From 5c7f59361ce801dfc38c641b0811d45d9b9c0c84 Mon Sep 17 00:00:00 2001 From: Allan Karlson Date: Mon, 18 Nov 2019 10:15:25 +0100 Subject: [PATCH 01/28] fix parameter running out of bounds causes by wrong index --- spotpy/algorithms/dds.py | 8 ++++---- spotpy/algorithms/padds.py | 9 ++++----- spotpy/examples/spot_setup_dds.py | 15 +++++++++++++++ spotpy/examples/tutorial_padds.py | 24 +++++++++++++++++++++--- tests/test_dds.py | 18 ++++++++++++++++++ tests/test_padds.py | 16 ++++++++++++++++ 6 files changed, 78 insertions(+), 12 deletions(-) diff --git a/spotpy/algorithms/dds.py b/spotpy/algorithms/dds.py index 11c488cc..1cd368b5 100644 --- a/spotpy/algorithms/dds.py +++ b/spotpy/algorithms/dds.py @@ -396,9 +396,9 @@ def calculate_next_s_test(self, previous_x_curr, rep, rep_limit, r): 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 + dec_var = np.int(np.ceil(amount_params * self.np_random.rand())) - 1 + new_value = self.dds_generator.neigh_value_mixed(previous_x_curr, r, dec_var, self.min_bound[dec_var], + self.max_bound[dec_var]) + new_x_curr[dec_var] = new_value # change relevant decision variable value in s_test return new_x_curr diff --git a/spotpy/algorithms/padds.py b/spotpy/algorithms/padds.py index a53c364a..0fdef401 100644 --- a/spotpy/algorithms/padds.py +++ b/spotpy/algorithms/padds.py @@ -305,11 +305,10 @@ def calculate_next_s_test(self, previous_x_curr, rep, rep_limit, r): 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 - + dec_var = np.int(np.ceil(amount_params * self.np_random.rand())) - 1 + new_value = self.dds_generator.neigh_value_mixed(previous_x_curr, r, dec_var, self.min_bound[dec_var], + self.max_bound[dec_var]) + new_x_curr[dec_var] = new_value # change relevant decision variable value in s_test return new_x_curr diff --git a/spotpy/examples/spot_setup_dds.py b/spotpy/examples/spot_setup_dds.py index 67e93585..e39e85dd 100644 --- a/spotpy/examples/spot_setup_dds.py +++ b/spotpy/examples/spot_setup_dds.py @@ -53,6 +53,21 @@ def _objfunc_switcher(self, name): 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)] + elif name == "cmf_style": + self.objfunc = ackley10 + self.params = [Uniform(.5, 5., optguess=1.5, doc='saturated depth at beginning'), + Uniform(.001, .8, optguess=.1, doc='porosity of matrix [m3 Pores / m3 Soil]'), + Uniform(1., 240., optguess=10., + doc='ssaturated conductivity of macropores [m/day]'), + Uniform(.0001, .5, optguess=.05, doc='macropore fraction [m3/m3]'), + Uniform(.005, 1., optguess=.05, + doc='mean distance between the macropores [m]'), + Uniform(0., 1., optguess=0., + doc='water content when matric potential pointing towards -infinity'), + Uniform(.5, 1., optguess=.99, + doc='wetness above which the parabolic extrapolation is used instead of VGM'), + Uniform(0., 50, optguess=.1, + doc='exchange rate [1/day] for macropore-matrix-exchange')] def parameters(self): if self.params is None: diff --git a/spotpy/examples/tutorial_padds.py b/spotpy/examples/tutorial_padds.py index 7ebd5f3a..789dbb93 100644 --- a/spotpy/examples/tutorial_padds.py +++ b/spotpy/examples/tutorial_padds.py @@ -1,6 +1,7 @@ # -*- coding: utf-8 -*- import numpy as np import sys +from spotpy.parameter import Uniform try: import spotpy except ModuleNotFoundError: @@ -48,10 +49,27 @@ def ZDT1(x): class padds_spot_setup(object): - def __init__(self): + def __init__(self, default=True): 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))) + if default: + 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))) + else: + self.params = [Uniform(.5, 5., optguess=1.5, doc='saturated depth at beginning'), + Uniform(.001, .8, optguess=.1, doc='porosity of matrix [m3 Pores / m3 Soil]'), + Uniform(1., 240., optguess=10., + doc='ssaturated conductivity of macropores [m/day]'), + Uniform(.0001, .5, optguess=.05, doc='macropore fraction [m3/m3]'), + Uniform(.005, 1., optguess=.05, + doc='mean distance between the macropores [m]'), + Uniform(0., 1., optguess=0., + doc='water content when matric potential pointing towards -infinity'), + Uniform(.5, 1., optguess=.99, + doc='wetness above which the parabolic extrapolation is used instead of VGM'), + Uniform(0., 50, optguess=.1, + doc='exchange rate [1/day] for macropore-matrix-exchange')] + for i in range(8,30): + self.params.append(Uniform(str(i+1), 0, 1, 0, 0, 0, 1,doc="param no " + str(i+1))) def parameters(self): diff --git a/tests/test_dds.py b/tests/test_dds.py index 9f51027b..da6b0df5 100644 --- a/tests/test_dds.py +++ b/tests/test_dds.py @@ -126,6 +126,24 @@ def test_run_own_initial_1(self): def test_run_own_initial_2(self): self.run_a_dds("own_input_2") + def outside_bound(self, x_curr, min_bound, max_bound): + out_left = min_bound > x_curr # [x x_curr # [x Date: Mon, 22 Jun 2020 09:39:31 +0200 Subject: [PATCH 02/28] SCEUA: added maximal number of loops to be exectued during a sampling call --- spotpy/algorithms/sceua.py | 61 ++++++++++++++++++++++++++++---------- 1 file changed, 46 insertions(+), 15 deletions(-) diff --git a/spotpy/algorithms/sceua.py b/spotpy/algorithms/sceua.py index bdcc5f9a..e212ab22 100644 --- a/spotpy/algorithms/sceua.py +++ b/spotpy/algorithms/sceua.py @@ -5,10 +5,10 @@ :author: Tobias Houska and Stijn Van Hoey ''' -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals + + + + from . import _algorithm import numpy as np @@ -63,6 +63,8 @@ def __init__(self, *args, **kwargs): kwargs['optimization_direction'] = 'minimize' kwargs['algorithm_name'] = 'Shuffled Complex Evolution (SCE-UA) algorithm' super(sceua, self).__init__(*args, **kwargs) + + print("LUKAS version anchor") def simulate(self, id_params_tuple): @@ -128,7 +130,7 @@ def simulate(self, id_params_tuple): # Replace the complex back into the population; 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): + def sample(self, repetitions, ngs=20, kstop=100, pcento=0.0000001, peps=0.0000001, max_loop_inc=None): """ Samples from parameter distributions using SCE-UA (Duan, 2004), converted to python by Van Hoey (2011), restructured and parallelized by Houska et al (2015). @@ -146,6 +148,8 @@ def sample(self, repetitions, ngs=20, kstop=100, pcento=0.0000001, peps=0.000000 the percentage change allowed in the past kstop loops below which convergence is assumed to be achieved. peps: float Value of the normalized geometric range of the parameters in the population below which convergence is deemed achieved. + max_loop_inc: int + Number of loops executed at max in this function call """ self.set_repetiton(repetitions) # Initialize SCE parameters: @@ -163,6 +167,9 @@ def sample(self, repetitions, ngs=20, kstop=100, pcento=0.0000001, peps=0.000000 bound = self.bu - self.bl # np.array self.stochastic_parameters = bound != 0 proceed = True + + # burnin_only, needed to indictat if only the burnin phase should be run + burnin_only = False if self.breakpoint == 'read' or self.breakpoint == 'readandwrite': data_frombreak = self.read_breakdata(self.dbname) icall = data_frombreak[0] @@ -177,7 +184,7 @@ def sample(self, repetitions, ngs=20, kstop=100, pcento=0.0000001, peps=0.000000 icall = 0 xf = np.zeros(npt) - print ('Starting burn-in sampling...') + print('Starting burn-in sampling...') # Burn in param_generator = ((rep, x[rep]) for rep in range(int(npt))) @@ -194,6 +201,17 @@ def sample(self, repetitions, ngs=20, kstop=100, pcento=0.0000001, peps=0.000000 idx = np.argsort(xf) xf = np.sort(xf) x = x[idx, :] + + print('Burn-in sampling completed...') + + # write brakpoint if only a single generation shall be computed and + # the main loop will not be executed + if (self.breakpoint == 'write' or self.breakpoint == 'readandwrite') \ + and max_loop_inc == 1: + work = (self.status.rep, (x, xf), gnrng) + self.write_breakdata(self.dbname, work) + burnin_only = True + else: raise ValueError("Don't know the breakpoint keyword {}".format(self.breakpoint)) @@ -221,16 +239,23 @@ def sample(self, repetitions, ngs=20, kstop=100, pcento=0.0000001, peps=0.000000 print( 'THE POPULATION HAS CONVERGED TO A PRESPECIFIED SMALL PARAMETER SPACE') - print ('Burn-in sampling completed...') + + # Begin evolution loops: nloop = 0 criter = [] criter_change_pcent = 1e+5 - - self.repeat.setphase('ComplexEvo') - print ('Starting Complex Evolution...') proceed = True + + # if only burnin, stop the following while loop to be started + if burnin_only: + proceed = False + print('ONLY THE BURNIN PHASE WAS COMPUTED') + else: + self.repeat.setphase('ComplexEvo') + print('Starting Complex Evolution...') + while icall < repetitions and gnrng > peps and criter_change_pcent > pcento and proceed == True: nloop += 1 print ('ComplexEvo loop #%d in progress...' % nloop) @@ -260,10 +285,10 @@ def sample(self, repetitions, ngs=20, kstop=100, pcento=0.0000001, peps=0.000000 self.discarded_runs+=1 print('Skipping saving') - if self.breakpoint == 'write' or self.breakpoint == 'readandwrite'\ - and self.status.rep >= self.backup_every_rep: - work = (self.status.rep, (x, xf), gnrng) - self.write_breakdata(self.dbname, work) + if self.breakpoint == 'write' or self.breakpoint == 'readandwrite'\ + and self.status.rep >= self.backup_every_rep: + work = (self.status.rep, (x, xf), gnrng) + self.write_breakdata(self.dbname, work) # End of Loop on Complex Evolution; @@ -315,7 +340,13 @@ def sample(self, repetitions, ngs=20, kstop=100, pcento=0.0000001, peps=0.000000 'CONVERGENCY HAS ACHIEVED BASED ON OBJECTIVE FUNCTION CRITERIA!!!') elif self.status.stop: proceed = False - break + break + + # stop, if max number of loop iteration was reached + elif max_loop_inc and nloop >= max_loop_inc: + proceed = False + print('THE MAXIMAL NUMBER OF LOOPS PER EXECUTION WAS REACHED') + break # End of the Outer Loops print('SEARCH WAS STOPPED AT TRIAL NUMBER: %d' % self.status.rep) From e89fbb7c5f220dbaae04f7bfde403878ef96affd Mon Sep 17 00:00:00 2001 From: lu-kas <> Date: Mon, 22 Jun 2020 11:00:00 +0200 Subject: [PATCH 03/28] moved write breakpoint --- spotpy/algorithms/sceua.