[WIP] added functionality to test current implementation of nsgaii algorithm and new version of nsgaii

.gitignore

@@ -8,8 +8,11 @@ __pycache__/
 
 #other files
 *.out
-*.in
-docs/*
+
+*.png
+*.csv
+site/*
+#mkdocs.yml
 
 # Distribution / packaging
 .Python
@@ -30,6 +33,9 @@ wheels/
 .installed.cfg
 *.egg
 
+# Python Unittest tool `py.test`
+.pytest_cache/*
+
 # PyInstaller
 #  Usually these files are written by a python script from a template
 #  before PyInstaller builds the exe, so as to inject date/other infos into it.
@@ -112,3 +118,6 @@ spotpy/hymod/likelihood_test/*
 
 # a doc folder for some notices
 _doc
+
+# spacemacs projects
+.projectile

.travis.yml

@@ -1,28 +1,39 @@
 language: python
 python:
-  - "2.7"
-  - "3.5"
-  - "3.6"
+  #- "2.7"
+  #- "3.5"
+  - "3.7"
+#  - "3.8" #Errors on Travis, build times out because no output was received...
 
-  # command to install dependencies
+# this ubuntu distribution has sure the libopenmpi-dev packages available
+dist: bionic
+
+# command to install dependencies
 install:
-  #- pip install pandas
+  - sudo apt-get update
+  - sudo apt install libopenmpi-dev openmpi-bin
+  - pip install mpi4py
   - python setup.py install
   - pip install coveralls
   - pip install pathos
   - pip install numpy
   - pip install matplotlib
-  - pip install scipy
   - pip install pandas
-  - pip install pytest pytest-pep8 pytest-cov
+  - pip install scipy
+  - pip install click
+  - pip install deap
+  # Need to force pytest-cov to v2.6 as current version (2.6.1) is deprecated and pytest:3.10.1
+  - pip install pytest==3.10.1 pytest-pep8 pytest-cov==2.6
   # Use docutils to generate html describe
   - pip install docutils
-  #- pip install matplotlib
-  #- travis_wait pip install scipy
+  - pip install numba
+  - pip install tables
+
 
 script:
+  - 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
-  - py.test  spotpy/unittests/test_* spotpy/examples/tutorial_rosenbrock.py  spotpy/examples/tutorial_nsgaii.py --cov spotpy --cov-report term-missing -v
 
 after_success:
   - coveralls

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
@@ -61,13 +62,15 @@ Some features you can use with the SPOTPY package are:
   * Fourier Amplitude Sensitivity Test (`FAST`)
   * Artificial Bee Colony (`ABC`)
   * Fitness Scaled Chaotic Artificial Bee Colony (`FSCABC`)
+  * Dynamically Dimensioned Search algorithm (`DDS`)
+  * Pareto Archived - Dynamicallly Dimensioned Search algorithm (`PA-DDS`)
 
 * Wide range of objective functions (also known as loss function, fitness function or energy function) to validate the sampled results. Available functions are
 
   * Bias
   * PBias
-  * Nash-Sutcliff (`NSE`)
-  * logarithmic Nash-Sutcliff (`logNSE`)
+  * Nash-Sutcliffe (`NSE`)
+  * logarithmic Nash-Sutcliffe (`logNSE`)
   * logarithmic probability (`logp`)
   * Correlation Coefficient (`r`)
   * Coefficient of Determination (`r^2`)
@@ -77,7 +80,20 @@ Some features you can use with the SPOTPY package are:
   * Relative Root Mean Squared Error (`RRMSE`)
   * Agreement Index (`AI`)
   * Covariance, Decomposed MSE (`dMSE`)
-  * Kling-Gupta Efficiency (`KGE`).
+  * Kling-Gupta Efficiency (`KGE`)
+  * Non parametric Kling-Gupta Efficiency (`KGE_non_parametric`)
+
+* Wide range of hydrological signatures functions to validate the sampled results:
+
+  * Slope
+  * Flooding/Drought events
+  * Flood/Drought frequency
+  * Flood/Drought duration
+  * Flood/Drought variance
+  * Mean flow
+  * Median flow
+  * Skewness
+  * compare percentiles of discharge
 
 * Prebuild parameter distribution functions: 
 
@@ -90,48 +106,6 @@ Some features you can use with the SPOTPY package are:
   * Wald
   * Weilbull
 
