diff --git a/.gitignore b/.gitignore index 4a039b03..f13e9241 100644 --- a/.gitignore +++ b/.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 diff --git a/.travis.yml b/.travis.yml index 77e229ce..12c199e6 100644 --- a/.travis.yml +++ b/.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 diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 00000000..e5706096 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/README.md b/README.md index 99a04167..1c325503 100644 --- a/README.md +++ b/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. + diff --git a/README.rst b/README.rst index b0939f36..cfe820de 100644 --- a/README.rst +++ b/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 ``__ +Documentation is available at ``__ 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 `__ 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/ diff --git a/docs/Ackley.md b/docs/Ackley.md new file mode 100644 index 00000000..8d5b3b2d --- /dev/null +++ b/docs/Ackley.md @@ -0,0 +1,105 @@ + +# The Ackley + +The Ackley function is another challenging optimization problem. With just one small global optimum. It is defined as: + +$$f_{Ackley}(x_0 \cdots x_n) = -20 exp(-0.2 \sqrt{\frac{1}{n} \sum_{i=1}^n x_i^2}) - exp(\frac{1}{n} \sum_{i=1}^n cos(2\pi x_i)) + 20 + e$$ + +where the control variables are *-32.768 < x_i < 32.768*, with *f(x_i=0) = 0*. + +![Ackley](../img/ackley.png) + +*Figure 4: Response surface of the two dimensional Ackley function. Check out `/examples/3dplot.py`to produce such plots.* + +## Creating the setup file + +This time we want to challenge our algorithms with a high number of dimensions. We use the `__init__` function to create 50 parameters. +We give every parameter just a number and not a name. See `/examples/spotpy_setup_ackley.py` for the following code: + + class spotpy_setup(object): + def __init__(self,dim=30): + self.dim=dim + self.params = [] + for i in range(self.dim): + self.params.append(spotpy.parameter.Uniform(str(i),-32.768,32.768,2.5,-20.0)) + + def parameters(self): + return spotpy.parameter.generate(self.params) + + def simulation(self, vector): + firstSum = 0.0 + secondSum = 0.0 + for c in vector: + firstSum += c**2.0 + secondSum += np.cos(2.0*np.pi*c) + n = float(len(vector)) + return [-20.0*np.exp(-0.2*np.sqrt(firstSum/n)) - np.exp(secondSum/n) + 20 + np.e] + + def evaluation(self): + observations=[0] + return observations + + def objectivefunction(self,simulation,evaluation): + objectivefunction= -spotpy.objectivefunctions.rmse(evaluation,simulation) + return objectivefunction + +## Sampling + +Now that we crated our setup file, we want to start to investigate our function. +One way is to analyse the results of the sampling is to have a look at the objective function trace of the sampled parameters. + +We start directly with all algorithms. First we have to create a new file: + + import spotpy + from spotpy.examples.spotpy_setup_ackley import spotpy_setup # Load your just created file from above + +Now we create samplers for every algorithm and Now sample 25,000 parameter combinations for every algorithm + + spotpy_setup=spotpy_setup() + sampler=spotpy.algorithms.mc(spotpy_setup, dbname='ackleyMC', dbformat='csv') + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.lhs(spotpy_setup, dbname='ackleyLHS', dbformat='csv') + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.mle(spotpy_setup, dbname='ackleyMLE', dbformat='csv') + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.mcmc(spotpy_setup, dbname='ackleyMCMC', dbformat='csv') + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.sceua(spotpy_setup, dbname='ackleySCEUA', dbformat='csv') + sampler.sample(rep,ngs=2) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.sa(spotpy_setup, dbname='ackleySA', dbformat='csv') + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.demcz(spotpy_setup, dbname='ackleyDEMCz', dbformat='csv') + sampler.sample(rep,nChains=30) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.rope(spotpy_setup, dbname='ackleyROPE', dbformat='csv') + sampler.sample(rep) + results.append(sampler.getdata()) + + +## Plotting + +To plot our results, we need just a few lines of code: + + algorithms=['MC','LHS','MLE','MCMC','SCEUA','SA','DEMCz','ROPE'] + evaluation = spotpy_setup.evaluation() + spotpy.analyser.plot_likelihoodtraces(results,evaluation,algorithms) + +This should give you something like this: + +![Trace plot](../img/ackleytracelike.png) + +*Figure 5: Objective function trace of 30 dimensional Ackley function.* + \ No newline at end of file diff --git a/docs/Advanced_hints.md b/docs/Advanced_hints.md new file mode 100644 index 00000000..da74b6c3 --- /dev/null +++ b/docs/Advanced_hints.md @@ -0,0 +1,182 @@ + +# Advanced settings + +This chapter will show you, how to get the full power out of SPOTPY: + +## Adjust algorithms +All algorithms come along with a standard setting, to provide acceptable parameter estimations, without expert knowledge. +If you want to change the standard settings (which is absolutely recommended if you know what you are doing), try something like this: + + sampler = spotpy.algorithms.sceua(spotpy_setup,() dbname='RosenSCEUA', dbformat='csv') + sampler.sample(10000,ngs=10,kstop=100,pcento=0.001,peps=0.01) + +Every algorithm has it's settings, which can be user defined. Read the paper about the algorithms to know what the settings do or +play around with this settings and see what happens. + + +## MPI - Parallel Computing +If you want to use the parallel setup of the algorithms you need to have [mpi4py](http://mpi4py.scipy.org/ "mpi4py") installed on your system. +To tell SPOTPY to use MPI, just give this information to the sampler: + + sampler = spotpy.algorithms.sceua(spotpy_setup,() dbname='RosenSCEUA', dbformat='csv', parallel='mpi') + sampler.sample(10000) + +Now save this file and start it from a console: `mpirun -c 20 your_script.py`, where 20 is the number of cores you want to use. +This should give you and speed of neerly 20 times compared with the standard sequential sampling. + +Keep in mind that `MC`, `LHS` `FAST` and `ROPE` can use as much cpu-cores as you have. `SCE-UA` will run its number of complexes +'ngs' in parallel (see the paper [Muttil et al., 2007](http://vuir.vu.edu.au/767/) for details), which you can +change by typing: + + sampler = spotpy.algorithms.sceua(spotpy_setup,() dbname='RosenSCEUA', dbformat='csv') + sampler.sample(10000,ngs=20) + +DE-MCz will parallelize the selcted number of chains [terBrack and Vrugt, 2008](http://link.springer.com/article/10.1007/s11222-008-9104-9). You can adjust this number by typing: + + sampler = spotpy.algorithms.demcz(spotpy_setup,() dbname='RosenSCEUA', dbformat='csv') + sampler.sample(10000,nChains=20) + +Th algorithms `MLE`, `MCMC` and `SA` can not run in parallel. + +## FAST - Sensitivity analysis +SPOTPY gives you the opportunity to start a sensitivity analysis of your model. In this case, we included a global sensitivity analysis called "Extended FAST" based on +Saltelli et al. (1999). This is besides the Sobol´ sensitivity test the only algorithm available that is taking parameter interaction into account. + +The algorithm will tell you, how sensitive your parameters are on whatever is given back by your objective function. Before you start to sample, you should know how how many +iterations you need to get an reliable information about your parameter. The number of iteration can be calculate after [Henkel et al. GLOBAL SENSITIVITY ANALYSIS OF NONLINEAR MATHEMATICAL MODELS - AN +IMPLEMENTATION OF TWO COMPLEMENTING VARIANCE-BASED ALGORITHMS, 2012] (https://www.informs-sim.org/wsc12papers/includes/files/con308.pdf): + +$$N = (1+4M^2(1+(k-2)d))k$$ + +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 + + sampler = spotpy.algorithms.fast(spotpy_setup,() dbname='Fast_sensitivity', dbformat='csv') + +and you can analyse your results with + + results = sampler.get_data() + analyser.plot_fast_sensitivity(results,number_of_sensitiv_pars=5) + +This will show you a Plot with the total Sensitivity index of all your parameters and in this case the five most sensitive parameters (can be adjusted). + +## Plotting time +If you want create plots out of your samples and you don't want to sample your results again do something like this: + + algorithms=['MC','LHS','MLE','MCMC','SCEUA','SA','DEMCz','ROPE'] + results=[] + for algorithm in algorithms: + results.append(spotpy.analyser.load_csv_results('Rosen'+algorithm)) + +This will load your results directly from your created csv-files. +Use this code instead of the sampling code. + +## Multi objective calibration +If you have more than one series of observations, or simulation or objectivefunction: You can include them into your analysis, if you use 'MC', 'LHS' or 'FAST': + +Let us say you have a model producing biomass and soil moisture simulations, then you can return both in a list of your simulation function: + + class spot_setup(object): + def simulations(self,vector): + biomass, soil_moisture = model(vector) + return [list(biomass), list(soil_moisture)] + +Then you consequently need also evaluation data lists for that: + + def evaluation(self): + eval_biomass, eval_soil_moisture = load_evaluation_data() + return [list(eval_biomass), list(eval_soil_moisture)] + +SPOTPY will transfer those lists into the objective function. Here you can separate them again and, if you want, return also more than one objective function: + + def objectivefunction(self,eval,sim): + obj1=spotpy.objectivefunctions.bias(eval[0],sim[0])#Biomass data + obj2=spotpy.objectivefunctions.rmse(eval[1],sim[1])#Soil moisture data + return [obj1,obj2] + +Just note that this works not for 'MLE', 'MCMC', 'SCE-UA', 'SA', 'DE-MCz' and 'ROPE', because they need one explicit objective function during the optimization. + +## 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 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: + + 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 + spotpy.parameter.List('y',[0,1,2,5,7,8,9,0,1]) #Give possible y values as a List + ] + def parameters(self): + return spotpy.parameter.generate(self.params) + + def simulation(self,vector): + x=np.array(vector) + simulations= [sum(100.0*(x[1:] - x[:-1]**2.0)**2.0 + (1 - x[:-1])**2.0)] + return simulations + + def evaluation(self): + observations=[0] + return observations + + def objectivefunction(self,simulation,evaluation): + objectivefunction=-spotpy.objectivefunctions.rmse(evaluation,simulation) + return objectivefunction + +After that you will be able to sample the parameter combinations with the normal `MC` algorithm: + + 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 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 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 self-made txt database into a new save function: + + class spot_setup(object): + slow = 1000 + 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 + spotpy.parameter.List('y',[0,1,2,5,7,8,9,0,1])] #Give possible y values as a List + + self.database = file('MyOwnDatabase.txt','w') #Create a file with writing rights + + def parameters(self): + return spotpy.parameter.generate(self.params) + + def simulation(self,vector): + x=np.array(vector) + for i in xrange(self.slow): + s = np.sin(i) + simulations= [sum(100.0*(x[1:] - x[:-1]**2.0)**2.0 + (1 - x[:-1])**2.0)] + return simulations + + def evaluation(self): + observations=[0] + return observations + + def objectivefunction(self,simulation,evaluation): + objectivefunction=-spotpy.objectivefunctions.rmse(evaluation,simulation) + return objectivefunction + + def save(self, objectivefunctions, parameter, simulations): + line=str(objectivefunctions)+','+str(parameter).strip('[]')+','+str(simulations).strip('[]')+'\n' + self.database.write(line) + +This example can save all results gained from sampling into a txt file. To tell SPOTPY that you want to use your own database, just leave +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 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 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) + diff --git a/docs/Algorithm_guide.md b/docs/Algorithm_guide.md new file mode 100644 index 00000000..027f769e --- /dev/null +++ b/docs/Algorithm_guide.md @@ -0,0 +1,168 @@ + +# Algorithm Guide + +SPOTPY comes along with some very powerful techniques for parameter optimization. The conducted algorithms can be very efficient, but also inefficient, +depending on your specific parameter search problem. This is why we have developed a decision tree to help you choosing the right algorithm: + +![Algorithm Guide](../img/decision_tree.png) + +*Figure 8: Decision-tree as a guidance for the choice of an algorithm in SPOTPY for a specific optimization problem.* + + +# Algorithm Overview + +To understand, what the algorithms are doing, we can provide a short overview. + + +## MC +### Monte Carlo + +This algorithm is probably the simplest one. It relays on repeated random parameter samplings which are tested in the simulation function. +The algorithm does not learn or adopt its method during the sampling, which makes it easy to parallelize. In principle, this algorithm can solve any parameter search problem, +but with an increasing number of parameters, the number of needed iterations is rising exponential. This can be optimized by selecting an appropriate distribution for the parameters. + +These steps are performed during the sampling: + +* Create random parameter set +* Run simulation function with the parameter set +* Compare resulting simulation with evaluation values and calculate a objective function +* Save objective function, parameter set and simulation in a database + +Because of its simplicity and easy integration the Monte Carlo sampling is widely used. + +## LHS +### Latin Hypercube Sampling + +The Latin Hypercube sampling combines the dimensions of the parameter function with the number of iterations into one matrix. This matrix assures that every +parameter combination is scanned. The needed number of iterations (N_{max}) can be calculated with the following formula: + +$$N_{max} = (k!)^{p-1}$$ + +with k as divisions per parameter and p as number of parameters. + +These steps are performed during the sampling: + +* Check the minimal and maximal value of every parameter +* Create the Latin HyperCube matrix +* Run simulation function with every row of the matrix +* Save every objective function, parameter set and simulation in a database + +For further detailed information check out McKay et al. (1979). + +## MCMC +### Markov Chain Monte Carlo + +The famous Metropolis MCMC is one of the most used parameter optimization method. It can learn during the sampling and can deal with non-monotonically response functions. +The sampling method can reject regions of the parameter search space and tries to find the global optimum. The more often a region of the parameter search space was sampled, +the more likely it is to be the global optimum. + +These steps are performed during the sampling: + +* 10% of the repetitions are designed to perform a Monte Carlo sampling (burn-in period) +* Save likelihood, parameter set and simulation in a database +* The best parameter set is taking after the burn-in as an inital parameter set for the Metropolis sampler +* A random value with a Gaussian distribution around the last best found parameter set is drawn to generate a new parameter-set (mean= last best parameter set, standard deviation=step-size of parameters function) +* Run simulation function with the new parameter set +* Calculate a hardcoded logProbability as likelihood +* Decide if the new parameter is accepted through a Metropolis decision +* Save the last accepted run with likelihood, parameter set and simulation in a database + +The MCMC algorithm can find a (quasi-) global optimum, but with a still remaining risk to stuck in local minima, depending on the chosen step-size in the parameter function. +Is the step-size to large, the sampler jumps too often away from the optimum. Is the step size to low, it can get stuck in local minima. + +For further detailed information check out Metropolis et al. (1953). + +## MLE +### Maximum Likelihood Estimation + +If one is just interested in a fast calibration of a simple model (with nearly monotonically response function or a compact parameter search space), the MLE is an efficient choice. +To test whether the MLE algorithm is applicable for calibrating the desired model, it is recommend to test the model with MC first. +MLE maximizes the likelihood during the sampling, by adapting the parameter only in directions with an increasing likelihood. + +These steps are performed during the sampling: + +* 10% of the repetitions are designed to perform a Monte Carlo sampling (burn-in period) +* Save likelihood, parameter set and simulation in a database +* The best parameter set is taking after the burn-in as an initial parameter set for the Metropolis sampler +* A random value with a Gaussian distribution around the last best found parameter set is drawn to generate a new parameter-set (mean= last best parameter set, standard deviation=step-size of parameters function) +* Run simulation function with the new parameter set +* Compare resulting simulation with evaluation values and calculate a likelihood +* Accept new parameter if it is better than the so far best found parameter set +* Save the best run with likelihood, parameter set and simulation in a database + +Adopted in the right way, the MLE can be a very efficient algorithm. But the risk to stuck in local optima is high. + +## SA +### Simulated Annealing + +Simulated Annealing can be a robust algorithm, but needs to be adopted to every new parameter search problem. +The algorithm starts with a high chance to jump to a new point (high temperature), which is getting more and more unlikely with increasing repetitions (cooling down). +If everything works, the algorithm freezes at the global optimal parameter-set. + +* The optimal-guess parameter set is taking as an initial parameter set for Simulated Annealing +* Run simulation function with the new parameter set +* Compare resulting simulation with evaluation values and calculate a objective function +* Save objectivefunction, parameter set and simulation in a database +* Generated a new parameter set +* Run simulation function with the new parameter set +* Accept new objective function if its better than the last best found, or, if worse, accept new objective function with Boltzmann distribution, depending on the temperature of the system +* Save the last accepted run with objective function, parameter set and simulation in a database + +For further detailed information check out Kirkpatrick et al. (1985). + +## ROPE +### RObust Parameter Estimation + +Another non-Bayesian approach is the ROPE algorithm. It determines parameter uncertainty with the concept of data depth. +This has the benefit, that the resulting parameter sets have proven to be more likely giving good results when space or time period of the model changes, e.g. for validation. + +* 25% of the repetitions are designed to perform a Monte Carlo sampling (burn-in period) +* The best 10% of the results are taken to generate samples (another 25% of the repetitions) in the remaining multi-dimensional parameter space. +* Run simulation function with generated parameter sets +* Save every objective function, parameter set and simulation in a database +* The best 10% of the results are taken to generate samples (another 25% of the repetitions) in the remaining multi-dimensional parameter space. +* Run simulation function with generated parameter sets +* Save every objective function, parameter set and simulation in a database +* The best 10% of the results are taken to generate samples (another 25% of the repetitions) in the remaining multi-dimensional parameter space. +* Run simulation function with generated parameter sets +* Save every objective function, parameter set and simulation in a database + +For further detailed information check out Bardossy et al. (2008). + +## SCE-UA +### Shuffled Complex Evolution - University of Arizona + +SCE-UA is designed to overcome the risk of MCMC to stuck in local optima. The risk was reduced by starting several chains/complexes that evolve individually in the parameter space. +The population is periodically shuffled and new complexes are created with information from the previous complex. +SCE-UA has found to be very robust in finding the global optimum of hydrological models and is one the most widely used algorithm in hydrological applications today. + +* 10% of the repetitions are designed to perform a Monte Carlo sampling (burn-in period) +* Generate a complex and evolve each complex independent by taking evolution steps +* Run simulation function with generated parameter sets +* Calculate a hardcoded RootMeanSquaredError (RMSE) as objective function +* Save every objective function, parameter set and simulation in a database +* Check convergence, if criterion is reached, stop +* Shuffle complexes: Combine complexes and sort the objective functions +* Check complex number reduction +* Generate a new complex + +For further detailed information check out Duan et al. (1994). + + +## DE-MCz +### Differential Evolution Markov Chain + +One of the most recent algorithms we present here is the DE MCz. +It requires a minimal number of three chains that learn from each other during the sampling. +It has the same Metropolis decision as the MCMC algorithm and has found to be quite efficient compared with other MCMC techniques. +Like SCE-UA and SA, DE-MCz does not require any prior distribution information, which reduces the uncertainty due to subjective assumptions during the analysis. + +* Initialize matrix by sampling from the prior +* Sample uniformly for all chains +* Generate a new parameter set +* Run simulation function with generated parameter sets +* Calculate a hardcoded logProbability as likelihood +* Decide if the new parameter is accepted through a Metropolis decision +* Check convergence, if criterion is reached, stop + +For further detailed information check out terBraak et al. (2008). diff --git a/docs/Griewank.md b/docs/Griewank.md new file mode 100644 index 00000000..3323e5bd --- /dev/null +++ b/docs/Griewank.md @@ -0,0 +1,113 @@ + +# The Griewank + +The Griewank function is known to be a challenge for parameter estimation methods. It is defined as: + +$$f_{Griewank}(x,y) = \frac{x²+y²}{4000}-cos(\frac{x}{\sqrt{2}})cos(\frac{y}{\sqrt{3}})+1$$ + +where the control variables are *-50 < x < 50* and *-50 < y < 50*, with *f(x=0,y=0) = 0*. + +![Griewank](../img/griewank.png) + +*Figure 2: Response surface of the two dimensional Griewank function. Check out `/examples/3dplot.py`to produce such plots.* + +## Creating the setup file + +This time we create a more general model setup. We use the `__init__` function to set the number of parameters we want to analyse. +In this case we select two parameters. See `/examples/spotpy_setup_griewank.py` for the following code: + + import numpy as np + import spotpy + + class spotpy_setup(object): + def __init__(self): + self.dim=2 + self.parameternames=['x','y'] + self.params=[] + for parname in self.parameternames: + spotpy.parameter.Uniform(parname,-10,10,1.5,3.0) + + def parameters(self): + return spotpy.parameter.generate(self.params) + + def simulation(self, vector): + n = len(vector) + fr = 4000 + s = 0 + p = 1 + for j in range(n): + s = s+vector[j]**2 + for j in range(n): + p = p*np.cos(vector[j]/np.sqrt(j+1)) + simulation = [s/fr-p+1] + return simulation + + def evaluation(self): + observations=[0] + return observations + + def objectivefunction(self,simulation,evaluation): + objectivefunction= -spotpy.objectivefunctions.rmse(evaluation,simulation) + return objectivefunction + +## Sampling + +Now that we crated our setup file, we want to start to investigate our function. +One way is to analyse the results of the sampling is to have a look at the objective function trace of the sampled parameters. + +We start directly with all algorithms. First we have to create a new file: + + import spotpy + from spotpy_setup_griewank import spotpy_setup # Load your just created file from above + +Now we create samplers for every algorithm and sample 5,000 parameter combinations for every algorithm: + + results=[] + spotpy_setup=spotpy_setup() + rep=5000 + + sampler=spotpy.algorithms.mc(spotpy_setup, dbname='GriewankMC', dbformat='csv') + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.lhs(spotpy_setup, dbname='GriewankLHS', dbformat='csv') + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.mle(spotpy_setup, dbname='GriewankMLE', dbformat='csv') + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.mcmc(spotpy_setup, dbname='GriewankMCMC', dbformat='csv') + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.sceua(spotpy_setup, dbname='GriewankSCEUA', dbformat='csv') + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.sa(spotpy_setup, dbname='GriewankSA', dbformat='csv') + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.demcz(spotpy_setup, dbname='GriewankDEMCz', dbformat='csv') + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.rope(spotpy_setup, dbname='GriewankROPE', dbformat='csv') + sampler.sample(rep) + results.append(sampler.getdata()) + +## Plotting +To compare the results of the different algorithms, we choose to show our sampled parameter +combinations over a heat map of the Griewank function. This makes it easier to see, whether +the algorithms find the local minima or not: + + algorithms=['MC','LHS','MLE','MCMC','SCEUA','SA','DEMCz','ROPE'] + spotpy.analyser.plot_heatmap_griewank(results,algorithms) + +This should give you something like this: + +![Trace plot](../img/griewankheat.png) + +*Figure 3: Heat map of the two dimensional Griewank function.* diff --git a/docs/Hydrological_model.md b/docs/Hydrological_model.md new file mode 100644 index 00000000..09cf2bd4 --- /dev/null +++ b/docs/Hydrological_model.md @@ -0,0 +1,215 @@ +# A hydrological model + +This chapter shows you, how to link an external model with SPOTPY. This is might be the most interesting chapter for users. +We use an hydrological model as an example, the Catchment Modelling Framework (CMF). For detailed information and download options about this model, have a look at the [CMF Homepage](http://fb09-pasig.umwelt.uni-giessen.de:8000/ "CMF Homepage"). +This example will, most likely, be different from your actual use, but it will give you hints how to solve your specific issue. + +![CMF model](../img/cmf_model.png) + +*Figure 6: One soil column model build with cmf. The coloured bars represent soil layers, the grey ring a Free air carbon dioxide enrichment (FACE) ring and the black tube a Piezometer.* + +Our model is driven by some meteorological data and a varying groundwater table. Evapotransipration is simulate with the Shuttleworth Wallace concept +and infiltration with the Richards equation. The retention curve is build with the VanGenuchten equation. We want to calibrate the VanGenuchten parameter +*alpha*, *ksat*, *n* and *porosity* to simulate the observed soil mositure in the upper most soil layer. + +## Creating the setup file + +To create our setup file, first have to import SPOTPY, the model cmf and some extra packages, we will need: + + import spotpy + import cmf + from datetime import timedelta, datetime # Standard in Python to work with data that have date and time stamp + import Load_Data as loader # Used to import meterological data and the evaluation data from csv-files. + import numpy as np + +Before we start to write our spotpy_setup, we have to write a class or a function, which setting up your model. +In this case, we use the cmf model which directly callable from Python. We use the \__init\__ function to separate the forcing and +evaluation data loading from the runtime loop. The run function is supposed to be called with a set of parameters, which setup the rest of the model and runs it. +Important here is that our run function returns just the simulations, on which evaluation data is available. You will have to do the same, if you use a different model. + + class model(object): + ''' + Input: datastart: e.g. datetime(1998,6,1) + dataend: e.g. datetime(2000,1,1) + analysestart: e.g. datetime(1999,1,1) + + Output: Initialised model instance with forcing data (climate, groundwater) and evaluation data + (soil moisture) + ''' + def __init__(self,datastart,dataend,analysestart): + self.d_s=datastart + self.d_end=dataend + self.a_start=analysestart + self.bound= [[0.0001,0.6],[0.01,3],[1.05,1.4],[0.4,0.7]] # Physical parameter boundaries + DataLoader = loader.load_data(self.a_start,self.d_s,self.d_end) + cmf.set_parallel_threads(1) + + ###################### Load Forcing data #################################### + ClimateFilename = 'Climate_Face_new2.csv' + self.md=np.load(ClimateFilename+str(d_start.date())+str(self.d_end.date())+'.npy') + self.rain=cmf.timeseries.from_array(begin=self.d_s,step=timedelta(hours=1),data=self.md['Nd_mm_day']) + self.rHmean= cmf.timeseries.from_array(begin=self.d_s,step=timedelta(hours=1),data=self.md['Rh']) + self.Windspeed=cmf.timeseries.from_array(begin=self.d_s,step=timedelta(hours=1),data=self.md['Wind']) + self.Rs=cmf.timeseries.from_array(begin=self.d_s,step=timedelta(hours=1),data=self.md['Rs_meas']) + self.T=cmf.timeseries.from_array(begin=self.d_s,step=timedelta(hours=1),data=self.md['Temp']) + self.piezometer = 'P4' + self.gw_array = DataLoader.groundwater(self.piezometer) + ############################################################################# + + ###################### Load Evaluation data ################################# + eval_soil_moisture = DataLoader.soil_moisture('A1') + self.eval_dates = eval_soil_moisture['Date'] + self.observations = eval_soil_moisture['A1'] + ########################################################################### + + def _load_meteo(self,project): + #Create meteo station for project + meteo=project.meteo_stations.add_station('FACE',position = (0,0,0),timezone=1,timestep=cmf.h) + rain = self.rain + meteo.rHmean = self.rHmean + meteo.Windspeed = self.Windspeed + meteo.Rs = self.Rs + meteo.T = self.T + meteo.Tmax = meteo.T.reduce_max(begin = self.d_start, step = timedelta(days=1)) + meteo.Tmin = meteo.T.reduce_min(begin = self.d_start, step = timedelta(days=1)) + project.rainfall_stations.add('FACE',rain,(0,0,0)) + project.use_nearest_rainfall() + # Use the meteorological station for each cell of the project + project.use_nearest_meteo() + + def run(self,args): + return self._run(*args) + + def _run(self,alpha=None,n=None,porosity=None,ksat=None): + #return alpha,n,porosity,ksat + ''' + Runs the model instance + + Input: Parameter set (in this case VanGenuchten Parameter alpha,n,porosity,ksat) + Output: Simulated values on given observation days + ''' + #Check if given parameter set is in realistic boundaries + if alphaself.bound[0][1] or ksatself.bound[1][1] or nself.bound[2][1] or \ + porosityself.bound[3][1]: + print 'The following combination was ignored:' + print 'n= '+str(n) + print 'alpha='+str(alpha) + print 'ksat= '+str(ksat) + print 'porosity= '+str(porosity) + print '##############################' + return self.observations*-np.inf + else: + project=cmf.project() + cell = project.NewCell(x=0,y=0,z=0,area=1000, with_surfacewater=True) + print 'n= '+str(n) + print 'alpha='+str(alpha) + print 'ksat= '+str(ksat) + print 'porosity= '+str(porosity) + print '##############################' + r_curve = cmf.VanGenuchtenMualem(Ksat=ksat,phi=porosity,alpha=alpha,n=n) + layers=5 + ldepth=.01 + for i in range(layers): + depth = (i+1) * ldepth + cell.add_layer(depth,r_curve) + cell.install_connection(cmf.Richards) + cell.install_connection(cmf.ShuttleworthWallace) + cell.saturated_depth =.5 + solver = cmf.CVodeIntegrator(project,1e-6) + self._load_meteo(project) + gw = project.NewOutlet('groundwater',x=0,y=0,z=.9)#layers*ldepth) + cmf.Richards(cell.layers[-1],gw) + gw.potential = -.5 #IMPORTANT + gw.is_source=True + solver.t = self.d_start + Evalstep,evallist=0,[] + rundays=(self.d_end-self.d_start).days + for t in solver.run(solver.t,solver.t + timedelta(days=rundays),timedelta(hours=1)): + if self.gw_array['Date'].__contains__(t)==True: + Gw_Index=np.where(self.gw_array['Date']==t) + gw.potential=self.gw_array[self.piezometer][Gw_Index] + print gw.potential #TO DO: CHECK IF SOMETHING HAPPENS HERE!!!! + if t > self.a_start: + if Evalstep !=len(self.eval_dates) and t == self.eval_dates[Evalstep]: + evallist.append(cell.layers.wetness[0]*cell.layers.porosity[0]*100) + Evalstep+=1 + return evallist + +Now we can create our spotpy_setup class. Here we use to \__init\__ function, to initialize our model. +At this point it is recommended to load all needed data into the working storage (in this case meteorological data, soil moisture and groundwater table data). +Keep in mind, that the \__init\__ function is only called once during the sampling, while the other functions are called within every iteration of the algorithm. +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, 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) + self.cmfmodel = model(datastart,dataend,analysestart) + self.params = [spotpy.parameter.Normal('alpha',0.3,0.1,0.02,0.2), + spotpy.parameter.Normal('n',1.2,0.035,0.01,1.22), + spotpy.parameter.Normal('ksat',1,0.3,0.1,2.0), + spotpy.parameter.Normal('porosity',.55,0.04,0.02,0.6), + ] + +Now we have setup our model. It has a warm up from 06/01/1998 until 01/01/1999 and will then save soil moisture simulations whenever evaluation data is available. + +To define the VanGenuchten parameter boundaries we use a normal distribution. + + def parameters(self): + return spotpy.parameter.generate(self.params) + + def simulation(self,vector): + simulations= self.cmfmodel._run(alpha=vector[0],n=vector[1],ksat=vector[2],porosity=vector[3]) + return simulations + + def evaluation(self,evaldates=False): + if evaldates: + return self.cmfmodel.eval_dates + else: + return self.cmfmodel.observations + + def objectivefunction(self,simulation,evaluation): + #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 + + 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=[] + for algorithm in algorithms: + sampler.sample(10000) + results.append(sampler.getdata) + +## Plotting + + evaluation = spotpy_setup().evaluation() + evaldates= spotpy_setup().evaluation(evaldates=True) + + spotpy.analyser.plot_bestmodelruns(res,evaluation,algorithms=algorithms,dates=evaldates, ylabel='Soil moisture [%]') + +![CMF model](../img/cmf_bestmodelruns.png) + +*Figure 7: Best model runs of the different algorithms.* diff --git a/docs/Introduction.md b/docs/Introduction.md new file mode 100644 index 00000000..f55e433e --- /dev/null +++ b/docs/Introduction.md @@ -0,0 +1,9 @@ +# Introduction + +Before you start to use SPOTPY you should be familiar with Python. Otherwise I recommend you to read a [basic Python Tutorial](https://docs.python.org/2/tutorial/ " Basic Python tutorial") or [Think Python](http://www.greenteapress.com/thinkpython/ "Think Python") first. + +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 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. diff --git a/docs/Rosenbrock.md b/docs/Rosenbrock.md new file mode 100644 index 00000000..9a802266 --- /dev/null +++ b/docs/Rosenbrock.md @@ -0,0 +1,181 @@ + +# The Rosenbrock + +Before we start to calibrate a real model, we start with some numerical optimization problems. They are all about finding the optima, just like a model. +We start here with a simple two dimensional (parameter) function: The Rosenbrock (aka. Banana), which your might already know from the [Getting started chapter](../getting_started.md). +The Rosenbrock function is defined as: + +$$f_{Rosen}(x,y) = (100(y - x^2)^2 + (1-x)^2$$ + +where we defined control variables as *-10 < x < 10* and *-10 < y < 10*, with *f(x=1,y=1) = 0* + +![Rosenbrock](../img/rosen.png) + +*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 SPOTPY. + +## Creating the setup file + +First we have to build a setup Python file, containg all above mentioned informations about the Rosenbrock. +We start to have a look at the parameters *x* and *y*: + + import spotpy + import numpy as np + + class spotpy_setup(object): + def __init__(self): + self.params = [spotpy.parameter.Uniform('x',-10,10,1.5,3.0,-10,10), + spotpy.parameter.Uniform('y',-10,10,1.5,3.0,-10,10), + ] + def parameters(self): + return spotpy.parameter.generate(self.params) + +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. + +The next function should call our actual model, given a parameter combination from the above created function: + + def simulation(self,vector): + x=np.array(vector) + simulations= [sum(100.0*(x[1:] - x[:-1]**2.0)**2.0 + (1 - x[:-1])**2.0)] + return simulations + +Now we have to create a function, which is containing the response of our function, when it is called with the optimal values. +In this case, we know, that *f(x=1,y=1) = 0*. Accordingly, our evaluation function returns *0*: + + def evaluation(self): + 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 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. + + def objectivefunction(self,simulation,evaluation): + objectivefunction= -spotpy.objectivefunctions.rmse(evaluation,simulation) + return objectivefunction + +For other works keep in mind: + +* Simulation function must not return values besides for which evaluation values are available. + +* MCMC and DEMCz do not use the objective function and have their own likelihood function (logarithmic probability density). Both algorithms are very sensitive to the given likelihood function and would require a complex adaptation to other likelihood functions. + +Now you just have to save this file, e.g. `spotpy_setup_rosenbrock.py`. + +## Sampling + +Now that we crated our setup file, we want to start to investigate our function. +One way is to analyse the results of the sampling is to have a look at the objective function trace of the sampled parameters. + +We start directly with all algorithms. First we have to create a new file: + + import spotpy + from spotpy.examples.spotpy_setup_rosenbrock import spotpy_setup # Load your just created file from above + +Now we create samplers for every algorithm and sample 5,000 parameter combinations for every algorithm: + + results=[] + spot_setup=spot_setup() + rep=1000 + timeout=10 #Given in Seconds + + parallel = "seq" + dbformat = "csv" + + sampler=spotpy.algorithms.mc(spot_setup,parallel=parallel, dbname='RosenMC', dbformat=dbformat, sim_timeout=timeout) + print(describe(sampler)) + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.lhs(spot_setup,parallel=parallel, dbname='RosenLHS', dbformat=dbformat, sim_timeout=timeout) + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.mle(spot_setup, parallel=parallel, dbname='RosenMLE', dbformat=dbformat, sim_timeout=timeout) + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.mcmc(spot_setup, parallel=parallel, dbname='RosenMCMC', dbformat=dbformat, sim_timeout=timeout) + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.sceua(spot_setup, parallel=parallel, dbname='RosenSCEUA', dbformat=dbformat, sim_timeout=timeout) + sampler.sample(rep,ngs=4) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.sa(spot_setup, parallel=parallel, dbname='RosenSA', dbformat=dbformat, sim_timeout=timeout) + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.demcz(spot_setup,parallel=parallel, dbname='RosenDEMCz', dbformat=dbformat, sim_timeout=timeout) + sampler.sample(rep,nChains=4) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.rope(spot_setup, parallel=parallel, dbname='RosenROPE', dbformat=dbformat,sim_timeout=timeout) + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.abc(spot_setup, parallel=parallel, dbname='RosenABC', dbformat=dbformat,sim_timeout=timeout) + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.fscabc(spot_setup, parallel=parallel, dbname='RosenFSABC', dbformat=dbformat, sim_timeout=timeout) + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.demcz(spot_setup, parallel=parallel, dbname='RosenDEMCZ', dbformat=dbformat, sim_timeout=timeout) + sampler.sample(rep) + results.append(sampler.getdata()) + + sampler=spotpy.algorithms.dream(spot_setup, parallel=parallel, dbname='RosenDREAM', dbformat=dbformat, sim_timeout=timeout) + sampler.sample(rep) + results.append(sampler.getdata()) + +## Plotting + +To see the all results of your sampling, just type something like this: + + algorithms = ['mc','lhs','mle','mcmc','sceua','sa','demcz','rope','abc','fscabc', 'demcz', 'dream'] + spotpy.analyser.plot_parametertrace_algorithms(results, algorithms, spot_setup) + +This should give you an nice overview about, how the parameter settings evolve during the sampling of each algorithm: + +![Trace plot](../img/rosentracealgortihms.png) + +*Figure 2: Parameter traces as blue line of the two dimensional Rosenbrock function. The red dotted line is the optimal value for each parameter.* + +One can see here that every algorthm was able to find quasie optimal parameter settings during the sampling. +Especially `MLE` and `MCMC` yield very good results after the burn-in phase. +Parameter `y` has a higher uncertainty than parameter `x`, which is obvious when you have a look back at *Figure 1*. + +## Sensitivity Analysis + +One might be interested which parameter has now more importance. Therefore SPOTPY comes along with the +Fourier Amplitude Sensitivity Test (FAST). It will return you a FAST sensitivity index ranging from 0 to 1 for every parameter. +The higher the value, the higher the sensitivity of the parameter. + +To use this sampler we just have to type + + sampler=spotpy.algorithms.fast(spot_setup, dbname='RosenFAST', dbformat='csv') + sampler.sample(rep) + +Which should give you something like this + +`> Parameter First Total` + +`> x 0.883849 0.961862` + +`> y 0.020266 0.035932` + +This is indicating a much higher total sensitivity of parameter `x=0.96` then of parameter `y=0.04` in the investigated parameter bounds (see Figure 1). diff --git a/docs/Working with DREAM and Hymod.md b/docs/Working with DREAM and Hymod.md new file mode 100644 index 00000000..89496e5a --- /dev/null +++ b/docs/Working with DREAM and Hymod.md @@ -0,0 +1,287 @@ +# Calibration of HYMOD with DREAM + +This chapter shows you, how to link the external hydrological model HYMOD with SPOTPY (works only on Windows systems). + +We use the hydrological model HYMOD as an example, to calibrate it with the Differential Evolution Adaptive Metropolis (DREAM) algorithm. +For detailed information about the underlying theorie, have a look at the [Vrugt (2016)](https://doi.org/10.1016/j.envsoft.2015.08.013 "Vrugt (2016)"). +The SPOTPY package comes with an example which is desgined to help you to set up your own research project. + +First, we need to setup the model within a spot setup class. The model needs some meteorological input data and five parameters to estimate discharge: + +## Connect HYMOD with SPOTPY + +Here we use to \__init\__ function, to initialize the parameter for our model. + + class spot_setup(object): + def __init__(self): + + self.params = [spotpy.parameter.Uniform('x1',low=1.0 , high=500, optguess=412.33), + spotpy.parameter.Uniform('x2',low=0.1 , high=2.0, optguess=0.1725), + spotpy.parameter.Uniform('x3',low=0.1 , high=0.99, optguess=0.8127), + spotpy.parameter.Uniform('x4',low=0.0 , high=0.10, optguess=0.0404), + spotpy.parameter.Uniform('x5',low=0.1 , high=0.99, optguess=0.5592) + ] + self.curdir = os.getcwd() + self.owd = os.path.realpath(__file__)+os.sep+'..' + self.evals = list(np.genfromtxt(self.owd+os.sep+'hymod'+os.sep+'bound.txt',skip_header=65)[:,3])[:730] + self.Factor = 1944 * (1000 * 1000 ) / (1000 * 60 * 60 * 24) + print(len(self.evals)) + + def parameters(self): + return spotpy.parameter.generate(self.params) + +We use the simulation function to write one random parameter set into a parameter file, like it is needed for the HYMOD model, +start the model and read the model discharge output data: + + def simulation(self,x): + os.chdir(self.owd+os.sep+'hymod') + if sys.version_info.major == 2: + params = file('Param.in', 'w') + elif sys.version_info.major == 3: + params = open('Param.in','w') + else: + raise Exception("Your python is too old for this example") + for i in range(len(x)): + if i == len(x): + params.write(str(round(x[i],5))) + else: + params.write(str(round(x[i],5))+' ') + params.close() + os.system('HYMODsilent.exe') + + #try: + if sys.version_info.major == 2: + SimRR = file('Q.out', 'r') + elif sys.version_info.major == 3: + SimRR = open('Q.out', 'r') + else: + raise Exception("Your python is too old for this example") + simulations=[] + for i in range(64): + SimRR.readline() + for i in range(730): + val= SimRR.readline() + simulations.append(float(val)*self.Factor) + #except:#Assign bad values - model might have crashed + # SimRR = 795 * [np.nan] + os.chdir(self.curdir) + + return simulations + +And in a last step, we compare the observed and the simulated data. Here we choose one of the implemented Likelihood functions +in the SPOTPY package. Please mind that the selection of the Likelihood highly influences the results gained with this algorithm: + + def evaluation(self): + return self.evals + + def objectivefunction(self,simulation,evaluation, params=None): + like = spotpy.likelihoods.gaussianLikelihoodMeasErrorOut(evaluation,simulation) + return like + +## Sample with DREAM + +Now we can initialize the Hymod example: + + spot_setup=spot_setup() + +Create the Dream sampler of spotpy, alt_objfun is set to None to force SPOTPY +to jump into the def objectivefunction in the spot_setup class (default is +spotpy.objectivefunctions.log_p). Results are saved in a DREAM_hymod.csv file: + + sampler=spotpy.algorithms.dream(spot_setup, dbname='DREAM_hymod', dbformat='csv', alt_objfun=None) + +Select number of maximum repetitions, the number of chains used by dream (default = 5) and set the Gelman-Rubin convergence limit (default 1.2). +We further allow 100 runs after convergence is achieved: + + nChains = 4 + convergence_limit = 1.2 + runs_after_convergence = 100 + +We start the sampler and collect the gained r_hat convergence values after the sampling: + + r_hat = sampler.sample(rep,nChains=nChains,convergence_limit=convergence_limit, + runs_after_convergence=runs_after_convergence) + + +## Access the results +All other results can be accessed from the SPOTPY csv-database: + + results = spotpy.analyser.load_csv_results('DREAM_hymod') + +These results are structured as a numpy array. Accordingly, you can access the different columns by using simple Python code, +e.g. to access all the simulations: + + fields=[word for word in results.dtype.names if word.startswith('sim')] + print(results[fields) + +## Plot model uncertainty +For the analysis we provide some examples how to plor the data. +If you want to see the remaining posterior model uncertainty: + + fig= plt.figure(figsize=(16,9)) + ax = plt.subplot(1,1,1) + q5,q25,q75,q95=[],[],[],[] + for field in fields: + q5.append(np.percentile(results[field][-100:-1],2.5)) + q95.append(np.percentile(results[field][-100:-1],97.5)) + ax.plot(q5,color='dimgrey',linestyle='solid') + ax.plot(q95,color='dimgrey',linestyle='solid') + ax.fill_between(np.arange(0,730,1),list(q5),list(q95),facecolor='dimgrey',zorder=0, + linewidth=0,label='parameter uncertainty') + ax.plot(spot_setup.evaluation(),'r.',label='data') + ax.set_ylim(-50,450) + ax.set_xlim(0,729) + ax.legend() + fig.savefig('python_hymod.png',dpi=300) + +![Posterior model uncertainty](../img/python_hymod_simulation.png) +*Figure 1: Posterior model uncertainty of HYMOD.* + +## Plot convergence diagnostic +If you want to check the convergence of the DREAM algorithm: + + fig= plt.figure(figsize=(16,9)) + plt.subplot(2,1,1) + for i in range(int(max(results['chain']))+1): + index=np.where(results['chain']==i) + plt.plot(results['like1'][index], label='Chain '+str(i+1)) + + plt.ylabel('Likelihood value') + plt.legend() + + ax =plt.subplot(2,1,2) + r_hat=np.array(r_hat) + ax.plot([1.2]*len(r_hat),'k--') + for i in range(len(r_hat[0])): + ax.plot(r_hat[:,i],label='x'+str(i+1)) + + ax.set_yscale("log", nonposy='clip') + ax.set_ylim(-1,50) + ax.set_ylabel('R$^d$ - convergence diagnostic') + plt.xlabel('Number of chainruns') + plt.legend() + fig.savefig('python_hymod_convergence.png',dpi=300) + +![Convergence diagnostic](../img/python_hymod_convergence.png) + +*Figure 2: Gelman-Rubin onvergence diagnostic of DREAM results.* + + +## Plot parameter uncertainty +Or if you want to check the posterior parameter distribution: + + def find_min_max(spot_setup): + randompar=spot_setup.parameters()['random'] + for i in range(1000): + randompar=np.column_stack((randompar,spot_setup.parameters()['random'])) + return np.amin(randompar,axis=1),np.amax(randompar,axis=1) + + + min_vs,max_vs = find_min_max(spot_setup) + + fig= plt.figure(figsize=(16,16)) + plt.subplot(5,2,1) + x = results['par'+spot_setup.parameters()['name'][0]] + for i in range(int(max(results['chain']))): + index=np.where(results['chain']==i) + plt.plot(x[index],'.') + plt.ylabel('x1') + plt.ylim(min_vs[0],max_vs[0]) + + + plt.subplot(5,2,2) + x = results['par'+spot_setup.parameters()['name'][0]][int(len(results)*0.5):] + normed_value = 1 + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('x1') + plt.xlim(min_vs[0],max_vs[0]) + + + + plt.subplot(5,2,3) + x = results['par'+spot_setup.parameters()['name'][1]] + for i in range(int(max(results['chain']))): + index=np.where(results['chain']==i) + plt.plot(x[index],'.') + plt.ylabel('x2') + plt.ylim(min_vs[1],max_vs[1]) + + plt.subplot(5,2,4) + x = results['par'+spot_setup.parameters()['name'][1]][int(len(results)*0.5):] + normed_value = 1 + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('x2') + plt.xlim(min_vs[1],max_vs[1]) + + + + plt.subplot(5,2,5) + x = results['par'+spot_setup.parameters()['name'][2]] + for i in range(int(max(results['chain']))): + index=np.where(results['chain']==i) + plt.plot(x[index],'.') + plt.ylabel('x3') + plt.ylim(min_vs[2],max_vs[2]) + + + plt.subplot(5,2,6) + x = results['par'+spot_setup.parameters()['name'][2]][int(len(results)*0.5):] + normed_value = 1 + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('x3') + plt.xlim(min_vs[2],max_vs[2]) + + + plt.subplot(5,2,7) + x = results['par'+spot_setup.parameters()['name'][3]] + for i in range(int(max(results['chain']))): + index=np.where(results['chain']==i) + plt.plot(x[index],'.') + plt.ylabel('x4') + plt.ylim(min_vs[3],max_vs[3]) + + + plt.subplot(5,2,8) + x = results['par'+spot_setup.parameters()['name'][3]][int(len(results)*0.5):] + normed_value = 1 + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('x4') + plt.xlim(min_vs[3],max_vs[3]) + + + plt.subplot(5,2,9) + x = results['par'+spot_setup.parameters()['name'][4]] + for i in range(int(max(results['chain']))): + index=np.where(results['chain']==i) + plt.plot(x[index],'.') + plt.ylabel('x5') + plt.ylim(min_vs[4],max_vs[4]) + plt.xlabel('Iterations') + + plt.subplot(5,2,10) + x = results['par'+spot_setup.parameters()['name'][4]][int(len(results)*0.5):] + normed_value = 1 + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('x5') + plt.xlabel('Parameter range') + plt.xlim(min_vs[4],max_vs[4]) + fig.savefig('python_parameters.png',dpi=300) + +![Parameter distribution](../img/python_hymod_parameters.png) + +*Figure 3: Posterior parameter distribution of HYMOD.* diff --git a/docs/getting_started.md b/docs/getting_started.md new file mode 100644 index 00000000..946e7f5d --- /dev/null +++ b/docs/getting_started.md @@ -0,0 +1,61 @@ +# Getting started + +To start your experience with SPOTPY you need to have SPOTPY installed. Please see the [Installation chapter](index.md#installation) for further details. + +You will find the following example in the `getting_started.py` file under the folder `/examples`. There is no need for copy paste work. + +To use SPOTPY we have to import it and use one of the pre-build examples: + + import spotpy # Load the SPOT package into your working storage + from spotpy import analyser # Load the Plotting extension + from spotpy.examples.spot_setup_rosenbrock import spot_setup # Import the two dimensional Rosenbrock example + +The example comes along with parameter boundaries, the Rosenbrock function, the optimal value of the function and RMSE as a objective function. +So we can directly start to analyse the Rosenbrock function with one of the algorithms. We start with a simple Monte Carlo sampling: + + # Give Monte Carlo algorithm the example setup and saves results in a RosenMC.csv file + sampler = spotpy.algorithms.mc(spot_setup(), dbname='RosenMC', dbformat='csv') + +Now we can sample with the implemented Monte Carlo algorithm: + + sampler.sample(100000) # Sample 100.000 parameter combinations + results=sampler.getdata() # Get the results of the sampler + +Now we want to have a look at the results. First we want to know, what the algorithm has done during the 100.000 iterations: + + spotpy.analyser.plot_parameterInteraction(results) # Use the analyser to show the parameter interaction + +This should give you a parameter interaction plot of your results, which should look like Fig. 1: + +![Rosenbrock](img/rosen_interaction.png) + +*Figure 1: Parameter interaction of the two dimensional Rosenbrock function* + +We can see that the parameters *x* and *y*, which drive the the Rosenbrock function, vary between *-10* and *10*. + +If you want to see the best 10% of your samples, which is called posterior parameter distribution, you have to do something like this: + + posterior=spotpy.analyser.get_posterior(results, percentage=10) + spotpy.analyser.plot_parameterInteraction(posterior) + +This should give you a parameter interaction plot of your best 10% samples, which should look like Fig. 2: + +![Rosenbrock](img/rosen_interaction_post.png) + +*Figure 2: Posterior parameter interaction of the two dimensional Rosenbrock function* + +This has reduced your parameter uncertainty for *x* of around 70% and for *y* for 40%, which is telling us, that we have a higher uncertainty for *y* than for *x* in our Rosenbrock example. + +Note that your `results` are a normal NumPy array. That means you are not limited to pre-build plotting features and you can try out things on your own, e.g. like typing `plot(results['like'])`. + +Let us find out the best found parameter set: + + print(spotpy.analyser.get_best_parameterset(results)) + +And you will get something like + + >>> x=1.05 y=1.12 + +depending on your random results. 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The package is puplished 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 "doi:10.1371/journal.pone.0145180"), 2015 + +The simplicity and flexibility enables the use and test of different +algorithms without the need of complex codes: + +``` +sampler = spotpy.algorithms.sceua(model_setup()) # Initialize your model with a setup file +sampler.sample(10000) # Run the model +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. +Some features you can use with the SPOTPY package are: + +* Fitting models to evaluation data with different algorithms. +Available algorithms are Monte Carlo (`MC`), Markov-Chain Monte-Carlo (`MCMC`), +Maximum Likelihood Estimation (`MLE`), Latin-Hypercube Sampling (`LHS`), +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`). + +* Wide range of objective functions, likelihood functions and hydroligcal signatures to validate the sampled results. +Available objective functions are: +Bias, Nash-Sutcliff (`NSE`), log Nash-Sutcliff (`logNSE`), Logarithmic probability (`logp`), Correlation Coefficient (`r`), +Coefficient of Determination (`r^2`), Mean Squared Error (`MSE`), Root Mean Squared Error (`RMSE`), Mean Absolute Error (`MAE`), +Relative Root Mean Squared Error (`RRMSE`), Agreement Index (`AI`), Covariance, Decomposed MSE (`dMSE`) and Kling-Gupta Efficiency (`KGE`). + +* Prebuild parameter distribution functions: Uniform, Normal, logNormal, Chisquare, +Exponential, Gamma, Wald, Weilbull + +* Suited to perform uncertainty-, sensitivity analysis or calibration + of a model. + +* Multi-objective support + +* MPI support for fast parallel computing + +* A progress bar monitoring the sampling loops. Enables you to plan your coffee breakes. + +* Use of Numpy functions as often as possible. This makes your coffe breakes short. + +* Different databases solutions: `ram` storage for fast sampling a simple and `csv` or `sql` tables +the save solution for long duration samplings. + +* Automatic best run selecting and plotting + +* Parameter trace plotting + +* Parameter interaction plot including the Gaussian-kde function + +* Regression analysis between simulation and evaluation data + +* Posterior distribution plot + +* Convergence diagnostics with Gelman-Rubin and the Geweke plot + +## Relationship to other packages +A surprisingly small range of similar parameter estimation packages is available + +* [Pest](http://www.pesthomepage.org/ "Pest") Independent program for Model-Independent Parameter Estimation and Uncertainty Analysis +* [OpenBugs](http://www.mrc-bsu.cam.ac.uk/software/bugs/ "BUGS") Independent program for performing Bayesian inference Using Gibbs Sampling +* [JAGS](http://mcmc-jags.sourceforge.net/ "JAGS") Independent program similar to OpenBUGS +* [PyMC](https://github.com/pymc-devs/pymc "PyMC") Comprehensive Python package to analyse models with MCMC techniques +* [STAN](http://mc-stan.org/ "STAN") Available in Python (amongst others) implementing MCMC techniques like NUTS, HMC and L-BFGS +* [emcee](http://dan.iel.fm/emcee/current/ "emcee") Python package using a Affine Invariant Markov chain Monte Carlo Ensemble sampler +* [BIP](http://bayesian-inference.googlecode.com/hg/trunk/BIP/Docs/build/html/index.html "BIP") Python package for bayesian inference with a DREAM sampler + +All of them have their pros and cons. To benchmark SPOTPY against these packages would be difficult because of wide variety of settings in different algorithms. +The most related one is certainly PyMC, which brings many ideas into this framework. +At the moment is PyMC limited to MCMC algorithms when analysing external deterministic models. +To test other algorithms in such a straightforward way was the main reason why SPOTPY was developed. +Consequently, none of the packages can offer such a wide range of different algorithms like SPOTPY. + +## Installation + +### Dependencies +The SPOTPY is an open-source package written in pure Python. It runs on all major platforms (Windows, Linux, Mac). +SPOTPY requires just some standard packages: + +* [NumPy](http://www.numpy.org/ "Numpy") +* [Scipy](http://www.scipy.org/ "Scipy") + +Optional packages are: + +* [Matplotlib](http://matplotlib.org/ "Matplotlib") +* [Pandas](http://pandas.pydata.org/ "Pandas") +* [mpi4py](http://mpi4py.scipy.org/ "mpi4py") + +all packages are pre-installed e.g. in the following packages: + +* [Python xy](https://code.google.com/p/pythonxy/ "Python xy") version >2.7 +* [WinPython](http://winpython.sourceforge.net/ "WinPython) version >2.7 + +### Download +SPOTPY is available on [PYPI](https://pypi.python.org/pypi/spotpy "spotpy") and [GitHub](https://github.com/thouska/spotpy "spotpy"). +This should make installation easy, as you just have to use a command window and type + + pip install spotpy + +Alternatively, you can download the latest version of SPOTPY with a [SVN-Client](http://tortoisesvn.net/index.de.html "TortoiseSVN"). + +With this software you just have to check out: `svn://fb09-pasig.umwelt.uni-giessen.de/spotpy/trunk` + +The recommended place for the SPOTPY package is in the site-packages folder in your Python Path, just the location of all other Python packages. + +## Project layout + +SPOTPY can work with any parameter distributions. A standard setup uses pre-build distributions from NumPy. +To benchmark the model-runs with a value, SPOTPY comes along with a wide range of pre-build objective functions. +All algorithms realized in the SPOTPY package can work with the Distributions and objective functions. One can use them for +uncertainty-, sensitivity analysis or parameter optimization. +The three packages together can be run in any combination and results are stored in the `ram` storage or in a `csv` file. +The results can be analysed with some pre-build statistics and plotting features. + + +![Overview](img/overview_new.png) + + +*Above: Overview about functionality of the SPOTPY package* + + + + __init__.py # Ensures that all needed files are loaded. + analyser.py # Plotting features and statistic analysis. + database.py # Ensures a secure data harbour during the sampling. + objectivefunctions.py # Library of pre-build evaluation functions + likelihoods.py # Library of pre-build evaluation functions + signatures.py # Library of pre-build evaluation functions + + algorithms/ + __init__.py # Ensures the availability of all algorithms + demcz.py # Differential Evolution Markov Chain Monte Carlo + lhs.py # Latin Hypercube Sampling + mcmc.py # Metropolis Markov Chain Monte Carlo + mle.py # Maximum Likelihood Estimation + mc.py # Monte Carlo + sceua.py # Shuffled Complex Evolution + sa.py # Simulated annealing + rope.py # RObust Parameter Estimation + fast.py # Fourier Amplitude Sensitivity Testing + abc.py # Artificial Bee Colony + fscabc.py # Fitness Scaled Chaotic Artificial Bee Colony + dream.py # Differential Evolution Adaptive Metropolis + + parallel/ + mpi.py #Basic Parralel Computing features + mpipool.py #Basic Parralel Computing features + mproc.py #Basic Parralel Computing features + sequential.py #Basic Parralel Computing features + + examples/ + spotpy_setup_ackley.py # Example SPOTPY setup to analyse the Ackley function + spotpy_setup_griewank.py # Example SPOTPY setup to analyse the Griewank function + spotpy_setup_rosenbrock.py # Example SPOTPY setup to analyse the Rosenbrock function + getting_started.py # Recommended test file for starters + 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 + 3dplot.py # Response surface plot of example files + diff --git a/docs/license.md b/docs/license.md new file mode 100644 index 00000000..2800400c --- /dev/null +++ b/docs/license.md @@ -0,0 +1,22 @@ +#The MIT License (MIT) + +Copyright © 2015, Tobias Houska. All rights reserved. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. + diff --git a/docs/static_qr_code_without_logo.jpg b/docs/static_qr_code_without_logo.jpg new file mode 100644 index 00000000..880ba1cc Binary files /dev/null and b/docs/static_qr_code_without_logo.jpg differ diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 00000000..44b31de5 --- /dev/null +++ b/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 © 2015, Tobias Houska. + + + diff --git a/readthedocs.yml b/readthedocs.yml new file mode 100644 index 00000000..a0c39652 --- /dev/null +++ b/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 + diff --git a/setup.py b/setup.py index 0b0a1952..8b786c28 100644 --- a/setup.py +++ b/setup.py @@ -1,26 +1,21 @@ # Copyright (c) 2015, Tobias Houska -from setuptools import setup +from setuptools import setup, find_packages import os setup( name = 'spotpy', - version = '1.3.25', + version = '1.5.10', 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 = 'tobias.houska@umwelt.uni-giessen.de', - url = 'http://www.uni-giessen.de/cms/faculties/f09/institutes/ilr/hydro/download/spotpy', + url = 'https://spotpy.readthedocs.io/en/latest/', license = 'MIT', - packages = ["spotpy", "spotpy.examples", "spotpy.examples.hymod_python", "spotpy.examples.hymod_exe", "spotpy.algorithms", "spotpy.parallel", "spotpy.gui"], - package_data={ - 'spotpy.examples.hymod_exe': ['*'], - 'spotpy.examples.hymod_python': ['*'], - }, - #include_package_data = True, + packages=find_packages(exclude=["tests*", "docs*"]), use_2to3 = True, - keywords = 'Monte Carlo, MCMC, MLE, SCE-UA, Simulated Annealing, DE-MCz, DREAM, ROPE, Artifical Bee Colony, Uncertainty, Calibration, Model, Signatures', + keywords = 'Monte Carlo, MCMC, MLE, SCE-UA, Simulated Annealing, DE-MCz, DREAM, ROPE, Artifical Bee Colony, DDS, PA-DDS, Uncertainty, Calibration, Model, Signatures', classifiers = [ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', @@ -28,10 +23,10 @@ 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Programming Language :: Python', - 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', - 'Programming Language :: Python :: 3.4', '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'], ) diff --git a/spotpy/__init__.py b/spotpy/__init__.py index f641d6fb..89b1022b 100644 --- a/spotpy/__init__.py +++ b/spotpy/__init__.py @@ -20,23 +20,24 @@ - 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/ Please cite our paper, if you are using SPOTPY. ''' -from . import database # Writes the results of the sampler in a user defined output file -from . import algorithms # Contains all the different algorithms implemented in SPOTPY -from . import parameter # Contains different distributions to discribe the prior information for every model parameter -from . import analyser # Contains some examples to analyse the results of the different algorithms -from . import objectivefunctions # Quantifies goodness of fit between simulation and evaluation data with objectivefunctions -from . import signatures # Quantifies goodness of fit between simulation and evaluation data with hydrological signatures -from . import likelihoods # Quantifies goodness of fit between simulation and evaluation data with likelihood functions -from . import examples # Contains tutorials how to use SPOTPY -from . import describe # Contains some helper functions to describe smaplers and setups - -__version__ = '1.3.25' \ No newline at end of file +from . import database # Writes the results of the sampler in a user defined output file +from . import algorithms # Contains all the different algorithms implemented in SPOTPY +from . import parameter # Contains different distributions to describe the prior information for every model parameter +from . import analyser # Contains some examples to analyse the results of the different algorithms +from . import objectivefunctions # Quantifies goodness of fit between simulation and evaluation data with objective functions +from . import likelihoods # Quantifies goodness of fit between simulation and evaluation data with likelihood functions +from . import examples # Contains tutorials how to use SPOTPY +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__ = '1.5.10' \ No newline at end of file diff --git a/spotpy/algorithms/__init__.py b/spotpy/algorithms/__init__.py index 99442c82..2de3ea62 100644 --- a/spotpy/algorithms/__init__.py +++ b/spotpy/algorithms/__init__.py @@ -30,5 +30,7 @@ from .abc import abc # Artificial Bee Colony from .fscabc import fscabc # Fitness Scaling Artificial Bee Colony from .dream import dream # DiffeRential Evolution Adaptive Metropolis -from .list import list # Samples from given spotpy database from .nsgaii import NSGAII # A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II +from .list_sampler import list_sampler # Samples from given spotpy database +from .dds import dds # Dynamically Dimensioned Search algorithm +from .padds import padds # Pareto Archived - Dynamicallly Dimensioned Search algorithm \ No newline at end of file diff --git a/spotpy/algorithms/_algorithm.py b/spotpy/algorithms/_algorithm.py index 5cd9815b..b366f892 100644 --- a/spotpy/algorithms/_algorithm.py +++ b/spotpy/algorithms/_algorithm.py @@ -5,17 +5,17 @@ This file holds the standards for every algorithm. ''' - from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals -from spotpy import database, objectivefunctions +from spotpy import database from spotpy import parameter import numpy as np import time import threading + try: from queue import Queue except ImportError: @@ -27,7 +27,6 @@ from Queue import Queue - class _RunStatistic(object): """ this class checks for each run if the objectivefunction got better and holds the @@ -36,35 +35,67 @@ class _RunStatistic(object): Usage: status = _RunStatistic() status(rep,like,params) - """ - def __init__(self): + def __init__(self, repetitions, algorithm_name, optimization_direction, parnames): + self.optimization_direction = optimization_direction #grid, mazimize, minimize + print('Initializing the ',algorithm_name,' with ',repetitions,' repetitions') + if optimization_direction == 'minimize': + self.compare = self.minimizer + print('The objective function will be minimized') + if optimization_direction == 'maximize': + self.compare = self.maximizer + print('The objective function will be maximized') + if optimization_direction == 'grid': + self.compare = self.grid + self.rep = 0 - self.params = None - self.objectivefunction = -1e308 - self.bestrep = 0 + self.parnames = parnames + self.parameters= len(parnames) + self.params_min = [np.nan]*self.parameters + self.params_max = [np.nan]*self.parameters + self.objectivefunction_min = 1e308 + self.objectivefunction_max = -1e308 self.starttime = time.time() self.last_print = time.time() - self.repetitions = None + self.repetitions = repetitions + self.stop = False + + def minimizer(self, objval, params): + if objval < self.objectivefunction_min: + self.objectivefunction_min = objval + self.params_min = list(params) + + def maximizer(self, objval, params): + if objval > self.objectivefunction_max: + self.objectivefunction_max = objval + self.params_max = list(params) + + def grid(self, objval, params): + if objval < self.objectivefunction_min: + self.objectivefunction_min = objval + self.params_min = list(params) + if objval > self.objectivefunction_max: + self.objectivefunction_max = objval + self.params_max = list(params) - def __call__(self, rep, objectivefunction, params): - self.curparmeterset = params + + def __call__(self, objectivefunction, params, block_print=False): self.rep+=1 - if type(objectivefunction) == type([]): - if objectivefunction[0] > self.objectivefunction: - # Show only the first best objectivefunction when working with - # more than one objectivefunction - self.objectivefunction = objectivefunction[0] - self.params = params - self.bestrep = self.rep + if type(objectivefunction) == type([]): #TODO: change to iterable + self.compare(objectivefunction[0], params) + elif type(objectivefunction) == type(np.array([])): + pass else: - if objectivefunction > self.objectivefunction: - self.params = params - self.objectivefunction = objectivefunction - self.bestrep = self.rep - self.print_status() + self.compare(objectivefunction, params) + + + if self.rep == self.repetitions: + self.stop = True + + if not block_print: + self.print_status() def print_status(self): # get str showing approximate timeleft to end of simulation in H, M, S @@ -73,14 +104,59 @@ def print_status(self): if acttime - self.last_print >= 2: avg_time_per_run = (acttime - self.starttime) / (self.rep + 1) timestr = time.strftime("%H:%M:%S", time.gmtime(round(avg_time_per_run * (self.repetitions - (self.rep + 1))))) - - text = '%i of %i (best like=%g) est. time remaining: %s' % (self.rep, self.repetitions, - self.objectivefunction, timestr) + if self.optimization_direction == 'minimize': + text = '%i of %i, minimal objective function=%g, time remaining: %s' % ( + self.rep, self.repetitions, self.objectivefunction_min, timestr) + + if self.optimization_direction == 'maximize': + text = '%i of %i, maximal objective function=%g, time remaining: %s' % ( + self.rep, self.repetitions, self.objectivefunction_max, timestr) + + if self.optimization_direction == 'grid': + text = '%i of %i, min objf=%g, max objf=%g, time remaining: %s' % ( + self.rep, self.repetitions, self.objectivefunction_min, self.objectivefunction_max, timestr) + print(text) self.last_print = time.time() - + + def print_status_final(self): + print('\n*** Final SPOTPY summary ***') + print('Total Duration: ' + str(round((time.time() - self.starttime), 2)) + ' seconds') + print('Total Repetitions:', self.rep) + + if self.optimization_direction == 'minimize': + print('Minimal objective value: %g' % (self.objectivefunction_min)) + print('Corresponding parameter setting:') + for i in range(self.parameters): + text = '%s: %g' % (self.parnames[i], self.params_min[i]) + print(text) + + if self.optimization_direction == 'maximize': + print('Maximal objective value: %g' % (self.objectivefunction_max)) + print('Corresponding parameter setting:') + for i in range(self.parameters): + text = '%s: %g' % (self.parnames[i], self.params_max[i]) + print(text) + + if self.optimization_direction == 'grid': + print('Minimal objective value: %g' % (self.objectivefunction_min)) + print('Corresponding parameter setting:') + for i in range(self.parameters): + text = '%s: %g' % (self.parnames[i], self.params_min[i]) + print(text) + + print('Maximal objective value: %g' % (self.objectivefunction_max)) + print('Corresponding parameter setting:') + for i in range(self.parameters): + text = '%s: %g' % (self.parnames[i], self.params_max[i]) + print(text) + + print('******************************\n') + + def __repr__(self): - return 'Best objectivefunction: %g' % self.objectivefunction + return 'Min objectivefunction: %g \n Max objectivefunction: %g' % ( + self.objectivefunction_min, self.objectivefunction_max) class _algorithm(object): @@ -112,67 +188,77 @@ class _algorithm(object): seq: Sequentiel sampling (default): Normal iterations on one core of your cpu. mpc: Multi processing: Iterations on all available cores on your (single) pc mpi: Message Passing Interface: Parallel computing on high performance computing clusters, py4mpi needs to be installed - save_thresholde: float or list + save_threshold: float or list Compares the given value/list of values with return value/list of values from spot_setup.objectivefunction. If the objectivefunction value is higher, the results are saved in the database. If not they are ignored (saves storage). db_precision:np.float type set np.float16, np.float32 or np.float64 for rounding of floats in the output database Default is np.float16 - alt_objfun: str or None, default: 'rmse' - alternative objectivefunction to be used for algorithm - * None: the objfun defined in spot_setup.objectivefunction is used - * any str: if str is found in spotpy.objectivefunctions, - this objectivefunction is used, else falls back to None - e.g.: 'log_p', 'rmse', 'bias', 'kge' etc. sim_timeout: float, int or None, default: None the defined model given in the spot_setup class can be controlled to break after 'sim_timeout' seconds if sim_timeout is not None. If the model run has been broken simlply '[nan]' will be returned. - - + random_state: int or None, default: None + the algorithms uses the number in random_state as seed for numpy. This way stochastic processes can be reproduced. """ + _unaccepted_parameter_types = (parameter.List, ) + def __init__(self, spot_setup, dbname=None, dbformat=None, dbinit=True, - parallel='seq', save_sim=True, alt_objfun=None, breakpoint=None, - backup_every_rep=100, save_threshold=-np.inf, db_precision=np.float16,sim_timeout = None): + dbappend=False, parallel='seq', save_sim=True, breakpoint=None, + backup_every_rep=100, save_threshold=-np.inf, db_precision=np.float16, + sim_timeout=None, random_state=None, optimization_direction='grid', algorithm_name=''): + # Initialize the user defined setup class self.setup = spot_setup - self.model = self.setup.simulation - # Philipp: Changed from Tobi's version, now we are using both new class defined parameters - # as well as the parameters function. The new method get_parameters - # can deal with a missing parameters function - # - # For me (Philipp) it is totally unclear why all the samplers should call this function - # again and again instead of - # TODO: just storing a definite list of parameter objects here + param_info = parameter.get_parameters_array(self.setup, unaccepted_parameter_types=self._unaccepted_parameter_types) + self.all_params = param_info['random'] + self.constant_positions = parameter.get_constant_indices(spot_setup) + if self.constant_positions: + self.non_constant_positions = [] + for i, val in enumerate(self.all_params): + if self.all_params[i] not in self.constant_positions: + self.non_constant_positions.append(i) + else: + self.non_constant_positions = np.arange(0,len(self.all_params)) self.parameter = self.get_parameters - self.parnames = self.parameter()['name'] - + self.parnames = param_info['name'] + self.algorithm_name = algorithm_name # Create a type to hold the parameter values using a namedtuple - self.partype = parameter.get_namedtuple_from_paramnames( - self.setup, self.parnames) + self.partype = parameter.ParameterSet(param_info) - # use alt_objfun if alt_objfun is defined in objectivefunctions, - # else self.setup.objectivefunction - self.objectivefunction = getattr( - objectivefunctions, alt_objfun or '', None) or self.setup.objectivefunction self.evaluation = self.setup.evaluation() self.save_sim = save_sim + self.optimization_direction = optimization_direction self.dbname = dbname or 'customDb' - self.dbformat = dbformat or 'custom' + self.dbformat = dbformat or 'ram' self.db_precision = db_precision self.breakpoint = breakpoint self.backup_every_rep = backup_every_rep + # Two parameters to control the data base handling + # 'dbinit' triggers the initial creation of the data base file + # 'dbappend' used to append to the existing data base, after restart self.dbinit = dbinit + self.dbappend = dbappend + + # Set the random state + if random_state is None: #ToDo: Have to discuss if these 3 lines are neccessary. + random_state = np.random.randint(low=0, high=2**30) + np.random.seed(random_state) # If value is not None a timeout will set so that the simulation will break after sim_timeout seconds without return a value self.sim_timeout = sim_timeout self.save_threshold = save_threshold - + + self._return_all_likes = False #allows multi-objective calibration if set to True, is set by the algorithm + if breakpoint == 'read' or breakpoint == 'readandwrite': print('Reading backupfile') - self.dbinit = False - self.breakdata = self.read_breakdata(self.dbname) + try: + open(self.dbname+'.break') + except FileNotFoundError: + print('Backupfile not found') + self.dbappend = True # Now a repeater (ForEach-object) is loaded # A repeater is a convinent wrapper to repeat tasks @@ -204,13 +290,9 @@ def __init__(self, spot_setup, dbname=None, dbformat=None, dbinit=True, # the normal work on the chains self.repeat = ForEach(self.simulate) - # In MPI, this command will do nothing on the master process - # but the worker processes are going to wait for jobs. - # Hence the workers will only receive parameters for the - # simulate function, new calculation phases and the termination - self.repeat.start() - self.status = _RunStatistic() - + # method "save" needs to know whether objective function result is list or float, default is float + self.like_struct_typ = type(1.1) + def __str__(self): return '{type}({mtype}())->{dbname}'.format( type=type(self).