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/spotpy/algorithms/sceua.py b/spotpy/algorithms/sceua.py index e212ab22..601fc594 100644 --- a/spotpy/algorithms/sceua.py +++ b/spotpy/algorithms/sceua.py @@ -170,6 +170,7 @@ def sample(self, repetitions, ngs=20, kstop=100, pcento=0.0000001, peps=0.000000 # burnin_only, needed to indictat if only the burnin phase should be run burnin_only = False + if self.breakpoint == 'read' or self.breakpoint == 'readandwrite': data_frombreak = self.read_breakdata(self.dbname) icall = data_frombreak[0] @@ -202,16 +203,11 @@ def sample(self, repetitions, ngs=20, kstop=100, pcento=0.0000001, peps=0.000000 xf = np.sort(xf) x = x[idx, :] - print('Burn-in sampling completed...') - - # write brakpoint if only a single generation shall be computed and - # the main loop will not be executed - if (self.breakpoint == 'write' or self.breakpoint == 'readandwrite') \ - and max_loop_inc == 1: - work = (self.status.rep, (x, xf), gnrng) - self.write_breakdata(self.dbname, work) + if max_loop_inc == 1: burnin_only = True + print('Burn-in sampling completed...') + else: raise ValueError("Don't know the breakpoint keyword {}".format(self.breakpoint)) @@ -249,9 +245,15 @@ def sample(self, repetitions, ngs=20, kstop=100, pcento=0.0000001, peps=0.000000 proceed = True # if only burnin, stop the following while loop to be started + # write brakpoint if only a single generation shall be computed and + # the main loop will not be executed if burnin_only: + if self.breakpoint == 'write' or self.breakpoint == 'readandwrite': + work = (self.status.rep, (x, xf), gnrng) + self.write_breakdata(self.dbname, work) proceed = False print('ONLY THE BURNIN PHASE WAS COMPUTED') + else: self.repeat.setphase('ComplexEvo') print('Starting Complex Evolution...') From 9676bfc06d0929a5c5828c0ccac630d4a487936d Mon Sep 17 00:00:00 2001 From: lu-kas <> Date: Fri, 4 Sep 2020 10:39:54 +0200 Subject: [PATCH 04/28] cleanup for PR --- spotpy/algorithms/sceua.py | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/spotpy/algorithms/sceua.py b/spotpy/algorithms/sceua.py index 601fc594..7d901f6b 100644 --- a/spotpy/algorithms/sceua.py +++ b/spotpy/algorithms/sceua.py @@ -5,10 +5,10 @@ :author: Tobias Houska and Stijn Van Hoey ''' - - - - +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals from . import _algorithm import numpy as np @@ -64,8 +64,6 @@ def __init__(self, *args, **kwargs): kwargs['algorithm_name'] = 'Shuffled Complex Evolution (SCE-UA) algorithm' super(sceua, self).__init__(*args, **kwargs) - print("LUKAS version anchor") - def simulate(self, id_params_tuple): """This overwrites the simple wrapper function of _algorithms.py @@ -253,7 +251,7 @@ def sample(self, repetitions, ngs=20, kstop=100, pcento=0.0000001, peps=0.000000 self.write_breakdata(self.dbname, work) proceed = False print('ONLY THE BURNIN PHASE WAS COMPUTED') - + else: self.repeat.setphase('ComplexEvo') print('Starting Complex Evolution...') @@ -446,4 +444,4 @@ def _sampleinputmatrix(self, nrows, npars): x = np.zeros((nrows, npars)) for i in range(nrows): x[i, :] = self.parameter()['random'] - return x \ No newline at end of file + return x From 4418bc215a60af2af4e09b12d40bec009797b4e4 Mon Sep 17 00:00:00 2001 From: lu-kas <> Date: Fri, 4 Sep 2020 10:41:48 +0200 Subject: [PATCH 05/28] removed whitespaces --- spotpy/algorithms/sceua.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/spotpy/algorithms/sceua.py b/spotpy/algorithms/sceua.py index 7d901f6b..86679eb1 100644 --- a/spotpy/algorithms/sceua.py +++ b/spotpy/algorithms/sceua.py @@ -5,9 +5,9 @@ :author: Tobias Houska and Stijn Van Hoey ''' -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function from __future__ import unicode_literals from . import _algorithm import numpy as np @@ -64,7 +64,6 @@ def __init__(self, *args, **kwargs): kwargs['algorithm_name'] = 'Shuffled Complex Evolution (SCE-UA) algorithm' super(sceua, self).__init__(*args, **kwargs) - def simulate(self, id_params_tuple): """This overwrites the simple wrapper function of _algorithms.py and makes a two phase mpi parallelization possbile: From bc7a24ad13ca43472790433d7a03525b0008dc3f Mon Sep 17 00:00:00 2001 From: thouska Date: Fri, 4 Sep 2020 14:09:01 +0200 Subject: [PATCH 06/28] Updated version number Slight performance improvement for sceua example --- setup.py | 2 +- spotpy/__init__.py | 2 +- spotpy/examples/tutorial_sceua_hymod.py | 6 +++--- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/setup.py b/setup.py index 8b786c28..728f6be6 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ setup( name = 'spotpy', - version = '1.5.10', + version = '1.5.11', description = 'A Statistical Parameter Optimization Tool', long_description=open(os.path.join(os.path.dirname(__file__), "README.rst")).read(), diff --git a/spotpy/__init__.py b/spotpy/__init__.py index 89b1022b..322f1456 100644 --- a/spotpy/__init__.py +++ b/spotpy/__init__.py @@ -40,4 +40,4 @@ from . import describe # Contains some helper functions to describe samplers and set-ups from .hydrology import signatures # Quantifies goodness of fit between simulation and evaluation data with hydrological signatures -__version__ = '1.5.10' \ No newline at end of file +__version__ = '1.5.11' \ No newline at end of file diff --git a/spotpy/examples/tutorial_sceua_hymod.py b/spotpy/examples/tutorial_sceua_hymod.py index 25c7129c..3fd3626f 100644 --- a/spotpy/examples/tutorial_sceua_hymod.py +++ b/spotpy/examples/tutorial_sceua_hymod.py @@ -21,10 +21,10 @@ # 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) + spot_setup=spot_setup(spotpy.objectivefunctions.rmse) #Select number of maximum allowed repetitions - rep=500 + rep=1000 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 @@ -32,7 +32,7 @@ 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) + sampler.sample(rep, ngs=7, kstop=3, 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') From f1bc4fd7b4b48dea239892a2f07462bdce81e7c1 Mon Sep 17 00:00:00 2001 From: thouska Date: Mon, 7 Sep 2020 13:05:09 +0200 Subject: [PATCH 07/28] Test upload on testpypi --- .travis.yml | 17 ++++++++++++++--- setup.py | 2 ++ 2 files changed, 16 insertions(+), 3 deletions(-) diff --git a/.travis.yml b/.travis.yml index 2144899a..66560d89 100644 --- a/.travis.yml +++ b/.travis.yml @@ -31,8 +31,19 @@ install: script: - - py.test tests/test_* --cov spotpy --cov-report term-missing -v + - py.test tests/test_* --cov spotpy --cov-report term-missing -v # - mpirun -c 4 python spotpy/examples/tutorial_parallel_computing_hymod.py 100 - pip uninstall spotpy -y -after_success: - - coveralls + +jobs: + include: + - stage: Coveralls + name: Report Coveralls + after_success: python3 -m coveralls + + # Test Deploy source distribution + - stage: deploy + name: PyPi Test Deploy + script: python3 setup.py sdist --formats=gztar bdist_wheel + after_success: + - python3 -m twine upload --verbose --skip-existing --repository-url https://test.pypi.org/legacy/ dist/* \ No newline at end of file diff --git a/setup.py b/setup.py index 728f6be6..b2fe2a8b 100644 --- a/setup.py +++ b/setup.py @@ -13,6 +13,8 @@ author_email = 'tobias.houska@umwelt.uni-giessen.de', url = 'https://spotpy.readthedocs.io/en/latest/', license = 'MIT', + install_requires=[ + 'scipy', 'numpy', 'random', 'time', 'math', 'copy', 'threading', 'logging'], 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, DDS, PA-DDS, Uncertainty, Calibration, Model, Signatures', From 2ddcc72854ac79fc98d200511c9a90e0df314880 Mon Sep 17 00:00:00 2001 From: thouska Date: Mon, 7 Sep 2020 13:15:18 +0200 Subject: [PATCH 08/28] Removed unecessarry module from pre-install --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index b2fe2a8b..a751b762 100644 --- a/setup.py +++ b/setup.py @@ -14,7 +14,7 @@ url = 'https://spotpy.readthedocs.io/en/latest/', license = 'MIT', install_requires=[ - 'scipy', 'numpy', 'random', 'time', 'math', 'copy', 'threading', 'logging'], + 'scipy', 'numpy', 'random', 'time', 'math', 'copy', 'logging'], 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, DDS, PA-DDS, Uncertainty, Calibration, Model, Signatures', From a02e532e7657d82484231d3058b927d7e2546e55 Mon Sep 17 00:00:00 2001 From: thouska Date: Mon, 7 Sep 2020 13:21:41 +0200 Subject: [PATCH 09/28] Further cleanding of pre-install requirements --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index a751b762..1c3a7b17 100644 --- a/setup.py +++ b/setup.py @@ -14,7 +14,7 @@ url = 'https://spotpy.readthedocs.io/en/latest/', license = 'MIT', install_requires=[ - 'scipy', 'numpy', 'random', 'time', 'math', 'copy', 'logging'], + 'scipy', 'numpy', 'logging'], 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, DDS, PA-DDS, Uncertainty, Calibration, Model, Signatures', From be6d393cf8d02e034cb96bea2ca1d4eee5bb7f22 Mon Sep 17 00:00:00 2001 From: Christian Krause Date: Mon, 7 Sep 2020 14:16:29 +0200 Subject: [PATCH 10/28] avoids spawning of timeout thread if no timeout set fixes #253 --- spotpy/algorithms/_algorithm.py | 49 ++++++++++++++++++--------------- 1 file changed, 27 insertions(+), 22 deletions(-) diff --git a/spotpy/algorithms/_algorithm.py b/spotpy/algorithms/_algorithm.py index b41705c7..1a2fc94c 100644 --- a/spotpy/algorithms/_algorithm.py +++ b/spotpy/algorithms/_algorithm.py @@ -432,26 +432,31 @@ def simulate(self, 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,all_params): - # Call self.model with a namedtuple instead of another sequence - 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, all_params)) - sim_thread.daemon = True - sim_thread.start() - - - # If self.sim_timeout is not None the self.model will break after self.sim_timeout seconds otherwise is runs as - # long it needs to run - 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]' and will not be saved. Otherwise get the result from the thread - model_result = None - if not que.empty(): - model_result = que.get() + if self.sim_timeout: + # we need a layer to fetch returned data from a threaded process into a queue. + def model_layer(q,all_params): + # Call self.model with a namedtuple instead of another sequence + 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, all_params)) + sim_thread.daemon = True + sim_thread.start() + + # If self.sim_timeout is not None the self.model will break after self.sim_timeout seconds otherwise is runs as + # long it needs to run + 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]' and will not be saved. Otherwise get the result from the thread + model_result = None + if not que.empty(): + model_result = que.get() + + else: + model_result = self.setup.simulation(self.partype(*all_params)) + return id, params, model_result From e76384bdcfdd1ccb7e58f4fbf89772fa2f45b1b2 Mon Sep 17 00:00:00 2001 From: thouska Date: Mon, 7 Sep 2020 14:40:04 +0200 Subject: [PATCH 11/28] Enable taged deployment with travis --- .travis.yml | 24 ++++++++++++++++++------ setup.py | 5 +++++ 2 files changed, 23 insertions(+), 6 deletions(-) diff --git a/.travis.yml b/.travis.yml index 66560d89..aa79a230 100644 --- a/.travis.yml +++ b/.travis.yml @@ -5,6 +5,9 @@ python: - "3.7" # - "3.8" #Errors on Travis, build times out because no output was received... +# Uses cache to build faster +cache: pip + # this ubuntu distribution has sure the libopenmpi-dev packages available dist: bionic @@ -41,9 +44,18 @@ jobs: name: Report Coveralls after_success: python3 -m coveralls - # Test Deploy source distribution - - stage: deploy - name: PyPi Test Deploy - script: python3 setup.py sdist --formats=gztar bdist_wheel - after_success: - - python3 -m twine upload --verbose --skip-existing --repository-url https://test.pypi.org/legacy/ dist/* \ No newline at end of file +# Publish spotpy on PyPi if taged +deploy: + # If you need to deploy files Travis has built, use the next line + skip_cleanup: true + provider: pypi + distributions: sdist bdist_wheel gztar + on: + # In this case I want to deploy only when a tag is present... + tags: true + # ... and when tag is on master and respects the form "v0.0.0" + branch: + - master + - /v?(\d+\.)?(\d+\.)?(\*|\d+)$/ + user: thouska + password : ${deploying} \ No newline at end of file diff --git a/setup.py b/setup.py index 1c3a7b17..ef554167 100644 --- a/setup.py +++ b/setup.py @@ -3,10 +3,15 @@ from setuptools import setup, find_packages import os +# Type of python distribution +[bdist_wheel] +universal=0 + setup( name = 'spotpy', version = '1.5.11', description = 'A Statistical Parameter Optimization Tool', + description-file = README.rst long_description=open(os.path.join(os.path.dirname(__file__), "README.rst")).read(), author = 'Tobias Houska, Philipp Kraft, Alejandro Chamorro-Chavez and Lutz Breuer', From c644da407f3a90f63c24f82d511db9c63da7bef9 Mon Sep 17 00:00:00 2001 From: thouska Date: Mon, 7 Sep 2020 14:45:19 +0200 Subject: [PATCH 12/28] Fix string --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index ef554167..731312fa 100644 --- a/setup.py +++ b/setup.py @@ -11,7 +11,7 @@ name = 'spotpy', version = '1.5.11', description = 'A Statistical Parameter Optimization Tool', - description-file = README.rst + description-file = 'README.rst', long_description=open(os.path.join(os.path.dirname(__file__), "README.rst")).read(), author = 'Tobias Houska, Philipp Kraft, Alejandro Chamorro-Chavez and Lutz Breuer', From 260c3ca66c5df3bbfb4c2001008b17602f76dfad Mon Sep 17 00:00:00 2001 From: thouska Date: Mon, 7 Sep 2020 14:48:32 +0200 Subject: [PATCH 13/28] Skipping description-file --- setup.py | 1 - 1 file changed, 1 deletion(-) diff --git a/setup.py b/setup.py index 731312fa..0396ad68 100644 --- a/setup.py +++ b/setup.py @@ -11,7 +11,6 @@ name = 'spotpy', version = '1.5.11', description = 'A Statistical Parameter Optimization Tool', - description-file = 'README.rst', long_description=open(os.path.join(os.path.dirname(__file__), "README.rst")).read(), author = 'Tobias Houska, Philipp Kraft, Alejandro Chamorro-Chavez and Lutz Breuer', From acf63adc560a693145856bc800f1d4afb79a2dcd Mon Sep 17 00:00:00 2001 From: thouska Date: Mon, 7 Sep 2020 14:51:45 +0200 Subject: [PATCH 14/28] Remove specified pypi build --- setup.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/setup.py b/setup.py index 0396ad68..1c3a7b17 100644 --- a/setup.py +++ b/setup.py @@ -3,10 +3,6 @@ from setuptools import setup, find_packages import os -# Type of python distribution -[bdist_wheel] -universal=0 - setup( name = 'spotpy', version = '1.5.11', From 719a32ed7382470d9f4622505d8cfdc65e82f534 Mon Sep 17 00:00:00 2001 From: thouska Date: Mon, 7 Sep 2020 15:51:27 +0200 Subject: [PATCH 15/28] Try to fix #256 --- .travis.yml | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/.travis.yml b/.travis.yml index aa79a230..6a2d50e6 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,7 +1,6 @@ language: python python: #- "2.7" - #- "3.5" - "3.7" # - "3.8" #Errors on Travis, build times out because no output was received... @@ -46,10 +45,8 @@ jobs: # Publish spotpy on PyPi if taged deploy: - # If you need to deploy files Travis has built, use the next line - skip_cleanup: true provider: pypi - distributions: sdist bdist_wheel gztar + distributions: "sdist bdist_wheel gztar" on: # In this case I want to deploy only when a tag is present... tags: true @@ -58,4 +55,5 @@ deploy: - master - /v?(\d+\.)?(\d+\.)?(\*|\d+)$/ user: thouska - password : ${deploying} \ No newline at end of file + password : ${deploying} + skip_existing: true \ No newline at end of file From 294c89894bc0c51c5b7dd35a442c16b4a2c3d265 Mon Sep 17 00:00:00 2001 From: thouska Date: Mon, 7 Sep 2020 15:59:36 +0200 Subject: [PATCH 16/28] Upate version number --- setup.py | 2 +- spotpy/__init__.