-* A toolbox of hydrological signatures, to see a hydrological fit of modeled data to the measured one. 
-
-To use this signatures, you call the _get_**HydrologicalSignature** function and entering the measured and the modeled data.
-Setting `mode='calc_Dev'` performs our desired calculation. Sometimes additional parameters are needed, as for example
-a timestamp list (in a pandas object) or threshold values. The closer the return value is to zero, the more fits the simulated 
-data with measured one. 
-
-
-	spotpy.signatures.getSlopeFDC(measured_data,results,mode='calc_Dev')
-
-By changing the value of `mode` to `'get_signature'`, we get the signature value _only_ for the parameter `data`.
-
-By changing the value of `mode` to `'get_raw_data'`, we get a raw calculation of flood-signatures, belonging to each signature calculation.
-
-**tutorial_signatures.py** contains al available functions and it's mode settings.
-
-  * List of available hydrological signatures, additional required parameters are in brackets
-    * getMeanFlow
-    * getMedianFlow
-    * getSkewness
-    * getCoeffVariation
-    * getQ001
-    * getQ01
-    * getQ1
-    * getQ5
-    * getQ10
-    * getQ20
-    * getQ85
-    * getQ95
-    * getQ99
-    * getSlopeFDC
-    * getAverageFloodOverflowPerSection(datetime_series, threshold_value)
-    * getAverageFloodFrequencyPerSection(datetime_series, threshold_value)
-    * getAverageFloodDuration(datetime_series, threshold_value)
-    * getAverageBaseflowUnderflowPerSection(datetime_series, threshold_value)
-    * getAverageBaseflowFrequencyPerSection(datetime_series, threshold_value)
-    * getFloodFrequency(datetime_series, threshold_value)
-    * getBaseflowFrequency (datetime_series, threshold_value)
-    * getLowFlowVar(datetime_series)
-    * getHighFlowVar(datetime_series)
-    * getBaseflowIndex(datetime_series)
-
 * Wide range to adapt algorithms to perform uncertainty-, sensitivity analysis or calibration
   of a model.
 
@@ -174,7 +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.
 
@@ -187,4 +161,22 @@ 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
+=================
+Patches/enhancements/new algorithms and any other contributions to this package are very welcome!
+
+1. Fork it ( http://github.com/thouska/spotpy/fork )
+2. Create your feature branch (``git checkout -b my-new-feature``)
+3. Add your modifications
+4. Add short summary of your modifications on ``CHANGELOG.rst``
+5. Commit your changes (``git commit -m "Add some feature"``)
+6. Push to the branch (``git push origin my-new-feature``)
+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.
+

README.rst

@@ -1,16 +1,6 @@
-.. image:: https://img.shields.io/pypi/v/spotpy.png
-  :target: https://pypi.python.org/pypi/spotpy
-.. image:: https://img.shields.io/travis/thouska/spotpy/master.png
-  :target: https://travis-ci.org/thouska/spotpy
-.. image:: https://img.shields.io/badge/license-MIT-blue.png
-  :target: http://opensource.org/licenses/MIT
-.. image:: https://coveralls.io/repos/github/thouska/spotpy/badge.svg?branch=master
-  :target: https://coveralls.io/github/thouska/spotpy?branch=master
-
-
-
-Purpose
--------
+SPOTPY
+========
+**A Statistical Parameter Optimization Tool for Python**
 
 SPOTPY is a Python tool 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
@@ -47,13 +37,15 @@ We want to make this task as easy as possible. Some features you can use with th
   * Fourier Amplitude Sensitivity Test (`FAST`)
   * Artificial Bee Colony (`ABC`)
   * Fitness Scaled Chaotic Artificial Bee Colony (`FSCABC`)
-
+  * Dynamically Dimensioned Search algorithm (`DDS`)
+  * Pareto Archived - Dynamicallly Dimensioned Search algorithm (`PA-DDS`)
+
 * Wide range of objective functions (also known as loss function, fitness function or energy function) to validate the sampled results. Available functions are
 
   * Bias
   * Procentual Bias (`PBias`)
-  * Nash-Sutcliff (`NSE`)
-  * logarithmic Nash-Sutcliff (`logNSE`)
+  * Nash-Sutcliffe (`NSE`)
+  * logarithmic Nash-Sutcliffe (`logNSE`)
   * logarithmic probability (`logp`)
   * Correlation Coefficient (`r`)
   * Coefficient of Determination (`r^2`)
@@ -64,6 +56,7 @@ We want to make this task as easy as possible. Some features you can use with th
   * Agreement Index (`AI`)
   * Covariance, Decomposed MSE (`dMSE`)
   * Kling-Gupta Efficiency (`KGE`)
+  * Non parametric Kling-Gupta Efficiency (`KGE_non_parametric`)
 
 * Wide range of likelihood functions to validate the sampled results:
 
@@ -137,7 +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
@@ -151,8 +144,9 @@ Or, after downloading the source code and making sure python is in your path:
 
 	python setup.py install
 
+
 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.
 
 
@@ -184,4 +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/