__name__, @@ -224,85 +306,115 @@ def get_parameters(self): """ Returns the parameter array from the setup """ - return parameter.get_parameters_array(self.setup) + pars = parameter.get_parameters_array(self.setup) + return pars[self.non_constant_positions] def set_repetiton(self, repetitions): - self.status.repetitions = repetitions - + self.status = _RunStatistic(repetitions, self.algorithm_name, + self.optimization_direction, self.parnames) + # In MPI, this command will do nothing on the master process + # but the worker processes are going to wait for jobs. + # Hence the workers will only receive parameters for the + # simulate function, new calculation phases and the termination + self.repeat.start() + def final_call(self): self.repeat.terminate() try: self.datawriter.finalize() except AttributeError: # Happens if no database was assigned pass - print('End of sampling') - text = 'Best run at %i of %i (best like=%g) with parameter set:' % ( - self.status.bestrep, self.status.repetitions, self.status.objectivefunction) - print(text) - print(self.status.params) - text = 'Duration:' + str(round((time.time() - self.status.starttime), 2)) + ' s' - print(text) + self.status.print_status_final() + def _init_database(self, like, randompar, simulations): if self.dbinit: print('Initialize database...') self.datawriter = database.get_datawriter(self.dbformat, - self.dbname, self.parnames, like, randompar, simulations, save_sim=self.save_sim, - dbinit=self.dbinit, db_precision=self.db_precision, setup=self.setup) + self.dbname, self.parnames, like, randompar, simulations, + save_sim=self.save_sim, dbappend=self.dbappend, + dbinit=self.dbinit, db_precision=self.db_precision, + setup=self.setup) self.dbinit = False + + def __is_list_type(self, data): + if type(data) == type: + return data == list or data == type(np.array([])) + else: + return type(data) == list or type(data) == type(np.array([])) + def save(self, like, randompar, simulations, chains=1): # Initialize the database if no run was performed so far self._init_database(like, randompar, simulations) - - #try if like is a list of values compare it with save threshold setting - try: + # Test if like and the save threshold are float/list and compare accordingly + if self.__is_list_type(like) and self.__is_list_type(self.save_threshold): if all(i > j for i, j in zip(like, self.save_threshold)): #Compares list/list self.datawriter.save(like, randompar, simulations, chains=chains) - #If like value is not a iterable, it is assumed to be a float - except TypeError: # This is also used if not threshold was set - try: - if like>self.save_threshold: #Compares float/float - self.datawriter.save(like, randompar, simulations, chains=chains) - except TypeError:# float/list would result in an error, because it does not make sense - if like[0]>self.save_threshold: #Compares list/float - self.datawriter.save(like, randompar, simulations, chains=chains) + if (not self.__is_list_type(like)) and (not self.__is_list_type(self.save_threshold)): + if like>self.save_threshold: #Compares float/float + self.datawriter.save(like, randompar, simulations, chains=chains) + if self.__is_list_type(like) and (not self.__is_list_type(self.save_threshold)): + if like[0]>self.save_threshold: #Compares list/float + self.datawriter.save(like, randompar, simulations, chains=chains) + if (not self.__is_list_type(like)) and self.__is_list_type(self.save_threshold): #Compares float/list + if (like > self.save_threshold).all: + self.datawriter.save(like, randompar, simulations, chains=chains) def read_breakdata(self, dbname): ''' Read data from a pickle file if a breakpoint is set. Reason: In case of incomplete optimizations, old data can be restored. ''' import pickle with open(dbname+'.break', 'rb') as breakfile: - return pickle.load(breakfile) + work,backuptime,repos,obmin,obmax=pickle.load(breakfile) + self.status.starttime=self.status.starttime-backuptime + self.status.rep=repos + self.status.objectivefunction_min=obmin + self.status.objectivefunction_max=obmax + return work def write_breakdata(self, dbname, work): ''' Write data to a pickle file if a breakpoint has been set.''' import pickle + work=(work,self.status.last_print-self.status.starttime,self.status.rep,self.status.objectivefunction_min,self.status.objectivefunction_max) with open(str(dbname)+'.break', 'wb') as breakfile: pickle.dump(work, breakfile) def getdata(self): return self.datawriter.getdata() - def postprocessing(self, rep, randompar, simulation, chains=1, save=True, negativlike=False): - like = self.getfitness(simulation=simulation, params=randompar) + def update_params(self, params): + #Add potential Constant parameters + self.all_params[self.non_constant_positions] = params + return self.all_params + + + def postprocessing(self, rep, params, simulation, chains=1, save_run=True, negativlike=False, block_print=False): # TODO: rep not necessaray + + params = self.update_params(params) + if negativlike is True: + like = -self.getfitness(simulation=simulation, params=params) + else: + like = self.getfitness(simulation=simulation, params=params) + # Save everything in the database, if save is True # This is needed as some algorithms just want to know the fitness, # before they actually save the run in a database (e.g. sce-ua) - if save is True: - if negativlike is True: - self.save(-like, randompar, simulations=simulation, chains=chains) - self.status(rep, -like, randompar) - else: - self.save(like, randompar, simulations=simulation, chains=chains) - self.status(rep, like, randompar) - if type(like)==type([]): - return like[0] - else: + + self.status(like,params,block_print=block_print) + if save_run is True and simulation is not None: + self.save(like, params, simulations=simulation, chains=chains) + if self._return_all_likes: return like - + else: + try: + iter(like) + return like[0] + except TypeError: # Happens if iter(like) fails, i.e. if like is just one value + return like + def getfitness(self, simulation, params): """ @@ -310,11 +422,11 @@ def getfitness(self, simulation, params): """ try: #print('Using parameters in fitness function') - return self.objectivefunction(evaluation=self.evaluation, simulation=simulation, params = (params,self.parnames)) + return self.setup.objectivefunction(evaluation=self.evaluation, simulation=simulation, params = (params,self.parnames)) except TypeError: # Happens if the user does not allow to pass parameter in the spot_setup.objectivefunction #print('Not using parameters in fitness function') - return self.objectivefunction(evaluation=self.evaluation, simulation=simulation) + return self.setup.objectivefunction(evaluation=self.evaluation, simulation=simulation) def simulate(self, id_params_tuple): """This is a simple wrapper of the model, returning the result together with @@ -322,16 +434,18 @@ 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 + all_params = self.all_params # we need a layer to fetch returned data from a threaded process into a queue. - def model_layer(q,params): + def model_layer(q,all_params): # Call self.model with a namedtuple instead of another sequence - q.put(self.model(self.partype(*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 que = Queue() - sim_thread = threading.Thread(target=model_layer, args=(que, params)) + sim_thread = threading.Thread(target=model_layer, args=(que, all_params)) sim_thread.daemon = True sim_thread.start() @@ -341,8 +455,8 @@ def model_layer(q,params): sim_thread.join(self.sim_timeout) # If no result from the thread is given, i.e. the thread was killed from the watcher the default result is - # '[nan]' otherwise get the result from the thread - model_result = [np.NAN] + # '[nan]' and will not be saved. Otherwise get the result from the thread + model_result = None if not que.empty(): model_result = que.get() return id, params, model_result diff --git a/spotpy/algorithms/abc.py b/spotpy/algorithms/abc.py index edc3e948..be0d4fba 100644 --- a/spotpy/algorithms/abc.py +++ b/spotpy/algorithms/abc.py @@ -10,16 +10,13 @@ from __future__ import print_function from __future__ import unicode_literals from . import _algorithm -import spotpy import numpy as np -import time import random -import itertools class abc(_algorithm): """ - This class holds the Artificial Bee Colony(ABC) algorithm, based on Karaboga (2007). + This class holds the Artificial Bee Colony (ABC) algorithm, based on Karaboga (2007). D. Karaboga, AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION,TECHNICAL REPORT-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department 2005. D. Karaboga, B. Basturk, A powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm, Journal of Global Optimization, Volume:39, Issue:3,pp:459-171, November 2007,ISSN:0925-5001 , doi: 10.1007/s10898-007-9149-x @@ -57,7 +54,8 @@ def __init__(self, *args, **kwargs): * True: Simulation results will be saved * False: Simulation results will not be saved """ - + kwargs['optimization_direction'] = 'maximize' + kwargs['algorithm_name'] = 'Artificial Bee Colony (ABC) algorithm' super(abc, self).__init__(*args, **kwargs) @@ -80,11 +78,8 @@ def sample(self, repetitions, eb=48, a=(1 / 10), peps=0.0001, ownlimit=False, li limit: int sets the limit """ - print('Starting the ABC algotrithm with '+str(repetitions)+ ' repetitions...') self.set_repetiton(repetitions) - # Initialize the Progress bar - #starttime = time.time() - #intervaltime = starttime + print('Starting the ABC algotrithm with '+str(repetitions)+ ' repetitions...') # Initialize ABC parameters: randompar = self.parameter()['random'] self.nopt = randompar.size @@ -96,38 +91,22 @@ def sample(self, repetitions, eb=48, a=(1 / 10), peps=0.0001, ownlimit=False, li lb, ub = self.parameter()['minbound'], self.parameter()['maxbound'] # Initialization work = [] + icall = 0 + gnrng = 1e100 # Calculate the objective function param_generator = ( (rep, self.parameter()['random']) for rep in range(eb)) for rep, randompar, simulations in self.repeat(param_generator): # Calculate fitness - like = self.postprocessing(rep, randompar, simulations, chains = 1) - #like = self.objectivefunction( - # evaluation=self.evaluation, simulation=simulations) - - # Save everything in the database - #self.save(like, randompar, simulations=simulations) - #self.status(rep, like, randompar) + like = self.postprocessing(rep, randompar, simulations, chains = 1, negativlike=True) c = 0 p = 0 - # (fit_x,x,fit_v,v,limit,normalized fitness) work.append([like, randompar, like, randompar, c, p]) - # Progress bar - #acttime = time.time() - # get str showing approximate timeleft to end of simulation in H, - # M, S - #timestr = time.strftime("%H:%M:%S", time.gmtime(round(((acttime - starttime) / - # (rep + 1)) * (repetitions - (rep + 1))))) - # Refresh progressbar every second - #if acttime - intervaltime >= 2: - # text = '%i of %i (best like=%g) est. time remaining: %s' % (rep, repetitions, - # self.status.objectivefunction, timestr) - # print(text) - # intervaltime = time.time() + icall +=1 + if self.status.stop: + print('Stopping sampling') + break - icall = 0 - gnrng = 1e100 - # and criter_change>pcento: while icall < repetitions and gnrng > peps: psum = 0 # Employed bee phase @@ -143,32 +122,22 @@ def sample(self, repetitions, eb=48, a=(1 / 10), peps=0.0001, ownlimit=False, li work[i][3][j] = lb[j] if work[i][3][j] > ub[j]: work[i][3][j] = ub[j] - ''' - #Scout bee phase - if work[i][4] >= self.limit: - work[i][3]=self.parameter()['random'] - work[i][4]=0 - ''' + # Calculate the objective function param_generator = ((rep, work[rep][3]) for rep in range(eb)) for rep, randompar, simulations in self.repeat(param_generator): # Calculate fitness - clike = self.postprocessing(icall+eb, randompar, simulations, chains = 2) - - #clike = self.objectivefunction( - # evaluation=self.evaluation, simulation=simulations) + clike = self.postprocessing(icall+eb, randompar, simulations, chains = 2, negativlike=True) if clike > work[rep][0]: work[rep][1] = work[rep][3] work[rep][0] = clike work[rep][4] = 0 else: - work[rep][4] = work[rep][4] + 1 - - #self.save( - # clike, work[rep][3], simulations=simulations, chains=icall) - #self.status(rep, work[rep][0], work[rep][1]) + work[rep][4] = work[rep][4] + 1 icall += 1 - # Probability distribution for roulette wheel selection + if self.status.stop: + print('Stopping samplig') + break # Probability distribution for roulette wheel selection bn = [] for i, val in enumerate(work): psum = psum + (1 / work[i][0]) @@ -204,19 +173,17 @@ def sample(self, repetitions, eb=48, a=(1 / 10), peps=0.0001, ownlimit=False, li param_generator = ((rep, work[rep][3]) for rep in range(eb)) for rep, randompar, simulations in self.repeat(param_generator): # Calculate fitness - clike = self.postprocessing(icall+eb, randompar, simulations, chains = 3) - #clike = self.objectivefunction( - # evaluation=self.evaluation, simulation=simulations) + clike = self.postprocessing(icall+eb, randompar, simulations, chains = 3, negativlike=True) if clike > work[rep][0]: work[rep][1] = work[rep][3] work[rep][0] = clike work[rep][4] = 0 else: work[rep][4] = work[rep][4] + 1 - #self.save( - # clike, work[rep][3], simulations=simulations, chains=icall) - #self.status(rep, work[rep][0], work[rep][1]) icall += 1 + if self.status.stop: + print('Stopping samplig') + break # Scout bee phase for i, val in enumerate(work): if work[i][4] >= self.limit: @@ -224,17 +191,13 @@ def sample(self, repetitions, eb=48, a=(1 / 10), peps=0.0001, ownlimit=False, li work[i][4] = 0 t, work[i][0], simulations = self.simulate( (icall, work[i][1])) - clike = self.postprocessing(icall+eb, randompar, simulations, chains = 4) - #clike = self.objectivefunction( - # evaluation=self.evaluation, simulation=simulations) - #self.save( - # clike, work[rep][3], simulations=simulations, chains=icall) + clike = self.postprocessing(icall+eb, randompar, simulations, chains = 4, negativlike=True) work[i][0] = clike icall += 1 - gnrng = -self.status.objectivefunction - #text = '%i of %i (best like=%g) est. time remaining: %s' % ( - # icall, repetitions, self.status.objectivefunction, timestr) - #print(text) + if self.status.stop: + print('Stopping samplig') + break + gnrng = -self.status.objectivefunction_max if icall >= repetitions: print('*** OPTIMIZATION SEARCH TERMINATED BECAUSE THE LIMIT') print('ON THE MAXIMUM NUMBER OF TRIALS ') @@ -243,5 +206,6 @@ def sample(self, repetitions, eb=48, a=(1 / 10), peps=0.0001, ownlimit=False, li if gnrng < peps: print( - 'THE POPULATION HAS CONVERGED TO A PRESPECIFIED SMALL PARAMETER SPACE') + 'THE POPULATION HAS CONVERGED TO A PRESPECIFIED SMALL PARAMETER SPACE AT RUN') + print(icall) self.final_call() diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py new file mode 100644 index 00000000..11c488cc --- /dev/null +++ b/spotpy/algorithms/dds.py @@ -0,0 +1,404 @@ +import numpy as np +from . import _algorithm +from spotpy.parameter import ParameterSet + + +class DDSGenerator: + """ + This class is used by the DDS algorithm to generate a new sample of parameters based on the current one. + Current parameter are exchanged in `ParameterSet` objects. + """ + + def __init__(self, np_random): + self.np_random = np_random + + def neigh_value_continuous(self, s, x_min, x_max, r): + """ + select a RANDOM neighbouring real value of a SINGLE decision variable + CEE 509, HW 5 by Bryan Tolson, Mar 5, 2003 AND ALSO CEE PROJECT + variables: + x_range is the range of the real variable (s_max-s_min) + + :param s: is a current SINGLE decision variable VALUE + :param x_min: is the min of variable s + :param x_max: is the max of variable s + :param r: is the neighbourhood parameter (replaces V parameter~see not + It is defined as the ratio of the std deviation of the desired + normal random number/x_range. Eg: + std dev desired = r * x_range + for comparison: variance (V) = (r * x_range)^2 + :return: x_new, a new sample of values in beetween a given range + """ + + x_range = x_max - x_min + + x_new = s + self.np_random.normal(0, 1) * r * x_range + + # NEED to deal with variable upper and lower bounds: + # Originally bounds in DDS were 100# reflective + # But some times DVs are right on the boundary and with 100# reflective + # boundaries it is hard to detect them. Therefore, we decided to make the + # boundaries reflective with 50# chance and absorptive with 50# chance. + # M. Asadzadeh and B. Tolson Dec 2008 + + p_abs_or_ref = self.np_random.rand() + + if x_new < x_min: # works for any pos or neg x_min + if p_abs_or_ref <= 0.5: # with 50%chance reflect + x_new = x_min + (x_min - x_new) + else: # with 50% chance absorb + x_new = x_min + + # if reflection goes past x_max then value should be x_min since without reflection + # the approach goes way past lower bound. This keeps X close to lower bound when X current + # is close to lower bound: + if x_new > x_max: + x_new = x_min + + elif x_new > x_max: # works for any pos or neg x_max + if p_abs_or_ref <= 0.5: # with 50% chance reflect + x_new = x_max - (x_new - x_max) + else: # with 50% chance absorb + x_new = x_max + + # if reflection goes past x_min then value should be x_max for same reasons as above + if x_new < x_min: + x_new = x_max + + return x_new + + def neigh_value_discrete(self, s, s_min, s_max, r): + """ + Created by B.Tolson and B.Yung, June 2006 + Modified by B. Tolson & M. Asadzadeh, Sept 2008 + Modification: 1- Boundary for reflection at (s_min-0.5) & (s_max+0.5) + 2- Round the new value at the end of generation. + select a RANDOM neighbouring integer value of a SINGLE decision variable + discrete distribution is approximately normal + alternative to this appoach is reflecting triangular distribution (see Azadeh work) + + :param s: is a current SINGLE decision variable VALUE + :param s_min: is the min of variable s + :param s_max: is the max of variable s + :param r: r is the neighbourhood parameter (replaces V parameter~see notes) + It is defined as the ratio of the std deviation of the desired + normal random number/s_range. Eg: + std dev desired = r * s_range + for comparison: variance (V) = (r * s_range)^2 + :return: s_new, a new sample of values in beetween a given range + """ + + s_range = s_max - s_min + delta = self.np_random.normal(0, 1) * r * s_range + s_new = s + delta + + p_abs_or_ref = self.np_random.rand() + + if s_new < s_min - 0.5: # works for any pos or neg s_min + if p_abs_or_ref <= 0.5: # with 50% chance reflect + s_new = (s_min - 0.5) + ((s_min - 0.5) - s_new) + else: # with 50% chance absorb + s_new = s_min + + # if reflection goes past (s_max+0.5) then value should be s_min since without reflection + # the approach goes way past lower bound. This keeps X close to lower bound when X current + # is close to lower bound: + if s_new > s_max + 0.5: + s_new = s_min + + elif s_new > s_max + 0.5: # works for any pos or neg s_max + if p_abs_or_ref <= 0.5: # with 50% chance reflect + s_new = (s_max + 0.5) - (s_new - (s_max + 0.5)) + else: # with 50% chance absorb + s_new = s_max + + # if reflection goes past (s_min-0.5) then value should be s_max for same reasons as above + if s_new < s_min - 0.5: + s_new = s_max + + s_new = np.round(s_new) # New value must be integer + if s_new == s: # pick a number between s_max and s_min by a Uniform distribution + sample = s_min - 1 + np.ceil((s_max - s_min) * self.np_random.rand()) + if sample < s: + s_new = sample + else: # must increment option number by one + s_new = sample + 1 + return s_new + + def neigh_value_mixed(self, x_curr, r, j, x_min, x_max): + """ + + :param x_curr: + :type x_curr: ParameterSet + :param r: + :param j: + :return: + """ + s = x_curr[j] + + if not x_curr.as_int[j]: + return self.neigh_value_continuous(s, x_min, x_max, r) + else: + return self.neigh_value_discrete(s, x_min, x_max, r) + + +class dds(_algorithm): + """ + Implements the Dynamically dimensioned search algorithm for computationally efficient watershed model + calibration + by + Tolson, B. A. and C. A. Shoemaker (2007), Dynamically dimensioned search algorithm for computationally efficient + watershed model calibration, Water Resources Research, 43, W01413, 10.1029/2005WR004723. + Asadzadeh, M. and B. A. Tolson (2013), Pareto archived dynamically dimensioned search with hypervolume-based + selection for multi-objective optimization, Engineering Optimization. 10.1080/0305215X.2012.748046. + + http://www.civil.uwaterloo.ca/btolson/software.aspx + + Method: + "The DDS algorithm is a novel and simple stochastic single-solution based heuristic global search + algorithm that was developed for the purpose of finding good global solutions + (as opposed to globally optimal solutions) within a specified maximum function (or model) evaluation limit." + (Page 3) + + The DDS algorithm is a simple greedy algorithm, always using the best solution (min or max) from the current + point of view. This may not lead to the global optimization. + + """ + + def __init__(self, *args, **kwargs): + """ + Input + ---------- + spot_setup: class + model: function + Should be callable with a parameter combination of the parameter-function + and return an list of simulation results (as long as evaluation list) + parameter: function + When called, it should return a random parameter combination. Which can + be e.g. uniform or Gaussian + objectivefunction: function + Should return the objectivefunction for a given list of a model simulation and + observation. + evaluation: function + Should return the true values as return by the model. + + dbname: str + * Name of the database where parameter, objectivefunction value and simulation results will be saved. + + dbformat: str + * ram: fast suited for short sampling time. no file will be created and results are saved in an array. + * csv: A csv file will be created, which you can import afterwards. + + parallel: str + * seq: Sequentiel sampling (default): Normal iterations on one core of your cpu. + * mpi: Message Passing Interface: Parallel computing on cluster pcs (recommended for unix os). + + save_sim: boolean + * True: Simulation results will be saved + * False: Simulation results will not be saved + :param r: neighborhood size perturbation parameter (r) that defines the random perturbation size standard + deviation as a fraction of the decision variable range. Default is 0.2. + :type r: float + + """ + + try: + self.r = kwargs.pop("r") + except KeyError: + self.r = 0.2 # default value + kwargs['optimization_direction'] = 'maximize' + kwargs['algorithm_name'] = 'Dynamically Dimensioned Search (DDS) algorithm' + super(dds, self).__init__(*args, **kwargs) + + self.np_random = np.random + + #self.status.params_max = ParameterSet(self.parameter()) + + # self.generator_repetitions will be set in `sample` and is needed to generate a + # generator which sends back actual parameter s_test + self.generator_repetitions = -1 + + self.dds_generator = DDSGenerator(self.np_random) + + def _set_np_random(self, f_rand): + self.np_random = f_rand + self.dds_generator.np_random = f_rand + + def get_next_x_curr(self): + """ + Fake a generator to run self.repeat to use multiprocessing + """ + # We need to shift position and length of the sampling process + for rep in range(self.generator_repetitions): + yield rep, self.calculate_next_s_test(self.params_max, rep, self.generator_repetitions, self.r) + + def sample(self, repetitions, trials=1, x_initial=np.array([])): + """ + Samples from the DDS Algorithm. + + DDS is a greedy type of algorithm since the current solution, also the best solution identified so far, + is never updated with a solution that has an inferior value of the objective function. + + That means in detail: + The DDS Algorithm starts with an initial phase: + If the user does not defines an own initial configuration The DDS algorithm start with searching a parameter + configuration in between the given parameter bounds. + + The next phase is the dds algorithm itself which runs in a loop `repetion` times: + Based on the parameter configuration x_new the algorithm run the model and simulation with the current parameter set + and calculates the objective function value called F_curr. + + If F_curr > F_best, where F_best is the current max value objective function value, we set x_best = x_curr and + F_best = F_curr. + + Select k of all parameters to include them in the neighborhood calculation. This is performed by calculating a + threshold probability_neighborhood (probability in neighbourhood). + + The neighbourhood calculation perturb x_best on standard normal distribution and reflect the result if it + breaks the parameter boundary. + The updated parameter configuration is called x_curr + + :param repetitions: Maximum number of runs. + :type repetitions: int + :param trials: amount of runs DDS algorithm will be performed + :param x_initial: set an initial trial set as a first parameter configuration. If the set is empty the algorithm + select an own initial parameter configuration + :return: a key-value set of all parameter combination which has been used. May changed in future. + """ + + # every iteration a map of all relevant values is stored, only for debug purpose. + # Spotpy will not need this values. + debug_results = [] + + self.set_repetiton(repetitions) + self.min_bound, self.max_bound = self.parameter( + )['minbound'], self.parameter()['maxbound'] + print('Starting the DDS algotrithm with '+str(repetitions)+ ' repetitions...') + + number_of_parameters = self.status.parameters # number_of_parameters is the amount of parameters + + if len(x_initial) == 0: + initial_iterations = np.int(np.max([5, round(0.005 * repetitions)])) + elif len(x_initial) != number_of_parameters: + raise ValueError("User specified 'x_initial' has not the same length as available parameters") + else: + initial_iterations = 1 + x_initial = np.array(x_initial) + if not (np.all(x_initial <= self.max_bound) and np.all( + x_initial >= self.min_bound)): + raise ValueError("User specified 'x_initial' but the values are not within the parameter range") + + # Users can define trial runs in within "repetition" times the algorithm will be executed + for trial in range(trials): + #objectivefunction_max = -1e308 + params_max = x_initial + # repitionno_best saves on which iteration the best parameter configuration has been found + repitionno_best = initial_iterations # needed to initialize variable and avoid code failure when small # iterations + params_max, repetions_left, objectivefunction_max = self.calc_initial_para_configuration(initial_iterations, trial, + repetitions, x_initial) + params_max = self.fix_status_params_format(params_max) + trial_best_value = list(params_max)#self.status.params_max.copy() + + # important to set this field `generator_repetitions` so that + # method `get_next_s_test` can generate exact parameters + self.generator_repetitions = repetions_left + self.params_max = params_max + for rep, x_curr, simulations in self.repeat(self.get_next_x_curr()): + + like = self.postprocessing(rep, x_curr, simulations, chains=trial) + if like > objectivefunction_max: + objectivefunction_max = like + self.params_max = list(x_curr) + self.params_max = self.fix_status_params_format(self.params_max) + + print('Best solution found has obj function value of ' + str(objectivefunction_max) + ' at ' + + str(repitionno_best) + '\n\n') + debug_results.append({"sbest": self.params_max, "trial_initial": trial_best_value,"objfunc_val": objectivefunction_max}) + self.final_call() + return debug_results + + def fix_status_params_format(self, params_max): + start_params = ParameterSet(self.parameter()) + start_params.set_by_array([j for j in params_max]) + return start_params + + def calc_initial_para_configuration(self, initial_iterations, trial, repetitions, x_initial): + #max_bound, min_bound = self.status.params_max.maxbound, self.status.params_max.minbound + parameter_bound_range = self.max_bound - self.min_bound + number_of_parameters = len(parameter_bound_range) + discrete_flag = ParameterSet(self.parameter()).as_int + params_max = x_initial + objectivefunction_max = -1e308 + # Calculate the initial Solution, if `initial_iterations` > 1 otherwise the user defined a own one. + # If we need to find an initial solution we iterating initial_iterations times to warm um the algorithm + # by trying which randomized generated input matches best + # initial_iterations is the number of function evaluations to initialize the DDS algorithm solution + if initial_iterations > 1: + print('Finding best starting point for trial ' + str(trial + 1) + ' using ' + str( + initial_iterations) + ' random samples.') + repetions_left = repetitions - initial_iterations # use this to reduce number of fevals in DDS loop + if repetions_left <= 0: + raise ValueError('# Initialization samples >= Max # function evaluations.') + + starting_generator = ( + (rep, [self.np_random.randint(np.int(self.min_bound[j]), np.int(self.max_bound[j]) + 1) if + discrete_flag[j] else self.min_bound[j] + parameter_bound_range[j] * self.np_random.rand() + for j in + range(number_of_parameters)]) for rep in range(int(initial_iterations))) + + for rep, x_curr, simulations in self.repeat(starting_generator): + like = self.postprocessing(rep, x_curr, simulations) # get obj function value + # status setting update + if like > objectivefunction_max: + objectivefunction_max = like + params_max = list(x_curr) + params_max = self.fix_status_params_format(params_max) + + else: # now initial_iterations=1, using a user supplied initial solution. Calculate obj func value. + repetions_left = repetitions - 1 # use this to reduce number of fevals in DDS loop + rep, x_test_param, simulations = self.simulate((0, x_initial)) # get from the inputs + like = self.postprocessing(rep, x_test_param, simulations) + if like > objectivefunction_max: + objectivefunction_max = like + params_max = list(x_test_param) + params_max = self.fix_status_params_format(params_max) + return params_max, repetions_left, objectivefunction_max + + def calculate_next_s_test(self, previous_x_curr, rep, rep_limit, r): + """ + Needs to run inside `sample` method. Calculate the next set of parameters based on a given set. + This is greedy algorithm belonging to the DDS algorithm. + + `probability_neighborhood` is a threshold at which level a parameter is added to neighbourhood calculation. + + Using a normal distribution + The decision variable + + `dvn_count` counts how many parameter configuration has been exchanged with neighbourhood values. + If no parameters has been exchanged just one will select and exchanged with it's neighbourhood value. + + :param previous_x_curr: A set of parameters + :param rep: Position in DDS loop + :param r: neighbourhood size perturbation parameter + :return: next parameter set + """ + amount_params = len(previous_x_curr) + new_x_curr = previous_x_curr.copy() # define new_x_curr initially as current (previous_x_curr for greedy) + + randompar = self.np_random.rand(amount_params) + probability_neighborhood = 1.0 - np.log(rep + 1) / np.log(rep_limit) + dvn_count = 0 # counter for how many decision variables vary in neighbour + + for j in range(amount_params): + if randompar[j] < probability_neighborhood: # then j th DV selected to vary in neighbour + dvn_count = dvn_count + 1 + new_value = self.dds_generator.neigh_value_mixed(previous_x_curr, r, j, self.min_bound[j],self.max_bound[j]) + new_x_curr[j] = new_value # change relevant dec var value in x_curr + + if dvn_count == 0: # no DVs selected at random, so select ONE + dec_var = np.int(np.ceil(amount_params * self.np_random.rand())) + new_value = self.dds_generator.neigh_value_mixed(previous_x_curr, r, dec_var - 1, self.min_bound[j],self.max_bound[j]) + + new_x_curr[dec_var - 1] = new_value # change relevant decision variable value in s_test + + return new_x_curr diff --git a/spotpy/algorithms/demcz.py b/spotpy/algorithms/demcz.py index 3ee2b18d..d325613a 100644 --- a/spotpy/algorithms/demcz.py +++ b/spotpy/algorithms/demcz.py @@ -13,11 +13,6 @@ import numpy as np - -class DEMCZError(Exception): - pass - - class demcz(_algorithm): """ Implements the DE-MC_Z algorithm from ter Braak and Vrugt (2008). @@ -60,7 +55,7 @@ class demcz(_algorithm): by the . The name of the may not be used to endorse or promote products derived from this software without specific prior written permission. - THIS SOFTWARE IS PROVIDED ``AS IS'' AND WITHOUT ANY EXPRESS OR + THIS SOFTWARE IS PROVIDED "AS IS" AND WITHOUT ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, WITHOUT LIMITATION, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. """ @@ -96,18 +91,11 @@ def __init__(self, *args, **kwargs): * mpi: Message Passing Interface: Parallel computing on cluster pcs (recommended for unix os). save_sim: boolean - *True: Simulation results will be saved - *False: Simulationt results will not be saved - - alt_objfun: str or None, default: 'log_p' - alternative objectivefunction to be used for algorithm - * None: the objfun defined in spot_setup.objectivefunction is used - * any str: if str is found in spotpy.objectivefunctions, - this objectivefunction is used, else falls back to None - e.g.: 'log_p', 'rmse', 'bias', 'kge' etc. + * True: Simulation results will be saved + * False: Simulationt results will not be saved """ - if 'alt_objfun' not in kwargs: - kwargs['alt_objfun'] = 'log_p' + kwargs['optimization_direction'] = 'maximize' + kwargs['algorithm_name'] = 'Differential Evolution Markov Chain (DE-MC) algorithm' super(demcz, self).__init__(*args, **kwargs) def check_par_validity(self, par): @@ -120,9 +108,7 @@ def check_par_validity(self, par): else: print('ERROR Bounds have not the same lenghts as Parameterarray') return par - # def simulate(self): - - + def sample(self, repetitions, nChains=3, burnIn=100, thin=1, convergenceCriteria=.8, variables_of_interest=None, DEpairs=2, adaptationRate='auto', eps=5e-2, @@ -146,15 +132,10 @@ def sample(self, repetitions, nChains=3, burnIn=100, thin=1, Returns ------- None : None - sample sets - self.history which contains the combined draws for all the chains - self.cur_iter which is the total number of iterations - self.acceptRatio which is the acceptance ratio - self.burnIn which is the number of burn in iterations done - self.R which is the gelman rubin convergence diagnostic for each dimension """ - print('Starting the DEMCz algotrithm with '+str(repetitions)+ ' repetitions...') + self.set_repetiton(repetitions) + print('Starting the DEMCz algotrithm with '+str(repetitions)+ ' repetitions...') self.min_bound, self.max_bound = self.parameter( )['minbound'], self.parameter()['maxbound'] @@ -166,7 +147,6 @@ def sample(self, repetitions, nChains=3, burnIn=100, thin=1, # minbound,maxbound=self.find_min_max() # select variables if necessary - if variables_of_interest is not None: slices = [] for var in variables_of_interest: @@ -195,186 +175,145 @@ def sample(self, repetitions, nChains=3, burnIn=100, thin=1, for rep, vector, simulations in self.repeat(param_generator): - burnInpar[i][rep] = vector - - likelist = self.postprocessing(i, list(vector), simulations, chains=rep) - #likelist = self.objectivefunction( - #evaluation=self.evaluation, simulation=simulations) + likelist = self.postprocessing(i, vector, simulations, chains=rep) simulationlist.append(simulations) self._logPs.append(likelist) - old_like[rep] = likelist - + old_like[rep] = likelist burnInpar[i][rep] = vector - # Save everything in the database - #self.save(likelist, list(vector), simulations=simulations) - #self.status(rep, likelist, vector) + if self.status.stop: + break + if not self.status.stop: history.record(burnInpar[i], self._logPs, 1) - gamma = None - self.accepts_ratio = 0.000001 - - - # initilize the convergence diagnostic object - grConvergence = _GRConvergence() - covConvergence = _CovarianceConvergence() - - # get the starting log objectivefunction and position for each of the - # chains - - currentVectors = burnInpar[-1] - - currentLogPs = self._logPs[-1] - - # 2)now loop through and sample - cur_iter = 0 - accepts_ratio_weighting = 1 - np.exp(-1.0 / 30) - lastRecalculation = 0 - # continue sampling if: - # 1) we have not drawn enough samples to satisfy the minimum number of iterations - # 2) or any of the dimensions have not converged - # 3) and we have not done more than the maximum number of iterations - - while cur_iter < maxChainDraws: - if cur_iter == burnIn: - history.start_sampling() - - # every5th iteration allow a big jump - if np.random.randint(5) == 0.0: - gamma = np.array([1.0]) - else: - gamma = np.array([2.38 / np.sqrt(2 * DEpairs * dimensions)]) - - if cur_iter >= burnIn: - proposalVectors = _dream_proposals( - currentVectors, history, dimensions, nChains, DEpairs, gamma, .05, eps) - for i in range(len(proposalVectors)): - proposalVectors[i] = self.check_par_validity( - proposalVectors[i]) - # print proposalVectors - else: - proposalVectors = [] - for i in range(nChains): - proposalVectors.append(self.parameter()['random']) - proposalVectors[i] = self.check_par_validity( - proposalVectors[i]) - - # if self.bounds_ok(minbound,maxbound,proposalVectors,nChains): - proposalLogPs = [] - old_simulationlist = simulationlist - old_likelist = self._logPs[-1] - new_simulationlist = [] - new_likelist = [] - - param_generator = ( - (rep, list(proposalVectors[rep])) for rep in range(int(nChains))) - for rep, vector, simulations in self.repeat(param_generator): - new_simulationlist.append(simulations) - like = self.postprocessing(cur_iter+nSeedIterations, list(vector), simulations, chains=rep) - - #like = self.objectivefunction( - # evaluation=self.evaluation, simulation=simulations) - self._logPs.append(like) - new_likelist.append(like) - proposalLogPs.append(like) - #self.status(rep, like, vector) - - # for i in range(nChains): - # simulations=self.model(proposalVectors[i])#THIS WILL WORK ONLY FOR MULTIPLE CHAINS - # new_simulationlist.append(simulations) - # like=self.objectivefunction(self.evaluation, simulations) - # new_likelist.append(like) - # proposalLogPs.append(like) - - - # apply the metrop decision to decide whether to accept or reject - # each chain proposal - decisions, acceptance = self._metropolis_hastings( - currentLogPs, proposalLogPs, nChains) - try: - self._update_accepts_ratio(accepts_ratio_weighting, acceptance) - except DEMCZError: - pass - - # if mAccept and cur_iter % 20 == 0: - # print self.accepts_ratio - - - # choose from list of possible choices if 1d_decision is True at - # specific index, else use default choice - # np.choose(1d_decision[:,None], (list of possible choices, default - # choice) - save_likes=[] - save_pars=[] - save_sims=[] - #print(len(self._logPs)) - - for curchain in range(nChains): - if decisions[curchain]: - save_likes.append(float(new_likelist[curchain])) - old_like[curchain]=float(new_likelist[curchain]) - save_pars.append(proposalVectors[curchain]) - save_sims.append(new_simulationlist[curchain]) + gamma = None + self.accepts_ratio = 0.000001 + + # initilize the convergence diagnostic object + grConvergence = _GRConvergence() + covConvergence = _CovarianceConvergence() + + # get the starting log objectivefunction and position for each of the + # chains + currentVectors = burnInpar[-1] + currentLogPs = self._logPs[-1] + + # 2)now loop through and sample + cur_iter = 0 + accepts_ratio_weighting = 1 - np.exp(-1.0 / 30) + lastRecalculation = 0 + # continue sampling if: + # 1) we have not drawn enough samples to satisfy the minimum number of iterations + # 2) or any of the dimensions have not converged + # 3) and we have not done more than the maximum number of iterations + + while cur_iter < maxChainDraws: + print(cur_iter, burnIn) + if cur_iter == burnIn: + print('starting') + history.start_sampling() + + # every5th iteration allow a big jump + if np.random.randint(5) == 0.0: + gamma = np.array([1.0]) else: - save_likes.append(old_like[curchain]) - save_pars.append(currentVectors[curchain]) - save_sims.append(old_simulationlist[curchain]) - - #print(len(save_pars) ) - currentVectors = np.choose( - decisions[:, np.newaxis], (currentVectors, proposalVectors)) - currentLogPs = np.choose(decisions, (currentLogPs, proposalLogPs)) - - simulationlist = [[new_simulationlist, old_simulationlist][ - int(x)][ix] for ix, x in enumerate(decisions)] - - likelist = list( - np.choose(decisions[:, np.newaxis], (new_likelist, old_likelist))) - - # we only want to recalculate convergence criteria when we are past - # the burn in period - - if cur_iter % thin == 0: - - historyStartMovementRate = adaptationRate - # try to adapt more when the acceptance rate is low and less - # when it is high - if adaptationRate == 'auto': - historyStartMovementRate = min( - (.234 / self.accepts_ratio) * .5, .95) - - history.record( - currentVectors, currentLogPs, historyStartMovementRate, grConvergence=grConvergence.R) - #for chain in range(nChains): - # if not any([x in simulationlist[chain] for x in [-np.Inf, np.Inf]]): - # self.save(save_likes[chain], - # save_pars[chain], - # simulations=save_sims[chain], - # chains=chain) - - if history.nsamples > 0 and cur_iter > lastRecalculation * 1.1 and history.nsequence_histories > dimensions: - lastRecalculation = cur_iter - grConvergence.update(history) - covConvergence.update(history, 'all') - covConvergence.update(history, 'interest') - if all(grConvergence.R < convergenceCriteria): - cur_iter = maxChainDraws - print( - 'All chains fullfil the convergence criteria. Sampling stopped.') - cur_iter += 1 - - - # else: - # print 'A proposal vector was ignored' - - # Progress bar - #acttime = time.time() - ## Refresh progressbar every second - #if acttime - intervaltime >= 2: - # text = str(cur_iter) + ' of ' + str(repetitions) - # print(text) - # intervaltime = time.time() + gamma = np.array([2.38 / np.sqrt(2 * DEpairs * dimensions)]) + + if cur_iter >= burnIn: + proposalVectors = _dream_proposals( + currentVectors, history, dimensions, nChains, DEpairs, gamma, .05, eps) + for i in range(len(proposalVectors)): + proposalVectors[i] = self.check_par_validity( + proposalVectors[i]) + # print proposalVectors + else: + proposalVectors = [] + for i in range(nChains): + proposalVectors.append(self.parameter()['random']) + proposalVectors[i] = self.check_par_validity( + proposalVectors[i]) + + # if self.bounds_ok(minbound,maxbound,proposalVectors,nChains): + proposalLogPs = [] + old_simulationlist = simulationlist + old_likelist = self._logPs[-1] + new_simulationlist = [] + new_likelist = [] + + param_generator = ( + (rep, list(proposalVectors[rep])) for rep in range(int(nChains))) + for rep, vector, simulations in self.repeat(param_generator): + new_simulationlist.append(simulations) + like = self.postprocessing(cur_iter+nSeedIterations, list(vector), simulations, chains=rep) + self._logPs.append(like) + new_likelist.append(like) + proposalLogPs.append(like) + if self.status.stop: + cur_iter = maxChainDraws + break + + if not self.status.stop: + # apply the metrop decision to decide whether to accept or reject + # each chain proposal + decisions, acceptance = self._metropolis_hastings( + currentLogPs, proposalLogPs, nChains) + self._update_accepts_ratio(accepts_ratio_weighting, acceptance) + # choose from list of possible choices if 1d_decision is True at + # specific index, else use default choice + # np.choose(1d_decision[:,None], (list of possible choices, default + # choice) + save_likes=[] + save_pars=[] + save_sims=[] + + for curchain in range(nChains): + if decisions[curchain]: + save_likes.append(float(new_likelist[curchain])) + old_like[curchain]=float(new_likelist[curchain]) + save_pars.append(proposalVectors[curchain]) + save_sims.append(new_simulationlist[curchain]) + else: + save_likes.append(old_like[curchain]) + save_pars.append(currentVectors[curchain]) + save_sims.append(old_simulationlist[curchain]) + + currentVectors = np.choose( + decisions[:, np.newaxis], (currentVectors, proposalVectors)) + currentLogPs = np.choose(decisions, (currentLogPs, proposalLogPs)) + + simulationlist = [[new_simulationlist, old_simulationlist][ + int(x)][ix] for ix, x in enumerate(decisions)] + + likelist = list( + np.choose(decisions[:, np.newaxis], (new_likelist, old_likelist))) + + # we only want to recalculate convergence criteria when we are past + # the burn in period + + if cur_iter % thin == 0: + + historyStartMovementRate = adaptationRate + # try to adapt more when the acceptance rate is low and less + # when it is high + if adaptationRate == 'auto': + historyStartMovementRate = min( + (.234 / self.accepts_ratio) * .5, .95) + + history.record( + currentVectors, currentLogPs, historyStartMovementRate, grConvergence=grConvergence.R) + + if history.nsamples > 0 and cur_iter > lastRecalculation * 1.1 and history.nsequence_histories > dimensions: + lastRecalculation = cur_iter + grConvergence.update(history) + covConvergence.update(history, 'all') + covConvergence.update(history, 'interest') + if all(grConvergence.R < convergenceCriteria): + cur_iter = maxChainDraws + print( + 'All chains fullfil the convergence criteria. Sampling stopped.') + cur_iter+=1 + # 3) finalize # only make the second half of draws available because that's the only # part used by the convergence diagnostic @@ -385,22 +324,9 @@ def sample(self, repetitions, nChains=3, burnIn=100, thin=1, self.R = grConvergence.R text = 'Gelman Rubin R=' + str(self.R) print(text) + self.status.rep = self.status.repetitions self.final_call() - #self.repeat.terminate() - #try: - # self.datawriter.finalize() - #except AttributeError: # Happens if no database was assigned - # pass -# -# print('End of sampling') -# text = '%i of %i (best like=%g)' % ( -# self.status.rep, repetitions, self.status.objectivefunction) -# print(text) -# print('Best parameter set') -# print(self.status.params) -# text = 'Duration:' + str(round((acttime - starttime), 2)) + ' s' -# print(text) def _update_accepts_ratio(self, weighting, acceptances): self.accepts_ratio = weighting * \ @@ -492,8 +418,6 @@ def combined_history(self): return self.group_combined_history('all') def group_combined_history(self, name): - # print self._combined_history - # print self.relevantHistoryStart return self._combined_history[(int(np.ceil(self.relevantHistoryStart)) * self._nChains):(self.relevantHistoryEnd * self._nChains), self.group_indicies[name]] @property @@ -631,30 +555,6 @@ def _dream_proposals(currentVectors, history, dimensions, nChains, DEpairs, gamm return proposalVectors -def _dream2_proposals(currentVectors, history, dimensions, nChains, DEpairs, - - gamma, jitter, eps): - """ - generates and returns proposal vectors given the current states - NOT USED ATM - """ - - sampleRange = history.ncombined_history - currentIndex = np.arange(sampleRange - nChains, sampleRange)[:, np.newaxis] - combined_history = history.combined_history - - # choose some chains without replacement to combine - chains = _random_no_replace(1, sampleRange - 1, nChains) - - # makes sure we have already selected the current chain so it is not replaced - # this ensures that the the two chosen chains cannot be the same as the - # chain for which the jump is - chains += (chains >= currentIndex) - - proposalVectors = combined_history[chains[:, 0], :] - return proposalVectors - - class _GRConvergence: """ Gelman Rubin convergence diagnostic calculator class. It currently only calculates the naive diff --git a/spotpy/algorithms/dream.py b/spotpy/algorithms/dream.py index caa95889..055dcc65 100644 --- a/spotpy/algorithms/dream.py +++ b/spotpy/algorithms/dream.py @@ -55,9 +55,8 @@ def __init__(self, *args, **kwargs): * False: Simulation results will not be saved """ - - if 'alt_objfun' not in kwargs: - kwargs['alt_objfun'] = 'log_p' + kwargs['optimization_direction'] = 'maximize' + kwargs['algorithm_name'] = 'DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm' super(dream, self).__init__(*args, **kwargs) def check_par_validity_bound(self, par): @@ -124,9 +123,9 @@ def get_new_proposal_vector(self,cur_chain,newN,nrN): random_chain1,random_chain2 = self.get_other_random_chains(cur_chain) new_parameterset=[] #position = self.chain_samples-1#self.nChains*self.chain_samples+self.chain_samples+cur_chain-1 - cur_par_set = self.bestpar[cur_chain][self.nChainruns[cur_chain]-1] - random_par_set1 = self.bestpar[random_chain1][self.nChainruns[random_chain1]-1] - random_par_set2 = self.bestpar[random_chain2][self.nChainruns[random_chain2]-1] + cur_par_set = list(self.bestpar[cur_chain][self.nChainruns[cur_chain]-1]) + random_par_set1 = list(self.bestpar[random_chain1][self.nChainruns[random_chain1]-1]) + random_par_set2 = list(self.bestpar[random_chain2][self.nChainruns[random_chain2]-1]) for i in range(self.N):#Go through parameters @@ -144,9 +143,9 @@ def get_new_proposal_vector(self,cur_chain,newN,nrN): # return new_par def update_mcmc_status(self,par,like,sim,cur_chain): - self.bestpar[cur_chain][self.nChainruns[cur_chain]]=par + self.bestpar[cur_chain][self.nChainruns[cur_chain]]=list(par) self.bestlike[cur_chain]=like - self.bestsim[cur_chain]=sim + self.bestsim[cur_chain]=list(sim) def get_r_hat(self, parameter_array): """ @@ -220,8 +219,8 @@ def get_r_hat(self, parameter_array): return R_stat#[R_stat, MR_stat] def sample(self, repetitions,nChains=5, nCr=3, eps=10e-6, convergence_limit=1.2, runs_after_convergence=100,acceptance_test_option=6): - print('Starting the DREAM algotrithm with '+str(repetitions)+ ' repetitions...') self.set_repetiton(repetitions) + print('Starting the DREAM algotrithm with '+str(repetitions)+ ' repetitions...') if nChains <3: print('Please use at least n=3 chains!') return None @@ -248,7 +247,7 @@ def sample(self, repetitions,nChains=5, nCr=3, eps=10e-6, convergence_limit=1.2, #firstcall = True - print('Inititalize ',self.nChains, ' chain(s)...') + print('Initialize ', self.nChains, ' chain(s)...') self.iter=0 #for i in range(10): startpoints = self.get_regular_startingpoint(nChains) @@ -272,7 +271,7 @@ def sample(self, repetitions,nChains=5, nCr=3, eps=10e-6, convergence_limit=1.2, self.N = len(self.parameter()['random']) nrN=1 newN = [True]*self.N - while self.iter <= self.repetitions - self.burnIn: + while self.iter < self.repetitions: param_generator = ((curChain,self.get_new_proposal_vector(curChain,newN,nrN)) for curChain in range(int(self.nChains))) for cChain,par,sim in self.repeat(param_generator): pCr = np.random.randint(0,nCr) @@ -290,20 +289,9 @@ def sample(self, repetitions,nChains=5, nCr=3, eps=10e-6, convergence_limit=1.2, if nrN == 0: ids=[np.random.randint(0,self.N)] nrN=1 - #print(self.bestpar[cChain][self.nChainruns[cChain]-1]) like = self.postprocessing(self.iter, par, sim, chains=cChain) - #like = self.objectivefunction( - # evaluation=self.evaluation, simulation=sim) - #self.status(self.iter, like, par) - -# logMetropHastRatio = np.abs(self.bestlike[cChain])/np.abs(like) #Fast convergence high uncertainty -# u = np.random.uniform(low=0.0, high=1) - -# logMetropHastRatio = like - self.bestlike[cChain] # Slow convergence, low uncertainty -# u = np.log(np.random.uniform(low=0.0, high=1)) # set a option which type of comparision should be choose: - metro_opt=acceptance_test_option if metro_opt == 1: @@ -334,33 +322,22 @@ def sample(self, repetitions,nChains=5, nCr=3, eps=10e-6, convergence_limit=1.2, self.update_mcmc_status(par,like,sim,cChain) self.accepted[cChain] += 1 # monitor acceptance - #self.save(like, par, simulations=sim,chains=cChain) else: self.update_mcmc_status(self.bestpar[cChain][self.nChainruns[cChain]-1],self.bestlike[cChain],self.bestsim[cChain],cChain) - #self.save(self.bestlike[cChain], self.bestpar[cChain][self.nChainruns[cChain]], - # simulations=self.bestsim[cChain],chains=cChain) - # Progress bar - #acttime = time.time() + if self.status.stop: + self.iter = self.repetitions + print('Stopping samplig') + break self.iter+=1 self.nChainruns[cChain] +=1 - #if acttime - intervaltime >= 2 and self.iter >=2 and self.nChainruns[-1] >=3: - # self.r_hats.append(self.get_r_hat(self.bestpar)) - # #print(self.r_hats[-1]) - # text = '%i of %i (best like=%g)' % ( - # self.iter + self.burnIn, repetitions, self.status.objectivefunction) + r_hat = self.get_r_hat(self.bestpar) - #self.gammalevel+=.1 - #print((r_hat < 1.2).all()) self.r_hats.append(r_hat) # Refresh progressbar every two seconds acttime = time.time() if acttime - intervaltime >= 2 and self.iter >=2 and self.nChainruns[-1] >=3: - #self.r_hats.append(self.get_r_hat(self.bestpar)) - #text = '%i of %i (best like=%g)' % ( - # self.iter + self.burnIn, repetitions, self.status.objectivefunction) - #print(text) text = "Acceptance rates [%] =" +str(np.around((self.accepted)/float(((self.iter-self.burnIn)/self.nChains)),decimals=4)*100).strip('array([])') print(text) text = "Convergence rates =" +str(np.around((r_hat),decimals=4)).strip('array([])') diff --git a/spotpy/algorithms/fast.py b/spotpy/algorithms/fast.py index 4bff4957..9556e2ec 100644 --- a/spotpy/algorithms/fast.py +++ b/spotpy/algorithms/fast.py @@ -61,12 +61,10 @@ def __init__(self, *args, **kwargs): save_sim: boolean *True: Simulation results will be saved - *False: Simulationt results will not be saved + *False: Simulation results will not be saved ''' + kwargs['algorithm_name'] = 'Fourier Amplitude Sensitivity Test (FAST)' super(fast, self).__init__(*args, **kwargs) -# _algorithm.__init__(self, spot_setup, dbname=dbname, -# dbformat=dbformat, parallel=parallel, save_sim=save_sim, -# save_threshold=save_threshold) def scale_samples(self, params, bounds): ''' @@ -109,6 +107,7 @@ def matrix(self, bounds, N, M=4): # Discretization of the frequency space, s s = (2 * math.pi / N) * np.arange(N) + #s = math.pi / 2.0 * (2 * np.arange(1,N+1) - N-1) / N # Transformation to get points in the X space X = np.empty([N * D, D]) @@ -185,12 +184,12 @@ def compute_first_order(self, outputs, N, M, omega): def compute_total_order(self, outputs, N, omega): f = np.fft.fft(outputs) - Sp = np.power(np.absolute(f[np.arange(1, int(N / 2))]) / N, 2) + Sp = np.power(np.absolute(f[np.arange(1, int((N + 1) / 2))]) / N, 2) V = 2 * np.sum(Sp) Dt = 2 * sum(Sp[np.arange(int(omega / 2))]) return (1 - Dt / V) - def sample(self, repetitions): + def sample(self, repetitions, M=4): """ Samples from the FAST algorithm. @@ -199,8 +198,8 @@ def sample(self, repetitions): repetitions: int Maximum number of runs. """ - print('Starting the FAST algotrithm with '+str(repetitions)+ ' repetitions...') self.set_repetiton(repetitions) + print('Starting the FAST algotrithm with '+str(repetitions)+ ' repetitions...') print('Creating FAST Matrix') # Get the names of the parameters to analyse names = self.parameter()['name'] @@ -214,7 +213,7 @@ def sample(self, repetitions): bounds = [] for i in range(len(parmin)): bounds.append([parmin[i], parmax[i]]) - Matrix = self.matrix(bounds, N, M=4) + Matrix = self.matrix(bounds, N, M=M) lastbackup=0 if self.breakpoint == 'read' or self.breakpoint == 'readandwrite': data_frombreak = self.read_breakdata(self.dbname) @@ -238,6 +237,6 @@ def sample(self, repetitions): data = self.datawriter.getdata() # this is likely to crash if database does not assign name 'like1' Si = self.analyze( - bounds, data['like1'], len(bounds), names, print_to_console=True) + bounds, data['like1'], len(bounds), names, M=M, print_to_console=True) except AttributeError: # Happens if no database was assigned pass diff --git a/spotpy/algorithms/fscabc.py b/spotpy/algorithms/fscabc.py index c6097c45..1538f760 100644 --- a/spotpy/algorithms/fscabc.py +++ b/spotpy/algorithms/fscabc.py @@ -11,14 +11,12 @@ from __future__ import unicode_literals from ._algorithm import _algorithm import numpy as np -import time import random - class fscabc(_algorithm): """ - This class holds the Fitness Scaled Chaotic Artificial Bee Colony(FSCABC) algorithm, + This class holds the Fitness Scaled Chaotic Artificial Bee Colony (FSCABC) algorithm, based on: Yudong Zhang, Lenan Wu, and Shuihua Wang (2011). Magnetic Resonance Brain Image @@ -62,7 +60,8 @@ def __init__(self, *args, **kwargs): * True: Simulation results will be saved * False: Simulation results will not be saved """ - + kwargs['optimization_direction'] = 'maximize' + kwargs['algorithm_name'] = 'Fitness Scaled Chaotic Artificial Bee Colony (FSCABC) algorithm' super(fscabc, self).__init__(*args, **kwargs) def mutate(self, r): @@ -70,10 +69,8 @@ def mutate(self, r): return x - def sample(self, repetitions, eb=48, a=(1 / 10), peps=0.0001, kpow=5, ownlimit=False, limit=24): + def sample(self, repetitions, eb=48, a=(1 / 10), peps=0.0001, kpow=4, limit=None): """ - - Parameters ---------- repetitions: int @@ -86,30 +83,22 @@ def sample(self, repetitions, eb=48, a=(1 / 10), peps=0.0001, kpow=5, ownlimit=F convergence criterion kpow: float exponent for power scaling method - ownlimit: boolean - determines if an userdefined limit is set or not limit: int sets the limit for scout bee phase - breakpoint: None, 'write', 'read' or 'readandwrite' - None does nothing, 'write' writes a breakpoint for restart as specified in backup_every_rep, 'read' reads a breakpoint file with dbname + '.break', 'readandwrite' does both - backup_every_rep: int - writes a breakpoint after every generation, if more at least the specified number of samples are carried out after writing the last breakpoint """ - print('Starting the FSCABC algotrithm with '+str(repetitions)+ ' repetitions...') self.set_repetiton(repetitions) - # Initialize the progress bar - starttime = time.time() - intervaltime = starttime + print('Starting the FSCABC algotrithm with '+str(repetitions)+ ' repetitions...') # Initialize FSCABC parameters: - randompar = self.parameter()['random'] + parset = self.parameter() + randompar = parset['random'] + lb, ub = parset['minbound'], parset['maxbound'] self.nopt = randompar.size random.seed() lastbackup=0 - if ownlimit == True: - self.limit = limit + if limit == None: + self.limit = int(eb/2) else: - self.limit = eb - lb, ub = self.parameter()['minbound'], self.parameter()['maxbound'] + self.limit = int(limit) # Generate chaos r = 0.25 while r == 0.25 or r == 0.5 or r == 0.75: @@ -117,7 +106,6 @@ def sample(self, repetitions, eb=48, a=(1 / 10), peps=0.0001, kpow=5, ownlimit=F icall = 0 gnrng = 1e100 - # and criter_change>pcento: if self.breakpoint == 'read' or self.breakpoint == 'readandwrite': data_frombreak = self.read_breakdata(self.dbname) @@ -125,7 +113,6 @@ def sample(self, repetitions, eb=48, a=(1 / 10), peps=0.0001, kpow=5, ownlimit=F work = data_frombreak[1] gnrng = data_frombreak[2] r = data_frombreak[3] - acttime = time.time() # Here database needs to be reinvoked elif self.breakpoint is None or self.breakpoint == 'write': # Initialization @@ -135,33 +122,16 @@ def sample(self, repetitions, eb=48, a=(1 / 10), peps=0.0001, kpow=5, ownlimit=F (rep, self.parameter()['random']) for rep in range(eb)) for rep, randompar, simulations in self.repeat(param_generator): # Calculate fitness - like = self.postprocessing(rep, randompar, simulations) - #like = self.objectivefunction( - # evaluation=self.evaluation, simulation=simulations) - - # Save everything in the database - #self.save(like, randompar, simulations=simulations) - - # Update status information (always do that after saving) - #self.status(rep, like, randompar) - + like = self.postprocessing(rep, randompar, simulations, negativlike=True) c = 0 p = 0 # (fit_x,x,fit_v,v,limit,normalized fitness) work.append([like, randompar, like, randompar, c, p]) - # Progress bar - #acttime = time.time() - # get str showing approximate timeleft to end of simulation in H, - # M, S -# timestr = time.strftime("%H:%M:%S", time.gmtime(round(((acttime - starttime) / -# (rep + 1)) * (repetitions - (rep + 1))))) - # Refresh progressbar every second -# if acttime - intervaltime >= 2: -# text = '%i of %i (best like=%g) est. time remaining: %s' % (rep, repetitions, -# self.status.objectivefunction, timestr) -# print(text) -# intervaltime = time.time() - + icall +=1 + if self.status.stop: + #icall = repetitions + print('Stopping samplig') + break #Bee Phases while icall < repetitions and gnrng > peps: @@ -188,23 +158,18 @@ def sample(self, repetitions, eb=48, a=(1 / 10), peps=0.0001, kpow=5, ownlimit=F param_generator = ((rep, work[rep][3]) for rep in range(eb)) for rep, randompar, simulations in self.repeat(param_generator): # Calculate fitness - clike = self.postprocessing(icall, randompar, simulations, chains=1) - #clike = self.objectivefunction( - # evaluation=self.evaluation, simulation=simulations) + clike = self.postprocessing(icall, randompar, simulations, chains=1, negativlike=True) if clike > work[rep][0]: work[rep][1] = work[rep][3] work[rep][0] = clike work[rep][4] = 0 else: work[rep][4] = work[rep][4] + 1 - - # Save everything in the database - #self.save( - # clike, work[rep][3], simulations=simulations, chains=icall) - - # Update status information (always do that after saving) - #self.status(rep, work[rep][0], work[rep][1]) icall += 1 + if self.status.stop: + print('Stopping samplig') + break + # Fitness scaling bn = [] csum = 0 @@ -239,19 +204,17 @@ def sample(self, repetitions, eb=48, a=(1 / 10), peps=0.0001, kpow=5, ownlimit=F param_generator = ((rep, work[rep][3]) for rep in range(eb)) for rep, randompar, simulations in self.repeat(param_generator): # Calculate fitness - clike = self.postprocessing(icall, randompar, simulations, chains=2) - #clike = self.objectivefunction( - # evaluation=self.evaluation, simulation=simulations) + clike = self.postprocessing(icall, randompar, simulations, chains=2, negativlike=True) if clike > work[rep][0]: work[rep][1] = work[rep][3] work[rep][0] = clike work[rep][4] = 0 else: work[rep][4] = work[rep][4] + 1 - #self.status(rep, work[rep][0], work[rep][1]) - #self.save( - # clike, work[rep][3], simulations=simulations, chains=icall) icall += 1 + if self.status.stop: + print('Stopping samplig') + break # Scout bee phase for i, val in enumerate(work): if work[i][4] >= self.limit: @@ -261,21 +224,18 @@ def sample(self, repetitions, eb=48, a=(1 / 10), peps=0.0001, kpow=5, ownlimit=F work[i][4] = 0 t, work[i][0], simulations = self.simulate( (icall, work[i][1])) - clike = self.postprocessing(icall, randompar, simulations, chains=3) - #clike = self.objectivefunction( - # evaluation=self.evaluation, simulation=simulations) - #self.save( - # clike, work[rep][3], simulations=simulations, chains=icall) + clike = self.postprocessing(icall, randompar, simulations, chains=3, negativlike=True) work[i][0] = clike icall += 1 - gnrng = -self.status.objectivefunction - #text = '%i of %i (best like=%g)' % ( - # icall, repetitions, self.status.objectivefunction) - #print(text) + if self.status.stop: + print('Stopping samplig') + break + gnrng = -self.status.objectivefunction_max + if self.breakpoint == 'write' or self.breakpoint == 'readandwrite'\ and icall >= lastbackup+self.backup_every_rep: - work = (icall, work, gnrng, r) - self.write_breakdata(self.dbname, work) + savework = (icall, work, gnrng, r) + self.write_breakdata(self.dbname, savework) lastbackup = icall if icall >= repetitions: print('*** OPTIMIZATION SEARCH TERMINATED BECAUSE THE LIMIT') @@ -287,4 +247,3 @@ def sample(self, repetitions, eb=48, a=(1 / 10), peps=0.0001, kpow=5, ownlimit=F print( 'THE POPULATION HAS CONVERGED TO A PRESPECIFIED SMALL PARAMETER SPACE') self.final_call() - diff --git a/spotpy/algorithms/lhs.py b/spotpy/algorithms/lhs.py index 5c957185..d9ac0535 100644 --- a/spotpy/algorithms/lhs.py +++ b/spotpy/algorithms/lhs.py @@ -51,15 +51,18 @@ def __init__(self, *args, **kwargs): * True: Simulation results will be saved * False: Simulation results will not be saved """ - + kwargs['algorithm_name'] = 'Latin Hypercube Sampling (LHS)' super(lhs, self).__init__(*args, **kwargs) def sample(self, repetitions): """ - test + Parameters + ---------- + repetitions: int + maximum number of function evaluations allowed during optimization """ - print('Starting the LHS algotrithm with '+str(repetitions)+ ' repetitions...') self.set_repetiton(repetitions) + print('Starting the LHS algotrithm with '+str(repetitions)+ ' repetitions...') print('Creating LatinHyperCube Matrix') # Get the names of the parameters to analyse names = self.parameter()['name'] @@ -83,7 +86,7 @@ def sample(self, repetitions): # A generator that produces the parameters param_generator = ((rep, matrix[rep]) - for rep in range(int(repetitions) - 1)) + for rep in range(int(repetitions))) for rep, randompar, simulations in self.repeat(param_generator): # A function that calculates the fitness of the run and the manages the database self.postprocessing(rep, randompar, simulations) diff --git a/spotpy/algorithms/list.py b/spotpy/algorithms/list_sampler.py similarity index 83% rename from spotpy/algorithms/list.py rename to spotpy/algorithms/list_sampler.py index 01352390..3138ea43 100644 --- a/spotpy/algorithms/list.py +++ b/spotpy/algorithms/list_sampler.py @@ -4,20 +4,13 @@ This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). :author: Tobias Houska ''' - from . import _algorithm -import spotpy -import numpy as np -import time -import random -import itertools - - -class list(_algorithm): +from .. import analyser +class list_sampler(_algorithm): """ This class holds the List sampler, which samples from a given spotpy database """ - + _excluded_parameter_classes = () def __init__(self, *args, **kwargs): """ Input @@ -50,20 +43,21 @@ def __init__(self, *args, **kwargs): * True: Simulation results will be saved * False: Simulation results will not be saved """ - - super(list, self).__init__(*args, **kwargs) - + kwargs['algorithm_name'] = 'List Sampler' + super(list_sampler, self).__init__(*args, **kwargs) def sample(self, repetitions=None): """ Parameters ---------- + Optional: repetitions: int - maximum number of function evaluations allowed during optimization + maximum number of function evaluations allowed during sampling + If not given number if iterations will be determined based on given list """ - parameters = spotpy.analyser.load_csv_parameter_results(self.dbname) + parameters = analyser.load_csv_parameter_results(self.dbname) self.dbname=self.dbname+'list' if not repetitions: repetitions=len(parameters) @@ -71,9 +65,9 @@ def sample(self, repetitions=None): # Initialization print('Starting the List sampler with '+str(repetitions)+ ' repetitions...') - param_generator = ((rep, parameters[rep]) + param_generator = ((rep, list(parameters[rep])) for rep in range(int(repetitions))) for rep, randompar, simulations in self.repeat(param_generator): # A function that calculates the fitness of the run and the manages the database - self.postprocessing(rep, randompar, simulations) + self.postprocessing(rep, list(randompar), simulations) self.final_call() diff --git a/spotpy/algorithms/mc.py b/spotpy/algorithms/mc.py index e3555c80..a22e853b 100644 --- a/spotpy/algorithms/mc.py +++ b/spotpy/algorithms/mc.py @@ -11,13 +11,12 @@ from __future__ import unicode_literals from . import _algorithm - class mc(_algorithm): """ The Monte Carlo algorithm generates random parameters from their respective distribution functions. """ - + _unaccepted_parameter_types = () def __init__(self, *args, **kwargs): """ Input @@ -50,7 +49,7 @@ def __init__(self, *args, **kwargs): * True: Simulation results will be saved * False: Simulation results will not be saved """ - + kwargs['algorithm_name'] = 'Monte Carlo (MC) sampler' super(mc, self).__init__(*args, **kwargs) def sample(self, repetitions): @@ -60,13 +59,13 @@ def sample(self, repetitions): Input ---------- repetitions: int - Maximum number of runs. + Maximum number of runs. """ - print('Starting the MC algorithm with {} repetitions...'.format(repetitions)) self.set_repetiton(repetitions) + print('Starting the MC algorithm with {} repetitions...'.format(repetitions)) # A generator that produces parametersets if called param_generator = ((rep, self.parameter()['random']) - for rep in range(int(repetitions) - 1)) + for rep in range(int(repetitions))) for rep, randompar, simulations in self.repeat(param_generator): # A function that calculates the fitness of the run and the manages the database self.postprocessing(rep, randompar, simulations) diff --git a/spotpy/algorithms/mcmc.py b/spotpy/algorithms/mcmc.py index c116a4cf..7682f68f 100644 --- a/spotpy/algorithms/mcmc.py +++ b/spotpy/algorithms/mcmc.py @@ -53,8 +53,8 @@ def __init__(self, *args, **kwargs): * True: Simulation results will be saved * False: Simulation results will not be saved """ - if 'alt_objfun' not in kwargs: - kwargs['alt_objfun'] = 'log_p' + kwargs['optimization_direction'] = 'maximize' + kwargs['algorithm_name'] = 'Markov Chain Monte Carlo (MCMC) sampler' super(mcmc, self).__init__(*args, **kwargs) def check_par_validity(self, par): @@ -99,8 +99,8 @@ def update_mcmc_status(self,par,like,sim,cur_chain): def sample(self, repetitions,nChains=1): - print('Starting the MCMC algotrithm with '+str(repetitions)+ ' repetitions...') self.set_repetiton(repetitions) + print('Starting the MCMC algotrithm with '+str(repetitions)+ ' repetitions...') # Prepare storing MCMC chain as array of arrays. self.nChains = int(nChains) #Ensure initialisation of chains and database @@ -116,7 +116,7 @@ def sample(self, repetitions,nChains=1): self.nChainruns=[[0]]*self.nChains self.min_bound, self.max_bound = self.parameter( )['minbound'], self.parameter()['maxbound'] - print('Inititalize ',self.nChains, ' chain(s)...') + print('Initialize ', self.nChains, ' chain(s)...') self.iter=0 param_generator = ((curChain,self.parameter()['random']) for curChain in range(int(self.nChains))) for curChain,randompar,simulations in self.repeat(param_generator): @@ -144,7 +144,7 @@ def sample(self, repetitions,nChains=1): #Refresh MCMC progressbar every two second if acttime - intervaltime >= 2 and self.iter >=2: text = '%i of %i (best like=%g)' % ( - self.iter + self.burnIn, repetitions, self.status.objectivefunction) + self.iter + self.burnIn, repetitions, self.status.objectivefunction_max) text = "Acceptance rates [%] =" +str(np.around((self.accepted)/float(((self.iter-self.burnIn)/self.nChains)),decimals=4)*100).strip('array([])') print(text) intervaltime = time.time() diff --git a/spotpy/algorithms/mle.py b/spotpy/algorithms/mle.py index cf774f91..2a6fec48 100644 --- a/spotpy/algorithms/mle.py +++ b/spotpy/algorithms/mle.py @@ -6,48 +6,50 @@ from . import _algorithm import numpy as np -import time -class mle(_algorithm): - ''' - Implements the Maximum Likelihood Estimation algorithm. - - Input - ---------- - spot_setup: class - model: function - Should be callable with a parameter combination of the parameter-function - and return an list of simulation results (as long as evaluation list) - parameter: function - When called, it should return a random parameter combination. Which can - be e.g. uniform or Gaussian - objectivefunction: function - Should return the objectivefunction for a given list of a model simulation and - observation. - evaluation: function - Should return the true values as return by the model. - - dbname: str - * Name of the database where parameter, objectivefunction value and simulation results will be saved. - - dbformat: str - * ram: fast suited for short sampling time. no file will be created and results are saved in an array. - * csv: A csv file will be created, which you can import afterwards. - save_sim: boolean - *True: Simulation results will be saved - *False: Simulationt results will not be saved - ''' - - def __init__(self, spot_setup, dbname=None, dbformat=None, parallel='seq', save_sim=True, save_threshold=-np.inf, - sim_timeout=None): - if parallel != 'seq': - raise Exception('ERROR: Please set parallel=seq as MLE is only useable in sequetial mode') +class mle(_algorithm): + """ + This class holds the Maximum Likelihood (MLE) algorithm, + based on a simple uphill method as presented by Houska et al (2015): + Houska, T., Kraft, P., Chamorro-Chavez, A. and Breuer, L. (2015) + SPOTting Model Parameters Using a Ready-Made Python Package, PLoS ONE. + """ - _algorithm.__init__(self, spot_setup, dbname=dbname, - dbformat=dbformat, parallel=parallel, save_sim=save_sim, save_threshold=save_threshold, - sim_timeout=sim_timeout) + def __init__(self, *args, **kwargs): + ''' + Implements the Maximum Likelihood Estimation algorithm. + + Input + ---------- + spot_setup: class + model: function + Should be callable with a parameter combination of the parameter-function + and return an list of simulation results (as long as evaluation list) + parameter: function + When called, it should return a random parameter combination. Which can + be e.g. uniform or Gaussian + objectivefunction: function + Should return the objectivefunction for a given list of a model simulation and + observation. + evaluation: function + Should return the true values as return by the model. + + dbname: str + * Name of the database where parameter, objectivefunction value and simulation results will be saved. + + dbformat: str + * ram: fast suited for short sampling time. no file will be created and results are saved in an array. + * csv: A csv file will be created, which you can import afterwards. + + save_sim: boolean + * True: Simulation results will be saved + * False: Simulation results will not be saved + ''' + kwargs['optimization_direction'] = 'maximize' + kwargs['algorithm_name'] = 'Maximum Likelihood Estimation (MLE) algorithm' + super(mle, self).__init__(*args, **kwargs) def check_par_validity(self, par): @@ -62,8 +64,8 @@ def check_par_validity(self, par): return par def sample(self, repetitions): - print('Starting the MLE algotrithm with '+str(repetitions)+ ' repetitions...') self.set_repetiton(repetitions) + print('Starting the MLE algotrithm with '+str(repetitions)+ ' repetitions...') # Define stepsize of MLE stepsizes = self.parameter()['step'] # array of stepsizes accepted = 0.0 @@ -78,7 +80,7 @@ def sample(self, repetitions): for i in range(burnIn): randompar = self.parameter()['random'] pars.append(randompar) - simulations = self.model(randompar) + _, _, simulations = self.simulate((i, randompar)) sims.append(simulations) like = self.postprocessing(i, randompar, simulations) likes.append(like) @@ -86,21 +88,19 @@ def sample(self, repetitions): old_like = max(likes) old_par = pars[likes.index(old_like)] - #old_simulations = sims[likes.index(old_like)] print('Beginn Random Walk') for rep in range(repetitions - burnIn): # Suggest new candidate from Gaussian proposal distribution. # Use stepsize provided for every dimension. new_par = np.random.normal(loc=old_par, scale=stepsizes) new_par = self.check_par_validity(new_par) - new_simulations = self.model(new_par) + _, _, new_simulations = self.simulate((i, new_par)) new_like = self.postprocessing(rep+burnIn, new_par, new_simulations) # Accept new candidate in Monte-Carlo fashing. if (new_like > old_like): accepted = accepted + 1.0 # monitor acceptance old_par = new_par - #old_simulations = new_simulations old_like = new_like - self.status(rep, new_like, new_par) + #self.status(rep, new_like, new_par) self.final_call() diff --git a/spotpy/algorithms/nsgaii.py b/spotpy/algorithms/nsgaii.py index 12b8fc4d..be01890e 100644 --- a/spotpy/algorithms/nsgaii.py +++ b/spotpy/algorithms/nsgaii.py @@ -1,7 +1,8 @@ +# -*- coding: utf-8 -*- ''' -Copyright (c) 2018 by Benjamin Manns +Copyright (c) 2018 by Tobias Houska This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). -:author: Benjamin Manns +:author: Iacopo Ferrario This file contains the NSGA-II Algorithm implemented for SPOTPY based on: - K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: @@ -13,21 +14,130 @@ ''' import numpy as np - +import math from spotpy.algorithms import _algorithm +import copy + + + +class TournamentSelection: + + def __init__(self,pressure = 2): + self.pressure = pressure + + def calc(self,pop_rank): + + n_select = len(pop_rank) + n_random = n_select * self.pressure #n_select * n_parents * pressure + + n_perms = math.ceil(n_random / len(pop_rank)) + + P = random_permuations(n_perms, len(pop_rank))[:n_random] + + P = np.reshape(P, (n_select, self.pressure)) + + n_tournament,_ = P.shape + + ret = np.full(n_tournament,-1,dtype=np.int) + + for i in range(n_tournament): + a,b = P[i] + + if pop_rank[a] < pop_rank[b]: + ret[i] = a + else: + ret[i] = b + + return ret + + +def random_permuations(n, l): + perms = [] + for _ in range(n): + perms.append(np.random.permutation(l)) + return np.concatenate(perms) + + +class Crossover: + + def __init__(self,crossProb=0.9): + + self.crossProbThreshold = crossProb + + def calc(self,pop,n_var): + + n_pop = pop.shape[0] + crossProbability = np.random.random((n_pop)) + do_cross = crossProbability < self.crossProbThreshold + R = np.random.randint(0,n_pop,(n_pop,2)) + parents = R[do_cross] + crossPoint = np.random.randint(1,n_var,parents.shape[0]) + d = pop[parents,:] + child = [] + for i in range(parents.shape[0]): + child.append(np.concatenate([d[i,0,:crossPoint[i]],d[i,1,crossPoint[i]:]])) + child = np.vstack(child) + pop[do_cross,:] = child + return pop + + + + +class PolynomialMutation: + + def __init__(self,prob_mut,eta_mut): + + self.prob_mut = prob_mut + self.eta_mut = eta_mut + + def calc(self,x,xl,xu): + + X = copy.deepcopy(x) + Y = np.full(X.shape,np.inf) + + do_mutation = np.random.random(X.shape) < self.prob_mut + + m = np.sum(np.sum(do_mutation)) + + Y[:,:] = X + + xl = np.repeat(xl[None,:],X.shape[0],axis=0)[do_mutation] #selecting who is mutating + xu = np.repeat(xu[None,:],X.shape[0],axis=0)[do_mutation] + + X = X[do_mutation] + + delta1 = (X - xl) / (xu - xl) + delta2 = (xu - X) / (xu -xl) + + mut_pow = 1.0/(self.eta_mut + 1.0) + + rand = np.random.random(X.shape) + mask = rand <= 0.5 + mask_not = np.logical_not(mask) + + deltaq = np.zeros(X.shape) + + xy = 1.0 - delta1 + val = 2.0 * rand + (1.0 - 2.0 * rand) * (np.power(xy, (self.eta_mut + 1.0))) + d = np.power(val, mut_pow) - 1.0 + deltaq[mask] = d[mask] + + xy = 1.0 - delta2 + val = 2.0 * (1.0 - rand) + 2.0 * (rand - 0.5) * (np.power(xy, (self.eta_mut + 1.0))) + d = 1.0 - (np.power(val, mut_pow)) + deltaq[mask_not] = d[mask_not] + + ret = X + deltaq * (xu - xl) + ret[ret < xl] = xl[ret < xl] + ret[ret > xu] = xu[ret > xu] + + Y[do_mutation] = ret + + return Y -class ParaPop: - def __init__(self, params, m_vals=[], sim=None): - self.params = params - self.m_vals = m_vals - self.sim = sim - def __str__(self): - return " with content " + str(self.m_vals) - def __repr__(self): - return self.__str__() class NSGAII(_algorithm): @@ -69,270 +179,227 @@ def __init__(self, *args, **kwargs): * True: Simulation results will be saved * False: Simulation results will not be saved """ - + self._return_all_likes=True #alloes multi-objective calibration + kwargs['optimization_direction'] = 'minimize' + kwargs['algorithm_name'] = 'Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II' super(NSGAII, self).__init__(*args, **kwargs) - # self.all_objectives = [self.setup.