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 1c3a7b17..6f5235f2 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ setup( name = 'spotpy', - version = '1.5.11', + version = '1.5.12', description = 'A Statistical Parameter Optimization Tool', long_description=open(os.path.join(os.path.dirname(__file__), "README.rst")).read(), diff --git a/spotpy/__init__.py b/spotpy/__init__.py index 322f1456..8cddb744 100644 --- a/spotpy/__init__.py +++ b/spotpy/__init__.py @@ -40,4 +40,4 @@ from . import describe # Contains some helper functions to describe samplers and set-ups from .hydrology import signatures # Quantifies goodness of fit between simulation and evaluation data with hydrological signatures -__version__ = '1.5.11' \ No newline at end of file +__version__ = '1.5.12' \ No newline at end of file From 8b0e9fed43ece0d520397e65e2488a5a65453171 Mon Sep 17 00:00:00 2001 From: thouska Date: Mon, 7 Sep 2020 16:17:13 +0200 Subject: [PATCH 17/28] Remove gztar from pypi upload --- .travis.yml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/.travis.yml b/.travis.yml index 6a2d50e6..bdb3ccb0 100644 --- a/.travis.yml +++ b/.travis.yml @@ -46,7 +46,7 @@ jobs: # Publish spotpy on PyPi if taged deploy: provider: pypi - distributions: "sdist bdist_wheel gztar" + distributions: "sdist bdist_wheel" on: # In this case I want to deploy only when a tag is present... tags: true @@ -55,5 +55,4 @@ deploy: - master - /v?(\d+\.)?(\d+\.)?(\*|\d+)$/ user: thouska - password : ${deploying} - skip_existing: true \ No newline at end of file + password : ${deploying} \ No newline at end of file From 309e8d9624bddf9a667faabe13a0f4cbff4a49b5 Mon Sep 17 00:00:00 2001 From: thouska Date: Tue, 8 Sep 2020 11:30:21 +0200 Subject: [PATCH 18/28] Reomve logging from preinstall and update version number --- setup.py | 4 ++-- spotpy/__init__.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/setup.py b/setup.py index 6f5235f2..b2af788d 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ setup( name = 'spotpy', - version = '1.5.12', + version = '1.5.13', description = 'A Statistical Parameter Optimization Tool', long_description=open(os.path.join(os.path.dirname(__file__), "README.rst")).read(), @@ -14,7 +14,7 @@ url = 'https://spotpy.readthedocs.io/en/latest/', license = 'MIT', install_requires=[ - 'scipy', 'numpy', 'logging'], + 'scipy', 'numpy'], 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, DDS, PA-DDS, Uncertainty, Calibration, Model, Signatures', diff --git a/spotpy/__init__.py b/spotpy/__init__.py index 8cddb744..26d052b9 100644 --- a/spotpy/__init__.py +++ b/spotpy/__init__.py @@ -40,4 +40,4 @@ from . import describe # Contains some helper functions to describe samplers and set-ups from .hydrology import signatures # Quantifies goodness of fit between simulation and evaluation data with hydrological signatures -__version__ = '1.5.12' \ No newline at end of file +__version__ = '1.5.13' \ No newline at end of file From 01772a855ea40a19b35c1c4a74789441dbe09f91 Mon Sep 17 00:00:00 2001 From: thouska Date: Wed, 9 Sep 2020 14:35:24 +0200 Subject: [PATCH 19/28] Unifies PADDS tutorial, introduces CHANGELOG.md #225 --- CHANGELOG.md | 10 + spotpy/algorithms/padds.py | 3 + .../spot_setup_hymod_python_pareto.py | 12 +- spotpy/examples/tutorial_dds_hymod.py | 2 +- spotpy/examples/tutorial_padds_hymod.py | 204 ++++++++---------- 5 files changed, 103 insertions(+), 128 deletions(-) create mode 100644 CHANGELOG.md diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 00000000..8b43b17b --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,10 @@ +# Changelog + +All notable changes to this project will be documented in this file. + +## [Unreleased Changes](https://github.com/thouska/spotpy/compare/v1.5.13...master) + +## Spotpy Version [1.5.13](https://github.com/thouska/spotpy/compare/v1.5.12...v1.5.13) (2020-09-07) + +* Introducing package dependencies as requested [#249](https://github.com/thouska/spotpy/issues/249) +* Introducing chanchelog.md as requested [#225](https://github.com/thouska/spotpy/issues/225) diff --git a/spotpy/algorithms/padds.py b/spotpy/algorithms/padds.py index 23f3885c..7557b2de 100644 --- a/spotpy/algorithms/padds.py +++ b/spotpy/algorithms/padds.py @@ -90,6 +90,9 @@ def __init__(self, *args, **kwargs): except KeyError: self.r = 0.2 # default value + kwargs['optimization_direction'] = 'minimize' + kwargs['algorithm_name'] = 'Pareto Archived Dynamically Dimensioned Search (PADDS) algorithm' + super(padds, self).__init__(*args, **kwargs) self.np_random = np.random diff --git a/spotpy/examples/spot_setup_hymod_python_pareto.py b/spotpy/examples/spot_setup_hymod_python_pareto.py index ceb2fffa..b4e97b31 100644 --- a/spotpy/examples/spot_setup_hymod_python_pareto.py +++ b/spotpy/examples/spot_setup_hymod_python_pareto.py @@ -61,12 +61,6 @@ 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 + return [-abs(spotpy.objectivefunctions.bias(evaluation, simulation)), + spotpy.objectivefunctions.rsquared(evaluation, simulation), + spotpy.objectivefunctions.nashsutcliffe(evaluation, simulation)] \ No newline at end of file diff --git a/spotpy/examples/tutorial_dds_hymod.py b/spotpy/examples/tutorial_dds_hymod.py index f94a061c..7eb52eb2 100644 --- a/spotpy/examples/tutorial_dds_hymod.py +++ b/spotpy/examples/tutorial_dds_hymod.py @@ -27,7 +27,7 @@ # 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) + spot_setup=spot_setup(spotpy.objectivefunctions.nashsutcliffe) #Select number of maximum allowed repetitions rep=1000 diff --git a/spotpy/examples/tutorial_padds_hymod.py b/spotpy/examples/tutorial_padds_hymod.py index 7a9c695c..c239285f 100644 --- a/spotpy/examples/tutorial_padds_hymod.py +++ b/spotpy/examples/tutorial_padds_hymod.py @@ -16,10 +16,17 @@ sys.path.append(".") import spotpy -from spotpy.examples.spot_setup_hymod_python_pareto import spot_setup +from spotpy.examples.spot_setup_hymod_python import spot_setup import matplotlib.pyplot as plt +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 np.array([like1, like2, like3]) + if __name__ == "__main__": parallel ='seq' # Runs everthing in sequential mode np.random.seed(2000) # Makes the results reproduceable @@ -27,138 +34,99 @@ # 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() + spot_setup=spot_setup(multi_obj_func) #Select number of maximum allowed repetitions - rep=3000 + rep=1000 - # Create the SCE-UA sampler of spotpy, alt_objfun is set to None to force SPOTPY + # Create the PADDS 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 + #Start the sampler, one can specify the desired metric "ones", "chc", "hvc", "crowd_distance" 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') - - + # Load the results gained with the sceua sampler, stored in padds_hymod.csv + #results = spotpy.analyser.load_csv_results('padds_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) + + + fig, ax=plt.subplots(3) + for likenr in range(3): + ax[likenr].plot(results['like'+str(likenr+1)]) + ax[likenr].set_ylabel('like'+str(likenr+1)) + fig.savefig('hymod_padds_objectivefunction.png', dpi=300) - plt.ylabel('RMSE') - plt.xlabel('Iteration') # 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)) - 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):] + + 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) - 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) + 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 + 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(spot_setup.evaluation(),'r.',label='data') + ax11.set_ylim(-50,450) + ax11.set_xlim(0,729) + ax11.legend() + fig.savefig('python_hymod.png',dpi=300) + ######################################################### \ No newline at end of file From 3574b35daa4be5fb2feaf25877e9111ed46896c0 Mon Sep 17 00:00:00 2001 From: thouska Date: Wed, 16 Sep 2020 14:39:06 +0200 Subject: [PATCH 20/28] Update describtion for further iinformation --- spotpy/examples/hymod_python/hymod.py | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/spotpy/examples/hymod_python/hymod.py b/spotpy/examples/hymod_python/hymod.py index ae10739b..f74bebdf 100644 --- a/spotpy/examples/hymod_python/hymod.py +++ b/spotpy/examples/hymod_python/hymod.py @@ -1,9 +1,26 @@ +# -*- coding: utf-8 -*- +''' +Copyright (c) 2015 by Tobias Houska + +This file is part of Statistical Parameter Estimation Tool (SPOTPY). + +:author: Tobias Houska and Benjamin Manns + +: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. +''' + from numba import jit 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. + See https://www.proc-iahs.net/368/180/2015/piahs-368-180-2015.pdf for a scientific paper: + + Quan, Z.; Teng, J.; Sun, W.; Cheng, T. & Zhang, J. (2015): Evaluation of the HYMOD model + for rainfall–runoff simulation using the GLUE method. Remote Sensing and GIS for Hydrology + and Water Resources, 180 - 185, IAHS Publ. 368. DOI: 10.5194/piahs-368-180-2015. :param cmax: :param bexp: From 99a8f9cd518dbb4f09eb96a30af51e9834370c1d Mon Sep 17 00:00:00 2001 From: thouska Date: Fri, 18 Sep 2020 09:48:26 +0200 Subject: [PATCH 21/28] Fix generalizedLikelihoodFunction as proposed #256 --- spotpy/examples/tutorial_dream_hymod.py | 9 +++++++-- spotpy/likelihoods.py | 13 ++++++++++--- 2 files changed, 17 insertions(+), 5 deletions(-) diff --git a/spotpy/examples/tutorial_dream_hymod.py