advanced_hints.md
+        - Algorithm_guide: Algorithm_guide.md
+
+    - License: license.md
+
+
+copyright: Copyright &copy; 2015, <a href="http://www.uni-giessen.de/cms/fbz/fb09/institute/ilr/wasser/mitarbeiter/houska">Tobias Houska</a>.
+
+
+

readthedocs.yml

@@ -0,0 +1,15 @@
+# .readthedocs.yml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+
+# Build documentation with MkDocs
+mkdocs:
+  configuration: mkdocs.yml
+
+# Optionally build your docs in additional formats such as PDF and ePub
+formats: all
+

setup.py

@@ -5,16 +5,17 @@
 
 setup(
   name = 'spotpy',
-  version = '1.5.8',
+  version = '1.5.13',
   description = 'A Statistical Parameter Optimization Tool',
   long_description=open(os.path.join(os.path.dirname(__file__),
                                        "README.rst")).read(),
   author = 'Tobias Houska, Philipp Kraft, Alejandro Chamorro-Chavez and Lutz Breuer',
   author_email = '[email protected]',
-  url = 'http://www.uni-giessen.de/cms/faculties/f09/institutes/ilr/hydro/download/spotpy',
+  url = 'https://spotpy.readthedocs.io/en/latest/',
   license = 'MIT',
+  install_requires=[
+          'scipy', 'numpy'],
   packages=find_packages(exclude=["tests*", "docs*"]),
-  include_package_data = True,
   use_2to3 = True,
   keywords = 'Monte Carlo, MCMC, MLE, SCE-UA, Simulated Annealing, DE-MCz, DREAM, ROPE, Artifical Bee Colony, DDS, PA-DDS, Uncertainty, Calibration, Model, Signatures',
   classifiers = [
@@ -28,5 +29,6 @@
         'Programming Language :: Python :: 3.5',
         'Programming Language :: Python :: 3.6',
         'Programming Language :: Python :: 3.7',
+        'Programming Language :: Python :: 3.8',
         'Topic :: Software Development :: Libraries :: Python Modules'],
         )

spotpy/__init__.py

@@ -20,9 +20,10 @@
                - mpi4py (optional) (http://mpi4py.scipy.org/)
                - pathos (optional) (https://pypi.python.org/pypi/pathos/)
                - sqlite3 (optional) (https://pypi.python.org/pypi/sqlite3/)
+               - numba (optional) (https://pypi.python.org/pypi/numba/)
 
                :help: For specific questions, try to use the documentation website at:
-                http://fb09-pasig.umwelt.uni-giessen.de/spotpy/
+                https://spotpy.readthedocs.io/en/latest/
 
 For general things about parameter optimization techniques have a look at:
 https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/tree/master/
@@ -39,4 +40,4 @@
 from . import describe            # Contains some helper functions to describe samplers and set-ups
 from .hydrology import signatures # Quantifies goodness of fit between simulation and evaluation data with hydrological signatures
 
-__version__ = '1.5.8'
+__version__ = '1.5.13'