__getattribute__(m) for m in dir(self.setup) if "objectivefunc" in m] - self.param_len = len(self.get_parameters()) - self.generation = 0 - self.length_of_objective_func = 0 - self.objfun_maxmin_list = None - self.objfun_normalize_list= [] + def fastSort(self,x): + n = x.shape[0] + S = np.zeros((n,n),dtype=bool) + Np = np.zeros(n) + + + for i in range(n): + for j in range(n): + S[i,j] = self.dominates(x[i,:],x[j,:]) + + nDom = np.sum(S,axis=0) # the n solutions that dominates i + Np[nDom == 0] = 1 # if i == 0, i is non-dominated, set i rank to 1, i belongs to first non-dominated front + k = 1 + # loop over pareto fronts + while np.sum(Np == 0) > 0: + l = np.arange(n)[Np==k] # first non-dominated front + for i in l: # loop over the non-dominated front + nDom[S[i,:]] = nDom[S[i,:]] -1 # reduce by 1 the rank of the solutions that i dominates + k += 1 + # now nDom has been reduced by 1, so the next non-dominated front will be nDom == 0 + # and Np == 0 ensure that we don't pass over the first ranked non-dom solutions + Np[(nDom == 0) & (Np == 0) ] = k + + return Np.astype(int) + + + + def dominates(self,a,b): + if len(a.shape) >1: + ret = (np.sum(a <= b,axis =1) == a.shape[1]) & (np.sum(a < b,axis=1) >0) + else: + ret = (np.sum(a <= b) == len(a)) & (np.sum(a < b) >0) + return ret - def fast_non_dominated_sort(self, P): - S = {} - n = {} - F = {} - rank = {} - F[1] = {} - if self.objfun_maxmin_list is None: - self.length_of_objective_func = 0 - # In the first iteration create this list to have it prepared for later use - self.objfun_maxmin_list = [] - for _ in self.objectivefunction(self.setup.evaluation(), self.setup.simulation(P[0])): - self.length_of_objective_func += 1 - self.objfun_maxmin_list.append([]) + def crowdDist(self,x): + n = x.shape[0] - param_generator = ((p, list(P[p])) for p in P) - calculated_sims = list(self.repeat(param_generator)) - for p, par_p, sim_p in calculated_sims: + nobj = x.shape[1] - S[p] = {} - S_p_index = 0 - n[p] = 0 - for q, par_q, sim_q in calculated_sims: + dist = np.zeros(n) - # check whether parameter set p or q is dominating so we test all objective functions here - # https://cims.nyu.edu/~gn387/glp/lec1.pdf / Definition / Dominance Relation + ord = np.argsort(x,axis=0) - # m_diffs = np.array([]) - m_vals_p = np.array([]) - m_vals_q = np.array([]) + X = x[ord,range(nobj)] + + dist = np.vstack([X,np.full(nobj,np.inf)]) - np.vstack([np.full(nobj,-np.inf),X]) - for i, m in enumerate(self.objectivefunction(self.setup.evaluation(), sim_q)): - m_vals_q = np.append(m_vals_q, m) - self.objfun_maxmin_list[i].append(m) + norm = np.max(X,axis=0) - np.min(X,axis=0) + dist_to_last,dist_to_next = dist, np.copy(dist) + dist_to_last,dist_to_next = dist_to_last[:-1]/norm ,dist_to_next[1:]/norm + J = np.argsort(ord,axis=0) + ret = np.sum(dist_to_last[J, np.arange(nobj)] + dist_to_next[J, np.arange(nobj)], axis=1) / nobj - for i, m in enumerate(self.objectivefunction(self.setup.evaluation(), sim_p)): - m_vals_p = np.append(m_vals_p, m) - self.objfun_maxmin_list[i].append(m) + return ret - m_diffs = m_vals_q - m_vals_p + def crowdDist2(self,x): + n = x.shape[0] + + dist = np.zeros(n) - # TODO ist Minimieren oder Maximieren richtig? + for obj in range(x.shape[1]): + ord = np.argsort(x[:,obj]) + dist[ord[[0,-1]]] = np.inf - pp_q = ParaPop(np.array(P[q]), list(m_vals_q.tolist()), sim_q) + norm = np.max(x[:,obj]) - np.min(x[:,obj]) - pp_p = ParaPop(np.array(P[q]), list(m_vals_p.tolist()), sim_p) + for i in range(1,n-1): + dist[i] = dist[ord[i]] + (x[ord[i+1],obj] - x[ord[i-1],obj])/norm - # Allow here also more then 2 - # if p dominates q - if (m_diffs >= 0).all and (m_diffs > 0).any(): + return dist - S[p][S_p_index] = pp_q - S_p_index += 1 - # elif q dominates p: - elif (m_diffs <= 0).all() and (m_diffs < 0).any(): - # else: - n[p] += 1 - if n[p] == 0: - rank[p] = 1 - F[1][p] = pp_p - i = 1 - while len(F[i]) > 0: - Q = {} - # q_ind is just a useless indices which may has to change somehow, it is only for the dict we use - q_ind = 0 - for p in F[i]: - for q in S[p]: - n[q] -= 1 - if n[q] == 0: - rank[q] = i + 1 - Q[q_ind] = S[p][q] - q_ind += 1 + def sample(self, generations, n_obj, n_pop = None, skip_duplicates = False, + selection = TournamentSelection(pressure=2), + crossover = Crossover(crossProb=0.9), + mutation = PolynomialMutation(prob_mut=0.25,eta_mut=30)): + + self.n_obj = n_obj + self.selection = selection + self.crossover = crossover + self.mutation = mutation + + self.n_pop = n_pop + self.generations= generations + self.set_repetiton(self.generations*self.n_pop) + self.skip_duplicates = skip_duplicates - i += 1 - F[i] = Q + Pt = np.vstack([self.parameter()['random'] for i in range(self.n_pop)]) + + #Burn-in + #TODO: I would suggest to make the burin-in sample indiviudual for each cpu-core in case of parallel usage, compare dream.py, but not sure if this is defined in the publication + # evaluate population + param_generator = ((i,Pt[i,:]) for i in range(self.n_pop)) + + ret = list(self.repeat(param_generator)) + + Of = [] + for p in range(self.n_pop): + index,parameters,simulation_results = ret[p] + Of.append(self.postprocessing(0, parameters, simulation_results, chains=p)) + Of = np.vstack(Of) - return F - def crowding_distance_assignement(self, I): - l = len(I) + nonDomRank = self.fastSort(Of) - if l > 2: + crDist = np.empty(self.n_pop) + for rk in range(1,np.max(nonDomRank)+1): + crDist[nonDomRank == rk] = self.crowdDist(Of[nonDomRank ==rk,:]) - I = list(I.values()) - ################## - # I = sort(I,m) # - ################## + # sorting - sorting_m = [] + rank = np.lexsort((-crDist,nonDomRank)) + Ptsort = Pt[rank] + Ofsort = Of[rank] - for i in I: - sorting_m.append(i.m_vals) + Of_parent = Ofsort[:,:] + Pt_parent = Ptsort[:,:] + + # selection - sorting_m = np.array(sorting_m) + offsprings = self.selection.calc(pop_rank = rank) - new_order = np.argsort(np.sqrt(np.sum(sorting_m ** 2, 1))) + Qt = Ptsort[offsprings,:] - I = np.array(I) - I = I[new_order] + + # crossover + try: + n_var = self.setup.n_var + except AttributeError: + n_var = len(parameters) - distance_I = list(np.repeat(0, l)) - distance_I[0] = distance_I[l - 1] = np.inf + Qt = self.crossover.calc(pop = Qt,n_var = n_var) - for_distance = [] + # mutation + self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] + self.varminbound = np.array([]) + self.varmaxbound = np.array([]) + for i in range(len(self.min_bound)): + self.varminbound = np.append(self.varminbound,self.min_bound[i]) + self.varmaxbound = np.append(self.varmaxbound,self.max_bound[i]) - for i in I: - for_distance.append(i.m_vals) + Qt = self.mutation.calc(x = Qt,xl = self.varminbound,xu = self.varmaxbound) + + for igen in range(1,self.generations - 1): - which_obj_fn = 0 - for_distance = np.array(for_distance) - for k in for_distance.T: - tmp_dist = k[2:l] - k[0:l - 2] + Rt = np.vstack([Pt_parent,Qt]) - distance_I[1:l - 1] += tmp_dist / self.objfun_normalize_list[which_obj_fn] - which_obj_fn += 1 + if self.skip_duplicates: - return distance_I + # evaluate population + param_generator = ((i,Qt[i,:]) for i in range(self.n_pop)) - else: - return [np.inf] - - def sample(self, generations=2, paramsamp=20): - self.repetitions = int(generations) - self.status.repetitions = self.repetitions*paramsamp*2 - R_0 = {} - - for i in range(paramsamp * 2): - R_0[i] = list(self.setup.parameters()['random']) - - while self.generation < self.repetitions: - - F = self.fast_non_dominated_sort(R_0) - - print("GENERATION: " + str(self.generation) + " of " + str(generations)) - - # Debuggin Issue - # import matplotlib.pyplot as pl - # from mpl_toolkits.mplot3d import Axes3D - # layer = 0 - # fig = pl.figure() - # - # if self.length_of_objective_func == 2: - # - # for i in F: - # if layer == 0: - # l_color = "b" - # else: - # l_color = "#" - # for _ in range(6): - # l_color += ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F"][ - # np.random.randint(16)] - # for j in F[i]: - # pl.plot(F[i][j].m_vals[0], F[i][j].m_vals[1], color=l_color, marker='o') - # layer += 1 - # - # pl.show() - # - # elif self.length_of_objective_func == 3: - # - # ax = fig.add_subplot(111, projection='3d') - # - # for i in F: - # for j in F[i]: - # ax.scatter(F[i][j].m_vals[0], F[i][j].m_vals[1], - # F[i][j].m_vals[2]) # , lay_col[layer] + 'o') - # layer += 1 - # - # # ax.set_xlabel(m1_name) - # # ax.set_ylabel(m2_name) - # # ax.set_zlabel(m3_name) - # - # pl.show() - - # Now sort again - complete_sort_all_p = [] - - # post-proccesing min-max values of each objective function: - # reset the normalize list - self.objfun_normalize_list = [] - for i in self.objfun_maxmin_list: - # fill the normalize list - self.objfun_normalize_list.append(abs(max(i)-min(i))) - - # reset the objfun_maxmin_list - self.objfun_maxmin_list = None - - cChain = 0 - for k in F: - - # Save fronts we have now before sorting and mutation - for o in F[k]: - - self.postprocessing(self.generation, F[k][o].params, F[k][o].sim, cChain) - - F_I_distance = self.crowding_distance_assignement(F[k]) - - # print(F_I_distance) - - # sort within the list and then add to general list - # the partial order <_n is defined as follows: - # i <_n j if(i_rank < j_rank) or (i_rank = j_rank - # and i_distance > j_distance ) - i_distance_order = np.argsort(F_I_distance) - - F_ar = np.array(list(F[k].values())) - if len(F[k]) > 0: - for a in F_ar[i_distance_order]: - complete_sort_all_p.append(a) - - # TODO Why is algorithm to take all values again, I think only the first which are the best - cChain +=1 - - N = paramsamp - complete_sort_all_p = np.array(complete_sort_all_p) - P_new = complete_sort_all_p[0:N] - Q_new = {} - M = len(P_new) - if M < N: - P_new = np.append(P_new, complete_sort_all_p[0:N - M]) - if N > len(P_new): - exit("We still have to few parameters after selecting parent elements") - - list_index = 0 - while list_index < N: - # select pairs... with tournament, because this is a sorted list we just use the - # first occurence of a pick "out of M (= length of P_new) - tmp_parm_1 = P_new[np.random.randint(0, M, 1)[0]].params - tmp_parm_2 = P_new[np.random.randint(0, M, 1)[0]].params - - # select cross over point - xover_point = np.random.randint(0, self.param_len - 1, 1)[0] - - # cross over - A = np.append(tmp_parm_2[0:xover_point], tmp_parm_1[xover_point:]) + ret = list(self.repeat(param_generator)) + + Of = [] + for p in range(self.n_pop): + index, parameters,simulation_results = ret[p] + Of.append(self.postprocessing(igen, parameters, simulation_results, chains=p)) + Of = np.vstack(Of) - # mutation - sign = [0, 1, -1][np.random.randint(0, 3, 1)[0]] - Q_new[list_index] = A + sign * (A / 4) # was 100 before + Of = np.vstack([Of_parent ,Of]) + + nonDomRank = self.fastSort(Of) + + crDist = np.empty(len(Of)) + for rk in range(1,np.max(nonDomRank)+1): + crDist[nonDomRank == rk] = self.crowdDist(Of[nonDomRank ==rk,:]) + else: + + n_pop_combined = self.n_pop *2 + # evaluate population + param_generator = ((i,Rt[i,:]) for i in range( n_pop_combined )) + + + #import pdb;pdb.set_trace() + ret = list(self.repeat(param_generator)) + + Of = [] + for p in range(n_pop_combined): + index, parameters,simulation_results = ret[p] + Of.append(self.postprocessing(igen, parameters, simulation_results, chains=p)) + Of = np.vstack(Of) + + nonDomRank = self.fastSort(Of) - list_index += 1 + crDist = np.empty(n_pop_combined) + for rk in range(1,np.max(nonDomRank)+1): + crDist[nonDomRank == rk] = self.crowdDist(Of[nonDomRank ==rk,:]) + + + # sorting + rank = np.lexsort((-crDist,nonDomRank))[:self.n_pop] + Ptsort = Rt[rank] + Ofsort = Of[rank] + + Pt_parent = Ptsort[:,:] + + Of_parent = Ofsort[:,:] + + + # selection + offsprings = self.selection.calc(pop_rank = rank) + + Qt = Ptsort[offsprings,:] + # crossover + Qt = self.crossover.calc(pop = Qt,n_var = n_var) + # mutation + Qt = self.mutation.calc(x = Qt,xl = self.varminbound,xu =self.varmaxbound) - self.generation += 1 - # merge P and Q - for i in P_new.tolist(): - Q_new[list_index] = i.params - list_index += 1 - R_0 = Q_new self.final_call() diff --git a/spotpy/algorithms/padds.py b/spotpy/algorithms/padds.py new file mode 100644 index 00000000..7e9e8670 --- /dev/null +++ b/spotpy/algorithms/padds.py @@ -0,0 +1,802 @@ +import numpy as np +from copy import deepcopy + +from spotpy.algorithms.dds import DDSGenerator +from . import _algorithm +from spotpy.parameter import ParameterSet +import copy +from scipy.spatial.qhull import ConvexHull, QhullError + +class BestValue(object): + """ + BestValue holds a parameter set and a best objective value, which is used by the PADDS Algorithm. + Updates are done within the algorithm + """ + + def __init__(self, para_func, obj_value): + self.para_func = para_func + self.parameters = ParameterSet(self.para_func()) + self.best_obj_val = obj_value + self.best_rep = 0 + + + def copy(self): + to_copy = BestValue(self.parameters.copy(), self.best_obj_val) + to_copy.best_rep = self.best_rep + return to_copy + + def __str__(self): + return "BestValue(best_obj_val = " + str(self.best_obj_val) + ", best_rep = " + str(self.best_rep) + ", " \ + + str(self.parameters) + ")" + + def reset_rep(self): + self.best_rep = 0 + + def fix_format(self): + start_params = ParameterSet(self.para_func()) + start_params.set_by_array([j for j in self.parameters]) + self.parameters = start_params + + +class padds(_algorithm): + """ + Implements the Pareto Archived Dynamically Dimensioned Search (short PADDS algorithm) by + Tolson, B. A. and Asadzadeh M. (2013) + https://www.researchgate.net/publication/259982925_Pareto_archived_dynamically_dimensioned_search_with_hypervolume-based_selection_for_multi-objective_optimization + + PADDS using the DDS algorithm with a pareto front included. Two metrics are implemented, + which is the simple "one" metric and the "crowd distance" metric. + """ + + def __init__(self, *args, **kwargs): + """ + Input + ---------- + spot_setup: class + model: function + Should be callable with a parameter combination of the parameter-function + and return an list of simulation results (as long as evaluation list) + parameter: function + When called, it should return a random parameter combination. Which can + be e.g. uniform or Gaussian + objectivefunction: function + Should return the objectivefunction for a given list of a model simulation and + observation. + evaluation: function + Should return the true values as return by the model. + + dbname: str + * Name of the database where parameter, objectivefunction value and simulation results will be saved. + + dbformat: str + * ram: fast suited for short sampling time. no file will be created and results are saved in an array. + * csv: A csv file will be created, which you can import afterwards. + + parallel: str + * seq: Sequentiel sampling (default): Normal iterations on one core of your cpu. + * mpi: Message Passing Interface: Parallel computing on cluster pcs (recommended for unix os). + + save_sim: boolean + * True: Simulation results will be saved + * False: Simulation results will not be saved + :param r: neighborhood size perturbation parameter (r) that defines the random perturbation size standard + deviation as a fraction of the decision variable range. Default is 0.2. + :type r: float + + """ + + try: + self.r = kwargs.pop("r") + except KeyError: + self.r = 0.2 # default value + self._return_all_likes=True #alloes multi-objective calibration + super(padds, self).__init__(*args, **kwargs) + + self.np_random = np.random + + + self.best_value = BestValue(self.parameter, None) + + self.dds_generator = DDSGenerator(self.np_random) + # self.generator_repetitions will be set in `sample` and is needed to generate a + # generator which sends back actual parameter s_test + self.generator_repetitions = -1 + self.pareto_front = np.array([]) + self.dominance_flag = -2 + self.obj_func_current = None + self.parameter_current = None + + # because we have a pareto front we need another save type + self.like_struct_typ = type([]) + + def _set_np_random(self, f_rand): + self.np_random = f_rand + if hasattr(self,"hvc"): + self.hvc._set_np_random(f_rand) + self.dds_generator.np_random = f_rand + + def roulette_wheel(self, metric): + cumul_metric = np.cumsum(metric) + probability = self.np_random.rand() * cumul_metric[-1] + levels = (cumul_metric >= probability) + length = cumul_metric.shape[0] + return np.array(range(length))[levels][0] + + def get_next_x_curr(self): + """ + Fake a generator to run self.repeat to use multiprocessing + """ + # We need to shift position and length of the sampling process + for rep in range(self.generator_repetitions): + if self.dominance_flag == -1: # if the last generated solution was dominated + index = self.roulette_wheel(self.metric) + self.best_value.parameters, self.best_value.best_obj_val = self.pareto_front[index][1], self.pareto_front[index][0] + else: # otherwise use the last generated solution + self.best_value.parameters, self.best_value.best_obj_val = (self.parameter_current, self.obj_func_current) + + # This line is needed to get an array of data converted into a parameter object + self.best_value.fix_format() + + yield rep, self.calculate_next_s_test(self.best_value.parameters, rep, self.generator_repetitions, self.r) + + def calculate_initial_parameterset(self, repetitions, initial_objs, initial_params): + self.obj_func_current = np.array([0.0]) + self.parameter_current = np.array([0.0] * self.number_of_parameters) + self.parameter_range = self.best_value.parameters.maxbound - self.best_value.parameters.minbound + self.pareto_front = np.array([[np.array([]), np.array([0] * self.number_of_parameters)]]) + #self.pareto_front = np.array([np.append([np.inf] * self.like_struct_len, [0] * self.number_of_parameters)]) + + if(len(initial_objs) != len(initial_params)): + raise ValueError("User specified 'initial_objs' and 'initial_params' have no equal length") + + if len(initial_objs) == 0: + initial_iterations = np.int(np.max([5, round(0.005 * repetitions)])) + self.calc_initial_pareto_front(initial_iterations) + elif initial_params.shape[1] != self.number_of_parameters: + raise ValueError("User specified 'initial_params' has not the same length as available parameters") + else: + if not (np.all(initial_params <= self.best_value.parameters.maxbound) and np.all( + initial_params >= self.best_value.parameters.minbound)): + raise ValueError("User specified 'initial_params' but the values are not within the parameter range") + initial_iterations = initial_params.shape[0] + + for i in range(initial_params.shape[0]): + self.parameter_current = initial_params[i] + if len(initial_objs[i]) > 0: + self.obj_func_current = initial_objs[i] + else: + self.obj_func_current = self.getfitness(simulation=[], params=self.parameter_current) + + if i == 0: # Initial value + self.pareto_front = np.array([[self.obj_func_current, self.parameter_current]]) + dominance_flag = 1 + else: + self.pareto_front, dominance_flag = nd_check(self.pareto_front, self.obj_func_current, + self.parameter_current.copy()) + self.dominance_flag = dominance_flag + + return initial_iterations, copy.deepcopy(self.parameter_current) + + def sample(self, repetitions, trials=1, initial_objs=np.array([]), initial_params=np.array([]), metric="ones"): + # every iteration a map of all relevant values is stored, only for debug purpose. + # Spotpy will not need this values. + debug_results = [] + print('Starting the PADDS algotrithm with ' + str(repetitions) + ' repetitions...') + print('WARNING: THE PADDS algorithm as implemented in SPOTPY is in an beta stage and not ready for production use!') + self.set_repetiton(repetitions) + self.number_of_parameters = len(self.best_value.parameters) # number_of_parameters is the amount of parameters + + if metric == "hvc": + self.hvc = HVC(np_random=self.np_random) + + self.min_bound, self.max_bound = self.parameter()['minbound'], self.parameter()['maxbound'] + + # Users can define trial runs in within "repetition" times the algorithm will be executed + for trial in range(trials): + self.best_value.best_obj_val = 1e-308 + repitionno_best, self.best_value.parameters = self.calculate_initial_parameterset(repetitions, initial_objs, initial_params) + + repetions_left = repetitions - repitionno_best + + # Main Loop of PA-DDS + self.metric = self.calc_metric(metric) + + # important to set this field `generator_repetitions` so that + # method `get_next_s_test` can generate exact parameters + self.generator_repetitions = repetions_left + + for rep, x_curr, simulations in self.repeat(self.get_next_x_curr()): + self.obj_func_current = self.postprocessing(rep, x_curr, simulations) + num_imp = np.sum(self.obj_func_current <= self.best_value.best_obj_val) + num_deg = np.sum(self.obj_func_current > self.best_value.best_obj_val) + + if num_imp == 0 and num_deg > 0: + self.dominance_flag = -1 # New solution is dominated by its parents + else: # Do dominance check only if new solution is not dominated by its parent + self.pareto_front, self.dominance_flag = nd_check(self.pareto_front, self.obj_func_current, x_curr.copy()) + if self.dominance_flag != -1: # means, that new parameter set is a new non-dominated solution + self.metric = self.calc_metric(metric) + self.parameter_current = x_curr + # update the new status structure + self.status.params_max, self.status.params_min = self.parameter_current, self.parameter_current + + print('Best solution found has obj function value of ' + str(self.best_value.best_obj_val) + ' at ' + + str(repitionno_best) + '\n\n') + debug_results.append({"sbest": self.best_value.parameters , "objfunc_val": self.best_value.best_obj_val}) + + self.final_call() + return debug_results + + def calc_metric(self, metric): + """ + calculate / returns metric field + :return: set of metric of choice + """ + if metric == "ones": + return np.array([1] * self.pareto_front.shape[0]) + elif metric == "crowd_distance": + return crowd_dist(np.array([w for w in self.pareto_front[:,0]])) + elif metric == "chc": + return chc(np.array([w for w in self.pareto_front[:,0]])) + elif metric == "hvc": + return self.hvc(np.array([w for w in self.pareto_front[:,0]])) + else: + raise AttributeError("metric argument is invalid") + + def calc_initial_pareto_front(self, its): + """ + calculate the initial pareto front + :param its: amount of initial parameters + """ + + + dominance_flag = -1 + for i in range(its): + for j in range(self.number_of_parameters): + if self.best_value.parameters.as_int[j]: + self.parameter_current[j] = self.np_random.randint(self.best_value.parameters.minbound[j], + self.best_value.parameters.maxbound[j]) + else: + self.parameter_current[j] = self.best_value.parameters.minbound[j] + self.parameter_range[ + j] * self.np_random.rand() # uniform random + + id, params, model_simulations = self.simulate((range(len(self.parameter_current)), self.parameter_current)) + self.obj_func_current = self.getfitness(simulation=model_simulations, params=self.parameter_current) + # First value will be used to initialize the values + if i == 0: + self.pareto_front = np.vstack( + [self.pareto_front[0], np.array([self.obj_func_current.copy(), self.parameter_current.copy() + 0])]) + else: + (self.pareto_front, dominance_flag) = nd_check(self.pareto_front, self.obj_func_current, + self.parameter_current.copy()) + + self.dominance_flag = dominance_flag + + + def calculate_next_s_test(self, previous_x_curr, rep, rep_limit, r): + """ + Needs to run inside `sample` method. Calculate the next set of parameters based on a given set. + This is greedy algorithm belonging to the DDS algorithm. + + `probability_neighborhood` is a threshold at which level a parameter is added to neighbourhood calculation. + + Using a normal distribution + The decision variable + + `dvn_count` counts how many parameter configuration has been exchanged with neighbourhood values. + If no parameters has been exchanged just one will select and exchanged with it's neighbourhood value. + + :param previous_x_curr: A set of parameters + :param rep: Position in DDS loop + :param r: neighbourhood size perturbation parameter + :return: next parameter set + """ + amount_params = len(previous_x_curr) + new_x_curr = previous_x_curr.copy() # define new_x_curr initially as current (previous_x_curr for greedy) + + randompar = self.np_random.rand(amount_params) + + probability_neighborhood = 1.0 - np.log(rep + 1) / np.log(rep_limit) + dvn_count = 0 # counter for how many decision variables vary in neighbour + + for j in range(amount_params): + if randompar[j] < probability_neighborhood: # then j th DV selected to vary in neighbour + dvn_count = dvn_count + 1 + new_value = self.dds_generator.neigh_value_mixed(previous_x_curr, r, j, self.min_bound[j],self.max_bound[j]) + new_x_curr[j] = new_value # change relevant dec var value in x_curr + + if dvn_count == 0: # no DVs selected at random, so select ONE + dec_var = np.int(np.ceil(amount_params * self.np_random.rand())) + new_value = self.dds_generator.neigh_value_mixed(previous_x_curr, r, dec_var - 1, self.min_bound[dec_var - 1],self.max_bound[dec_var - 1]) + + new_x_curr[dec_var - 1] = new_value # change relevant decision variable value in s_test + + return new_x_curr + + +def nd_check(nd_set_input, objective_values, parameter_set): + """ + It is the Non Dominated Solution Check (ND Check) + + Meaning of dominance_flag + dominance_flag = -1: parameter_set is dominated by pareto front + dominance_flag = 0: parameter_set is a new non-dominated solution but not dominating + dominance_flag = 1: parameter_set is a new non-dominated solution and dominating + + + :param nd_set_input: Pareto Front + :param objective_values: objective values + :param parameter_set: parameter set + :return: a new pareto front and a value if it was dominated or not (0,1,-1) + """ + # Algorithm from PADDS Matlab Code + + nd_set = deepcopy(nd_set_input) + dominance_flag = 0 + + # These are simply reshaping problems if we want to loop over arrays but we have a single float given + try: + like_struct_len = objective_values.shape[0] + except IndexError: + objective_values = objective_values.reshape((1,)) + like_struct_len = objective_values.shape[0] + try: + # TODO delete pareto_high + pareto_high = nd_set.shape[1] + except IndexError: + nd_set = nd_set.reshape(1, nd_set.shape[0]) + pareto_high = nd_set.shape[1] + + i = -1 # solution counter + while i < nd_set.shape[0] - 1: + i += 1 + + try: + _ = objective_values < nd_set[i][0] + except ValueError: + nd_set[i][0] = np.array([np.inf]*objective_values.shape[0]) + + num_eql = np.sum(objective_values == nd_set[i][0]) + num_imp = np.sum(objective_values < nd_set[i][0]) + num_deg = np.sum(objective_values > nd_set[i][0]) + + if num_imp == 0 and num_deg > 0: # parameter_set is dominated + dominance_flag = -1 + return nd_set, dominance_flag + elif num_eql == like_struct_len: + # Objective functions are the same for parameter_set and archived solution i + # TODO check if this line still works + nd_set[i][0], nd_set[i][1] = objective_values, parameter_set # Replace solution i in ND_set with X + dominance_flag = 0 # X is non - dominated + return nd_set, dominance_flag + elif num_imp > 0 and num_deg == 0: # X dominates ith solution in the ND_set + nd_set = np.delete(nd_set, i, 0) + i = i - 1 + dominance_flag = 1 + + if nd_set.size == 0: # that means the array is completely empty + nd_set = np.array([objective_values, parameter_set]) # Set solution i in ND_set with X + else: # If X dominated a portion of solutions in ND_set + nd_set = np.vstack( + [nd_set, np.array([objective_values, parameter_set])]) # Add the new solution to the end of ND_set (for later use and comparing! + + return nd_set, dominance_flag + + +def crowd_dist(points): + """ + This function calculates the Normalized Crowding Distance for each member + or "points". Deb book p236 + The structure of PF_set is as follows: + PF_set = [obj_1, obj_2, ... obj_m, DV_1, DV_2, ..., DV_n] + + e.g. v = np.array([[1,10], [2,9.8], [3,5], [4,4], [8,2], [10,1]]); CDInd = crowd_dist(v) + + :param points: mainly is this a pareto front, but could be any set of data which a crowd distance should be calculated from + :return: the crowd distance distance + """ + + # Normalize Objective Function Space + try: # Python / Numpy interprets arrays sometimes with wrong shape, this is a fix + length_x = points.shape[1] + except IndexError: + points = points.reshape((1, points.shape[0])) + length_x = points.shape[1] + + max_f = np.nanmax(points, 0) + min_f = np.nanmin(points, 0) + + levels = (max_f == min_f) + length_y = points.shape[0] + + indicies = np.array(range(length_x))[levels] + max_f[indicies] += 1 + + MAX_f = np.transpose(max_f.repeat(length_y).reshape((length_x, length_y))) + MIN_f = np.transpose(min_f.repeat(length_y).reshape((length_x, length_y))) + + points = np.divide(points - MIN_f, MAX_f - MIN_f) + + # resave Length + length_x = points.shape[1] + length_y = points.shape[0] + + pointsWithNoNan = points[:, ~np.any(np.isnan(points), axis=0)] + # Initialization + zero_column = np.array([[0] * length_y]).reshape((length_y, 1)) + index_column = np.array(range(length_y)).reshape((length_y, 1)) + temp = np.concatenate((pointsWithNoNan, zero_column, index_column), 1) + ij = temp.shape[1] - 2 + endpointIndx = np.array([0] * 2 * length_x) + + # Main Calculation + if length_y <= length_x + 1: # Less than or equal # obj + 1 solutions are non-dominated + temp[:, ij] = 1 # The crowding distance is 1 for all archived solutions + return temp[:, ij] + else: # More than 2 solutions are non - dominated + for i in range(length_x): + # https://stackoverflow.com/a/22699957/5885054 + temp = temp[temp[:, i].argsort()] + temp[0, ij] = temp[0, ij] + 2 * (temp[1, i] - temp[0, i]) + temp[length_y - 1, ij] = temp[length_y - 1, ij] + 2 * (temp[length_y - 1, i] - temp[length_y - 2, i]) + + for j in range(1, length_y - 1): + temp[j, ij] = temp[j, ij] + (temp[j + 1, i] - temp[j - 1, i]) + + endpointIndx[2 * (i - 1) + 0] = temp[0, -1] + endpointIndx[2 * (i - 1) + 1] = temp[-1, -1] + + # Endpoints of Pareto Front + temp = temp[temp[:, temp.shape[ + 1] - 1].argsort()] # Sort points based on the last column to restore the original order of points in the archive + endpointIndx = np.unique(endpointIndx) + + non_endpointIndx = np.array(range(length_y)).reshape((length_y, 1)) + non_endpointIndx = np.delete(non_endpointIndx, endpointIndx, 0) + + non_endpointIndx = non_endpointIndx.reshape((non_endpointIndx.shape[0])) + + Y = points[endpointIndx, :] + X = points[non_endpointIndx, :] + IDX = dsearchn(X, Y) # Identify the closest point in the objective space to each endpoint (dsearchn in Matlab) + if IDX.size > 0: + for i in range(endpointIndx.shape[0]): + temp[endpointIndx[i], ij] = np.nanmax([temp[endpointIndx[i], ij], temp[non_endpointIndx[IDX[ + i]], ij]]) # IF the closest point to the endpoint has a higher CD value, assign that to the endpoint; otherwise leave the CD value of the endpoint unchanged + return temp[:, ij] + + +def dsearchn(x, y): + """ + Implement Octave / Matlab dsearchn without triangulation + :param x: Search Points in + :param y: Were points are stored + :return: indices of points of x which have minimal distance to points of y + """ + IDX = [] + for line in range(y.shape[0]): + distances = np.sqrt(np.nansum(np.power(x - y[line, :], 2), axis=1)) + found_min_dist_ind = (np.nanmin(distances, axis=0) == distances) + length = found_min_dist_ind.shape[0] + IDX.append(np.array(range(length))[found_min_dist_ind][0]) + return np.array(IDX) + + +class HVC(): + def __init__(self, *args, **kwargs): + self.fakerandom = ('fakerandom' in kwargs and kwargs['fakerandom']) or ('np_random' in kwargs) + self.has_random_class = ('np_random' in kwargs) + self.random_class = (self.has_random_class and kwargs['np_random']) + from deap.tools._hypervolume import hv + self.hv = hv.hypervolume + self.h_vol_c = self.hv_wrapper + + def hv_wrapper(self, points): + ref = np.max(points, axis=0) + return self.hv(points, ref) + + def _set_np_random(self,f_rand): + self.random_class = f_rand + self.has_random_class = True + + def hype_indicator_sampled(self,points, bounds, nrOfSamples): + try: + nrP, dim = points.shape + except ValueError: + nrP, dim = points.shape[0], 1 + + F = np.array([0] * nrP) + BoxL = np.min(points, 0) + + S = np.dot(self.__rand(nrOfSamples, dim), np.diag(bounds - BoxL)) + np.dot(np.ones([nrOfSamples, dim]), np.diag(BoxL)) + + dominated = np.array([0] * nrOfSamples) + dominated_ind = np.array([0] * nrOfSamples) + + ROWS = np.transpose(np.array([range(nrOfSamples)])) + + for j in range(nrP): + B = S - np.repeat([points[j, :]], S.shape[0], 0) + ind = np.sum(B >= 0, 1) == dim + dominated[ind] += 1 + dominated_ind[ind] = j + Index = np.where(dominated == 2) + S = np.delete(S, Index, 0) + ROWS = np.delete(ROWS, Index, 0) + dominated = np.delete(dominated, Index, 0) + dominated_ind = np.delete(dominated_ind, Index, 0) + + Index = np.array(range(S.shape[0])) + Logical = dominated == 1 + ind = np.transpose(Index[Logical]) + Index = dominated_ind[ind] + + Index = np.sort(Index) + + for j in Index: + F[j] = F[j] + 1 + F = np.transpose(F) * np.prod(bounds - BoxL) / nrOfSamples + return F # transpose?? + + + def hv_apprx(self,points): + p_xlen = np.shape(points)[0] + p_ylen = np.shape(points)[1] + indexis = np.array(range(p_xlen)) + 1 + temp = np.hstack((points, indexis.reshape(p_xlen, 1))) + endpointIndx = np.zeros(2 * p_ylen) + + for i in range(p_ylen): + temp = self.sortrows(temp, i) + + endpointIndx[2 * (i)] = temp[0, -1] + endpointIndx[2 * (i) + 1] = temp[-1:, -1:] + endpointIndx = np.int32(np.unique(endpointIndx) - 1) + + nrOfSamples = np.max([10000, 2 * p_xlen]) # Dictates the accuracy of approximation + + HVInf = self.hype_indicator_sampled(points, np.array([1] * p_ylen), nrOfSamples) + + nonZero_Indx = np.where(HVInf > 0)[0] + Y = points[endpointIndx, :] + X = points[nonZero_Indx, :] + + IDX = dsearchn(X, Y) + + for i in range(len(endpointIndx)): + if len(IDX) > 0: + HVInf[endpointIndx[i]] = HVInf[nonZero_Indx[IDX[i]]] + else: + HVInf[endpointIndx[i]] = 1 + + return HVInf + + + def __rand(self, x, y): + if self.fakerandom: + if self.has_random_class: + return self.random_class.rand(x,y) + else: + dim = x * y + 1 + step = 1.0 / dim + data = np.arange(step, 1, step)[0:x * y] + if y <= 1: + reshaper = [x] + else: + reshaper = [x, y] + return data.reshape(reshaper) + else: + return np.random.rand(x,y) + + + + def sortrows(self, arr, index): + """ + https://gist.github.com/stevenvo/e3dad127598842459b68 + :param arr: + :param index: + :return: + """ + return arr[arr[:, index].argsort()] + + def hv_exact(self,points): + p_xlen = np.shape(points)[0] + p_ylen = np.shape(points)[1] + + indexis = np.array(range(p_xlen)) + 1 + + temp = np.hstack((points, indexis.reshape(p_xlen, 1))) + endpointIndx = np.zeros(2 * p_ylen) + + for i in range(p_ylen): + temp = self.sortrows(temp, i) + + endpointIndx[2 * (i)] = temp[0, -1] + endpointIndx[2 * (i) + 1] = temp[-1:, -1:] + + endpointIndx = np.int32(np.unique(endpointIndx) - 1) + + if len(endpointIndx) == p_xlen: + return np.ones(p_xlen) + + indexis = np.delete(indexis, endpointIndx) + + totalHV = self.h_vol_c(points) + + HVInf = np.zeros(p_xlen) + + for i in range(len(indexis)): + y = points + y = np.delete(y, indexis[i] - 1, 0) + subhv = self.h_vol_c(y) + + HVInf[indexis[i] - 1] = totalHV - subhv + + non_endpointIndx = np.array(range(p_xlen)) + non_endpointIndx = non_endpointIndx[[i for i in range(p_xlen) if i not in endpointIndx]] + + Y = points[endpointIndx, :] + X = points[non_endpointIndx, :] + IDX = dsearchn(X, Y) + + for i in range(len(endpointIndx)): + if len(IDX) > 0: + HVInf[endpointIndx[i]] = HVInf[non_endpointIndx[IDX[i]]] + else: + HVInf[endpointIndx[i]] = 1 + + return HVInf + + + def __call__(self, points): + p_xlen = np.shape(points)[0] + p_ylen = np.shape(points)[1] + + if p_xlen <= p_ylen + 1: + return np.array([1] * p_xlen) + + max_f = np.max(points, 0) + min_f = np.min(points, 0) + max_f = max_f.reshape(1, p_ylen) + min_f = min_f.reshape(1, p_ylen) + + ind = max_f == min_f + max_f[ind] = max_f[ind] + 1 + + MAX_f = np.repeat(max_f, p_xlen, 0) + MIN_f = np.repeat(min_f, p_xlen, 0) + + points = (points - MIN_f) / (MAX_f - MIN_f) + + if p_xlen <= p_ylen + 1: + return np.array([1] * p_xlen) + + if p_ylen <= 4: + return self.hv_exact(points) + elif p_ylen > 4: + return self.hv_apprx(points) + + + +def chc(points): + """ + function CHC_metric = CHC(points) + Comments + Original written by Masoud Asadzadeh, University of Waterloo, June 03 2011 + + This code calls the qhull by SciPy (see also http://www.qhull.org/) + + This function calculates Convex Hull Contribution CHC. See: + Asadzadeh, M., B. A. Tolson, and D. H. Burn (2014), A new selection metric for multiobjective hydrologic model calibration, doi:10.1002/2013WR014970. + There are four mutually exclusive sets of points in CH: + i. Points inside the convex hull + ii. Vertices of top facet only + iii. Vertices of bottom facets only + iv. Vertices in the intersection of top and bottom facets + + CHC is ZERO for points in i and ii. CHC for points iii is calculated as + their contribution to the volume of the convex hull of set of "points". + CHC for points iv is calculated as the CHC of closest point in iii. + + e.g. CHC_metric = CHC([1 10; 2 9.8; 3 5; 4 4; 8 2; 10 1]) + Normalize the objective space + :param points: + :return: + """ + + max_f = np.max(points, 0) + min_f = np.min(points, 0) + p_xlen = np.shape(points)[0] + p_ylen = np.shape(points)[1] + + max_f = max_f.reshape(1, p_ylen) + min_f = min_f.reshape(1, p_ylen) + MAX_f = np.repeat(max_f, p_xlen, 0) + MIN_f = np.repeat(min_f, p_xlen, 0) + + CHC_metric = np.array([0.0] * p_xlen) + + points = (points - MIN_f) / (MAX_f - MIN_f) + + if p_xlen <= p_ylen + 1: + return np.array([1] * p_xlen) + + try: + hull = ConvexHull(points) + except ValueError: + hull = None + return np.array([1.] * np.max(points.shape)) + + Totalv = hull.volume + # hull.vertices + norm = hull.equations[:, :-1] + num = hull.nsimplex + vertices = [] + + for s in hull.simplices: + vertices.append(list(s + 1)) + vertices = np.array(vertices) + + all_CHpts_ind = np.unique(vertices) + ZEROind = all_CHpts_ind == 0 + all_CHpts_ind[ZEROind] = [] + + # Identify points in groups ii, iii, iv as defined above + top_facets_ind = np.min(norm, 1) >= 0 # facets with outward norm that has only non-negative components + + ii_iv_CHpts_ind = np.unique(vertices[top_facets_ind]) # points on top of CH, i.e. groups ii and iv + + ZEROind = ii_iv_CHpts_ind == 0 + ii_iv_CHpts_ind[ZEROind] = [] + other_facets_ind = np.array(range(norm.shape[0])) # All facets + other_facets_ind = np.delete(other_facets_ind, other_facets_ind[top_facets_ind]) + + iii_iv_CHpts_ind = np.unique(vertices[other_facets_ind, :]) # points on bottom of CH, i.e. groups iii and iv + + ZEROind = iii_iv_CHpts_ind == 0 + + iii_iv_CHpts_ind = np.delete(iii_iv_CHpts_ind, iii_iv_CHpts_ind[ZEROind]) + + bor_ind = np.array([y in ii_iv_CHpts_ind for y in iii_iv_CHpts_ind]) + + bor_CHpts_ind = iii_iv_CHpts_ind[bor_ind] + bot_CHpts_ind = iii_iv_CHpts_ind + bot_CHpts_ind = bot_CHpts_ind[bor_ind == False] # Remove border points from bottom points + + # When number of bottom points and border points are not enough to form CH + if bot_CHpts_ind.shape[0] == 0: + CHC_metric[bot_CHpts_ind - 1] = 1 + CHC_metric[bor_CHpts_ind - 1] = 1 + return CHC_metric + + for i in range(bot_CHpts_ind.shape[0]): + y = points[all_CHpts_ind - 1] # Only consider points that are on the vertices of convex hull + # Meaning that forget the points inside the convex hull + ind = np.array([j == bot_CHpts_ind[i] for j in all_CHpts_ind]) + y = y[ind == False] + try: + convhull = ConvexHull(y) + Sub_v = convhull.volume + + if Sub_v > Totalv: # just in case of numerical issues + CHC_metric[bot_CHpts_ind[i] - 1] = 0 + else: + CHC_metric[bot_CHpts_ind[i] - 1] = Totalv - Sub_v + except QhullError: + # an error occured + CHC_metric = np.array([0.0] * p_xlen) + CHC_metric[bot_CHpts_ind - 1] = 1 + CHC_metric[bor_CHpts_ind - 1] = 1 + return CHC_metric + + if np.max(CHC_metric) == 0: # In case no solution has valid CHC value + CHC_metric[bot_CHpts_ind - 1] = 1 + CHC_metric[bor_CHpts_ind - 1] = 1 + + Y = points[bor_CHpts_ind - 1, :] + X = points[bot_CHpts_ind - 1, :] + IDX = dsearchn(X, Y) + for i in range(bor_CHpts_ind.shape[0]): + CHC_metric[bor_CHpts_ind[i] - 1] = CHC_metric[bot_CHpts_ind[IDX[i]] - 1] + + return CHC_metric diff --git a/spotpy/algorithms/rope.py b/spotpy/algorithms/rope.py index 90554467..caf1ae2a 100644 --- a/spotpy/algorithms/rope.py +++ b/spotpy/algorithms/rope.py @@ -21,8 +21,7 @@ class rope(_algorithm): Hydrol. Earth Syst. Sci. Discuss., 5(3), 1641–1675, 2008. ''' - def __init__(self, spot_setup, dbname=None, dbformat=None, - parallel='seq', save_sim=True, save_threshold=-np.inf,sim_timeout = None): + def __init__(self, *args, **kwargs): ''' Input @@ -60,25 +59,11 @@ def __init__(self, spot_setup, dbname=None, dbformat=None, :param save_sim: boolean *True: Simulation results will be saved - *False: Simulationt results will not be saved + *False: Simulation results will not be saved ''' - _algorithm.__init__(self, spot_setup, dbname=dbname, - dbformat=dbformat, parallel=parallel, - save_sim=save_sim, save_threshold=save_threshold,sim_timeout = sim_timeout) - - def create_par(self, min_bound, max_bound): - return np.random.uniform(low=min_bound, high=max_bound) - - def check_par_validity(self, par): - if len(par) == len(self.min_bound) and len(par) == len(self.max_bound): - for i in range(len(par)): - if par[i] < self.min_bound[i]: - par[i] = self.min_bound[i] - if par[i] > self.max_bound[i]: - par[i] = self.max_bound[i] - else: - print('ERROR: Bounds have not the same lenghts as Parameterarray') - return par + kwargs['optimization_direction'] = 'maximize' + kwargs['algorithm_name'] = 'RObust Parameter Estimation (ROPE) algorithm' + super(rope, self).__init__(*args, **kwargs) def get_best_runs(self, likes, pars, runs, percentage): ''' @@ -105,8 +90,8 @@ def sample(self, repetitions=None, repetitions_first_run=None, next step after in all following subsets NDIR = The number of samples to draw """ - # Repetitions_following_runs raus - # Braucht zu lang (npar >8) + #Reported behaviour: + # Takes ways´to long for npar >8 # wenn mehr parameter produziert werden sollen als reingehen, rechnet er sich tot (ngen>n) #Subsets < 5 führt manchmal zu Absturz print('Starting the ROPE algotrithm with '+str(repetitions)+ ' repetitions...') @@ -114,7 +99,7 @@ def sample(self, repetitions=None, repetitions_first_run=None, if repetitions_first_run is None: #Take the first have of the repetitions as burn-in - first_run = int(repetitions / 2) + first_run = int(repetitions / 2.0) else: #Make user defined number of burn-in repetitions @@ -124,44 +109,35 @@ def sample(self, repetitions=None, repetitions_first_run=None, / (subsets-1)) # Needed to avoid an error in integer division somewhere in depth function if repetitions_following_runs % 2 != 0: + print('Warning: Burn-in samples and total number of repetions are not compatible.\n' + 'SPOTPY will automatically adjust the number of total repetitions.') repetitions_following_runs+=1 if NDIR is None: - NDIR = int(repetitions_following_runs / 100) + NDIR = int(repetitions_following_runs / 100.0) self.NDIR = NDIR starttime = time.time() intervaltime = starttime - self.min_bound, self.max_bound = self.parameter( - )['minbound'], self.parameter()['maxbound'] - #randompar = list(self.parameter()['optguess']) - #simulations = self.model(randompar) - #like = self.postprocessing(rep, randompar, simulations) - + parset =self.parameter() + self.min_bound, self.max_bound = parset['minbound'], parset['maxbound'] + # Init ROPE with one subset likes = [] pars = [] - - - - - # Get the names of the parameters to analyse - names = self.parameter()['name']# distribution - parmin, parmax = self.parameter()['minbound'], self.parameter()[ - 'maxbound'] - segment = 1 / float(first_run) + segment = 1.0 / float(first_run) # Get the minimum and maximum value for each parameter from the # Create a matrix to store the parameter sets - matrix = np.empty((first_run, len(parmin))) + matrix = np.empty((first_run, len(self.min_bound))) # Create the LatinHypercube matrix as in McKay et al. (1979): for i in range(int(first_run)): segmentMin = i * segment pointInSegment = segmentMin + (random.random() * segment) - parset = pointInSegment * (parmax - parmin) + parmin + parset = pointInSegment * (self.max_bound - self.min_bound) + self.min_bound matrix[i] = parset - for i in range(len(names)): + for i in range(len(self.min_bound)): random.shuffle(matrix[:, i]) # A generator that produces the parameters @@ -175,14 +151,14 @@ def sample(self, repetitions=None, repetitions_first_run=None, # Progress bar acttime = time.time() # Refresh progressbar every second + if self.status.stop: + break if acttime - intervaltime >= 2: text = '1 Subset: Run %i of %i (best like=%g)' % ( - rep, first_run, self.status.objectivefunction) + rep, first_run, self.status.objectivefunction_max) print(text) intervaltime = time.time() - - for subset in range(subsets - 1): if subset == 0: best_pars = self.get_best_runs(likes, pars, repetitions_following_runs, @@ -200,7 +176,6 @@ def sample(self, repetitions=None, repetitions_first_run=None, trials += 1 pars = [] likes = [] - print(len(new_pars)) if(int(repetitions_following_runs) > len(new_pars)): repetitions_following_runs = len(new_pars) param_generator = ( @@ -210,6 +185,9 @@ def sample(self, repetitions=None, repetitions_first_run=None, like = self.postprocessing(first_run + rep + repetitions_following_runs * subset, ropepar, simulations) likes.append(like) pars.append(ropepar) + if self.status.stop: + print('Stopping samplig') + break # Progress bar acttime = time.time() @@ -220,9 +198,11 @@ def sample(self, repetitions=None, repetitions_first_run=None, subset + 2, rep, repetitions_following_runs, - self.status.objectivefunction) + self.status.objectivefunction_max) print(text) intervaltime = time.time() + if self.status.stop: + break self.final_call() @@ -255,7 +235,7 @@ def programm_depth(self, pars, runs): CL = np.zeros(NP) TL = np.zeros(shape=(LLEN, NP)) - #test=[np.zeros(NP)] + while (IPOS < NPOSI): for IM in range(LLEN): # LLEN=1000 Random Vectors of dim NP for j in range(NP): @@ -265,17 +245,10 @@ def programm_depth(self, pars, runs): for L in range(LLEN): ITRY = ITRY + 1 if LNDEP[L] >= 1: - #test.append(TL[L, :]) CL = np.vstack((CL, TL[L, :])) IPOS = IPOS + 1 print((IPOS, ITRY)) - #CL=np.array(test) - #print('##') - #print(type(CL[0])) - #print('###') - #print(type(np.array(test)[0])) - #print('####') - #CL=np.array(test) + CL = np.delete(CL, 0, 0) CL = CL[:NPOSI] return CL @@ -369,4 +342,4 @@ def fDEP(self, N, NP, X, TL, EPS, LLEN): dirac = 0 NUMH = N1 LNDEP[LU] = min(LNDEP[LU], min(NUMH + NT, N - NUMH)) - return LNDEP + return LNDEP \ No newline at end of file diff --git a/spotpy/algorithms/sa.py b/spotpy/algorithms/sa.py index 7d3514cf..15bc3f31 100644 --- a/spotpy/algorithms/sa.py +++ b/spotpy/algorithms/sa.py @@ -53,7 +53,8 @@ def __init__(self, *args, **kwargs): * True: Simulation results will be saved * False: Simulation results will not be saved """ - + kwargs['optimization_direction'] = 'maximize' + kwargs['algorithm_name'] = 'Simulated Annealing (SA) algorithm' super(sa, self).__init__(*args, **kwargs) def check_par_validity(self, par): @@ -76,46 +77,32 @@ def sample(self, repetitions, Tini=80, Ntemp=50, alpha=0.99): repetitions: int Maximum number of runs. """ - print('Starting the SA algotrithm with '+str(repetitions)+ ' repetitions...') self.set_repetiton(repetitions) - # Tini=80#repetitions/100 - # Ntemp=6 + print('Starting the SA algotrithm with '+str(repetitions)+ ' repetitions...') self.min_bound, self.max_bound = self.parameter( )['minbound'], self.parameter()['maxbound'] stepsizes = self.parameter()['step'] - #starttime = time.time() - #intervaltime = starttime Eopt = 999999 Titer = Tini - #vmin,vmax = self.find_min_max() x = self.parameter()['optguess'] Xopt = x - simulations = self.model(x) - #SimOpt = simulations + _, _, simulations = self.simulate((1, x)) Enew = self.postprocessing(1, x, simulations) Eopt = Enew - #self.save(Eopt, Xopt, simulations=simulations) - # k=(vmax-vmin)/self.parameter()['step'] - rep = 0 + rep = 1 # Because the model has been started once already while (Titer > 0.001 * Tini and rep < repetitions): for counter in range(Ntemp): - if (Enew > Eopt): - # print 'Better' + if (Enew > Eopt): # Run was better Eopt = Enew Xopt = x - #SimOpt = simulations Eopt = Enew x = np.random.uniform(low=Xopt - stepsizes, high=Xopt + stepsizes) else: accepted = frandom(Enew, Eopt, Titer) if accepted == True: - # print Xopt Xopt = x - #SimOpt = self.model(x) - #Eopt = self.objectivefunction( - # evaluation=self.evaluation, simulation=simulations) x = np.random.uniform(low=Xopt - stepsizes, high=Xopt + stepsizes) else: @@ -123,33 +110,18 @@ def sample(self, repetitions, Tini=80, Ntemp=50, alpha=0.99): x = self.check_par_validity(x) - simulations = self.model(x) + _, _, simulations = self.simulate((rep+1, x)) Enew = self.postprocessing(rep+1, x, simulations) - # self.objectivefunction( -# evaluation=self.evaluation, simulation=simulations) -# -# self.save(Eopt, Xopt, simulations=SimOpt) -# self.status(rep, Enew, Xopt) rep += 1 + if self.status.stop: + break Titer = alpha * Titer self.final_call() -# text = '%i of %i (best like=%g)' % ( -# rep, repetitions, self.status.objectivefunction) -# print(text) -# try: -# self.datawriter.finalize() -# except AttributeError: # Happens if no database was assigned -# pass -# text = 'Duration:' + str(round((acttime - starttime), 2)) + ' s' -# print(text) -# data = self.datawriter.getdata() -# return data def frandom(Enew, Eold, Titer): - # dE=Enew-Eold dE = Eold - Enew accepted = False if (dE > 0): @@ -157,21 +129,7 @@ def frandom(Enew, Eold, Titer): rn = np.random.rand() if (rn <= P): # New configuration accepted - # print 'accepted' accepted = True else: - # print 'else' accepted = True - return accepted - - -def fgener(param, vmin, vmax, k): # random displacement - rv = np.random.rand() - k = 10 - rd = 2.0 * (rv - 0.5) * param / float(k) - new = param + rd - if (new < vmin): - new = vmin - if (new > vmax): - new = vmax - return new + return accepted \ No newline at end of file diff --git a/spotpy/algorithms/sceua.py b/spotpy/algorithms/sceua.py index 151d7101..bdcc5f9a 100644 --- a/spotpy/algorithms/sceua.py +++ b/spotpy/algorithms/sceua.py @@ -10,9 +10,7 @@ from __future__ import print_function from __future__ import unicode_literals from . import _algorithm -import spotpy import numpy as np -import time class sceua(_algorithm): @@ -62,23 +60,10 @@ def __init__(self, *args, **kwargs): * True: Simulation results will be saved * False: Simulation results will not be saved """ - - if 'alt_objfun' not in kwargs: - kwargs['alt_objfun'] = 'rmse' + kwargs['optimization_direction'] = 'minimize' + kwargs['algorithm_name'] = 'Shuffled Complex Evolution (SCE-UA) algorithm' super(sceua, self).__init__(*args, **kwargs) - - def find_min_max(self): - randompar = self.parameter()['random'] - for i in range(1000): - randompar = np.column_stack( - (randompar, self.parameter()['random'])) - return np.amin(randompar, axis=1), np.amax(randompar, axis=1) - """ - def simulate(self,params): - if self.repeat.phase=='burnin': - id,params = params - simulations = - """ + def simulate(self, id_params_tuple): """This overwrites the simple wrapper function of _algorithms.py @@ -88,16 +73,12 @@ def simulate(self, id_params_tuple): """ if not self.repeat.phase: # burn-in - id, params = id_params_tuple - return id, params, self.model(params) + return _algorithm.simulate(self, id_params_tuple) else: # complex-evolution - igs, x, xf, icall, cx, cf, sce_vars = id_params_tuple - self.npg, self.nopt, self.ngs, self.nspl, self.nps, self.bl, self.bu, self.status = sce_vars + igs, x, xf, cx, cf, sce_vars = id_params_tuple + self.npg, self.nopt, self.ngs, self.nspl, self.nps, self.bl, self.bu, self.stochastic_parameters, discarded_runs = sce_vars # Partition the population into complexes (sub-populations); -# cx=np.zeros((self.npg,self.nopt)) -# cf=np.zeros((self.npg)) - # print(igs) k1 = np.arange(self.npg, dtype=int) k2 = k1 * self.ngs + igs cx[k1, :] = x[k2, :] @@ -113,11 +94,9 @@ def simulate(self, id_params_tuple): lcs[0] = 1 for k3 in range(1, self.nps): for i in range(1000): - #lpos = 1 + int(np.floor(self.npg+0.5-np.sqrt((self.npg+0.5)**2 - self.npg*(self.npg+1)*np.random.random()))) lpos = int(np.floor( self.npg + 0.5 - np.sqrt((self.npg + 0.5)**2 - self.npg * (self.npg + 1) * np.random.random()))) - # idx=find(lcs(1:k3-1)==lpos) - # check of element al eens gekozen + # check if the element has already been chosen idx = (lcs[0:k3] == lpos).nonzero() if idx[0].size == 0: break @@ -125,16 +104,14 @@ def simulate(self, id_params_tuple): lcs.sort() # Construct the simplex: - s = np.zeros((self.nps, self.nopt)) s = cx[lcs, :] sf = cf[lcs] - snew, fnew, icall, simulation = self._cceua(s, sf, icall) + snew, fnew, simulation, discarded_runs = self._cceua(s, sf, discarded_runs) likes.append(fnew) pars.append(snew) - #self.status(igs, -fnew, snew) sims.append(simulation) - #self.datawriter.save(-fnew,list(snew), simulations = list(simulation),chains = igs) + # Replace the worst point in Simplex with the new point: s[-1, :] = snew sf[-1] = fnew @@ -147,10 +124,9 @@ def simulate(self, id_params_tuple): idx = np.argsort(cf) cf = np.sort(cf) cx = cx[idx, :] - + # Replace the complex back into the population; - - return igs, likes, pars, sims, cx, cf, k1, k2 + return igs, likes, pars, sims, cx, cf, k1, k2, discarded_runs def sample(self, repetitions, ngs=20, kstop=100, pcento=0.0000001, peps=0.0000001): """ @@ -162,20 +138,16 @@ def sample(self, repetitions, ngs=20, kstop=100, pcento=0.0000001, peps=0.000000 repetitions: int maximum number of function evaluations allowed during optimization ngs: int - number of complexes (sub-populations), take more then the number of + number of complexes (sub-populations), take more than the number of analysed parameters kstop: int - maximum number of evolution loops before convergency - pcento: int - the percentage change allowed in kstop loops before convergency + the number of past evolution loops and their respective objective value to assess whether the marginal improvement at the current loop (in percentage) is less than pcento + pcento: float + the percentage change allowed in the past kstop loops below which convergence is assumed to be achieved. peps: float - Convergence criterium + Value of the normalized geometric range of the parameters in the population below which convergence is deemed achieved. """ - print('Starting the SCE-UA algorithm with '+str(repetitions)+ ' repetitions...') self.set_repetiton(repetitions) - # Initialize the Progress bar - starttime = time.time() - #intervaltime = starttime # Initialize SCE parameters: self.ngs = ngs randompar = self.parameter()['random'] @@ -185,135 +157,114 @@ def sample(self, repetitions, ngs=20, kstop=100, pcento=0.0000001, peps=0.000000 self.nspl = self.npg npt = self.npg * self.ngs self.iseed = 1 + self.discarded_runs = 0 self.bl, self.bu = self.parameter()['minbound'], self.parameter()[ 'maxbound'] bound = self.bu - self.bl # np.array + self.stochastic_parameters = bound != 0 + proceed = True if self.breakpoint == 'read' or self.breakpoint == 'readandwrite': data_frombreak = self.read_breakdata(self.dbname) icall = data_frombreak[0] x = data_frombreak[1][0] xf = data_frombreak[1][1] gnrng = data_frombreak[2] - acttime = time.time() + elif self.breakpoint is None or self.breakpoint == 'write': # Create an initial population to fill array x(npt,self.self.nopt): x = self._sampleinputmatrix(npt, self.nopt) - - # Set Ininitial parameter position - # iniflg=1 - nloop = 0 icall = 0 xf = np.zeros(npt) - print ('burn-in started...') + print ('Starting burn-in sampling...') # Burn in param_generator = ((rep, x[rep]) for rep in range(int(npt))) for rep, randompar, simulations in self.repeat(param_generator): # Calculate the objective function - like = self.postprocessing(icall, randompar, simulations, negativlike=True) - - #like = self.objectivefunction( - # evaluation=self.evaluation, simulation=simulations) - # Save everything in the database - + like = self.postprocessing(icall, randompar, simulations,chains=0) xf[rep] = like - #self.save(-like, randompar, simulations=simulations) - #self.status(rep, -like, randompar) - icall += 1 - # Progress bar - #acttime = time.time() - #if acttime - intervaltime >= 2: - # text = '%i of %i (best like=%g)' % ( - # rep, repetitions, self.status.objectivefunction) - # print(text) - # intervaltime = time.time() - + icall+=1 + if self.status.stop: + print('Stopping samplig. Maximum number of repetitions reached already during burn-in') + proceed = False + break # Sort the population in order of increasing function values; idx = np.argsort(xf) xf = np.sort(xf) x = x[idx, :] + else: + raise ValueError("Don't know the breakpoint keyword {}".format(self.breakpoint)) - # Record the best and worst points; + # Record the best points; bestx = x[0, :] bestf = xf[0] - # worstx=x[-1,:] - # worstf=xf[-1] BESTF = bestf BESTX = bestx - ICALL = icall - # Compute the standard deviation for each parameter - # xnstd=np.std(x,axis=0) # Computes the normalized geometric range of the parameters gnrng = np.exp( - np.mean(np.log((np.max(x, axis=0) - np.min(x, axis=0)) / bound))) + np.mean(np.log((np.max(x[:, self.stochastic_parameters], axis=0) - np.min(x[:, self.stochastic_parameters], axis=0)) / bound[self.stochastic_parameters]))) # Check for convergency; - if icall >= repetitions: + if self.status.rep >= repetitions: print('*** OPTIMIZATION SEARCH TERMINATED BECAUSE THE LIMIT') print('ON THE MAXIMUM NUMBER OF TRIALS ') print(repetitions) print('HAS BEEN EXCEEDED. SEARCH WAS STOPPED AT TRIAL NUMBER:') - print(icall) + print(self.status.rep) print('OF THE INITIAL LOOP!') if gnrng < peps: print( 'THE POPULATION HAS CONVERGED TO A PRESPECIFIED SMALL PARAMETER SPACE') - print ('burn-in completed...') + print ('Burn-in sampling completed...') # Begin evolution loops: nloop = 0 criter = [] - criter_change = 1e+5 + criter_change_pcent = 1e+5 - #starttime = time.time() - #intervaltime = starttime - #acttime = time.time() self.repeat.setphase('ComplexEvo') - - print ('ComplexEvo started...') - - while icall < repetitions and gnrng > peps and criter_change > pcento: + print ('Starting Complex Evolution...') + proceed = True + while icall < repetitions and gnrng > peps and criter_change_pcent > pcento and proceed == True: nloop += 1 - # print nloop - # print 'Start MPI' + print ('ComplexEvo loop #%d in progress...' % nloop) # Loop on complexes (sub-populations); cx = np.zeros((self.npg, self.nopt)) cf = np.zeros((self.npg)) - + remaining_runs = repetitions - self.status.rep + if remaining_runs <= self.ngs: + self.ngs = remaining_runs-1 + proceed = False + sce_vars = [self.npg, self.nopt, self.ngs, self.nspl, - self.nps, self.bl, self.bu, self.status] - param_generator = ((rep, x, xf, icall, cx, cf, sce_vars) + self.nps, self.bl, self.bu, self.stochastic_parameters, self.discarded_runs] + param_generator = ((rep, x, xf, cx, cf, sce_vars) for rep in range(int(self.ngs))) - for igs, likes, pars, sims, cx, cf, k1, k2 in self.repeat(param_generator): - icall += len(likes) + for igs, likes, pars, sims, cx, cf, k1, k2, discarded_runs in self.repeat(param_generator): x[k2, :] = cx[k1, :] xf[k2] = cf[k1] - #print(len(likes)) + self.discard_runs = discarded_runs for i in range(len(likes)): - #print(icall) - like = self.postprocessing(icall+i, pars[i], sims[i], chains=i, negativlike=True) - #self.save(-likes[i], pars[i], - # simulations=sims[i], chains=igs) - #self.status(icall, -likes[i], pars[i]) + if not self.status.stop: + like = self.postprocessing(i, pars[i], sims[i], chains=i+1) + else: + #Collect data from all slaves but do not save + proceed=False + like = self.postprocessing(i, pars[i], sims[i], chains=i+1, save_run=False) + self.discarded_runs+=1 + print('Skipping saving') + if self.breakpoint == 'write' or self.breakpoint == 'readandwrite'\ - and icall >= self.backup_every_rep: - work = (icall, (x, xf), gnrng) + and self.status.rep >= self.backup_every_rep: + work = (self.status.rep, (x, xf), gnrng) self.write_breakdata(self.dbname, work) - - # Progress bar -# acttime = time.time() -# if acttime - intervaltime >= 2: -# text = '%i of %i (best like=%g)' % ( -# icall, repetitions, self.status.objectivefunction) -# print(text) -# intervaltime = time.time() # End of Loop on Complex Evolution; # Shuffled the complexes; @@ -324,58 +275,61 @@ def sample(self, repetitions, ngs=20, kstop=100, pcento=0.0000001, peps=0.000000 # Record the best and worst points; bestx = x[0, :] bestf = xf[0] - # worstx=x[-1,:] - # worstf=xf[-1] # appenden en op einde reshapen!! BESTX = np.append(BESTX, bestx, axis=0) BESTF = np.append(BESTF, bestf) - ICALL = np.append(ICALL, icall) # Computes the normalized geometric range of the parameters gnrng = np.exp( - np.mean(np.log((np.max(x, axis=0) - np.min(x, axis=0)) / bound))) + np.mean(np.log((np.max(x[:, self.stochastic_parameters], axis=0) - np.min(x[:, self.stochastic_parameters], axis=0)) / bound[self.stochastic_parameters]))) + criter = np.append(criter, bestf) + # Check for convergency; - if icall >= repetitions: + if self.status.rep >= repetitions: print('*** OPTIMIZATION SEARCH TERMINATED BECAUSE THE LIMIT') print('ON THE MAXIMUM NUMBER OF TRIALS ') print(repetitions) print('HAS BEEN EXCEEDED.') - if gnrng < peps: + elif gnrng < peps: print( 'THE POPULATION HAS CONVERGED TO A PRESPECIFIED SMALL PARAMETER SPACE') - criter = np.append(criter, bestf) - if nloop >= kstop: # nodig zodat minimum zoveel doorlopen worden - criter_change = np.abs( - criter[nloop - 1] - criter[nloop - kstop]) * 100 - criter_change = criter_change / \ - np.mean(np.abs(criter[nloop - kstop:nloop])) - print ('updated convergence criteria: %f' % criter_change) - if criter_change < pcento: - text = 'THE BEST POINT HAS IMPROVED IN LAST %d LOOPS BY LESS THAN THE THRESHOLD %f' % ( - kstop, pcento) - print(text) + elif nloop >= kstop: # necessary so that the area of high posterior density is visited as much as possible + print ('Objective function convergence criteria is now being updated and assessed...') + absolute_change = np.abs( + criter[nloop - 1] - criter[nloop - kstop])*100 + denominator = np.mean(np.abs(criter[(nloop - kstop):nloop])) + if denominator == 0.0: + criter_change_pcent = 0.0 + else: + criter_change_pcent = absolute_change / denominator + print ('Updated convergence criteria: %f' % criter_change_pcent) + if criter_change_pcent <= pcento: + print('THE BEST POINT HAS IMPROVED IN LAST %d LOOPS BY LESS THAN THE USER-SPECIFIED THRESHOLD %f' % ( + kstop, pcento)) print( 'CONVERGENCY HAS ACHIEVED BASED ON OBJECTIVE FUNCTION CRITERIA!!!') + elif self.status.stop: + proceed = False + break + # End of the Outer Loops - text = 'SEARCH WAS STOPPED AT TRIAL NUMBER: %d' % icall - print(text) - text = 'NORMALIZED GEOMETRIC RANGE = %f' % gnrng - print(text) - text = 'THE BEST POINT HAS IMPROVED IN LAST %d LOOPS BY %f' % ( - kstop, criter_change) - print(text) + print('SEARCH WAS STOPPED AT TRIAL NUMBER: %d' % self.status.rep) + print('NUMBER OF DISCARDED TRIALS: %d' % self.discarded_runs) + print('NORMALIZED GEOMETRIC RANGE = %f' % gnrng) + print('THE BEST POINT HAS IMPROVED IN LAST %d LOOPS BY %f PERCENT' % ( + kstop, criter_change_pcent)) # reshape BESTX - BESTX = BESTX.reshape(BESTX.size // self.nopt, self.nopt) + #BESTX = BESTX.reshape(BESTX.size // self.nopt, self.nopt) self.final_call() - def _cceua(self, s, sf, icall): + def _cceua(self, s, sf, discarded_runs): # This is the subroutine for generating a new point in a simplex # # s(.,.) = the sorted simplex in order of increasing function values @@ -391,7 +345,7 @@ def _cceua(self, s, sf, icall): # iviol = flag indicating if constraints are violated # = 1 , yes # = 0 , no - + constant_parameters = np.invert(self.stochastic_parameters) self.nps, self.nopt = s.shape alpha = 1.0 beta = 0.5 @@ -405,7 +359,7 @@ def _cceua(self, s, sf, icall): # Attempt a reflection point snew = ce + alpha * (ce - sw) - + snew[constant_parameters] = sw[constant_parameters] # Check if is outside the bounds: ibound = 0 s1 = snew - self.bl @@ -419,43 +373,34 @@ def _cceua(self, s, sf, icall): ibound = 2 if ibound >= 1: - snew = self._sampleinputmatrix(1, self.nopt)[0] # checken!! + snew = self._sampleinputmatrix(1, self.nopt)[0] ## fnew = functn(self.nopt,snew); - simulations = self.model(snew) - like = self.postprocessing(icall, snew, simulations, save=False) - #like = self.objectivefunction( - # evaluation=self.evaluation, simulation=simulations) - # bcf.algorithms._makeSCEUAformat(self.model,self.observations,snew) + _, _, simulations = _algorithm.simulate(self, (1, snew)) + like = self.postprocessing(1, snew, simulations, save_run=False, block_print=True) + discarded_runs+=1 + fnew = like - #fnew = self.model(snew) - icall += 1 # Reflection failed; now attempt a contraction point: if fnew > fw: snew = sw + beta * (ce - sw) - simulations = self.model(snew) - like = self.postprocessing(icall, snew, simulations, save=False) -# like = self.objectivefunction( -# evaluation=self.evaluation, simulation=simulations) + snew[constant_parameters] = sw[constant_parameters] + + _, _, simulations = _algorithm.simulate(self, (2, snew)) + like = self.postprocessing(2, snew, simulations, save_run=False, block_print=True) + discarded_runs+=1 fnew = like - icall += 1 # Both reflection and contraction have failed, attempt a random point; if fnew > fw: - snew = self._sampleinputmatrix(1, self.nopt)[0] # checken!! - simulations = self.model(snew) - like = self.postprocessing(icall, snew, simulations, save=False) -# like = self.objectivefunction( -# evaluation=self.evaluation, simulation=simulations) - # bcf.algorithms._makeSCEUAformat(self.model,self.observations,snew) - fnew = like - # print 'NSE = '+str((fnew-1)*-1) - #fnew = self.model(snew) - icall += 1 - + snew = self._sampleinputmatrix(1, self.nopt)[0] + _, _, simulations = _algorithm.simulate(self, (3, snew)) + like = self.postprocessing(3, snew, simulations, save_run=False, block_print=True) + discarded_runs+=1 + fnew = like # END OF CCE - return snew, fnew, icall, simulations + return snew, fnew, simulations, discarded_runs def _sampleinputmatrix(self, nrows, npars): ''' @@ -468,8 +413,4 @@ def _sampleinputmatrix(self, nrows, npars): x = np.zeros((nrows, npars)) for i in range(nrows): x[i, :] = self.parameter()['random'] - return x - # Matrix=np.empty((nrows,npars)) - # for i in range(nrows): - # Matrix[i]= self.parameter()['random'] - # return Matrix + return x \ No newline at end of file diff --git a/spotpy/analyser.py b/spotpy/analyser.py index 4ec27f41..7767d8ee 100644 --- a/spotpy/analyser.py +++ b/spotpy/analyser.py @@ -7,28 +7,19 @@ :author: Tobias Houska Holds functions to analyse results out of the database. -Note: This part of SPOTPY is in alpha status and not ready for production use. +Note: This part of SPOTPY is in alpha status and not yet ready for production use. ''' - - - - import numpy as np import spotpy - - - font = {'family' : 'calibri', 'weight' : 'normal', 'size' : 18} def load_csv_results(filename, usecols=None): """ - Get an array of your results in the given file, without the first and the - last column. The first line may have a different objectivefunction and the last - line may be incomplete, which would result in an error. + Get an array of your results in the given file. :filename: Expects an available filename, without the csv, in your working directory :type: str @@ -37,11 +28,26 @@ def load_csv_results(filename, usecols=None): :rtype: array """ if usecols == None: - #return np.genfromtxt(filename+'.csv',delimiter=',',names=True,skip_footer=1,invalid_raise=False)[1:] return np.genfromtxt(filename+'.csv',delimiter=',',names=True,invalid_raise=False) else: return np.genfromtxt(filename+'.csv',delimiter=',',names=True,skip_footer=1,invalid_raise=False,usecols=usecols)[1:] +def load_hdf5_results(filename): + """ + Get an array of your results in the given file. + + :filename: Expects an available filename, without the .h5 ending, + in your working directory + :type: str + + :return: Result array, simulation is an ndarray, + which is different to structured arrays return by the csv/sql/ram databases + :rtype: array + """ + import h5py + with h5py.File(filename+'.h5', 'r') as f: + return f[filename][()] + def load_csv_parameter_results(filename, usecols=None): """ Get an array of your results in the given file, without the first and the @@ -113,8 +119,6 @@ def get_parameters(results): """ fields=[word for word in results.dtype.names if word.startswith('par')] results = results[fields] - #print results.dtype.names# = get_parameternames(results) - #results.dtype.names = get_parameternames(results) return results def get_parameternames(results): @@ -209,7 +213,7 @@ def get_percentiles(results,sim_number=''): p95.append(np.percentile(list(results[fields[i]]),95)) return p5,p25,p50,p75,p95 -def calc_like(results,evaluation): +def calc_like(results,evaluation,objectivefunction): """ Calculate another objectivefunction of your results @@ -218,6 +222,9 @@ def calc_like(results,evaluation): :evaluation: Expects values, which correspond to your simulations :type: list + + :objectivefunction: Takes evaluation and simulation data and returns a objectivefunction, e.g. spotpy.objectvefunction.rmse + :type: function :return: New objectivefunction list :rtype: list @@ -225,8 +232,7 @@ def calc_like(results,evaluation): likes=[] sim=get_modelruns(results) for s in sim: - likes.append(spotpy.objectivefunctions.rmse(list(s),evaluation)) - #likes.append(objectivefunctions.agreementindex(list(s),evaluation)) + likes.append(objectivefunction(evaluation,list(s))) return likes def compare_different_objectivefunctions(like1,like2): @@ -251,24 +257,30 @@ def compare_different_objectivefunctions(like1,like2): print('like1 is signifikant different to like2: p<0.05' ) return out -def get_posterior(results,percentage=10): +def get_posterior(results,percentage=10, maximize=True): """ Get the best XX% of your result array (e.g. best 10% model runs would be a threshold setting of 0.9) - :results: Expects an numpy array which should have as first axis an index "like". This will be sorted. + :results: Expects an numpy array which should have as first axis an index "like1". This will be sorted . :type: array - :threshold: Optional, ratio of values that will be deleted + :percentag: Optional, ratio of values that will be deleted. :type: float + + :maximize: If True (default), higher "like1" column values are assumed to be better. + If False, lower "like1" column values are assumed to be better. :return: Posterior result array :rtype: array """ - reduction_factor = (100.0-percentage)/100.0 - return np.sort(results,axis=0)[int(len(results)*reduction_factor):] + if maximize: + index = np.where(results['like1']>=np.percentile(results['like1'],100.0-percentage)) + else: + index = np.where(results['like1']>=np.percentile(results['like1'],100.0-percentage)) + return results[index] -def plot_parameter_uncertainty(posterior_results,evaluation): - import pylab as plt +def plot_parameter_uncertainty(posterior_results,evaluation, fig_name='Posterior_parameter_uncertainty.png'): + import matplotlib.pyplot as plt simulation_fields = get_simulation_fields(posterior_results) fig= plt.figure(figsize=(16,9)) @@ -291,8 +303,8 @@ def plot_parameter_uncertainty(posterior_results,evaluation): plt.xlabel('Number of Observation Points') plt.ylabel ('Simulated value') plt.legend(loc='upper right') - fig.savefig('Posteriot_parameter_uncertainty.png',dpi=300) - text='A plot of the parameter uncertainty has been saved as "Posteriot_parameter_uncertainty.png"' + fig.savefig(fig_name,dpi=300) + text='A plot of the parameter uncertainty has been saved as '+fig_name print(text) @@ -342,9 +354,10 @@ def get_min_max(spotpy_setup): :return: Possible minimal and maximal values of all parameters in the parameters function of the spotpy_setup class :rtype: Two arrays """ - randompar = spotpy_setup.parameters()['random'] + parameter_obj = spotpy.parameter.generate(spotpy.parameter.get_parameters_from_setup(spotpy_setup)) + randompar = parameter_obj['random'] for i in range(1000): - randompar = np.column_stack((randompar, spotpy_setup.parameters()['random'])) + randompar = np.column_stack((randompar, parameter_obj['random'])) return np.amin(randompar, axis=1), np.amax(randompar, axis=1) def get_parbounds(spotpy_setup): @@ -378,29 +391,32 @@ def get_sensitivity_of_fast(results,like_index=1,M=4, print_to_console=True): """ import math likes=results['like'+str(like_index)] - print(likes.size) + print('Number of model runs:', likes.size) parnames = get_parameternames(results) parnumber=len(parnames) - if likes.size % (parnumber) == 0: - N = int(likes.size / parnumber) - else: - print(""" - Error: Number of samples in model output file must be a multiple of D, + print('Number of parameters:', parnumber) + + rest = likes.size % (parnumber) + if rest != 0: + print("""" + Number of samples in model output file must be a multiple of D, where D is the number of parameters in your parameter file. - """) - return np.nan + We handle this by ignoring the last """, rest, """runs.""") + likes = likes[:-rest ] + N = int(likes.size / parnumber) # Recreate the vector omega used in the sampling - omega = np.empty([parnumber]) + omega = np.zeros([parnumber]) omega[0] = math.floor((N - 1) / (2 * M)) m = math.floor(omega[0] / (2 * M)) - print(m) + + print('m =', m) if m >= (parnumber - 1): omega[1:] = np.floor(np.linspace(1, m, parnumber - 1)) else: omega[1:] = np.arange(parnumber - 1) % m + 1 - print(omega) + print('Omega =', omega) # Calculate and Output the First and Total Order Values if print_to_console: print("Parameter First Total") @@ -417,19 +433,16 @@ def get_sensitivity_of_fast(results,like_index=1,M=4, print_to_console=True): (parnames[i], Si['S1'][i], Si['ST'][i])) return Si -def plot_fast_sensitivity(results,likes=['mean'],like_indices=None,number_of_sensitiv_pars=10): +def plot_fast_sensitivity(results,like_index=1,number_of_sensitiv_pars=10,fig_name='FAST_sensitivity.png'): """ Example, how to plot the sensitivity for every parameter of your result array, created with the FAST algorithm - :results: Expects an numpy array which should have as first axis an index "like" or "like1". + :results: Expects an numpy array which should have an header defined with the keyword like. :type: array - :likes: Optional, header of your objectivefunction + :like: Default 'like1', Collum of which the sensitivity indices will be estimated on :type: list - :like_indices: Optional, index of objectivefunction to base the sensitivity on, default=None first objectivefunction is taken - :type: int - :number_of_sensitiv_pars: Optional, this number of most sensitive parameters will be shown in the legend :type: int @@ -443,7 +456,7 @@ def plot_fast_sensitivity(results,likes=['mean'],like_indices=None,number_of_sen fig=plt.figure(figsize=(16,6)) ax = plt.subplot(1,1,1) - Si = get_sensitivity_of_fast(results) + Si = get_sensitivity_of_fast(results, like_index=like_index) names = [] values = [] @@ -451,49 +464,63 @@ def plot_fast_sensitivity(results,likes=['mean'],like_indices=None,number_of_sen no_values = [] index=[] no_index=[] - threshold = 0.2 - + + try: + threshold = np.sort(list(Si.values())[1])[-number_of_sensitiv_pars] + except IndexError: + threshold = 0 + + first_sens_call=True + first_insens_call=True try: Si.values() except AttributeError: exit("Our SI is wrong: " +str(Si)) for j in range(len(list(Si.values())[1])): - if list(Si.values())[1][j]>threshold: - names.append(parnames[j]) + if list(Si.values())[1][j]>=threshold: + names.append(j) values.append(list(Si.values())[1][j]) index.append(j) + if first_sens_call: + ax.bar(j, list(Si.values())[1][j], color='blue', label='Sensitive Parameters') + else: + ax.bar(j, list(Si.values())[1][j], color='blue') + first_sens_call=False + + else: - no_names.append(parnames[j]) + #names.append('') no_values.append(list(Si.values())[1][j]) no_index.append(j) - - if len(no_index) > 0 and len(no_values) > 0: - ax.bar(no_index,no_values,color='orange', label = 'Insensitive parameter') - - else: - ax.bar(index, values, color='blue', label='Sensitive Parameters') + if first_insens_call: + ax.bar(j,list(Si.values())[1][j],color='orange', label = 'Insensitive parameter') + else: + ax.bar(j,list(Si.values())[1][j],color='orange') + first_insens_call=False ax.set_ylim([0,1]) ax.set_xlabel('Model Paramters') ax.set_ylabel('Total Sensititivity Index') ax.legend() - #ax.set_xticklabels(names[1:]) - ax.set_xticklabels(['0']+parnames) + ax.set_xticks(np.arange(0,len(parnames))) + xtickNames = ax.set_xticklabels(parnames, color='grey') + + plt.setp(xtickNames, rotation=90) + for name_id in names: + ax.get_xticklabels()[name_id].set_color("black") + + #ax.set_xticklabels(['0']+parnames) ax.plot(np.arange(-1,len(parnames)+1,1),[threshold]*(len(parnames)+2),'r--') ax.set_xlim(-0.5,len(parnames)-0.5) - fig.savefig('FAST_sensitivity.png',dpi=300) - - - - + plt.tight_layout() + fig.savefig(fig_name,dpi=300) + -def plot_heatmap_griewank(results,algorithms): +def plot_heatmap_griewank(results,algorithms, fig_name='heatmap_griewank.png'): """Example Plot as seen in the SPOTPY Documentation""" import matplotlib.pyplot as plt - from matplotlib import colors - cnames=list(colors.cnames) from matplotlib import ticker from matplotlib import cm @@ -512,17 +539,15 @@ def plot_heatmap_griewank(results,algorithms): x, y = np.meshgrid(x, y) z=1+ (x**2+y**2)/4000 - np.cos(x/np.sqrt(2))*np.cos(y/np.sqrt(3)) - #z = 100.0*(x - x**2.0)**2.0 + (1 - y)**2.0 - # - #norm = cm.colors.Normalize(vmax=abs(z).max(), vmin=-abs(z).max()) + cmap = plt.get_cmap('autumn') - #levels = np.linspace(-5, 5, 20) + rows=2.0 for i in range(subplots): amount_row = int(np.ceil(subplots/rows)) ax = plt.subplot(rows, amount_row, i+1) - CS = ax.contourf(x, y, z,locator=ticker.LogLocator(),cmap=cm.rainbow)#cmap)#,levels=levels) - #CS = ax.contourf(x, y, z,cmap=cm.rainbow)#cmap)#,levels=levels) + CS = ax.contourf(x, y, z,locator=ticker.LogLocator(),cmap=cm.rainbow) + ax.plot(results[i]['par0'],results[i]['par1'],'ko',alpha=0.2,markersize=1.9) ax.xaxis.set_ticks([]) if i==0: @@ -539,16 +564,13 @@ def plot_heatmap_griewank(results,algorithms): ax.set_title(algorithms[i]) - #plt.tight_layout() - fig.savefig('test.png', bbox_inches='tight') # <------ this + fig.savefig(fig_name, bbox_inches='tight') -def plot_objectivefunction(results,evaluation,limit=None,sort=True): +def plot_objectivefunction(results,evaluation,limit=None,sort=True, fig_name = 'objective_function.png'): """Example Plot as seen in the SPOTPY Documentation""" import matplotlib.pyplot as plt - from matplotlib import colors - cnames=list(colors.cnames) - likes=calc_like(results,evaluation) + likes=calc_like(results,evaluation,spotpy.objectivefunctions.rmse) data=likes #Calc confidence Interval mean = np.average(data) @@ -582,56 +604,43 @@ def plot_objectivefunction(results,evaluation,limit=None,sort=True): #plt.ylim(ymin=-1,ymax=1.39) else: plt.plot(bestlike) + plt.savefig(fig_name) -def plot_parametertrace_algorithms(results,algorithmnames=None,parameternames=None,xticks=[0,2000,4000]): +def plot_parametertrace_algorithms(result_lists, algorithmnames, spot_setup, + fig_name='parametertrace_algorithms.png'): """Example Plot as seen in the SPOTPY Documentation""" import matplotlib.pyplot as plt - from matplotlib import colors - cnames=list(colors.cnames) font = {'family' : 'calibri', 'weight' : 'normal', 'size' : 20} plt.rc('font', **font) fig=plt.figure(figsize=(17,5)) - rep=len(results[0]) - subplots=len(results) - rows=2 + subplots=len(result_lists) + parameter = spotpy.parameter.get_parameters_array(spot_setup) + rows=len(parameter['name']) for j in range(rows): for i in range(subplots): ax = plt.subplot(rows,subplots,i+1+j*subplots) - if j==0: - if parameternames: - data=results[i]['par'+parameternames[0]] - else: - data=results[i]['par0'] - if j==1: - if parameternames: - data=results[i]['par'+parameternames[1]] - else: - data=results[i]['par1'] - ax.set_xlabel(algorithmnames[i-subplots]) - + data=result_lists[i]['par'+parameter['name'][j]] ax.plot(data,'b-') - ax.plot([1]*rep,'r--') - ax.set_xlim(0,rep) - ax.set_ylim(-50,50) - ax.xaxis.set_ticks(xticks) - if i==0 and j==0: - ax.set_ylabel('x') - ax.yaxis.set_ticks([-50,0,50]) - if i==0 and j==1: - ax.set_ylabel('y') - ax.yaxis.set_ticks([-50,0,50]) - if j==0: - ax.xaxis.set_ticks([]) + if i==0: + ax.set_ylabel(parameter['name'][j]) + rep = len(data) if i>0: ax.yaxis.set_ticks([]) - - plt.tight_layout() - fig.savefig('test2.png', bbox_inches='tight') + if j==rows-1: + ax.set_xlabel(algorithmnames[i-subplots]) + else: + ax.xaxis.set_ticks([]) + ax.plot([1]*rep,'r--') + ax.set_xlim(0,rep) + ax.set_ylim(parameter['minbound'][j],parameter['maxbound'][j]) + + #plt.tight_layout() + fig.savefig(fig_name, bbox_inches='tight') -def plot_parametertrace(results,parameternames=None): +def plot_parametertrace(results,parameternames=None,fig_name='Parameter_trace.png'): """ Get a plot with all values of a given parameter in your result array. The plot will be saved as a .png file. @@ -646,8 +655,6 @@ def plot_parametertrace(results,parameternames=None): :rtype: figure """ import matplotlib.pyplot as plt - from matplotlib import colors - cnames=list(colors.cnames) fig=plt.figure(figsize=(16,9)) if not parameternames: parameternames=get_parameternames(results) @@ -664,11 +671,11 @@ def plot_parametertrace(results,parameternames=None): ax.set_title('Parametertrace') ax.legend() i+=1 - fig.savefig(names+'_trace.png') - text='The figure as been saved as "'+names+'trace.png"' + fig.savefig(fig_name) + text='The figure as been saved as "'+fig_name print(text) -def plot_posterior_parametertrace(results,parameternames=None,threshold=0.1): +def plot_posterior_parametertrace(results,parameternames=None,threshold=0.1, fig_name='Posterior_parametertrace.png'): """ Get a plot with all values of a given parameter in your result array. The plot will be saved as a .png file. @@ -683,8 +690,6 @@ def plot_posterior_parametertrace(results,parameternames=None,threshold=0.1): :rtype: figure """ import matplotlib.pyplot as plt - from matplotlib import colors - cnames=list(colors.cnames) fig=plt.figure(figsize=(16,9)) results=sort_like(results) @@ -703,11 +708,11 @@ def plot_posterior_parametertrace(results,parameternames=None,threshold=0.1): ax.set_title('Parametertrace') ax.legend() i+=1 - fig.savefig(names+'_trace.png') - text='The figure as been saved as "'+names+'trace.png"' + fig.savefig(fig_name) + text='The figure as been saved as '+fig_name print(text) -def plot_posterior(results,evaluation,dates=None,ylabel='Posterior model simulation',xlabel='Time',objectivefunction='NSE',objectivefunctionmax=True,calculatelike=True,sort=True, bestperc=0.1): +def plot_posterior(results,evaluation,dates=None,ylabel='Posterior model simulation',xlabel='Time',bestperc=0.1, fig_name='bestmodelrun.png'): """ Get a plot with the maximum objectivefunction of your simulations in your result array. @@ -734,37 +739,12 @@ def plot_posterior(results,evaluation,dates=None,ylabel='Posterior model simulat Returns: figure. Plot of the simulation with the maximum objectivefunction value in the result array as a blue line and dots for the evaluation data. - A really great idea. A way you might use me is - >>> bcf.analyser.plot_bestmodelrun(results,evaluation, ylabel='Best model simulation') - """ import matplotlib.pyplot as plt - from matplotlib import colors - import random - - cnames=list(colors.cnames) - - - plt.rc('font', **font) - if sort: - results=sort_like(results) - if calculatelike: - likes=calc_like(results, evaluation) - maximum=max(likes) - par=get_parameters(results) - sim=get_modelruns(results) - index=likes.index(maximum) - bestmodelrun=list(sim[index]) - bestparameterset=list(par[index]) - - else: - if objectivefunctionmax==True: - index,maximum=get_maxlikeindex(results) - else: - index,maximum=get_minlikeindex(results) - sim=get_modelruns(results) - bestmodelrun=list(sim[index][0])#Transform values into list to ensure plotting - bestparameterset=list(get_parameters(results)[index][0]) + index,maximum=get_maxlikeindex(results) + sim=get_modelruns(results) + bestmodelrun=list(sim[index][0])#Transform values into list to ensure plotting + bestparameterset=list(get_parameters(results)[index][0]) parameternames=list(get_parameternames(results) ) bestparameterstring='' @@ -774,58 +754,34 @@ def plot_posterior(results,evaluation,dates=None,ylabel='Posterior model simulat bestparameterstring+='\n' bestparameterstring+=parameternames[i]+'='+str(round(bestparameterset[i],4))+',' fig=plt.figure(figsize=(16,8)) - if dates is not None: - chains=int(max(results['chain'])) - colors=list(cnames) - random.shuffle(colors) - - for s in sim[5000:]: - plt.plot(dates,list(s),'c-',alpha=0.05) - plt.plot(dates,bestmodelrun,'b-',label='Simulations: '+objectivefunction+'='+str(round(maxNSE,4))) - plt.plot(dates,evaluation,'ro',label='Evaluation') - else: - pl_i = 1 - for s in sim: - # why we plotting dates again is we in case that it is none, this will cause an error?? - #plt.plot(dates, list(s), 'c-', alpha=0.05) - plt.plot(pl_i, list(s), 'c-', alpha=0.05) - pl_i+=1 - - plt.plot(bestmodelrun,'b-',label='Simulations: '+objectivefunction+'='+str(round(maxNSE,4))) - plt.plot(evaluation,'ro',label='Evaluation') + plt.plot(bestmodelrun,'b-',label='Simulation='+str(round(maxNSE,4))) + plt.plot(evaluation,'ro',label='Evaluation') plt.legend() plt.ylabel(ylabel) plt.xlabel(xlabel) - plt.ylim(0,70) #DELETE WHEN NOT USED WITH SOIL MOISTUR RESULTS plt.title('Maximum objectivefunction of Simulations with '+bestparameterstring[0:-2]) -# plt.text(0, 0, bestparameterstring[0:-2], -# horizontalalignment='left', -# verticalalignment='bottom') - fig.savefig('bestmodelrun.png') - text='The figure as been saved as "bestmodelrun.png"' + fig.savefig(fig_name) + text='The figure as been saved as '+fig_name print(text) -def plot_bestmodelrun(results,evaluation): +def plot_bestmodelrun(results,evaluation,fig_name ='Best_model_run.png'): """ Get a plot with the maximum objectivefunction of your simulations in your result array. The plot will be saved as a .png file. - Args: - results (array): Expects an numpy array which should of an index "like" for + :results: Expects an numpy array which should of an index "like" for objectivefunctions and "sim" for simulations. + type: Array - evaluation (list): Should contain the values of your observations. Expects that this list has the same lenght as the number of simulations in your result array. + :evaluation: Should contain the values of your observations. Expects that this list has the same lenght as the number of simulations in your result array. + :type: list Returns: figure. Plot of the simulation with the maximum objectivefunction value in the result array as a blue line and dots for the evaluation data. - - A really great idea. A way you might use me is - >>> bcf.analyser.plot_bestmodelrun(results,evaluation, ylabel='Best model simulation') - """ - import pylab as plt + import matplotlib.pyplot as plt fig= plt.figure(figsize=(16,9)) for i in range(len(evaluation)): if evaluation[i] == -9999: @@ -837,13 +793,13 @@ def plot_bestmodelrun(results,evaluation): plt.xlabel('Number of Observation Points') plt.ylabel ('Simulated value') plt.legend(loc='upper right') - text='A plot of the best model run has been saved as "Best_model_run.png"' + fig.savefig(fig_name,dpi=300) + text='A plot of the best model run has been saved as '+fig_name print(text) - fig.savefig('Best_model_run.png',dpi=300) - + -def plot_bestmodelruns(results,evaluation,algorithms=None,dates=None,ylabel='Best model simulation',xlabel='Date',objectivefunctionmax=True,calculatelike=True): +def plot_bestmodelruns(results,evaluation,algorithms=None,dates=None,ylabel='Best model simulation',xlabel='Date',objectivefunctionmax=True,calculatelike=True,fig_name='bestmodelrun.png'): """ Get a plot with the maximum objectivefunction of your simulations in your result array. @@ -875,9 +831,6 @@ def plot_bestmodelruns(results,evaluation,algorithms=None,dates=None,ylabel='Bes """ import matplotlib.pyplot as plt - from matplotlib import colors - cnames=list(colors.cnames) - plt.rc('font', **font) fig=plt.figure(figsize=(17,8)) @@ -917,11 +870,11 @@ def plot_bestmodelruns(results,evaluation,algorithms=None,dates=None,ylabel='Bes plt.xlabel(xlabel) plt.ylim(15,50) #DELETE WHEN NOT USED WITH SOIL MOISTUR RESULTS - fig.savefig('bestmodelrun.png') - text='The figure as been saved as "bestmodelrun.png"' + fig.savefig(fig_name) + text='The figure as been saved as '+fig_name print(text) -def plot_objectivefunctiontraces(results,evaluation,algorithms,filename='Like_trace'): +def plot_objectivefunctiontraces(results,evaluation,algorithms,fig_name='Like_trace.png'): import matplotlib.pyplot as plt from matplotlib import colors cnames=list(colors.cnames) @@ -934,7 +887,7 @@ def plot_objectivefunctiontraces(results,evaluation,algorithms,filename='Like_tr for i in range(len(results)): ax = plt.subplot(1,len(results),i+1) - likes=calc_like(results[i],evaluation) + likes=calc_like(results[i],evaluation,spotpy.objectivefunctions.rmse) ax.plot(likes,'b-') ax.set_ylim(0,25) ax.set_xlim(0,len(results[0])) @@ -947,13 +900,11 @@ def plot_objectivefunctiontraces(results,evaluation,algorithms,filename='Like_tr ax.yaxis.set_ticks([]) plt.tight_layout() - fig.savefig(str(filename)+'.png') + fig.savefig(fig_name) -def plot_regression(results,evaluation): +def plot_regression(results,evaluation,fig_name='regressionanalysis.png'): import matplotlib.pyplot as plt - from matplotlib import colors - cnames=list(colors.cnames) fig=plt.figure(figsize=(16,9)) simulations=get_modelruns(results) for sim in simulations: @@ -961,33 +912,29 @@ def plot_regression(results,evaluation): plt.ylabel('simulation') plt.xlabel('evaluation') plt.title('Regression between simulations and evaluation data') - fig.savefig('regressionanalysis.png') - text='The figure as been saved as "regressionanalysis.png"' + fig.savefig(fig_name) + text='The figure as been saved as '+fig_name print(text) -def plot_parameterInteraction(results): +def plot_parameterInteraction(results, fig_name ='ParameterInteraction.png'): '''Input: List with values of parameters and list of strings with parameter names Output: Dotty plot of parameter distribution and gaussian kde distribution''' import matplotlib.pyplot as plt - from matplotlib import colors import pandas as pd - cnames=list(colors.cnames) parameterdistribtion=get_parameters(results) parameternames=get_parameternames(results) df = pd.DataFrame(np.asarray(parameterdistribtion).T.tolist(), columns=parameternames) pd.plotting.scatter_matrix(df, alpha=0.2, figsize=(12, 12), diagonal='kde') - plt.savefig('ParameterInteraction',dpi=300) + plt.savefig(fig_name,dpi=300) -def plot_allmodelruns(modelruns,observations,dates=None): +def plot_allmodelruns(modelruns,observations,dates=None, fig_name='bestmodel.png'): '''Input: Array of modelruns and list of Observations Output: Plot with all modelruns as a line and dots with the Observations ''' import matplotlib.pyplot as plt - from matplotlib import colors - cnames=list(colors.cnames) fig=plt.figure(figsize=(16,9)) ax = plt.subplot(1,1,1) if dates is not None: @@ -1008,36 +955,30 @@ def plot_allmodelruns(modelruns,observations,dates=None): ax.set_xlabel = 'Best model simulation' ax.set_ylabel = 'Evaluation points' ax.set_title = 'Maximum objectivefunction of Simulations' - fig.savefig('bestmodel.png') - text='The figure as been saved as "Modelruns.png"' + fig.savefig(fig_name) + text='The figure as been saved as '+fig_name print(text) -def plot_autocorellation(parameterdistribution,parametername): +def plot_autocorellation(parameterdistribution,parametername,fig_name='Autocorrelation.png'): '''Input: List of sampled values for one Parameter Output: Parameter Trace, Histogramm and Autocorrelation Plot''' import matplotlib.pyplot as plt - from matplotlib import colors import pandas as pd - cnames=list(colors.cnames) - fig=plt.figure(figsize=(16,9)) - ax = plt.subplot(1,1,1) - pd.tools.plotting.autocorrelation_plot(parameterdistribution) - plt.savefig('Autocorellation'+str(parametername),dpi=300) + pd.plotting.autocorrelation_plot(parameterdistribution) + plt.savefig(fig_name,dpi=300) -def plot_gelman_rubin(r_hat_values): +def plot_gelman_rubin(r_hat_values,fig_name='gelman_rub.png'): '''Input: List of R_hat values of chains (see Gelman & Rubin 1992) Output: Plot as seen for e.g. in (Sadegh and Vrugt 2014)''' import matplotlib.pyplot as plt - from matplotlib import colors - cnames=list(colors.cnames) fig=plt.figure(figsize=(16,9)) ax = plt.subplot(1,1,1) ax.plot(r_hat_values) ax.plot([1.2]*len(r_hat_values),'k--') ax.set_xlabel='r_hat' - + plt.savefig(fig_name,dpi=300) def gelman_rubin(x): '''NOT USED YET''' @@ -1068,8 +1009,7 @@ def plot_Geweke(parameterdistribution,parametername): '''Input: Takes a list of sampled values for a parameter and his name as a string Output: Plot as seen for e.g. in BUGS or PyMC''' import matplotlib.pyplot as plt - from matplotlib import colors - cnames=list(colors.cnames) + # perform the Geweke test Geweke_values = _Geweke(parameterdistribution) @@ -1087,18 +1027,15 @@ def plot_Geweke(parameterdistribution,parametername): plt.plot( [-2]*len(Geweke_values), 'r-.') def _compute_first_order(outputs, N, M, omega): - '''Needed for FAST sensitivity''' f = np.fft.fft(outputs) - Sp = np.power(np.absolute(f[np.arange(1, int(N / 2))]) / N, 2) + Sp = np.power(np.absolute(f[np.arange(1, int((N + 1) / 2))]) / N, 2) V = 2 * np.sum(Sp) D1 = 2 * np.sum(Sp[np.arange(1, M + 1) * int(omega) - 1]) return D1 / V def _compute_total_order(outputs, N, omega): - '''Needed for FAST sensitivity''' f = np.fft.fft(outputs) - #print(f) - Sp = np.power(np.absolute(f[np.arange(1, int(N / 2))]) / N, 2) + Sp = np.power(np.absolute(f[np.arange(1, int((N + 1) / 2))]) / N, 2) V = 2 * np.sum(Sp) Dt = 2 * sum(Sp[np.arange(int(omega / 2))]) return (1 - Dt / V) diff --git a/spotpy/cli.py b/spotpy/cli.py new file mode 100644 index 00000000..04ad2807 --- /dev/null +++ b/spotpy/cli.py @@ -0,0 +1,97 @@ +from __future__ import division, print_function, unicode_literals + +from . import algorithms, database, describe +import click +import inspect +import io +import os + + +def get_config_from_file(): + """ + Gets the spotpy configuration from a config file 'spotpy.conf'. + + Example: + + sampler = mc + dbtype = csv + parallel = seq + # This is a comment + runs = 10 + """ + config = {} + if os.path.exists('spotpy.conf'): + with io.open('spotpy.conf') as f: + for line in f: + if not line.strip().startswith('#'): + try: + k, v = line.split('=', 1) + config[k.strip()] = v.strip() + except ValueError: + pass + return config + + +def get_sampler_from_string(sampler_name): + return getattr(algorithms, sampler_name) + + +def make_type_from_module(module, *exclude): + + def use(cl): + # Check if class name starts with an exclusion term + return inspect.isclass(cl) and not any([cl.__name__.startswith(ex) for ex in ('_', ) + exclude]) + members = inspect.getmembers(module, use) + return click.Choice([n for n, m in members if not n.startswith('_')]) + + +@click.group(context_settings=dict(help_option_names=['-h', '--help'])) +def cli(): + pass + + +@cli.command() +@click.pass_context +@click.option('--sampler', '-s', type=make_type_from_module(algorithms), default='mc', + help='Select the spotpy sampler') +@click.option('--dbformat', type=click.Choice(database.__dir__()), default='ram', + help='The type of the database') +@click.option('--dbname', type=click.STRING, help='The name of the database, leave open for ram') +@click.option('--parallel', '-p', type=click.Choice(['seq', 'mpc', 'mpi']), default='seq', + help='Parallelization: seq = no parallelization, mpi = MPI (for clusters), mpc = multiprocessing') +@click.option('--runs', '-n', type=click.INT, default=1, help='Number of runs') +@click.option('--config', '-c', is_flag=True, + help='Print only the configuration, can be used to create a config file with your_model.py > spotpy.conf') +def run(ctx, **kwargs): + """ + Runs a sampler for automatic calibration + """ + setup = ctx.obj + if kwargs.pop('config', None): + click.echo('\n'.join('{} = {}'.format(k, v) for k, v in kwargs.items())) + else: + sampler_name = kwargs.pop('sampler') + sampler_class = get_sampler_from_string(sampler_name) + runs = kwargs.pop('runs') + sampler = sampler_class(setup, **kwargs) + sampler.sample(runs) + + +@cli.command() +@click.pass_context +def gui(ctx): + """ + Shows a GUI for manual calibration + """ + from spotpy.gui.mpl import GUI + setup = ctx.obj + gui = GUI(setup) + gui.show() + + +def main(setup): + # Prevent help text from wrapping + cli.help = '\b\n' + describe.setup(setup).replace('\n\n', '\n\b\n') + config = get_config_from_file() + cli(obj=setup, auto_envvar_prefix='SPOTPY', default_map=config) + diff --git a/spotpy/database.py b/spotpy/database.py deleted file mode 100644 index ed42a1e9..00000000 --- a/spotpy/database.py +++ /dev/null @@ -1,352 +0,0 @@ -# -*- coding: utf-8 -*- -''' -Copyright (c) 2015 by Tobias Houska - -This file is part of Statistical Parameter Optimization Tool (SPOTPY). - -:author: Tobias Houska - -This is the parent class of all algorithms, which can handle the database -structure during the sample. -''' -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals -import numpy as np -import io -import time -from itertools import product -import sqlite3 -import sys -if sys.version_info[0] >= 3: - unicode = str - - -class database(object): - """ - Parent class for database. It can handle the basic functionalities of all - databases. - """ - - def __init__(self, dbname, parnames, like, randompar, simulations=None, - chains=1, save_sim=True, db_precision=np.float16, **kwargs): - # Just needed for the first line in the database - self.chains = chains - self.dbname = dbname - self.like = like - self.randompar = randompar - self.simulations = simulations - self.save_sim = save_sim - self.db_precision = db_precision - if not save_sim: - simulations = None - self.dim_dict = {} - self.singular_data_lens = [self._check_dims(name, obj) for name, obj in [( - 'like', like), ('par', randompar), ('simulation', simulations)]] - self._make_header(simulations,parnames) - - self.last_flush = time.time() - - def _check_dims(self, name, obj): - '''checks dimensionality of obj and stores function in dict''' - if obj is None: - # None object - self.dim_dict[name] = self._empty_list - return (0,) - elif hasattr(obj, '__len__'): - if hasattr(obj, 'shape'): - # np.array style obj - self.dim_dict[name] = self._array_to_list - return obj.shape - elif all([hasattr(x, '__len__') for x in obj]): - # nested list, checked only for singular nesting - # assumes all lists have same length - self.dim_dict[name] = self._nestediterable_to_list - return (len(obj), len(obj[0])) - else: - # simple iterable - self.dim_dict[name] = self._iterable_to_list - return (len(obj),) - else: - # scalar (int, float) - self.dim_dict[name] = self._scalar_to_list - return (1,) - - def _empty_list(self, obj): - return [] - - def _scalar_to_list(self, obj): - #print('scalar') - return [obj] - - def _iterable_to_list(self, obj): - #print('iterable') - return list(obj) - - def _array_to_list(self, obj): - #print('array') - values = [] - for val in obj: - values.append(val) - return values - #return obj.flatten().tolist() - - def _nestediterable_to_list(self, obj): - #print('nested') - values = [] - for nestedlist in obj: - #print(len(nestedlist)) - for val in nestedlist: - values.append(val) - #print(len(values)) - return values - #return np.array(obj).flatten().tolist() - - def _make_header(self, simulations,parnames): - self.header = [] - self.header.extend(['like' + '_'.join(map(str, x)) - for x in product(*self._tuple_2_xrange(self.singular_data_lens[0]))]) - self.header.extend(['par{0}'.format(x) for x in parnames]) - #print(self.singular_data_lens[2]) - #print(type(self.singular_data_lens[2])) - if self.save_sim: - for i in range(len(simulations)): - if type(simulations[0]) == type([]) or type(simulations[0]) == type(np.array([])): - for j in range(len(simulations[i])): - self.header.extend(['simulation' + str(i+1)+'_'+str(j+1)]) - else: - self.header.extend(['simulation' + '_'+str(i)]) - #for x in product(*self._tuple_2_xrange(self.singular_data_lens[2]))]) - - self.header.append('chain') - - def _tuple_2_xrange(self, t): - return (range(1, x + 1) for x in t) - - -class ram(database): - """ - This class saves the process in the working storage. It can be used if - time matters. - """ - - def __init__(self, *args, **kwargs): - # init base class - super(ram, self).__init__(*args, **kwargs) - # init the status vars - self.ram = [] - - def save(self, objectivefunction, parameterlist, simulations=None, - chains=1): - self.ram.append(tuple(self.dim_dict['like'](objectivefunction) + - self.dim_dict['par'](parameterlist) + - self.dim_dict['simulation'](simulations) + - [chains])) - - def finalize(self): - """ - Is called in a last step of every algorithm. - Forms the List of values into a strutured numpy array in order to have - the same structure as a csv database. - """ - dt = {'names': self.header, - 'formats': [np.float] * len(self.header)} - i = 0 - Y = np.zeros(len(self.ram), dtype=dt) - - for line in self.ram: - Y[i] = line - i+=1 - - self.data = Y - - def getdata(self): - """ - Returns a finalized database""" - return self.data - - -class csv(database): - """ - This class saves the process in the working storage. It can be used if - safety matters. - """ - - def __init__(self, *args, **kwargs): - # init base class - super(csv, self).__init__(*args, **kwargs) - # store init item only if dbinit - if kwargs.get('dbinit', True): - # Create a open file, which needs to be closed after the sampling - self.db = io.open(self.dbname + '.csv', 'w') - # write header line - self.db.write(unicode(','.join(self.header) + '\n')) - else: - # Continues writing file - self.db = io.open(self.dbname + '.csv', 'a') - - def save(self, objectivefunction, parameterlist, simulations=None, chains=1): - coll = (self.dim_dict['like'](objectivefunction) + - self.dim_dict['par'](parameterlist) + - self.dim_dict['simulation'](simulations) + - [chains]) - try: - # Apply rounding of floats - coll = map(self.db_precision, coll) - self.db.write( - ','.join(map(str, coll)) + '\n') - except IOError: - input("Please close the file " + self.dbname + - " When done press Enter to continue...") - # Apply rounding of floats - coll = map(self.db_precision, coll) - self.db.write( - ','.join(map(str, coll)) + '\n') - - acttime = time.time() - # Force writing to disc at least every two seconds - if acttime - self.last_flush >= 2: - self.db.flush() - self.last_flush = time.time() - - def finalize(self): - self.db.close() - - def getdata(self): - data = np.genfromtxt(self.dbname + '.csv', delimiter=',', names=True) - return data - - -class PickalableSWIG: - def __setstate__(self, state): - self.__init__(*state['args']) - def __getstate__(self): - return {'args': self.args} - - -class PickalableSQL3Connect(sqlite3.Connection, PickalableSWIG): - def __init__(self, *args,**kwargs): - self.args = args - sqlite3.Connection.__init__(self,*args,**kwargs) - - -class PickalableSQL3Cursor(sqlite3.Cursor, PickalableSWIG): - def __init__(self, *args,**kwargs): - self.args = args - sqlite3.Cursor.__init__(self,*args,**kwargs) - - - - -class sql(database): - - """ - This class saves the process in the working storage. It can be used if - safety matters. - """ - - def __init__(self, *args, **kwargs): - import os - # init base class - super(sql, self).__init__(*args, **kwargs) - # Create a open file, which needs to be closed after the sampling - try: - os.remove(self.dbname + '.db') - except: - pass - - self.db = PickalableSQL3Connect(self.dbname + '.db') - self.db_cursor = PickalableSQL3Cursor(self.db) - # Create Table -# self.db_cursor.execute('''CREATE TABLE IF NOT EXISTS '''+self.dbname+''' -# (like1 real, parx real, pary real, simulation1 real, chain int)''') - self.db_cursor.execute('''CREATE TABLE IF NOT EXISTS '''+self.dbname+''' - ('''+' real ,'.join(self.header)+''')''') - - def save(self, objectivefunction, parameterlist, simulations=None, chains=1): - coll = (self.dim_dict['like'](objectivefunction) + - self.dim_dict['par'](parameterlist) + - self.dim_dict['simulation'](simulations) + - [chains]) - # Apply rounding of floats - coll = map(self.db_precision, coll) - try: - self.db_cursor.execute("INSERT INTO "+self.dbname+" VALUES ("+'"'+str('","'.join(map(str, coll)))+'"'+")") - - except Exception: - input("Please close the file " + self.dbname + - " When done press Enter to continue...") - self.db_cursor.execute("INSERT INTO "+self.dbname+" VALUES ("+'"'+str('","'.join(map(str, coll)))+'"'+")") - - self.db.commit() - - def finalize(self): - self.db.close() - - def getdata(self): - self.db = PickalableSQL3Connect(self.dbname + '.db') - self.db_cursor = PickalableSQL3Cursor(self.db) - - if sys.version_info[0] >= 3: - headers = [(row[1],"= 3: + unicode = str + +from importlib import import_module + +class database(object): + """ + Parent class for database. It can handle the basic functionalities of all + databases. + """ + + def __init__(self, dbname, parnames, like, randompar, simulations=None, + chains=1, save_sim=True, db_precision=np.float32, **kwargs): + # Just needed for the first line in the database + self.chains = chains + self.dbname = dbname + self.like = like + self.randompar = randompar + self.simulations = simulations + self.save_sim = save_sim + self.db_precision = db_precision + if not save_sim: + simulations = None + self.dim_dict = {} + self.singular_data_lens = [self._check_dims(name, obj) for name, obj in [( + 'like', like), ('par', randompar), ('simulation', simulations)]] + self._make_header(simulations, parnames) + + self.last_flush = time.time() + + def _check_dims(self, name, obj): + '''checks dimensionality of obj and stores function in dict''' + if obj is None: + # None object + self.dim_dict[name] = self._empty_list + return (0,) + elif hasattr(obj, '__len__'): + if hasattr(obj, 'shape'): + # np.array style obj + self.dim_dict[name] = self._array_to_list + return obj.shape + elif all([hasattr(x, '__len__') for x in obj]): + # nested list, checked only for singular nesting + # assumes all lists have same length + self.dim_dict[name] = self._nestediterable_to_list + return (len(obj), len(obj[0])) + else: + # simple iterable + self.dim_dict[name] = self._iterable_to_list + return (len(obj),) + else: + # scalar (int, float) + self.dim_dict[name] = self._scalar_to_list + return (1,) + + def _empty_list(self, obj): + return [] + + def _scalar_to_list(self, obj): + # print('scalar') + return [obj] + + def _iterable_to_list(self, obj): + # print('iterable') + return list(obj) + + def _array_to_list(self, obj): + # print('array') + values = [] + for val in obj: + values.append(val) + return values + # return obj.flatten().tolist() + + def _nestediterable_to_list(self, obj): + # print('nested') + values = [] + for nestedlist in obj: + # print(len(nestedlist)) + for val in nestedlist: + values.append(val) + # print(len(values)) + return values + # return np.array(obj).flatten().tolist() + + def _make_header(self, simulations, parnames): + self.header = [] + self.header.extend(['like' + '_'.join(map(str, x)) + for x in product(*self._tuple_2_xrange(self.singular_data_lens[0]))]) + self.header.extend(['par{0}'.format(x) for x in parnames]) + # print(self.singular_data_lens[2]) + # print(type(self.singular_data_lens[2])) + if self.save_sim: + for i in range(len(simulations)): + if isinstance(simulations[0], list) or type(simulations[0]) == type(np.array([])): + for j in range(len(simulations[i])): + self.header.extend(['simulation' + str(i + 1) + '_' + str(j + 1)]) + else: + self.header.extend(['simulation' + '_' + str(i)]) + # for x in product(*self._tuple_2_xrange(self.singular_data_lens[2]))]) + + self.header.append('chain') + + def _tuple_2_xrange(self, t): + return (range(1, x + 1) for x in t) + + +class noData(database): + """ + This class saves the process in the working storage. It can be used if + safety matters. + """ + + def __init__(self, *args, **kwargs): + pass + + def save(self, objectivefunction, parameterlist, simulations=None, chains=1): + pass + + def finalize(self): + pass + + def getdata(self): + pass + +class custom(database): + """ + This class is a simple wrapper over the database API, and can be used + when the user provides a custom save function. + """ + + def __init__(self, *args, **kwargs): + if 'setup' not in kwargs: + raise ValueError(""" + You are using the 'custom' Datawriter. To use it, the + setup must be specified on creation, but it is missing + """) + self.setup = kwargs['setup'] + if not hasattr(self.setup, 'save'): + raise AttributeError('Your setup needs a "save" method in order to use the "custom" dbformat') + + super(custom, self).__init__(*args, **kwargs) + + def save(self, objectivefunction, parameterlist, simulations, *args, **kwargs): + self.setup.save(objectivefunction, parameterlist, simulations, *args, **kwargs) + + def finalize(self): + pass + + def getdata(self): + pass + + + diff --git a/spotpy/database/csv.py b/spotpy/database/csv.py new file mode 100644 index 00000000..9fc7c596 --- /dev/null +++ b/spotpy/database/csv.py @@ -0,0 +1,62 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals +from .base import database +import numpy as np +import io +import time +import sys +if sys.version_info[0] >= 3: + unicode = str + +class csv(database): + """ + This class saves the process in the working storage. It can be used if + safety matters. + """ + + def __init__(self, *args, **kwargs): + # init base class + super(csv, self).__init__(*args, **kwargs) + # store init item only if dbinit + if kwargs.get('dbappend', False) is False: + print("* Database file '{}.csv' created.".format(self.dbname)) + # Create a open file, which needs to be closed after the sampling + mode = 'w' + if sys.version_info.major < 3: + mode += 'b' + self.db = io.open(self.dbname + '.csv', mode) + # write header line + self.db.write(unicode(','.join(self.header) + '\n')) + else: + print("* Appending to database file '{}.csv'.".format(self.dbname)) + # Continues writing file + mode = 'a' + if sys.version_info.major < 3: + mode += 'b' + self.db = io.open(self.dbname + '.csv', mode) + + def save(self, objectivefunction, parameterlist, simulations=None, chains=1): + coll = (self.dim_dict['like'](objectivefunction) + + self.dim_dict['par'](parameterlist) + + self.dim_dict['simulation'](simulations) + + [chains]) + # Apply rounding of floats + coll = map(self.db_precision, coll) + self.db.write( + ','.join(map(str, coll)) + '\n') + + acttime = time.time() + # Force writing to disc at least every two seconds + if acttime - self.last_flush >= 2: + self.db.flush() + self.last_flush = time.time() + + def finalize(self): + self.db.flush() # Just to make sure that everything is written in file + self.db.close() + + def getdata(self): + data = np.genfromtxt(self.dbname + '.csv', delimiter=',', names=True) + return data diff --git a/spotpy/database/hdf5.py b/spotpy/database/hdf5.py new file mode 100644 index 00000000..ea5de0a8 --- /dev/null +++ b/spotpy/database/hdf5.py @@ -0,0 +1,109 @@ +# -*- coding: utf-8 -*- +''' +Copyright (c) 2015 by Tobias Houska + +This file is part of Statistical Parameter Optimization Tool (SPOTPY). + +:author: Tobias Houska + +This is the parent class of all algorithms, which can handle the database +structure during the sample. +''' +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals + +import numpy as np +from .base import database +try: + import tables +except ImportError: + print('ImportError: Pytables is not correctly installed. Please also make sure you', + 'installed the hdf5 extension (https://www.hdfgroup.org/downloads/hdf5/)') + raise +import sys +if sys.version_info[0] >= 3: + unicode = str + + +class hdf5(database): + """ + A database class to store the result in hdf5 tables. + + This is only available if PyTables is installed + """ + def get_table_def(self): + """ + Returns a dict of column definitions using multidimensional + hdf5 columns. Columns for parameters and likelihoods are atomic and resemble + the csv datawriter. If ``save_sim=True``, the simulation array is saved as an array value in + a single multidimensional table cell + + cf.: https://www.pytables.org/usersguide/tutorials.html#multidimensional-table-cells-and-automatic-sanity-checks + + """ + # Position of likelihood columns + like_pos = 0 + # Start position of parameter columns + param_pos = np.array(self.like).size + # Start position of simulation columns + sim_pos = param_pos + np.array(self.randompar).size + chain_pos = sim_pos + + dtype = np.dtype(self.db_precision) + columns = { + self.header[i]: tables.Col.from_dtype(dtype, pos=i) + for i in range(like_pos, sim_pos) + } + + if self.save_sim: + # Get the shape of the simulation + sim_shape = np.array(self.simulations).shape + # Get the appropriate dtype for the n-d cell + # (tables.Col.from_dtype does not take a shape parameter) + sim_dtype = np.dtype((self.db_precision, sim_shape)) + columns['simulation'] = tables.Col.from_dtype( + sim_dtype, pos=sim_pos + ) + chain_pos += 1 + # Add a column chains + columns['chains'] = tables.UInt16Col(pos=chain_pos) + + return columns + + def __init__(self, *args, **kwargs): + """ + Create a new datawriter for hdf5 files + :param args: + :param kwargs: + """ + # init base class + super(hdf5, self).__init__(*args, **kwargs) + # store init item only if dbinit + if not kwargs.get('dbappend', False): + # Create an open file, which needs to be closed after the sampling + self.db = tables.open_file(self.dbname + '.h5', 'w', self.dbname) + self.table = self.db.create_table('/', self.dbname, description=self.get_table_def()) + else: + # Continues writing file + self.db = tables.open_file(self.dbname + '.h5', 'a') + self.table = self.db.root[self.dbname] + + def save(self, objectivefunction, parameterlist, simulations=None, chains=1): + new_row = self.table.row + + coll = self.dim_dict['like'](objectivefunction) + self.dim_dict['par'](parameterlist) + for header, value in zip(self.header, coll): + new_row[header] = value + if self.save_sim: + new_row['simulation'] = simulations + new_row['chains'] = chains + new_row.append() + + def finalize(self): + self.db.close() + + def getdata(self): + with tables.open_file(self.dbname + '.h5', 'a') as db: + return db.root[self.dbname][:] diff --git a/spotpy/database/ram.py b/spotpy/database/ram.py new file mode 100644 index 00000000..e5652c70 --- /dev/null +++ b/spotpy/database/ram.py @@ -0,0 +1,63 @@ +# -*- coding: utf-8 -*- +''' +Copyright (c) 2015 by Tobias Houska + +This file is part of Statistical Parameter Optimization Tool (SPOTPY). + +:author: Tobias Houska + +This is the parent class of all algorithms, which can handle the database +structure during the sample. +''' +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals + +import numpy as np +from .base import database +import sys +if sys.version_info[0] >= 3: + unicode = str + + +class ram(database): + """ + This class saves the process in the working storage. It can be used if + time matters. + """ + + def __init__(self, *args, **kwargs): + # init base class + super(ram, self).__init__(*args, **kwargs) + # init the status vars + self.ram = [] + + def save(self, objectivefunction, parameterlist, simulations=None, + chains=1): + self.ram.append(tuple(self.dim_dict['like'](objectivefunction) + + self.dim_dict['par'](parameterlist) + + self.dim_dict['simulation'](simulations) + + [chains])) + + def finalize(self): + """ + Is called in a last step of every algorithm. + Forms the List of values into a strutured numpy array in order to have + the same structure as a csv database. + """ + dt = {'names': self.header, + 'formats': [np.float] * len(self.header)} + i = 0 + Y = np.zeros(len(self.ram), dtype=dt) + + for line in self.ram: + Y[i] = line + i += 1 + + self.data = Y + + def getdata(self): + """ + Returns a finalized database""" + return self.data diff --git a/spotpy/database/sql.py b/spotpy/database/sql.py new file mode 100644 index 00000000..f103f3be --- /dev/null +++ b/spotpy/database/sql.py @@ -0,0 +1,88 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals +import numpy as np +import sqlite3 +import sys +from .base import database + +if sys.version_info[0] >= 3: + unicode = str + + +class PickalableSWIG: + def __setstate__(self, state): + self.__init__(*state['args']) + def __getstate__(self): + return {'args': self.args} + + +class PickalableSQL3Connect(sqlite3.Connection, PickalableSWIG): + def __init__(self, *args,**kwargs): + self.args = args + sqlite3.Connection.__init__(self,*args,**kwargs) + + +class PickalableSQL3Cursor(sqlite3.Cursor, PickalableSWIG): + def __init__(self, *args,**kwargs): + self.args = args + sqlite3.Cursor.__init__(self,*args,**kwargs) + + + +class sql(database): + """ + This class saves the process in the working storage. It can be used if + safety matters. + """ + + def __init__(self, *args, **kwargs): + import os + # init base class + super(sql, self).__init__(*args, **kwargs) + # Create a open file, which needs to be closed after the sampling + try: + os.remove(self.dbname + '.db') + except: + pass + + self.db = PickalableSQL3Connect(self.dbname + '.db') + self.db_cursor = PickalableSQL3Cursor(self.db) + # Create Table + # self.db_cursor.execute('''CREATE TABLE IF NOT EXISTS '''+self.dbname+''' + # (like1 real, parx real, pary real, simulation1 real, chain int)''') + self.db_cursor.execute('''CREATE TABLE IF NOT EXISTS ''' + self.dbname + ''' + (''' + ' real ,'.join(self.header) + ''')''') + + def save(self, objectivefunction, parameterlist, simulations=None, chains=1): + coll = (self.dim_dict['like'](objectivefunction) + + self.dim_dict['par'](parameterlist) + + self.dim_dict['simulation'](simulations) + + [chains]) + # Apply rounding of floats + coll = map(self.db_precision, coll) + self.db_cursor.execute( + "INSERT INTO " + self.dbname + " VALUES (" + '"' + str('","'.join(map(str, coll))) + '"' + ")") + + self.db.commit() + + def finalize(self): + self.db.close() + + def getdata(self): + self.db = PickalableSQL3Connect(self.dbname + '.db') + self.db_cursor = PickalableSQL3Cursor(self.db) + + if sys.version_info[0] >= 3: + headers = [(row[1], " (3, 5): + + from pathlib import Path + import webbrowser + + + + class rst: + """ + Creates a reStructuredText description of a sampler or a setup + + Usage: + >>>description = spotpy.describe.rst(sampler) + >>>print(description) # Prints the rst source text + >>># Add additional text section + >>>description.append('#. One idea' + '\n' + '#. Another one.', title='Ideas', titlelevel=2) + >>>description.append_image('media/image.png') + >>>print(description.as_html()) # Create html + >>>description.show_in_browser() + """ + + caption_characters = '=-#~*+^' + + def __init__(self, setup_or_sampler): + """ + Creates a reStructuredText description of a sampler or a setup + :param setup_or_sampler: Either a spotpy.algorithm sampler or a spotpy setup + """ + if isinstance(setup_or_sampler, _algorithm): + self.setup = setup_or_sampler.setup + self.sampler = setup_or_sampler + self.rst_text = [self._sampler_text()] + else: + self.setup = setup_or_sampler + self.sampler = None + self.rst_text = [] + + if self.setup: + self.rst_text.append(self._setup_text()) + + def append(self, text='', title=None, titlelevel=1): + """ + Appends additional descriptions in reStructured text to the generated text + :param text: The rst text to add + :param title: A title for the text + :param titlelevel: The level of the section (0->h1.title, 1->h1, 2->h2, etc.) + :return: + """ + res = '\n' + if title: + res += rst._as_rst_caption(title, titlelevel) + self.rst_text.append(res + text) + + def append_image(self, imgpath, **kwargs): + """ + Links an image to the output + :param imgpath: Path to the image (must be found from the http server) + :param kwargs: Any keyword with value is translated in rst as `:keyword: value` + and added to the image description + + >>>description.append_image('https://img.shields.io/travis/thouska/spotpy/master.svg', + ... target='https://github.com/thouska', + ... width='200px') + """ + rst = '.. image:: {}'.format(imgpath) + for k, v in kwargs.items(): + rst += '\n :{}: {}'.format(k, v) + rst += '\n' + self.append(rst) + + def append_math(self, latex): + """ + Appends a block equation to the output + :param latex: Latex formula + """ + rst = '.. math::\n' + rst += ' ' + latex + '\n' + self.append(rst) + + def __str__(self): + return '\n'.join(self.rst_text) + + @classmethod + def _as_rst_caption(cls, s, level=1): + """ + Marks text as a section caption + :param s: String to be marked as caption + :param level: Caption level 0-6, translates to 0=h1.title, 1=h1, 2=h2, etc. + :return: The string as rst caption + """ + return s + '\n' + cls.caption_characters[level] * len(s) + '\n\n' + + css = """ + body, table, div, p, dl { + font-family: Lucida Grande, Verdana, Geneva, Arial, sans-serif; + font-size: 16px; + } + li>p { + margin: 0px; + } + /* @group Heading Levels */ + h1.title { + background-color: #fff; + color: #0040A0; + text-align: left; + font-size: 200%; + border: solid 2px #1f6992; + } + h1 { + background-color: #1f6992; + color: #fff; + padding: .2em .5em; + font-size: 150%; + } + h2 { + background-color: #cde; + color: #000; + padding: .2em .5em; + border-bottom: solid 2px #1f6992; + font-size: 120%; + } + + h3 { + font-size: 100%; + border-bottom: solid 2px #0040A0; + } + div.line { + font-family: "Lucida Console", "Lucida Sans Typewriter","DejaVu Sans Mono",monospace; + font-size: 100%; + } + + img { + max-width: 720px; + } + + """ + + def as_html(self, css=None): + """ + Converts the generated reStructuredText as html5 + + :css: A string containing a cascading style sheet. If None, the default css is used + :return: The html document as string + """ + if publish_string is None: + raise NotImplementedError('The docutils package needs to be installed') + args = {'input_encoding': 'unicode', + 'output_encoding': 'unicode'} + res = publish_string(source=str(self), + writer_name='html5', + settings_overrides=args) + style_idx = res.index('') + css = css or self.css + # Include css + res = res[:style_idx] + css + res[style_idx:] + return res + + def show_in_browser(self, filename=None, css=None): + """ + Writes the content as html to disk and opens a browser showing the result + + :param filename: The html filename, if None use .html + :param css: A style string, if None the default style is used + """ + html = self.as_html(css).replace('unicode', 'utf-8') + fn = filename or type(self.setup).__name__ + '.html' + path = Path(fn).absolute() + path.write_text(html, encoding='utf-8') + webbrowser.open_new_tab(path.as_uri()) + + def _sampler_text(self): + """ + Generates the rst for the sampler + :return: + """ + obj = self.sampler + cname = rst._as_rst_caption(type(obj).__name__, 0) + s = [ + '- **db format:** ' + obj.dbformat, + '- **db name:** ' + obj.dbname, + '- **save simulation:** ' + str(obj.save_sim), + '- **parallel:** ' + type(obj.repeat).__module__.split('.')[-1], + '', '' + ] + return cname + _getdoc(obj).strip('\n') + '\n\n' + '\n'.join(s) + + def _setup_text(self): + """ + Generates the rst for the setup + :return: + """ + # Get class name + obj = self.setup + cname = rst._as_rst_caption(type(obj).__name__, 0) + # Add doc string + mdoc = _getdoc(obj).strip('\n').replace('\r', '\n') + '\n\n' + # Get parameters from class + param_caption = rst._as_rst_caption('Parameters', 1) + params = '\n'.join('#. **{p.name}:** {p}'.format(p=p) for p in get_parameters_from_setup(obj)) + return cname + mdoc + param_caption + params + diff --git a/spotpy/examples/MyOwnDatabase.txt b/spotpy/examples/MyOwnDatabase.txt new file mode 100644 index 00000000..5eaa3fd3 --- /dev/null +++ b/spotpy/examples/MyOwnDatabase.txt @@ -0,0 +1 @@ +obj_functions parameters simulations diff --git a/spotpy/examples/NSGA2.csv b/spotpy/examples/NSGA2.csv new file mode 100644 index 00000000..9197c4d8 --- /dev/null +++ b/spotpy/examples/NSGA2.csv @@ -0,0 +1,511 @@ +like1,like2,like3,par0,par1,par2,par3,par4,simulation1_1,simulation2_1,simulation3_1,chain +69.56,23.84,150.6,0.3828,0.7446,0.6465,0.7314,0.83,69.56,23.84,150.6,0.0 +13.29,6.305,40.66,0.3252,0.678,0.1247,0.603,0.3013,13.29,6.305,40.66,1.0 +79.06,11.414,218.6,0.2927,0.874,0.5547,0.2489,0.955,79.06,11.414,218.6,2.0 +3.541,64.8,87.1,0.4397,0.0518,0.9644,0.4207,0.1848,3.541,64.8,87.1,3.0 +37.22,42.22,112.25,0.4146,0.4685,0.8457,0.83,0.8105,37.22,42.22,112.25,4.0 +29.0,58.5,183.6,0.3228,0.3313,0.7397,0.0561,0.9717,29.0,58.5,183.6,5.0 +202.2,10.04,50.9,0.8066,0.9526,0.636,0.5635,0.2585,202.2,10.04,50.9,6.0 +4.457,19.38,163.8,0.1271,0.1869,0.62,0.33,0.1343,4.457,19.38,163.8,7.0 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+1,14 @@ +""" +Shows the usage of the command line interface CLI + +""" + +from __future__ import division, print_function, unicode_literals + +from spotpy.cli import main +from spotpy.examples.spot_setup_hymod_python import spot_setup + + +if __name__ == '__main__': + setup = spot_setup() + main(setup) diff --git a/spotpy/examples/cmf_data/__init__.py b/spotpy/examples/cmf_data/__init__.py new file mode 100644 index 00000000..c91096bc --- /dev/null +++ b/spotpy/examples/cmf_data/__init__.py @@ -0,0 +1,31 @@ +# -*- coding: utf-8 -*- +''' +Copyright (c) 2015 by Tobias Houska +This file is part of Statistical Parameter Estimation Tool (SPOTPY). + +:author: Tobias Houska + +: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. + +This package enables the comprehensive use of different Bayesian and Heuristic calibration +techniques in one Framework. It comes along with an algorithms folder for the +sampling and an analyser class for the plotting of results by the sampling. + +:dependencies: - Numpy >1.8 (http://www.numpy.org/) + - Pandas >0.13 (optional) (http://pandas.pydata.org/) + - Matplotlib >1.4 (optional) (http://matplotlib.org/) + - CMF (optional) (http://fb09-pasig.umwelt.uni-giessen.de:8000/) + - mpi4py (optional) (http://mpi4py.scipy.org/) + - pathos (optional) (https://pypi.python.org/pypi/pathos/) + - sqlite3 (optional) (https://pypi.python.org/pypi/sqlite3/) + + :help: For specific questions, try to use the documentation website at: + http://fb09-pasig.umwelt.uni-giessen.de/spotpy/ + +For general things about parameter optimization techniques have a look at: +https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/tree/master/ + +Please cite our paper, if you are using SPOTPY. +''' diff --git a/spotpy/examples/dds/__init__.py b/spotpy/examples/dds/__init__.py new file mode 100644 index 00000000..c91096bc --- /dev/null +++ b/spotpy/examples/dds/__init__.py @@ -0,0 +1,31 @@ +# -*- coding: utf-8 -*- +''' +Copyright (c) 2015 by Tobias Houska +This file is part of Statistical Parameter Estimation Tool (SPOTPY). + +:author: Tobias Houska + +: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. + +This package enables the comprehensive use of different Bayesian and Heuristic calibration +techniques in one Framework. It comes along with an algorithms folder for the +sampling and an analyser class for the plotting of results by the sampling. + +:dependencies: - Numpy >1.8 (http://www.numpy.org/) + - Pandas >0.13 (optional) (http://pandas.pydata.org/) + - Matplotlib >1.4 (optional) (http://matplotlib.org/) + - CMF (optional) (http://fb09-pasig.umwelt.uni-giessen.de:8000/) + - mpi4py (optional) (http://mpi4py.scipy.org/) + - pathos (optional) (https://pypi.python.org/pypi/pathos/) + - sqlite3 (optional) (https://pypi.python.org/pypi/sqlite3/) + + :help: For specific questions, try to use the documentation website at: + http://fb09-pasig.umwelt.uni-giessen.de/spotpy/ + +For general things about parameter optimization techniques have a look at: +https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/tree/master/ + +Please cite our paper, if you are using SPOTPY. +''' diff --git a/spotpy/examples/dds/benchmark_dds.py b/spotpy/examples/dds/benchmark_dds.py new file mode 100644 index 00000000..16e928af --- /dev/null +++ b/spotpy/examples/dds/benchmark_dds.py @@ -0,0 +1,117 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals +from pprint import pprint +import numpy as np +import matplotlib.pylab as plt +import json + +import time + +try: + import spotpy +except ImportError: + import sys + + sys.path.append(".") + import spotpy + +from spotpy.examples.spot_setup_hymod_python import spot_setup + + + +spot_setup = spot_setup() + +# Create samplers for every algorithm: +results = [] + +benchmarks_dict = [] +benchmarks_duration = {"dds":[], "sceua":[], "dds_like":[],"sceua_like":[]} +reps = [300, 1000, 3000, 4000, 5000, 10000] + + +for rep in reps: + + timeout = 10 # Given in Seconds + + parallel = "seq" + dbformat = "csv" + + start = time.time() + dds_sampler = spotpy.algorithms.DDS(spot_setup, parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) + dds_sampler.sample(rep, trials=1) + results.append(dds_sampler.getdata()) + dds_elapsed = time.time() - start + + start = time.time() + sceua_sampler = spotpy.algorithms.sceua(spot_setup, parallel=parallel, dbname='SCEUA', dbformat=dbformat, + sim_timeout=timeout, alt_objfun=None) + sceua_sampler.sample(rep) + results.append(sceua_sampler.getdata()) + sceua_elapsed = time.time() - start + + + print("#########################################") + + #print(dds_elapsed, dds_sampler.status.params) + + print(sceua_elapsed, sceua_sampler.status.params) + + benchmarks_dict.append({ + "rep": rep, + "dds_time": dds_elapsed, + "sceua_time": sceua_elapsed, + "dds_like": dds_sampler.status.objectivefunction, + "sceua_like": sceua_sampler.status.objectivefunction, + "dds_param": list(dds_sampler.status.params), + "sceua_param": list(sceua_sampler.status.params) + }) + benchmarks_duration["dds"].append(dds_elapsed) + benchmarks_duration["sceua"].append(sceua_elapsed) + benchmarks_duration["sceua_like"].append(sceua_sampler.status.objectivefunction) + benchmarks_duration["dds_like"].append(dds_sampler.status.objectivefunction) + +print(json.dumps(benchmarks_dict)) + + +def autolabel(rects): + """ + Attach a text label above each bar displaying its height + """ + for rect in rects: + height = rect.get_height() + ax.text(rect.get_x() + rect.get_width()/2., 1.05*height, + '%d' % int(height), + ha='center', va='bottom') + + + +fig = plt.figure(figsize=(10, 6)) +ax = plt.subplot(111) + +rep_labels = [str(j) for j in reps] +x_pos = [i for i, _ in enumerate(rep_labels)] + + +X = np.arange(len(benchmarks_duration["dds"])) +dds_plot = ax.bar(x_pos, benchmarks_duration["dds_like"], color = 'b', width = 0.45) +sceua_plot = ax.bar([j+0.45 for j in x_pos], benchmarks_duration["sceua_like"], color = 'g', width = 0.45) + +#dds_plot = ax.bar(x_pos, benchmarks_duration["dds"], color = 'b', width = 0.45) +#sceua_plot = ax.bar([j+0.45 for j in x_pos], benchmarks_duration["sceua"], color = 'g', width = 0.45) + + + +plt.xticks(x_pos, rep_labels) +plt.legend(("DDS", "SCEUA")) +plt.xlabel("Repetitions") +plt.ylabel("Best Objective Function Value") + +autolabel(dds_plot) +autolabel(sceua_plot) + +plt.show() +plt.savefig("MPI_TEST") +# + diff --git a/spotpy/examples/dds/dds_parallel.py b/spotpy/examples/dds/dds_parallel.py new file mode 100644 index 00000000..15af96d0 --- /dev/null +++ b/spotpy/examples/dds/dds_parallel.py @@ -0,0 +1,46 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals +import numpy as np +import sys +import os +import matplotlib.pylab as plt +import json +import time + +try: + import spotpy +except ImportError: + import sys + + sys.path.append(".") + import spotpy + +from spotpy.examples.spot_setup_hymod_python import spot_setup + +spot_setup = spot_setup() + + +path = os.path.abspath(os.path.dirname(__file__)) +json_path = path + "/dds_parallel_data.json" +benchmarks_duration = json.load(open(json_path)) + + +rep = int(sys.argv[1]) +timeout = 10 # Given in Seconds +parallel = "mpi" +dbformat = "csv" +start = time.time() +dds_sampler = spotpy.algorithms.dds(spot_setup, parallel=parallel, dbname='DDS', dbformat=dbformat, sim_timeout=timeout) +dds_sampler.sample(rep, trials=1) +dds_elapsed = time.time() - start +print(dds_elapsed) + +benchmarks_duration["dds_duration"].append(dds_elapsed) +benchmarks_duration["dds_like"].append(dds_sampler.status.objectivefunction_max) +benchmarks_duration["rep"].append(rep) + +print(benchmarks_duration) + +json.dump(benchmarks_duration, open(json_path,"w")) \ No newline at end of file diff --git a/spotpy/examples/dds/dds_parallel_data.json b/spotpy/examples/dds/dds_parallel_data.json new file mode 100644 index 00000000..f3e7bcd8 --- /dev/null +++ b/spotpy/examples/dds/dds_parallel_data.json @@ -0,0 +1 @@ +{"dds_duration": [], "rep": [], "dds_like": []} \ No newline at end of file diff --git a/spotpy/examples/dds/dds_parallel_plot.py b/spotpy/examples/dds/dds_parallel_plot.py new file mode 100644 index 00000000..cd6a85e8 --- /dev/null +++ b/spotpy/examples/dds/dds_parallel_plot.py @@ -0,0 +1,51 @@ +import numpy as np + +import matplotlib.pylab as plt +import json +import matplotlib as mp + +data_normalizer = mp.colors.Normalize() +color_map = mp.colors.LinearSegmentedColormap( + "my_map", + { + "red": [(0, 1.0, 1.0), + (1.0, .5, .5)], + "green": [(0, 0.5, 0.5), + (1.0, 0, 0)], + "blue": [(0, 0.50, 0.5), + (1.0, 0, 0)] + } +) + + +def autolabel(ax, rects): + """ + Attach a text label above each bar displaying its height + """ + for rect in rects: + height = rect.get_height() + ax.text(rect.get_x() + rect.get_width() / 2., 1.05 * height, + '%f' % height, + ha='center', va='bottom') + + +def subplot(data, name, ylabel): + fig = plt.figure(figsize=(20, 6)) + ax = plt.subplot(111) + rep_labels = [str(j) for j in reps] + x_pos = [i for i, _ in enumerate(rep_labels)] + X = np.arange(len(data)) + ax_plot = ax.bar(x_pos, data, color=color_map(data_normalizer(data)), width=0.45) + + plt.xticks(x_pos, rep_labels) + plt.xlabel("Repetitions") + plt.ylabel(ylabel) + + autolabel(ax, ax_plot) + plt.savefig(name + ".png") + + +parallel_data = json.loads('{"dds_duration": [1.1293659210205078, 3.254117250442505, 4.888171672821045, 18.719818592071533, 34.56907820701599, 169.47716689109802, 337.86882615089417, 1644.955144405365, 3348.948029756546], "rep": [30, 500, 1000, 5000, 10000, 50000, 100000, 500000, 1000000], "dds_like": [-8384.884435178812, -8269.480874403698, -8268.453892284442, -8268.51195094138, -8269.65509041187, -8268.1421690868, -8267.791798085422, -8267.79178644684, -8268.141980514703]}') +reps = parallel_data["rep"] +subplot(parallel_data["dds_duration"], "DDS_PARALLEL_DURATION_all", "Duration of Run in Seconds") +subplot(parallel_data["dds_like"], "DDS_PARALLEL_OBJECTIVEFUNCTION_all", "Best Objective Function Value") diff --git a/spotpy/examples/dds/dds_parallel_run.py b/spotpy/examples/dds/dds_parallel_run.py new file mode 100644 index 00000000..c97126d2 --- /dev/null +++ b/spotpy/examples/dds/dds_parallel_run.py @@ -0,0 +1,8 @@ +import subprocess +import os +path = os.path.abspath(os.path.dirname(__file__)) +for r in [500,1000,5000,10000,50000,100000,500000]: + args = ["mpirun", "-c 6", "python", path + "/dds_parallel.py", str(r)] + print(args) + subprocess.run(args) + exit(8) \ No newline at end of file diff --git a/spotpy/examples/gui_cmf_lumped.py b/spotpy/examples/gui_cmf_lumped.py deleted file mode 100644 index 38ad20f6..00000000 --- a/spotpy/examples/gui_cmf_lumped.py +++ /dev/null @@ -1,21 +0,0 @@ -""" -Shows the usage of the matplotlib GUI - -Needs at least Python 3.5 -""" - -from __future__ import division, print_function, unicode_literals - - -import datetime -import spotpy -from spotpy.gui.mpl import GUI -from spotpy.examples.spot_setup_cmf_lumped import SingleStorage - -if __name__ == '__main__': - # Create the model - model = SingleStorage(datetime.datetime(1980, 1, 1), - datetime.datetime(1985, 12, 31)) - spotpy.describe.setup(model) - gui = GUI(model) - gui.show() diff --git a/spotpy/examples/gui_hymod.py b/spotpy/examples/gui_hymod.py new file mode 100644 index 00000000..d0f7f9b3 --- /dev/null +++ b/spotpy/examples/gui_hymod.py @@ -0,0 +1,30 @@ +""" +Shows the usage of the matplotlib GUI + +Needs at least Python 3.5 +""" + +from __future__ import division, print_function, unicode_literals + + +import spotpy +from spotpy.gui.mpl import GUI +from spotpy.examples.spot_setup_hymod_python import spot_setup +from spotpy.objectivefunctions import rmse + +if __name__ == '__main__': + setup_class=spot_setup(rmse) + + #Select number of maximum allowed repetitions + rep=10000 + + # Create the SCE-UA sampler of spotpy, alt_objfun is set to None to force SPOTPY + # to jump into the def objectivefunction in the spot_setup class (default is + # spotpy.objectivefunctions.rmse) + sampler=spotpy.algorithms.sceua(setup_class, dbname='SCEUA_hymod', dbformat='csv', alt_objfun=None) + + #Start the sampler, one can specify ngs, kstop, peps and pcento id desired + #sampler.sample(rep,ngs=10, kstop=50, peps=0.1, pcento=0.1) + + gui = GUI(spot_setup()) + gui.show() diff --git a/spotpy/examples/hymod_exe/Param.in b/spotpy/examples/hymod_exe/Param.in new file mode 100644 index 00000000..de97657f --- /dev/null +++ b/spotpy/examples/hymod_exe/Param.in @@ -0,0 +1 @@ +401.08218 0.23071 0.43731 0.06884 0.16352 \ No newline at end of file diff --git a/spotpy/examples/hymod_exe/hymod_input.csv b/spotpy/examples/hymod_exe/hymod_input.csv new file mode 100644 index 00000000..3c158e03 --- /dev/null +++ b/spotpy/examples/hymod_exe/hymod_input.csv @@ -0,0 +1,1828 @@ +Date;rainfall[mm];TURC [mm d-1];Discharge[ls-1] +01.01.2012;2.052861283;0.35;nan +02.01.2012;0;0.26;nan +03.01.2012;0.58456085;0.39;nan +04.01.2012;0.123880377;0.53;nan +05.01.2012;0;0.44;nan +06.01.2012;0;0.63;nan 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+30.12.2016;0;0;3.061955 +31.12.2016;0;0;2.959312 diff --git a/spotpy/examples/hymod_python/hymod.py b/spotpy/examples/hymod_python/hymod.py index 68ee7cda..ae10739b 100644 --- a/spotpy/examples/hymod_python/hymod.py +++ b/spotpy/examples/hymod_python/hymod.py @@ -1,3 +1,5 @@ +from numba import jit + def hymod(Precip, PET, cmax,bexp,alpha,Rs,Rq): """ @@ -20,7 +22,6 @@ def hymod(Precip, PET, cmax,bexp,alpha,Rs,Rq): # Initialize state(s) of quick tank(s) x_quick = [0,0,0] t = 0 - outflow = [] output = [] # START PROGRAMMING LOOP WITH DETERMINING RAINFALL - RUNOFF AMOUNTS @@ -48,21 +49,21 @@ def hymod(Precip, PET, cmax,bexp,alpha,Rs,Rq): output.append(QS + outflow) t = t+1 - return output +@jit def power(X,Y): X=abs(X) # Needed to capture invalid overflow with netgative values return X**Y - +@jit def linres(x_slow,inflow,Rs): # Linear reservoir x_slow = (1 - Rs) * x_slow + (1 - Rs) * inflow outflow = (Rs / (1 - Rs)) * x_slow return x_slow,outflow - +@jit def excess(x_loss,cmax,bexp,Pval,PETval): # this function calculates excess precipitation and evaporation xn_prev = x_loss diff --git a/spotpy/examples/hymod_unix/__init__.py b/spotpy/examples/hymod_unix/__init__.py new file mode 100644 index 00000000..c91096bc --- /dev/null +++ b/spotpy/examples/hymod_unix/__init__.py @@ -0,0 +1,31 @@ +# -*- coding: utf-8 -*- +''' +Copyright (c) 2015 by Tobias Houska +This file is part of Statistical Parameter Estimation Tool (SPOTPY). + +:author: Tobias Houska + +: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. + +This package enables the comprehensive use of different Bayesian and Heuristic calibration +techniques in one Framework. It comes along with an algorithms folder for the +sampling and an analyser class for the plotting of results by the sampling. + +:dependencies: - Numpy >1.8 (http://www.numpy.org/) + - Pandas >0.13 (optional) (http://pandas.pydata.org/) + - Matplotlib >1.4 (optional) (http://matplotlib.org/) + - CMF (optional) (http://fb09-pasig.umwelt.uni-giessen.de:8000/) + - mpi4py (optional) (http://mpi4py.scipy.org/) + - pathos (optional) (https://pypi.python.org/pypi/pathos/) + - sqlite3 (optional) (https://pypi.python.org/pypi/sqlite3/) + + :help: For specific questions, try to use the documentation website at: + http://fb09-pasig.umwelt.uni-giessen.de/spotpy/ + +For general things about parameter optimization techniques have a look at: +https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/tree/master/ + +Please cite our paper, if you are using SPOTPY. +''' diff --git a/spotpy/examples/hymod_unix/bound.txt b/spotpy/examples/hymod_unix/bound.txt new file 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1.1000000e+001 9.0000000e+000 1.9620000e+003 5.0404000e+000 4.7772000e+000 0.0000000e+000 0.0000000e+000 8.5856000e+000 0.0000000e+000 + 1.2000000e+001 9.0000000e+000 1.9620000e+003 4.6157000e+000 4.8511000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 + 1.3000000e+001 9.0000000e+000 1.9620000e+003 4.4741000e+000 5.1530000e+000 0.0000000e+000 2.0150000e-001 0.0000000e+000 0.0000000e+000 + 1.4000000e+001 9.0000000e+000 1.9620000e+003 4.0777000e+000 3.7513000e+000 0.0000000e+000 4.0630000e-001 4.2652600e+001 1.7400000e-002 + 1.5000000e+001 9.0000000e+000 1.9620000e+003 1.1156900e+001 2.2361000e+000 0.0000000e+000 2.9250000e-001 0.0000000e+000 2.1000000e-002 + 1.6000000e+001 9.0000000e+000 1.9620000e+003 6.8244000e+000 2.9261000e+000 0.0000000e+000 0.0000000e+000 1.5954000e+000 9.6600000e-002 + 1.7000000e+001 9.0000000e+000 1.9620000e+003 5.3519000e+000 3.4230000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 + 1.8000000e+001 9.0000000e+000 1.9620000e+003 4.3608000e+000 4.1149000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 + 1.9000000e+001 9.0000000e+000 1.9620000e+003 3.9644000e+000 5.3350000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 + 2.0000000e+001 9.0000000e+000 1.9620000e+003 3.7662000e+000 5.4795000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 + 2.1000000e+001 9.0000000e+000 1.9620000e+003 3.5680000e+000 5.7278000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 + 2.2000000e+001 9.0000000e+000 1.9620000e+003 3.4547000e+000 4.7755000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 + 2.3000000e+001 9.0000000e+000 1.9620000e+003 3.4547000e+000 5.0611000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 + 2.4000000e+001 9.0000000e+000 1.9620000e+003 3.3981000e+000 4.1354000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 + 2.5000000e+001 9.0000000e+000 1.9620000e+003 3.3697000e+000 3.7895000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 + 2.6000000e+001 9.0000000e+000 1.9620000e+003 3.3697000e+000 9.8620000e-001 0.0000000e+000 1.2600000e-001 0.0000000e+000 0.0000000e+000 + 2.7000000e+001 9.0000000e+000 1.9620000e+003 3.3131000e+000 4.6725000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 + 2.8000000e+001 9.0000000e+000 1.9620000e+003 3.2565000e+000 4.4010000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 + 2.9000000e+001 9.0000000e+000 1.9620000e+003 3.2282000e+000 4.9763000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 + 3.0000000e+001 9.0000000e+000 1.9620000e+003 3.1715000e+000 4.9871000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 0.0000000e+000 diff --git a/spotpy/examples/hymod_unix/bound_units.xlsx b/spotpy/examples/hymod_unix/bound_units.xlsx new file mode 100644 index 00000000..54207683 Binary files /dev/null and b/spotpy/examples/hymod_unix/bound_units.xlsx differ diff --git a/spotpy/examples/hymod_unix/hymod_3.6 b/spotpy/examples/hymod_unix/hymod_3.6 new file mode 100644 index 00000000..a11d46c3 Binary files /dev/null and b/spotpy/examples/hymod_unix/hymod_3.6 differ diff --git a/spotpy/examples/hymod_unix/hymod_3.7 b/spotpy/examples/hymod_unix/hymod_3.7 new file mode 100644 index 00000000..a69a7d0c Binary files /dev/null and b/spotpy/examples/hymod_unix/hymod_3.7 differ diff --git a/spotpy/examples/hymod_unix/hymod_cython/Makefile b/spotpy/examples/hymod_unix/hymod_cython/Makefile new file mode 100644 index 00000000..056dbcde --- /dev/null +++ b/spotpy/examples/hymod_unix/hymod_cython/Makefile @@ -0,0 +1,19 @@ +all: prepare hymod + +prepare: + @echo "This scripts works best with Linux / Ubuntu" + @echo "cython needs to be installed (with pip install cython or make cython)" + cython --cplus -3 hymod.pyx + +cython: + @echo pip i cython + pip install cython + +hymod: + g++ hymod.cpp main_hymod.cpp -o hymod `python3-config --includes` `python3-config --cflags` `python3-config --ldflags` -fPIC + +clean: + rm -r hymod.dSYM hymod.cpp hymod.h + +run: + ./hymod \ No newline at end of file diff --git a/spotpy/examples/hymod_unix/hymod_cython/compile_hymod.sh b/spotpy/examples/hymod_unix/hymod_cython/compile_hymod.sh new file mode 100644 index 00000000..ee54a5ce --- /dev/null +++ b/spotpy/examples/hymod_unix/hymod_cython/compile_hymod.sh @@ -0,0 +1,9 @@ +#!/bin/bash + +# works with Linux + +echo "This scripts works best with Linux / Ubuntu" +echo "cython needs to be installed (with pip install cython)" + +cython --cplus -3 hymod.pyx +g++ hymod.cpp main_hymod.cpp -o hymod $(python3-config --includes) $(python3-config --cflags) $(python3-config --ldflags) -fPIC diff --git a/spotpy/examples/hymod_unix/hymod_cython/hymod.pyx b/spotpy/examples/hymod_unix/hymod_cython/hymod.pyx new file mode 100644 index 00000000..db53be61 --- /dev/null +++ b/spotpy/examples/hymod_unix/hymod_cython/hymod.pyx @@ -0,0 +1,128 @@ +import sys, re, os + + + +#cdef public list hymod(list Precip, list PET, float cmax, float bexp, float alpha, float Rs, float Rq): # public function declaration +def hymod(Precip, PET, cmax,bexp,alpha,Rs,Rq): + """ + See https://www.proc-iahs.net/368/180/2015/piahs-368-180-2015.pdf for a scientific paper. + + :param cmax: + :param bexp: + :param alpha: + :param Rs: + :param Rq: + :return: Dataset of water in hymod (has to be calculated in litres) + :rtype: list + """ + + # HYMOD PROGRAM IS SIMPLE RAINFALL RUNOFF MODEL + x_loss = 0.0 + # Initialize slow tank state + x_slow = 2.3503 / (Rs * 22.5) + x_slow = 0 # --> works ok if calibration data starts with low discharge + # Initialize state(s) of quick tank(s) + x_quick = [0,0,0] + t = 0 + outflow = [] + output = [] + # START PROGRAMMING LOOP WITH DETERMINING RAINFALL - RUNOFF AMOUNTS + + while t <= len(Precip)-1: + Pval = Precip[t] + PETval = PET[t] + # Compute excess precipitation and evaporation + ER1, ER2, x_loss = excess(x_loss, cmax, bexp, Pval, PETval) + # Calculate total effective rainfall + ET = ER1 + ER2 + # Now partition ER between quick and slow flow reservoirs + UQ = alpha * ET + US = (1 - alpha) * ET + # Route slow flow component with single linear reservoir + x_slow, QS = linres(x_slow, US, Rs) + # Route quick flow component with linear reservoirs + inflow = UQ + + for i in range(3): + # Linear reservoir + x_quick[i], outflow = linres(x_quick[i], inflow, Rq) + inflow = outflow + + # Compute total flow for timestep + output.append(QS + outflow) + t = t+1 + + + return output + + +def power( X, Y): + X=abs(X) # Needed to capture invalid overflow with netgative values + return X**Y + +#cdef public tuple linres(float x_slow, float inflow, float Rs): +def linres( x_slow, inflow, Rs): + # Linear reservoir + x_slow = (1 - Rs) * x_slow + (1 - Rs) * inflow + outflow = (Rs / (1 - Rs)) * x_slow + return x_slow,outflow + + +#cdef public tuple excess(float x_loss, float cmax,float bexp,float Pval,float PETval): +def excess(x_loss,cmax,bexp,Pval,PETval): + # this function calculates excess precipitation and evaporation + xn_prev = x_loss + ct_prev = cmax * (1 - power((1 - ((bexp + 1) * (xn_prev) / cmax)), (1 / (bexp + 1)))) + # Calculate Effective rainfall 1 + ER1 = max((Pval - cmax + ct_prev), 0.0) + Pval = Pval - ER1 + dummy = min(((ct_prev + Pval) / cmax), 1) + xn = (cmax / (bexp + 1)) * (1 - power((1 - dummy), (bexp + 1))) + + # Calculate Effective rainfall 2 + ER2 = max(Pval - (xn - xn_prev), 0) + + # Alternative approach + evap = (1 - (((cmax / (bexp + 1)) - xn) / (cmax / (bexp + 1)))) * PETval # actual ET is linearly related to the soil moisture state + xn = max(xn - evap, 0) # update state + + return ER1,ER2,xn + + +cdef public void hymod_run(owd): + if not hasattr(sys, 'argv'): + sys.argv = [''] + if len(sys.argv) != 6: + print("Hymod reads in from file called 'Param.in'") + with open("Param.in", "r") as param_in: + param_line = param_in.readline() + x = [float(i) for i in re.split("\s+", param_line) if len(i) > 0] + else: + print("Hymod reads in from stdin") + x = sys.argv + x.pop(0) + + # try to use path provided from cpp + # owd = os.path.dirname(os.path.realpath(__file__)) + + hymod_path = owd + os.sep + 'hymod_input.csv' + Precip, PET = [], [] + + with open(hymod_path, "r") as hymod_settings_file: + for f_index, f_line in enumerate(hymod_settings_file): + if f_index == 0: + continue + f_data = re.split(";", f_line) + Precip.append(float(f_data[1])) + PET.append(float(f_data[2])) + + + x = [float(i) for i in x] + result = hymod(Precip,PET, x[0], x[1], x[2], x[3], x[4]) + + with open("Q.out", "w") as Qout: + for r in result: + Qout.write(str(r) + "\n") + + #np.savetxt("Q.out", result, delimiter=";") + \ No newline at end of file diff --git a/spotpy/examples/hymod_unix/hymod_cython/main_hymod.cpp b/spotpy/examples/hymod_unix/hymod_cython/main_hymod.cpp new file mode 100644 index 00000000..b3748b13 --- /dev/null +++ b/spotpy/examples/hymod_unix/hymod_cython/main_hymod.cpp @@ -0,0 +1,51 @@ +#include +#include "Python.h" +#include "hymod.h" +#include + +int main(int argc, char *argv[]) +{ + + //PyInit_hymod(); //Needed! called PyInit_hello() for Python3 + + int status = PyImport_AppendInittab("hymod", PyInit_hymod); + + + + if (status == -1) + { + std::cout << "We got a error " << status << std::endl; + return -1; //error + } + + Py_Initialize(); //Needed! + + PyObject *module = PyImport_ImportModule("hymod"); + //std::cout << "Module is: " << module << std::endl; + + if (module == NULL) + { + Py_Finalize(); + return -1; //error + } + + /*const std::filesystem::path owd; + std::filesystem::current_path(&owd); + + getcwd(cwd, sizeof(cwd))*/ + + char cwd[256]; + if (getcwd(cwd, sizeof(cwd)) == NULL) + { + std::cout << "Error in getcwd" << std::endl; + Py_Finalize(); + return -2; //error + } + + PyObject *py_cwd = Py_BuildValue("s", cwd); + hymod_run(py_cwd); + + Py_Finalize(); //Needed! + + return 0; +} \ No newline at end of file diff --git a/spotpy/examples/plot_nsgaii_tutorial.py b/spotpy/examples/plot_nsgaii_tutorial.py new file mode 100644 index 00000000..ceae0593 --- /dev/null +++ b/spotpy/examples/plot_nsgaii_tutorial.py @@ -0,0 +1,62 @@ +import numpy as np +import sys +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +import pandas as pd + + + +# user config + +n_var = 5 +n_obj = 3 + +last = None +first = None +n_pop = 50 + +# output calibration + +df = pd.read_csv("NSGA2.csv") + +df["like3"] = df.like3 * -1 + + +if last: + df = df.iloc[-last:,:] +elif first: + df = df.iloc[:first,:] +else: + pass + + + +# plot objective functions +fig = plt.figure() +for i,name in enumerate(df.columns[:n_obj]): + ax = fig.add_subplot(n_obj,1,i +1) + df.loc[::5,name].plot(lw=0.5,figsize=(18,8),ax = ax,color="black") + plt.title(name) +plt.show() + + + +x,y,z = df.iloc[-n_pop:,0],df.iloc[-n_pop:,1],df.iloc[-n_pop:,2] +fig = plt.figure() +ax = fig.add_subplot(111, projection='3d') +ax.scatter(x,y,z,marker="o") +ax.set_xlabel("pbias") +ax.set_ylabel("rmse") +ax.set_zlabel("rsquared") +plt.show() + +# plot parameters +fig = plt.figure() +for i,name in enumerate(df.columns[n_obj:8]): + ax = fig.add_subplot(5,1,i +1) + df.loc[:,name].plot(lw=0.5,figsize=(18,8),ax = ax,color="black") + plt.title(name) +plt.show() + + + diff --git a/spotpy/examples/spot_setup_cmf_lumped.py b/spotpy/examples/spot_setup_cmf_lumped.py index 749fc798..411d3d65 100644 --- a/spotpy/examples/spot_setup_cmf_lumped.py +++ b/spotpy/examples/spot_setup_cmf_lumped.py @@ -27,11 +27,11 @@ class DataProvider(object): def __init__(self): # Load data from file using numpy magic - data = np.recfromcsv('cmf_data/fulda_climate.csv') + data = np.recfromcsv('cmf_data/fulda_climate.csv', encoding='utf-8') def bstr2date(bs): """Helper function to convert date byte string to datetime object""" - return datetime.datetime.strptime(bs.decode(), '%d.%m.%Y') + return datetime.datetime.strptime(bs, '%d.%m.