b/spotpy/examples/tutorial_dream_hymod.py index c7cc3d9a..72a9f7dc 100644 --- a/spotpy/examples/tutorial_dream_hymod.py +++ b/spotpy/examples/tutorial_dream_hymod.py @@ -31,11 +31,16 @@ # Select five chains and set the Gelman-Rubin convergence limit nChains = 4 convergence_limit = 1.2 + + # Other possible settings to modify the DREAM algorithm, for details see Vrugt (2016) + nCr = 3 + eps = 10e-6 runs_after_convergence = 100 + acceptance_test_option = 6 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) + r_hat = sampler.sample(rep, nChains, nCr, eps, convergence_limit, + runs_after_convergence,acceptance_test_option) diff --git a/spotpy/likelihoods.py b/spotpy/likelihoods.py index dfc37a57..b6545f06 100644 --- a/spotpy/likelihoods.py +++ b/spotpy/likelihoods.py @@ -12,7 +12,7 @@ # Dream has now a bunch of options. We tested only likelihoods, which do not need a additional parameter # from this we saw that # ExponentialTransformErrVarShapingFactor with option 2 and 6 is good -# InverseErrorVarianceShapingFactor with option 2 ist good +# InverseErrorVarianceShapingFactor with option 2 is good # gaussianLikelihoodMeasErrorOut with option 6 is good # NoisyABCGaussianLikelihood with Option 6 is good # ABCBoxcarLikelihood with Option 2 and 6 is good @@ -33,6 +33,14 @@ class LikelihoodError(Exception): def __generateMeaserror(data): return np.array(data) * 0.1 +def __calcSimpleDeviation_bias(data, comparedata, mu_h): + __standartChecksBeforeStart(data, comparedata) + d = np.array(data) + c = np.array(comparedata) + #Adjust c for multiplicative bias parameter: + mu_t = np.exp(mu_h * c) + Et = c * mu_t + return d - Et def __calcSimpleDeviation(data, comparedata): __standartChecksBeforeStart(data, comparedata) @@ -429,7 +437,6 @@ def generalizedLikelihoodFunction(data, comparedata, measerror=None, params=None """ __standartChecksBeforeStart(data, comparedata) - errorArr = __calcSimpleDeviation(data, comparedata) if measerror is None: measerror = __generateMeaserror(data) measerror = np.array(measerror) @@ -505,7 +512,7 @@ def generalizedLikelihoodFunction(data, comparedata, measerror=None, params=None mu_xi = 0.0 n = data.__len__() - + errorArr = __calcSimpleDeviation_bias(data, comparedata, muh) sum_at = 0 # formula for a_t is from page 3, (6) for j in range(n - 1): From dae6c696b69f795c8c1f44fb2a1eaa10eac8a1e4 Mon Sep 17 00:00:00 2001 From: lu-kas <> Date: Tue, 22 Sep 2020 13:08:47 +0200 Subject: [PATCH 22/28] add params_min/max into break point pickle --- spotpy/algorithms/_algorithm.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/spotpy/algorithms/_algorithm.py b/spotpy/algorithms/_algorithm.py index 1a2fc94c..c92f0d78 100644 --- a/spotpy/algorithms/_algorithm.py +++ b/spotpy/algorithms/_algorithm.py @@ -366,17 +366,19 @@ def read_breakdata(self, dbname): Reason: In case of incomplete optimizations, old data can be restored. ''' import pickle with open(dbname+'.break', 'rb') as breakfile: - work,backuptime,repos,obmin,obmax=pickle.load(breakfile) + work,backuptime,repos,obmin,obmax,pmin,pmax=pickle.load(breakfile) self.status.starttime=self.status.starttime-backuptime self.status.rep=repos self.status.objectivefunction_min=obmin self.status.objectivefunction_max=obmax + self.status.params_min=pmin + self.status.params_max=pmax 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) + work=(work,self.status.last_print-self.status.starttime,self.status.rep,self.status.objectivefunction_min,self.status.objectivefunction_max,self.status.params_min,self.status.params_max) with open(str(dbname)+'.break', 'wb') as breakfile: pickle.dump(work, breakfile) From d0c7494275e9fc451b5a6904b9a88240a5dd1664 Mon Sep 17 00:00:00 2001 From: thouska Date: Thu, 24 Sep 2020 13:29:48 +0200 Subject: [PATCH 23/28] Redesign for tutorial of hydrological model calibration #259 --- ...brating a hydrological model with DREAM.md | 174 +++++++++++ docs/Hydrological_model.md | 215 ------------- docs/Working with DREAM and Hymod.md | 287 ------------------ mkdocs.yml | 3 +- spotpy/analyser.py | 44 ++- spotpy/examples/tutorial_cmf_lumped.py | 4 +- spotpy/examples/tutorial_dream_hymod.py | 142 +-------- 7 files changed, 227 insertions(+), 642 deletions(-) create mode 100644 docs/Calibrating a hydrological model with DREAM.md delete mode 100644 docs/Hydrological_model.md delete mode 100644 docs/Working with DREAM and Hymod.md diff --git a/docs/Calibrating a hydrological model with DREAM.md b/docs/Calibrating a hydrological model with DREAM.md new file mode 100644 index 00000000..d85f79e7 --- /dev/null +++ b/docs/Calibrating a hydrological model with DREAM.md @@ -0,0 +1,174 @@ +# 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 import some functions we will need in the following: + + from spotpy.parameter import Uniform + from spotpy.examples.hymod_python.hymod import hymod + import os + +Now, we can 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 class parameter, to initialize the parameter for our model. + + class spot_setup(object): + 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) + + 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) + 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' + #Load Observation data from file + 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: + 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() + +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): + #Here the model is actualy startet with one paramter combination that it gets from spotpy for each time the model is called + 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:] + +And in a last step, we compare the observed and the simulated data. Here we set up the setup class in a way, that it can receive any +likein the SPOTPY package. Please mind that the selection of the Likelihood highly influences the results gained with this algorithm: + + def evaluation(self): + #The first year of simulation data is ignored (warm-up) + return self.trueObs[366:] + + 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') + +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: + + #Select number of maximum repetitions + rep=5000 + + # Select five chains and set the Gelman-Rubin convergence limit + nChains = 4 + convergence_limit = 1.2 + + # Other possible settings to modify the DREAM algorithm, for details see Vrugt (2016) + nCr = 3 + eps = 10e-6 + runs_after_convergence = 100 + acceptance_test_option = 6 + +We start the sampler and collect the gained r_hat convergence values after the sampling: + + r_hat = sampler.sample(rep, nChains, nCr, eps, convergence_limit) + + +## 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,len(q5),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: + + spotpy.analyser.plot_gelman_rubin(results, r_hat, fig_name = 'python_hymod_convergence.png') + +![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: + + parameters = spotpy.parameter.get_parameters_array(spot_setup) + + fig, ax = plt.subplots(nrows=5, ncols=2) + for par_id in range(len(parameters)): + plot_parameter_trace(ax[par_id][0], results, parameters[par_id]) + plot_posterior_parameter_histogram(ax[par_id][1], results, parameters[par_id]) + + ax[-1][0].set_xlabel('Iterations') + ax[-1][1].set_xlabel('Parameter range') + + plt.show() + fig.savefig('hymod_parameters.png',dpi=300) + +![Parameter distribution](../img/python_hymod_parameters.png) + +*Figure 3: Posterior parameter distribution of HYMOD.* diff --git a/docs/Hydrological_model.md b/docs/Hydrological_model.md deleted file mode 100644 index 09cf2bd4..00000000 --- a/docs/Hydrological_model.md +++ /dev/null @@ -1,215 +0,0 @@ -# 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/Working with DREAM and Hymod.md b/docs/Working with DREAM and Hymod.md deleted file mode 100644 index 89496e5a..00000000 --- a/docs/Working with DREAM and Hymod.md +++ /dev/null @@ -1,287 +0,0 @@ -# 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/mkdocs.yml b/mkdocs.yml index 44b31de5..a68e12e2 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -20,8 +20,7 @@ nav: - 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 + - Calibrating a hydrological model with DREAM: Calibrating a hydrological model with DREAM.md - Advanced_hints: Advanced_hints.md - Algorithm_guide: Algorithm_guide.md diff --git a/spotpy/analyser.py b/spotpy/analyser.py index 7767d8ee..1c7db8de 100644 --- a/spotpy/analyser.py +++ b/spotpy/analyser.py @@ -279,6 +279,21 @@ def get_posterior(results,percentage=10, maximize=True): index = np.where(results['like1']>=np.percentile(results['like1'],100.0-percentage)) return results[index] +def plot_parameter_trace(ax, results, parameter): + #THis function plots the parameter setting for each run + for i in range(int(max(results['chain']))): + index=np.where(results['chain']==i) + ax.plot(results['par'+parameter['name']][index],'.') + ax.set_ylabel(parameter['name']) + ax.set_ylim(parameter['minbound'], parameter['maxbound']) + +def plot_posterior_parameter_histogram(ax, results, parameter): + #This functing is plotting the last 10% of the results + 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']) + def plot_parameter_uncertainty(posterior_results,evaluation, fig_name='Posterior_parameter_uncertainty.png'): import matplotlib.pyplot as plt @@ -969,16 +984,31 @@ def plot_autocorellation(parameterdistribution,parametername,fig_name='Autocorre plt.savefig(fig_name,dpi=300) -def plot_gelman_rubin(r_hat_values,fig_name='gelman_rub.png'): +def plot_gelman_rubin(results, 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 - 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) + + fig= plt.figure(figsize=(12,16)) + ax1 = plt.subplot(2,1,1) + for i in range(int(max(results['chain']))+1): + index=np.where(results['chain']==i) + ax1.plot(results['like1'][index], label='Chain '+str(i+1)) + + ax1.set_ylabel('Likelihood value') + ax1.legend() + + ax2 =plt.subplot(2,1,2) + r_hat=np.array(r_hat_values) + ax2.plot([1.2]*len(r_hat),'k--') + for i in range(len(r_hat[0])): + ax2.plot(r_hat[:,i],label='x'+str(i+1)) + + ax2.set_yscale("log", nonpositive='clip') + ax2.set_ylabel('R$^d$ - convergence diagnostic') + ax2.set_xlabel('Number of chainruns') + ax2.legend() + fig.savefig(fig_name,dpi=300) def gelman_rubin(x): '''NOT USED YET''' diff --git a/spotpy/examples/tutorial_cmf_lumped.py b/spotpy/examples/tutorial_cmf_lumped.py index 49a87713..a79904fa 100644 --- a/spotpy/examples/tutorial_cmf_lumped.py +++ b/spotpy/examples/tutorial_cmf_lumped.py @@ -46,11 +46,11 @@ def get_runs(default=1): model = SingleStorage(datetime.datetime(1980, 1, 1), datetime.datetime(1985, 12, 31)) - runs = get_runs(default=5) + runs = get_runs(default=100) # Create the sampler sampler = spotpy.algorithms.mc(model, parallel=parallel(), - dbname=model.dbname, dbformat='hdf5', + dbname=model.dbname, dbformat='csv', save_sim=True, sim_timeout=300) # Now we can sample with the implemented Monte Carlo algorithm: diff --git a/spotpy/examples/tutorial_dream_hymod.py b/spotpy/examples/tutorial_dream_hymod.py index 72a9f7dc..36de14fc 100644 --- a/spotpy/examples/tutorial_dream_hymod.py +++ b/spotpy/examples/tutorial_dream_hymod.py @@ -14,14 +14,15 @@ from spotpy.examples.spot_setup_hymod_python import spot_setup import matplotlib.pyplot as plt from spotpy.likelihoods import gaussianLikelihoodMeasErrorOut as GausianLike - +from spotpy.analyser import plot_parameter_trace +from spotpy.analyser import plot_posterior_parameter_histogram 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(GausianLike) - # Create the Dream sampler of spotpy, al_objfun is set to None to force SPOTPY + # 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) @@ -71,27 +72,7 @@ # Example plot to show the convergence ################# - 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) - 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) + spotpy.analyser.plot_gelman_rubin(results, r_hat) ######################################################## @@ -100,111 +81,14 @@ # Example plot to show the parameter distribution ###### parameters = spotpy.parameter.get_parameters_array(spot_setup) + fig, ax = plt.subplots(nrows=5, ncols=2) + for par_id in range(len(parameters)): + plot_parameter_trace(ax[par_id][0], results, parameters[par_id]) + plot_posterior_parameter_histogram(ax[par_id][1], results, parameters[par_id]) - min_vs,max_vs = parameters['minbound'], parameters['maxbound'] - - fig= plt.figure(figsize=(16,16)) - plt.subplot(5,2,1) - 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('cmax') - plt.ylim(min_vs[0],max_vs[0]) - - - plt.subplot(5,2,2) - 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('cmax') - plt.xlim(min_vs[0],max_vs[0]) - - - - plt.subplot(5,2,3) - 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('bexp') - plt.ylim(min_vs[1],max_vs[1]) - - plt.subplot(5,2,4) - 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('bexp') - plt.xlim(min_vs[1],max_vs[1]) - - - - plt.subplot(5,2,5) - 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('alpha') - plt.ylim(min_vs[2],max_vs[2]) - - - plt.subplot(5,2,6) - 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('alpha') - plt.xlim(min_vs[2],max_vs[2]) - - - plt.subplot(5,2,7) - 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('Ks') - plt.ylim(min_vs[3],max_vs[3]) - - - plt.subplot(5,2,8) - 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('Ks') - plt.xlim(min_vs[3],max_vs[3]) - - - plt.subplot(5,2,9) - 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('Kq') - plt.ylim(min_vs[4],max_vs[4]) - plt.xlabel('Iterations') - - plt.subplot(5,2,10) - 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('Kq') - plt.xlabel('Parameter range') - plt.xlim(min_vs[4],max_vs[4]) - plt.show() - fig.savefig('python_parameters.png',dpi=300) - ######################################################## + ax[-1][0].set_xlabel('Iterations') + ax[-1][1].set_xlabel('Parameter range') + plt.show() + fig.savefig('hymod_parameters.png',dpi=300) + ####################################################### \ No newline at end of file From a42c26dc883ed806e09bc42b25d49f4336e524cd Mon Sep 17 00:00:00 2001 From: thouska Date: Thu, 24 Sep 2020 13:45:40 +0200 Subject: [PATCH 24/28] Fix in updated gelman rubin test --- spotpy/parameter.py | 4 ++-- tests/test_analyser.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/spotpy/parameter.py b/spotpy/parameter.py index c03e9608..c786172c 100644 --- a/spotpy/parameter.py +++ b/spotpy/parameter.py @@ -431,7 +431,7 @@ def __init__(self, *args, **kwargs): default is median of rndfunc(*rndargs, size=1000) :optguess: (optional) number for start point of parameter default is quantile(0.5) - quantile(0.4) of - rndfunc(*rndargs, size=1000) + rndfunc(*rndargs, size=1000) """ super(Exponential, self).__init__(rnd.exponential, 'Exponential', *args, **kwargs) @@ -474,7 +474,7 @@ def __init__(self, *args, **kwargs): default is median of rndfunc(*rndargs, size=1000) :optguess: (optional) number for start point of parameter default is quantile(0.5) - quantile(0.4) of - rndfunc(*rndargs, size=1000) + rndfunc(*rndargs, size=1000) """ super(Wald, self).__init__(rnd.wald, 'Wald', *args, **kwargs) diff --git a/tests/test_analyser.py b/tests/test_analyser.py index ba35f13f..7c8444d8 100644 --- a/tests/test_analyser.py +++ b/tests/test_analyser.py @@ -362,7 +362,7 @@ def test_plot_autocorellation(self): 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) + spotpy.analyser.plot_gelman_rubin(self.hymod_results, self.r_hat, fig_name =self.fig_name) self.assertGreaterEqual(abs(os.path.getsize(self.fig_name)), 100) From bd8bb8407358a50876ddc41692dca816de844011 Mon Sep 17 00:00:00 2001 From: thouska Date: Fri, 25 Sep 2020 14:08:36 +0200 Subject: [PATCH 25/28] Introduction of new documentation slides #259 --- docs/Advanced_hints.md | 24 +-- ...ayesian_uncertainty_analysis_with_DREAM.md | 128 +++++++++++++ ...brating a hydrological model with DREAM.md | 174 ------------------ docs/Calibration_with_SCE-UA.md | 88 +++++++++ docs/How_to_link_a_model_to_SPOTPY.md | 126 +++++++++++++ docs/Sensitivity_analysis_with_FAST.md | 63 +++++++ docs/index.md | 6 +- mkdocs.yml | 5 +- spotpy/algorithms/sceua.py | 2 +- spotpy/analyser.py | 2 +- spotpy/examples/spot_setup_hymod_python.py | 2 +- spotpy/examples/tutorial_fast_hymod.py | 11 +- spotpy/examples/tutorial_sceua_hymod.py | 128 +++---------- 13 files changed, 441 insertions(+), 318 deletions(-) create mode 100644 docs/Bayesian_uncertainty_analysis_with_DREAM.md delete mode 100644 docs/Calibrating a hydrological model with DREAM.md create mode 100644 docs/Calibration_with_SCE-UA.md create mode 100644 docs/How_to_link_a_model_to_SPOTPY.md create mode 100644 docs/Sensitivity_analysis_with_FAST.md diff --git a/docs/Advanced_hints.md b/docs/Advanced_hints.md index da74b6c3..a86e1166 100644 --- a/docs/Advanced_hints.md +++ b/docs/Advanced_hints.md @@ -20,7 +20,7 @@ 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. @@ -38,29 +38,7 @@ DE-MCz will parallelize the selcted number of chains [terBrack and Vrugt, 2008]( 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: diff --git a/docs/Bayesian_uncertainty_analysis_with_DREAM.md b/docs/Bayesian_uncertainty_analysis_with_DREAM.md new file mode 100644 index 00000000..c0a4d104 --- /dev/null +++ b/docs/Bayesian_uncertainty_analysis_with_DREAM.md @@ -0,0 +1,128 @@ +# Bayesian uncertainty analysis of HYMOD with DREAM + +This chapter shows you, how to calibrate an external hydrological model (HYMOD) with SPOTPY. + +We use the previously created hydrological model HYMOD spotpy_setup class as an [example](https://github.com/thouska/spotpy/blob/master/spotpy/examples/spot_setup_hymod_python.py), +to perform a parameter uncertainty analysis and Bayesian calibration with the Differential Evolution Adaptive Metropolis (DREAM) algorithm. +For detailed information about the underlying theory, have a look at the [Vrugt (2016)](https://doi.org/10.1016/j.envsoft.2015.08.013 "Vrugt (2016)"). + + +First some relevant functions are imported Hymod example: + + import numpy as np + import spotpy + import matplotlib.pyplot as plt + from spotpy.likelihoods import gaussianLikelihoodMeasErrorOut as GausianLike + from spotpy.analyser import plot_parameter_trace + from spotpy.analyser import plot_posterior_parameter_histogram + +Further we need the spotpy_setup class, which links the model to spotpy: + + from spotpy.examples.spot_setup_hymod_python import spot_setup + +It is important that this function is initialized before it is further used by SPOTPY: + + spot_setup=spot_setup() + +In this special case, we want to change the implemented rmse objective function with a likelihood function, which is compatible with the Bayesian +calibration approach, used by the DREAM sampler: + + # Initialize the Hymod example (will only work on Windows systems) + spot_setup=spot_setup(GausianLike) + + +## Sample with DREAM + +Now we create a sampler, by using one of the implemented algorithms in SPOTPY. +The algorithm needs the inititalized spotpy setup class, and wants you to define a database name and database format. Here we create a DREAM_hymod.csv file, +which will contain the returned likelihood function, the coresponding parameter set, the model results and a chain ID (Dream is starting different independent +optimization chains, which need to be in line, to receive robust results): + + sampler=spotpy.algorithms.dream(spot_setup, dbname='DREAM_hymod', dbformat='csv') + +To actually start the algorithm, spotpy needs some further details, like the maximum allowed number of 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: + + #Select number of maximum repetitions + rep=5000 + + # Select five chains and set the Gelman-Rubin convergence limit + nChains = 4 + convergence_limit = 1.2 + + # Other possible settings to modify the DREAM algorithm, for details see Vrugt (2016) + nCr = 3 + eps = 10e-6 + runs_after_convergence = 100 + acceptance_test_option = 6 + +We start the sampler and collect the gained r_hat convergence values after the sampling: + + r_hat = sampler.sample(rep, nChains, nCr, eps, convergence_limit) + + +## Access the results +All gained 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,len(q5),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: + + spotpy.analyser.plot_gelman_rubin(results, r_hat, fig_name='python_hymod_convergence.png') + +![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: + + parameters = spotpy.parameter.get_parameters_array(spot_setup) + + fig, ax = plt.subplots(nrows=5, ncols=2) + for par_id in range(len(parameters)): + plot_parameter_trace(ax[par_id][0], results, parameters[par_id]) + plot_posterior_parameter_histogram(ax[par_id][1], results, parameters[par_id]) + + ax[-1][0].set_xlabel('Iterations') + ax[-1][1].set_xlabel('Parameter range') + + plt.show() + fig.savefig('hymod_parameters.png',dpi=300) + +![Parameter distribution](../img/python_hymod_parameters.png) + +*Figure 3: Posterior parameter distribution of HYMOD.* + +The corresponding code is available for download [here](https://github.com/thouska/spotpy/blob/master/spotpy/examples/tutorial_dream_hymod.py). \ No newline at end of file diff --git a/docs/Calibrating a hydrological model with DREAM.md b/docs/Calibrating a hydrological model with DREAM.md deleted file mode 100644 index d85f79e7..00000000 --- a/docs/Calibrating a hydrological model with DREAM.md +++ /dev/null @@ -1,174 +0,0 @@ -# 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 import some functions we will need in the following: - - from spotpy.parameter import Uniform - from spotpy.examples.hymod_python.hymod import hymod - import os - -Now, we can 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 class parameter, to initialize the parameter for our model. - - class spot_setup(object): - 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) - - 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) - 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' - #Load Observation data from file - 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: - 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() - -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): - #Here the model is actualy startet with one paramter combination that it gets from spotpy for each time the model is called - 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:] - -And in a last step, we compare the observed and the simulated data. Here we set up the setup class in a way, that it can receive any -likein the SPOTPY package. Please mind that the selection of the Likelihood highly influences the results gained with this algorithm: - - def evaluation(self): - #The first year of simulation data is ignored (warm-up) - return self.trueObs[366:] - - 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') - -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: - - #Select number of maximum repetitions - rep=5000 - - # Select five chains and set the Gelman-Rubin convergence limit - nChains = 4 - convergence_limit = 1.2 - - # Other possible settings to modify the DREAM algorithm, for details see Vrugt (2016) - nCr = 3 - eps = 10e-6 - runs_after_convergence = 100 - acceptance_test_option = 6 - -We start the sampler and collect the gained r_hat convergence values after the sampling: - - r_hat = sampler.sample(rep, nChains, nCr, eps, convergence_limit) - - -## 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,len(q5),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: - - spotpy.analyser.plot_gelman_rubin(results, r_hat, fig_name = 'python_hymod_convergence.png') - -![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: - - parameters = spotpy.parameter.get_parameters_array(spot_setup) - - fig, ax = plt.subplots(nrows=5, ncols=2) - for par_id in range(len(parameters)): - plot_parameter_trace(ax[par_id][0], results, parameters[par_id]) - plot_posterior_parameter_histogram(ax[par_id][1], results, parameters[par_id]) - - ax[-1][0].set_xlabel('Iterations') - ax[-1][1].set_xlabel('Parameter range') - - plt.show() - fig.savefig('hymod_parameters.png',dpi=300) - -![Parameter distribution](../img/python_hymod_parameters.png) - -*Figure 3: Posterior parameter distribution of HYMOD.* diff --git a/docs/Calibration_with_SCE-UA.md b/docs/Calibration_with_SCE-UA.md new file mode 100644 index 00000000..fac6380e --- /dev/null +++ b/docs/Calibration_with_SCE-UA.md @@ -0,0 +1,88 @@ +# Calibration of HYMOD with SCE-UA + +This chapter shows you, how to calibrate an external hydrological model (HYMOD) with SPOTPY. + +We use the previously created hydrological model HYMOD spotpy_setup class as an [example](https://github.com/thouska/spotpy/blob/master/spotpy/examples/spot_setup_hymod_python.py), +to perform a calibration with the Shuffled Complex Evolution Algorithm (SCE-UA) algorithm. +For detailed information about the underlying theory, have a look at +Duan, Q., Sorooshian, S. and Gupta, V. K. (1994) +Optimal use of the SCE-UA global optimization method for calibrating watershed models, J. Hydrol.. + +First some relevant functions are imported Hymod example: + + import numpy as np + import spotpy + from spotpy.examples.spot_setup_hymod_python import spot_setup + import matplotlib.pyplot as plt + +As SCE-UA is minizing the objective function we need a objective function that gets better with decreasing values. +The Root Mean Squared Error objective function is a suitable choice: + + spot_setup=spot_setup(spotpy.objectivefunctions.rmse) + +Now we create a sampler, by using one of the implemented algorithms in SPOTPY. +The algorithm needs the inititalized spotpy setup class, and wants you to define a database name and database format. Here we create a SCEUA_hymod.csv file, +which will contain the returned likelihood function, the coresponding parameter set, the model results and a chain ID (SCE-UA is starting different independent +optimization complexes, which need to be in line, to receive robust results, they are herein called chains): + + sampler=spotpy.algorithms.sceua(spot_setup, dbname='SCEUA_hymod', dbformat='csv') + +To actually start the algorithm, spotpy needs some further details, like the maximum allowed number of 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: + + #Select number of maximum repetitions + rep=5000 + +We start the sampler and set some optional algorithm specific settings (check out the publication of SCE-UA for details): + + sampler.sample(rep, ngs=7, kstop=3, peps=0.1, pcento=0.1) + + +## Access the results +All gained results can be accessed from the SPOTPY csv-database: + + results = spotpy.analyser.load_csv_results('SCEUA_hymod') + +These results are stored in a simple structered numpy array, giving you all the flexibility at hand to analyse the results. +Herein we show a example how the objective function was minimized during sampling: + + fig= plt.figure(1,figsize=(9,5)) + plt.plot(results['like1']) + plt.show() + plt.ylabel('RMSE') + plt.xlabel('Iteration') + fig.savefig('SCEUA_objectivefunctiontrace.png',dpi=300) + +![Posterior model uncertainty](../img/SCEUA_objectivefunctiontrace.png) +*Figure 1: Root Mean Squared Error of Hymod model results during the optimization with SCE-UA algorithm.