%Y') # Get begin, step and end from the date column self.begin = bstr2date(data.date[0]) @@ -107,7 +107,7 @@ def __init__(self, begin=None, end=None): """ - self.dbname = 'cmf-singlestorage' + self.dbname = 'cmf_singlestorage' # Loads driver data self.data = DataProvider() @@ -146,7 +146,7 @@ def setparameters(self, par=None): """ Sets the parameters of the model by creating the connections """ - par = par or spotpy.parameter.create_set(self, random=False) + par = par or spotpy.parameter.create_set(self, valuetype='optguess') # Some shortcuts to gain visibility c = self.project[0] @@ -203,7 +203,10 @@ def objectivefunction(simulation, evaluation): """ Calculates the goodness of the simulation """ - return spotpy.objectivefunctions.nashsutcliffe(evaluation, simulation) + return [ + spotpy.objectivefunctions.nashsutcliffe(evaluation, simulation), + spotpy.objectivefunctions.pbias(evaluation, simulation) + ] def evaluation(self): """ diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py new file mode 100644 index 00000000..67e93585 --- /dev/null +++ b/spotpy/examples/spot_setup_dds.py @@ -0,0 +1,83 @@ +import spotpy +from spotpy.parameter import Uniform +from spotpy.objectivefunctions import rmse +import numpy as np + + +def ackley10(vector): + length = len(vector) + sum1 = 0 + sum2 = 0 + for i in range(length): + sum1 = sum1 + vector[i] ** 2 + sum2 = sum2 + np.cos(2 * np.pi * vector[i]) + return -1*(-20 * np.exp(-0.2 * (sum1 / length) ** 0.5) - np.exp(sum2 / length)) + + +def griewank10(vector): + sum1 = 0 + term2 = 1 + term3 = 1 + + for i in range(len(vector)): + sum1 = sum1 + (vector[i] ** 2) / 4000 + term2 = term2 * np.cos(vector[i] / (i + 1) ** 0.5) + + return -1*(sum1 - term2 + term3) + + +class spot_setup(object): + """ + Setup for a simple example to run DDS Algorithm + """ + + def __init__(self): + self.params = None + self.objfunc = None + + def _objfunc_switcher(self, name): + """ + Set new parameter and objective function while setup is instanced in a test case + :param name: function name which overwrites initial objective function + :return: + """ + + if name == "ackley": + self.objfunc = ackley10 + self.params = [Uniform(str(j), -2, 2, 1.5, 3.0, -2, 2, doc=str(j) + ' value of Rosenbrock function') + for j in range(10)] + elif name == "griewank": + self.objfunc = griewank10 + self.params = [Uniform('d' + str(j), -500, 700, 1.5, 3.0, -500, 700, + doc=str(j) + 'distinc parameter within a boundary', as_int=True) + for j in range(2)] + [Uniform('c' + str(j), -500, 700, 1.5, 3.0, -500, 700, + doc=str(j) + 'continuous parameter within a boundary') + for j in range(8)] + + def parameters(self): + if self.params is None: + self.params = [ + Uniform("0", -10, 10, 1.5, 3.0, -10, 10, doc='x value of Rosenbrock function'), + Uniform("1", -10, 10, 1.5, 3.0, -10, 10, doc='y value of Rosenbrock function'), + Uniform("z", -10, 10, 1.5, 3.0, -10, 10, doc='z value of Rosenbrock function')] + return spotpy.parameter.generate(self.params) + + def simulation(self, vector): + x = np.array(vector) + # simulations = [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] + simulations = x * np.random.rand(len(vector)) + #simulations = x * np.sum(vector) + return simulations + + def evaluation(self): + # observations = [0] + observations = [2, 3, 4] + return observations + + def objectivefunction(self, simulation, evaluation, params): + + if self.objfunc is None: + return -1*rmse(evaluation, simulation) + else: + pars, names = params + return self.objfunc(pars) diff --git a/spotpy/examples/spot_setup_dtlz1.py b/spotpy/examples/spot_setup_dtlz1.py new file mode 100644 index 00000000..b2d99746 --- /dev/null +++ b/spotpy/examples/spot_setup_dtlz1.py @@ -0,0 +1,61 @@ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals +import numpy as np +import spotpy +import time + +def g1(x,k): + return 100*( k + np.sum(np.square(x - 0.5) - np.cos(20*np.pi*(x -0.5)), axis=1)) + + +def dtlz1(x,n_var,n_obj): + + + k = n_var - n_obj + 1 + + X, X_M = x[:, :n_obj - 1], x[:, n_obj - 1:] + g = g1(X_M,k) + + f = [] + for i in range(0,n_obj): + _f = 0.5 * (1 + g) + _f *= np.prod(X[:, :X.shape[1] -i],axis=1) + if i> 0: + _f *= 1 - X[:,X.shape[1] -i] + f.append(_f) + + return f + + + + + + +class spot_setup(object): + def __init__(self,n_var=5,n_obj=3): + self.n_var=n_var + self.n_obj = n_obj + + self.params = [] + for i in range(self.n_var): + self.params.append(spotpy.parameter.Uniform(str(i),0,1)) + def parameters(self): + return spotpy.parameter.generate(self.params) + + def simulation(self, vector): + vars = np.array(vector)[None] + sim = dtlz1(vars,n_var=self.n_var,n_obj=self.n_obj) + #time.sleep(0.1) + return sim + def evaluation(self): + observations=[0]*self.n_obj + return observations + + def objectivefunction(self, simulation,evaluation): + obj = [] + for i,f in enumerate(simulation): + obj.append(spotpy.objectivefunctions.mae(evaluation = [evaluation[i]], simulation = [simulation[i]])) + return obj diff --git a/spotpy/examples/spot_setup_hymod_exe.py b/spotpy/examples/spot_setup_hymod_exe.py index dbb64dd4..ef55bc6e 100644 --- a/spotpy/examples/spot_setup_hymod_exe.py +++ b/spotpy/examples/spot_setup_hymod_exe.py @@ -1,9 +1,7 @@ ''' Copyright 2015 by Tobias Houska This file is part of Statistical Parameter Estimation Tool (SPOTPY). - :author: Tobias Houska - This example implements the external hydrological model HYMOD into SPOTPY. ''' from __future__ import absolute_import @@ -42,17 +40,22 @@ def simulation(self,x): if self.parallel == 'seq': call = '' elif self.parallel == 'mpi': + #Running n parallel, care has to be taken when files are read or written + #Therefor we check the ID of the current computer core call = str(int(os.environ['OMPI_COMM_WORLD_RANK'])+2) + #And generate a new folder with all underlying files copy_tree(self.hymod_path, self.hymod_path+call) elif self.parallel == 'mpc': + #Running n parallel, care has to be taken when files are read or written + #Therefor we check the ID of the current computer core call =str(os.getpid()) + #And generate a new folder with all underlying files copy_tree(self.hymod_path, self.hymod_path+call) else: raise 'No call variable was assigned' os.chdir(self.hymod_path+call) - try: if sys.version_info.major == 2: params = file('Param.in', 'w') @@ -79,11 +82,8 @@ def simulation(self,x): SimRR.readline() for i in range(730): val= SimRR.readline() - #print(i,val) simulations.append(float(val)*self.Factor) SimRR.close() - #except: - # SimRR = 795 * [0] except: 'Model has failed' simulations=[np.nan]*795 #Assign bad values - model might have crashed @@ -96,6 +96,5 @@ def evaluation(self): return self.evals def objectivefunction(self,simulation,evaluation, params=None): - #like = spotpy.likelihoods.gaussianLikelihoodMeasErrorOut(evaluation,simulation) # Works good - like = spotpy.objectivefunctions.nashsutcliffe(evaluation,simulation) # Works good - return like \ No newline at end of file + like = spotpy.objectivefunctions.nashsutcliffe(evaluation,simulation) # Just an example, please choose an appropriate objective function depending on the used algorithm + return like diff --git a/spotpy/examples/spot_setup_hymod_python.py b/spotpy/examples/spot_setup_hymod_python.py index 57b326e3..876f1f41 100644 --- a/spotpy/examples/spot_setup_hymod_python.py +++ b/spotpy/examples/spot_setup_hymod_python.py @@ -12,22 +12,35 @@ from __future__ import print_function from __future__ import unicode_literals -import spotpy +from spotpy.parameter import Uniform +from spotpy.objectivefunctions import rmse from spotpy.examples.hymod_python.hymod import hymod import os class spot_setup(object): - def __init__(self): + cmax = Uniform(low=1.0 , high=500, optguess=412.33) + bexp = Uniform(low=0.1 , high=2.0, optguess=0.1725) + alpha = Uniform(low=0.1 , high=0.99, optguess=0.8127) + Ks = Uniform(low=0.001 , high=0.10, optguess=0.0404) + Kq = Uniform(low=0.1 , high=0.99, optguess=0.5592) + #fake1 =spotpy.parameter.Uniform(low=0.1 , high=10, optguess=0.5592) + #fake2 =spotpy.parameter.Uniform(low=0.1 , high=10, optguess=0.5592) + + def __init__(self, obj_func=None): + #Just a way to keep this example flexible and applicable to various examples + self.obj_func = obj_func #Transform [mm/day] into [l s-1], where 1.783 is the catchment area self.Factor = 1.783 * 1000 * 1000 / (60 * 60 * 24) #Load Observation data from file self.PET,self.Precip = [], [] self.date,self.trueObs = [], [] + #Find Path to Hymod on users system self.owd = os.path.dirname(os.path.realpath(__file__)) self.hymod_path = self.owd+os.sep+'hymod_python' climatefile = open(self.hymod_path+os.sep+'hymod_input.csv', 'r') headerline = climatefile.readline()[:-1] + #Read model forcing in working storage (this is done only ones) if ';' in headerline: self.delimiter = ';' else: @@ -39,29 +52,27 @@ def __init__(self): self.Precip.append(float(values[1])) self.PET.append(float(values[2])) self.trueObs.append(float(values[3])) - climatefile.close() - self.params = [spotpy.parameter.Uniform('cmax',low=1.0 , high=500, optguess=412.33), - spotpy.parameter.Uniform('bexp',low=0.1 , high=2.0, optguess=0.1725), - spotpy.parameter.Uniform('alpha',low=0.1 , high=0.99, optguess=0.8127), - spotpy.parameter.Uniform('Ks',low=0.0 , high=0.10, optguess=0.0404), - spotpy.parameter.Uniform('Kq',low=0.1 , high=0.99, optguess=0.5592), - spotpy.parameter.Uniform('fake1',low=0.1 , high=10, optguess=0.5592), - spotpy.parameter.Uniform('fake2',low=0.1 , high=10, optguess=0.5592)] - def parameters(self): - return spotpy.parameter.generate(self.params) - def simulation(self,x): + #Here the model is actualy startet with one paramter combination data = hymod(self.Precip, self.PET, x[0], x[1], x[2], x[3], x[4]) sim=[] for val in data: sim.append(val*self.Factor) + #The first year of simulation data is ignored (warm-up) return sim[366:] def evaluation(self): return self.trueObs[366:] def objectivefunction(self,simulation,evaluation, params=None): - like = spotpy.likelihoods.gaussianLikelihoodMeasErrorOut(evaluation,simulation) + #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 + like = rmse(evaluation,simulation) + else: + #Way to ensure flexible spot setup class + like = self.obj_func(evaluation,simulation) return like \ No newline at end of file diff --git a/spotpy/examples/spot_setup_hymod_python_pareto.py b/spotpy/examples/spot_setup_hymod_python_pareto.py new file mode 100644 index 00000000..ceb2fffa --- /dev/null +++ b/spotpy/examples/spot_setup_hymod_python_pareto.py @@ -0,0 +1,72 @@ +''' +Copyright 2015 by Tobias Houska +This file is part of Statistical Parameter Estimation Tool (SPOTPY). + +:author: Tobias Houska + +This example implements the python version of hymod into SPOTPY. +''' + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals + +import spotpy +from spotpy.examples.hymod_python.hymod import hymod +import os +import numpy as np + +class spot_setup(object): + cmax = spotpy.parameter.Uniform(low=1.0 , high=500, optguess=412.33) + bexp = spotpy.parameter.Uniform(low=0.1 , high=2.0, optguess=0.1725) + alpha = spotpy.parameter.Uniform(low=0.1 , high=0.99, optguess=0.8127) + Ks = spotpy.parameter.Uniform(low=0.0 , high=0.10, optguess=0.0404) + Kq = spotpy.parameter.Uniform(low=0.1 , high=0.99, optguess=0.5592) + + def __init__(self): + #Transform [mm/day] into [l s-1], where 1.783 is the catchment area + self.Factor = 1.783 * 1000 * 1000 / (60 * 60 * 24) + #Load Observation data from file + self.PET,self.Precip = [], [] + self.date,self.trueObs = [], [] + self.owd = os.path.dirname(os.path.realpath(__file__)) + self.hymod_path = self.owd+os.sep+'hymod_python' + climatefile = open(self.hymod_path+os.sep+'hymod_input.csv', 'r') + headerline = climatefile.readline()[:-1] + + if ';' in headerline: + self.delimiter = ';' + else: + self.delimiter = ',' + self.header = headerline.split(self.delimiter) + for line in climatefile: + values = line.strip().split(self.delimiter) + self.date.append(str(values[0])) + self.Precip.append(float(values[1])) + self.PET.append(float(values[2])) + self.trueObs.append(float(values[3])) + + climatefile.close() + + + def simulation(self,x): + data = hymod(self.Precip, self.PET, x[0], x[1], x[2], x[3], x[4]) + sim=[] + for val in data: + sim.append(val*self.Factor) + return sim[366:] + + def evaluation(self): + return self.trueObs[366:] + + def objectivefunction(self,simulation,evaluation, params=None): + return np.array([ + spotpy.likelihoods.gaussianLikelihoodMeasErrorOut(evaluation, simulation), + -spotpy.objectivefunctions.rmse(evaluation, simulation), + -spotpy.objectivefunctions.mse(evaluation, simulation), + -spotpy.objectivefunctions.pbias(evaluation, simulation), + spotpy.likelihoods.NashSutcliffeEfficiencyShapingFactor(evaluation, simulation), + spotpy.likelihoods.ABCBoxcarLikelihood(evaluation, simulation), + spotpy.likelihoods.LikelihoodAR1NoC(evaluation, simulation) + ]) \ No newline at end of file diff --git a/spotpy/examples/spot_setup_hymod_unix.py b/spotpy/examples/spot_setup_hymod_unix.py new file mode 100644 index 00000000..cf003f7a --- /dev/null +++ b/spotpy/examples/spot_setup_hymod_unix.py @@ -0,0 +1,109 @@ +''' +Copyright 2015 by Tobias Houska +This file is part of Statistical Parameter Estimation Tool (SPOTPY). + +:author: Tobias Houska + +This example implements the external hydrological model HYMOD into SPOTPY. +''' +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals +import numpy as np +try: + import spotpy +except ImportError: + import sys + sys.path.append(".") + import spotpy +import os +import multiprocessing as mp +from distutils.dir_util import copy_tree, remove_tree +#from shutil import rmtree +import sys + +class spot_setup(object): + def __init__(self,parallel='seq'): + + self.params = [spotpy.parameter.Uniform('cmax',low=1.0 , high=500, optguess=412.33), + spotpy.parameter.Uniform('bexp',low=0.1 , high=2.0, optguess=0.1725), + spotpy.parameter.Uniform('alpha',low=0.1 , high=0.99, optguess=0.8127), + spotpy.parameter.Uniform('Ks',low=0.0 , high=0.10, optguess=0.0404), + spotpy.parameter.Uniform('Kq',low=0.1 , high=0.99, optguess=0.5592)] + + self.curdir = os.getcwd() + self.owd = os.path.dirname(os.path.realpath(__file__)) + self.hymod_path = self.owd+os.sep+'hymod_unix' + self.evals = list(np.genfromtxt(self.hymod_path+os.sep+'bound.txt',skip_header=65)[:,3])[:730] + self.Factor = 1944 * (1000 * 1000 ) / (1000 * 60 * 60 * 24) + self.parallel = parallel + + def parameters(self): + return spotpy.parameter.generate(self.params) + + def simulation(self,x): + + if self.parallel == 'seq': + call = '' + elif self.parallel == 'mpi': + #Running n parallel, care has to be taken when files are read or written + #Therefor we check the ID of the current computer core + call = str(int(os.environ['OMPI_COMM_WORLD_RANK'])+2) + #And generate a new folder with all underlying files + copy_tree(self.hymod_path, self.hymod_path+call) + + elif self.parallel == 'mpc': + #Running n parallel, care has to be taken when files are read or written + #Therefor we check the ID of the current computer core + call =str(os.getpid()) + #And generate a new folder with all underlying files + copy_tree(self.hymod_path, self.hymod_path+call) + else: + raise 'No call variable was assigned' + + os.chdir(self.hymod_path+call) + try: + if sys.version_info.major == 2: + params = file('Param.in', 'w') + elif sys.version_info.major == 3: + params = open('Param.in','w') + + for i in range(len(x)): + if i == len(x): + params.write(str(round(x[i],5))) + else: + params.write(str(round(x[i],5))+' ') + params.close() + + os.system('./hymod_%s.%s' % (sys.version_info.major, sys.version_info.minor)) + + #try: + if sys.version_info.major == 2: + SimRR = file('Q.out', 'r') + elif sys.version_info.major == 3: + SimRR = open('Q.out', 'r') + else: + raise Exception("Your python is too old for this example") + simulations=[] + for i in range(64): + SimRR.readline() + for i in range(730): + val= SimRR.readline() + simulations.append(float(val)*self.Factor) + SimRR.close() + except: + 'Model has failed' + simulations=[np.nan]*795 #Assign bad values - model might have crashed + os.chdir(self.curdir) + if self.parallel == 'mpi' or self.parallel == 'mpc': + remove_tree(self.hymod_path+call) + return simulations + + def evaluation(self): + return self.evals + + def objectivefunction(self,simulation,evaluation, params=None): + like = spotpy.objectivefunctions.nashsutcliffe(evaluation,simulation) # Just an example, please choose an appropriate objective function depending on the used algorithm + return like + diff --git a/spotpy/examples/spot_setup_rosenbrock.py b/spotpy/examples/spot_setup_rosenbrock.py index 7c33c429..99632b05 100644 --- a/spotpy/examples/spot_setup_rosenbrock.py +++ b/spotpy/examples/spot_setup_rosenbrock.py @@ -9,8 +9,8 @@ from __future__ import division from __future__ import print_function from __future__ import unicode_literals -import numpy as np +import numpy as np from spotpy.parameter import Uniform from spotpy.objectivefunctions import rmse @@ -18,12 +18,15 @@ class spot_setup(object): """ A 3 dimensional implementation of the Rosenbrock function - Result at (0,0,0) is 0. + Result at (1,1,1) is 0. """ x = Uniform(-10, 10, 1.5, 3.0, -10, 10, doc='x value of Rosenbrock function') y = Uniform(-10, 10, 1.5, 3.0, -10, 10, doc='y value of Rosenbrock function') z = Uniform(-10, 10, 1.5, 3.0, -10, 10, doc='z value of Rosenbrock function') - + + def __init__(self,obj_func=None): + self.obj_func = obj_func + def simulation(self, vector): x=np.array(vector) simulations= [sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)] @@ -33,6 +36,14 @@ def evaluation(self): observations = [0] return observations - def objectivefunction(self, simulation, evaluation): - objectivefunction = -rmse(evaluation=evaluation, simulation=simulation) - return objectivefunction \ No newline at end of file + def objectivefunction(self, simulation, evaluation, params=None): + + #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 + like = rmse(evaluation,simulation) + else: + #Way to ensure on flexible spot setup class + like = self.obj_func(evaluation,simulation) + return like diff --git a/spotpy/examples/tutorial_Parameterlist_iterator.py b/spotpy/examples/tutorial_Parameterlist_iterator.py index 3f0d4307..724bed36 100644 --- a/spotpy/examples/tutorial_Parameterlist_iterator.py +++ b/spotpy/examples/tutorial_Parameterlist_iterator.py @@ -15,11 +15,13 @@ class spot_setup(object): - slow = 1000 + 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 - spotpy.parameter.List('y',[0,1,2,5,7,8,9,0,1]) #Give possible y values as a List - ] + self.slow = 1000 + self.params = [spotpy.parameter.List('x',[1,2,3,4,5,6,7,8,9,0]), #Give possible x values as a List + spotpy.parameter.List('y',[0,1,2,5,6,7,8,9,0,1]) #Give possible y values as a List + + ] def parameters(self): return spotpy.parameter.generate(self.params) @@ -40,4 +42,4 @@ def objectivefunction(self,simulation,evaluation): 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 \ No newline at end of file +sampler.sample(10) #Choose equaly or less repetitions as you have parameters in your List diff --git a/spotpy/examples/tutorial_cmf_lumped.py b/spotpy/examples/tutorial_cmf_lumped.py index 2f76fcab..49a87713 100644 --- a/spotpy/examples/tutorial_cmf_lumped.py +++ b/spotpy/examples/tutorial_cmf_lumped.py @@ -50,7 +50,7 @@ def get_runs(default=1): # Create the sampler sampler = spotpy.algorithms.mc(model, parallel=parallel(), - dbname=model.dbname, dbformat='csv', + dbname=model.dbname, dbformat='hdf5', save_sim=True, sim_timeout=300) # Now we can sample with the implemented Monte Carlo algorithm: diff --git a/spotpy/examples/tutorial_dds_hymod.py b/spotpy/examples/tutorial_dds_hymod.py new file mode 100644 index 00000000..f94a061c --- /dev/null +++ b/spotpy/examples/tutorial_dds_hymod.py @@ -0,0 +1,99 @@ +# -*- coding: utf-8 -*- +''' +Copyright 2015 by Tobias Houska +This file is part of Statistical Parameter Estimation Tool (SPOTPY). + +:author: Tobias Houska + +This class holds example code how to use the dream algorithm +''' + +import numpy as np +try: + import spotpy +except ImportError: + import sys + sys.path.append(".") + import spotpy + +from spotpy.examples.spot_setup_hymod_python import spot_setup +import matplotlib.pyplot as plt + + +if __name__ == "__main__": + parallel ='seq' # Runs everthing in sequential mode + np.random.seed(2000) # Makes the results reproduceable + + # Initialize the Hymod example + # In this case, we tell the setup which algorithm we want to use, so + # we can use this exmaple for different algorithms + spot_setup=spot_setup(users_objective_function=spotpy.objectivefunctions.nashsutcliffe) + + #Select number of maximum allowed repetitions + rep=1000 + + # Create the SCE-UA sampler of spotpy, alt_objfun is set to None to force SPOTPY + # to jump into the def objectivefunction in the spot_setup class (default is + # spotpy.objectivefunctions.rmse) + sampler=spotpy.algorithms.dds(spot_setup, dbname='DDS_hymod', dbformat='csv') + + #Start the sampler, one can specify ngs, kstop, peps and pcento id desired + sampler.sample(rep) + results = sampler.getdata() + + fig= plt.figure(1,figsize=(9,5)) + plt.plot(results['like1']) + plt.show() + plt.ylabel('Objective function') + plt.xlabel('Iteration') + fig.savefig('hymod_objectivefunction.png',dpi=300) + + # Example plot to show the parameter distribution ###### + def plot_parameter_trace(ax, results, parameter): + ax.plot(results['par'+parameter.name],'.') + ax.set_ylabel(parameter.name) + ax.set_ylim(parameter.minbound, parameter.maxbound) + + def plot_parameter_histogram(ax, results, parameter): + #chooses the last 20% of the sample + ax.hist(results['par'+parameter.name][int(len(results)*0.9):], + bins =np.linspace(parameter.minbound,parameter.maxbound,20)) + ax.set_ylabel('Density') + ax.set_xlim(parameter.minbound, parameter.maxbound) + + fig= plt.figure(2,figsize=(9,9)) + + ax1 = plt.subplot(5,2,1) + plot_parameter_trace(ax1, results, spot_setup.cmax) + + ax2 = plt.subplot(5,2,2) + plot_parameter_histogram(ax2,results, spot_setup.cmax) + + ax3 = plt.subplot(5,2,3) + plot_parameter_trace(ax3, results, spot_setup.bexp) + + ax4 = plt.subplot(5,2,4) + plot_parameter_histogram(ax4, results, spot_setup.bexp) + + ax5 = plt.subplot(5,2,5) + plot_parameter_trace(ax5, results, spot_setup.alpha) + + ax6 = plt.subplot(5,2,6) + plot_parameter_histogram(ax6, results, spot_setup.alpha) + + ax7 = plt.subplot(5,2,7) + plot_parameter_trace(ax7, results, spot_setup.Ks) + + ax8 = plt.subplot(5,2,8) + plot_parameter_histogram(ax8, results, spot_setup.Ks) + + ax9 = plt.subplot(5,2,9) + plot_parameter_trace(ax9, results, spot_setup.Kq) + ax9.set_xlabel('Iterations') + + ax10 = plt.subplot(5,2,10) + plot_parameter_histogram(ax10, results, spot_setup.Kq) + ax10.set_xlabel('Parameter range') + + plt.show() + fig.savefig('hymod_parameters.png',dpi=300) \ No newline at end of file diff --git a/spotpy/examples/tutorial_dream_hymod.py b/spotpy/examples/tutorial_dream_hymod.py index 737443c5..c7cc3d9a 100644 --- a/spotpy/examples/tutorial_dream_hymod.py +++ b/spotpy/examples/tutorial_dream_hymod.py @@ -12,30 +12,30 @@ import spotpy #from spotpy.examples.spot_setup_hymod_exe import spot_setup from spotpy.examples.spot_setup_hymod_python import spot_setup -import pylab as plt - +import matplotlib.pyplot as plt +from spotpy.likelihoods import gaussianLikelihoodMeasErrorOut as GausianLike if __name__ == "__main__": parallel ='seq' # Initialize the Hymod example (will only work on Windows systems) #spot_setup=spot_setup(parallel=parallel) - spot_setup=spot_setup() + spot_setup=spot_setup(GausianLike) # Create the Dream sampler of spotpy, al_objfun is set to None to force SPOTPY # to jump into the def objectivefunction in the spot_setup class (default is # spotpy.objectivefunctions.log_p) #Select number of maximum repetitions - rep=10000 + rep=5000 # Select five chains and set the Gelman-Rubin convergence limit nChains = 4 convergence_limit = 1.2 runs_after_convergence = 100 - sampler=spotpy.algorithms.dream(spot_setup, dbname='DREAM_hymod', dbformat='csv', alt_objfun=None) + sampler=spotpy.algorithms.dream(spot_setup, dbname='DREAM_hymod', dbformat='csv') r_hat = sampler.sample(rep,nChains=nChains,convergence_limit=convergence_limit, - runs_after_convergence=runs_after_convergence, acceptance_test_option = 6) + runs_after_convergence=runs_after_convergence) @@ -66,7 +66,7 @@ # Example plot to show the convergence ################# - fig= plt.figure(figsize=(16,9)) + fig= plt.figure(figsize=(12,16)) plt.subplot(2,1,1) for i in range(int(max(results['chain']))+1): index=np.where(results['chain']==i) @@ -93,115 +93,110 @@ # Example plot to show the parameter distribution ###### - - def find_min_max(spot_setup): - randompar=spot_setup.parameters()['random'] - for i in range(1000): - randompar=np.column_stack((randompar,spot_setup.parameters()['random'])) - return np.amin(randompar,axis=1),np.amax(randompar,axis=1) + parameters = spotpy.parameter.get_parameters_array(spot_setup) - min_vs,max_vs = find_min_max(spot_setup) + min_vs,max_vs = parameters['minbound'], parameters['maxbound'] fig= plt.figure(figsize=(16,16)) plt.subplot(5,2,1) - x = results['par'+str(spot_setup.parameters()['name'][0])] + x = results['par'+str(parameters['name'][0])] for i in range(int(max(results['chain']))): index=np.where(results['chain']==i) plt.plot(x[index],'.') - plt.ylabel('x1') + plt.ylabel('cmax') plt.ylim(min_vs[0],max_vs[0]) plt.subplot(5,2,2) - x = results['par'+spot_setup.parameters()['name'][0]][int(len(results)*0.5):] + x = results['par'+parameters['name'][0]][int(len(results)*0.5):] normed_value = 1 hist, bins = np.histogram(x, bins=20, density=True) widths = np.diff(bins) hist *= normed_value plt.bar(bins[:-1], hist, widths) - plt.ylabel('x1') + plt.ylabel('cmax') plt.xlim(min_vs[0],max_vs[0]) plt.subplot(5,2,3) - x = results['par'+spot_setup.parameters()['name'][1]] + x = results['par'+parameters['name'][1]] for i in range(int(max(results['chain']))): index=np.where(results['chain']==i) plt.plot(x[index],'.') - plt.ylabel('x2') + plt.ylabel('bexp') plt.ylim(min_vs[1],max_vs[1]) plt.subplot(5,2,4) - x = results['par'+spot_setup.parameters()['name'][1]][int(len(results)*0.5):] + x = results['par'+parameters['name'][1]][int(len(results)*0.5):] normed_value = 1 hist, bins = np.histogram(x, bins=20, density=True) widths = np.diff(bins) hist *= normed_value plt.bar(bins[:-1], hist, widths) - plt.ylabel('x2') + plt.ylabel('bexp') plt.xlim(min_vs[1],max_vs[1]) plt.subplot(5,2,5) - x = results['par'+spot_setup.parameters()['name'][2]] + x = results['par'+parameters['name'][2]] for i in range(int(max(results['chain']))): index=np.where(results['chain']==i) plt.plot(x[index],'.') - plt.ylabel('x3') + plt.ylabel('alpha') plt.ylim(min_vs[2],max_vs[2]) plt.subplot(5,2,6) - x = results['par'+spot_setup.parameters()['name'][2]][int(len(results)*0.5):] + x = results['par'+parameters['name'][2]][int(len(results)*0.5):] normed_value = 1 hist, bins = np.histogram(x, bins=20, density=True) widths = np.diff(bins) hist *= normed_value plt.bar(bins[:-1], hist, widths) - plt.ylabel('x3') + plt.ylabel('alpha') plt.xlim(min_vs[2],max_vs[2]) plt.subplot(5,2,7) - x = results['par'+spot_setup.parameters()['name'][3]] + x = results['par'+parameters['name'][3]] for i in range(int(max(results['chain']))): index=np.where(results['chain']==i) plt.plot(x[index],'.') - plt.ylabel('x4') + plt.ylabel('Ks') plt.ylim(min_vs[3],max_vs[3]) plt.subplot(5,2,8) - x = results['par'+spot_setup.parameters()['name'][3]][int(len(results)*0.5):] + x = results['par'+parameters['name'][3]][int(len(results)*0.5):] normed_value = 1 hist, bins = np.histogram(x, bins=20, density=True) widths = np.diff(bins) hist *= normed_value plt.bar(bins[:-1], hist, widths) - plt.ylabel('x4') + plt.ylabel('Ks') plt.xlim(min_vs[3],max_vs[3]) plt.subplot(5,2,9) - x = results['par'+spot_setup.parameters()['name'][4]] + x = results['par'+parameters['name'][4]] for i in range(int(max(results['chain']))): index=np.where(results['chain']==i) plt.plot(x[index],'.') - plt.ylabel('x5') + plt.ylabel('Kq') plt.ylim(min_vs[4],max_vs[4]) plt.xlabel('Iterations') plt.subplot(5,2,10) - x = results['par'+spot_setup.parameters()['name'][4]][int(len(results)*0.5):] + x = results['par'+parameters['name'][4]][int(len(results)*0.5):] normed_value = 1 hist, bins = np.histogram(x, bins=20, density=True) widths = np.diff(bins) hist *= normed_value plt.bar(bins[:-1], hist, widths) - plt.ylabel('x5') + plt.ylabel('Kq') plt.xlabel('Parameter range') plt.xlim(min_vs[4],max_vs[4]) plt.show() diff --git a/spotpy/examples/tutorial_fast_hymod.py b/spotpy/examples/tutorial_fast_hymod.py new file mode 100644 index 00000000..4ea0f05e --- /dev/null +++ b/spotpy/examples/tutorial_fast_hymod.py @@ -0,0 +1,39 @@ +# -*- coding: utf-8 -*- +''' +Copyright 2015 by Tobias Houska +This file is part of Statistical Parameter Estimation Tool (SPOTPY). + +:author: Tobias Houska + +This class holds example code how to use the dream algorithm +''' + +try: + import spotpy +except ImportError: + import sys + sys.path.append(".") + import spotpy +from spotpy.examples.spot_setup_hymod_python import spot_setup + + +if __name__ == "__main__": + parallel ='seq' + # Initialize the Hymod example + spot_setup = spot_setup() + + #Select number of maximum repetitions + rep = 1000 + + #Start a sensitivity analysis + sampler = spotpy.algorithms.fast(spot_setup, dbname='FAST_hymod', dbformat='csv') + sampler.sample(rep) + + # Load the results gained with the fast sampler, stored in FAST_hymod.csv + results = spotpy.analyser.load_csv_results('FAST_hymod') + + # Example plot to show the sensitivity index of each parameter + spotpy.analyser.plot_fast_sensitivity(results, number_of_sensitiv_pars=3) + + # Example to get the sensitivity index of each parameter + SI = spotpy.analyser.get_sensitivity_of_fast(results) diff --git a/spotpy/examples/tutorial_likelihood.py b/spotpy/examples/tutorial_likelihood.py index 06e3de14..a0b9c4c3 100644 --- a/spotpy/examples/tutorial_likelihood.py +++ b/spotpy/examples/tutorial_likelihood.py @@ -350,7 +350,7 @@ def objectivefunction(self, simulation=simulation, evaluation=evaluation, params sampler = spotpy.algorithms.lhs(spot_setup, dbname='RosenMC', dbformat='csv', alt_objfun=None) sampler.sample(rep) results.append(sampler.getdata()) -import pprint + import pickle pickle.dump(results, open('mle_LimitsOfAcceptability_v2.p', 'wb')) diff --git a/spotpy/examples/tutorial_nsgaii.py b/spotpy/examples/tutorial_nsgaii.py index e3cb0f86..444f28ee 100644 --- a/spotpy/examples/tutorial_nsgaii.py +++ b/spotpy/examples/tutorial_nsgaii.py @@ -17,76 +17,193 @@ import spotpy import spotpy.algorithms -from spotpy.examples.hymod_python.hymod import hymod -import os import unittest - - -class spot_setup(object): - def __init__(self): - # Transform [mm/day] into [l s-1], where 1.783 is the catchment area - self.Factor = 1.783 * 1000 * 1000 / (60 * 60 * 24) - # Load Observation data from file - self.PET, self.Precip = [], [] - self.date, self.trueObs = [], [] - self.owd = os.path.dirname(os.path.realpath(__file__)) - self.hymod_path = self.owd + os.sep + 'hymod_python' - climatefile = open(self.hymod_path + os.sep + 'hymod_input.csv', 'r') - headerline = climatefile.readline()[:-1] - - if ';' in headerline: - self.delimiter = ';' - else: - self.delimiter = ',' - self.header = headerline.split(self.delimiter) - for line in climatefile: - values = line.strip().split(self.delimiter) - self.date.append(str(values[0])) - self.Precip.append(float(values[1])) - self.PET.append(float(values[2])) - self.trueObs.append(float(values[3])) - - climatefile.close() - self.params = [spotpy.parameter.Uniform('cmax', low=1.0, high=500, optguess=412.33), - spotpy.parameter.Uniform('bexp', low=0.1, high=2.0, optguess=0.1725), - spotpy.parameter.Uniform('alpha', low=0.1, high=0.99, optguess=0.8127), - spotpy.parameter.Uniform('Ks', low=0.0, high=0.10, optguess=0.0404), - spotpy.parameter.Uniform('Kq', low=0.1, high=0.99, optguess=0.5592), - spotpy.parameter.Uniform('fake1', low=0.1, high=10, optguess=0.5592), - spotpy.parameter.Uniform('fake2', low=0.1, high=10, optguess=0.5592)] - - def parameters(self): - return spotpy.parameter.generate(self.params) - - def simulation(self, x): - data = hymod(self.Precip, self.PET, x[0], x[1], x[2], x[3], x[4]) - sim = [] - for val in data: - sim.append(val * self.Factor) - return sim[366:] - - def evaluation(self): - return self.trueObs[366:] - - def objectivefunction(self, simulation, evaluation, params=None): - # like = spotpy.likelihoods.gaussianLikelihoodMeasErrorOut(evaluation,simulation) - # return -like - # like1 = spotpy.signatures.getSkewness(evaluation, simulation) - like1 = spotpy.objectivefunctions.agreementindex(evaluation, simulation) +from spotpy.examples.spot_setup_hymod_python import spot_setup +import matplotlib.pyplot as plt +import numpy as np + + +def multi_obj_func(evaluation, simulation): + #used to overwrite objective function in hymod example + like1 = abs(spotpy.objectivefunctions.pbias(evaluation, simulation)) + like2 = spotpy.objectivefunctions.rmse(evaluation, simulation) + like3 = spotpy.objectivefunctions.rsquared(evaluation, simulation)*-1 + return [like1, like2, like3] + +if __name__ == "__main__": + + + generations=40 + n_pop = 20 + skip_duplicates = True + + sp_setup=spot_setup(obj_func= multi_obj_func) + sampler = spotpy.algorithms.NSGAII(spot_setup=sp_setup, dbname='NSGA2', dbformat="csv") + + sampler.sample(generations, n_obj= 3, n_pop = n_pop, skip_duplicates = skip_duplicates) + #sampler.sample(generations=5, paramsamp=40) + + +# # user config +# +# n_var = 5 +# +# +# last = None +# first = None +# +# # output calibration +# +# df = pd.read_csv("NSGA2.csv") +# +# if last: +# df = df.iloc[-last:,:] +# elif first: +# df = df.iloc[:first,:] +# else: +# pass +# +# +# +# print(len(df)) +# # plot objective functions +# fig = plt.figure() +# for i,name in enumerate(df.columns[:n_obj]): +# ax = fig.add_subplot(n_obj,1,i +1) +# df.loc[::5,name].plot(lw=0.5,figsize=(18,8),ax = ax,color="black") +# plt.title(name) +# plt.show() +# +# last = 100 +# first = None +# +# x,y,z = df.iloc[-last:,0],df.iloc[-last:,1],df.iloc[-last:,2] +# fig = plt.figure() +# ax = fig.add_subplot(111, projection='3d') +# ax.scatter(x,y,z,marker="o") +# ax.set_xlabel("f0") +# ax.set_ylabel("f1") +# ax.set_zlabel("f2") +# plt.show() +# +# # plot parameters +# fig = plt.figure() +# for i,name in enumerate(df.columns[n_obj:8]): +# ax = fig.add_subplot(5,1,i +1) +# df.loc[:,name].plot(lw=0.5,figsize=(18,8),ax = ax,color="black") +# plt.title(name) +# plt.show() +# + + + + results = sampler.getdata() + + fig= plt.figure(1,figsize=(9,5)) + plt.plot(results['like1']) + plt.show() + plt.ylabel('Objective function') + plt.xlabel('Iteration') + fig.savefig('hymod_objectivefunction.png',dpi=300) + + # Example plot to show the parameter distribution ###### + def plot_parameter_trace(ax, results, parameter): + ax.plot(results['par'+parameter.name],'.') + ax.set_ylabel(parameter.name) + ax.set_ylim(parameter.minbound, parameter.maxbound) + + def plot_parameter_histogram(ax, results, parameter): + #chooses the last 20% of the sample + ax.hist(results['par'+parameter.name][int(len(results)*0.9):], + bins =np.linspace(parameter.minbound,parameter.maxbound,20)) + ax.set_ylabel('Density') + ax.set_xlim(parameter.minbound, parameter.maxbound) + + fig= plt.figure(2,figsize=(9,9)) + + ax1 = plt.subplot(5,2,1) + plot_parameter_trace(ax1, results, spot_setup.cmax) + + ax2 = plt.subplot(5,2,2) + plot_parameter_histogram(ax2,results, spot_setup.cmax) + + ax3 = plt.subplot(5,2,3) + plot_parameter_trace(ax3, results, spot_setup.bexp) + + ax4 = plt.subplot(5,2,4) + plot_parameter_histogram(ax4, results, spot_setup.bexp) + + ax5 = plt.subplot(5,2,5) + plot_parameter_trace(ax5, results, spot_setup.alpha) + + ax6 = plt.subplot(5,2,6) + plot_parameter_histogram(ax6, results, spot_setup.alpha) + + ax7 = plt.subplot(5,2,7) + plot_parameter_trace(ax7, results, spot_setup.Ks) + + ax8 = plt.subplot(5,2,8) + plot_parameter_histogram(ax8, results, spot_setup.Ks) + + ax9 = plt.subplot(5,2,9) + plot_parameter_trace(ax9, results, spot_setup.Kq) + ax9.set_xlabel('Iterations') + + ax10 = plt.subplot(5,2,10) + plot_parameter_histogram(ax10, results, spot_setup.Kq) + ax10.set_xlabel('Parameter range') + + plt.show() + fig.savefig('hymod_parameters.png',dpi=300) + + + # Example plot to show remaining parameter uncertainty # + fields=[word for word in results.dtype.names if word.startswith('sim')] + fig= plt.figure(3, figsize=(16,9)) + ax11 = plt.subplot(1,1,1) + q5,q25,q75,q95=[],[],[],[] + for field in fields: + q5.append(np.percentile(results[field][-100:-1],2.5)) + q95.append(np.percentile(results[field][-100:-1],97.5)) + ax11.plot(q5,color='dimgrey',linestyle='solid') + ax11.plot(q95,color='dimgrey',linestyle='solid') + ax11.fill_between(np.arange(0,len(q5),1),list(q5),list(q95),facecolor='dimgrey',zorder=0, + linewidth=0,label='parameter uncertainty') + ax11.plot(sp_setup.evaluation(),'r.',label='data') + ax11.set_ylim(-50,450) + ax11.set_xlim(0,729) + ax11.legend() + fig.savefig('python_hymod.png',dpi=300) + ######################################################### + + + x,y,z = results['like1'][-n_pop:],results['like2'][-n_pop:],results['like3'][-n_pop:] + fig = plt.figure(4) + ax12 = fig.add_subplot(111, projection='3d') + ax12.scatter(x,y,z,marker="o") + ax12.set_xlabel("pbias") + ax12.set_ylabel("rmse") + ax12.set_zlabel("rsquared") + plt.show() + + + +class Test_NSGAII(unittest.TestCase): + def multi_obj_func(evaluation, simulation): + #used to overwrite objective function in hymod example + like1 = abs(spotpy.objectivefunctions.pbias(evaluation, simulation)) like2 = spotpy.objectivefunctions.rmse(evaluation, simulation) - like3 = spotpy.signatures.getCoeffVariation(evaluation, simulation) + like3 = spotpy.objectivefunctions.rsquared(evaluation, simulation)*-1 return [like1, like2, like3] - - -class Test_NSGAII(unittest.TestCase): def setUp(self): - self.sp_setup = spot_setup() - self.sampler = spotpy.algorithms.NSGAII(self.sp_setup, dbname='NSGA2', dbformat="csv") + generations=40 + n_pop = 20 + skip_duplicates = True - self.sampler.sample(generations=5, paramsamp=40) + self.sp_setup=spot_setup(obj_func= multi_obj_func) + self.sampler = spotpy.algorithms.NSGAII(self.sp_setup, dbname='NSGA2', dbformat="csv") + self.sampler.sample(generations, n_obj= 3, n_pop = n_pop, skip_duplicates = skip_duplicates) def test_sampler_output(self): - self.assertGreaterEqual(400, len(self.sampler.getdata())) - self.assertLessEqual(300, len(self.sampler.getdata())) - + self.assertEqual(200, len(self.sampler.getdata())) diff --git a/spotpy/examples/tutorial_nsgaii_dltz1.py b/spotpy/examples/tutorial_nsgaii_dltz1.py new file mode 100644 index 00000000..96031b5b --- /dev/null +++ b/spotpy/examples/tutorial_nsgaii_dltz1.py @@ -0,0 +1,76 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals + +import spotpy +from spotpy.examples.spot_setup_dtlz1 import spot_setup +import numpy as np +import sys +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +import pandas as pd + + +if __name__ == "__main__": + #Create samplers for every algorithm: + results=[] + n_obj = 3 + spot_setup=spot_setup(n_var=5, n_obj=n_obj) + generations=10 + n_pop = 30 + skip_duplicates = False + + sampler=spotpy.algorithms.NSGAII(spot_setup=spot_setup, + dbname='NSGA2', + dbformat='csv', + save_sim=True) + sampler.sample(generations,n_obj=3, n_pop=n_pop,skip_duplicates=skip_duplicates) + + + last = None + first = None + + # output calibration + + df = pd.read_csv("NSGA2.csv") + + df["like3"] = df.like3 * -1 + + + if last: + df = df.iloc[-last:,:] + elif first: + df = df.iloc[:first,:] + else: + pass + + + + # plot objective functions + fig = plt.figure() + for i,name in enumerate(df.columns[:n_obj]): + ax = fig.add_subplot(n_obj,1,i +1) + df.loc[::5,name].plot(lw=0.5,figsize=(18,8),ax = ax,color="black") + plt.title(name) + plt.show() + + + + x,y,z = df.iloc[-n_pop:,0],df.iloc[-n_pop:,1],df.iloc[-n_pop:,2] + fig = plt.figure() + ax = fig.add_subplot(111, projection='3d') + ax.scatter(x,y,z,marker="o") + ax.set_xlabel("x") + ax.set_ylabel("y") + ax.set_zlabel("z") + plt.show() + + # plot parameters + fig = plt.figure() + for i,name in enumerate(df.columns[n_obj:8]): + ax = fig.add_subplot(5,1,i +1) + df.loc[:,name].plot(lw=0.5,figsize=(18,8),ax = ax,color="black") + plt.title(name) + plt.show() + diff --git a/spotpy/examples/tutorial_own_database.py b/spotpy/examples/tutorial_own_database.py index b75e0fcd..2fc4f5e9 100644 --- a/spotpy/examples/tutorial_own_database.py +++ b/spotpy/examples/tutorial_own_database.py @@ -54,5 +54,5 @@ def save(self, objectivefunctions, parameter, simulations, *args, **kwargs): # 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 equal or less repetitions as you have parameters in your List +sampler.sample(9) # Choose equal or less repetitions as you have parameters in your List spot_setup.database.close() # Close the created txt file diff --git a/spotpy/examples/tutorial_padds.py b/spotpy/examples/tutorial_padds.py new file mode 100644 index 00000000..3dd54cdc --- /dev/null +++ b/spotpy/examples/tutorial_padds.py @@ -0,0 +1,81 @@ +# -*- coding: utf-8 -*- +import numpy as np +import sys +try: + import spotpy +except ModuleNotFoundError: + sys.path.append(".") + import spotpy + + +def ZDT1(x): + """ + Zitzler–Deb–Thiele's function number 1. Is used to benchmark or test algorithms, see also + https://en.wikipedia.org/wiki/Test_functions_for_optimization and Deb et al. 2002 IEEE. + + + ## The schematic tradoff looks like this + # /\ + # | + #1 . + # | + # | + # | . + # | + # | . + # | + # | . + # | + # | . + # | + # | . + # | . + # | . + # |------------------------------------------.------> + # 1 + + ZDT1 needs 30 parameters, which are in [0,1]. + :param x: + :return: Two Value Tuple + """ + a = x[0] # objective 1 value + g = 0 + for i in range(1,30): + g = g + x[i] + g = 1 + 9 * g / 29 + b = g * (1 - (x[0] / g) ** 0.5) # objective 2 value + return np.array([a,b]) + + +class padds_spot_setup(object): + def __init__(self): + self.params = [] + for i in range(30): + self.params.append(spotpy.parameter.Uniform(str(i+1), 0, 1, 0, 0, 0, 1,doc="param no " + str(i+1))) + + def parameters(self): + return spotpy.parameter.generate(self.params) + + def simulation(self, vector): + firstSum = 0.0 + secondSum = 0.0 + for c in range(len(vector)): + firstSum += c**2.0 + secondSum += np.cos(2.0*np.pi*vector[c]) + n = float(len(vector)) + return [-20.0*np.exp(-0.2*np.sqrt(firstSum/n)) - np.exp(secondSum/n) + 20 + np.e] + + def evaluation(self): + observations = [0] + return observations + + def objectivefunction(self, simulation, evaluation, params): + para, names = params + if len(para) != 30: + raise Exception("params must have length 30") + return ZDT1(para) + +spot_setup = padds_spot_setup() + +sampler = spotpy.algorithms.padds(spot_setup, dbname='padds_hymod', dbformat='csv') +res = sampler.sample(10000,trials=1) diff --git a/spotpy/examples/tutorial_padds_hymod.py b/spotpy/examples/tutorial_padds_hymod.py new file mode 100644 index 00000000..7a9c695c --- /dev/null +++ b/spotpy/examples/tutorial_padds_hymod.py @@ -0,0 +1,164 @@ +# -*- coding: utf-8 -*- +''' +Copyright 2015 by Tobias Houska +This file is part of Statistical Parameter Estimation Tool (SPOTPY). + +:author: Tobias Houska + +This class holds example code how to use the dream algorithm +''' + +import numpy as np +try: + import spotpy +except ImportError: + import sys + sys.path.append(".") + import spotpy + +from spotpy.examples.spot_setup_hymod_python_pareto import spot_setup +import matplotlib.pyplot as plt + + +if __name__ == "__main__": + parallel ='seq' # Runs everthing in sequential mode + np.random.seed(2000) # Makes the results reproduceable + + # Initialize the Hymod example + # In this case, we tell the setup which algorithm we want to use, so + # we can use this exmaple for different algorithms + spot_setup=spot_setup() + + #Select number of maximum allowed repetitions + rep=3000 + + # Create the SCE-UA sampler of spotpy, alt_objfun is set to None to force SPOTPY + # to jump into the def objectivefunction in the spot_setup class (default is + # spotpy.objectivefunctions.rmse) + sampler=spotpy.algorithms.padds(spot_setup, dbname='padds_hymod', dbformat='csv') + + #Start the sampler, one can specify ngs, kstop, peps and pcento id desired + print(sampler.sample(rep, metric="crowd_distance")) + + # Load the results gained with the sceua sampler, stored in SCEUA_hymod.csv + #results = spotpy.analyser.load_csv_results('DDS_hymod') + + + results = sampler.getdata() + from pprint import pprint + #pprint(results) + pprint(results['chain']) + + for likno in range(1,5): + fig_like1 = plt.figure(1,figsize=(9,5)) + plt.plot(results['like'+str(likno)]) + plt.show() + fig_like1.savefig('hymod_padds_objectivefunction_' + str(likno) + '.png', dpi=300) + + + plt.ylabel('RMSE') + plt.xlabel('Iteration') + + + # Example plot to show the parameter distribution ###### + fig= plt.figure(2,figsize=(9,9)) + normed_value = 1 + + plt.subplot(5,2,1) + x = results['parcmax'] + for i in range(int(max(results['chain'])-1)): + index=np.where(results['chain']==i+1) #Ignores burn-in chain + plt.plot(x[index],'.') + plt.ylabel('cmax') + plt.ylim(spot_setup.cmax.minbound, spot_setup.cmax.maxbound) + + + plt.subplot(5,2,2) + x = x[int(len(results)*0.9):] #choose the last 10% of the sample + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('cmax') + plt.xlim(spot_setup.cmax.minbound, spot_setup.cmax.maxbound) + + + plt.subplot(5,2,3) + x = results['parbexp'] + for i in range(int(max(results['chain'])-1)): + index=np.where(results['chain']==i+1) + plt.plot(x[index],'.') + plt.ylabel('bexp') + plt.ylim(spot_setup.bexp.minbound, spot_setup.bexp.maxbound) + + plt.subplot(5,2,4) + x = x[int(len(results)*0.9):] + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('bexp') + plt.xlim(spot_setup.bexp.minbound, spot_setup.bexp.maxbound) + + + + plt.subplot(5,2,5) + x = results['paralpha'] + print(x) + for i in range(int(max(results['chain'])-1)): + index=np.where(results['chain']==i+1) + plt.plot(x[index],'.') + plt.ylabel('alpha') + plt.ylim(spot_setup.alpha.minbound, spot_setup.alpha.maxbound) + + + plt.subplot(5,2,6) + x = x[int(len(results)*0.9):] + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('alpha') + plt.xlim(spot_setup.alpha.minbound, spot_setup.alpha.maxbound) + + + plt.subplot(5,2,7) + x = results['parKs'] + for i in range(int(max(results['chain'])-1)): + index=np.where(results['chain']==i+1) + plt.plot(x[index],'.') + plt.ylabel('Ks') + plt.ylim(spot_setup.Ks.minbound, spot_setup.Ks.maxbound) + + + plt.subplot(5,2,8) + x = x[int(len(results)*0.9):] + + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('Ks') + plt.xlim(spot_setup.Ks.minbound, spot_setup.Ks.maxbound) + + + plt.subplot(5,2,9) + x = results['parKq'] + for i in range(int(max(results['chain'])-1)): + index=np.where(results['chain']==i+1) + plt.plot(x[index],'.') + plt.ylabel('Kq') + plt.ylim(spot_setup.Kq.minbound, spot_setup.Kq.maxbound) + plt.xlabel('Iterations') + + plt.subplot(5,2,10) + x = x[int(len(results)*0.9):] + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('Kq') + plt.xlabel('Parameter range') + plt.xlim(spot_setup.Kq.minbound, spot_setup.Kq.maxbound) + plt.show() + fig.savefig('hymod_parameters.png',dpi=300) \ No newline at end of file diff --git a/spotpy/examples/tutorial_parallel_computing_hymod.py b/spotpy/examples/tutorial_parallel_computing_hymod.py index 72e19217..5fe05ba7 100644 --- a/spotpy/examples/tutorial_parallel_computing_hymod.py +++ b/spotpy/examples/tutorial_parallel_computing_hymod.py @@ -8,8 +8,12 @@ This class holds example code how to use the dream algorithm ''' -import spotpy -from spotpy.examples.spot_setup_hymod_exe import spot_setup +try: + import spotpy +except ImportError: + import sys + sys.path.append(".") + import spotpy #from spotpy.examples.spot_setup_hymod_python import spot_setup import sys @@ -20,11 +24,15 @@ #If you are working on a windows computer, this will be True if 'win' in sys.platform: parallel ='mpc' + from spotpy.examples.spot_setup_hymod_exe import spot_setup + # If not you probably have a Mac or Unix system. Then save this file and start it - # from your terminal with "mpirun -c 20 your_script.py" + # from your terminal with "mpirun -c 20 python your_script.py" else: parallel = 'mpi' + from spotpy.examples.spot_setup_hymod_unix import spot_setup + # Initialize the Hymod example (this runs on Windows systems only) # Checkout the spot_setup_hymod_exe.py to see how you need to adopt # your spot_setup class to run in parallel @@ -33,13 +41,13 @@ spot_setup=spot_setup(parallel=parallel) # Initialize a sampler that is suited for parallel computing (all except MLE, MCMC and SA) - sampler=spotpy.algorithms.mc(spot_setup, dbname='DREAM_hymod', dbformat='csv', + sampler=spotpy.algorithms.mc(spot_setup, dbname='Parallel_hymod', dbformat='csv', parallel=parallel) # Sample in parlallel sampler.sample(rep) # Load results from file - results = spotpy.analyser.load_csv_results('DREAM_hymod') + #results = spotpy.analyser.load_csv_results('Parallel_hymod') # Plot best model run - spotpy.analyser.plot_bestmodelrun(results,spot_setup.evaluation()) \ No newline at end of file + #spotpy.analyser.plot_bestmodelrun(results,spot_setup.evaluation()) \ No newline at end of file diff --git a/spotpy/examples/tutorial_rosenbrock.py b/spotpy/examples/tutorial_rosenbrock.py index d699b2cd..d4c8c36d 100644 --- a/spotpy/examples/tutorial_rosenbrock.py +++ b/spotpy/examples/tutorial_rosenbrock.py @@ -26,86 +26,87 @@ #Create samplers for every algorithm: results=[] -spot_setup=spot_setup() rep=1000 timeout=10 #Given in Seconds parallel = "seq" dbformat = "csv" -sampler=spotpy.algorithms.mc(spot_setup,parallel=parallel, dbname='RosenMC', dbformat=dbformat, sim_timeout=timeout) -print(describe(sampler)) -sampler.sample(rep) -results.append(sampler.getdata()) - +# Bayesian algorithms should be run with a likelihood function +bayesian_likelihood_func = spotpy.likelihoods.gaussianLikelihoodMeasErrorOut -sampler=spotpy.algorithms.lhs(spot_setup,parallel=parallel, dbname='RosenLHS', dbformat=dbformat, sim_timeout=timeout) +sampler = spotpy.algorithms.mc(spot_setup(), + parallel=parallel, dbname='RosenMC', dbformat=dbformat, sim_timeout=timeout) print(describe(sampler)) sampler.sample(rep) results.append(sampler.getdata()) -sampler=spotpy.algorithms.mle(spot_setup, parallel=parallel, dbname='RosenMLE', dbformat=dbformat, sim_timeout=timeout) -print(describe(sampler)) +sampler = spotpy.algorithms.lhs(spot_setup(), + parallel=parallel, dbname='RosenLHS', dbformat=dbformat, sim_timeout=timeout) sampler.sample(rep) results.append(sampler.getdata()) -# -sampler=spotpy.algorithms.mcmc(spot_setup, parallel=parallel, dbname='RosenMCMC', dbformat=dbformat, sim_timeout=timeout) -print(describe(sampler)) + + +sampler=spotpy.algorithms.mle(spot_setup(obj_func=bayesian_likelihood_func), + parallel=parallel, dbname='RosenMLE', dbformat=dbformat, sim_timeout=timeout) sampler.sample(rep) results.append(sampler.getdata()) -sampler=spotpy.algorithms.sceua(spot_setup, parallel=parallel, dbname='RosenSCEUA', dbformat=dbformat, sim_timeout=timeout) -print(describe(sampler)) + +sampler=spotpy.algorithms.sceua(spot_setup(), + parallel=parallel, dbname='RosenSCEUA', dbformat=dbformat, sim_timeout=timeout) sampler.sample(rep,ngs=4) results.append(sampler.getdata()) -sampler=spotpy.algorithms.sa(spot_setup, parallel=parallel, dbname='RosenSA', dbformat=dbformat, sim_timeout=timeout) -print(describe(sampler)) + +sampler=spotpy.algorithms.sa(spot_setup(obj_func=bayesian_likelihood_func), + parallel=parallel, dbname='RosenSA', dbformat=dbformat, sim_timeout=timeout) sampler.sample(rep) results.append(sampler.getdata()) -sampler=spotpy.algorithms.demcz(spot_setup,parallel=parallel, dbname='RosenDEMCz', dbformat=dbformat, sim_timeout=timeout) -print(describe(sampler)) -sampler.sample(rep,nChains=4) -results.append(sampler.getdata()) -sampler=spotpy.algorithms.rope(spot_setup, parallel=parallel, dbname='RosenROPE', dbformat=dbformat,sim_timeout=timeout) -print(describe(sampler)) +sampler=spotpy.algorithms.rope(spot_setup(), + parallel=parallel, dbname='RosenROPE', dbformat=dbformat,sim_timeout=timeout) sampler.sample(rep) results.append(sampler.getdata()) -sampler=spotpy.algorithms.abc(spot_setup, parallel=parallel, dbname='RosenABC', dbformat=dbformat,sim_timeout=timeout) -print(describe(sampler)) +sampler=spotpy.algorithms.abc(spot_setup(), + parallel=parallel, dbname='RosenABC', dbformat=dbformat,sim_timeout=timeout) sampler.sample(rep) results.append(sampler.getdata()) -sampler=spotpy.algorithms.fscabc(spot_setup, parallel=parallel, dbname='RosenFSABC', dbformat=dbformat, sim_timeout=timeout) -print(describe(sampler)) +sampler=spotpy.algorithms.fscabc(spot_setup(), + parallel=parallel, dbname='RosenFSABC', dbformat=dbformat, sim_timeout=timeout) sampler.sample(rep) results.append(sampler.getdata()) -sampler=spotpy.algorithms.demcz(spot_setup, parallel=parallel, dbname='RosenDEMCZ', dbformat=dbformat, sim_timeout=timeout) -print(describe(sampler)) +sampler=spotpy.algorithms.mcmc(spot_setup(obj_func=bayesian_likelihood_func), + parallel=parallel, dbname='RosenMCMC', dbformat=dbformat, sim_timeout=timeout) sampler.sample(rep) results.append(sampler.getdata()) -sampler=spotpy.algorithms.dream(spot_setup, parallel=parallel, dbname='RosenDREAM', dbformat=dbformat, sim_timeout=timeout) -print(describe(sampler)) -sampler.sample(rep) +sampler=spotpy.algorithms.demcz(spot_setup(obj_func = bayesian_likelihood_func), + parallel=parallel, dbname='RosenDEMCz', dbformat=dbformat, sim_timeout=timeout) +sampler.sample(rep,nChains=4) +results.append(sampler.getdata()) + +sampler=spotpy.algorithms.dream(spot_setup(obj_func = bayesian_likelihood_func), + parallel=parallel, dbname='RosenDREAM', dbformat=dbformat, sim_timeout=timeout) +sampler.sample(rep,nChains=4) results.append(sampler.getdata()) print(results[0].dtype) # Check for Travis: Get the last sampled parameter for x -evaluation = spot_setup.evaluation() +evaluation = spot_setup().evaluation() # Example how to plot the data -#algorithms = ['mc','lhs','mle','mcmc','sceua','sa','demcz','rope','abc','fscabc', 'demcz', 'dream'] -#spotpy.analyser.plot_parametertrace_algorithms(results,algorithmnames=algorithms,parameternames=['x','y']) +algorithms = ['mc','lhs','mle','sceua','sa','rope','abc','fscabc', 'mcmc', 'demcz', 'dream'] +spotpy.analyser.plot_parametertrace_algorithms(results, algorithms, spot_setup()) diff --git a/spotpy/examples/tutorial_sceua_hymod.py b/spotpy/examples/tutorial_sceua_hymod.py new file mode 100644 index 00000000..25c7129c --- /dev/null +++ b/spotpy/examples/tutorial_sceua_hymod.py @@ -0,0 +1,150 @@ +# -*- coding: utf-8 -*- +''' +Copyright 2015 by Tobias Houska +This file is part of Statistical Parameter Estimation Tool (SPOTPY). + +:author: Tobias Houska + +This class holds example code how to use the dream algorithm +''' + +import numpy as np +import spotpy +from spotpy.examples.spot_setup_hymod_python import spot_setup +import matplotlib.pyplot as plt + + +if __name__ == "__main__": + parallel ='seq' # Runs everthing in sequential mode + np.random.seed(2000) # Makes the results reproduceable + + # Initialize the Hymod example + # In this case, we tell the setup which algorithm we want to use, so + # we can use this exmaple for different algorithms + spot_setup=spot_setup(users_objective_function=spotpy.objectivefunctions.rmse) + + #Select number of maximum allowed repetitions + rep=500 + filename = 'SCEUA_hymod' + # Create the SCE-UA sampler of spotpy, alt_objfun is set to None to force SPOTPY + # to jump into the def objectivefunction in the spot_setup class (default is + # spotpy.objectivefunctions.rmse) + sampler=spotpy.algorithms.sceua(spot_setup, dbname='SCEUA_hymod', dbformat='csv') + + #Start the sampler, one can specify ngs, kstop, peps and pcento id desired + sampler.sample(rep)#,ngs=10, kstop=50, peps=0.1, pcento=0.1) + + # Load the results gained with the sceua sampler, stored in SCEUA_hymod.csv + results = spotpy.analyser.load_csv_results('SCEUA_hymod') + + + print(len(results), 'runs were saved.') + + fig= plt.figure(1,figsize=(9,5)) + plt.plot(results['like1']) + plt.show() + plt.ylabel('RMSE') + plt.xlabel('Iteration') + fig.savefig('hymod_objectivefunction.png',dpi=300) + + # Example plot to show the parameter distribution ###### + fig= plt.figure(2,figsize=(9,9)) + normed_value = 1 + + plt.subplot(5,2,1) + x = results['parcmax'] + for i in range(int(max(results['chain'])-1)): + index=np.where(results['chain']==i+1) #Ignores burn-in chain + plt.plot(x[index],'.') + plt.ylabel('cmax') + plt.ylim(spot_setup.cmax.minbound, spot_setup.cmax.maxbound) + + + plt.subplot(5,2,2) + x = x[int(len(results)*0.9):] #choose the last 10% of the sample + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('cmax') + plt.xlim(spot_setup.cmax.minbound, spot_setup.cmax.maxbound) + + + plt.subplot(5,2,3) + x = results['parbexp'] + for i in range(int(max(results['chain'])-1)): + index=np.where(results['chain']==i+1) + plt.plot(x[index],'.') + plt.ylabel('bexp') + plt.ylim(spot_setup.bexp.minbound, spot_setup.bexp.maxbound) + + plt.subplot(5,2,4) + x = x[int(len(results)*0.9):] + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('bexp') + plt.xlim(spot_setup.bexp.minbound, spot_setup.bexp.maxbound) + + + + plt.subplot(5,2,5) + x = results['paralpha'] + for i in range(int(max(results['chain'])-1)): + index=np.where(results['chain']==i+1) + plt.plot(x[index],'.') + plt.ylabel('alpha') + plt.ylim(spot_setup.alpha.minbound, spot_setup.alpha.maxbound) + + + plt.subplot(5,2,6) + x = x[int(len(results)*0.9):] + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('alpha') + plt.xlim(spot_setup.alpha.minbound, spot_setup.alpha.maxbound) + + + plt.subplot(5,2,7) + x = results['parKs'] + for i in range(int(max(results['chain'])-1)): + index=np.where(results['chain']==i+1) + plt.plot(x[index],'.') + plt.ylabel('Ks') + plt.ylim(spot_setup.Ks.minbound, spot_setup.Ks.maxbound) + + + plt.subplot(5,2,8) + x = x[int(len(results)*0.9):] + + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('Ks') + plt.xlim(spot_setup.Ks.minbound, spot_setup.Ks.maxbound) + + + plt.subplot(5,2,9) + x = results['parKq'] + for i in range(int(max(results['chain'])-1)): + index=np.where(results['chain']==i+1) + plt.plot(x[index],'.') + plt.ylabel('Kq') + plt.ylim(spot_setup.Kq.minbound, spot_setup.Kq.maxbound) + plt.xlabel('Iterations') + + plt.subplot(5,2,10) + x = x[int(len(results)*0.9):] + hist, bins = np.histogram(x, bins=20, density=True) + widths = np.diff(bins) + hist *= normed_value + plt.bar(bins[:-1], hist, widths) + plt.ylabel('Kq') + plt.xlabel('Parameter range') + plt.xlim(spot_setup.Kq.minbound, spot_setup.Kq.maxbound) + plt.show() + fig.savefig('hymod_parameters.png',dpi=300) \ No newline at end of file diff --git a/spotpy/gui/mpl.py b/spotpy/gui/mpl.py index 5732bd59..09e3179f 100644 --- a/spotpy/gui/mpl.py +++ b/spotpy/gui/mpl.py @@ -191,7 +191,7 @@ def run(self, _=None): sim = self.setup.simulation(parset) objf = as_scalar(self.setup.objectivefunction(sim, self.setup.evaluation())) label = ('{:0.4g}=M('.format(objf) - + ', '.join('{f}={v:0.4g}'.format(f=f, v=v) for f, v in zip(parset._fields, parset)) + + ', '.join('{f}={v:0.4g}'.format(f=f, v=v) for f, v in zip(parset.name, parset)) + ')') self.lines.extend(self.ax.plot(sim, '-', label=label)) self.ax.legend() diff --git a/spotpy/hydrology/__init__.py b/spotpy/hydrology/__init__.py new file mode 100644 index 00000000..cfe25ae1 --- /dev/null +++ b/spotpy/hydrology/__init__.py @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- +''' +Copyright (c) 2015 by Tobias Houska + +This file is part of Statistical Parameter Estimation Tool (SPOTPY). + +:author: Tobias Houska +''' diff --git a/spotpy/hydrology/signatures.py b/spotpy/hydrology/signatures.py new file mode 100644 index 00000000..e7f7c987 --- /dev/null +++ b/spotpy/hydrology/signatures.py @@ -0,0 +1,571 @@ +# -*- coding: utf-8 -*- +""" +Copyright 2017 by Tobias Houska +This file is part of Statistical Parameter Estimation Tool (SPOTPY). + +:author: Tobias Houska, Benjamin Manns, Philipp Kraft + +This module calculates scalars from timeseries to describe signature behaviour. + +The signature behaviour indices are collected from different sources: + +.. [WESMCM2015] *Uncertainty in hydrological hydrology* + by I. K. Westerberg and H. K. McMillan, 2015 + `DOI: 10.5194/hess-19-3951-2015 `_ +.. [CLBGS2000] *Flow variables for ecological studies in temperate streams: groupings based on covariance*, + B Clausen, BJF Biggs 2000 + `DOI:10.1016/S0022-1694(00)00306-1 `_ +.. [YADV2007] *Regionalization of constraints on expected watershed response behavior for improved + predictions in ungauged basins*, M Yadev, T Wagener, H Gupta, 2007 + `DOI:10.1016/j.advwatres.2007.01.005`_ + +All methods without further parameters in this module should follow +the following template, where "signature" is replaced with the actual name + +>>> def get_signature(data, measurements_per_day=None): +... return sum(data) + +**Where:** + +- `data` is the data time series as any sequence of floats +- `measurements_per_day` is the raster length of the timeseries in seconds. + For daily time series, `measurements_per_day=86400`. Many hydrology ignore the `measurements_per_day`parameter, + but it needs to be provided to ensure a consistent interface + +**Please follow these guidelines for the docstring of a signature behaviour function:** + +- use a `:return:` keyword to give the returned variable(s) short names. Seperate several variables + with a comma and do not use a comma in the return line for anything else +- if possibile, use a `:limit:` keyword to define a strict and loose limit of deviation. + If the limit is given as % eg. `:limit: 10%, 90%`, then the limit is understood as a relative limit + (in terms of the orignal value). If no % is given, eg. `:limit: 0.1, 0.9` the value is used as an absolute value. + Absolute values make more sense when the signature behaviour index is already a relative value (eg. BFI, skewness) + + +If a method needs additional parameters, create a class for that method, +with the parameters as attributes of an instance and a __call__ method +with the same interface as the function above. + +>>> class Signature: +... def __init__(self, parameter): +... self.parameter = parameter +... def __call__(self, data, measurements_per_day=None): +... return sum(data) ** self.parameter + +For typical parametrizations, create instances of this class inside this module, +cf. to QuantileSignature and the get_qXXX methods +""" + + +import numpy as np +import inspect +import sys + + +class SignatureMethod: + """ + Wraps a signature method from this module to access multiple variables + """ + @classmethod + def find_all(cls): + """ + Finds and all signature methods of this module and returns + them as a list + :return: List of signature methods + """ + + current_module = sys.modules[__name__] + methods = inspect.getmembers(current_module) + return [cls(m, name) for name, m in methods if name.startswith('get_')] + + @classmethod + def run(cls, list_of_methods, data, measurements_per_day=1): + """ + Runs all signature methods from the given list and returns + the result as a list of (variable, result) tuples. + Multivariate hydrology are handled accordingly + + :param list_of_methods: A list of signature method objects + :param data: A time series sequence + :param measurements_per_day: Number of measurements per day (needed for some hydrology) + :return: a list of (variable, result) tuples, len(result) might be + longer then len(list_of_methods) + """ + res = [] + for m in list_of_methods: + res.extend(m(data, measurements_per_day)) + return res + + @classmethod + def extract_from_doc(cls, token, doc): + for doc_line in doc.split('\n'): + if doc_line.strip().startswith(token): + var_str = doc_line[len(token):].strip() + return [v.strip() for v in var_str.split(',')] + return [] + + def __init__(self, method, name): + self.method = method + self.name = name[4:] + + doc = inspect.getdoc(self.method) + self.variables = self.extract_from_doc(':return:', doc) or [self.name] + + def __call__(self, data, measurements_per_day=1): + """ + Calculates the signature behaviour indices of this method for data + :param data: The runoff timeseries data as a numeric sequence + :param measurements_per_day: the measurements_per_day of the timeseries (unused) + :return: A list of variable / value pairs + """ + res = self.method(data, measurements_per_day) + if len(self.variables) > 1: + return [(var, val) for var, val in zip(self.variables, res)] + else: + return [(self.variables[0], res)] + + def __repr__(self): + return 'Sig({n})->{v}'.format(n=self.name, v=', '.join(self.variables)) + + + +def remove_nan(data): + """ + Returns the the data timeseries with NaN and inifinite values removed + + :param data: The timeseries data as a numeric sequence + :return: data[np.isfinite(data)], might be shorter than the input + """ + return np.array(data)[np.isfinite(data)] + + +def fill_nan(data): + """ + Returns the timeseries where any gaps (represented by NaN) are + filled, using a linear approximation between the neighbors. + Gaps at the beginning or end are filled with the first resp. last + valid entry + + :param data: The timeseries data as a numeric sequence + :return: The filled timeseries as array + """ + # All data indices + x = np.arange(len(data)) + # Valid data indices + xp = np.flatnonzero(np.isfinite(data)) + # Valid data + fp = remove_nan(data) + # Interpolate missing values + return np.interp(x, xp, fp) + + +def summarize(data, step, f): + """ + Summarizes data for step using function f + + Example: Get yearly minimum from a daily timeseries: + + >>> yearly_min = summarize(data, 365, np.min) + + :param data: the timeseries + :param step: int the number of time steps to summarize + :param f: a function to summarize, eg. np.min, np.max etc. + Must except an arraylike as the only parameter and return a + float + :return: The summarized timeseries + """ + if len(data) < step: + return np.array([f(data)]) + return np.fromiter((f(data[i:i+step]) + for i in range(0, len(data), step)), + count=len(data) // step, dtype=float) + + +class Quantile(object): + """ + Calculates the % exceedance of the flow duration curve. + + Used as a signature behaviour index by [WESMCM2015]_ + + :return: Q_{} + """ + def __init__(self, quantile): + self.quantile = quantile + self.__doc__ = inspect.getdoc(type(self)).replace('', '{:0.4g}'.format(self.quantile)) + + def __call__(self, data, measurements_per_day=None): + """ + Calculates the flow % of the time exceeded from a runoff time series. + + :param data: The timeseries data as a numeric sequence + :param measurements_per_day: Unused + :return: quantile signature behaviour index + """ + return np.percentile(remove_nan(data), 100 - self.quantile) + + def __repr__(self): + return 'q({:0.2f}%)'.format(self.quantile) + + +get_q0_01 = Quantile(0.01) +get_q0_1 = Quantile(0.1) +get_q1 = Quantile(1) +get_q5 = Quantile(5) +get_q50 = Quantile(50) +get_q85 = Quantile(85) +get_q95 = Quantile(95) +get_q99 = Quantile(99) + + +def get_mean(data, measurements_per_day=None): + """ + Calculates the mean from a runoff time series. + + Used as a signature behaviour index by [WESMCM2015]_ and [CLBGS2000]_ + + :param data: The runoff timeseries data as a numeric sequence + :param measurements_per_day: the measurements_per_day of the timeseries (unused) + :return: Q_{mean} + :limit: 10%, 80% + """ + return np.mean(remove_nan(data)) + + +def get_skewness(data, measurements_per_day=None): + """ + Skewness, i.e. the mean flow data divided by Q50. + + See: [CLBGS2000]_ and [WESMCM2015]_ + + :param data: The runoff timeseries data as a numeric sequence + :param measurements_per_day: the measurements_per_day of the timeseries (unused) + :return: SKW + :limit: 0.2, 2.0 + + """ + return get_mean(data) / get_q50(data) if get_q50(data) > 0 else 0 + + +def get_qcv(data, measurements_per_day=None): + """ + Coefficient of variation, i.e. standard deviation divided by mean flow + + See: [CLBGS2000]_ + + :param data: The runoff timeseries data as a numeric sequence + :param measurements_per_day: the measurements_per_day of the timeseries (unused) + :return: Q_{CV} + :limit: 10%, 80% + """ + return remove_nan(data).std() / get_mean(data) + + +def get_sfdc(data, measurements_per_day=None): + """ + The slope in the middle part of the flow duration curve, calculated between the 33rd and 66th + streamflow percentiles + + [YADV2007]_: + + .. math:: + S_{fdc} = \\frac{\\ln Q_{66} - \\ln Q_{33}}{0.66 - 0.33} + + :math:`Q_{X}` is the X'th percentile of the normalized discharge :math:`Q / \\overline{Q_{mean}}` + + Used by:[WESMCM2015]_, [YADV2007]_, + + :param data: The runoff timeseries data as a numeric sequence + :param measurements_per_day: the measurements_per_day of the timeseries (unused) + :return: S_{FDC} + + """ + mean = get_mean(data) + + Q33 = Quantile(33)(data)/mean + Q66 = Quantile(66)(data)/mean + + # Determine if the fdc has a slope at this tage and return the + # corresponding values + if Q33 == 0 and Q66 == 0: + return 0 + elif Q33 == 0 and not Q66 == 0: + return -np.log(Q66) / (2/3 - 1/3) + elif not Q33 == 0 and Q66 == 0: + return np.log(Q33) / (2/3 - 1/3) + else: + return (np.log(Q33) - np.log(Q66)) / (2/3 - 1/3) + + +def calc_baseflow(data, measurements_per_day=1): + """ + Calculates the 5 day baseflow after Gustard et al 1992, p. 21 + See: + Report No. 108, Low flow estimation in the United Kingdom, . Gustard, A. Bullock December 1992 and J. M. Dixon" + http://nora.nerc.ac.uk/id/eprint/6050/1/IH_108.pdf + + :param data: + :param measurements_per_day: + :return: The baseflow timeseries in the same resolution as data + """ + period_length = 5 # days + if measurements_per_day < 1: + raise ValueError('At least a daily measurement frequency is needed to calculate baseflow') + + # Remove NaN values + data = fill_nan(data) + + def irange(seq): + """ Returns the indices of a sequence""" + return range(len(seq)) + + # Calculate daily mean + daily_flow = summarize(data, measurements_per_day, np.mean) + + # Get minimum flow for each 5 day period (Step 1 in Gustard et al 1992) + Q = summarize(daily_flow, period_length, np.min) + + + def is_baseflow(i, Q): + """ + Returns True if a 5 day period can be considered as baseflow + :param i: Actual 5day period index + :param Q: 5day period minimum values + :return: True if Q[i] is a baseflow + """ + if 0 < i < len(Q)-1: + # The boolean expression after the or works with the assumption + # that flow values of 0 can always be considered as baseflow. + return (Q[i] * 0.9 < min(Q[i - 1], Q[i + 1])) or (Q[i] == 0) + else: + return False + + # Get each 5 day period index, where the baseflow condition is fullfilled + # (Step 2 in Gustard et al 1992) + QB_pos = [i for i in irange(Q) + if is_baseflow(i, Q)] + + QB_raw = Q[QB_pos] + # get interpolated values for each minflow timestep (Step 3) + QB_int = np.interp(irange(Q), QB_pos, QB_raw) + + # If QBi > Qi then QBi = Qi (Step 4) + QB = np.where(QB_int > Q, Q, QB_int) + + # Return the baseflow interpolated to the data line + # using a time axis t in the unit of period indices (eg 1/(5 days)) + t = np.linspace(0, len(QB) - 1, len(data)) + return np.interp(t, irange(QB), QB) + + +def get_bfi(data, measurements_per_day=1): + """ + Returns the baseflow index after Gustard et al 1992 + + See: + Report No. 108, Low flow estimation in the United Kingdom, . Gustard, A. Bullock December 1992 and J. M. Dixon + http://nora.nerc.ac.uk/id/eprint/6050/1/IH_108.pdf + + :param data: The runoff timeseries data as a numeric sequence + :param measurements_per_day: the measurements_per_day of the timeseries in seconds, default is daily + :return: BFI + :limit: 0.1, 0.8 + """ + + # Calculates the timeseries for the baseflow follwing Gustard et al 1992, p. 20ff, Step 1-4 + baseflow = calc_baseflow(data, measurements_per_day) + + return baseflow.mean() / get_mean(data) + + +def flow_event(data, event_condition, *ec_args): + """ + Returns the frequency and mean duration of events. + + Events can be eg. high flow, low flow or whatever can be determined from a single value + of the timeseries. This function is used be get_qhf, get_qhd, get_qlf, get_qhd. + + In difference to [WESMCM2016]_ the frequency is in occurences per timestep and hence quite a small number (multiply with 365 to gain :math:`yr^{-1}`) and + the mean duration is in days, if measurements_per_day is given. Without a step size the mean duration is in multiples of + the timeseries measurements_per_day, whatever it is (5min, 1h, 1day, 1y...). + + :param data: the timeseries + :param event_condition: a callable checking for a single value if an event is happening + :param ec_args: Additional arguments for the event condition + :return: frequency, mean duration + """ + + # A list of events. An event is characterized by its duration + events = [] + actual_event = False + for v in data: + if event_condition(v, *ec_args): # We have an event! + if not actual_event: # A new event starts + events.append(1) + actual_event = True + else: # A current event continues + events[-1] += 1 + else: # No event + actual_event = False + + if not events: + return 0.0, 0.0 + + else: + freq = len(events) / len(data) + mean_duration = np.mean(events) + + return freq, mean_duration + + +def get_qhf(data, measurements_per_day=1): + """ + Calculates the frequency of high flow events defined as :math:`Q > 9 \\cdot Q_{50}` + + cf. [CLBGS2000]_, [WESMCM2015]_. The frequency is given as :math: :math:`yr^{-1}` + + :param data: the timeseries + :param measurements_per_day: the measurements_per_day of the timeseries + :return: Q_{HF}, Q_{HD} + """ + + def highflow(value, median): + return value > 9 * median + + fq, md = flow_event(data, highflow, np.median(data)) + + return fq * measurements_per_day * 365, md / measurements_per_day + + +def get_qlf(data, measurements_per_day=1): + """ + Calculates the frequency of low flow events defined as + :math:`Q < 0.2 \\cdot \\overline{Q_{mean}}` + + cf. [CLBGS2000]_, [WESMCM2015]_. The frequency is given + in :math:`yr^{-1}` and for the whole timeseries + + :param data: the timeseries + :param measurements_per_day: the measurements_per_day of the timeseries + :return: Q_{LF}, Q_{LD} + """ + + def lowflow(value, mean): + return value < 0.2 * mean + fq, md = flow_event(data, lowflow, np.mean(data)) + return fq * measurements_per_day * 365, md / measurements_per_day + + +def get_ac(data, measurements_per_day=1): + """ + Calculates the autocorrelation for 1 day + + cf. [WESMCM2015]_ + + :param data: the timeseries + :param measurements_per_day: the measurements_per_day of the timeseries + :return: Q_{AC} + """ + + front = fill_nan(data[measurements_per_day:]) + back = fill_nan(data[:-measurements_per_day]) + + return np.corrcoef(front, back)[0, 1] + + +def get_qlv(data, measurements_per_day=1): + """ + Calculates the low flow variability as low flow per median flow + + Here low flow (:math:`LF_{mean/yr}`) is defined as the mean of + the minimum flow per year + + cf. [WESMCM2015]_ + + .. math:: + Q_{LV} = \\frac{LF_{mean/yr}}{Q_{50}} + + :param data: the timeseries + :param measurements_per_day: the measurements_per_day of the timeseries + :return: Q_{LV} + """ + + year = measurements_per_day * 365 + # Calculate mean annual low flow + data = fill_nan(data) + lf = np.mean(summarize(data, year, np.min)) + + return lf / get_q50(data) if get_q50(data) > 0 else 0 + + +def get_qhv(data, measurements_per_day=1): + """ + Calculates the high flow variability as high flow per median flow + + Here high flow (:math:`HF_{mean/yr}`) is defined as the mean of the maximum flow per year + + cf. [WESMCM2015]_ + + .. math:: + Q_{HV} = \\frac{LF_{mean/yr}}{Q_{50}} + + :param data: the timeseries + :param measurements_per_day: the measurements_per_day of the timeseries + :return: Q_{HV} + """ + + year = measurements_per_day * 365 + # Calculate mean annual low flow + data = fill_nan(data) + lf = np.mean(summarize(data, year, np.max)) + + return lf / get_q50(data) if get_q50(data) > 0 else 0 + + +def get_recession(data, measurements_per_day=None): + """ + Calculates the recession parameters as given by [WESMCM2015]_ + + [WESMCM2015]_ say: + + In the theoretical case where flow Q is a power function of storage, + and evaporation is negligible, the relationship is + + .. math:: + \\frac{dQ}{dt} = -Q^b / T_0 + + T0 and b are the intercept of a linear regression of :math:`\\log dQ/dt` with + :math:`\\log Q` + + This function returns a tuple with slope, intercept and :math:`R^2`of the + correlation of the derivation of runoff with the runoff itself + + + :param data: the timeseries + :param measurements_per_day: + :return: b, T_0, R^2 + """ + + q = fill_nan(data) + # Only use median of flows above 0, to avoid mathmatical errors. + q = q / np.median(q[q>0]) + dqdt = np.diff(q) + # Use only recession situation (dqdt < 0) + q = q[:-1][dqdt < 0] + dqdt = dqdt[dqdt < 0] + r2 = np.corrcoef(np.log(q), np.log(-dqdt))[0, 1] ** 2 + + b, t0 = np.polyfit(np.log(q), np.log(-dqdt), 1) + + return b, 1/np.exp(t0), r2 + + +def get_zero_q_freq(data, measurements_per_day=None): + """ + Calculates the percent of timesteps with Q = 0 mm/timestep + :param data: the timeseries + :param measurements_per_day: + :return: ZERO_Q_FREQ + """ + return ((len(data) - np.count_nonzero(data)) / len(data)) * 100 + + diff --git a/spotpy/likelihoods.py b/spotpy/likelihoods.py index 1c799a3c..dfc37a57 100644 --- a/spotpy/likelihoods.py +++ b/spotpy/likelihoods.py @@ -8,7 +8,6 @@ We modified the formula so, that a best fit of model can be archived by maximizing negative likelihood to zero ''' - #### TEST RESULTS WITH DREAM and hymod.py #### # Dream has now a bunch of options. We tested only likelihoods, which do not need a additional parameter # from this we saw that @@ -23,6 +22,7 @@ import math import warnings + class LikelihoodError(Exception): """ Define an own error class to know it is an error made by a likelihood calculation to warn the use for wrong inputs @@ -30,9 +30,8 @@ class LikelihoodError(Exception): pass - def __generateMeaserror(data): - return np.array(data)*0.1 + return np.array(data) * 0.1 def __calcSimpleDeviation(data, comparedata): @@ -50,6 +49,16 @@ def __standartChecksBeforeStart(data, comparedata): raise LikelihoodError("Data with no content can not be used as a foundation of calculation a likelihood") +def __jitter_measerror_if_needed(fun_name, measerror): + size = measerror[measerror == 0.0].size + if size > 0: + warnings.warn( + "[" + fun_name + "] realized that there are distinct distributed values. " + "We jittered the values but the result can be far away from the truth.") + + measerror[measerror == 0.0] = np.random.uniform(0.01, 0.1, size) + return measerror + class TimeSeries: """ @@ -108,44 +117,6 @@ def AR_1_Coeff(data): return TimeSeries.acf(data, 1) / TimeSeries.acf(data, 0) -class Stats: - @staticmethod - def getKyrtosis(data): - """ - Calculate the forth central moment, see https://de.wikipedia.org/wiki/W%C3%B6lbung_(Statistik) - - :param data: a numerical list - :type data: list - :return: the kyrtosis of the data - :rtype: float - """ - s = np.std(data) - n = data.__len__() - m = np.mean(data) - npdata = np.array(data) - - return np.sum(((npdata - m) / s) ** 4) / n - - @staticmethod - def getSkewnessParameter(data): - """ - Calculate the third central moment, see https://de.wikipedia.org/wiki/Schiefe_(Statistik) - - :param data: a numerical list - :type data: list - :return: the skewness of the data - :rtype: float - """ - - s = np.std(data) - n = data.__len__() - m = np.mean(data) - npdata = np.array(data) - result = (np.sum(((npdata - m) / s) ** 3) / n) - if result == 0.0: - result = 0.001 - return result - def logLikelihood(data, comparedata, measerror=None): """ This formula is based on the gaussian likelihood: homo/heteroscedastic data error formula which can be used in both @@ -177,14 +148,11 @@ def logLikelihood(data, comparedata, measerror=None): if measerror is None: measerror = __generateMeaserror(data) measerror = np.array(measerror) - size = measerror[measerror == 0.0].size - if size > 0: - warnings.warn("[logLikelihood] reaslized that there are distinct distributed values. We jittered the values but the result can be far away from the truth.") - - measerror[measerror == 0.0] = np.random.uniform(0.01,0.1,size) + measerror = __jitter_measerror_if_needed("logLikelihood", measerror) # TODO: Maximize is done but in positive way (from negative to zero is hard) - return -data.__len__()/2*np.log(2*np.pi) - np.nansum(np.log(measerror)) - 0.5*np.sum(((data-comparedata)/measerror)**2) + return -data.__len__() / 2 * np.log(2 * np.pi) - np.nansum(np.log(measerror)) - 0.5 * np.sum( + ((data - comparedata) / measerror) ** 2) def gaussianLikelihoodMeasErrorOut(data, comparedata): @@ -245,20 +213,15 @@ def gaussianLikelihoodHomoHeteroDataError(data, comparedata, measerror=None): if measerror is None: measerror = __generateMeaserror(data) measerror = np.array(measerror) - - size = measerror[measerror == 0.0].size - if size > 0: - warnings.warn("[gaussianLikelihoodHomoHeteroDataError] reaslized that there are distinct distributed values. We jittered the values but the result can be far away from the truth.") - - measerror[measerror == 0.0] = np.random.uniform(0.01, 0.1, size) + measerror = __jitter_measerror_if_needed("gaussianLikelihoodHomoHeteroDataError", measerror) # TODO Maximizing with negative to zero? # original: -np.prod((1 / (np.sqrt(2 * np.pi * measerror**2)))*np.exp(-0.5 * ((data-comparedata)/(measerror))**2)) - return -np.sum((1 / (np.sqrt(2 * np.pi * measerror**2)))*np.exp(-0.5 * ((data-comparedata)/(measerror))**2)) - + return -np.sum( + (1 / (np.sqrt(2 * np.pi * measerror ** 2))) * np.exp(-0.5 * ((data - comparedata) / (measerror)) ** 2)) -def LikelihoodAR1WithC(data, comparedata, measerror=None,params=None): +def LikelihoodAR1WithC(data, comparedata, measerror=None, params=None): """ Suppose the error residuals assume an AR(1)-process @@ -300,11 +263,7 @@ def LikelihoodAR1WithC(data, comparedata, measerror=None,params=None): measerror = __generateMeaserror(data) measerror = np.array(measerror) - size = measerror[measerror == 0.0].size - if size > 0: - warnings.warn( - "[LikelihoodAR1WithC] reaslized that there are distinct distributed values. We jittered the values but the result can be far away from the truth.") - measerror[measerror == 0.0] = np.random.uniform(0.01, 0.1, size) + measerror = __jitter_measerror_if_needed("LikelihoodAR1WithC", measerror) paramDependencies = ["likelihood_phi"] @@ -314,21 +273,21 @@ def LikelihoodAR1WithC(data, comparedata, measerror=None,params=None): missingparams = [] randomparset, parameternames = params randomparset = np.array(randomparset) + parameternames = np.array(parameternames) for nm in paramDependencies: if nm not in parameternames: missingparams.append(nm) if missingparams.__len__() > 0: raise LikelihoodError( - "Unfortunately contains your param list not all parameters which are needed for this class." - "Following parameter are needed, too: "+str(missingparams)) - + "Unfortunately contains your param list not all parameters which are needed for this class." + "Following parameter are needed, too: " + str(missingparams)) phi = float(randomparset[parameternames == 'likelihood_phi']) - # Break the calculation if given parameter are not valid - if abs(phi) >= 1: - warnings.warn("The parameter 'phi' should be real between -1 and 1 and is: " + str(phi)) - return np.NAN + # Break the calculation if given parameter are not valid + if abs(phi) >= 1: + warnings.warn("The parameter 'phi' should be real between -1 and 1 and is: " + str(phi)) + return np.NAN expect = np.nanmean(data) errorArr = np.array(__calcSimpleDeviation(data, comparedata)) @@ -340,13 +299,12 @@ def LikelihoodAR1WithC(data, comparedata, measerror=None,params=None): sum_2 = np.sum(((errorArr[1:] - c - phi * errorArr[:-1]) / (measerror[1:])) ** 2) - #TODO Its maximaized but maybe from negative to zero, is that possible? + # TODO Its maximaized but maybe from negative to zero, is that possible? return -(-(n / 2) * np.log(2 * np.pi) - 0.5 * np.log(measerror[0] ** 2 / (1 - phi ** 2)) - ( - (errorArr[0] - (c / (1 - phi))) ** 2 / (2 * measerror[0] ** 2 / (1 - phi ** 2))) - sum_1 - 0.5 * sum_2) - + (errorArr[0] - (c / (1 - phi))) ** 2 / (2 * measerror[0] ** 2 / (1 - phi ** 2))) - sum_1 - 0.5 * sum_2) -def LikelihoodAR1NoC(data, comparedata, measerror=None,params=None): +def LikelihoodAR1NoC(data, comparedata, measerror=None, params=None): """ Based on the formula in `LikelihoodAR1WithC` we assuming that :math:`c = 0` and that means that the formula of `log-likelihood` is: @@ -378,14 +336,9 @@ def LikelihoodAR1NoC(data, comparedata, measerror=None,params=None): errorArr = np.array(__calcSimpleDeviation(data, comparedata)) - # I summarize from 2 to n, but range starts in 1 (in python it is zero index), so just shift it with one measerror = np.array(measerror) - size = measerror[measerror == 0.0].size - if size > 0: - warnings.warn( - "[LikelihoodAR1NoC] reaslized that there are distinct distributed values. We jittered the values but the result can be far away from the truth.") - measerror[measerror == 0.0] = np.random.uniform(0.01, 0.1, size) + measerror = __jitter_measerror_if_needed("LikelihoodAR1NoC", measerror) paramDependencies = ["likelihood_phi"] @@ -400,8 +353,7 @@ def LikelihoodAR1NoC(data, comparedata, measerror=None,params=None): if missingparams.__len__() > 0: raise LikelihoodError( "Unfortunately contains your param list not all parameters which are needed for this class." - "Following parameter are needed, too: "+str(missingparams)) - + "Following parameter are needed, too: " + str(missingparams)) parameternames = np.array(parameternames) randomparset = np.array(randomparset) @@ -412,14 +364,13 @@ def LikelihoodAR1NoC(data, comparedata, measerror=None,params=None): warnings.warn("The parameter 'phi' should be real between -1 and 1 and is: " + str(phi)) return np.NAN - sum_1 = np.sum(np.log(measerror[1:])) - sum_2 = np.sum(( (errorArr[1:] - phi * errorArr[:-1]) / measerror[1:]) ** 2) + sum_2 = np.sum(((errorArr[1:] - phi * errorArr[:-1]) / measerror[1:]) ** 2) # TODO Maximizing with negative to zero? - return -float(-(n / 2) * np.log(2 * np.pi) + 0.5 * np.log(1 - phi ** 2) - 0.5 * (1 - phi ** 2) * (1 / measerror[0] ** 2) * \ - errorArr[0] ** 2 - sum_1 - 0.5 * sum_2) - + return -float( + -(n / 2) * np.log(2 * np.pi) + 0.5 * np.log(1 - phi ** 2) - 0.5 * (1 - phi ** 2) * (1 / measerror[0] ** 2) * \ + errorArr[0] ** 2 - sum_1 - 0.5 * sum_2) def generalizedLikelihoodFunction(data, comparedata, measerror=None, params=None): @@ -477,34 +428,28 @@ def generalizedLikelihoodFunction(data, comparedata, measerror=None, params=None :rtype: float """ - - __standartChecksBeforeStart(data, comparedata) errorArr = __calcSimpleDeviation(data, comparedata) if measerror is None: measerror = __generateMeaserror(data) measerror = np.array(measerror) comparedata = np.array(comparedata) + measerror = __jitter_measerror_if_needed("generalizedLikelihoodFunction", measerror) - size = measerror[measerror == 0.0].size - if size > 0: - warnings.warn( - "[generalizedLikelihoodFunction] reaslized that there are distinct distributed values. We jittered the values but the result can be far away from the truth.") - measerror[measerror == 0.0] = np.random.uniform(0.01, 0.1, size) - - paramDependencies = ["likelihood_beta","likelihood_xi","likelihood_sigma0","likelihood_sigma1","likelihood_phi1","likelihood_muh"] + paramDependencies = ["likelihood_beta", "likelihood_xi", "likelihood_sigma0", "likelihood_sigma1", + "likelihood_phi1", "likelihood_muh"] if params is None: # for this params look into http://onlinelibrary.wiley.com/doi/10.1029/2009WR008933/epdf, page 5 - beta = np.random.uniform(-0.99,1,1) - xi = np.random.uniform(0.01,10,1) - sigma0 = np.random.uniform(0,1,1) + beta = np.random.uniform(-0.99, 1, 1) + xi = np.random.uniform(0.01, 10, 1) + sigma0 = np.random.uniform(0, 1, 1) sigma1 = np.random.uniform(0, 1, 1) phi1 = np.random.uniform(0, .99, 1) muh = np.random.uniform(0, 100, 1) else: - missingparams=[] + missingparams = [] randomparset, parameternames = params parameternames = np.array(parameternames) randomparset = np.array(randomparset) @@ -515,8 +460,8 @@ def generalizedLikelihoodFunction(data, comparedata, measerror=None, params=None if missingparams.__len__() > 0: raise LikelihoodError( - "Unfortunately contains your param list not all parameters which are needed for this class." - "Following parameter are needed, too: "+str(missingparams)) + "Unfortunately contains your param list not all parameters which are needed for this class." + "Following parameter are needed, too: " + str(missingparams)) beta = float(randomparset[np.where(parameternames == 'likelihood_beta')]) xi = float(randomparset[np.where(parameternames == 'likelihood_xi')]) @@ -526,27 +471,25 @@ def generalizedLikelihoodFunction(data, comparedata, measerror=None, params=None muh = float(randomparset[np.where(parameternames == 'likelihood_muh')]) # Break the calculation if given parameter are not valid - if beta <=-1 or beta > 1: - warnings.warn("The parameter 'beta' should be greater then -1 and less equal 1 and is: "+str(beta)) + if beta <= -1 or beta > 1: + warnings.warn("The parameter 'beta' should be greater then -1 and less equal 1 and is: " + str(beta)) return np.NAN if xi < 0.1 or xi > 10: - warnings.warn("The parameter 'xi' should be between 0.1 and 10 and is: "+str(xi)) + warnings.warn("The parameter 'xi' should be between 0.1 and 10 and is: " + str(xi)) return np.NAN if sigma0 < 0 or sigma0 > 1: - warnings.warn("The parameter 'sigma0' should be between 0 and 1 and is: "+str(sigma0)) + warnings.warn("The parameter 'sigma0' should be between 0 and 1 and is: " + str(sigma0)) return np.NAN if sigma1 < 0 or sigma1 > 1: - warnings.warn("The parameter 'sigma1' should be between 0 and 1 and is: "+str(sigma1)) + warnings.warn("The parameter 'sigma1' should be between 0 and 1 and is: " + str(sigma1)) return np.NAN if phi1 < 0 or phi1 > 1: - warnings.warn("The parameter 'phi1' should be between 0 and 1 and is: "+str(phi1)) + warnings.warn("The parameter 'phi1' should be between 0 and 1 and is: " + str(phi1)) return np.NAN if muh < 0 or muh > 100: - warnings.warn("The parameter 'muh' should be between 0 and 100 and is: "+str(muh)) + warnings.warn("The parameter 'muh' should be between 0 and 100 and is: " + str(muh)) return np.NAN - - try: omegaBeta = np.sqrt(math.gamma(3 * (1 + beta) / 2)) / ((1 + beta) * np.sqrt(math.gamma((1 + beta) / 2) ** 3)) M_1 = math.gamma(1 + beta) / (np.sqrt(math.gamma(3 * (1 + beta) / 2)) * np.sqrt(math.gamma((1 + beta) / 2))) @@ -556,7 +499,6 @@ def generalizedLikelihoodFunction(data, comparedata, measerror=None, params=None except ValueError: raise LikelihoodError("Please check your parameter input there is something wrong with the parameter") - if xi != 0.0: mu_xi = M_1 * (xi - (xi ** (-1))) else: @@ -564,9 +506,8 @@ def generalizedLikelihoodFunction(data, comparedata, measerror=None, params=None n = data.__len__() - sum_at = 0 - #formula for a_t is from page 3, (6) + # formula for a_t is from page 3, (6) for j in range(n - 1): t = j + 1 if t > 0 and t < n and type(t) == type(1): @@ -579,27 +520,25 @@ def generalizedLikelihoodFunction(data, comparedata, measerror=None, params=None sum_at += np.abs(a_xi_t) ** (2 / (1 + beta)) - # page 3 formula 5 of this paper expalin that sigma[t] = sigma0 + sigma1*E[t] - # where E[t] is calles y(x) in the main paper (discrepance) and sigma0 and sigma1 are input parameter which also + # page 3 formula 5 of this paper explain that sigma[t] = sigma0 + sigma1*E[t] + # where E[t] is called y(x) in the main paper (discrepancy) and sigma0 and sigma1 are input parameter which also # can be generate by the function itself. Then # E[t] = Y_{ht}*mu[t] # where Y_{ht} should be the simulated model data and mu_t = exp(mu_h * Y_{ht}). # So, mu_h is "a bias parameter to be inferred from the model." (cite, page 3, formula (3)) - mu_t = np.mean(muh*comparedata) + mu_t = np.exp(muh * comparedata) - E = comparedata*mu_t + E = comparedata * mu_t - sigmas = sigma0 +sigma1*E + sigmas = sigma0 + sigma1 * E if sigmas[sigmas <= 0.0].size > 0: warnings.warn("Sorry, you comparedata have negative values. Maybe you model has some inaccurate" - " assumptions or there is another error." - " We cannot calculate this likelihood") + " assumptions or there is another error." + " We cannot calculate this likelihood") return np.NAN - return n * np.log(omegaBeta * (2 * sigma_xi) / np.abs(xi + (1 / xi))) - np.sum(np.log( sigmas )) - cBeta * sum_at - - + return n * np.log(omegaBeta * (2 * sigma_xi) / np.abs(xi + (1 / xi))) - np.sum(np.log(sigmas)) - cBeta * sum_at def LaplacianLikelihood(data, comparedata, measerror=None): @@ -612,7 +551,8 @@ def LaplacianLikelihood(data, comparedata, measerror=None): p = -\\sum_{t=1}^n \\log(2\\sigma_t)-\\sum_{t=1}^n (\\frac{|e_t(x)|}{\\sigma_t}) - `Usage:` Maximizing the likelihood value guides to the best model, because the less :math:`\\sum_{t=1}^n (\\frac{|e_t(x)|}{\\sigma_t})` + `Usage:` Maximizing the likelihood value guides to the best model, + because the less :math:`\\sum_{t=1}^n (\\frac{|e_t(x)|}{\\sigma_t})` is the better fits the model simulation data to the observed data. :param data: observed measurements as a numerical list @@ -630,11 +570,7 @@ def LaplacianLikelihood(data, comparedata, measerror=None): if measerror is None: measerror = __generateMeaserror(data) measerror = np.array(measerror) - - size = measerror[measerror == 0.0].size - if size > 0: - warnings.warn("[LaplacianLikelihood] reaslized that there are distinct distributed values. We jittered the values but the result can be far away from the truth.") - measerror[measerror == 0.0] = np.random.uniform(0.01,0.01,size) + measerror = __jitter_measerror_if_needed("LaplacianLikelihood", measerror) # Log from negative value makes no sense at all return -1 * np.sum(np.log(2 * np.abs(measerror))) - np.sum(np.abs(errArr) / measerror) @@ -652,8 +588,8 @@ def SkewedStudentLikelihoodHomoscedastic(data, comparedata, measerror=None): For detailed mathematical question take a look into hessd-12-2155-2015.pdf (https://www.hydrol-earth-syst-sci-discuss.net/12/2155/2015/hessd-12-2155-2015.pdf) pages 2164-2165 - `Usage:` Maximizing the likelihood value guides to the best model. Be aware that only a right model asumption leads to - a result which makes sense. + `Usage:` Maximizing the likelihood value guides to the best model. Be aware that only a right model + assumption leads to a result which makes sense. :param data: observed measurements as a numerical list :type data: list @@ -672,12 +608,12 @@ def SkewedStudentLikelihoodHomoscedastic(data, comparedata, measerror=None): res = np.array(__calcSimpleDeviation(data, comparedata)) - #TODO Maximizing with negative to zero? + # TODO Maximizing with negative to zero? # Original: -np.prod(1 / (np.sqrt(2 * np.pi) * measerror) * np.exp(-1 * (res ** 2) / (2))) - return -np.sum( ( 1 / (np.sqrt(2 * np.pi) * measerror) * np.exp(-1 * (res ** 2) / (2)))) + return -np.sum((1 / (np.sqrt(2 * np.pi) * measerror) * np.exp(-1 * (res ** 2) / (2)))) -def SkewedStudentLikelihoodHeteroscedastic(data, comparedata, measerror=None,params=None): +def SkewedStudentLikelihoodHeteroscedastic(data, comparedata, measerror=None, params=None): """ Under the assumption that the data are heteroscedastic, i.e. the they have for every measurement another error and that the residuals are non-Gaussian distributed we perform a likelihoodcalculation based on this formualar, having @@ -745,12 +681,7 @@ def SkewedStudentLikelihoodHeteroscedastic(data, comparedata, measerror=None,par measerror = __generateMeaserror(data) measerror = np.array(measerror) - - size = measerror[measerror == 0.0].size - if size > 0: - warnings.warn( - "[SkewedStudentLikelihoodHeteroscedastic] reaslized that there are distinct distributed values. We jittered the values but the result can be far away from the truth.") - measerror[measerror == 0.0] = np.random.uniform(0.01, 0.1, size) + measerror = __jitter_measerror_if_needed("SkewedStudentLikelihoodHeteroscedastic", measerror) diff = np.array(__calcSimpleDeviation(data, comparedata)) @@ -766,8 +697,8 @@ def SkewedStudentLikelihoodHeteroscedastic(data, comparedata, measerror=None,par missingparams = [] randomparset, parameternames = params - randomparset = np.array(randomparset) + parameternames = np.array(parameternames) for nm in paramDependencies: if nm not in parameternames: @@ -778,41 +709,43 @@ def SkewedStudentLikelihoodHeteroscedastic(data, comparedata, measerror=None,par "Unfortunately contains your param list not all parameters which are needed for this class." "Following parameter are needed, too: " + str(missingparams)) - nu = randomparset[parameternames == 'likelihood_nu'] - k = randomparset[parameternames == 'likelihood_kappa'] - phi = randomparset[parameternames == 'likelihood_phi'] + nu = randomparset[parameternames == 'likelihood_nu'][0] + k = randomparset[parameternames == 'likelihood_kappa'][0] + phi = randomparset[parameternames == 'likelihood_phi'][0] - if abs(phi) > 1: - warnings.warn("[SkewedStudentLikelihoodHeteroscedastic] The parameter 'phi' should be between -1 and 1 and is: "+str(phi)) - return np.NAN - if nu <= 2: - warnings.warn( - "[SkewedStudentLikelihoodHeteroscedastic] The parameter 'nu' should be greater then 2 and is: " + str( - nu)) - return np.NAN - if k <= 0: - warnings.warn( - "[SkewedStudentLikelihoodHeteroscedastic] The parameter 'k' should be greater then 0 and is: " + str( - k)) - return np.NAN + if abs(phi) > 1: + warnings.warn( + "[SkewedStudentLikelihoodHeteroscedastic] The parameter 'phi' should be between -1 and 1 and is: " + str( + phi)) + return np.NAN + if nu <= 2: + warnings.warn( + "[SkewedStudentLikelihoodHeteroscedastic] The parameter 'nu' should be greater then 2 and is: " + str( + nu)) + return np.NAN + if k <= 0: + warnings.warn( + "[SkewedStudentLikelihoodHeteroscedastic] The parameter 'k' should be greater then 0 and is: " + str( + k)) + return np.NAN eta_all = diff[1:] - phi * diff[:-1] * np.sqrt(1 - phi ** 2) c_1 = ((k ** 2 - 1 / (k ** 2)) * 2 * math.gamma((nu + 1) / 2) * np.sqrt(nu / (nu - 2)) * (nu - 2)) / ( - (k + (1 / k)) * math.gamma(nu / 2) * np.sqrt(np.pi * nu) * (nu - 1)) + (k + (1 / k)) * math.gamma(nu / 2) * np.sqrt(np.pi * nu) * (nu - 1)) for_c2 = -1 * (c_1) ** 2 + (k ** 3 + 1 / k ** 3) / (k + 1 / k) c_2 = np.sqrt(for_c2) # TODO Maximizing with negative to zero? - return np.log(-np.prod( (2 * c_2 * math.gamma((nu + 1) / 2) * np.sqrt(nu / (nu - 2))) / ( - (k + 1 / k) * math.gamma(nu / 2) * np.sqrt(np.pi * nu) * np.sqrt(1 - phi ** 2) * measerror[1:]) \ - * (1 + (1 / (nu - 2)) * ( - (c_1 + c_2 * eta_all) / (k ** (np.sign(c_1 + c_2 * eta_all)))) ** 2) ** ( - -(nu + 1) / 2))) + return np.log(-np.prod((2 * c_2 * math.gamma((nu + 1) / 2) * np.sqrt(nu / (nu - 2))) / ( + (k + 1 / k) * math.gamma(nu / 2) * np.sqrt(np.pi * nu) * np.sqrt(1 - phi ** 2) * measerror[1:]) \ + * (1 + (1 / (nu - 2)) * ( + (c_1 + c_2 * eta_all) / (k ** (np.sign(c_1 + c_2 * eta_all)))) ** 2) ** ( + -(nu + 1) / 2))) -def SkewedStudentLikelihoodHeteroscedasticAdvancedARModel(data, comparedata, measerror=None,params=None): +def SkewedStudentLikelihoodHeteroscedasticAdvancedARModel(data, comparedata, measerror=None, params=None): """ This function is based of the previos one, called `SkewedStudentLikelihoodHeteroscedastic`. We expand @@ -852,13 +785,7 @@ def SkewedStudentLikelihoodHeteroscedasticAdvancedARModel(data, comparedata, mea measerror = __generateMeaserror(data) measerror = np.array(measerror) - - size = measerror[measerror == 0.0].size - - if size > 0: - warnings.warn( - "[SkewedStudentLikelihoodHeteroscedasticAdvancedARModel] reaslized that there are distinct distributed values. We jittered the values but the result can be far away from the truth.") - measerror[measerror == 0.0] = np.random.uniform(0.01, 0.1, size) + measerror = __jitter_measerror_if_needed("SkewedStudentLikelihoodHeteroscedasticAdvancedARModel", measerror) res = np.array(__calcSimpleDeviation(data, comparedata)) @@ -888,9 +815,10 @@ def SkewedStudentLikelihoodHeteroscedasticAdvancedARModel(data, comparedata, mea k = randomparset[parameternames == 'likelihood_kappa'][0] phi = randomparset[parameternames == 'likelihood_phi'][0] - if abs(phi) > 1: - warnings.warn("[SkewedStudentLikelihoodHeteroscedasticAdvancedARModel] The parameter 'phi' should be between -1 and 1 and is: "+str(phi)) + warnings.warn( + "[SkewedStudentLikelihoodHeteroscedasticAdvancedARModel] The parameter 'phi' should be between -1 and 1 and is: " + str( + phi)) return np.NAN if nu <= 2: warnings.warn( @@ -906,25 +834,24 @@ def SkewedStudentLikelihoodHeteroscedasticAdvancedARModel(data, comparedata, mea N = data.__len__() eta_all = (res[1:] - phi * res[:-1] + phi / (N) * np.sum(res)) * np.sqrt(1 - phi ** 2) - c_1 = ((k ** 2 - 1 / (k ** 2)) * 2 * math.gamma((nu + 1) / 2) * np.sqrt(nu / (nu - 2)) * (nu - 2)) / ((k + (1 / k)) * math.gamma(nu / 2) * np.sqrt(np.pi * nu) * (nu - 1)) + c_1 = ((k ** 2 - 1 / (k ** 2)) * 2 * math.gamma((nu + 1) / 2) * np.sqrt(nu / (nu - 2)) * (nu - 2)) / ( + (k + (1 / k)) * math.gamma(nu / 2) * np.sqrt(np.pi * nu) * (nu - 1)) for_c2 = -1 * (c_1) ** 2 + (k ** 3 + 1 / k ** 3) / (k + 1 / k) c_2 = np.sqrt(for_c2) # TODO Maximizing with negative to zero? datas = ((2 * c_2 * math.gamma((nu + 1) / 2) * np.sqrt(nu / (nu - 2))) / ( - (k + 1 / k) * math.gamma(nu / 2) * np.sqrt(np.pi * nu) * np.sqrt(1 - phi ** 2) * measerror[1:]) \ - * (1 + (1 / (nu - 2)) * ( - (c_1 + c_2 * eta_all) / (k ** (np.sign(c_1 + c_2 * eta_all)))) ** 2) ** ( - -(nu + 1) / 2)) + (k + 1 / k) * math.gamma(nu / 2) * np.sqrt(np.pi * nu) * np.sqrt(1 - phi ** 2) * measerror[1:]) \ + * (1 + (1 / (nu - 2)) * ( + (c_1 + c_2 * eta_all) / (k ** (np.sign(c_1 + c_2 * eta_all)))) ** 2) ** ( + -(nu + 1) / 2)) return np.log(-np.prod((2 * c_2 * math.gamma((nu + 1) / 2) * np.sqrt(nu / (nu - 2))) / ( - (k + 1 / k) * math.gamma(nu / 2) * np.sqrt(np.pi * nu) * np.sqrt(1 - phi ** 2) * measerror[1:]) \ - * (1 + (1 / (nu - 2)) * ( - (c_1 + c_2 * eta_all) / (k ** (np.sign(c_1 + c_2 * eta_all)))) ** 2) ** ( - -(nu + 1) / 2))) - - + (k + 1 / k) * math.gamma(nu / 2) * np.sqrt(np.pi * nu) * np.sqrt(1 - phi ** 2) * measerror[1:]) \ + * (1 + (1 / (nu - 2)) * ( + (c_1 + c_2 * eta_all) / (k ** (np.sign(c_1 + c_2 * eta_all)))) ** 2) ** ( + -(nu + 1) / 2))) def NoisyABCGaussianLikelihood(data, comparedata, measerror=None): @@ -954,7 +881,7 @@ def NoisyABCGaussianLikelihood(data, comparedata, measerror=None): :rtype: float """ - __standartChecksBeforeStart(data,comparedata) + __standartChecksBeforeStart(data, comparedata) if measerror is None: measerror = __generateMeaserror(data) sigmas = np.array(measerror) @@ -962,8 +889,9 @@ def NoisyABCGaussianLikelihood(data, comparedata, measerror=None): size = sigmas[sigmas == 0.0].size if size > 0: - warnings.warn("[NoisyABCGaussianLikelihood] reaslized that there are distinct distributed values. We jittered the values but the result can be far away from the truth.") - sigmas[sigmas == 0.0] = np.random.uniform(0.01,0.1,size) + warnings.warn( + "[NoisyABCGaussianLikelihood] reaslized that there are distinct distributed values. We jittered the values but the result can be far away from the truth.") + sigmas[sigmas == 0.0] = np.random.uniform(0.01, 0.1, size) if measerror == 0.0: warnings.warn( @@ -975,8 +903,8 @@ def NoisyABCGaussianLikelihood(data, comparedata, measerror=None): comparedata = np.array(comparedata) # The euclidean distance has a bit diffrent formula then the original paper showed - return -m/2*np.log(2*np.pi)-m*np.log(measerror)-0.5*1/(measerror**2)* np.sqrt( np.sum( ((data-comparedata)/sigmas)**2 )) - + return -m / 2 * np.log(2 * np.pi) - m * np.log(measerror) - 0.5 * 1 / (measerror ** 2) * np.sqrt( + np.sum(((data - comparedata) / sigmas) ** 2)) def ABCBoxcarLikelihood(data, comparedata, measerror=None): @@ -1008,16 +936,12 @@ def ABCBoxcarLikelihood(data, comparedata, measerror=None): comparedata = np.array(comparedata) measerror = np.array(measerror) - size = measerror[measerror == 0.0].size - if size > 0: - warnings.warn("[ABCBoxcarLikelihood] reaslized that there are distinct distributed values. We jittered the values but the result can be far away from the truth.") - measerror[measerror == 0.0] = np.random.uniform(0.01,0.1,size) + measerror = __jitter_measerror_if_needed("ABCBoxcarLikelihood", measerror) # Usage of euclidean distance changes the formula a bit # TODO Maximizing with negative to zero? - return np.min(measerror-np.sqrt(((data-comparedata)/measerror)**2)) - + return np.min(measerror - np.sqrt(((data - comparedata) / measerror) ** 2)) def LimitsOfAcceptability(data, comparedata, measerror=None): @@ -1050,10 +974,7 @@ def LimitsOfAcceptability(data, comparedata, measerror=None): comparedata = np.array(comparedata) measerror = np.array(measerror) - size = measerror[measerror == 0.0].size - if size > 0: - warnings.warn("[LimitsOfAcceptability] reaslized that there are distinct distributed values. We jittered the values but the result can be far away from the truth.") - measerror[measerror == 0.0] = np.random.uniform(0.01, 0.1, size) + measerror = __jitter_measerror_if_needed("LimitsOfAcceptability", measerror) # Use simple non euclidean but weighted distance measurement. return np.sum(np.abs((data - comparedata) / measerror) <= measerror) @@ -1094,10 +1015,10 @@ def InverseErrorVarianceShapingFactor(data, comparedata, G=10): else: # Gives an better convergence, so close values are more less and apart values are more great. # (0 is the best so to say). - return -G*np.log(errArr)**3 + return -G * np.log(errArr) ** 3 -def NashSutcliffeEfficiencyShapingFactor(data, comparedata,G=10): +def NashSutcliffeEfficiencyShapingFactor(data, comparedata, G=10): """ This function use the opposite ratio of variance of the error terms between observed and simulated and the variance of the observed data as a base to claculate the @@ -1129,22 +1050,24 @@ def NashSutcliffeEfficiencyShapingFactor(data, comparedata,G=10): errArr = np.array(__calcSimpleDeviation(data, comparedata)) if np.nanvar(data) == 0.0: - warnings.warn("[NashSutcliffeEfficiencyShapingFactor] reaslized that the variance of the data is zero. Thereforee is no likelihood calculation possible") + warnings.warn( + "[NashSutcliffeEfficiencyShapingFactor] reaslized that the variance of the data is zero. Thereforee is no likelihood calculation possible") return np.NAN else: - ratio = np.nanvar(errArr)/np.nanvar(data) + ratio = np.nanvar(errArr) / np.nanvar(data) if ratio > 1: - warnings.warn("[NashSutcliffeEfficiencyShapingFactor]: The ratio between residual variation and observation " - "variation is bigger then one and therefore" - "we can not calculate the liklihood. Please use another function which fits to this data and / or " - "model") + warnings.warn( + "[NashSutcliffeEfficiencyShapingFactor]: The ratio between residual variation and observation " + "variation is bigger then one and therefore" + "we can not calculate the liklihood. Please use another function which fits to this data and / or " + "model") return np.NAN else: - return G*np.log(1-ratio) + return G * np.log(1 - ratio) -def ExponentialTransformErrVarShapingFactor(data, comparedata,G=10): +def ExponentialTransformErrVarShapingFactor(data, comparedata, G=10): """ This function use the variance of the error terms between observed and simulated data as a base to claculate the likelihood. @@ -1172,7 +1095,8 @@ def ExponentialTransformErrVarShapingFactor(data, comparedata,G=10): errArr = np.array(__calcSimpleDeviation(data, comparedata)) - return -G*np.nanvar(errArr) + return -G * np.nanvar(errArr) + def sumOfAbsoluteErrorResiduals(data, comparedata): """ @@ -1194,5 +1118,5 @@ def sumOfAbsoluteErrorResiduals(data, comparedata): """ __standartChecksBeforeStart(data, comparedata) - errArr = np.array(__calcSimpleDeviation(data,comparedata)) - return -1*np.log(np.sum(np.abs(errArr))) + errArr = np.array(__calcSimpleDeviation(data, comparedata)) + return -1 * np.log(np.sum(np.abs(errArr))) diff --git a/spotpy/objectivefunctions.py b/spotpy/objectivefunctions.py index 2c485a89..d5c7e626 100644 --- a/spotpy/objectivefunctions.py +++ b/spotpy/objectivefunctions.py @@ -34,7 +34,7 @@ def bias(evaluation, simulation): """ if len(evaluation) == len(simulation): obs, sim = np.array(evaluation), np.array(simulation) - bias = np.sum(obs - sim) / len(obs) + bias = np.nansum(obs - sim) / len(obs) return float(bias) else: @@ -62,7 +62,7 @@ def pbias(evaluation, simulation): if len(evaluation) == len(simulation): sim = np.array(simulation) obs = np.array(evaluation) - return 100 * (float(np.sum(sim - obs)) / float(np.sum(obs))) + return 100 * (float(np.nansum(sim - obs)) / float(np.nansum(obs))) else: logging.warning("evaluation and simulation lists does not have the same length.") @@ -90,10 +90,10 @@ def nashsutcliffe(evaluation, simulation): if len(evaluation) == len(simulation): s, e = np.array(simulation), np.array(evaluation) # s,e=simulation,evaluation - mean_observed = np.mean(e) + mean_observed = np.nanmean(e) # compute numerator and denominator - numerator = sum((e - s) ** 2) - denominator = sum((e - mean_observed)**2) + numerator = np.nansum((e - s) ** 2) + denominator = np.nansum((e - mean_observed)**2) # compute coefficient return 1 - (numerator / denominator) @@ -225,7 +225,7 @@ def mse(evaluation, simulation): if len(evaluation) == len(simulation): obs, sim = np.array(evaluation), np.array(simulation) - mse = np.mean((obs - sim)**2) + mse = np.nanmean((obs - sim)**2) return mse else: logging.warning("evaluation and simulation lists does not have the same length.") @@ -427,6 +427,67 @@ def kge(evaluation, simulation, return_all=False): logging.warning("evaluation and simulation lists does not have the same length.") return np.nan +def _spearmann_corr(x, y): + """Separmann correlation coefficient""" + col = [list(a) for a in zip(x, y)] + xy = sorted(col, key=lambda x: x[0], reverse=False) + # rang of x-value + for i, row in enumerate(xy): + row.append(i+1) + + a = sorted(xy, key=lambda x: x[1], reverse=False) + # rang of y-value + for i, row in enumerate(a): + row.append(i+1) + + MW_rank_x = np.nanmean(np.array(a)[:,2]) + MW_rank_y = np.nanmean(np.array(a)[:,3]) + + numerator = np.nansum([float((a[j][2]-MW_rank_x)*(a[j][3]-MW_rank_y)) for j in range(len(a))]) + denominator1 = np.sqrt(np.nansum([(a[j][2]-MW_rank_x)**2. for j in range(len(a))])) + denominator2 = np.sqrt(np.nansum([(a[j][3]-MW_rank_x)**2. for j in range(len(a))])) + return float(numerator/(denominator1*denominator2)) + +def kge_non_parametric(evaluation, simulation, return_all=False): + """ + Non parametric Kling-Gupta Efficiency + + Corresponding paper: + Pool, Vis, and Seibert, 2018 Evaluating model performance: towards a non-parametric variant of the Kling-Gupta efficiency, Hydrological Sciences Journal. + + output: + kge: Kling-Gupta Efficiency + + author: Nadine Maier and Tobias Houska + optional_output: + cc: correlation + alpha: ratio of the standard deviation + beta: ratio of the mean + """ + if len(evaluation) == len(simulation): + ## self-made formula + cc = _spearmann_corr(evaluation, simulation) + + ### scipy-Version + #cc = stm.spearmanr(evaluation, simulation, axis=0)[0] + + ### pandas version + #a = pd.DataFrame({'eva': evaluation, 'sim': simulation}) + #cc = a.ix[:,1].corr(a.ix[:,0], method = 'spearman') + + fdc_sim = np.sort(simulation / (np.nanmean(simulation)*len(simulation))) + fdc_obs = np.sort(evaluation / (np.nanmean(evaluation)*len(evaluation))) + alpha = 1 - 0.5 * np.nanmean(np.abs(fdc_sim - fdc_obs)) + + beta = np.mean(simulation) / np.mean(evaluation) + kge = 1 - np.sqrt((cc - 1)**2 + (alpha - 1)**2 + (beta - 1)**2) + if return_all: + return kge, cc, alpha, beta + else: + return kge + else: + logging.warning("evaluation and simulation lists does not have the same length.") + return np.nan def rsr(evaluation, simulation): """ @@ -448,7 +509,7 @@ def rsr(evaluation, simulation): def volume_error(evaluation, simulation): """ - Returns the Volumer Error (Ve). + Returns the Volume Error (Ve). It is an indicator of the agreement between the averages of the simulated and observed runoff (i.e. long-term water balance). used in this paper: diff --git a/spotpy/parallel/mpi.py b/spotpy/parallel/mpi.py index d08e6b4b..1fbd77f1 100644 --- a/spotpy/parallel/mpi.py +++ b/spotpy/parallel/mpi.py @@ -45,6 +45,14 @@ class ForEach(object): Repitition objects are owned by spot algorithms and can be used to repeat a task in a for loop. Using the repetition instead of a normal iterable allows for parallelization, in this case using mpi + + Attributes: + size: number of mpi processes + rank: current process number + process: callable to execute + phase: The phase of the job (used by process) + on_worker_terminate: An optional callable, that gets executed + when the worker processes terminate """ def __repr__(self): @@ -63,6 +71,7 @@ def __init__(self, process): self.rank = self.comm.Get_rank() self.process = process self.phase = None + self.on_worker_terminate = None if self.rank == 0: # The slots are a place for the master to remember which worker is doing something # Idle slots contain None @@ -115,22 +124,28 @@ def __wait(self): Breaks when master sent StopIteration :return: Nothing, calls exit() """ - assert self.is_worker() - while True: - # Wait for a message - obj = self.comm.recv(source=0, tag=tag.job) - # Handle messages - if type(obj) is StopIteration: - # Stop message - break - elif type(obj) is PhaseChange: - # Phase change - self.phase = obj.phase - else: # obj is a job for self.process - # Send the object back for processing it - res = self.process(obj) - self.comm.send(res, dest=0, tag=tag.answer) - exit() + try: + assert self.is_worker() + while True: + # Wait for a message + obj = self.comm.recv(source=0, tag=tag.job) + # Handle messages + if type(obj) is StopIteration: + # Stop message + break + elif type(obj) is PhaseChange: + # Phase change + self.phase = obj.phase + else: # obj is a job for self.process + # Send the object back for processing it + res = self.process(obj) + self.comm.send(res, dest=0, tag=tag.answer) + + if callable(self.on_worker_terminate): + self.on_worker_terminate() + + finally: + exit() def __send(self, jobiter): """ diff --git a/spotpy/parallel/mproc.py b/spotpy/parallel/mproc.py index 3c6b79e2..7594e9df 100644 --- a/spotpy/parallel/mproc.py +++ b/spotpy/parallel/mproc.py @@ -12,12 +12,14 @@ import pathos.multiprocessing as mp +process_count = None + class PhaseChange(object): """ Object to identify a change of a simulation phase """ - def __init__(self,phase): - self.phase=phase + def __init__(self, phase): + self.phase = phase class ForEach(object): """ @@ -25,29 +27,29 @@ class ForEach(object): We using the pathos multiprocessing module and the orderd map function where results are saved until results in the given order are caluculated. We yielding back the result so a generator object is created. """ - def __init__(self,process): - self.size = mp.cpu_count() + def __init__(self, process): + self.size = process_count or mp.cpu_count() self.process = process self.phase=None self.pool = mp.ProcessingPool(self.size) def is_idle(self): return False + def terminate(self): pass def start(self): pass - def setphase(self,phasename): - self.phase=phasename - + def setphase(self, phasename): + self.phase = phasename def f(self, job): data = self.process(job) return data - def __call__(self,jobs): + def __call__(self, jobs): results = self.pool.imap(self.f, jobs) for i in results: yield i diff --git a/spotpy/parallel/umproc.py b/spotpy/parallel/umproc.py index 0852e5ad..e16bbed8 100644 --- a/spotpy/parallel/umproc.py +++ b/spotpy/parallel/umproc.py @@ -12,12 +12,14 @@ import pathos.multiprocessing as mp +process_count = None + class PhaseChange(object): """ Object to identify a change of a simulation phase """ - def __init__(self,phase): - self.phase=phase + def __init__(self, phase): + self.phase = phase class ForEach(object): """ @@ -25,19 +27,21 @@ class ForEach(object): We using the pathos multiprocessing module and the unordered map function where results are yield back while some processes are still running. """ - def __init__(self,process): - self.size = mp.cpu_count() + def __init__(self, process): + self.size = process_count or mp.cpu_count() self.process = process - self.phase=None + self.phase = None self.pool = mp.ProcessingPool(self.size) def is_idle(self): return False + def terminate(self): pass def start(self): pass + def setphase(self,phasename): self.phase=phasename diff --git a/spotpy/parameter.py b/spotpy/parameter.py index c1dc3232..c03e9608 100644 --- a/spotpy/parameter.py +++ b/spotpy/parameter.py @@ -9,11 +9,10 @@ import numpy.random as rnd import numpy as np import sys +import copy if sys.version_info[0] >= 3: unicode = str - -from collections import namedtuple from itertools import cycle @@ -111,20 +110,21 @@ def attributes(self, names, raise_for_missing=None, as_dict=False): ) # Return the name, the distribution parameter values, and a tuple of unprocessed args and kwargs if as_dict: - return dict((n, a) for n, a in zip(names, attributes)) + # Creates the name / value dict with entries where the value is not None + return dict((n, a) for n, a in zip(names, attributes) if a is not None) else: return attributes def __len__(self): return len(self.args) + len(self.kwargs) - def get(self, argname): + def get(self, argname, default=None): """ Checks if argname is in kwargs, if present it is returned and removed else none. :param argname: :return: """ - return self.kwargs.pop(argname, None) + return self.kwargs.pop(argname, default) def check_complete(self): """ @@ -136,9 +136,21 @@ def check_complete(self): error = '{}: {} arguments where given but only {} could be used'.format(self.classname, total_args, self.processed_args) raise TypeError(error) + def _round_sig(x, sig=3): + """ + Rounds x to sig significant digits + :param x: The value to round + :param sig: Number of significant digits + :return: rounded value + """ from math import floor, log10 - return round(x, sig-int(floor(log10(abs(x))))-1) + # Check for zero to avoid math value error with log10(0.0) + if abs(x) < 1e-12: + return 0 + else: + return round(x, sig-int(floor(log10(abs(x))))-1) + class Base(object): """ @@ -159,7 +171,7 @@ def __init__(*args, **kwargs): The Uniform parameter class is the reference implementation. """ __rndargs__ = () - def __init__(self, rndfunc, *args, **kwargs): + def __init__(self, rndfunc, rndfuncname, *args, **kwargs): """ :name: Name of the parameter :rndfunc: Function to draw a random number, @@ -175,6 +187,7 @@ def __init__(self, rndfunc, *args, **kwargs): rndfunc(*rndargs, size=1000) """ self.rndfunc = rndfunc + self.rndfunctype = rndfuncname arghelper = _ArgumentHelper(self, *args, **kwargs) self.name = arghelper.name() arghelper.alias('default', 'optguess') @@ -185,10 +198,10 @@ def __init__(self, rndfunc, *args, **kwargs): param_args = arghelper.attributes(['step', 'optguess', 'minbound', 'maxbound'], as_dict=True) # Draw one sample of size 1000 sample = self(size=1000) - self.step = param_args.get('step') or (np.percentile(sample, 50) - np.percentile(sample, 40)) - self.optguess = param_args.get('optguess') or np.median(sample) - self.minbound = param_args.get('minbound') or _round_sig(np.min(sample)) - self.maxbound = param_args.get('maxbound') or _round_sig(np.max(sample)) + self.step = param_args.get('step', _round_sig(np.percentile(sample, 50) - np.percentile(sample, 40))) + self.optguess = param_args.get('optguess', _round_sig(np.median(sample))) + self.minbound = param_args.get('minbound', _round_sig(np.min(sample))) + self.maxbound = param_args.get('maxbound', _round_sig(np.max(sample))) else: @@ -198,6 +211,8 @@ def __init__(self, rndfunc, *args, **kwargs): self.maxbound = 0.0 self.description = arghelper.get('doc') + + self.as_int = not not arghelper.get("as_int") arghelper.check_complete() def __call__(self, **kwargs): @@ -210,7 +225,7 @@ def astuple(self): """ Returns a tuple of a realization and the other parameter properties """ - return self(), self.name, self.step, self.optguess, self.minbound, self.maxbound + return self(), self.name, self.step, self.optguess, self.minbound, self.maxbound, self.as_int def __repr__(self): """ @@ -259,7 +274,7 @@ def __init__(self, *args, **kwargs): default is quantile(0.5) - quantile(0.4) of rndfunc(*rndargs, size=1000) """ - super(Uniform, self).__init__(rnd.uniform, *args, **kwargs) + super(Uniform, self).__init__(rnd.uniform, 'Uniform', *args, **kwargs) class List(Base): @@ -267,14 +282,14 @@ class List(Base): A specialization to sample from a list (or other iterable) of parameter sets. Usage: - >>>list_param = List([1,2,3,4], repeat=True) - >>>list_param() + list_param = List([1,2,3,4], repeat=True) + list_param() 1 """ __rndargs__ = ('values', ) def __init__(self, *args, **kwargs): self.repeat = kwargs.pop('repeat', False) - super(List, self).__init__(None, *args, **kwargs) + super(List, self).__init__(None, 'List', *args, **kwargs) self.values, = self.rndargs # Hack to avoid skipping the first value. See __call__ function below. @@ -312,7 +327,7 @@ def __call__(self, size=None): raise IndexError(text) def astuple(self): - return self(), self.name, 0, 0, 0, 0 + return self(), self.name, 0, 0, 0, 0, self.as_int class Constant(Base): @@ -322,7 +337,9 @@ class Constant(Base): __rndargs__ = 'scalar', def __init__(self, *args, **kwargs): - super(Constant, self).__init__(self, *args, **kwargs) + super(Constant, self).__init__(self, 'Constant', *args, **kwargs) + + value = property(lambda self: self.rndargs[0]) def __call__(self, size=None): """ @@ -331,12 +348,12 @@ def __call__(self, size=None): :return: """ if size: - return np.ones(size, dtype=float) * self.rndargs[0] + return np.ones(size, dtype=float) * self.value else: - return self.rndargs[0] + return self.value def astuple(self): - return self(), self.name, self.rndargs[0], self.rndargs[0], 0, 0 + return self(), self.name, 0, self.value, self.value, self.value, self.as_int class Normal(Base): @@ -357,7 +374,7 @@ def __init__(self, *args, **kwargs): rndfunc(*rndargs, size=1000) """ - super(Normal, self).__init__(rnd.normal, *args, **kwargs) + super(Normal, self).__init__(rnd.normal, 'Normal', *args, **kwargs) class logNormal(Base): @@ -377,7 +394,7 @@ def __init__(self, *args, **kwargs): default is quantile(0.5) - quantile(0.4) of rndfunc(*rndargs, size=1000) """ - super(logNormal, self).__init__(rnd.lognormal, *args, **kwargs) + super(logNormal, self).__init__(rnd.lognormal, 'logNormal', *args, **kwargs) class Chisquare(Base): @@ -396,7 +413,7 @@ def __init__(self, *args, **kwargs): default is quantile(0.5) - quantile(0.4) of rndfunc(*rndargs, size=1000) """ - super(Chisquare, self).__init__(rnd.chisquare, *args, **kwargs) + super(Chisquare, self).__init__(rnd.chisquare, 'Chisquare', *args, **kwargs) class Exponential(Base): @@ -416,7 +433,7 @@ def __init__(self, *args, **kwargs): default is quantile(0.5) - quantile(0.4) of rndfunc(*rndargs, size=1000) """ - super(Exponential, self).__init__(rnd.exponential, *args, **kwargs) + super(Exponential, self).__init__(rnd.exponential, 'Exponential', *args, **kwargs) class Gamma(Base): @@ -438,7 +455,7 @@ def __init__(self, *args, **kwargs): rndfunc(*rndargs, size=1000) """ - super(Gamma, self).__init__(rnd.gamma, *args, **kwargs) + super(Gamma, self).__init__(rnd.gamma, 'Gamma', *args, **kwargs) class Wald(Base): @@ -459,7 +476,7 @@ def __init__(self, *args, **kwargs): default is quantile(0.5) - quantile(0.4) of rndfunc(*rndargs, size=1000) """ - super(Wald, self).__init__(rnd.wald, *args, **kwargs) + super(Wald, self).__init__(rnd.wald, 'Wald', *args, **kwargs) class Weibull(Base): @@ -478,7 +495,8 @@ def __init__(self, *args, **kwargs): default is quantile(0.5) - quantile(0.4) of rndfunc(*rndargs, size=1000) """ - super(Weibull, self).__init__(rnd.weibull, *args, **kwargs) + super(Weibull, self).__init__(rnd.weibull, 'Weibull', *args, **kwargs) + class Triangular(Base): """ @@ -498,8 +516,162 @@ def __init__(self, *args, **kwargs): default is quantile(0.5) - quantile(0.4) of rndfunc(*rndargs, size=1000) """ - super(Triangular, self).__init__(rnd.triangular, *args, **kwargs) + super(Triangular, self).__init__(rnd.triangular, 'Triangular', *args, **kwargs) + + +class ParameterSet(object): + """ + A Pickable parameter set to use named parameters in a setup + Is not created by a user directly, but in algorithm. + Older versions used a namedtuple, which is not pickable. + + An instance of ParameterSet is sent to the users setup.simulate method. + + Usage: + ps = ParameterSet(...) + + Update values by arguments or keyword arguments + + ps(0, 1, 2) + ps(a=1, c=2) + + Assess parameter values of this parameter set + + ps[0] == ps['a'] == ps.a + + A parameter set is a sequence: + + list(ps) + + Assess the parameter set properties as arrays + [ps.maxbound, ps.minbound, ps.optguess, ps.step, ps.random] + + + """ + def __init__(self, param_info): + """ + Creates a set of parameters from a parameter info array. + To create the parameter set from a setup use either: + setup = ... + ps = ParameterSet(get_parameters_array(setup)) + + or you can just use a function for this: + + ps = create_set(setup) + + :param param_info: A record array containing the properties of the parameters + of this set. + """ + self.__lookup = dict(("p" + x if x.isdigit() else x, i) for i, x in enumerate(param_info['name'])) + self.__info = param_info + + def __call__(self, *values, **kwargs): + """ + Populates the values ('random') of the parameter set with new data + :param values: Contains the new values or omitted. + If given, the number of values needs to match the number + of parameters + :param kwargs: Can be used to set only single parameter values + :return: + """ + if values: + if len(self.__info) != len(values): + raise ValueError('Given values do are not the same length as the parameter set') + self.__info['random'][:] = values + for k in kwargs: + try: + self.__info['random'][self.__lookup[k]] = kwargs[k] + except KeyError: + raise TypeError('{} is not a parameter of this set'.format(k)) + return self + + def __len__(self): + return len(self.__info['random']) + + def __iter__(self): + return iter(self.__info['random']) + + def __getitem__(self, item): + """ + Provides item access + ps[0] == ps['a'] + + :raises: KeyError, IndexError and TypeError + """ + if type(item) is str: + item = self.__lookup[item] + return self.__info['random'][item] + + def __setitem__(self, key, value): + """ + Provides setting of item + ps[0] = 1 + ps['a'] = 2 + """ + if key in self.__lookup: + key = self.__lookup[key] + self.__info['random'][key] = value + + def __getattr__(self, item): + """ + Provides the attribute access like + print(ps.a) + """ + if item.startswith('_'): + raise AttributeError('{} is not a member of this parameter set'.format(item)) + elif item in self.__lookup: + return self.__info['random'][self.__lookup[item]] + elif item in self.__info.dtype.names: + return self.__info[item] + else: + raise AttributeError('{} is not a member of this parameter set'.format(item)) + + def __setattr__(self, key, value): + """ + Provides setting of attributes + ps.a = 2 + """ + # Allow normal usage + if key.startswith('_') or key not in self.__lookup: + return object.__setattr__(self, key, value) + else: + self.__info['random'][self.__lookup[key]] = value + def __str__(self): + return 'parameters({})'.format( + ', '.join('{}={:g}'.format(k, self.__info['random'][i]) + for i, k in enumerate(self.__info['name']) + ) + ) + + def __repr__(self): + return 'spotpy.parameter.ParameterSet()' + + def __dir__(self): + """ + Helps to show the field names in an interactive environment like IPython. + See: http://ipython.readthedocs.io/en/stable/config/integrating.html + + :return: List of method names and fields + """ + attrs = [attr for attr in vars(type(self)) if not attr.startswith('_')] + return attrs + list(self.__info['name']) + list(self.__info.dtype.names) + + def set_by_array(self,array): + for i, a in enumerate(array): + self.__setitem__(i,a) + + def copy(self): + return ParameterSet(copy.deepcopy(self.__info)) + +def get_classes(): + keys = [] + current_module = sys.modules[__name__] + for key in dir(current_module): + if isinstance( getattr(current_module, key), type ): + keys.append(key) + return keys + def generate(parameters): """ @@ -509,12 +681,27 @@ def generate(parameters): """ dtype = [('random', '>> import spotpy - >>> from spotpy.examples.spot_setup_rosenbrock import spot_setup - >>> model = spot_setup() - >>> param_set = spotpy.parameter.create_set(model, x=2) - >>> result = model.simulation(param_set) + import spotpy + from spotpy.examples.spot_setup_rosenbrock import spot_setup + model = spot_setup() + param_set = spotpy.parameter.create_set(model, x=2) + result = model.simulation(param_set) :param setup: A spotpy compatible Model object - :param random: If True, undefined parameters are filled with a random realisation of the - parameter distribution, when False undefined parameters are filled with optguess + :param valuetype: Select between 'optguess' (defaul), 'random', 'minbound' and 'maxbound'. :param kwargs: Any keywords can be used to set certain parameters to fixed values :return: namedtuple of parameter values """ @@ -560,36 +757,48 @@ def create_set(setup, random=False, **kwargs): params = get_parameters_array(setup) # Create the namedtuple from the parameter names - partype = get_namedtuple_from_paramnames(setup, params['name']) - - # Get the values - if random: - # Use the generated values from the distribution - pardict = dict(zip(params['name'], params['random'])) - else: - # Use opt guess instead of a random value - pardict = dict(zip(params['name'], params['optguess'])) - - # Overwrite parameters with keyword arguments - pardict.update(kwargs) + partype = ParameterSet(params) # Return the namedtuple with fitting names - return partype(**pardict) + return partype(*params[valuetype], **kwargs) -def get_namedtuple_from_paramnames(owner, parnames): +def get_constant_indices(setup, unaccepted_parameter_types=(Constant,)): """ - Returns the namedtuple classname for parameter names - :param owner: Owner of the parameters, usually the spotpy setup - :param parnames: Sequence of parameter names - :return: Class + Returns a list of the class defined parameters, and + overwrites the names of the parameters. + By defining parameters as class members, as shown below, + one can omit the parameters function of the setup. + + Usage: + from spotpy import parameter + class SpotpySetup: + # Add parameters p1 & p2 to the setup. + p1 = parameter.Uniform(20, 100) + p2 = parameter.Gamma(2.3) + + setup = SpotpySetup() + parameters = parameter.get_parameters_from_setup(setup) """ - # Get name of owner class - typename = type(owner).__name__ - parnames = ["p" + x if x.isdigit() else x for x in list(parnames)] - return namedtuple('Par_' + typename, # Type name created from the setup name - parnames) # get parameter names + # Get all class variables + cls = type(setup) + class_variables = vars(cls).items() + + par_index = [] + i=0 + contained_class_parameter=False + #for i in range(len(class_variables)): + for attrname, attrobj in class_variables: + # Check if it is an spotpy parameter + if isinstance(attrobj, Base): + contained_class_parameter=True + if isinstance(attrobj, unaccepted_parameter_types): + par_index.append(i) + i+=1 + + if contained_class_parameter: + return par_index def get_parameters_from_setup(setup): @@ -601,25 +810,28 @@ def get_parameters_from_setup(setup): Usage: - >>> from spotpy import parameter - >>> class SpotpySetup: - >>> # Add parameters p1 & p2 to the setup. - >>> p1 = parameter.Uniform(20, 100) - >>> p2 = parameter.Gamma(2.3) - >>> - >>> setup = SpotpySetup() - >>> parameters = spotpy.parameter.get_parameters_from_setup(setup) + from spotpy import parameter + class SpotpySetup: + # Add parameters p1 & p2 to the setup. + p1 = parameter.Uniform(20, 100) + p2 = parameter.Gamma(2.3) + + setup = SpotpySetup() + parameters = parameter.get_parameters_from_setup(setup) """ # Get all class variables cls = type(setup) class_variables = vars(cls).items() - + parameters = [] for attrname, attrobj in class_variables: # Check if it is an spotpy parameter if isinstance(attrobj, Base): - # Set the attribute name +# if isinstance(attrobj, unaccepted_parameter_types): +# pass +# # Set the attribute name +# else: if not attrobj.name: attrobj.name = attrname # Add parameter to dict @@ -641,20 +853,4 @@ def get_parameters_from_setup(setup): # Generate the parameters array from it and append it to our list parameters.extend(setup.parameters) - return parameters - - -if __name__ == '__main__': - class TestSetup: - a = Uniform(0, 1) - b = Triangular(0, 1, right=10, doc='A Triangular parameter') - - def __init__(self): - self.parameters = [Uniform('c', 0, 1), Uniform('d', 0, 2)] - - params = get_parameters_from_setup(TestSetup) - assert len(params) == 4, 'Expected 2 Parameters in the test setup, got {}'.format(len(params)) - assert params[0] is TestSetup.a, 'Expected parameter a to be first in params' - assert params[1] is TestSetup.b - print('\n'.join(str(p) for p in params)) - + return parameters \ No newline at end of file diff --git a/spotpy/signatures.py b/spotpy/signatures.py deleted file mode 100644 index 3dac391d..00000000 --- a/spotpy/signatures.py +++ /dev/null @@ -1,1510 +0,0 @@ -# -*- coding: utf-8 -*- -''' -Copyright 2017 by Tobias Houska -This file is part of Statistical Parameter Estimation Tool (SPOTPY). -:author: Tobias Houska, Benjamin Manns -This module contains a framework to analyze data sets of hydrological signatures. -''' - - -import numpy as np -import copy -import warnings -import datetime - -try: - import pandas -except ImportError: - print('Please install Pandas to use these signature functions') - - - -class SuitableInput: - def __init__(self, datm): - """ - Calculates which section type the the date time series suits best (year, month, day, hour). - - :param datm: - :type datm: pandas datetime object - :return: the section type which suites best - :rtype: string - """ - self.datm = datm - self.allowed_sections = ['year', 'month', 'day', 'hour'] - - def calc(self): - if self.datm.__len__() > 1: - diff = (self.datm[1].to_pydatetime() - self.datm[0].to_pydatetime()).total_seconds() - anything_found = False - found_section = '' - while not anything_found: - try: - section = self.allowed_sections.pop() - except IndexError: - break - - if section == "year": - anything_found, found_section = diff / (3600 * 24 * 365) <= 1.0, 'year' - elif section == "month": - anything_found, found_section = diff / (3600 * 24 * 30) <= 1.0, 'month' - elif section == "day": - anything_found, found_section = diff / (3600 * 24) <= 1.0, 'day' - elif section == "hour": - anything_found, found_section = diff <= 3600, 'hour' - else: - raise Exception("The section [" + section + "] is not defined in " + str(self)) - return found_section - - -def __isSorted(df): - """ - If the pandas object is not sorted the comparision will failed with a ValueError which will be caught - and noted as a unsorted list - - :param df: time series - :type df: pandas datetime object - :return: if the pandas object is sorted - :rtype: bool - - """ - try: - if sum(df == np.sort(df)) == df.__len__(): - return True - else: - return False - except ValueError: - return False - - -def __calcMedianFlow(data): - """ - Simply calculate the median (flow exceeded 50% of the time) of the data - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - - :param data: A list of float data - :type data: list - :return: Median - :rtype: float - """ - return np.percentile(data, 50) - - -def __percentilwrapper(array, index): - """ - A Percentil Wrapper to have a easy chance to modify the percentil calculation for the following functions - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - - :param array: float array - :type array: list - :param index: which percentil should be used - :type index: int - :return: the percentil - :rtype: float - """ - return np.percentile(array, index) - - -def __calcMeanFlow(data): - """ - Simply calculate the mean of the data - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - - :param data: A list of float data - :type data: list - :return: Mean - :rtype: float - """ - return np.mean(data) - - -class HydroSignaturesError(Exception): - """ - Define an own error class to know it is an error made by a hydroindex calculation to warn the use for wrong inputs - """ - pass - - -class _SignaturesBasicFunctionality(object): - def __init__(self, data, comparedata=None, mode=None): - """ - A basic class to give a blueprint of a signature calculation class - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - """ - self.data = data - self.mode = mode - self.additional_arguments = {} - self.preProcessFunction_additional_arguments = {} - self.hasPreProcess = False - self.preProcessFunction = None - - if comparedata is not None: - self.comparedata = comparedata - if data.__len__() != comparedata.__len__(): - raise HydroSignaturesError("Simulation and observation data have not the same length") - self.which_case = "compare" - else: - self.which_case = "single" - - def pre_process(self, pre_func=None, **kwargs): - if pre_func is not None: - self.hasPreProcess = True - for k, v in kwargs.items(): - self.preProcessFunction_additional_arguments[k] = v - self.preProcessFunction = pre_func - - def analyze(self, func, **kwargs): - - # pack the kwargs into a dict so it can be expressed as keyword arguments later on - - for k, v in kwargs.items(): - self.additional_arguments[k] = v - - if self.mode == 'get_signature': - #print('Calculation Signature') - return self.get_signature(func) - elif self.mode == 'get_raw_data': - #print('Calculation raw data') - return self.raw_data(func) - elif self.mode == 'calc_Dev': - #print('Calculation difference') - try: - return self.run_compared_signature(func) - except AttributeError: - warnings.warn("The signature method need a not None value for `comparedata` if you use `calc_Dev`") - return None - - else: - raise ValueError( - "'%s' is not a valid keyword for signature processing" % self.mode) - - def get_signature(self, func): - if self.which_case == "compare": - return self.run_compared_signature(func) - elif self.which_case == "single": - return self.run_signature(self.data, func) - else: - raise HydroSignaturesError("No case set!") - - def raw_data(self, func): - if func.__name__ == "__calcFloodDuration": - return func(self.data, **self.additional_arguments)[1] - else: - return func(self.data, **self.additional_arguments) - - def run_signature(self, data, func): - return func(data, **self.additional_arguments) - - def run_compared_signature(self, func): - if not self.hasPreProcess: - return self.calcDev(self.run_signature(self.data, func), self.run_signature(self.comparedata, func)) - else: - return self.preProcessFunction(self.run_signature(self.data, func), - self.run_signature(self.comparedata, func), - **self.preProcessFunction_additional_arguments) - - @staticmethod - def calcDev(a, b): - """ - Calculate the relative error / derivation of two values - If one parameter is zero the result is just 1, for example b = 0, so calculate: - :math:`\\frac{a+0}{a} = 1` and also - :math:`a = b \\Leftrightarrow return = 0` [approximately] - - See https://en.wikipedia.org/wiki/Approximation_error - - :param a: Value a - :type a: float - :param b: Value b - :type b: float - :return: relative error of a and b (numeric definition) - :rtype: float - """ - if type(a) == type({}) or type(b) == type({}) or a is None or b is None: - raise HydroSignaturesError("You specified no pre process because your data are in a dict or NONE!") - else: - - if a != 0: - return (a - b) / a - elif b != 0: - return (a - b) / b - else: - return 0 - - -def getSlopeFDC(data, comparedata=None, mode='get_signature'): - """ - The main idea is to use a threshold by the mean of the data and use the first occurrence of a 33% exceed and a 66% - exceed and calculate the factor of how many times is the 66% exceed higher then the 33% exceed. - If 33% or 66% exceed does not exists then just give 0 back for a slope of 0 (horizontal line) - - :math:`slope = \\frac{treshold(mean*1,33 <= data)}{treshold(mean*1,66 <= data)}` - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - :return: the calculation will be return which is set by mode, this might be float numbers or war data in dict format which can be plot to visualize the signatures - :rtype: dict / float - - """ - basics = _SignaturesBasicFunctionality(data, comparedata=comparedata, mode=mode) - - return basics.analyze(__calcSlopeFDC) - - -def __calcSlopeFDC(data): - upper33_data = np.sort(data)[np.sort(data) >= 1.33 * np.mean(data)] - upper66_data = np.sort(data)[np.sort(data) >= 1.66 * np.mean(data)] - if upper33_data.__len__() > 0 and upper66_data.__len__() > 0: - if upper66_data[0] != 0: - return upper33_data[0] / upper66_data[0] - else: - return 0.0 - else: - return 0.0 - - -def getAverageFloodOverflowPerSection(data, comparedata=None, mode='get_signature', datetime_series=None, - threshold_value=3): - """ - All measurements are scanned where there are overflow events. Based on the section we summarize events per year, - month, day, hour. - Of course we need a datetime_series which has the the suitable resolution. So, for example, if you group the - overflow events hourly but you have only daily data the function will work but not very useful. - - However for every section the function collect the overflow value, i.e. value - threshold and calc the deviation - of the means of this overflows. - - The idea is based on values from "REDUNDANCY AND THE CHOICE OF HYDROLOGIC INDICES FOR CHARACTERIZING STREAMFLOW REGIMES - JULIAN D. OLDEN* and N. L. POFF" (RiverResearchApp_2003.pdf, page 109). An algorithms to calculate this data is not - given, so we developed an own. - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - :param datetime_series: datetime series - :type datetime_series: pandas datetime object - :param threshold_value: which times the median we use for a threshold - :type threshold_value: float - :return: deviation of means of overflow value or raw data - :rtype: dict / float - """ - - if datetime_series is None: - raise HydroSignaturesError("datetime_series is None. Please specify a datetime_series.") - - if data.__len__() != datetime_series.__len__(): - raise HydroSignaturesError("Simulation / observation data and the datetime_series have not the same length") - - basics = _SignaturesBasicFunctionality(data, comparedata=comparedata, mode=mode) - - basics.pre_process(__calcDeviationForAverageFloodOverflowPerSection) - return basics.analyze(__calcFloodDuration, datetime_series=datetime_series, threshold_value=threshold_value, - which_flow="flood") - - -def __calcDeviationForAverageFloodOverflowPerSection(a, b): - for_mean_a = [] - for_mean_b = [] - a = a[0] - b = b[0] - - for y in a: - if a[y].__len__() > 0: - for elem in range(a[y].__len__()): - for ov in a[y][elem]["overflow"]: - for_mean_a.append(ov) - for y in b: - if b[y].__len__() > 0: - for elem in range(b[y].__len__()): - for ov in b[y][elem]["overflow"]: - for_mean_b.append(ov) - if for_mean_a.__len__() > 0 and for_mean_b.__len__() > 0: - return _SignaturesBasicFunctionality.calcDev(np.mean(for_mean_a), np.mean(for_mean_b)) - else: - warnings.warn("No data in for_mean_a to calculate mean of") - - -def __calcFloodDuration(data, datetime_series, threshold_value, which_flow): - """ - With a given data set we use the datetime_series and save all continuous floods, measured by a given - threshold_value times the median of the data. The start and end time of this event is recorded. Based on the best - suitable section we create the list of the calculated values per year, month, day, hour. - Important to know is that the user can input a date-time object with several intervals, so it could be every second - or every day recorded data. - This does not matter at all, we just save the beginning and ending date-time, the difference of threshold and - measurement and the amount of how many steps are in the flood event. - This function is used by several "getFlood*"-Functions which then calculate the desired hydrological index. - - The idea is based on values from "REDUNDANCY AND THE CHOICE OF HYDROLOGIC INDICES FOR CHARACTERIZING STREAMFLOW REGIMES - JULIAN D. OLDEN* and N. L. POFF" (RiverResearchApp_2003.pdf, page 109). An algorithms to calculate this data is not - given, so we developed an own. - - :param data: measurement / simulation of a flow - :type data: list - :param datetime_series: datetime series - :type datetime_series: pandas datetime object - :param threshold_value: which times the median we use for a threshold - :type threshold_value: float - :param which_flow: in ["flood","baseflow"] - :type which_flow: string - :return: objects per section with the flood event - :rtype: dict - """ - s = SuitableInput(datetime_series) - section = s.calc() - duration_per_section = {} - tmp_duration_logger_per_sec = {} - threshold_on_per_year = {} - event_mapped_to_datetime = [] - index = 0 - - if which_flow not in ["flood", "baseflow"]: - raise HydroSignaturesError("which_flow should be flood or baseflow") - if section not in ["year", "month", "day", "hour"]: - raise HydroSignaturesError("Your section: " + section + " is not valid. See pydoc of this function") - - tmpStdDurLG = {'start': "0000-00-00", 'end': '0000-00-00', 'duration': 0,} - if which_flow == "flood": - tmpStdDurLG['overflow'] = [] - elif which_flow == "baseflow": - tmpStdDurLG['underflow'] = [] - - if __isSorted(datetime_series): - for d in datetime_series: - if section == "year": - sec_key = pandas.Timestamp(datetime.datetime(year=d.to_pydatetime().year, month=1, day=1)) - elif section == "month": - sec_key = pandas.Timestamp( - datetime.datetime(year=d.to_pydatetime().year, month=d.to_pydatetime().month, day=1)) - elif section == "day": - sec_key = d - elif section == "hour": - sec_key = pandas.Timestamp(datetime.datetime(year=d.to_pydatetime().year, month=d.to_pydatetime().month, - day=d.to_pydatetime().day, hour=d.to_pydatetime().hour)) - else: - raise HydroSignaturesError("Your section: " + section + " is not valid. See pydoc of this function") - - if sec_key not in duration_per_section: - # Define a bunch of arrays to handle, save and organize the analyze of the data as most as possible at the same time - duration_per_section[sec_key] = [] - tmp_duration_logger_per_sec[sec_key] = copy.deepcopy(tmpStdDurLG) - threshold_on_per_year[sec_key] = False - - # And save the old years duration object: - if index > 0: - - tmp_lastsec_d = datetime_series[index - 1] - if section == "year": - tmp_lastsec = pandas.Timestamp(datetime.datetime(year=tmp_lastsec_d.to_pydatetime().year, month=1, day=1)) - elif section == "month": - tmp_lastsec = pandas.Timestamp( - datetime.datetime(year=tmp_lastsec_d.to_pydatetime().year, month=tmp_lastsec_d.to_pydatetime().month, day=1)) - elif section == "day": - tmp_lastsec = tmp_lastsec_d - elif section == "hour": - tmp_lastsec = pandas.Timestamp( - datetime.datetime(year=tmp_lastsec_d.to_pydatetime().year, month=tmp_lastsec_d.to_pydatetime().month, - day=tmp_lastsec_d.to_pydatetime().day, hour=tmp_lastsec_d.to_pydatetime().hour)) - else: - raise HydroSignaturesError( - "Your section: " + section + " is not valid. See pydoc of this function") - - - if tmp_duration_logger_per_sec[tmp_lastsec]["duration"] > 0: - tmp_duration_logger_per_sec[tmp_lastsec]["end"] = datetime_series[ - index - 1].to_pydatetime().strftime("%Y-%m-%d %H:%M:%S") - duration_per_section[tmp_lastsec].append( - copy.deepcopy(tmp_duration_logger_per_sec[tmp_lastsec])) - tmp_duration_logger_per_sec[tmp_lastsec] = copy.deepcopy(tmpStdDurLG) - - event_happend = False - if which_flow == "flood": - if data[index] > threshold_value: - event_happend = True - diff = data[index] - threshold_value - tmp_duration_logger_per_sec[sec_key]["overflow"].append(diff) - else: - event_happend = False - elif which_flow == "baseflow": - if data[index] < threshold_value: - event_happend = True - diff = data[index] - threshold_value - tmp_duration_logger_per_sec[sec_key]["underflow"].append(diff) - else: - event_happend = False - - tmp_dict_for_eventMap = {"datetime": d} - if event_happend: - - - tmp_dict_for_eventMap[which_flow]=diff - event_mapped_to_datetime.append(tmp_dict_for_eventMap) - if not threshold_on_per_year[sec_key]: - threshold_on_per_year[sec_key] = True - tmp_duration_logger_per_sec[sec_key]["start"] = d.to_pydatetime().strftime("%Y-%m-%d %H:%M:%S") - tmp_duration_logger_per_sec[sec_key]["duration"] = tmp_duration_logger_per_sec[sec_key]["duration"] + 1 - else: - tmp_dict_for_eventMap[which_flow] = 0 - event_mapped_to_datetime.append(tmp_dict_for_eventMap) - if threshold_on_per_year[sec_key]: - threshold_on_per_year[sec_key] = False - tmp_duration_logger_per_sec[sec_key]["end"] = datetime_series[index - 1].to_pydatetime().strftime( - "%Y-%m-%d %H:%M:%S") - if tmp_duration_logger_per_sec[sec_key]["duration"] > 0: - # Here we save the logged flood into the big array - duration_per_section[sec_key].append(copy.deepcopy(tmp_duration_logger_per_sec[sec_key])) - - tmp_duration_logger_per_sec[sec_key] = copy.deepcopy(tmpStdDurLG) - else: - threshold_on_per_year[sec_key] = False - index += 1 - else: - raise HydroSignaturesError("The timeseries is not sorted, so a calculation can not be performed") - - event_mapped_to_datetime = pandas.DataFrame(event_mapped_to_datetime) - event_mapped_to_datetime = event_mapped_to_datetime.set_index("datetime") - return duration_per_section, event_mapped_to_datetime - - -def getMeanFlow(data, comparedata=None, mode='get_signature'): - """ - Simply calculate the mean of the data - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - - :return: Mean - :rtype: float - - """ - basics = _SignaturesBasicFunctionality(data=data, comparedata=comparedata, mode=mode) - return basics.analyze(__calcMeanFlow) - - -def getMedianFlow(data, comparedata=None, mode='get_signature'): - """ - Simply calculate the median (flow exceeded 50% of the time) of the data - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - - :return: Median - :rtype: float - - """ - basics = _SignaturesBasicFunctionality(data=data, comparedata=comparedata, mode=mode) - return basics.analyze(__calcMedianFlow) - - -def getSkewness(data, comparedata=None, mode='get_signature'): - """ - Skewness, i.e. the mean flow data divided by Q50 (50 percentil / median flow) . - - See paper "B. Clausen, B.J.F. Biggs / Journal of Hydrology 237 (2000) 184-197", (M_A1_MeanDailyFlows .pdf, page 185) - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - - :return: derivation of the skewness - :rtype: float - - """ - basics = _SignaturesBasicFunctionality(data=data, comparedata=comparedata, mode=mode) - return basics.analyze(__Skewness) - - -def __Skewness(data): - return __calcMeanFlow(data) / __calcMedianFlow(data) - - -def getCoeffVariation(data, comparedata=None, mode='get_signature'): - """ - Coefficient of variation, i.e. standard deviation divided by mean flow - - See paper "B. Clausen, B.J.F. Biggs / Journal of Hydrology 237 (2000) 184-197", (M_A1_MeanDailyFlows .pdf, page 185) - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - - :return: derivation of the coefficient of variation - :rtype: float - - """ - basics = _SignaturesBasicFunctionality(data=data, comparedata=comparedata, mode=mode) - return basics.analyze(_CoeffVariation) - - -def _CoeffVariation(data): - return np.std(data) / __calcMeanFlow(data) - - -def getQ001(data, comparedata=None, mode='get_signature'): - """ - The value of the 0.01 percentiles - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - - :return: derivation of the 0.01 percentiles - :rtype: float - - """ - basics = _SignaturesBasicFunctionality(data=data, comparedata=comparedata, mode=mode) - return basics.analyze(__percentilwrapper, index=0.01) - - -def getQ01(data, comparedata=None, mode='get_signature'): - """ - The value of the 0.1 percentiles - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - - :return: derivation of the 0.1 percentiles - :rtype: float - - """ - basics = _SignaturesBasicFunctionality(data=data, comparedata=comparedata, mode=mode) - return basics.analyze(__percentilwrapper, index=0.1) - - -def getQ1(data, comparedata=None, mode='get_signature'): - """ - The value of the 1 percentiles - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - - :return: derivation of the 1 percentiles - :rtype: float - - """ - basics = _SignaturesBasicFunctionality(data=data, comparedata=comparedata, mode=mode) - return basics.analyze(__percentilwrapper, index=1) - - -def getQ5(data, comparedata=None, mode='get_signature'): - """ - The value of the 5 percentiles - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - - :return: derivation of the 5 percentiles - :rtype: float - - """ - basics = _SignaturesBasicFunctionality(data=data, comparedata=comparedata, mode=mode) - return basics.analyze(__percentilwrapper, index=1) - - -def getQ10(data, comparedata=None, mode='get_signature'): - """ - The value of the 10 percentiles - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - - :return: derivation of the 10 percentiles - :rtype: float - - """ - basics = _SignaturesBasicFunctionality(data=data, comparedata=comparedata, mode=mode) - return basics.analyze(__percentilwrapper, index=10) - - -def getQ20(data, comparedata=None, mode='get_signature'): - """ - The value of the 20 percentiles - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - - :return: derivation of the 20 percentiles - :rtype: float - - """ - basics = _SignaturesBasicFunctionality(data=data, comparedata=comparedata, mode=mode) - return basics.analyze(__percentilwrapper, index=20) - - -def getQ85(data, comparedata=None, mode='get_signature'): - """ - The value of the 85 percentiles - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - - :return: derivation of the 85 percentiles - :rtype: float - - """ - basics = _SignaturesBasicFunctionality(data=data, comparedata=comparedata, mode=mode) - return basics.analyze(__percentilwrapper, index=85) - - -def getQ95(data, comparedata=None, mode='get_signature'): - """ - The value of the 95 percentiles - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - - :return: derivation of the 95 percentiles - :rtype: float - - """ - basics = _SignaturesBasicFunctionality(data=data, comparedata=comparedata, mode=mode) - return basics.analyze(__percentilwrapper, index=95) - - -def getQ99(data, comparedata=None, mode='get_signature'): - """ - The value of the 99 percentiles - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - - :return: derivation of the 99 percentiles - :rtype: float - - """ - basics = _SignaturesBasicFunctionality(data=data, comparedata=comparedata, mode=mode) - return basics.analyze(__percentilwrapper, index=99) - - -def getAverageFloodFrequencyPerSection(data, comparedata=None, mode='get_signature', datetime_series=None, - threshold_value=3): - """ - This function calculates the average frequency per every section in the given interval of the datetime_series. - So if the datetime is recorded all 5 min we use this fine interval to count all records which are in flood. - - The idea is based on values from "REDUNDANCY AND THE CHOICE OF HYDROLOGIC INDICES FOR CHARACTERIZING STREAMFLOW REGIMES - JULIAN D. OLDEN* and N. L. POFF" (RiverResearchApp_2003.pdf, page 109). An algorithms to calculate this data is not - given, so we developed an own. - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - :param datetime_series: datetime series - :type datetime_series: pandas datetime object - :param threshold_value: a threshold where a flood event is defined with - :type threshold_value: float - :return: deviation of means of flood frequency per best suitable section or a whole dict if mode was set to raw data - :rtype: float / dict - """ - - if datetime_series is None: - raise HydroSignaturesError("datetime_series is None. Please specify a datetime_series.") - - if data.__len__() != datetime_series.__len__(): - raise HydroSignaturesError("Simulation / observation data and the datetime_series have not the same length") - - basics = _SignaturesBasicFunctionality(data, comparedata=comparedata, mode=mode) - - basics.pre_process(__calcDeviationForAverageFloodFrequencyPerSection) - return basics.analyze(__calcFloodDuration, datetime_series=datetime_series, threshold_value=threshold_value, - which_flow="flood") - - -def __calcDeviationForAverageFloodFrequencyPerSection(a, b): - sum_dev = 0.0 - a = a[0] - b = b[0] - for y in a: - sum_dur_1 = 0.0 - sum_dur_2 = 0.0 - for elem in a[y]: - sum_dur_1 += elem["duration"] - for elem in b[y]: - sum_dur_2 += elem["duration"] - - sum_dev += _SignaturesBasicFunctionality.calcDev(sum_dur_1, sum_dur_2) - return sum_dev / a.__len__() - - -def getAverageFloodDuration(data, comparedata=None, mode='get_signature', datetime_series=None, threshold_value=3): - """ - Get high and low-flow yearly-average event duration which has a threshold of threshold_value, this could be any float - - The idea is based on values from "REDUNDANCY AND THE CHOICE OF HYDROLOGIC INDICES FOR CHARACTERIZING STREAMFLOW REGIMES - JULIAN D. OLDEN* and N. L. POFF" (RiverResearchApp_2003.pdf, page 109). An algorithms to calculate this data is not - given, so we developed an own. - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - :param datetime_series: datetime series - :type datetime_series: pandas datetime object - :param threshold_value: a threshold where a flood event is defined with - :type threshold_value: float - :return: deviation of means of flood durations or a dict if mode was set to raw data - :rtype: float / dict - """ - - if datetime_series is None: - raise HydroSignaturesError("datetime_series is None. Please specify a datetime_series.") - - if data.__len__() != datetime_series.__len__(): - raise HydroSignaturesError("Simulation / observation data and the datetime_series have not the same length") - - basics = _SignaturesBasicFunctionality(data, comparedata=comparedata, mode=mode) - - s = SuitableInput(datetime_series) - section = s.calc() - basics.pre_process(__calcDeviationForAverageFloodDuration, section=section) - return basics.analyze(__calcFloodDuration, datetime_series=datetime_series, threshold_value=threshold_value, - which_flow="flood") - - -def __calcDeviationForAverageFloodDuration(a, b, section): - sum_dev = 0.0 - a = a[0] - - b = b[0] - for y in a: - sum_diff_1 = 0.0 - sum_diff_2 = 0.0 - if a[y].__len__() > 0: - for elem in range(a[y].__len__()): - d_start_a = datetime.datetime.strptime(a[y][elem]["start"], "%Y-%m-%d %H:%M:%S") - d_end_a = datetime.datetime.strptime(a[y][elem]["end"], "%Y-%m-%d %H:%M:%S") - if d_end_a.date() == d_start_a.date(): - sum_diff_1 += 24 * 3600 - else: - d_diff_a = d_end_a - d_start_a - sum_diff_1 += d_diff_a.seconds - sum_diff_av_1 = sum_diff_1 / a[y].__len__() - else: - sum_diff_av_1 = 0 - - if b[y].__len__() > 0: - for elem in range(b[y].__len__()): - d_start_b = datetime.datetime.strptime(b[y][elem]["start"], "%Y-%m-%d %H:%M:%S") - d_end_b = datetime.datetime.strptime(b[y][elem]["end"], "%Y-%m-%d %H:%M:%S") - if d_end_b.date() == d_start_b.date(): - d_diff_b = datetime.timedelta(1) - else: - d_diff_b = d_end_b - d_start_b - sum_diff_2 += d_diff_b.seconds - sum_diff_av_2 = sum_diff_2 / b[y].__len__() - else: - sum_diff_av_2 = 0 - - if section == "year": - sum_dev += _SignaturesBasicFunctionality.calcDev(sum_diff_av_1 / (365 * 24 * 3600), - sum_diff_av_2 / (365 * 24 * 3600)) - elif section == "month": - sum_dev += _SignaturesBasicFunctionality.calcDev(sum_diff_av_1 / (30 * 24 * 3600), - sum_diff_av_2 / (30 * 24 * 3600)) - elif section == "day": - sum_dev += _SignaturesBasicFunctionality.calcDev(sum_diff_av_1 / (24 * 3600), - sum_diff_av_2 / (24 * 3600)) - elif section == "hour": - sum_dev += _SignaturesBasicFunctionality.calcDev(sum_diff_av_1 / (3600), sum_diff_av_2 / (3600)) - else: - raise HydroSignaturesError("Your section: " + section + " is not valid. See pydoc of this function") - - return sum_dev / a.__len__() - - -def getAverageBaseflowUnderflowPerSection(data, comparedata=None, mode='get_signature', datetime_series=None, - threshold_value=3): - """ - All measurements are scanned where there are overflow events. Based on the best suitable section we summarize events - per year, month, day, hour. - - However for every section the function collect the overflow value, i.e. value - threshold and calc the deviation - of the means of this overflows. - - The idea is based on values from "REDUNDANCY AND THE CHOICE OF HYDROLOGIC INDICES FOR CHARACTERIZING STREAMFLOW REGIMES - JULIAN D. OLDEN* and N. L. POFF" (RiverResearchApp_2003.pdf, page 109). An algorithms to calculate this data is not - given, so we developed an own. - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - :param datetime_series: datetime series - :type datetime_series: pandas datetime object - :param threshold_value: a threshold where a baseflow event is defined with - :type threshold_value: float - :return: deviation of means of underflow value or a dict if mode was set to raw data - :rtype: float / dict - - """ - - if datetime_series is None: - raise HydroSignaturesError("datetime_series is None. Please specify a datetime_series.") - - if data.__len__() != datetime_series.__len__(): - raise HydroSignaturesError("Simulation / observation data and the datetime_series have not the same length") - - basics = _SignaturesBasicFunctionality(data, comparedata=comparedata, mode=mode) - - basics.pre_process(__calcDeviationForAverageBaseflowUnderflowPerSection) - return basics.analyze(__calcFloodDuration, datetime_series=datetime_series, threshold_value=threshold_value, - which_flow="baseflow") - - -def __calcDeviationForAverageBaseflowUnderflowPerSection(a, b): - for_mean_a = [] - for_mean_b = [] - a = a[0] - b = b[0] - for y in a: - if a[y].__len__() > 0: - for elem in range(a[y].__len__()): - for ov in a[y][elem]["underflow"]: - for_mean_a.append(ov) - for y in b: - if b[y].__len__() > 0: - for elem in range(b[y].__len__()): - for ov in b[y][elem]["underflow"]: - for_mean_b.append(ov) - - return _SignaturesBasicFunctionality.calcDev(np.mean(for_mean_a), np.mean(for_mean_b)) - - -def getAverageBaseflowFrequencyPerSection(data, comparedata=None, mode='get_signature', datetime_series=None, - threshold_value=3): - """ - This function calculates the average frequency per every section in the given interval of the datetime_series. - So if the datetime is recorded all 5 min we use this fine interval to count all records which are in flood. - - The idea is based on values from "REDUNDANCY AND THE CHOICE OF HYDROLOGIC INDICES FOR CHARACTERIZING STREAMFLOW REGIMES - JULIAN D. OLDEN* and N. L. POFF" (RiverResearchApp_2003.pdf, page 109). An algorithms to calculate this data is not - given, so we developed an own. - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - :param datetime_series: datetime series - :type datetime_series: pandas datetime object - :param threshold_value: a threshold where a baseflow event is defined with - :type threshold_value: float - :return: deviation of means of baseflow frequency per section or a dict if mode was set to raw data - :rtype: float / dict - """ - - if datetime_series is None: - raise HydroSignaturesError("datetime_series is None. Please specify a datetime_series.") - - if data.__len__() != datetime_series.__len__(): - raise HydroSignaturesError("Simulation / observation data and the datetime_series have not the same length") - - basics = _SignaturesBasicFunctionality(data, comparedata=comparedata, mode=mode) - - basics.pre_process(__calcDevForAverageBaseflowFrequencyPerSection) - return basics.analyze(__calcFloodDuration, datetime_series=datetime_series, threshold_value=threshold_value, - which_flow="baseflow") - - -def __calcDevForAverageBaseflowFrequencyPerSection(a, b): - sum_dev = 0.0 - a = a[0] - b = b[0] - for y in a: - sum_dur_1 = 0.0 - sum_dur_2 = 0.0 - for elem in a[y]: - sum_dur_1 += elem["duration"] - for elem in b[y]: - sum_dur_2 += elem["duration"] - - sum_dev += _SignaturesBasicFunctionality.calcDev(sum_dur_1, sum_dur_2) - - return sum_dev / a.__len__() - - -def getAverageBaseflowDuration(data, comparedata=None, mode='get_signature', datetime_series=None, threshold_value=3): - """ - Get high and low-flow yearly-average event duration which have a threshold of threshold_value - - The idea is based on values from "REDUNDANCY AND THE CHOICE OF HYDROLOGIC INDICES FOR CHARACTERIZING STREAMFLOW REGIMES - JULIAN D. OLDEN* and N. L. POFF" (RiverResearchApp_2003.pdf, page 109). An algorithms to calculate this data is not - given, so we developed an own. - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - :param datetime_series: datetime series - :type datetime_series: pandas datetime object - :param threshold_value: a threshold where a baseflow event is defined with - :type threshold_value: float - :return: deviation of means of baseflow duration or a dict if mode was set to raw data - :rtype: float / dict - - """ - - if datetime_series is None: - raise HydroSignaturesError("datetime_series is None. Please specify a datetime_series.") - - if data.__len__() != datetime_series.__len__(): - raise HydroSignaturesError("Simulation / observation data and the datetime_series have not the same length") - - basics = _SignaturesBasicFunctionality(data, comparedata=comparedata, mode=mode) - - s = SuitableInput(datetime_series) - section = s.calc() - basics.pre_process(__calcDevForAverageBaseflowDuration, section=section) - return basics.analyze(__calcFloodDuration, datetime_series=datetime_series, threshold_value=threshold_value, - which_flow="baseflow") - - -def __calcDevForAverageBaseflowDuration(a, b, section): - sum_dev = 0.0 - a = a[0] - b = b[0] - - for y in a: - sum_diff_1 = 0.0 - sum_diff_2 = 0.0 - if a[y].__len__() > 0: - for elem in range(a[y].__len__()): - d_start_a = datetime.datetime.strptime(a[y][elem]["start"], "%Y-%m-%d %H:%M:%S") - d_end_a = datetime.datetime.strptime(a[y][elem]["end"], "%Y-%m-%d %H:%M:%S") - if d_end_a.date() == d_start_a.date(): - sum_diff_1 += 24 * 3600 - else: - d_diff_a = d_end_a - d_start_a - sum_diff_1 += d_diff_a.seconds - sum_diff_av_1 = sum_diff_1 / a[y].__len__() - else: - sum_diff_av_1 = 0 - - if b[y].__len__() > 0: - for elem in range(b[y].__len__()): - d_start_b = datetime.datetime.strptime(b[y][elem]["start"], "%Y-%m-%d %H:%M:%S") - d_end_b = datetime.datetime.strptime(b[y][elem]["end"], "%Y-%m-%d %H:%M:%S") - if d_end_b.date() == d_start_b.date(): - d_diff_b = datetime.timedelta(1) - else: - d_diff_b = d_end_b - d_start_b - sum_diff_2 += d_diff_b.seconds - sum_diff_av_2 = sum_diff_2 / b[y].__len__() - else: - sum_diff_av_2 = 0 - - if section == "year": - sum_dev += _SignaturesBasicFunctionality.calcDev(sum_diff_av_1 / (365 * 24 * 3600), - sum_diff_av_2 / (365 * 24 * 3600)) - elif section == "month": - sum_dev += _SignaturesBasicFunctionality.calcDev(sum_diff_av_1 / (30 * 24 * 3600), - sum_diff_av_2 / (30 * 24 * 3600)) - elif section == "day": - sum_dev += _SignaturesBasicFunctionality.calcDev(sum_diff_av_1 / (24 * 3600), - sum_diff_av_2 / (24 * 3600)) - elif section == "hour": - sum_dev += _SignaturesBasicFunctionality.calcDev(sum_diff_av_1 / (3600), sum_diff_av_2 / (3600)) - else: - raise HydroSignaturesError("Your section: " + section + " is not valid. See pydoc of this function") - - return sum_dev / a.__len__() - - -def getFloodFrequency(data, comparedata=None, mode='get_signature', datetime_series=None, threshold_value=3): - """ - Get high and low-flow event frequencies which have a threshold of "threshold_value" - - The idea is based on values from "REDUNDANCY AND THE CHOICE OF HYDROLOGIC INDICES FOR CHARACTERIZING STREAMFLOW REGIMES - JULIAN D. OLDEN* and N. L. POFF" (RiverResearchApp_2003.pdf, page 109). An algorithms to calculate this data is not - given, so we developed an own. - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - :param datetime_series: datetime series - :type datetime_series: pandas datetime object - :param threshold_value: a threshold where a baseflow event is defined with - :type threshold_value: float - :return: mean of deviation of average flood frequency of the best suitable section or a dict if mode was set to raw data - :rtype: float / dict - - - """ - if datetime_series is None: - raise HydroSignaturesError("datetime_series is None. Please specify a datetime_series.") - - if data.__len__() != datetime_series.__len__(): - raise HydroSignaturesError("Simulation / observation data and the datetime_series have not the same length") - - basics = _SignaturesBasicFunctionality(data, comparedata=comparedata, mode=mode) - basics.pre_process(__calcDevForFloodFrequency) - return basics.analyze(__calcFlowLevelEventFrequency, datetime_series=datetime_series, - threshold_value=threshold_value, - flow_level_type="flood") - - -def getBaseflowFrequency(data, comparedata=None, mode='get_signature', datetime_series=None, threshold_value=3): - """ - Get high and low-flow event frequencies which have a threshold of "threshold_value" - - The idea is based on values from "REDUNDANCY AND THE CHOICE OF HYDROLOGIC INDICES FOR CHARACTERIZING STREAMFLOW REGIMES - JULIAN D. OLDEN* and N. L. POFF" (RiverResearchApp_2003.pdf, page 109). An algorithms to calculate this data is not - given, so we developed an own. - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - :param datetime_series: datetime series - :type datetime_series: pandas datetime object - :param threshold_value: a threshold where a baseflow event is defined with - :type threshold_value: float - :return: mean of deviation of average flood frequency of the best suitable section or a dict if mode was set to raw data - :rtype: float / dict - - - """ - if datetime_series is None: - raise HydroSignaturesError("datetime_series is None. Please specify a datetime_series.") - - if data.__len__() != datetime_series.__len__(): - raise HydroSignaturesError("Simulation / observation data and the datetime_series have not the same length") - - basics = _SignaturesBasicFunctionality(data, comparedata=comparedata, mode=mode) - basics.pre_process(__calcDevForFloodFrequency) - return basics.analyze(__calcFlowLevelEventFrequency, datetime_series=datetime_series, - threshold_value=threshold_value, - flow_level_type="baseflow") - - -def __calcDevForFloodFrequency(a, b): - - sum = 0.0 - rows_a = [row[1] for row in a.itertuples()] - rows_b = [row[1] for row in b.itertuples()] - for j in range(rows_a.__len__()): - sum += _SignaturesBasicFunctionality.calcDev(rows_a[j], rows_b[j]) - return sum / b.__len__() - - -def __calcFlowLevelEventFrequency(data, datetime_series, threshold_value, flow_level_type): - """ - Calc the high and low-flow event frequencies which have a threshold of "threshold_value" - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - - :param data: data where the flood frequency is calculated of - :type data: list - :param datetime_series: a pandas data object with sorted (may not be complete but sorted) dates - :type datetime_series: pandas datetime - :param threshold_value: a threshold where a baseflow event is defined with - :type threshold_value: float - :param flow_level_type: in ["flood","baseflow"]: - :type flow_level_type: string - :return: mean of deviation of average flood frequency of the best suitable section - :rtype: float - """ - - if flow_level_type not in ["flood", "baseflow"]: - raise HydroSignaturesError("flow_level_type should flood or baseflow") - - if __isSorted(datetime_series): - - count_per_section = {} - - index = 0 - - s = SuitableInput(datetime_series) - section = s.calc() - - for d in datetime_series: - - if section == "year": - sec_key = pandas.Timestamp(datetime.datetime(year=d.to_pydatetime().year, month=1, day=1)) - elif section == "month": - sec_key = pandas.Timestamp(datetime.datetime(year=d.to_pydatetime().year, month=d.to_pydatetime().month, day=1)) - elif section == "day": - sec_key = d - elif section == "hour": - sec_key = pandas.Timestamp(datetime.datetime(year=d.to_pydatetime().year, month=d.to_pydatetime().month, day=d.to_pydatetime().day, hour=d.to_pydatetime().hour)) - else: - raise HydroSignaturesError("Your section: " + section + " is not valid. See pydoc of this function") - - if sec_key not in count_per_section: - count_per_section[sec_key] = 0 - - if flow_level_type == "flood": - if data[index] > threshold_value: - count_per_section[sec_key] += 1 - elif flow_level_type == "baseflow": - if data[index] < threshold_value: - count_per_section[sec_key] += 1 - index += 1 - - else: - raise HydroSignaturesError("The time series is not sorted, so a calculation can not be performed") - - count_per_section_for_pandas = [] - - for key in count_per_section: - count_per_section_for_pandas.append({'count':count_per_section[key],'datetime':key}) - - - count_per_section_pandas = pandas.DataFrame(count_per_section_for_pandas) - - count_per_section_pandas = count_per_section_pandas.set_index("datetime") - return count_per_section_pandas.sort_index() - - -def getLowFlowVar(data, comparedata=None, mode='get_signature', datetime_series=None): - """ - - Mean of annual minimum flow divided by the median flow (Jowett and Duncan, 1990) - - Annular Data - - .. math:: - - Annualar Data= \\frac{\\sum_{i=1}^{N}(min(d_i)}{N*median(data)} - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - :param datetime_series: a pandas data object with sorted (may not be complete but sorted) dates - :type datetime_series: pandas datetime object - :return: mean of deviation of the low flow variation or a dict if mode was set to raw data - :rtype: float / dict - - """ - - if datetime_series is None: - raise HydroSignaturesError("datetime_series is None. Please specify a datetime_series.") - - if data.__len__() != datetime_series.__len__(): - raise HydroSignaturesError("Simulation / observation data and the datetime_series have not the same length") - - basics = _SignaturesBasicFunctionality(data, comparedata=comparedata, mode=mode) - return basics.analyze(__calcAnnularData, datetime_series=datetime_series, - what="min") - - - - -def getHighFlowVar(data, comparedata=None, mode='get_signature', datetime_series=None): - """ - Mean of annual maximum flow divided by the median flow (Jowett and Duncan, 1990) - - Annular Data - - .. math:: - - Annualar Data= \\frac{\\sum_{i=1}^{N}(max(d_i)}{N*median(data)} - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - :param datetime_series: a pandas data object with sorted (may not be complete but sorted) dates - :type datetime_series: pandas datetime object - :return: mean of deviation of the high flow variation or a dict if mode was set to raw data - :rtype: float / dict - - """ - - if datetime_series is None: - raise HydroSignaturesError("datetime_series is None. Please specify a datetime_series.") - - if data.__len__() != datetime_series.__len__(): - raise HydroSignaturesError("Simulation / observation data and the datetime_series have not the same length") - - basics = _SignaturesBasicFunctionality(data, comparedata=comparedata, mode=mode) - return basics.analyze(__calcAnnularData, datetime_series=datetime_series, - what="max") - - -def __calcAnnularData(data, datetime_series, what): - """ - Annular Data - - :math:`Annualar Data= \\frac{\\sum_{i=1}^{N}(max(d_i)}{N}` - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - - :param data: measurements - :type data: list - :param datetime_series: sorted pandas date time object - :type datetime_series: pandas datetime object - - :param what: string which switches the calculation method. Allowed are: min (the min value of a year) and max (the max value of a year) - :type what: string - - :return:mean of min/max per year - :rtype: float - """ - data_per_year_tmp = [] - data_per_year = {} - index = 0 - for d in datetime_series: - yr = d.to_pydatetime().year - if yr not in data_per_year: - if index > 0: - data_per_year[datetime_series[index - 1].to_pydatetime().year] = data_per_year_tmp - data_per_year[datetime_series[index - 1].to_pydatetime().year] = data_per_year_tmp - data_per_year_tmp = [] - data_per_year_tmp.append(data[index]) - index += 1 - - summarized = [] - if what == "min": - for y in data_per_year: - summarized.append(np.min(data_per_year[y])) - return np.mean(summarized) - elif what == "max": - for y in data_per_year: - summarized.append(np.max(data_per_year[y])) - return np.mean(summarized) - else: - raise HydroSignaturesError("The parameter what=" + what + " is not defined") - - -def getBaseflowIndex(data, comparedata=None, mode='get_signature', datetime_series=None): - """ - We may have to use baseflow devided with total discharge - See https://de.wikipedia.org/wiki/Niedrigwasser and - see also http://people.ucalgary.ca/~hayashi/kumamoto_2014/lectures/2_3_baseflow.pdf - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - and - "Report No. 108, Low flow estimation in the United Kingdom, . Gustard, A. Bullock December 1992 and J. M. Dixon" - (IH_108.pdf, page 20 ff) - - :param data: data to analyze - :type data: list - :param comparedata: data to analyze and compare with variable data - :type comparedata: list - :param mode: which mode of calculation should be used: one of get_signature, get_raw_data or calc_Dev - :type mode: string - - :param datetime_series: a pandas data object with sorted (may not be complete but sorted) dates - :type datetime_series: pandas datetime - - :return: deviation of base flow index - :rtype: float - """ - if datetime_series is None: - raise HydroSignaturesError("datetime_series is None. Please specify a datetime_series.") - - if data.__len__() != datetime_series.__len__(): - raise HydroSignaturesError("Simulation / observation data and the datetime_series have not the same length") - - basics = _SignaturesBasicFunctionality(data, comparedata=comparedata, mode=mode) - basics.pre_process(__calcDevForBaseflowIndex) - return basics.analyze(__calcBaseflowIndex, datetime_series=datetime_series) - - -def __calcDevForBaseflowIndex(a, b): - sum_sim = 0.0 - sum_obs = 0.0 - - for y in a: - sum_obs += a[y] - for y in b: - sum_sim += b[y] - - - sum_obs = sum_obs / a.__len__() - sum_sim = sum_sim / b.__len__() - return _SignaturesBasicFunctionality.calcDev(sum_sim, sum_obs) - - -def __calcBaseflowIndex(data, datetime_series): - """ - Basefow Index - - :math:`BasefowIndex = \\frac{BF}{TD}` where BF is the median of the data and TD the minimum of the data per year - - See paper "Uncertainty in hydrological signatures" by I. K. Westerberg and H. K. McMillan, Hydrol. Earth Syst. Sci., - 19, 3951 - 3968, 2015 (hess-19-3951-2015.pdf, page 3956) - and - "Report No. 108, Low flow estimation in the United Kingdom, . Gustard, A. Bullock December 1992 and J. M. Dixon" - (IH_108.pdf, page 20 ff) - - :param data: float list - :type data: list - :param datetime_series: sorted pandas datetime object - :type datetime_series: pandas datetime object - :return: BFI per year - :rtype: dict - """ - Min_per_year = {} - Q50_per_year = {} - data_per_year_tmp = [] - index = 0 - for d in datetime_series: - yr = d.to_pydatetime().year - if yr not in Min_per_year: - if index > 0: - Min_per_year[datetime_series[index - 1].to_pydatetime().year] = np.min(data_per_year_tmp) - Q50_per_year[datetime_series[index - 1].to_pydatetime().year] = np.median(data_per_year_tmp) - data_per_year_tmp = [] - data_per_year_tmp.append(data[index]) - index += 1 - - BFI = {} - for y in Min_per_year: - BFI[y] = Q50_per_year[y] / Min_per_year[y] - return BFI - - -def __help(): - print("Use .__doc__ to see description of every function") - - -def rel_error_list(a, b): - """ - See See https://dsp.stackexchange.com/a/8724 - :param a: - :param b: - :return: - """ - if a.__len__() == b.__len__(): - error = 0 - denominator = 0 - for i in range(a.__len__()): - error += (a[i] - b[i]) ** 2 - denominator += a[i] * +2 - normalized = error / denominator - return normalized - else: - raise Exception("Not the same length") diff --git a/spotpy/unittests/test_analyser.py b/spotpy/unittests/test_analyser.py deleted file mode 100644 index 8018255c..00000000 --- a/spotpy/unittests/test_analyser.py +++ /dev/null @@ -1,506 +0,0 @@ -# -*- coding: utf-8 -*- -''' -Copyright (c) 2018 by Benjamin Manns -This file is part of Statistical Parameter Estimation Tool (SPOTPY). -:author: Tobias Houska, Benjamin Manns - - -''' - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import matplotlib as mpl -mpl.use('Agg') -import matplotlib.pyplot as plt - -try: - import spotpy -except ImportError: - import sys - sys.path.append(".") - import spotpy - -import unittest -import numpy as np -import spotpy.analyser -import os -import pickle - - -class TestAnalyser(unittest.TestCase): - def setUp(self): - np.random.seed(42) - self.rep = 100 - if not os.path.isfile("setUp_pickle_file"): - from spotpy.examples.spot_setup_rosenbrock import spot_setup - spot_setup_object = spot_setup() - self.results = [] - parallel = "seq" - dbformat = "ram" - timeout = 5 - - self.sampler = spotpy.algorithms.dream(spot_setup_object, parallel=parallel, dbname='RosenDREAM', dbformat=dbformat, - sim_timeout=timeout) - - self.sampler.sample(self.rep) - self.results.append(self.sampler.getdata()) - self.results = np.array(self.results) - - def test_get_parameters(self): - - self.assertEqual(len(spotpy.analyser.get_parameters( - self.results - )[0][0]), 3) - - def test_get_parameternames(self): - self.assertEqual(spotpy.analyser.get_parameternames( - self.results - ),['x', 'y', 'z']) - - def test_get_parameter_fields(self): - self.assertEqual(len(spotpy.analyser.get_parameternames( - self.results - )), 3) - - def test_get_minlikeindex(self): - minlikeindex = spotpy.analyser.get_minlikeindex( - self.results - ) - self.assertEqual(len(minlikeindex), 2) - self.assertEqual(type(minlikeindex),type((1,1))) - - def test_get_maxlikeindex(self): - get_maxlikeindex = spotpy.analyser.get_maxlikeindex( - self.results - ) - self.assertEqual(len(get_maxlikeindex), 2) - self.assertEqual(type(get_maxlikeindex),type((1,1))) - - def test_get_like_fields(self): - get_like_fields = spotpy.analyser.get_like_fields( - self.results - ) - self.assertEqual(len(get_like_fields), 1) - self.assertEqual(type(get_like_fields),type([])) - - def test_calc_like(self): - calc_like = spotpy.analyser.calc_like( - self.results, - self.sampler.evaluation) - self.assertEqual(len(calc_like), 1) - self.assertEqual(type(calc_like), type([])) - - def test_get_best_parameterset(self): - get_best_parameterset_true = spotpy.analyser.get_best_parameterset( - self.results,True) - get_best_parameterset_false = spotpy.analyser.get_best_parameterset( - self.results, False) - self.assertEqual(len(get_best_parameterset_true[0]), 3) - self.assertEqual(type(get_best_parameterset_true[0]), np.void) - self.assertEqual(len(get_best_parameterset_false[0]), 3) - self.assertEqual(type(get_best_parameterset_false[0]), np.void) - - def test_get_modelruns(self): - get_modelruns = spotpy.analyser.get_modelruns( - self.results - ) - self.assertEqual(len(get_modelruns[0][0]), 1) - self.assertEqual(type(get_modelruns[0][0]), np.void) - - def test_get_header(self): - get_header = spotpy.analyser.get_header( - self.results - ) - self.assertEqual(len(get_header), 6) - self.assertEqual(type(get_header), type(())) - - def test_get_min_max(self): - from spotpy.examples.spot_setup_ackley import spot_setup as sp_ackley - sp_ackley = sp_ackley() - get_min_max = spotpy.analyser.get_min_max(spotpy_setup=sp_ackley) - self.assertEqual(len(get_min_max[0]), 30) - self.assertEqual(type(get_min_max), type(())) - - def test_get_parbounds(self): - from spotpy.examples.spot_setup_ackley import spot_setup as sp_ackley - sp_ackley = sp_ackley() - get_parbounds = spotpy.analyser.get_parbounds(spotpy_setup=sp_ackley) - - self.assertEqual(len(get_parbounds[0]), 2) - self.assertEqual(len(get_parbounds), 30) - self.assertEqual(type(get_parbounds), type([])) - - def test_get_percentiles(self): - get_percentiles = spotpy.analyser.get_percentiles( - self.results - ) - self.assertEqual(len(get_percentiles),5) - self.assertEqual(type(get_percentiles[0][0]), type(np.abs(-1.0))) - self.assertEqual(type(get_percentiles),type(())) - - def test__geweke(self): - sample1 = [] - for a in self.results: - sample1.append(a[0]) - - _geweke = spotpy.analyser._Geweke(sample1) - - self.assertEqual(len(_geweke), 20) - self.assertEqual(type(_geweke), type(np.array([]))) - - def test_plot_Geweke(self): - sample1 = [] - for a in self.results[0]: - sample1.append(a[0]) - spotpy.analyser.plot_Geweke(sample1,"sample1") - fig_name = "test_plot_Geweke.png" - plt.savefig(fig_name) - - # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so - # we expecting a plot with some content without testing the structure of the pot just - # the size - self.assertGreaterEqual(os.path.getsize(fig_name), 8855) - os.remove(fig_name) - - def test_get_sensitivity_of_fast(self): - from spotpy.examples.spot_setup_rosenbrock import spot_setup - spot_setup_object = spot_setup() - results = [] - parallel = "umpc" - dbformat = "ram" - timeout = 5 - self.sampler = spotpy.algorithms.fast(spot_setup_object, parallel=parallel, - dbname='test_get_sensitivity_of_fast', dbformat=dbformat, - sim_timeout=timeout) - self.sampler.sample(300) - results.append(self.sampler.getdata()) - results = np.array(results)[0] - get_sensitivity_of_fast = spotpy.analyser.get_sensitivity_of_fast(results) - self.assertEqual(len(get_sensitivity_of_fast), 2) - self.assertEqual(len(get_sensitivity_of_fast["S1"]), 3) - self.assertEqual(len(get_sensitivity_of_fast["ST"]), 3) - self.assertEqual(type(get_sensitivity_of_fast), type({})) - - def test_get_simulation_fields(self): - get_simulation_fields = spotpy.analyser.get_simulation_fields( - self.results - ) - self.assertEqual(['simulation_0'],get_simulation_fields) - - def test_compare_different_objectivefunctions(self): - - from spotpy.examples.spot_setup_hymod_python import spot_setup as sp - sp = sp() - - sampler = spotpy.algorithms.dream(sp, parallel="seq", dbname='test_compare_different_objectivefunctions', - dbformat="ram", - sim_timeout=5) - - sampler_mcmc = spotpy.algorithms.mcmc(sp, parallel="seq", dbname='test_compare_different_objectivefunctions', - dbformat="ram", - sim_timeout=5) - - sampler.sample(50) - sampler_mcmc.sample(50) - compare_different_objectivefunctions = spotpy.analyser.compare_different_objectivefunctions( - sampler_mcmc.bestlike, sampler.bestlike) - - - self.assertEqual(type(compare_different_objectivefunctions[1]),type(np.array([0.5])[0])) - - def test_plot_parameter_uncertainty(self): - get_posterior = spotpy.analyser.get_posterior( - self.results - ) - self.assertEqual(len(get_posterior[0]), self.rep) - self.assertEqual(type(get_posterior[0]), type(np.array([]))) - spotpy.analyser.plot_parameter_uncertainty(get_posterior, - self.sampler.evaluation) - - fig_name = "test_plot_parameter_uncertainty.png" - plt.savefig(fig_name) - - # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so - # we expecting a plot with some content without testing the structure of the pot just - # the size - self.assertGreaterEqual(os.path.getsize(fig_name), 8855) - # tidy up all - os.remove(fig_name) - - def test_plot_fast_sensitivity(self): - - from spotpy.examples.spot_setup_rosenbrock import spot_setup - spot_setup_object = spot_setup() - parallel = "seq" - dbformat = "ram" - timeout = 5 - - sampler = spotpy.algorithms.dream(spot_setup_object, parallel=parallel, - dbname='test_get_sensitivity_of_fast', dbformat=dbformat, - sim_timeout=timeout) - sampler.sample(300) - results = [] - results.append(sampler.getdata()) - results = np.array(results)[0] - print("Sampler is done with") - print(results) - spotpy.analyser.plot_fast_sensitivity(results) - - fig_name = "FAST_sensitivity.png" - - # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so - # we expecting a plot with some content without testing the structure of the pot just - # the size - self.assertGreaterEqual(os.path.getsize(fig_name), 8855) - # tidy up all - os.remove(fig_name) - - - - - - - def setup_griewank(self): - if not os.path.isfile("setup_griewank_pickle"): - from spotpy.examples.spot_setup_griewank import spot_setup - # Create samplers for every algorithm: - spot_setup = spot_setup() - rep = 100 - timeout = 10 # Given in Seconds - - parallel = "seq" - dbformat = "csv" - - sampler = spotpy.algorithms.mc(spot_setup, parallel=parallel, dbname='RosenMC', dbformat=dbformat, - sim_timeout=timeout) - sampler.sample(rep) - - fl = open("setup_griewank_pickle", "wb") - pickle.dump({"getdata": sampler.getdata(), "evaluation": sampler.evaluation}, fl) - fl.close() - - with open("setup_griewank_pickle", "rb") as file: - return pickle.load(file) - - def test_plot_heatmap_griewank(self): - fig_name = "test.png" - spotpy.analyser.plot_heatmap_griewank([self.setup_griewank()["getdata"]],["test"]) - - # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so - # we expecting a plot with some content without testing the structure of the pot just - # the size - self.assertGreaterEqual(os.path.getsize(fig_name), 8855) - os.remove(fig_name) - - def test_plot_objectivefunction(self): - fig_name = "test_plot_objectivefunction.png" - spotpy.analyser.plot_objectivefunction(self.results - , self.sampler.evaluation) - plt.savefig(fig_name) - - # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so - # we expecting a plot with some content without testing the structure of the pot just - # the size - self.assertGreaterEqual(os.path.getsize(fig_name), 8855) - os.remove(fig_name) - - def test_plot_parametertrace_algorithms(self): - spotpy.analyser.plot_parametertrace_algorithms([self.setup_griewank()["getdata"]],["test_plot_parametertrace_algorithms"]) - fig_name = "test2.png" - - # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so - # we expecting a plot with some content without testing the structure of the pot just - # the size - self.assertGreaterEqual(os.path.getsize(fig_name), 8855) - os.remove(fig_name) - - def test_plot_parametertrace(self): - spotpy.analyser.plot_parametertrace(self.setup_griewank()["getdata"], ["0","1"]) - fig_name = "0_1__trace.png" - - # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so - # we expecting a plot with some content without testing the structure of the pot just - # the size - self.assertGreaterEqual(os.path.getsize(fig_name), 8855) - os.remove(fig_name) - - def test_plot_posterior_parametertrace(self): - spotpy.analyser.plot_posterior_parametertrace(self.setup_griewank()["getdata"], ["0","1"]) - fig_name = "0_1__trace.png" - - # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so - # we expecting a plot with some content without testing the structure of the pot just - # the size - self.assertGreaterEqual(os.path.getsize(fig_name), 8855) - os.remove(fig_name) - - def test_plot_posterior(self): - spotpy.analyser.plot_posterior(self.results[0] - , self.sampler.evaluation) - fig_name = "bestmodelrun.png" - - # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so - # we expecting a plot with some content without testing the structure of the pot just - # the size - self.assertGreaterEqual(os.path.getsize(fig_name), 8855) - os.remove(fig_name) - - def test_plot_bestmodelrun(self): - samp = self.setup_griewank() - spotpy.analyser.plot_bestmodelrun(samp["getdata"], samp["evaluation"]) - fig_name="Best_model_run.png" - - # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so - # we expecting a plot with some content without testing the structure of the pot just - # the size - self.assertGreaterEqual(os.path.getsize(fig_name), 8855) - os.remove(fig_name) - - def test_plot_bestmodelruns(self): - spotpy.analyser.plot_bestmodelruns( - self.results, self.sampler.evaluation, - dates=range(1, 1+len(self.sampler.evaluation)), algorithms=["test"]) - fig_name = "bestmodelrun.png" - - # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so - # we expecting a plot with some content without testing the structure of the pot just - # the size - self.assertGreaterEqual(os.path.getsize(fig_name), 8855) - - def test_plot_objectivefunctiontraces(self): - spotpy.analyser.plot_objectivefunctiontraces(self.results - , self.sampler.evaluation - , ["test"]) - fig_name="Like_trace.png" - - # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so - # we expecting a plot with some content without testing the structure of the pot just - # the size - self.assertGreaterEqual(os.path.getsize(fig_name), 8855) - os.remove(fig_name) - - def test_plot_regression(self): - from spotpy.examples.spot_setup_rosenbrock import spot_setup - spot_setup_object = spot_setup() - parallel = "mpc" - dbformat = "ram" - timeout = 5 - sampler = spotpy.algorithms.mc(spot_setup_object, parallel=parallel, - dbname='test_plot_regression', dbformat=dbformat, - sim_timeout=timeout) - sampler.sample(300) - - spotpy.analyser.plot_regression(sampler.getdata(), sampler.evaluation) - fig_name="regressionanalysis.png" - - # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so - # we expecting a plot with some content without testing the structure of the pot just - # the size - self.assertGreaterEqual(os.path.getsize(fig_name), 8855) - os.remove(fig_name) - - def test_plot_parameterInteraction(self): - self.setup_MC_results() - spotpy.analyser.plot_parameterInteraction(pickle.load(open("test_analyser_MC_results","rb"))) - fig_name = "ParameterInteraction.png" - - # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so - # we expecting a plot with some content without testing the structure of the pot just - # the size - self.assertGreaterEqual(os.path.getsize(fig_name), 8855) - os.remove(fig_name) - - def test_plot_allmodelruns(self): - from spotpy.examples.spot_setup_hymod_python import spot_setup as sp - sp = sp() - - sampler = spotpy.algorithms.dream(sp, parallel="seq", dbname='test_plot_allmodelruns', - dbformat="ram", - sim_timeout=5) - - sampler.sample(50) - - modelruns = [] - for run in sampler.getdata(): - on_run = [] - for i in run: - on_run.append(i) - on_run = np.array(on_run)[:-9] - print(on_run) - modelruns.append(on_run.tolist()) - - test_plot_allmodelruns = spotpy.analyser.plot_allmodelruns(modelruns, sp.evaluation(), - dates=range(1, len(sp.evaluation()) + 1)) - - fig_name = "bestmodel.png" - - # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so - # we expecting a plot with some content without testing the structure of the pot just - # the size - self.assertGreaterEqual(os.path.getsize(fig_name), 8855) - - os.remove(fig_name) - - def test_plot_autocorellation(self): - self.setup_MC_results() - - results = [] - results.append(pickle.load(open("test_analyser_MC_results","rb"))) - results = np.array(results) - - spotpy.analyser.plot_autocorellation(results["parcmax"][0],"parcmax") - - fig_name="Autocorellationparcmax.png" - - # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so - # we expecting a plot with some content without testing the structure of the pot just - # the size - self.assertGreaterEqual(os.path.getsize(fig_name), 8855) - os.remove(fig_name) - - def test_plot_gelman_rubin(self): - from spotpy.examples.spot_setup_hymod_python import spot_setup as sp - sp = sp() - sampler = spotpy.algorithms.dream(sp, parallel="seq", dbname='test_plot_autocorellation', - dbformat="csv", - sim_timeout=5) - - r_hat = sampler.sample(100) - - fig_name = "gelman_rubin.png" - spotpy.analyser.plot_gelman_rubin(r_hat) - plt.savefig(fig_name) - self.assertGreaterEqual(abs(os.path.getsize(fig_name)), 100) - - os.remove(fig_name) - - def setup_MC_results(self): - - picklefilename = "test_analyser_MC_results" - if not os.path.isfile(picklefilename): - from spotpy.examples.spot_setup_hymod_python import spot_setup as sp - sp = sp() - - sampler = spotpy.algorithms.mc(sp, parallel="seq", dbname='test_plot_autocorellation', - dbformat="csv", - sim_timeout=5) - sampler.sample(100) - pickfil = open(picklefilename, "wb") - pickle.dump(sampler.getdata(), pickfil) - - -if __name__ == '__main__': - unittest.main(exit=False) - try: - os.remove("RosenMC.csv") - os.remove("setup_griewank_pickle") - os.remove("test_plot_autocorellation.csv") - os.remove("test_analyser_MC_results") - os.remove("Posteriot_parameter_uncertainty.png") - except FileNotFoundError: - pass - - diff --git a/spotpy/unittests/test_database.py b/spotpy/unittests/test_database.py deleted file mode 100644 index 1c244fc5..00000000 --- a/spotpy/unittests/test_database.py +++ /dev/null @@ -1,197 +0,0 @@ -import unittest - -try: - import spotpy -except ImportError: - import sys - sys.path.append("../../") - sys.path.append("../../spotpy") - sys.path.append(".") - import spotpy -import spotpy.database as db -import numpy as np - -#https://docs.python.org/3/library/unittest.html - -class TestDatabase(unittest.TestCase): - - def setUp(self): - self.parnames = ['x1','x2','x3','x4','x5'] - self.like = 0.213 - self.randompar = [175.21733934706367, 0.41669126598819262, 0.25265012080652388, 0.049706767415682945, 0.69674090782836173] - - self.simulations_multi = [] - for i in range(5): - self.simulations_multi.append(np.random.uniform(0, 1, 5).tolist()) - - self.simulations = np.random.uniform(0, 1, 5).tolist() - - #print(self.simulations) - - def objf(self): - return np.random.uniform(0,1,1)[0] - - - def test_csv_multiline(self): - csv = db.csv("UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations_multi, - chains=1, save_sim=True) - - csv.save(self.like ,self.randompar,self.simulations_multi) - csv.save(self.like ,self.randompar,self.simulations_multi) - # Save Simulations - - csv.finalize() - csvdata = csv.getdata() - self.assertEqual(str(type(csvdata)), str(type(np.array([])))) - self.assertEqual(len(csvdata[0]), 32) - self.assertEqual(len(csvdata), 2) - self.assertEqual(len(csv.header), 32) - - - def test_csv_multiline_false(self): - # Save not Simulations - csv = db.csv("UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations_multi, - chains=1, save_sim=False) - - csv.save(self.like, self.randompar, self.simulations_multi) - csv.save(self.like, self.randompar, self.simulations_multi) - - csv.finalize() - csvdata = csv.getdata() - self.assertEqual(str(type(csvdata)), str(type(np.array([])))) - self.assertEqual(len(csvdata[0]), 7) - self.assertEqual(len(csvdata), 2) - self.assertEqual(len(csv.header), 7) - - def test_csv_single(self): - csv = db.csv("UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations, - chains=1, save_sim=True) - - csv.save(self.like, self.randompar, self.simulations) - csv.save(self.like, self.randompar, self.simulations) - - csv.finalize() - csvdata = csv.getdata() - self.assertEqual(str(type(csvdata)), str(type(np.array([])))) - self.assertEqual(len(csvdata[0]), 12) - self.assertEqual(len(csvdata), 2) - self.assertEqual(len(csv.header), 12) - - def test_csv_single_false(self): - csv = db.csv("UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations, - chains=1, save_sim=False) - - csv.save(self.like, self.randompar, self.simulations) - csv.save(self.like, self.randompar, self.simulations) - - csv.finalize() - csvdata = csv.getdata() - self.assertEqual(str(type(csvdata)), str(type(np.array([])))) - self.assertEqual(len(csvdata[0]), 7) - self.assertEqual(len(csvdata), 2) - self.assertEqual(len(csv.header), 7) - - - def test_sql_multiline(self): - sql = db.sql("UnitTest_tmp",self.parnames, self.like, self.randompar, simulations=self.simulations_multi, - chains=1, save_sim=True) - sql.save(self.like, self.randompar, self.simulations_multi) - sql.finalize() - sqldata = sql.getdata() - self.assertEqual(str(type(sqldata)), str(type(np.array([])))) - self.assertEqual(len(sqldata[0]), 32) - self.assertEqual(len(sqldata), 1) - self.assertEqual(len(sql.header), 32) - - - def test_sql_multiline_false(self): - sql = db.sql("UnitTest_tmp",self.parnames, self.like, self.randompar, simulations=self.simulations_multi, - chains=1, save_sim=False) - sql.save(self.like, self.randompar, self.simulations_multi) - sql.finalize() - sqldata = sql.getdata() - self.assertEqual(str(type(sqldata)), str(type(np.array([])))) - self.assertEqual(len(sqldata[0]), 7) - self.assertEqual(len(sqldata), 1) - self.assertEqual(len(sql.header), 7) - - def test_sql_single(self): - sql = db.sql("UnitTest_tmp",self.parnames, self.like, self.randompar, simulations=self.simulations, - chains=1, save_sim=True) - sql.save(self.like, self.randompar, self.simulations) - sql.finalize() - sqldata = sql.getdata() - self.assertEqual(str(type(sqldata)), str(type(np.array([])))) - self.assertEqual(len(sqldata[0]), 12) - self.assertEqual(len(sqldata), 1) - self.assertEqual(len(sql.header), 12) - - def test_sql_single_false(self): - sql = db.sql("UnitTest_tmp",self.parnames, self.like, self.randompar, simulations=self.simulations, - chains=1, save_sim=False) - sql.save(self.like, self.randompar, self.simulations) - sql.finalize() - - sqldata = sql.getdata() - self.assertEqual(str(type(sqldata)), str(type(np.array([])))) - self.assertEqual(len(sqldata[0]), 7) - self.assertEqual(len(sqldata), 1) - self.assertEqual(len(sql.header), 7) - - def test_ram_multiline(self): - ram = db.ram("UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations_multi, - chains=1, save_sim=True) - ram.save(self.like, self.randompar, self.simulations_multi) - ram.finalize() - - ramdata = ram.getdata() - self.assertEqual(str(type(ramdata)), str(type(np.array([])))) - self.assertEqual(len(ram.header), 32) - self.assertEqual(len(ramdata[0]), 32) - self.assertEqual(len(ramdata), 1) - self.assertEqual(len(ramdata.dtype),len(ram.header)) - - def test_ram_multiline_false(self): - ram = db.ram("UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations_multi, - chains=1, save_sim=False) - ram.save(self.like, self.randompar, self.simulations_multi) - - ram.finalize() - ramdata = ram.getdata() - self.assertEqual(str(type(ramdata)), str(type(np.array([])))) - self.assertEqual(len(ramdata[0]), 7) - self.assertEqual(len(ramdata), 1) - self.assertEqual(len(ramdata.dtype), len(ram.header)) - self.assertEqual(len(ram.header), 7) - - def test_ram_single(self): - ram = db.ram("UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations, - chains=1, save_sim=True) - ram.save(self.like, self.randompar, self.simulations) - - ram.finalize() - ramdata = ram.getdata() - self.assertEqual(str(type(ramdata)), str(type(np.array([])))) - self.assertEqual(len(ramdata[0]), 12) - self.assertEqual(len(ramdata), 1) - self.assertEqual(len(ramdata.dtype), len(ram.header)) - self.assertEqual(len(ram.header), 12) - - def test_ram_single_false(self): - ram = db.ram("UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations, - chains=1, save_sim=False) - ram.save(self.like, self.randompar, self.simulations) - - ram.finalize() - ramdata = ram.getdata() - self.assertEqual(str(type(ramdata)), str(type(np.array([])))) - self.assertEqual(len(ramdata[0]), 7) - self.assertEqual(len(ramdata), 1) - self.assertEqual(len(ramdata.dtype), len(ram.header)) - self.assertEqual(len(ram.header), 7) - - - - -if __name__ == '__main__': - unittest.main() diff --git a/spotpy/unittests/test_fast.py b/spotpy/unittests/test_fast.py deleted file mode 100644 index 76f603d1..00000000 --- a/spotpy/unittests/test_fast.py +++ /dev/null @@ -1,37 +0,0 @@ -import unittest - -try: - import spotpy -except ImportError: - import sys - sys.path.append(".") - import spotpy - - -from spotpy.examples.spot_setup_hymod_python import spot_setup - - - -class TestFast(unittest.TestCase): - def setUp(self): - - self.spot_setup = spot_setup() - self.rep = 800 # REP must be a multiply of amount of parameters which are in 7 if using hymod - self.timeout = 10 # Given in Seconds - - - - def test_fast(self): - sampler = spotpy.algorithms.fast(self.spot_setup, parallel="seq", dbname='test_FAST', dbformat="ram", - sim_timeout=self.timeout) - results = [] - - sampler.sample(self.rep) - results = sampler.getdata() - self.assertEqual(805,len(results)) - - - - -if __name__ == '__main__': - unittest.main() diff --git a/spotpy/unittests/test_likelihood.py b/spotpy/unittests/test_likelihood.py deleted file mode 100644 index ff51e8cb..00000000 --- a/spotpy/unittests/test_likelihood.py +++ /dev/null @@ -1,164 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Copyright (c) 2017 by Benjamin Manns -This file is part of Statistical Parameter Estimation Tool (SPOTPY). -:author: Tobias Houska, Benjamin Manns - -This code tests the likelihood framework and present all existing function. -""" - -import numpy as np -try: - import spotpy -except ImportError: - import sys - sys.path.append(".") - import spotpy - -import unittest - -# We use all available likelihood functions. The pydoc of every function tells, if we can add a -# parameter `param` to the function which includes model parameter. The `param` must be None or a tuple with values -# and names. If `param` is None, the needed values are calculated by the function itself. - - -class TestLikelihood(unittest.TestCase): - def setUp(self): - np.random.seed(12) - self.data, self.comparedata = np.random.normal(1500, 2530, 20), np.random.normal(15, 25, 20) - self.do_print = True - - def test_logLikelihood(self): - l = spotpy.likelihoods.logLikelihood(self.data, self.comparedata) - self.assertGreaterEqual(np.abs(l),900) - self.assertEqual(type(np.float(l)),type(np.float(1))) - if self.do_print: - print("logLikelihood: " + str(l)) - - def test_gaussianLikelihoodMeasErrorOut(self): - l = spotpy.likelihoods.gaussianLikelihoodMeasErrorOut(self.data, self.comparedata) - self.assertGreaterEqual(-100, l) - self.assertEqual(type(np.float(l)), type(np.float(1))) - if self.do_print: - print("gaussianLikelihoodMeasErrorOut: " + str(l)) - - def test_gaussianLikelihoodHomoHeteroDataError(self): - l = spotpy.likelihoods.gaussianLikelihoodHomoHeteroDataError(self.data, self.comparedata) - self.assertGreaterEqual(1,np.abs(l)) - self.assertEqual(type(np.float(l)), type(np.float(1))) - if self.do_print: - print("gaussianLikelihoodHomoHeteroDataError: " + str(l)) - - def test_LikelihoodAR1NoC(self): - l = spotpy.likelihoods.LikelihoodAR1NoC(self.data, self.comparedata, params=([0.98], ["likelihood_phi"])) - self.assertNotEqual(None,l) - if self.do_print: - print("LikelihoodAR1NoC: " + str(l)) - - def test_LikelihoodAR1WithC(self): - l = spotpy.likelihoods.LikelihoodAR1WithC(self.data, self.comparedata) - self.assertNotEqual(None, l) - if self.do_print: - print("LikelihoodAR1WithC: " + str(l)) - - def test_generalizedLikelihoodFunction(self): - size = 1000 - data, comparedata = np.random.normal(1500, 2530, size), np.random.normal(355, 25, size) - - l = spotpy.likelihoods.generalizedLikelihoodFunction(data, comparedata, params= - ([np.random.uniform(-0.99, 1, 1), np.random.uniform(0.1, 10, 1), np.random.uniform(0, 1, 1), np.random.uniform(0, 1, 0), - np.random.uniform(0, 0.99, 1), np.random.uniform(0, 100, 1)], - ["likelihood_beta", "likelihood_xi", "likelihood_sigma0", "likelihood_sigma1", "likelihood_phi1", "likelihood_muh"])) - - self.assertNotEqual(None, l) - self.assertGreaterEqual(-10000, l) - self.assertEqual(type(np.float(l)), type(np.float(1))) - if self.do_print: - print("generalizedLikelihoodFunction: " + str(l)) - - def test_LaplacianLikelihood(self): - l = spotpy.likelihoods.LaplacianLikelihood(self.data, self.comparedata) - self.assertNotEqual(None, l) - self.assertEqual(type(np.float(l)), type(np.float(1))) - if self.do_print: - print("LaplacianLikelihood: " + str(l)) - - def test_SkewedStudentLikelihoodHomoscedastic(self): - l = spotpy.likelihoods.SkewedStudentLikelihoodHomoscedastic(self.data, self.comparedata) - self.assertGreaterEqual(0.5, np.abs(l)) - self.assertEqual(type(np.float(l)), type(np.float(1))) - if self.do_print: - print("SkewedStudentLikelihoodHomoscedastic: " + str(l)) - - def test_SkewedStudentLikelihoodHeteroscedastic(self): - l = spotpy.likelihoods.SkewedStudentLikelihoodHeteroscedastic(self.data, self.comparedata) - if not np.isnan(l): - self.assertGreaterEqual(-100, l) - self.assertEqual(type(np.float(l)), type(np.float(1))) - if self.do_print: - print("SkewedStudentLikelihoodHeteroscedastic: " + str(l)) - - def test_SkewedStudentLikelihoodHeteroscedasticAdvancedARModel(self): - l = spotpy.likelihoods.SkewedStudentLikelihoodHeteroscedasticAdvancedARModel(self.data, self.comparedata, params=( - [np.random.uniform(2.01, 100, 1)[0], np.random.uniform(0.01, 100, 1)[0], np.random.uniform(-.99, .99, 1)[0]], - ["likelihood_nu", "likelihood_kappa", "likelihood_phi"])) - - self.assertNotEqual(None, l) - self.assertEqual(type(np.float(l)), type(np.float(1))) - if self.do_print: - print("SkewedStudentLikelihoodHeteroscedasticAdvancedARModel: " + str(l)) - - def test_NoisyABCGaussianLikelihood(self): - l = spotpy.likelihoods.NoisyABCGaussianLikelihood(self.data, self.comparedata) - self.assertNotEqual(None, l) - self.assertEqual(type(np.float(l)), type(np.float(1))) - if self.do_print: - print("NoisyABCGaussianLikelihood: " + str(l)) - - def test_ABCBoxcarLikelihood(self): - l = spotpy.likelihoods.ABCBoxcarLikelihood(self.data, self.comparedata) - self.assertNotEqual(None, l) - self.assertNotEqual(np.nan, l) - self.assertEqual(type(np.float(l)), type(np.float(1))) - if self.do_print: - print("ABCBoxcarLikelihood: " + str(l)) - - def test_LimitsOfAcceptability(self): - l = spotpy.likelihoods.LimitsOfAcceptability(self.data, self.comparedata) - self.assertEqual(12, l) - self.assertNotEqual(None, l) - self.assertEqual(type(np.int(l)), type(int(1))) - if self.do_print: - print("LimitsOfAcceptability: " + str(l)) - - def test_InverseErrorVarianceShapingFactor(self): - l = spotpy.likelihoods.InverseErrorVarianceShapingFactor(self.data, self.comparedata) - self.assertGreaterEqual(-20000, l) - self.assertEqual(type(np.float(l)), type(np.float(1))) - if self.do_print: - print("inverseErrorVarianceShapingFactor: " + str(l)) - - def test_ExponentialTransformErrVarShapingFactor(self): - l = spotpy.likelihoods.ExponentialTransformErrVarShapingFactor(self.data, self.comparedata) - self.assertGreaterEqual(-30000, l) - self.assertEqual(type(np.float(l)), type(np.float(1))) - if self.do_print: - print("inverseErrorVarianceShapingFactor: " + str(l)) - - def test_NashSutcliffeEfficiencyShapingFactor(self): - l = spotpy.likelihoods.NashSutcliffeEfficiencyShapingFactor(self.data, self.comparedata) - self.assertNotEqual(None, l) - self.assertEqual(type(np.float(l)), type(np.float(1))) - if self.do_print: - print("NashSutcliffeEfficiencyShapingFactor: " + str(l)) - - def test_sumOfAbsoluteErrorResiduals(self): - l = spotpy.likelihoods.sumOfAbsoluteErrorResiduals(self.data, self.comparedata) - self.assertGreaterEqual(2,np.abs(np.abs(l)-10)) - self.assertEqual(type(np.float(l)), type(np.float(1))) - if self.do_print: - print("sumOfAbsoluteErrorResiduals: " + str(l)) - -if __name__ == '__main__': - while True: - unittest.main() diff --git a/spotpy/unittests/test_setup_parameters.py b/spotpy/unittests/test_setup_parameters.py deleted file mode 100644 index dc9a854f..00000000 --- a/spotpy/unittests/test_setup_parameters.py +++ /dev/null @@ -1,144 +0,0 @@ -""" -Tests the various possibilities to create and use parameters - -Focus especially the usage of parameters as class attributes -:author: philippkraft -""" -import sys -import unittest -try: - import spotpy -except ImportError: - import sys - sys.path.append(".") - import spotpy -from spotpy import parameter -import numpy as np -import inspect - - -class SpotSetupBase(object): - """ - The base for a number of test cases. - Each Test case should have for parameters a,b,c,d and - the sum of the parameters should be zero - """ - - def simulation(self, par): - return [par.a + par.b + par.c + par.d] - - def evaluation(self): - return [0] - - def objectivefunction(self, simulation, evaluation): - return np.abs(simulation[0] - evaluation[0]) - - @classmethod - def get_derived(cls): - """ - Returns a list of all derived classes in this module - """ - module = sys.modules[__name__] - def predicate(mcls): - return inspect.isclass(mcls) and issubclass(mcls, cls) and mcls is not cls - return [mcls for cname, mcls in inspect.getmembers(module, predicate)] - - def __repr__(self): - return '{}()'.format(type(self).__name__) - - - -class SpotSetupClassAttributes(SpotSetupBase): - """ - A Test case with two parameters as class parameters (a,b) - and 2 given from the parameter function - """ - a = parameter.Uniform(-1, 1) - b = parameter.Uniform(-1, 1) - c = parameter.Uniform(-1, 1) - d = parameter.Uniform(-1, 1) - - -class SpotSetupParameterFunction(SpotSetupBase): - """ - A Test case with 4 parameters given from the parameter function - """ - def parameters(self): - return parameter.generate([parameter.Uniform(name, -1, 1) for name in 'abcd']) - - -class SpotSetupMixedParameterFunction(SpotSetupBase): - """ - A Test case with two parameters as class parameters (a,b) - and 2 given from the parameter function - """ - a = parameter.Uniform(0, 1) - b = parameter.Uniform(1, 2) - - def parameters(self): - return parameter.generate([parameter.Uniform(name, -1, 1) for name in 'cd']) - - -class SpotSetupParameterList(SpotSetupBase): - """ - A Test case with 4 parameters given from the parameter list - """ - def __init__(self): - self.parameters = [parameter.Uniform(name, -1, 1) for name in 'abcd'] - - -class SpotSetupMixedParameterList(SpotSetupBase): - """ - A Test case with two parameters as class parameters (a,b) - and 2 given from the parameter function - """ - a = parameter.Uniform(0, 1) - b = parameter.Uniform(1, 2) - - def parameters(self): - return parameter.generate([parameter.Uniform(name, -1, 1) for name in 'cd']) - - -class TestSetupVariants(unittest.TestCase): - def setUp(self): - # Get all Setups from this module - self.objects = [cls() for cls in SpotSetupBase.get_derived()] - - def test_exists(self): - self.assertGreater(len(self.objects), 0) - - def parameter_count_test(self, o): - params = parameter.create_set(o) - param_names = ','.join(pn for pn in params._fields) - self.assertEqual(len(params), 4, '{} should have 4 parameters, but found only {} ({})' - .format(o, len(params), param_names)) - self.assertEqual(param_names, 'a,b,c,d', '{} Parameter names should be "a,b,c,d" but got "{}"' - .format(o, param_names)) - - def make_sampler(self, o): - sampler = spotpy.algorithms.mc(spot_setup=o, dbformat='ram') - sampler.sample(10) - - def test_parameter_class(self): - self.parameter_count_test(SpotSetupClassAttributes()) - - def test_parameter_function(self): - self.parameter_count_test(SpotSetupParameterFunction()) - - def test_parameter_list(self): - self.parameter_count_test(SpotSetupParameterList()) - - def test_parameter_mixed_list(self): - self.parameter_count_test(SpotSetupMixedParameterList()) - - def test_parameter_mixed_function(self): - self.parameter_count_test(SpotSetupMixedParameterFunction()) - - def test_sampler(self): - for o in self.objects: - self.make_sampler(o) - - -if __name__ == '__main__': - unittest.main(verbosity=3) - diff --git a/spotpy/unittests/test_signatures.py b/spotpy/unittests/test_signatures.py deleted file mode 100644 index 58f8e08c..00000000 --- a/spotpy/unittests/test_signatures.py +++ /dev/null @@ -1,410 +0,0 @@ -import unittest - -try: - import spotpy -except ImportError: - import sys - sys.path.append(".") - import spotpy - - - -import spotpy.signatures as sig -#from spotpy.examples.spot_setup_hymod_python import spot_setup -from spotpy.examples.spot_setup_hymod_python import spot_setup -import numpy as np -import os - -try: - import pandas as pd - test = True -except ImportError: - print('Please install Pandas to use these signature functions') - test = False - -#https://docs.python.org/3/library/unittest.html - - - -class TestSignatures(unittest.TestCase): - - def setUp(self): - self.spot_setup = spot_setup() - self.parameterset = self.spot_setup.parameters()['random'] - self.simulation = self.spot_setup.simulation(self.parameterset) - self.observation = self.spot_setup.evaluation() - - self.simulation = self.simulation[1:100] - self.observation = self.observation[1:100] - - self.timespanlen = self.simulation.__len__() - try: - self.ddd = pd.date_range("2015-01-01 11:00", freq="5min", periods=self.timespanlen) - self.dd_daily = pd.date_range("2015-05-01", periods=self.timespanlen) - self.usepandas = True - except NameError: - print('Please install Pandas to use these signature functions') - self.usepandas = False - - def test_getSlopeFDC(self): - sig_val = sig.getSlopeFDC(self.simulation, self.observation, mode="get_signature") - sig_raw = sig.getSlopeFDC(self.simulation, self.observation, mode="get_raw_data") - sig_dev = sig.getSlopeFDC(self.simulation, self.observation, mode="calc_Dev") - self.assertEqual(type(float(sig_val)), type(1.0)) - self.assertEqual(type(float(sig_raw)), type(1.0)) - self.assertEqual(type(float(sig_dev)), type(1.0)) - - - def test_getAverageFloodOverflowPerSection(self): - if self.usepandas: - th = np.random.randint(-10,10) - - sig_val = sig.getAverageFloodOverflowPerSection(self.simulation, self.observation, mode="get_signature", datetime_series=self.dd_daily, - threshold_value=th) - sig_raw = sig.getAverageFloodOverflowPerSection(self.simulation, self.observation, mode="get_raw_data", datetime_series=self.dd_daily, - threshold_value=th) - sig_dev = sig.getAverageFloodOverflowPerSection(self.simulation, self.observation, mode="calc_Dev", datetime_series=self.dd_daily, - threshold_value=th) - self.assertEqual(type(float(sig_val.astype(float))),type(1.0)) - - - - self.assertEqual(sig_raw.dtypes[0],"float64") - self.assertEqual(sig_raw["flood"].__len__(), 99) - #self.assertEqual(str(type(sig_raw.index.tolist()[0])),"") - self.assertEqual(type(float(sig_dev.astype(float))), type(1.0)) - - - def test_getMeanFlow(self): - self.assertEqual(type(1.0),type(float(sig.getMeanFlow(self.simulation,self.observation,mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getMeanFlow(self.simulation, None, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getMeanFlow(self.simulation, self.observation, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getMeanFlow(self.simulation,None, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getMeanFlow(self.simulation, self.observation, mode="calc_Dev")))) - - - def test_getMedianFlow(self): - self.assertEqual(type(1.0), type(float(sig.getMedianFlow(self.simulation, self.observation, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getMedianFlow(self.simulation, None, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getMedianFlow(self.simulation, self.observation, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getMedianFlow(self.simulation, None, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getMedianFlow(self.simulation, self.observation, mode="calc_Dev")))) - - def test_getSkewness(self): - self.assertEqual(type(1.0), type(float(sig.getSkewness(self.simulation, self.observation, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getSkewness(self.simulation, None, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getSkewness(self.simulation, self.observation, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getSkewness(self.simulation, None, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getSkewness(self.simulation, self.observation, mode="calc_Dev")))) - - - def test_getCoeffVariation(self): - self.assertEqual(type(1.0), type(float(sig.getCoeffVariation(self.simulation, self.observation, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getCoeffVariation(self.simulation, None, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getCoeffVariation(self.simulation, self.observation, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getCoeffVariation(self.simulation, None, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getCoeffVariation(self.simulation, self.observation, mode="calc_Dev")))) - - - def test_getQ001(self): - self.assertEqual(type(1.0), type(float(sig.getQ001(self.simulation, self.observation, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getQ001(self.simulation, None, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getQ001(self.simulation, self.observation, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getQ001(self.simulation, None, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getQ001(self.simulation, self.observation, mode="calc_Dev")))) - - - def test_getQ01(self): - self.assertEqual(type(1.0), type(float(sig.getQ01(self.simulation, self.observation, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getQ01(self.simulation, None, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getQ01(self.simulation, self.observation, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getQ01(self.simulation, None, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getQ01(self.simulation, self.observation, mode="calc_Dev")))) - - def test_getQ1(self): - self.assertEqual(type(1.0), type(float(sig.getQ1(self.simulation, self.observation, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getQ1(self.simulation, None, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getQ1(self.simulation, self.observation, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getQ1(self.simulation, None, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getQ1(self.simulation, self.observation, mode="calc_Dev")))) - - def test_getQ5(self): - self.assertEqual(type(1.0), type(float(sig.getQ5(self.simulation, self.observation, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getQ5(self.simulation, None, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getQ5(self.simulation, self.observation, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getQ5(self.simulation, None, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getQ5(self.simulation, self.observation, mode="calc_Dev")))) - - def test_getQ10(self): - self.assertEqual(type(1.0), type(float(sig.getQ10(self.simulation, self.observation, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getQ10(self.simulation, None, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getQ10(self.simulation, self.observation, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getQ10(self.simulation, None, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getQ10(self.simulation, self.observation, mode="calc_Dev")))) - - - def test_getQ20(self): - self.assertEqual(type(1.0), type(float(sig.getQ20(self.simulation, self.observation, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getQ20(self.simulation, None, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getQ20(self.simulation, self.observation, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getQ20(self.simulation, None, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getQ20(self.simulation, self.observation, mode="calc_Dev")))) - - - def test_getQ85(self): - self.assertEqual(type(1.0), type(float(sig.getQ85(self.simulation, self.observation, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getQ85(self.simulation, None, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getQ85(self.simulation, self.observation, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getQ85(self.simulation, None, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getQ85(self.simulation, self.observation, mode="calc_Dev")))) - - - - def test_getQ99(self): - self.assertEqual(type(1.0), type(float(sig.getQ99(self.simulation, self.observation, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getQ99(self.simulation, None, mode="get_signature")))) - self.assertEqual(type(1.0), type(float(sig.getQ99(self.simulation, self.observation, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getQ99(self.simulation, None, mode="get_raw_data")))) - self.assertEqual(type(1.0), type(float(sig.getQ99(self.simulation, self.observation, mode="calc_Dev")))) - - - def test_getAverageFloodFrequencyPerSection(self): - if self.usepandas: - for th in range(-10, 10): - th = th + np.random.uniform(-1, 1, 1)[0] - sig_val = sig.getAverageFloodFrequencyPerSection(self.simulation, self.observation, datetime_series=self.dd_daily, threshold_value=th, - mode="get_signature") - - sig_raw = sig.getAverageFloodFrequencyPerSection(self.simulation, self.observation, datetime_series=self.dd_daily, threshold_value=th, - mode="get_raw_data") - - sig_dev = sig.getAverageFloodFrequencyPerSection(self.simulation, self.observation, datetime_series=self.dd_daily, threshold_value=th, - mode="calc_Dev") - - self.assertEqual(sig_raw.dtypes[0], "float64") - self.assertEqual(sig_raw["flood"].__len__(), 99) - - #self.assertEqual(str(type(sig_raw.index.tolist()[0])), "") - - self.assertEqual(type(sig_dev), type(1.0)) - self.assertEqual(type(sig_val), type(1.0)) - - - def test_getAverageFloodDuration(self): - if self.usepandas: - for th in range(-10, 10): - th = th + np.random.uniform(-1, 1, 1)[0] - sig_val = sig.getAverageFloodDuration(self.simulation, self.observation, datetime_series=self.dd_daily, threshold_value=th, - mode="get_signature") - sig_raw = sig.getAverageFloodDuration(self.simulation, self.observation, datetime_series=self.dd_daily, threshold_value=th, - mode="get_raw_data") - sig_dev = sig.getAverageFloodDuration(self.simulation, self.observation, datetime_series=self.dd_daily, threshold_value=th, - mode="calc_Dev") - - self.assertEqual(sig_raw.dtypes[0], "float64") - self.assertEqual(sig_raw["flood"].__len__(), 99) - - #self.assertEqual(str(type(sig_raw.index.tolist()[0])), "") - - self.assertEqual(type(sig_dev), type(1.0)) - self.assertEqual(type(sig_val), type(1.0)) - - - def test_getAverageBaseflowUnderflowPerSection(self): - if self.usepandas: - for th in range(-10, 10): - th = th + np.random.uniform(-1, 1, 1)[0] - sig_val =sig.getAverageBaseflowUnderflowPerSection(self.simulation, self.observation, datetime_series=self.dd_daily, - threshold_value=th, mode="get_signature") - sig_raw = sig.getAverageBaseflowUnderflowPerSection(self.simulation, self.observation, datetime_series=self.dd_daily, - threshold_value=th, mode="get_raw_data") - sig_dev = sig.getAverageBaseflowUnderflowPerSection(self.simulation, self.observation, datetime_series=self.dd_daily, - threshold_value=th, mode="calc_Dev") - - self.assertTrue(sig_raw.dtypes[0] == "int64" or sig_raw.dtypes[0] == "float64") - self.assertEqual(sig_raw["baseflow"].__len__(), 99) - - #self.assertEqual(str(type(sig_raw.index.tolist()[0])), "") - - self.assertEqual(type(float(sig_dev.astype(float))), type(1.0)) - - self.assertEqual(type(float(sig_val.astype(float))), type(1.0)) - - def test_getAverageBaseflowFrequencyPerSection(self): - if self.usepandas: - for th in range(-10, 10): - th = th + np.random.uniform(-1,1,1)[0] - sig_val = sig.getAverageBaseflowFrequencyPerSection(self.simulation, self.observation, datetime_series=self.dd_daily, - threshold_value=th, mode="get_signature") - sig_raw = sig.getAverageBaseflowFrequencyPerSection(self.simulation, self.observation, datetime_series=self.ddd, threshold_value=th, - mode="get_raw_data") - sig_dev = sig.getAverageBaseflowFrequencyPerSection(self.simulation, self.observation, datetime_series=self.dd_daily, - threshold_value=th, mode="calc_Dev") - - self.assertTrue(sig_raw.dtypes[0] == "int64" or sig_raw.dtypes[0] == "float64") - self.assertEqual(sig_raw["baseflow"].__len__(), 99) - - #self.assertEqual(str(type(sig_raw.index.tolist()[0])), "") - - self.assertEqual(type(sig_dev), type(1.0)) - self.assertEqual(type(sig_val), type(1.0)) - - def test_getAverageBaseflowDuration(self): - if self.usepandas: - for th in range(-10, 10): - th = th + np.random.uniform(-1,1,1)[0] - - sig_val = sig.getAverageBaseflowDuration(self.simulation, self.observation, datetime_series=self.dd_daily, - threshold_value=th, mode="get_signature") - - sig_raw = sig.getAverageBaseflowDuration(self.simulation, self.observation, datetime_series=self.ddd, threshold_value=th, - mode="get_raw_data") - sig_dev = sig.getAverageBaseflowDuration(self.simulation, self.observation, datetime_series=self.dd_daily, - threshold_value=th, mode="calc_Dev") - - self.assertTrue(sig_raw.dtypes[0] == "int64" or sig_raw.dtypes[0] == "float64") - self.assertEqual(sig_raw["baseflow"].__len__(), 99) - - #self.assertEqual(str(type(sig_raw.index.tolist()[0])), "") - - self.assertEqual(type(sig_dev), type(1.0)) - self.assertEqual(type(sig_val), type(1.0)) - - - - - def test_getFloodFrequency(self): - if self.usepandas: - for th in range(-10, 10): - th = th + np.random.uniform(-1, 1, 1)[0] - sig_val = sig.getFloodFrequency(self.simulation, self.observation, - datetime_series=pd.date_range("2015-05-01", periods=self.timespanlen), threshold_value=th, - mode="get_signature") - sig_raw_dd = sig.getFloodFrequency(self.simulation, self.observation, - datetime_series=self.dd_daily, threshold_value=th, - mode="get_raw_data") - sig_raw_ddd = sig.getFloodFrequency(self.simulation, self.observation, - datetime_series=self.ddd, threshold_value=th, - mode="get_raw_data") - - sig_dev = sig.getFloodFrequency(self.simulation, self.observation, - datetime_series=pd.date_range("2015-05-01", periods=self.timespanlen), threshold_value=th, - mode="calc_Dev") - - - - - self.assertEqual(sig_raw_dd.dtypes[0], "int64") - self.assertEqual(sig_raw_dd["count"].__len__(), 99) - - self.assertEqual(sig_raw_ddd.dtypes[0], "int64") - self.assertEqual(sig_raw_ddd["count"].__len__(), 9) - - if type(sig_dev) != type(np.array([42.0])[0]) and type(sig_dev) != type(42.0): - self.assertRaises("type of sig_dev must be float or numpy.float") - - if type(sig_val) != type(np.array([42.0])[0]) and type(sig_val) != type(42.0): - self.assertRaises("type of sig_val must be float or numpy.float") - - - def test_getBaseflowFrequency(self): - if self.usepandas: - for th in range(-10, 10): - th = th + np.random.uniform(-1, 1, 1)[0] - sig_val = sig.getBaseflowFrequency(self.simulation, self.observation, - datetime_series=pd.date_range("2015-05-01", periods=self.timespanlen), threshold_value=th, - mode="get_signature") - sig_raw_dd = sig.getBaseflowFrequency(self.simulation, self.observation, - datetime_series=self.dd_daily, threshold_value=th, - mode="get_raw_data") - sig_raw_ddd = sig.getBaseflowFrequency(self.simulation, self.observation, - datetime_series=self.ddd, threshold_value=th, - mode="get_raw_data") - - sig_dev = sig.getBaseflowFrequency(self.simulation, self.observation, - datetime_series=pd.date_range("2015-05-01", periods=self.timespanlen), threshold_value=th, - mode="calc_Dev") - - self.assertEqual(sig_raw_dd.dtypes[0], "int64") - self.assertEqual(sig_raw_dd["count"].__len__(), 99) - - self.assertEqual(sig_raw_ddd.dtypes[0], "int64") - self.assertEqual(sig_raw_ddd["count"].__len__(), 9) - - - #self.assertEqual(str(type(sig_raw_dd.index.tolist()[0])), "") - #self.assertEqual(str(type(sig_raw_ddd.index.tolist()[0])), "") - - - self.assertEqual(type(float(sig_dev)), type(42.0) ) - self.assertEqual(type(float(sig_val)), type(1.0)) - - def test_getLowFlowVar(self): - if self.usepandas: - sig_sig_1 = sig.getLowFlowVar(self.simulation, self.observation, datetime_series=pd.date_range("2015-05-01", periods=self.timespanlen),mode="get_signature") - sig_sig_2 = sig.getLowFlowVar(self.simulation, None, - datetime_series=pd.date_range("2015-05-01", periods=self.timespanlen), - mode="get_signature") - sig_raw = sig.getLowFlowVar(self.simulation, self.observation, datetime_series=pd.date_range("2015-05-01", periods=self.timespanlen),mode="get_raw_data") - sig_raw_2 = sig.getLowFlowVar(self.simulation, None, - datetime_series=pd.date_range("2015-05-01", periods=self.timespanlen), - mode="get_raw_data") - sig_dev = sig.getLowFlowVar(self.simulation, self.observation, datetime_series=pd.date_range("2015-05-01", periods=self.timespanlen),mode="calc_Dev") - - self.assertEqual(type(1.0), type(float(sig_sig_1))) - self.assertEqual(type(1.0), type(float(sig_sig_2))) - self.assertEqual(type(1.0), type(float(sig_raw))) - self.assertEqual(type(1.0), type(float(sig_raw_2))) - self.assertEqual(type(1.0), type(float(sig_dev))) - - def test_getHighFlowVar(self): - if self.usepandas: - sig_sig_1 = sig.getHighFlowVar(self.simulation, self.observation, - datetime_series=pd.date_range("2015-05-01", periods=self.timespanlen), - mode="get_signature") - sig_sig_2 = sig.getHighFlowVar(self.simulation, None, - datetime_series=pd.date_range("2015-05-01", periods=self.timespanlen), - mode="get_signature") - sig_raw = sig.getHighFlowVar(self.simulation, self.observation, - datetime_series=pd.date_range("2015-05-01", periods=self.timespanlen), - mode="get_raw_data") - sig_raw_2 = sig.getHighFlowVar(self.simulation, None, - datetime_series=pd.date_range("2015-05-01", periods=self.timespanlen), - mode="get_raw_data") - sig_dev = sig.getHighFlowVar(self.simulation, self.observation, - datetime_series=pd.date_range("2015-05-01", periods=self.timespanlen), - mode="calc_Dev") - - self.assertEqual(type(1.0), type(float(sig_sig_1))) - self.assertEqual(type(1.0), type(float(sig_sig_2))) - self.assertEqual(type(1.0), type(float(sig_raw))) - self.assertEqual(type(1.0), type(float(sig_raw_2))) - self.assertEqual(type(1.0), type(float(sig_dev))) - - def test_getBaseflowIndex(self): - if self.usepandas: - sig_raw = sig.getBaseflowIndex(self.simulation, self.observation, - datetime_series=self.dd_daily, - mode="get_raw_data") - - self.assertEqual(type(sig_raw) , type({})) - self.assertGreater(sig_raw.__len__(),0) - - sig_sig_1 = sig.getBaseflowIndex(self.simulation, self.observation, - datetime_series=pd.date_range("2015-05-01", periods=self.timespanlen), - mode="get_signature") - sig_sig_2 = sig.getBaseflowIndex(self.simulation, None, - datetime_series=pd.date_range("2015-05-01", periods=self.timespanlen), - mode="get_signature") - - sig_dev = sig.getBaseflowIndex(self.simulation, self.observation, - datetime_series=pd.date_range("2015-05-01", periods=self.timespanlen), - mode="calc_Dev") - - self.assertEqual(type(1.0), type(float(sig_sig_1))) - self.assertEqual(type({}), type(sig_sig_2)) - self.assertEqual(type(1.0), type(float(sig_dev))) - -if __name__ == '__main__': - if test==True: # Happens if Pandas can be imported (not on travis so far) - unittest.main() \ No newline at end of file diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 00000000..5156db42 --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1,2 @@ +from . import testutils +from . import test_setup_parameters \ No newline at end of file diff --git a/tests/dds_tests/normal_list.txt b/tests/dds_tests/normal_list.txt new file mode 100644 index 00000000..106ad330 --- /dev/null +++ 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lhs mc mcmc mle rope sa # sceua +do + mpirun -c 6 python spotpy/examples/cli_cmf_lumped.py run -s $SAMPLER -n 10000 -p mpi +done diff --git a/tests/padds_tests/CHC_testdata.json b/tests/padds_tests/CHC_testdata.json new file mode 100644 index 00000000..01950e40 --- /dev/null +++ b/tests/padds_tests/CHC_testdata.json @@ -0,0 +1 @@ +[{"p": [[85.8188054768826, 86.55944180792586], [86.70630152632604, 87.51697338588765], [84.5722177714412, 86.92953610925], [86.54232318634665, 85.6841354876693], [87.1379457325233, 84.80451078305524], [87.84359409518048, 87.7982984625044], [85.09545212301622, 88.30543061349296], [87.14329627515937, 84.65748805922628], [86.38450425480222, 86.94982637544089]], "res": [0.0, 0.0, 0.1980235970887902, 0.0, 0.0, 0.4050002924906428, 0.1980235970887902, 0.4050002924906428, 0.0]}, {"p": [[7.825889162852209, 4.246441751767023, 6.542684331732198, 6.63187000673506, 5.166749943686848, 6.511631319699378, 7.532508525765445, 5.39785961201319], [7.033694180988696, 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100644 index 00000000..ad9682b8 --- /dev/null +++ b/tests/padds_tests/run_3.json @@ -0,0 +1,18 @@ +{ + "objfunc" : "ZDT1", + "evatrials" : 120, + "trial_runs" : 2, + "r_val" : 0.42, + "metric": "ones", + "results":[ + { + "sbest" : [0.05584,0.61647,0.00000,0.88926,0.57878,0.39500,0.65440,0.27478,0.34950,0.40215,1.00000,0.27649,0.25527,0.08841,0.29990,0.16057,0.02119,0.29092,0.22615,0.44949,0.00000,0.04376,0.00000,0.60798,0.44935,0.85983,0.01842,0.33763,0.00000,0.51219], + "objfunc_val":[0.055844, 3.641671] + }, + { + "sbest": [0.76884,0.34083,0.77033,0.03116,0.01427,0.26233,0.83944,0.40157,0.02387,0.06592,0.04617,0.52834,0.39722,0.00000,0.18781,0.05753,0.50057,0.67455,0.18398,0.18330,0.04044,0.00000,0.19888,0.00000,0.65601,0.54583,0.80648,0.08947,0.45019,0.24862], + "objfunc_val": [0.76884, 1.97629] + } + ] +} + diff --git a/tests/padds_tests/run_4.json b/tests/padds_tests/run_4.json new file mode 100644 index 00000000..71833fab --- /dev/null +++ b/tests/padds_tests/run_4.json @@ -0,0 +1,14 @@ +{ + "objfunc" : "ZDT1", + "evatrials" : 512, + "trial_runs" : 1, + "r_val" : 0.123, + "metric": "crowd_distance", + "results":[ + { + "sbest" : [0.79792, 0.00000, 0.44355, 0.30041, 0.15778, 0.00956, 0.00109, 0.29287, 0.44490, 0.20388, 0.00000, 0.43155, 0.30005, 0.15399, 0.16983, 0.46214, 0.41422, 0.81851, 0.12460, 0.47947, 0.06209, 0.00000, 0.99709, 0.09754, 0.26563, 0.04512, 0.36053, 0.75756, 0.07208, 0.15492], + "objfunc_val":[0.79792, 1.82069] + } + ] +} + diff --git a/tests/padds_tests/run_5.json b/tests/padds_tests/run_5.json new file mode 100644 index 00000000..d8a428bb --- /dev/null +++ b/tests/padds_tests/run_5.json @@ -0,0 +1,16 @@ +{ + "objfunc" : "ZDT1", + "evatrials" : 222, + "trial_runs" : 1, + "r_val" : 0.999, + "metric": "ones", + "initial_objs":[[]], + "initial_params": [[0.12519307, 0.92327596,0.90659523, 0.35615036, 0.87201232,0.33806262, 0.00786898, 0.63141019, 0.96851024, 0.75354514,0.86159084, 0.54991199, 0.57053675, 0.14343799, 0.17565052,0.46087274, 0.21685785, 0.63161391, 0.04039054, 0.87338488,0.82015038, 0.07409751, 0.27996075, 0.20705898, 0.55020237,0.23608541, 0.3669174 , 0.68879076, 0.73846853, 0.65078106]], + "results":[ + { + "sbest": [0.61069,0.00000,0.00000,0.00000,0.00000,0.30234,0.15275,0.00000,0.00000,0.00000,0.20714,0.00000,0.00000,0.00000,0.26151,0.00000,1.00000,0.25759,0.28883,0.00000,0.00000,0.88102,0.00000,0.00000,0.00000,0.00000,1.00000,0.22310,0.03402,0.00000], + "objfunc_val":[0.61069 ,1.21193] + } + ] +} + diff --git a/tests/padds_tests/run_6.json b/tests/padds_tests/run_6.json new file mode 100644 index 00000000..5e794165 --- /dev/null +++ b/tests/padds_tests/run_6.json @@ -0,0 +1,14 @@ +{ + "objfunc" : "ZDT1", + "evatrials" : 142, + "trial_runs" : 1, + "r_val" : 0.689, + "metric": "chc", + "results":[ + { + "sbest": [0.05366, 0.00000,0.00336, 0.30536, 0.00000, 0.11744, 0.00000, 0.20588, 0.00000, 0.24772, 0.00000, 0.58339, 0.32860, 0.00775, 0.30572, 0.00000, 0.00000, 0.21709, 0.36373, 0.03198, 0.27714, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 1.00000, 0.22899, 0.00000], + "objfunc_val":[0.053664, 1.958784] + } + ] +} + diff --git a/tests/padds_tests/run_7.json b/tests/padds_tests/run_7.json new file mode 100644 index 00000000..5afc1963 --- /dev/null +++ b/tests/padds_tests/run_7.json @@ -0,0 +1,18 @@ +{ + "objfunc" : "ZDT1", + "evatrials" : 223, + "trial_runs" : 2, + "r_val" : 0.256, + "metric": "chc", + "results":[ + { + "sbest": [0.50998, 0.25208, 0.00000, 0.09775, 0.15868, 0.53484, 0.80365, 0.01233, 0.08588, 0.42180, 0.20908, 0.00000, 0.39356, 0.29495, 0.16042, 0.00000, 0.17884, 0.16136, 0.11532, 0.37592, 0.28997, 0.00918, 0.00000, 0.20536, 0.65936, 0.05032, 0.13690, 0.10504, 0.22051, 0.05695], + "objfunc_val":[0.50998, 1.65150] + }, + { + "sbest": [0.02253, 0.07440, 0.00000, 0.58701, 0.69827, 0.00000, 0.05478, 0.65779, 0.78882, 0.00000, 0.69952, 0.81780, 0.00000, 0.50725, 0.45359, 0.09002, 0.00000, 0.00000, 0.15259, 0.00000, 0.33875, 0.00000, 0.29043, 0.54003, 0.10294, 0.15101, 0.35866, 0.00000, 0.09554, 0.00000], + "objfunc_val":[0.022529, 3.041644] + } + ] +} + diff --git a/tests/padds_tests/run_8.json b/tests/padds_tests/run_8.json new file mode 100644 index 00000000..67eab012 --- /dev/null +++ b/tests/padds_tests/run_8.json @@ -0,0 +1,18 @@ +{ + "objfunc" : "ZDT1", + "evatrials" : 223, + "trial_runs" : 2, + "r_val" : 0.745, + "metric": "hvc", + "results":[ + { + "sbest": [0.52709,0.08392,0.22482,0.00000,0.25033,0.12452,0.62693,0.24943,0.88157,0.00000,0.33692,0.00000,0.20865,0.29779,0.00000,0.00000,0.17705,0.00000,0.00000,0.79987,0.52464,0.15013,0.22015,0.00000,0.00000,0.00000,0.10893,0.00000,0.52070,0.16301], + "objfunc_val":[0.52709,1.62148] + }, + { + "sbest": [0.00000,0.00000,0.00000,0.85256,0.37026,0.00000,0.00000,0.43546,0.00000,0.00000,0.40134,0.74414,0.64239,0.08547,0.46782,0.00000,0.37347,0.00000,0.00000,0.29645,0.03278,0.00000,0.00558,0.00000,0.06120,0.16485,0.00000,0.45798,0.17679,1.00000], + "objfunc_val":[0.00000,3.03851] + } + ] +} + diff --git a/tests/padds_tests/run_9.json b/tests/padds_tests/run_9.json new file mode 100644 index 00000000..c4701581 --- /dev/null +++ b/tests/padds_tests/run_9.json @@ -0,0 +1,14 @@ +{ + "objfunc" : "ZDT1", + "evatrials" : 333, + "trial_runs" : 1, + "r_val" : 0.221, + "metric": "hvc", + "results":[ + { + "sbest": [0.07034,0.09803,0.27438,0.13823,0.00000,0.02806,0.14638,0.06090,0.00000,0.01408,0.00000,0.00000,0.14639,0.36101,0.22449,0.02401,0.00000,0.00000,0.07789,0.00000,0.00000,0.01056,0.03769,0.00000,0.17720,0.60322,0.02148,0.00000,0.56599,0.30216], + "objfunc_val": [0.070344,1.650221] + } + ] +} + diff --git a/tests/test_algorithms.py b/tests/test_algorithms.py new file mode 100644 index 00000000..e8a809d9 --- /dev/null +++ b/tests/test_algorithms.py @@ -0,0 +1,149 @@ +# -*- coding: utf-8 -*- +''' +Copyright (c) 2018 by Tobias Houska +This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). +:author: Tobias Houska +''' + +import unittest +try: + import spotpy +except ImportError: + import sys + sys.path.append(".") + import spotpy +#from spotpy.examples.tutorial_padds import padds_spot_setup +from spotpy.examples.spot_setup_rosenbrock import spot_setup +#from spotpy.describe import describe +import os +from spotpy.likelihoods import gaussianLikelihoodMeasErrorOut as GausianLike + + +#https://docs.python.org/3/library/unittest.html + +class TestAlgorithms(unittest.TestCase): + + def multi_obj_func(self, evaluation, simulation, params=None): + #used to overwrite objective function in hymod example + like1 = abs(spotpy.objectivefunctions.bias(evaluation, simulation)) + like2 = spotpy.objectivefunctions.rmse(evaluation, simulation) + like3 = spotpy.objectivefunctions.rsquared(evaluation, simulation)*-1 + return [like2, like3] + + def setUp(self): + # How many digits to match in case of floating point answers + self.tolerance = 7 + #Create samplers for every algorithm: + self.rep = 987 + self.timeout = 10 #Given in Seconds + + self.parallel = os.environ.get('SPOTPY_PARALLEL', 'seq') + self.dbformat = "ram" + + def test_mc(self): + sampler=spotpy.algorithms.mc(spot_setup(),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout) + sampler.sample(self.rep) + results = sampler.getdata() + self.assertEqual(len(results), self.rep) + + def test_lhs(self): + sampler=spotpy.algorithms.lhs(spot_setup(),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout) + sampler.sample(self.rep) + results = sampler.getdata() + self.assertEqual(len(results), self.rep) + + def test_mle(self): + sampler=spotpy.algorithms.mle(spot_setup(),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout) + sampler.sample(self.rep) + results = sampler.getdata() + self.assertEqual(len(results), self.rep) + + def test_mcmc(self): + sampler=spotpy.algorithms.mcmc(spot_setup(GausianLike),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout) + sampler.sample(self.rep) + results = sampler.getdata() + self.assertEqual(len(results), self.rep) + + def test_demcz(self): + sampler=spotpy.algorithms.demcz(spot_setup(GausianLike),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout) + sampler.sample(self.rep, convergenceCriteria=0) + results = sampler.getdata() + self.assertEqual(len(results), self.rep) + + def test_dream(self): + sampler=spotpy.algorithms.dream(spot_setup(GausianLike),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout) + sampler.sample(self.rep, convergence_limit=0.9, runs_after_convergence=500) + results = sampler.getdata() + self.assertEqual(len(results), self.rep) + + def test_sceua(self): + sampler=spotpy.algorithms.sceua(spot_setup(),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout) + sampler.sample(self.rep) + results = sampler.getdata() + self.assertLessEqual(len(results), self.rep) #Sceua save per definition not all sampled runs + + def test_abc(self): + sampler=spotpy.algorithms.abc(spot_setup(),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout) + sampler.sample(self.rep) + results = sampler.getdata() + self.assertEqual(len(results), self.rep) + + def test_fscabc(self): + sampler=spotpy.algorithms.fscabc(spot_setup(),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout) + sampler.sample(self.rep) + results = sampler.getdata() + self.assertEqual(len(results), self.rep) + + def test_rope(self): + sampler=spotpy.algorithms.rope(spot_setup(),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout) + sampler.sample(self.rep) + results = sampler.getdata() + self.assertEqual(len(results), self.rep) + + def test_sa(self): + sampler=spotpy.algorithms.sa(spot_setup(),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout) + sampler.sample(self.rep) + results = sampler.getdata() + self.assertEqual(len(results), self.rep) + + def test_list(self): + #generate a List sampler input + sampler=spotpy.algorithms.mc(spot_setup(),parallel=self.parallel, dbname='Rosen', dbformat='csv', sim_timeout=self.timeout) + sampler.sample(self.rep) + + sampler=spotpy.algorithms.list_sampler(spot_setup(),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout) + sampler.sample(self.rep) + results = sampler.getdata() + self.assertEqual(len(results), self.rep) + + def test_fast(self): + sampler=spotpy.algorithms.fast(spot_setup(),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout) + sampler.sample(self.rep, M=5) + results = sampler.getdata() + self.assertEqual(len(results), self.rep) #Si values should be returned + +# def test_padds(self): +# sampler=spotpy.algorithms.padds(padds_spot_setup(),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout) +# sampler.sample(self.rep) +# results = sampler.getdata() +# self.assertEqual(len(results)+5, self.rep) #Si values should be returned + + def test_nsgaii(self): + generations=40 + n_pop = 20 + skip_duplicates = True + sampler=spotpy.algorithms.NSGAII(spot_setup(self.multi_obj_func),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout) + sampler.sample(generations, n_obj= 3, n_pop = n_pop, skip_duplicates = skip_duplicates) + results = sampler.getdata() + self.assertLessEqual(len(results), generations*n_pop) + + @classmethod + def tearDownClass(cls): + try: + os.remove("Rosen.csv") + + except FileNotFoundError: + pass + +if __name__ == '__main__': + unittest.main(exit=False) diff --git a/tests/test_analyser.py b/tests/test_analyser.py new file mode 100644 index 00000000..ba35f13f --- /dev/null +++ b/tests/test_analyser.py @@ -0,0 +1,375 @@ +# -*- coding: utf-8 -*- +''' +Copyright (c) 2018 by Benjamin Manns +This file is part of Statistical Parameter Estimation Tool (SPOTPY). +:author: Tobias Houska, Benjamin Manns +''' + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals + +import matplotlib as mpl +mpl.use('Agg') +import matplotlib.pyplot as plt + +import spotpy +import unittest +import numpy as np +import spotpy.analyser +import os +import sys +from spotpy.examples.spot_setup_rosenbrock import spot_setup as rosenbrock_setup +from spotpy.examples.spot_setup_griewank import spot_setup as griewank_setup +from spotpy.examples.spot_setup_hymod_python import spot_setup as hymod_setup +from spotpy.likelihoods import gaussianLikelihoodMeasErrorOut as GausianLike + +class TestAnalyser(unittest.TestCase): + @classmethod + def setUpClass(self): + np.random.seed(42) + self.rep = 300 + self.parallel = "seq" + self.dbformat = "ram" + self.timeout = 5 + self.fig_name = 'test_output.png' + + sampler = spotpy.algorithms.mc(rosenbrock_setup(), + sim_timeout=self.timeout) + sampler.sample(self.rep) + self.results = sampler.getdata() + + sampler = spotpy.algorithms.mc(griewank_setup(), + sim_timeout=self.timeout) + sampler.sample(self.rep) + self.griewank_results = sampler.getdata() + if sys.version_info >= (3, 6): # FAST is only fully operational under + #Python 3 + sampler = spotpy.algorithms.fast(rosenbrock_setup(), + sim_timeout=self.timeout) + sampler.sample(self.rep) + self.sens_results = sampler.getdata() + #Hymod resuts are empty with Python <3.6 + sampler = spotpy.algorithms.dream(hymod_setup(GausianLike), + sim_timeout=self.timeout) + self.r_hat = sampler.sample(self.rep) + self.hymod_results = sampler.getdata() + + def test_setUp(self): + self.assertEqual(len(self.results), self.rep) + self.assertEqual(len(self.griewank_results), self.rep) + if sys.version_info >= (3, 6): + self.assertEqual(len(self.hymod_results), self.rep) + self.assertEqual(len(self.sens_results), self.rep) + + def test_get_parameters(self): + + self.assertEqual(len(spotpy.analyser.get_parameters( + self.results)[0]), 3) + + def test_get_parameternames(self): + self.assertEqual(spotpy.analyser.get_parameternames( + self.results + ),['x', 'y', 'z']) + + def test_get_parameter_fields(self): + self.assertEqual(len(spotpy.analyser.get_parameternames( + self.results + )), 3) + + def test_get_minlikeindex(self): + minlikeindex = spotpy.analyser.get_minlikeindex( + self.results + ) + self.assertEqual(len(minlikeindex), 2) + self.assertEqual(type(minlikeindex),type((1,1))) + + def test_get_maxlikeindex(self): + get_maxlikeindex = spotpy.analyser.get_maxlikeindex( + self.results + ) + self.assertEqual(len(get_maxlikeindex), 2) + self.assertEqual(type(get_maxlikeindex),type((1,1))) + + def test_get_like_fields(self): + get_like_fields = spotpy.analyser.get_like_fields( + self.results + ) + self.assertEqual(len(get_like_fields), 1) + self.assertEqual(type(get_like_fields),type([])) + + def test_calc_like(self): + calc_like = spotpy.analyser.calc_like( + self.results, + rosenbrock_setup().evaluation(),spotpy.objectivefunctions.rmse) + self.assertEqual(len(calc_like), len(self.results)) + self.assertEqual(type(calc_like), type([])) + + def test_get_best_parameterset(self): + get_best_parameterset_true = spotpy.analyser.get_best_parameterset( + self.results,True) + get_best_parameterset_false = spotpy.analyser.get_best_parameterset( + self.results, False) + self.assertEqual(len(get_best_parameterset_true[0]), 3) + self.assertEqual(type(get_best_parameterset_true[0]), np.void) + self.assertEqual(len(get_best_parameterset_false[0]), 3) + self.assertEqual(type(get_best_parameterset_false[0]), np.void) + + def test_get_modelruns(self): + get_modelruns = spotpy.analyser.get_modelruns( + self.results + ) + self.assertEqual(len(get_modelruns[0]), 1) + self.assertEqual(type(get_modelruns[0]), np.void) + + def test_get_header(self): + get_header = spotpy.analyser.get_header( + self.results + ) + self.assertEqual(len(get_header), 6) + self.assertEqual(type(get_header), type(())) + + def test_get_min_max(self): + get_min_max = spotpy.analyser.get_min_max(spotpy_setup=rosenbrock_setup()) + self.assertEqual(len(get_min_max[0]), 3) + self.assertEqual(type(get_min_max), type(())) + + def test_get_parbounds(self): + get_parbounds = spotpy.analyser.get_parbounds(spotpy_setup=rosenbrock_setup()) + self.assertEqual(len(get_parbounds[0]), 2) + self.assertEqual(len(get_parbounds), 3) + self.assertEqual(type(get_parbounds), type([])) + + def test_get_percentiles(self): + get_percentiles = spotpy.analyser.get_percentiles( + self.results) + self.assertEqual(len(get_percentiles),5) + self.assertEqual(type(get_percentiles[0][0]), type(np.abs(-1.0))) + self.assertEqual(type(get_percentiles),type(())) + + def test__geweke(self): + sample1 = [] + for a in self.results: + sample1.append(a[0]) + + _geweke = spotpy.analyser._Geweke(sample1) + + self.assertEqual(len(_geweke), 20) + self.assertEqual(type(_geweke), type(np.array([]))) + + def test_plot_Geweke(self): + sample1 = [] + for a in self.results: + sample1.append(a[0]) + spotpy.analyser.plot_Geweke(sample1,"sample1") + plt.savefig(self.fig_name) + + # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so + # we expecting a plot with some content without testing the structure of the plot, just + # the size + self.assertGreaterEqual(os.path.getsize(self.fig_name), 8855) + + + def test_get_sensitivity_of_fast(self): + if sys.version_info >= (3, 6): + get_sensitivity_of_fast = spotpy.analyser.get_sensitivity_of_fast(self.sens_results) + self.assertEqual(len(get_sensitivity_of_fast), 2) + self.assertEqual(len(get_sensitivity_of_fast["S1"]), 3) + self.assertEqual(len(get_sensitivity_of_fast["ST"]), 3) + self.assertEqual(type(get_sensitivity_of_fast), type({})) + + def test_get_simulation_fields(self): + get_simulation_fields = spotpy.analyser.get_simulation_fields( + self.results) + self.assertEqual(['simulation_0'],get_simulation_fields) + + def test_compare_different_objectivefunctions(self): + + + sampler = spotpy.algorithms.lhs(rosenbrock_setup(), + sim_timeout=self.timeout) + + sampler.sample(self.rep) + compare_different_objectivefunctions = spotpy.analyser.compare_different_objectivefunctions( + sampler.getdata()['like1'], self.results['like1']) + + + self.assertEqual(type(compare_different_objectivefunctions[1]),type(np.array([0.5])[0])) + + def test_plot_parameter_uncertainty(self): + if sys.version_info >= (3, 6): + posterior = spotpy.analyser.get_posterior(self.hymod_results,percentage=10) + #assertAlmostEqual tests on after comma accuracy, therefor we divide both by 100 + self.assertAlmostEqual(len(posterior)/100, self.rep*0.001, 1) + self.assertEqual(type(posterior), type(np.array([]))) + spotpy.analyser.plot_parameter_uncertainty(posterior, + hymod_setup().evaluation(), + fig_name=self.fig_name) + + # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so + # we expecting a plot with some content without testing the structure of the plot, just + # the size + self.assertGreaterEqual(os.path.getsize(self.fig_name), 8855) + + + def test_plot_fast_sensitivity(self): + + if sys.version_info >= (3, 6): + spotpy.analyser.plot_fast_sensitivity(self.sens_results,fig_name=self.fig_name) + # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so + # we expecting a plot with some content without testing the structure of the plot, just + # the size + self.assertGreaterEqual(os.path.getsize(self.fig_name), 8855) + + def test_plot_heatmap_griewank(self): + spotpy.analyser.plot_heatmap_griewank([self.griewank_results],["test"], + fig_name=self.fig_name) + + # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so + # we expecting a plot with some content without testing the structure of the plot, just + # the size + self.assertGreaterEqual(os.path.getsize(self.fig_name), 8855) + + + def test_plot_objectivefunction(self): + spotpy.analyser.plot_objectivefunction(self.results, + rosenbrock_setup().evaluation(), + fig_name=self.fig_name) + + # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so + # we expecting a plot with some content without testing the structure of the plot, just + # the size + self.assertGreaterEqual(os.path.getsize(self.fig_name), 8855) + + def test_plot_parametertrace_algorithms(self): + spotpy.analyser.plot_parametertrace_algorithms([self.griewank_results], + ["test_plot_parametertrace_algorithms"], + spot_setup=griewank_setup(), + fig_name=self.fig_name) + # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so + # we expecting a plot with some content without testing the structure of the plot, just + # the size + self.assertGreaterEqual(os.path.getsize(self.fig_name), 8855) + os.remove(self.fig_name) + + def test_plot_parametertrace(self): + spotpy.analyser.plot_parametertrace(self.griewank_results, ["0","1"], fig_name=self.fig_name) + + # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so + # we expecting a plot with some content without testing the structure of the plot, just + # the size + self.assertGreaterEqual(os.path.getsize(self.fig_name), 8855) + + def test_plot_posterior_parametertrace(self): + spotpy.analyser.plot_posterior_parametertrace(self.griewank_results, ["0","1"], fig_name=self.fig_name) + + # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so + # we expecting a plot with some content without testing the structure of the plot, just + # the size + self.assertGreaterEqual(os.path.getsize(self.fig_name), 8855) + + + def test_plot_posterior(self): + if sys.version_info >= (3, 6): + spotpy.analyser.plot_posterior(self.hymod_results + , hymod_setup().evaluation(),fig_name=self.fig_name) + + # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so + # we expecting a plot with some content without testing the structure of the plot, just + # the size + self.assertGreaterEqual(os.path.getsize(self.fig_name), 8855) + + def test_plot_bestmodelrun(self): + spotpy.analyser.plot_bestmodelrun(self.griewank_results, + griewank_setup().evaluation(), fig_name=self.fig_name) + + # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so + # we expecting a plot with some content without testing the structure of the plot, just + # the size + self.assertGreaterEqual(os.path.getsize(self.fig_name), 8855) + os.remove(self.fig_name) + + def test_plot_bestmodelruns(self): + if sys.version_info >= (3, 6): + spotpy.analyser.plot_bestmodelruns( + [self.hymod_results[0:10],self.hymod_results[10:20]], hymod_setup().evaluation(), + dates=range(1, 1+len(hymod_setup().evaluation())), algorithms=["test", "test2"], + fig_name=self.fig_name) + + # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so + # we expecting a plot with some content without testing the structure of the plot, just + # the size + self.assertGreaterEqual(os.path.getsize(self.fig_name), 8855) + + def test_plot_objectivefunctiontraces(self): + spotpy.analyser.plot_objectivefunctiontraces([self.results], + [rosenbrock_setup().evaluation()], + ["test"],fig_name=self.fig_name) + + # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so + # we expecting a plot with some content without testing the structure of the plot, just + # the size + self.assertGreaterEqual(os.path.getsize(self.fig_name), 8855) + + def test_plot_regression(self): + spotpy.analyser.plot_regression(self.results, rosenbrock_setup().evaluation(), + fig_name=self.fig_name) + + # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so + # we expecting a plot with some content without testing the structure of the plot, just + # the size + self.assertGreaterEqual(os.path.getsize(self.fig_name), 8855) + + def test_plot_parameterInteraction(self): + # Test only untder Python 3.6 as lower versions results in a strange ValueError + if sys.version_info >= (3, 6): + spotpy.analyser.plot_parameterInteraction(self.results, + fig_name = self.fig_name) + + # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so + # we expecting a plot with some content without testing the structure of the plot, just + # the size + self.assertGreaterEqual(os.path.getsize(self.fig_name), 8855) + + + def test_plot_allmodelruns(self): + if sys.version_info >= (3, 6): + modelruns = [] + for run in self.hymod_results: + on_run = [] + for i in run: + on_run.append(i) + on_run = np.array(on_run)[:-7] + modelruns.append(on_run.tolist()) + spotpy.analyser.plot_allmodelruns(modelruns, hymod_setup().evaluation(), + dates=range(1, len(hymod_setup().evaluation()) + 1), + fig_name=self.fig_name) + + # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so + # we expecting a plot with some content without testing the structure of the plot, just + # the size + self.assertGreaterEqual(os.path.getsize(self.fig_name), 8855) + + + def test_plot_autocorellation(self): + spotpy.analyser.plot_autocorellation(self.results["parx"],"parx", fig_name=self.fig_name) + + # approximately 8855 KB is the size of an empty matplotlib.pyplot.plot, so + # we expecting a plot with some content without testing the structure of the plot, just + # the size + self.assertGreaterEqual(os.path.getsize(self.fig_name), 8855) + + def test_plot_gelman_rubin(self): + if sys.version_info >= (3, 6): + spotpy.analyser.plot_gelman_rubin(self.r_hat, fig_name =self.fig_name) + self.assertGreaterEqual(abs(os.path.getsize(self.fig_name)), 100) + + + @classmethod + def tearDownClass(self): + os.remove('test_output.png') + + +if __name__ == '__main__': + unittest.main(exit=False) diff --git a/tests/test_cli.py b/tests/test_cli.py new file mode 100644 index 00000000..4aedeca1 --- /dev/null +++ b/tests/test_cli.py @@ -0,0 +1,60 @@ +# -*- coding: utf-8 -*- +''' +Copyright (c) 2018 by Tobias Houska +This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). +:author: Tobias Houska, Philipp Kraft +''' + +from __future__ import division, print_function, unicode_literals +import io + +from click.testing import CliRunner +from spotpy.cli import cli, run, get_config_from_file, get_sampler_from_string +from spotpy.algorithms import _algorithm +from spotpy.examples.spot_setup_rosenbrock import spot_setup +import unittest + +import os + + +class TestCLI(unittest.TestCase): + def test_cli_config(self): + + runner = CliRunner() + result = runner.invoke(cli, ['run', '--config']) + if result.exception: + raise result.exception + self.assertEqual(result.exit_code, 0) + + def test_cli_run(self): + setup = spot_setup() + runner = CliRunner() + result = runner.invoke(run, ['-n 10', '-s', 'lhs', '-p', 'seq'], obj=setup) + self.assertEqual(result.exit_code, 0) + + def test_config_file(self): + config = dict(n='10', sampler='mc', parallel='seq') + + # 1. Create config file + with io.open('spotpy.conf', 'w', encoding='utf-8') as f: + f.write('# A comment to be ignored\n') + f.write('Some nonsense to fail\n') + for k, v in config.items(): + f.write('{} = {}\n'.format(k, v)) + + try: + # 2. Read config file + config_from_file = get_config_from_file() + self.assertDictEqual(config, config_from_file, 'Configuration from file is not the same as before') + finally: + os.unlink('spotpy.conf') + + def test_sampler_from_string(self): + sampler_names = "abc|demcz|dream|fast|fscabc|lhs|mc|mcmc|mle|rope|sa|sceua".split('|') + samplers = [get_sampler_from_string(sampler_name) for sampler_name in sampler_names] + wrong_samplers = [n for n, c in zip(sampler_names, samplers) if not issubclass(c, _algorithm)] + self.assertFalse(wrong_samplers, 'Samplers not found from name: ' + ', '.join(wrong_samplers)) + + +if __name__ == '__main__': + unittest.main() \ No newline at end of file diff --git a/tests/test_database.py b/tests/test_database.py new file mode 100644 index 00000000..08a9d509 --- /dev/null +++ b/tests/test_database.py @@ -0,0 +1,350 @@ +# -*- coding: utf-8 -*- +''' +Copyright (c) 2018 by Tobias Houska +This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). +:author: Tobias Houska +''' +import unittest +import os +import glob +import spotpy +import spotpy.database as db +import numpy as np + +#https://docs.python.org/3/library/unittest.html + +class MockSetup: + """ + Mock class to use the save function of a spotpy setup + """ + def save(self, *args, **kwargs): + pass + + +class TestDatabase(unittest.TestCase): + @classmethod + def setUpClass(self): + self.parnames = ['x1', 'x2', 'x3', 'x4', 'x5'] + self.like = 0.213 + self.randompar = [175.21733934706367, 0.41669126598819262, 0.25265012080652388, 0.049706767415682945, 0.69674090782836173] + + self.simulations_multi = [] + for i in range(5): + self.simulations_multi.append(np.random.uniform(0, 1, 5).tolist()) + + self.simulations = np.random.uniform(0, 1, 5) + + @classmethod + def tearDownClass(self): + for filename in glob.glob("UnitTest_tmp*"): + os.remove(filename) + + def objf(self): + return np.random.uniform(0, 1, 1)[0] + + def test_csv_multiline(self): + csv = db.get_datawriter('csv', "UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations_multi, chains=1, save_sim=True) + + csv.save(self.like, self.randompar, self.simulations_multi) + csv.save(self.like, self.randompar, self.simulations_multi) + # Save Simulations + + csv.finalize() + csvdata = csv.getdata() + self.assertEqual(str(type(csvdata)), str(type(np.array([])))) + self.assertEqual(len(csvdata[0]), 32) + self.assertEqual(len(csvdata), 2) + self.assertEqual(len(csv.header), 32) + + + def test_csv_multiline_false(self): + # Save not Simulations + csv = db.get_datawriter('csv', "UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations_multi, chains=1, save_sim=False) + + csv.save(self.like, self.randompar, self.simulations_multi) + csv.save(self.like, self.randompar, self.simulations_multi) + + csv.finalize() + csvdata = csv.getdata() + self.assertEqual(str(type(csvdata)), str(type(np.array([])))) + self.assertEqual(len(csvdata[0]), 7) + self.assertEqual(len(csvdata), 2) + self.assertEqual(len(csv.header), 7) + + def test_csv_single(self): + csv = db.get_datawriter('csv', "UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations, + chains=1, save_sim=True) + + csv.save(self.like, self.randompar, self.simulations) + csv.save(self.like, self.randompar, self.simulations) + + csv.finalize() + csvdata = csv.getdata() + self.assertEqual(str(type(csvdata)), str(type(np.array([])))) + self.assertEqual(len(csvdata[0]), 12) + self.assertEqual(len(csvdata), 2) + self.assertEqual(len(csv.header), 12) + + def test_csv_append(self): + csv = db.get_datawriter( + 'csv', "UnitTest_tmp", + self.parnames, self.like, self.randompar, + simulations=self.simulations, chains=1, save_sim=True, + ) + + csv.save(self.like, self.randompar, self.simulations) + csv.save(self.like, self.randompar, self.simulations) + csv.finalize() + + csv_new = db.get_datawriter( + 'csv', "UnitTest_tmp", + self.parnames, self.like, self.randompar, + simulations=self.simulations, chains=1, save_sim=True, + dbappend=True + ) + + csv_new.save(self.like, self.randompar, self.simulations) + csv_new.save(self.like, self.randompar, self.simulations) + csv_new.finalize() + + csvdata = csv_new.getdata() + self.assertEqual(len(csvdata), 4) + + def test_csv_single_false(self): + csv = db.get_datawriter('csv', "UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations, + chains=1, save_sim=False) + + csv.save(self.like, self.randompar, self.simulations) + csv.save(self.like, self.randompar, self.simulations) + + csv.finalize() + csvdata = csv.getdata() + self.assertEqual(str(type(csvdata)), str(type(np.array([])))) + self.assertEqual(len(csvdata[0]), 7) + self.assertEqual(len(csvdata), 2) + self.assertEqual(len(csv.header), 7) + + def test_hdf5_multiline(self): + hdf5 = db.get_datawriter('hdf5', "UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations_multi, chains=1, save_sim=True) + + hdf5.save(self.like, self.randompar, self.simulations_multi) + hdf5.save(self.like, self.randompar, self.simulations_multi) + # Save Simulations + + hdf5.finalize() + hdf5data = hdf5.getdata() + self.assertEqual(str(type(hdf5data)), str(type(np.array([])))) + self.assertEqual(len(hdf5data[0]), 8) + self.assertEqual(len(hdf5data), 2) + self.assertEqual(len(hdf5.header), 32) + + + def test_hdf5_multiline_false(self): + # Save not Simulations + hdf5 = db.get_datawriter('hdf5', "UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations_multi, chains=1, save_sim=False) + + hdf5.save(self.like, self.randompar, self.simulations_multi) + hdf5.save(self.like, self.randompar, self.simulations_multi) + + hdf5.finalize() + hdf5data = hdf5.getdata() + self.assertEqual(str(type(hdf5data)), str(type(np.array([])))) + self.assertEqual(len(hdf5data[0]), 7) + self.assertEqual(len(hdf5data), 2) + self.assertEqual(len(hdf5.header), 7) + + def test_hdf5_single(self): + hdf5 = db.get_datawriter('hdf5', "UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations, + chains=1, save_sim=True) + + hdf5.save(self.like, self.randompar, self.simulations) + hdf5.save(self.like, self.randompar, self.simulations) + + hdf5.finalize() + hdf5data = hdf5.getdata() + self.assertEqual(str(type(hdf5data)), str(type(np.array([])))) + self.assertEqual(len(hdf5data[0]), 8) + self.assertEqual(len(hdf5data), 2) + self.assertEqual(len(hdf5.header), 12) + + def test_hdf5_append(self): + hdf5 = db.get_datawriter( + 'hdf5', "UnitTest_tmp", + self.parnames, self.like, self.randompar, + simulations=self.simulations, chains=1, save_sim=True, + ) + + hdf5.save(self.like, self.randompar, self.simulations) + hdf5.save(self.like, self.randompar, self.simulations) + hdf5.finalize() + + hdf5_new = db.get_datawriter( + 'hdf5', "UnitTest_tmp", + self.parnames, self.like, self.randompar, + simulations=self.simulations, chains=1, save_sim=True, + dbappend=True + ) + + hdf5_new.save(self.like, self.randompar, self.simulations) + hdf5_new.save(self.like, self.randompar, self.simulations) + hdf5_new.finalize() + + hdf5data = hdf5_new.getdata() + self.assertEqual(len(hdf5data), 4) + + + def test_hdf5_single_false(self): + hdf5 = db.get_datawriter('hdf5', "UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations, + chains=1, save_sim=False) + + hdf5.save(self.like, self.randompar, self.simulations) + hdf5.save(self.like, self.randompar, self.simulations) + + hdf5.finalize() + hdf5data = hdf5.getdata() + self.assertEqual(str(type(hdf5data)), str(type(np.array([])))) + self.assertEqual(len(hdf5data[0]), 7) + self.assertEqual(len(hdf5data), 2) + self.assertEqual(len(hdf5.header), 7) + + + def test_sql_multiline(self): + sql = db.get_datawriter('sql', "UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations_multi, chains=1, save_sim=True) + sql.save(self.like, self.randompar, self.simulations_multi) + sql.finalize() + sqldata = sql.getdata() + self.assertEqual(str(type(sqldata)), str(type(np.array([])))) + self.assertEqual(len(sqldata[0]), 32) + self.assertEqual(len(sqldata), 1) + self.assertEqual(len(sql.header), 32) + + + def test_sql_multiline_false(self): + sql = db.get_datawriter('sql', "UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations_multi, chains=1, save_sim=False) + sql.save(self.like, self.randompar, self.simulations_multi) + sql.finalize() + sqldata = sql.getdata() + self.assertEqual(str(type(sqldata)), str(type(np.array([])))) + self.assertEqual(len(sqldata[0]), 7) + self.assertEqual(len(sqldata), 1) + self.assertEqual(len(sql.header), 7) + + def test_sql_single(self): + sql = db.get_datawriter('sql', "UnitTest_tmp", self.parnames, self.like, self.randompar, + simulations=self.simulations, chains=1, save_sim=True) + sql.save(self.like, self.randompar, self.simulations) + sql.finalize() + sqldata = sql.getdata() + self.assertEqual(str(type(sqldata)), str(type(np.array([])))) + self.assertEqual(len(sqldata[0]), 12) + self.assertEqual(len(sqldata), 1) + self.assertEqual(len(sql.header), 12) + + def test_sql_single_false(self): + sql = db.get_datawriter('sql', "UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations, + chains=1, save_sim=False) + sql.save(self.like, self.randompar, self.simulations) + sql.finalize() + + sqldata = sql.getdata() + self.assertEqual(str(type(sqldata)), str(type(np.array([])))) + self.assertEqual(len(sqldata[0]), 7) + self.assertEqual(len(sqldata), 1) + self.assertEqual(len(sql.header), 7) + + def test_ram_multiline(self): + ram = db.get_datawriter('ram', "UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations_multi, chains=1, save_sim=True) + ram.save(self.like, self.randompar, self.simulations_multi) + ram.finalize() + + ramdata = ram.getdata() + self.assertEqual(str(type(ramdata)), str(type(np.array([])))) + self.assertEqual(len(ram.header), 32) + self.assertEqual(len(ramdata[0]), 32) + self.assertEqual(len(ramdata), 1) + self.assertEqual(len(ramdata.dtype), len(ram.header)) + + def test_ram_multiline_false(self): + ram = db.get_datawriter('ram', "UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations_multi, chains=1, save_sim=False) + ram.save(self.like, self.randompar, self.simulations_multi) + + ram.finalize() + ramdata = ram.getdata() + self.assertEqual(str(type(ramdata)), str(type(np.array([])))) + self.assertEqual(len(ramdata[0]), 7) + self.assertEqual(len(ramdata), 1) + self.assertEqual(len(ramdata.dtype), len(ram.header)) + self.assertEqual(len(ram.header), 7) + + def test_ram_single(self): + ram = db.get_datawriter('ram', "UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations, + chains=1, save_sim=True) + ram.save(self.like, self.randompar, self.simulations) + + ram.finalize() + ramdata = ram.getdata() + self.assertEqual(str(type(ramdata)), str(type(np.array([])))) + self.assertEqual(len(ramdata[0]), 12) + self.assertEqual(len(ramdata), 1) + self.assertEqual(len(ramdata.dtype), len(ram.header)) + self.assertEqual(len(ram.header), 12) + + def test_ram_single_false(self): + ram = db.get_datawriter('ram', "UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations, + chains=1, save_sim=False) + ram.save(self.like, self.randompar, self.simulations) + + ram.finalize() + ramdata = ram.getdata() + self.assertEqual(str(type(ramdata)), str(type(np.array([])))) + self.assertEqual(len(ramdata[0]), 7) + self.assertEqual(len(ramdata), 1) + self.assertEqual(len(ramdata.dtype), len(ram.header)) + self.assertEqual(len(ram.header), 7) + + def test_not_existing_dbformat(self): + with self.assertRaises(AttributeError): + _ = db.get_datawriter('xxx', "UnitTest_tmp", self.parnames, self.like, self.randompar, simulations=self.simulations, + chains=1, save_sim=True) + + def test_noData(self): + nodata = db.get_datawriter( + 'noData', "UnitTest_tmp", + self.parnames, np.array(self.like), self.randompar, + simulations=self.simulations, chains=1, save_sim=True + ) + nodata.save(self.like, self.randompar, self.simulations) + nodata.finalize() + self.assertEqual(nodata.getdata(), None) + + def test_custom(self): + custom = db.get_datawriter( + 'custom', "UnitTest_tmp", + self.parnames, self.like, self.randompar, + setup=MockSetup(), + simulations=self.simulations, chains=1, save_sim=True + ) + custom.save(self.like, self.randompar, self.simulations) + custom.finalize() + self.assertEqual(custom.getdata(),None) + + def test_custom_no_setup(self): + with self.assertRaises(ValueError): + _ = db.get_datawriter( + 'custom', "UnitTest_tmp", + self.parnames, self.like, self.randompar, + simulations=self.simulations, chains=1, save_sim=True + ) + + def test_custom_wrong_setup(self): + with self.assertRaises(AttributeError): + _ = db.get_datawriter( + 'custom', "UnitTest_tmp", + self.parnames, self.like, self.randompar, + setup=[], + simulations=self.simulations, chains=1, save_sim=True + ) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_dds.py b/tests/test_dds.py new file mode 100644 index 00000000..9f51027b --- /dev/null +++ b/tests/test_dds.py @@ -0,0 +1,182 @@ +# -*- coding: utf-8 -*- +''' +Copyright (c) 2018 by Tobias Houska +This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). +:author: Tobias Houska, Benjamin Manns +''' + +import unittest +import sys +import numpy as np + +try: + import spotpy +except ImportError: + sys.path.append(".") + import spotpy + +import os +from spotpy.examples.spot_setup_dds import spot_setup +import json + + +# replaces numpy.random module in a way +class FixedRandomizerEndOfDataException(Exception): + pass + + +class FixedRandomizer(): + def __init__(self): + self.debug = False + self.uniform_counter = 0 + self.normal_counter = 0 + self.uniform_list=list(np.loadtxt(os.path.dirname(__file__)+"/dds_tests/uniform_list.txt")) + + self.uniform_list*=3 + self.max_normal_counter = 10000 + self.max_uniform_counter = 30000 + + self.normal_list = list(np.loadtxt(os.path.dirname(__file__)+"/dds_tests/normal_list.txt")) + + def rand(self, dim_x=1, dim_y=1): + x = dim_x * [0] + for i in range(dim_x): + if self.uniform_counter < self.max_uniform_counter: + x[i] = self.uniform_list[self.uniform_counter] + self.uniform_counter = self.uniform_counter + 1 + if self.debug: + print("fixrand::rand() counter = "+str(self.uniform_counter)) + else: + raise FixedRandomizerEndOfDataException("No more data left. Counter is: "+str(self.uniform_counter)) + if len(x) == 1: + return x[0] + else: + return x + + def randint(self,x_from,x_to): + vals = [j for j in range(x_from,x_to)] + vals_size = len(vals) + if vals_size == 0: + raise ValueError("x_to >= x_from") + fraq = 1. / vals_size + if self.uniform_counter < self.max_uniform_counter: + q_uni = self.uniform_list[self.uniform_counter] + pos = np.int(np.floor(q_uni / fraq)) + self.uniform_counter += 1 + if self.debug: + print("fixrand::randint() counter = " + str(self.uniform_counter)) + return vals[pos] + else: + raise FixedRandomizerEndOfDataException("No more data left.") + + def normal(self,loc,scale,size=1): + x = [] + for j in range(size): + if self.normal_counter < self.max_normal_counter: + x.append(self.normal_list[self.normal_counter]*scale + loc) + self.normal_counter += 1 + if self.debug: + print("fixrand::normal() counter = " + str(self.normal_counter)) + + else: + raise FixedRandomizerEndOfDataException("No more data left.") + if len(x) == 1: + return x[0] + else: + return x + + +class TestDDS(unittest.TestCase): + def setUp(self): + self.spot_setup = spot_setup() + self.rep = 1000 + self.timeout = 1 # Given in Seconds + self.f_random = FixedRandomizer() + + def json_helper(self, run): + with open(os.path.dirname(__file__) + "/dds_tests/run_" + str(run) + ".json") as f: + data = json.load(f) + + return data + + def test_run_1(self): + self.run_a_dds(1) + + def test_run_2(self): + self.run_a_dds(2) + + def test_run_3(self): + self.run_a_dds(3) + + def test_run_4(self): + self.run_a_dds(4) + + def test_run_5(self): + self.run_a_dds(5) + + def test_run_6(self): + self.run_a_dds(6) + + def not_working_run_7(self): + self.run_a_dds(7) + + def test_run_own_initial_1(self): + self.run_a_dds("own_input_1") + + def test_run_own_initial_2(self): + self.run_a_dds("own_input_2") + + def run_a_dds(self, run): + original_result = self.json_helper(run) + + self.spot_setup._objfunc_switcher(original_result['objfunc']) + + sampler = spotpy.algorithms.dds(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout,r=original_result["r_val"]) + sampler._set_np_random(self.f_random) + + if original_result.get("s_initial") is not None: + # if a parameter initialisation is given, test this: + results = sampler.sample(original_result["evatrials"], + original_result["trial_runs"], x_initial=original_result["s_initial"]) + else: + results = sampler.sample(original_result["evatrials"], + original_result["trial_runs"]) + + for t in range(original_result["trial_runs"]): + print(results[t]["objfunc_val"], -1*original_result["results"][t]["objfunc_val"]) + self.assertAlmostEqual(results[t]["objfunc_val"], -1*original_result["results"][t]["objfunc_val"], + delta=0.000001) + py_sbest = results[t]["sbest"] + matlb_sbest = original_result["results"][t]["sbest"] + for k in range(len(py_sbest)): + print(py_sbest[k], matlb_sbest[k]) + self.assertAlmostEqual(py_sbest[k], matlb_sbest[k], delta=0.00001) + + py_trial_initial = results[t]["trial_initial"] + matlb_trial_initial = original_result["results"][t]["trial_initial"] + for k in range(len(py_sbest)): + print(t, k, py_trial_initial[k], matlb_trial_initial[k]) + self.assertAlmostEqual(py_trial_initial[k], matlb_trial_initial[k], delta=0.0001) + + def test_own_initial_out_of_borders_ackley_1(self): + self.spot_setup._objfunc_switcher("ackley") + sampler = spotpy.algorithms.dds(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + self.assertRaises(ValueError,sampler.sample,1000, x_initial=np.random.uniform(-2, 2, 9) + [3]) + + def test_own_initial_too_lees(self): + self.spot_setup._objfunc_switcher("ackley") + sampler = spotpy.algorithms.dds(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + self.assertRaises(ValueError, sampler.sample, 1000, x_initial=np.random.uniform(-2, 2, 9)) + + def test_own_initial_too_much(self): + self.spot_setup._objfunc_switcher("ackley") + sampler = spotpy.algorithms.dds(self.spot_setup, parallel="seq", dbname='test_DDS', dbformat="csv", + sim_timeout=self.timeout) + self.assertRaises(ValueError, sampler.sample, 1000, x_initial=np.random.uniform(-2, 2, 11)) + + +if __name__ == '__main__': + unittest.main() diff --git a/spotpy/unittests/test_describe.py b/tests/test_describe.py similarity index 60% rename from spotpy/unittests/test_describe.py rename to tests/test_describe.py index 7aba1b4d..5035963b 100644 --- a/spotpy/unittests/test_describe.py +++ b/tests/test_describe.py @@ -1,9 +1,10 @@ # -*- coding: utf-8 -*- -""" -Tests the describe module +''' +Copyright (c) 2018 by Tobias Houska +This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). +:author: Tobias Houska, Philipp Kraft +''' -:author: philippkraft -""" from __future__ import unicode_literals, absolute_import, division, print_function import sys import unittest @@ -17,7 +18,7 @@ class SpotSetup(object): """ - Just a fun setup using non ASCIÎ letters to chällenge describe in Python 2 + Just a fun setup using non ASCIÎ letters to challenge describe in Python 2 """ a = Uniform(-1, 1, doc='α parameter') beta = Uniform(-1, 1, doc='β parameter') @@ -69,6 +70,30 @@ def test_all_algorithms(self): sampler = scls(spot_setup=model, dbformat='ram', dbname='äöü') self.sampler_test(sampler) +if sys.version_info > (3, 5): + + class TestDescribeRst(unittest.TestCase): + + def test_setup_rst(self): + setup = SpotSetup() + rst = spotpy.describe.rst(setup) + + def test_sampler_rst(self): + for sname, scls in inspect.getmembers(spotpy.algorithms, inspect.isclass): + if not sname.startswith('_'): + model = SpotSetup() + sampler = scls(spot_setup=model, dbformat='ram', dbname='äöü') + rst = spotpy.describe.rst(sampler) + + rst.append('This is a test for ``rst.append().``\n' * 10, 'Appending', 1) + rst.append_math(r'c = \sqrt{a^2 + b^2}') + rst.append(title='Image', titlelevel=2) + rst.append_image('https://img.shields.io/travis/thouska/spotpy/master.svg', + target='https://travis-ci.org/thouska/spotpy', + width='200px') + + rst.as_html() + if __name__ == '__main__': unittest.main() diff --git a/tests/test_fast.py b/tests/test_fast.py new file mode 100644 index 00000000..33aa7734 --- /dev/null +++ b/tests/test_fast.py @@ -0,0 +1,38 @@ +# -*- coding: utf-8 -*- +''' +Copyright (c) 2018 by Tobias Houska +This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). +:author: Tobias Houska +''' + +import unittest +import sys + +try: + import spotpy +except ImportError: + sys.path.append(".") + import spotpy + +from spotpy.examples.spot_setup_hymod_python import spot_setup + +# Test only untder Python 3 as lower versions results in a strange fft error +if sys.version_info >= (3, 6): + + class TestFast(unittest.TestCase): + def setUp(self): + + self.spot_setup = spot_setup() + self.rep = 200 # REP must be a multiply of amount of parameters which are in 7 if using hymod + self.timeout = 10 # Given in Seconds + + def test_fast(self): + sampler = spotpy.algorithms.fast(self.spot_setup, parallel="seq", dbname='test_FAST', dbformat="ram", + sim_timeout=self.timeout) + results = [] + sampler.sample(self.rep) + results = sampler.getdata() + self.assertEqual(200,len(results)) + +if __name__ == '__main__': + unittest.main() diff --git a/spotpy/unittests/test_gui_mpl.py b/tests/test_gui_mpl.py similarity index 79% rename from spotpy/unittests/test_gui_mpl.py rename to tests/test_gui_mpl.py index 41510621..8889f953 100644 --- a/spotpy/unittests/test_gui_mpl.py +++ b/tests/test_gui_mpl.py @@ -1,3 +1,9 @@ +# -*- coding: utf-8 -*- +''' +Copyright (c) 2018 by Tobias Houska +This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). +:author: Tobias Houska, Philipp Kraft +''' import unittest import matplotlib @@ -15,7 +21,7 @@ import spotpy from spotpy.gui.mpl import GUI - from test_setup_parameters import SpotSetupMixedParameterFunction as Setup + from .test_setup_parameters import SpotSetupMixedParameterFunction as Setup class TestGuiMpl(unittest.TestCase): diff --git a/tests/test_likelihood.py b/tests/test_likelihood.py new file mode 100644 index 00000000..b07f7beb --- /dev/null +++ b/tests/test_likelihood.py @@ -0,0 +1,375 @@ +# -*- coding: utf-8 -*- +""" +Copyright (c) 2017 by Benjamin Manns +This file is part of Statistical Parameter Estimation Tool (SPOTPY). +:author: Tobias Houska, Benjamin Manns + +This code tests the likelihood framework and present all existing function. +""" + +import numpy as np + +try: + import spotpy +except ImportError: + import sys + + sys.path.append(".") + import spotpy + +import unittest +from spotpy.likelihoods import LikelihoodError + + +# We use all available likelihood functions. The pydoc of every function tells, if we can add a +# parameter `param` to the function which includes model parameter. The `param` must be None or a tuple with values +# and names. If `param` is None, the needed values are calculated by the function itself. + + +class TestLikelihood(unittest.TestCase): + def setUp(self): + np.random.seed(12) + self.normal_data, self.normal_comparedata = np.random.normal(1500, 2530, 20), np.random.normal(15, 25, 20) + self.binom_data, self.binom_comparedata = np.random.binomial(20, 0.1, 20), np.random.binomial(20, 0.1, 20) + self.do_print = True + + def test_logLikelihood(self): + l_normal = spotpy.likelihoods.logLikelihood(self.normal_data, self.normal_comparedata) + self.assertGreaterEqual(np.abs(l_normal), 900) + self.assertEqual(type(np.float(l_normal)), type(np.float(1))) + if self.do_print: + print("logLikelihood: " + str(l_normal)) + + l_binom = spotpy.likelihoods.logLikelihood(self.binom_data, self.binom_comparedata) + self.assertGreaterEqual(np.abs(l_binom), 900) + self.assertEqual(type(np.float(l_binom)), type(np.float(1))) + if self.do_print: + print("logLikelihood: " + str(l_binom)) + + def test_gaussianLikelihoodMeasErrorOut(self): + l_normal = spotpy.likelihoods.gaussianLikelihoodMeasErrorOut(self.normal_data, self.normal_comparedata) + self.assertGreaterEqual(-40, l_normal) + self.assertEqual(type(np.float(l_normal)), type(np.float(1))) + if self.do_print: + print("gaussianLikelihoodMeasErrorOut: " + str(l_normal)) + + l_binom = spotpy.likelihoods.gaussianLikelihoodMeasErrorOut(self.binom_data, self.binom_comparedata) + self.assertGreaterEqual(-40, l_binom) + self.assertEqual(type(np.float(l_binom)), type(np.float(1))) + if self.do_print: + print("gaussianLikelihoodMeasErrorOut: " + str(l_binom)) + + def test_gaussianLikelihoodHomoHeteroDataError(self): + l_normal = spotpy.likelihoods.gaussianLikelihoodHomoHeteroDataError(self.normal_data, self.normal_comparedata) + self.assertGreaterEqual(5, np.abs(l_normal)) + self.assertEqual(type(np.float(l_normal)), type(np.float(1))) + if self.do_print: + print("gaussianLikelihoodHomoHeteroDataError: " + str(l_normal)) + + l_binom = spotpy.likelihoods.gaussianLikelihoodHomoHeteroDataError(self.binom_data, self.binom_comparedata) + self.assertGreaterEqual(10, np.abs(l_binom)) + self.assertEqual(type(np.float(l_binom)), type(np.float(1))) + if self.do_print: + print("gaussianLikelihoodHomoHeteroDataError: " + str(l_binom)) + + def test_LikelihoodAR1NoC(self): + l_list = [] + + l_list.append(spotpy.likelihoods.LikelihoodAR1NoC(self.normal_data, self.normal_comparedata, + params=([0.98], ["likelihood_phi"]))) + + try: + l_list.append(spotpy.likelihoods.LikelihoodAR1NoC(self.normal_data, self.normal_comparedata, + params=([], []))) + except LikelihoodError as e: + print("LikelihoodError occurred: " + str(e)) + + l_list.append(spotpy.likelihoods.LikelihoodAR1NoC(self.normal_data, self.normal_comparedata, + params=([1.1], ["likelihood_phi"]))) + + l_list.append(spotpy.likelihoods.LikelihoodAR1NoC(self.binom_data, self.binom_data)) + + for l in l_list: + self.assertNotEqual(None, l) + if self.do_print: + print("LikelihoodAR1NoC: " + str(l)) + + def test_LikelihoodAR1WithC(self): + l_normal_list = [] + try: + l_normal_list.append(spotpy.likelihoods.LikelihoodAR1WithC(self.normal_data, self.normal_comparedata, + params=([], []))) + except LikelihoodError as e: + print("Likelihood Error occurred " + str(e)) + + l_normal_list.append(spotpy.likelihoods.LikelihoodAR1WithC(self.normal_data, self.normal_comparedata, + params=([0.98], ["likelihood_phi"]))) + l_normal_list.append(spotpy.likelihoods.LikelihoodAR1WithC(self.normal_data, self.normal_comparedata, + params=([1.1], ["likelihood_phi"]))) + + l_normal_list.append(spotpy.likelihoods.LikelihoodAR1WithC(self.binom_data, self.binom_comparedata)) + + for l_normal in l_normal_list: + self.assertNotEqual(None, l_normal) + if self.do_print: + print("LikelihoodAR1WithC: " + str(l_normal)) + + def test_generalizedLikelihoodFunction(self): + size = 1000 + data, comparedata = np.random.normal(1500, 2530, size), np.random.normal(355, 25, size) + + param_list = ["likelihood_beta", "likelihood_xi", "likelihood_sigma0", "likelihood_sigma1", "likelihood_phi1", + "likelihood_muh"] + l_normal_list = [] + + l_normal_list.append(spotpy.likelihoods.generalizedLikelihoodFunction(data, comparedata, params= + ([-0.09, 1, 0.5, 0.567, 0.98, 57.32], param_list))) + + try: + l_normal_list.append(spotpy.likelihoods.generalizedLikelihoodFunction(data, comparedata, params= + ([], []))) + except LikelihoodError as e: + print("Likelihood Error occurred " + str(e)) + + l_normal_list.append(spotpy.likelihoods.generalizedLikelihoodFunction(data, comparedata, params= + ([2, 1, 0.5, 0.567, 0.98, 57.32], param_list))) + l_normal_list.append(spotpy.likelihoods.generalizedLikelihoodFunction(data, comparedata, params= + ([-0.09, 11, 0.5, 0.567, 0.98, 57.32], param_list))) + l_normal_list.append(spotpy.likelihoods.generalizedLikelihoodFunction(data, comparedata, params= + ([-0.09, 1, 1.5, 0.567, 0.98, 57.32], param_list))) + l_normal_list.append(spotpy.likelihoods.generalizedLikelihoodFunction(data, comparedata, params= + ([-0.09, 1, 0.5, 1.567, 0.98, 57.32], param_list))) + l_normal_list.append(spotpy.likelihoods.generalizedLikelihoodFunction(data, comparedata, params= + ([-0.09, 1, 0.5, 0.567, 2.98, 57.32], param_list))) + l_normal_list.append(spotpy.likelihoods.generalizedLikelihoodFunction(data, comparedata, params= + ([-0.09, 1, 0.5, 0.567, 0.98, 101], param_list))) + l_normal_list.append(spotpy.likelihoods.generalizedLikelihoodFunction(data, comparedata, params= + ([-0.09, 0.0, 0.5, 0.567, 0.98, 101], param_list))) + + for l_normal in l_normal_list: + + self.assertNotEqual(None, l_normal) + self.assertEqual(type(np.float(l_normal)), type(np.float(1))) + if self.do_print: + print("generalizedLikelihoodFunction: " + str(l_normal)) + + l_binom = spotpy.likelihoods.generalizedLikelihoodFunction(self.binom_data, self.binom_comparedata) + + self.assertNotEqual(None, l_binom) + self.assertGreaterEqual(-10000, l_binom) + self.assertEqual(type(np.float(l_binom)), type(np.float(1))) + if self.do_print: + print("generalizedLikelihoodFunction: " + str(l_binom)) + + def test_LaplacianLikelihood(self): + l_normal = spotpy.likelihoods.LaplacianLikelihood(self.normal_data, self.normal_comparedata) + self.assertNotEqual(None, l_normal) + self.assertEqual(type(np.float(l_normal)), type(np.float(1))) + if self.do_print: + print("LaplacianLikelihood: " + str(l_normal)) + + l_binom = spotpy.likelihoods.LaplacianLikelihood(self.binom_data, self.binom_comparedata) + self.assertNotEqual(None, l_normal) + self.assertEqual(type(np.float(l_binom)), type(np.float(1))) + if self.do_print: + print("LaplacianLikelihood: " + str(l_binom)) + + def test_SkewedStudentLikelihoodHomoscedastic(self): + l_normal = spotpy.likelihoods.SkewedStudentLikelihoodHomoscedastic(self.normal_data, self.normal_comparedata) + self.assertGreaterEqual(12, np.abs(l_normal)) + self.assertEqual(type(np.float(l_normal)), type(np.float(1))) + if self.do_print: + print("SkewedStudentLikelihoodHomoscedastic: " + str(l_normal)) + + l_binom = spotpy.likelihoods.SkewedStudentLikelihoodHomoscedastic(self.binom_data, self.binom_comparedata) + self.assertGreaterEqual(17, np.abs(l_binom)) + self.assertEqual(type(np.float(l_binom)), type(np.float(1))) + if self.do_print: + print("SkewedStudentLikelihoodHomoscedastic: " + str(l_binom)) + + def test_SkewedStudentLikelihoodHeteroscedastic(self): + l_normal_list = [] + paramDependencies = ["likelihood_nu", "likelihood_kappa", "likelihood_phi"] + l_normal_list.append( + spotpy.likelihoods.SkewedStudentLikelihoodHeteroscedastic(self.normal_data, self.normal_comparedata, + params=([2.4, 0.15, 0.87], paramDependencies))) + try: + l_normal_list.append( + spotpy.likelihoods.SkewedStudentLikelihoodHeteroscedastic(self.normal_data, self.normal_comparedata, + params=([], []))) + except LikelihoodError as e: + print("An error occurred: " + str(e)) + + l_normal_list.append( + spotpy.likelihoods.SkewedStudentLikelihoodHeteroscedastic(self.normal_data, self.normal_comparedata, + params=([1, 0.15, 1.87], paramDependencies))) + + l_normal_list.append( + spotpy.likelihoods.SkewedStudentLikelihoodHeteroscedastic(self.normal_data, self.normal_comparedata, + params=([1, 0.15, 0.87], paramDependencies))) + + l_normal_list.append( + spotpy.likelihoods.SkewedStudentLikelihoodHeteroscedastic(self.normal_data, self.normal_comparedata, + params=([1, -0.15, 0.87], paramDependencies))) + for l_normal in l_normal_list: + if not np.isnan(l_normal): + self.assertGreaterEqual(-100, l_normal) + self.assertEqual(type(np.float(l_normal)), type(np.float(1))) + if self.do_print: + print("SkewedStudentLikelihoodHeteroscedastic: " + str(l_normal)) + + l_binom = spotpy.likelihoods.SkewedStudentLikelihoodHeteroscedastic(self.binom_data, self.binom_comparedata) + if not np.isnan(l_binom): + self.assertGreaterEqual(-100, l_binom) + self.assertEqual(type(np.float(l_binom)), type(np.float(1))) + if self.do_print: + print("SkewedStudentLikelihoodHeteroscedastic: " + str(l_binom)) + + def test_SkewedStudentLikelihoodHeteroscedasticAdvancedARModel(self): + l_normal_list = [] + params = ["likelihood_nu", "likelihood_kappa", "likelihood_phi"] + + l_normal_list.append(spotpy.likelihoods.SkewedStudentLikelihoodHeteroscedasticAdvancedARModel( + self.normal_data, self.normal_comparedata, params=([4, 43, 0.4], params))) + + try: + l_normal_list.append(spotpy.likelihoods.SkewedStudentLikelihoodHeteroscedasticAdvancedARModel( + self.normal_data, self.normal_comparedata, params=([], []))) + except LikelihoodError as e: + print("Likelihood Error occurred " + str(e)) + + l_normal_list.append(spotpy.likelihoods.SkewedStudentLikelihoodHeteroscedasticAdvancedARModel( + self.normal_data, self.normal_comparedata, params=([4, 43, 2.4], params))) + l_normal_list.append(spotpy.likelihoods.SkewedStudentLikelihoodHeteroscedasticAdvancedARModel( + self.normal_data, self.normal_comparedata, params=([1, 43, 0.4], params))) + l_normal_list.append(spotpy.likelihoods.SkewedStudentLikelihoodHeteroscedasticAdvancedARModel( + self.normal_data, self.normal_comparedata, params=([4, -3, 0.4], params))) + + for l_normal in l_normal_list: + self.assertNotEqual(None, l_normal) + self.assertEqual(type(np.float(l_normal)), type(np.float(1))) + if self.do_print: + print("SkewedStudentLikelihoodHeteroscedasticAdvancedARModel: " + str(l_normal)) + + l_binom = spotpy.likelihoods.SkewedStudentLikelihoodHeteroscedasticAdvancedARModel( + self.normal_data, self.normal_comparedata) + + self.assertNotEqual(None, l_binom) + self.assertEqual(type(np.float(l_binom)), type(np.float(1))) + if self.do_print: + print("SkewedStudentLikelihoodHeteroscedasticAdvancedARModel: " + str(l_binom)) + + def test_NoisyABCGaussianLikelihood(self): + l_normal = spotpy.likelihoods.NoisyABCGaussianLikelihood(self.normal_data, self.normal_comparedata) + self.assertNotEqual(None, l_normal) + self.assertEqual(type(np.float(l_normal)), type(np.float(1))) + if self.do_print: + print("NoisyABCGaussianLikelihood: " + str(l_normal)) + + l_binom = spotpy.likelihoods.NoisyABCGaussianLikelihood(self.binom_data, self.binom_data, + measerror=[0.0]) + self.assertNotEqual(None, l_binom) + self.assertEqual(type(np.float(l_binom)), type(np.float(1))) + if self.do_print: + print("NoisyABCGaussianLikelihood: " + str(l_binom)) + + def test_ABCBoxcarLikelihood(self): + l_normal = spotpy.likelihoods.ABCBoxcarLikelihood(self.normal_data, self.normal_comparedata) + self.assertNotEqual(None, l_normal) + self.assertNotEqual(np.nan, l_normal) + self.assertEqual(type(np.float(l_normal)), type(np.float(1))) + if self.do_print: + print("ABCBoxcarLikelihood: " + str(l_normal)) + + l_binom = spotpy.likelihoods.ABCBoxcarLikelihood(self.binom_data, self.binom_comparedata) + self.assertNotEqual(None, l_binom) + self.assertNotEqual(np.nan, l_binom) + self.assertEqual(type(np.float(l_binom)), type(np.float(1))) + if self.do_print: + print("ABCBoxcarLikelihood: " + str(l_binom)) + + def test_LimitsOfAcceptability(self): + l_normal = spotpy.likelihoods.LimitsOfAcceptability(self.normal_data, self.normal_comparedata) + self.assertEqual(12, l_normal) + self.assertNotEqual(None, l_normal) + self.assertEqual(type(np.int(l_normal)), type(int(1))) + if self.do_print: + print("LimitsOfAcceptability: " + str(l_normal)) + + l_binom = spotpy.likelihoods.LimitsOfAcceptability(self.binom_data, self.binom_comparedata) + self.assertEqual(5, l_binom) + self.assertNotEqual(None, l_binom) + self.assertEqual(type(np.int(l_binom)), type(int(1))) + if self.do_print: + print("LimitsOfAcceptability: " + str(l_binom)) + + def test_InverseErrorVarianceShapingFactor(self): + l_normal = spotpy.likelihoods.InverseErrorVarianceShapingFactor(self.normal_data, self.normal_comparedata) + self.assertGreaterEqual(-10, l_normal) + self.assertEqual(type(np.float(l_normal)), type(np.float(1))) + if self.do_print: + print("inverseErrorVarianceShapingFactor: " + str(l_normal)) + + l_binom = spotpy.likelihoods.InverseErrorVarianceShapingFactor(self.binom_data, self.binom_comparedata) + self.assertGreaterEqual(-10, l_binom) + self.assertEqual(type(np.float(l_binom)), type(np.float(1))) + if self.do_print: + print("inverseErrorVarianceShapingFactor: " + str(l_binom)) + + def test_ExponentialTransformErrVarShapingFactor(self): + l_binom = spotpy.likelihoods.ExponentialTransformErrVarShapingFactor(self.binom_data, self.binom_comparedata) + self.assertGreaterEqual(-30, l_binom) + self.assertEqual(type(np.float(l_binom)), type(np.float(1))) + if self.do_print: + print("inverseErrorVarianceShapingFactor: " + str(l_binom)) + + l_gauss = spotpy.likelihoods.ExponentialTransformErrVarShapingFactor(self.normal_data, self.normal_comparedata) + self.assertGreaterEqual(-30, l_gauss) + self.assertEqual(type(np.float(l_gauss)), type(np.float(1))) + if self.do_print: + print("inverseErrorVarianceShapingFactor: " + str(l_gauss)) + + def test_NashSutcliffeEfficiencyShapingFactor(self): + l_normal_list = [] + l_normal_list.append(spotpy.likelihoods.NashSutcliffeEfficiencyShapingFactor(self.normal_data, + self.normal_comparedata)) + + l_normal_list.append(spotpy.likelihoods.NashSutcliffeEfficiencyShapingFactor(self.normal_data, + self.normal_data)) + + l_normal_list.append(spotpy.likelihoods.NashSutcliffeEfficiencyShapingFactor(self.binom_data, + self.binom_comparedata)) + + try: + l_normal_list.append(spotpy.likelihoods.NashSutcliffeEfficiencyShapingFactor([], + [])) + except LikelihoodError as e: + print("Likelihood Error occurred: " + str(e)) + try: + l_normal_list.append(spotpy.likelihoods.NashSutcliffeEfficiencyShapingFactor([1], + [])) + except LikelihoodError as e: + print("Likelihood Error occurred " + str(e)) + + for l_normal in l_normal_list: + self.assertNotEqual(None, l_normal) + self.assertEqual(type(np.float(l_normal)), type(np.float(1))) + if self.do_print: + print("NashSutcliffeEfficiencyShapingFactor: " + str(l_normal)) + + def test_sumOfAbsoluteErrorResiduals(self): + l_normal = spotpy.likelihoods.sumOfAbsoluteErrorResiduals(self.normal_data, self.normal_comparedata) + self.assertGreaterEqual(7, np.abs(np.abs(l_normal) - 10)) + self.assertEqual(type(np.float(l_normal)), type(np.float(1))) + if self.do_print: + print("sumOfAbsoluteErrorResiduals: " + str(l_normal)) + + l_binom = spotpy.likelihoods.sumOfAbsoluteErrorResiduals(self.binom_data, self.binom_comparedata) + self.assertGreaterEqual(7, np.abs(np.abs(l_binom) - 10)) + self.assertEqual(type(np.float(l_binom)), type(np.float(1))) + if self.do_print: + print("sumOfAbsoluteErrorResiduals: " + str(l_binom)) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_list_and_constant_parameters_with_samplers.py b/tests/test_list_and_constant_parameters_with_samplers.py new file mode 100644 index 00000000..744c22d3 --- /dev/null +++ b/tests/test_list_and_constant_parameters_with_samplers.py @@ -0,0 +1,152 @@ +''' +Copyright (c) 2018 by Tobias Houska +This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). +:author: Tobias Houska +''' + +import unittest +import numpy as np +import inspect +try: + import spotpy +except ImportError: + import sys + sys.path.append(".") + import spotpy + +from spotpy import parameter +from spotpy.objectivefunctions import rmse +from itertools import cycle + + +class Rosenbrock(object): + """ + A 3 dimensional implementation of the Rosenbrock function + + Result at (1,1,1) is 0. + """ + + def simulation(self, vector): + vector = np.array(vector) + simulations = [sum(100.0 * (vector[1:] - vector[:-1] ** 2.0) ** 2.0 + (1 - vector[:-1]) ** 2.0)] + return simulations + + def evaluation(self): + observations = [0] + return observations + + def objectivefunction(self, simulation, evaluation): + objectivefunction = -rmse(evaluation=evaluation, simulation=simulation) + return objectivefunction + + +class RosenbrockWithConstant(Rosenbrock): + """ + A 3 dimensional implementation of the Rosenbrock function + + Result at (1,1,1) is 0. + """ + c = parameter.Constant(0, doc='Constant offset, should stay 0') + x = parameter.Uniform(-10, 10, 1.5, 3.0, -10, 10, doc='x value of Rosenbrock function') + y = parameter.Uniform(-10, 10, 1.5, 3.0, -10, 10, doc='y value of Rosenbrock function') + z = parameter.Uniform(-10, 10, 1.5, 3.0, -10, 10, doc='z value of Rosenbrock function') + +class RosenbrockWithList(Rosenbrock): + """ + A 3 dimensional implementation of the Rosenbrock function + + Result at (1,1,1) is 0. + """ + l = parameter.List(np.arange(0, 10), repeat=True, doc='list parameter for testing') + x = parameter.Uniform(-10, 10, 1.5, 3.0, -10, 10, doc='x value of Rosenbrock function') + y = parameter.Uniform(-10, 10, 1.5, 3.0, -10, 10, doc='y value of Rosenbrock function') + z = parameter.Uniform(-10, 10, 1.5, 3.0, -10, 10, doc='z value of Rosenbrock function') + + + +def get_all_samplers(): + def use(cl): + # Check if class name starts with an exclusion term + return (inspect.isclass(cl) and + not any([cl.__name__.startswith(ex) for ex in ('_', 'list')])) + + return inspect.getmembers(spotpy.algorithms, use) + + +class TestConstantSetups(unittest.TestCase): + def setUp(self): + self.rep = 1000 + self.setup_constant = RosenbrockWithConstant + self.setup_list = RosenbrockWithList + + def sampler_with_constant(self, sampler_class): + + sampler = sampler_class(self.setup_constant(), dbformat='ram', save_sim=False) + sampler.sample(self.rep) + + self.assertTrue(all(line[1] == 0 for line in sampler.datawriter.ram), + msg='Parameter c == 0 not true in all lines with sampler {}'.format(sampler)) + + def sampler_with_list(self, sampler_class, valid=False): + sampler = sampler_class(self.setup_list(), dbformat='ram', save_sim=False) + sampler.sample(self.rep) + iterator = cycle(np.arange(0,10)) + for i in range(self.rep): + self.assertEqual(sampler.datawriter.ram[i][1],next(iterator)) + + def test_abc_sampler(self): + self.sampler_with_constant(spotpy.algorithms.abc) + with self.assertRaises(TypeError): + self.sampler_with_list(spotpy.algorithms.abc) + + def test_demcz_sampler(self): + self.sampler_with_constant(spotpy.algorithms.demcz) + with self.assertRaises(TypeError): + self.sampler_with_list(spotpy.algorithms.demcz) + + def test_dream_sampler(self): + self.sampler_with_constant(spotpy.algorithms.dream) + with self.assertRaises(TypeError): + self.sampler_with_list(spotpy.algorithms.dream) + + def test_fscabc_sampler(self): + self.sampler_with_constant(spotpy.algorithms.fscabc) + with self.assertRaises(TypeError): + self.sampler_with_list(spotpy.algorithms.fscabc) + + def test_lhs_sampler(self): + self.sampler_with_constant(spotpy.algorithms.lhs) + with self.assertRaises(TypeError): + self.sampler_with_list(spotpy.algorithms.lhs) + + def test_mc_sampler(self): + self.sampler_with_constant(spotpy.algorithms.mc) + self.sampler_with_list(spotpy.algorithms.mc, valid = True) + + def test_mcmc_sampler(self): + self.sampler_with_constant(spotpy.algorithms.mcmc) + with self.assertRaises(TypeError): + self.sampler_with_list(spotpy.algorithms.mcmc) + + def test_mle_sampler(self): + self.sampler_with_constant(spotpy.algorithms.mle) + with self.assertRaises(TypeError): + self.sampler_with_list(spotpy.algorithms.mle) + + def test_rope_sampler(self): + self.sampler_with_constant(spotpy.algorithms.rope) + with self.assertRaises(TypeError): + self.sampler_with_list(spotpy.algorithms.rope) + + def test_sa_sampler(self): + self.sampler_with_constant(spotpy.algorithms.sa) + with self.assertRaises(TypeError): + self.sampler_with_list(spotpy.algorithms.sa) + + def test_sceua_sampler(self): + self.sampler_with_constant(spotpy.algorithms.sceua) + with self.assertRaises(TypeError): + self.sampler_with_list(spotpy.algorithms.sceua) + +if __name__ == '__main__': + unittest.main() \ No newline at end of file diff --git a/spotpy/unittests/test_objectivefunctions.py b/tests/test_objectivefunctions.py similarity index 88% rename from spotpy/unittests/test_objectivefunctions.py rename to tests/test_objectivefunctions.py index bef68339..ac8b29d9 100644 --- a/spotpy/unittests/test_objectivefunctions.py +++ b/tests/test_objectivefunctions.py @@ -1,3 +1,9 @@ +''' +Copyright (c) 2018 by Tobias Houska +This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). +:author: Tobias Houska, Benjamin Manns +''' + import unittest try: import spotpy @@ -37,6 +43,12 @@ def test_lognashsutcliffe(self): res = of.lognashsutcliffe(self.evaluation + 3, self.simulation + 3) self.assertAlmostEqual(res, -2.3300973555530344, self.tolerance) + def test_lognashsutcliffe_with_0_values(self): + evaluation, simulation = self.evaluation + 3, self.simulation + 3 + simulation[0] = 0 + res = of.lognashsutcliffe(evaluation, simulation, epsilon=0.00001) + self.assertAlmostEqual(res, -125.77518894078659, self.tolerance) + def test_lognashsutcliffe_for_invalid_obs_is_nan(self): res = of.lognashsutcliffe(self.evaluation, self.simulation) self.assertTrue(np.isnan(res)) @@ -170,6 +182,16 @@ def test_kge_return_all(self): for exp, actual in zip(expected, res): self.assertAlmostEqual(actual, exp, self.tolerance) + def test_kge_non_parametric(self): + res = of.kge_non_parametric(self.evaluation, self.simulation) + self.assertAlmostEqual(res, -0.84274521306792427, self.tolerance) + + def test_kge_non_parametric_return_all(self): + expected = (-0.8427452130679243, 0.030303030303030304, 0.970533493046538, -0.5666937901878675) + res = of.kge_non_parametric(self.evaluation, self.simulation, return_all=True) + for exp, actual in zip(expected, res): + self.assertAlmostEqual(actual, exp, self.tolerance) + def test_rsr(self): res = of.rsr(self.evaluation, self.simulation) self.assertAlmostEqual(res, 2.2619034190253462, self.tolerance) diff --git a/tests/test_padds.py b/tests/test_padds.py new file mode 100644 index 00000000..846b4d77 --- /dev/null +++ b/tests/test_padds.py @@ -0,0 +1,111 @@ +''' +Copyright (c) 2018 by Tobias Houska +This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). +:author: Tobias Houska, Benjamin Manns +''' + +import unittest +import sys +import numpy as np + +from spotpy.examples.tutorial_padds import padds_spot_setup +from tests.test_dds import FixedRandomizer + +try: + import spotpy +except ImportError: + sys.path.append(".") + import spotpy + +import os +from spotpy.examples.spot_setup_dds import spot_setup +import json +from spotpy.algorithms.padds import chc, HVC + +class TestPADDS(unittest.TestCase): + def setUp(self): + self.spot_setup = padds_spot_setup() + self.rep = 1000 + self.timeout = 1 # Given in Seconds + self.f_random = FixedRandomizer() + + def json_helper(self, run): + with open(os.path.dirname(__file__) + "/padds_tests/run_" + str(run) + ".json") as f: + data = json.load(f) + return data + + def test_run_1(self): + self.run_a_dds(1) + + def test_run_2(self): + self.run_a_dds(2) + + def test_run_3(self): + self.run_a_dds(3) + + def test_run_4(self): + self.run_a_dds(4) + + def test_run_5(self): + self.run_a_dds(5) + + def test_run_6(self): + self.run_a_dds(6) + + def test_run_7(self): + self.run_a_dds(7) + + def test_run_8(self): + self.run_a_dds(8) + + def test_run_9(self): + self.run_a_dds(9) + + def test_chc(self): + self.assertArrayEqual([0.01851852, 0. , 0.01851852, 0.01851852, 0.,0.01851852], + chc([[1,10], [2,9.8],[3,5] ,[4, 4], [8,2], [10,1]])) + with open(os.path.dirname(__file__) + "/padds_tests/CHC_testdata.json") as f: + data = json.load(f) + for i, test_data in enumerate(data): + self.assertArrayEqual(chc(np.array(test_data["p"])),test_data["res"],delta=1e-6) + + def test_hvc(self): + with open(os.path.dirname(__file__) + "/padds_tests/HVC_testdata.json") as f: + data = json.load(f) + hvc = HVC(fakerandom=True) + for i, test_data in enumerate(data): + self.assertArrayEqual(hvc(np.array(test_data["p"])),test_data["res"],delta=1e-6) + + def assertArrayEqual(self, a, b, delta=None): + for j, elem in enumerate(a): + try: + self.assertAlmostEqual(elem, b[j], delta=delta) + except IndexError: + self.assertRaises("Index out of bound for array b at index = "+str(j)) + + def run_a_dds(self, run): + original_result = self.json_helper(run) + + sampler = spotpy.algorithms.padds(self.spot_setup, parallel="seq", dbname='test_PADDS', dbformat="csv", + sim_timeout=self.timeout, r=original_result["r_val"]) + sampler._set_np_random(self.f_random) + + + if original_result.get("initial_objs") is not None: + # if a parameter initialisation is given, test this: + results = sampler.sample(original_result["evatrials"], + original_result["trial_runs"], initial_objs=np.array(original_result["initial_objs"]), initial_params=np.array(original_result["initial_params"]), metric=original_result["metric"]) + else: + results = sampler.sample(original_result["evatrials"], + original_result["trial_runs"],metric=original_result["metric"]) + + for t in range(original_result["trial_runs"]): + print(results[t]["objfunc_val"]) + print(original_result["results"][t]["objfunc_val"]) + + self.assertArrayEqual(original_result["results"][t]["objfunc_val"], results[t]["objfunc_val"], 1e-5) + self.assertArrayEqual(original_result["results"][t]["sbest"], results[t]["sbest"], 1e-5) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_parallel.py b/tests/test_parallel.py new file mode 100644 index 00000000..58868643 --- /dev/null +++ b/tests/test_parallel.py @@ -0,0 +1,45 @@ +# -*- coding: utf-8 -*- +''' +Copyright (c) 2018 by Tobias Houska +This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). +:author: Tobias Houska +''' + +import unittest +import spotpy +import numpy as np +from spotpy.examples.spot_setup_rosenbrock import spot_setup +from spotpy.describe import describe +import os + +class TestParallel(unittest.TestCase): + @classmethod + def setUpClass(self): + # How many digits to match in case of floating point answers + self.tolerance = 7 + #Create samplers for every algorithm: + self.rep = 21 + self.timeout = 10 #Given in Seconds + + self.dbformat = "ram" + + def test_seq(self): + sampler=spotpy.algorithms.mc(spot_setup(),parallel='seq', dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout) + sampler.sample(self.rep) + results = sampler.getdata() + self.assertEqual(len(results), self.rep) + + def test_mpc(self): + sampler=spotpy.algorithms.mc(spot_setup(),parallel='mpc', dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout) + sampler.sample(self.rep) + results = sampler.getdata() + self.assertEqual(len(results), self.rep) + + def test_umpc(self): + sampler=spotpy.algorithms.mc(spot_setup(),parallel='umpc', dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout) + sampler.sample(self.rep) + results = sampler.getdata() + self.assertEqual(len(results), self.rep) + +if __name__ == '__main__': + unittest.main(exit=False) diff --git a/spotpy/unittests/test_parameter.py b/tests/test_parameter.py similarity index 89% rename from spotpy/unittests/test_parameter.py rename to tests/test_parameter.py index d92a42af..a229c1e2 100644 --- a/spotpy/unittests/test_parameter.py +++ b/tests/test_parameter.py @@ -1,8 +1,14 @@ +''' +Copyright (c) 2018 by Tobias Houska +This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). +:author: Tobias Houska, Philipp Kraft +''' + import unittest +import sys try: import spotpy except ImportError: - import sys sys.path.append(".") import spotpy from spotpy import parameter @@ -10,8 +16,10 @@ # Import inspect to scan spotpy.parameter for all Parameter classes import inspect -from testutils import repeat - +if sys.version_info[0] >= 3: + from .testutils import repeat +else: + from testutils import repeat # https://docs.python.org/3/library/unittest.html @@ -65,11 +73,11 @@ def test_list_without_repeat_raises_index_error_on_invalid_extra_call(self): def test_astuple(self): _ = self.list_param() - v, name, step, optguess, minbound, maxbound = self.list_param.astuple() + v, name, step, optguess, minbound, maxbound, as_int = self.list_param.astuple() self.assertEqual(self.values[0], v) self.assertEqual("test", name) - # the values of step, optguess, minbound and maxbound don't matter + # the values of step, optguess, minbound, maxbound and as_int don't matter class TestUniformParameterDistribution(unittest.TestCase): @@ -86,6 +94,7 @@ def test_uniform_is_callable(self): def test_uniform_has_correct_bounds(self): unif = parameter.Uniform("test", low=0, high=1) + print(unif.minbound) self.assertGreaterEqual(unif.minbound, 0) self.assertLessEqual(unif.maxbound, 1) @@ -111,8 +120,23 @@ def test_constant_is_callable(self): def test_constant_gives_only_constants(self): nums = set([self.const() for _ in range(1000)]) - self.assertEqual(len(nums), 1) - self.assertEqual(nums.pop(), 10) + self.assertEqual(len(nums), 1, 'All results must be the same') + self.assertEqual(nums.pop(), 10, 'All results must be 10') + + def test_parameter_settings(self): + p = parameter.generate([self.const])[0] + self.assertEqual(p['random'], 10, 'Random value must always be 10') + self.assertEqual(p['optguess'], 10, 'Optguess value must always be 10') + self.assertEqual(p['maxbound'], 10, 'maxbound value must always be 10') + self.assertEqual(p['minbound'], 10, 'minbound value must always be 10') + self.assertEqual(p['step'], 0, 'step value must always be 0') + + def test_find_constant_parameters(self): + flex = parameter.Uniform('flexible', 0, 1) + p = parameter.generate([flex, self.const]) + constant_parameters = parameter.find_constant_parameters(p) + self.assertFalse(constant_parameters[0], 'Flexible parameter detected as constant') + self.assertTrue(constant_parameters[1], 'Constant parameter not detected') class TestNormalParameterDistribution(unittest.TestCase): @@ -336,6 +360,17 @@ def test_correct_with_extra_args(self): self.assertTrue(p_with_name.step == 1, 'Step overridden by class (name={})'.format(repr(p_with_name.name))) + def test_minbound_zero(self): + param = spotpy.parameter.Normal(name="parname", mean=0.6, stddev=0.2, optguess=0.6, minbound=0, maxbound=1) + self.assertEqual(param.minbound, 0) + + def test_maxbound_zero(self): + param = spotpy.parameter.Normal(name="parname", mean=-0.6, stddev=0.2, optguess=-0.6, minbound=-1, maxbound=0) + self.assertEqual(param.maxbound, 0) + + def test_optguess_zero(self): + param = spotpy.parameter.Normal(name="parname", mean=0.1, stddev=0.2, optguess=0.0, minbound=-1, maxbound=1) + self.assertEqual(param.optguess, 0) def make_args(pcls): """ diff --git a/tests/test_setup_parameters.py b/tests/test_setup_parameters.py new file mode 100644 index 00000000..0f89ce4f --- /dev/null +++ b/tests/test_setup_parameters.py @@ -0,0 +1,252 @@ +''' +Copyright (c) 2018 by Tobias Houska +This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). +:author: Tobias Houska, Philipp Kraft + +Tests the various possibilities to create and use parameters +Focus especially the usage of parameters as class attributes +''' +import sys +import unittest +try: + import spotpy +except ImportError: + sys.path.append(".") + import spotpy +from spotpy import parameter +import numpy as np +import inspect + + +class SpotSetupBase(object): + """ + The base for a number of test cases. + Each Test case should have for parameters a,b,c,d and + the sum of the parameters should be zero + """ + + def simulation(self, par): + return [par.a + par.b + par.c + par.d] + + def evaluation(self): + return [0] + + def objectivefunction(self, simulation, evaluation): + return np.abs(simulation[0] - evaluation[0]) + + @classmethod + def get_derived(cls): + """ + Returns a list of all derived classes in this module + """ + module = sys.modules[__name__] + def predicate(mcls): + return inspect.isclass(mcls) and issubclass(mcls, cls) and mcls is not cls + return [mcls for cname, mcls in inspect.getmembers(module, predicate)] + + def __repr__(self): + return '{}()'.format(type(self).__name__) + + + +class SpotSetupClassAttributes(SpotSetupBase): + """ + A Test case with two parameters as class parameters (a,b) + and 2 given from the parameter function + """ + a = parameter.Uniform(-1, 1) + b = parameter.Uniform(-1, 1) + c = parameter.Uniform(-1, 1) + d = parameter.Uniform(-1, 1) + + +class SpotSetupParameterFunction(SpotSetupBase): + """ + A Test case with 4 parameters given from the parameter function + """ + def parameters(self): + return parameter.generate([parameter.Uniform(name, -1, 1) for name in 'abcd']) + +class SpotSetupMixedParameterFunction(SpotSetupBase): + """ + A Test case with two parameters as class parameters (a,b) + and 2 given from the parameter function + """ + a = parameter.Uniform(0, 1) + b = parameter.Uniform(1, 2) + def parameters(self): + return parameter.generate([parameter.Uniform(name, -1, 1) for name in 'cd']) + + +class SpotSetupParameterList(SpotSetupBase): + """ + A Test case with 4 parameters given from the parameter list + """ + def __init__(self): + self.parameters = [parameter.Uniform(name, -1, 1) for name in 'abcd'] + + +class SpotSetupMixedParameterList(SpotSetupBase): + """ + A Test case with two parameters as class parameters (a,b) + and 2 given from the parameter function + """ + a = parameter.Uniform(0, 1) + b = parameter.Uniform(1, 2) + + def parameters(self): + return parameter.generate([parameter.Uniform(name, -1, 1) for name in 'cd']) + + +class TestParameterSet(unittest.TestCase): + def setUp(self): + model = SpotSetupParameterFunction() + param_info = model.parameters() + self.ps = parameter.ParameterSet(param_info) + + def test_create(self): + self.assertEqual(type(self.ps), parameter.ParameterSet) + + def test_assign(self): + values = [1] * len(self.ps) + self.ps(*values) + self.assertEqual(list(self.ps), values) + # Test if wrong number of parameters raises + with self.assertRaises(ValueError): + self.ps(*values[:-1]) + + def test_iter(self): + values = [1] * len(self.ps) + self.ps(*values) + ps_values = list(self.ps) + self.assertEqual(values, ps_values) + + def test_getitem(self): + values = [1] * len(self.ps) + self.ps(*values) + self.assertEqual(self.ps['a'], 1.0) + self.assertEqual(self.ps[0], 1.0) + + def test_getattr(self): + values = [1] * len(self.ps) + self.ps(*values) + + with self.assertRaises(AttributeError): + _ = self.ps.__x + + self.assertEqual(self.ps.a, 1.0) + self.assertEqual(list(self.ps.random), list(self.ps), 'Access to random variable does not equal list of names') + + with self.assertRaises(AttributeError): + _ = self.ps.x + + def test_setattr(self): + self.ps.a = 2 + self.assertEqual(self.ps[0], 2) + + def test_dir(self): + values = [1] * len(self.ps) + self.ps(*values) + + attrs = dir(self.ps) + for param in self.ps.name: + self.assertIn(param, attrs, 'Attribute {} not found in {}'.format(param, self.ps)) + for prop in ['maxbound', 'minbound', 'name', 'optguess', 'random', 'step']: + self.assertIn(prop, attrs, 'Property {} not found in {}'.format(prop, self.ps)) + + def test_str(self): + values = [1] * len(self.ps) + self.ps(*values) + self.assertEqual(str(self.ps), 'parameters(a=1, b=1, c=1, d=1)') + + def test_repr(self): + values = [1] * len(self.ps) + self.ps(*values) + self.assertEqual(repr(self.ps), 'spotpy.parameter.ParameterSet()') + + +class TestSetupVariants(unittest.TestCase): + def setUp(self): + # Get all Setups from this module + self.objects = [cls() for cls in SpotSetupBase.get_derived()] + + def test_exists(self): + self.assertGreater(len(self.objects), 0) + + def parameter_count_test(self, o): + params = parameter.create_set(o, valuetype='optguess') + param_names = ','.join(pn for pn in params.name) + self.assertEqual(len(params), 4, '{} should have 4 parameters, but found only {} ({})' + .format(o, len(params), param_names)) + self.assertEqual(param_names, 'a,b,c,d', '{} Parameter names should be "a,b,c,d" but got "{}"' + .format(o, param_names)) + + def make_sampler(self, o, algo=spotpy.algorithms.mc): + sampler = algo(spot_setup=o, dbformat='ram') + sampler.sample(100) + + def test_parameter_class(self): + self.parameter_count_test(SpotSetupClassAttributes()) + + def test_parameter_function(self): + self.parameter_count_test(SpotSetupParameterFunction()) + + def test_parameter_list(self): + self.parameter_count_test(SpotSetupParameterList()) + + def test_parameter_mixed_list(self): + self.parameter_count_test(SpotSetupMixedParameterList()) + + def test_parameter_mixed_function(self): + self.parameter_count_test(SpotSetupMixedParameterFunction()) + + def test_sampler(self): + for o in self.objects: + self.make_sampler(o) + + def test_abc_sampler(self): + for o in self.objects: + self.make_sampler(o, spotpy.algorithms.abc) + + def test_demcz_sampler(self): + for o in self.objects: + self.make_sampler(o, spotpy.algorithms.demcz) + + def test_dream_sampler(self): + for o in self.objects: + self.make_sampler(o, spotpy.algorithms.dream) + + def test_fscabc_sampler(self): + for o in self.objects: self.make_sampler(o, spotpy.algorithms.fscabc) + + def test_lhs_sampler(self): + for o in self.objects: + self.make_sampler(o, spotpy.algorithms.lhs) + + def test_mc_sampler(self): + for o in self.objects: + self.make_sampler(o, spotpy.algorithms.mc) + + def test_mcmc_sampler(self): + for o in self.objects: + self.make_sampler(o, spotpy.algorithms.mcmc) + + def test_mle_sampler(self): + for o in self.objects: self.make_sampler(o, spotpy.algorithms.mle) + + def test_rope_sampler(self): + for o in self.objects: + self.make_sampler(o, spotpy.algorithms.rope) + + def test_sa_sampler(self): + for o in self.objects: + self.make_sampler(o, spotpy.algorithms.sa) + + def test_sceua_sampler(self): + for o in self.objects: + self.make_sampler(o, spotpy.algorithms.sceua) + + +if __name__ == '_main__': + unittest.main(verbosity=3) + diff --git a/tests/test_signatures.py b/tests/test_signatures.py new file mode 100644 index 00000000..802c2638 --- /dev/null +++ b/tests/test_signatures.py @@ -0,0 +1,45 @@ +''' +Copyright (c) 2018 by Tobias Houska +This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). +:author: Tobias Houska, Philipp Kraft +''' + +import unittest +import numpy as np +try: + import spotpy +except ImportError: + import sys + sys.path.append(".") + import spotpy + +from spotpy.hydrology.signatures import SignatureMethod +import spotpy.hydrology as sig + +class TestSignatures(unittest.TestCase): + + def setUp(self): + + np.random.seed(0) + rain = np.random.normal(-1, 5, size=3650) + rain[rain < 0] = 0.0 + runoff = np.zeros(rain.shape, np.float) + stor = 0.0 + for i, prec in enumerate(rain): + runoff[i] = 0.1 * stor + stor += prec - runoff[i] + + self.runoff = runoff + self.rain = rain + + def test_signatures(self): + sbm_list = SignatureMethod.find_all() + + sig_result = SignatureMethod.run(sbm_list, self.runoff, 1) + + for name, value in sig_result: + self.assertNotEqual(value, np.nan, '{} returned no value'.format(name)) + +if __name__ == '__main__': + + unittest.main(verbosity=3) diff --git a/spotpy/unittests/testutils.py b/tests/testutils.py similarity index 71% rename from spotpy/unittests/testutils.py rename to tests/testutils.py index d123ab50..2cfd67ef 100644 --- a/spotpy/unittests/testutils.py +++ b/tests/testutils.py @@ -1,4 +1,10 @@ -"""Utility functions for unit test execution""" +''' +Copyright (c) 2018 by Tobias Houska +This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY). +:author: Tobias Houska, Benjamin Manns + +Utility functions for unit test execution +''' def repeat(times): """Repeats a single test the given number of times