* + + +## Plot parameter uncertainty +Or if you want to check the best model run, we first need to find the run_id with the minimal objective function value + bestindex,bestobjf = spotpy.analyser.get_minlikeindex(results) + +Than we select the best model run: + + best_model_run = results[bestindex] + +And filter results for simulation results only: + + fields=[word for word in best_model_run.dtype.names if word.startswith('sim')] + best_simulation = list(best_model_run[fields]) + +The best model run can be plotted by using basic Matplotlib skills: + + fig= plt.figure(figsize=(16,9)) + ax = plt.subplot(1,1,1) + ax.plot(best_simulation,color='black',linestyle='solid', label='Best objf.='+str(bestobjf)) + ax.plot(spot_setup.evaluation(),'r.',markersize=3, label='Observation data') + plt.xlabel('Number of Observation Points') + plt.ylabel ('Discharge [l s-1]') + plt.legend(loc='upper right') + fig.savefig('SCEUA_best_modelrun.png',dpi=300) + +![SCEUA_best_modelrun](../img/SCEUA_best_modelrun.png) + +*Figure 2: Best model run of HYMOD, calibrated with SCE-UA, using RMSE as objective function.* + +The corresponding code is available for download [here](https://github.com/thouska/spotpy/blob/master/spotpy/examples/tutorial_sceua_hymod.py). \ No newline at end of file diff --git a/docs/How_to_link_a_model_to_SPOTPY.md b/docs/How_to_link_a_model_to_SPOTPY.md new file mode 100644 index 00000000..ccb08d07 --- /dev/null +++ b/docs/How_to_link_a_model_to_SPOTPY.md @@ -0,0 +1,126 @@ +## Linking a model with SPOTPY + +The SPOTPY package comes with several examples, which are designed to help you to set up your own research project. + +Herein we show, how to link an external model with SPOTPY. + +The hydrological model HYMOD serves as an example, which is a commonly used model in the hydrological community. +It takes meteorological data as input and produces discharge as output. + +Basically any model can be linked to SPOTPY as long it is somehow start-able with the Python programming language. +Linking a model to SPOTPY is done by following five consecutive steps, which are grouped in a spotpy_setup class. +If these steps are followed, SPOTPY can work with this class and analyse the model in an automatized way, giving you +powerful tools at hand. + +## Step 0: Import relevant packages and generate a spotpy setup class + + from spotpy.parameter import Uniform + from spotpy.objectivefunctions import rmse + from spotpy.examples.hymod_python.hymod import hymod + import os + +Generating a spotpy setup class is as easy as creating a class in Python: + + class spot_setup(object): + +## Step 1: Define the parameter of the model as class parameters + +Now, we can fill the setup class. In our case the model comes along with five parameters. +Here we use Python class parameter, to initialize the parameter for our model and make them readable for SPOTPY. +Needed information is the prior distribution (herein we choose a Uniform distribution), the minimum allowed parameter +setting (low) and the maximum allow setting (high). + + cmax = Uniform(low=1.0 , high=500) + bexp = Uniform(low=0.1 , high=2.0) + alpha = Uniform(low=0.1 , high=0.99) + Ks = Uniform(low=0.001 , high=0.10) + Kq = Uniform(low=0.1 , high=0.99) + +## Step 2: Write the def __init__ function, which takes care of any things which need to be done only once + +In this step, we can load all the data needed to start the model and set information, which might be needed in the following, +but want change during the model assessment. In our case, we want to set the path to the model: + + def __init__(self,obj_func=None): + #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' + +Further we want to read in the forcing meteorological data and the observation data from file: + + self.PET,self.Precip = [], [] + self.date,self.trueObs = [], [] + #Load Observation data from file + 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: + 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() + +Finaly, in this case a transformation factor is needed, which brings the output of the model from [mm/day] into [l s-1], where 1.783 is the catchment area [km²] + + self.Factor = 1.783 * 1000 * 1000 / (60 * 60 * 24) + +The keyword `obj_func` is not necessary in most cases. Herein we use it to change the function in Step 5, which makes this example flexible and applicable to various algorithms. + + self.obj_func = obj_func + +## Step 3: Write the def simulation function, which starts your model + +This function need to do three things. First it needs to be able to receive a parameter set from SPOTPY. +This will be an list of, in our case five values. Each value represents one setting for one parameter. +The HYMOD model receives these settings through a function. However, one could also write the settings into a file, which is read by the model (most common way). +Finally we start the model with the parameter set and collect the data, which are at the end returned. + + def simulation(self,x): + #Here the model is actualy started with a unique parameter combination that it gets from spotpy for each time the model is called + 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:] + +## Step 4: Write the def evaluation function, which returns your observation data + +Model assessment needs data, which tell you something about the reality that the model aims to reproduce. The HYMOD model produces discharge data, so herein we need to return observed discharge data + + def evaluation(self): + #The first year of simulation data is ignored (warm-up) + return self.trueObs[366:] + +## Step 5: Write the def objectivefunction, which returns how good the model fits the observation data + +In this last step, we compare the observed and the simulated data. So we need a function which can receive both information (simulation and evaluation). +SPOTPY will internally take care of times when this function needs to be called and will bring the corresponding data to this function. +Important is that the first keyword is handled as simulation/model results and the second as observation/evaluation results. +The SPOTPY package gives you several functions at hand, which compare the model and observation data with each other. +Please note that the selection of such a function highly influences the results gained with a optimization algorithm. Further some optimization algorithms +have specific needs at this function, as they wont to minimize or maximize the returned value by playing around with the parameter settings. Further some algorithms +need specific likelihood function. Herein we choose the Root Mean Squared Error (rmse) as an example. +A value of 0 would be a perfect fit of the simulation and observation data, +inf would be the worst possible fit. + + def objectivefunction(self,simulation,evaluation, params=None): + #SPOTPY expects to get one or multiple values back, + #that define the performance 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 +Finally, save the file and check out the next example how to use the spotpy setup class with an algorithm. + +The shown example code is available [here](https://github.com/thouska/spotpy/blob/master/spotpy/examples/spot_setup_hymod_python.py) \ No newline at end of file diff --git a/docs/Sensitivity_analysis_with_FAST.md b/docs/Sensitivity_analysis_with_FAST.md new file mode 100644 index 00000000..72d2b874 --- /dev/null +++ b/docs/Sensitivity_analysis_with_FAST.md @@ -0,0 +1,63 @@ +# Sensitivity analysis of HYMOD with FAST + +SPOTPY gives you the opportunity to start a sensitivity analysis of your model. In this case, we included a global sensitivity analysis called "FAST" based on +Saltelli et al. (1999). + +The algorithm will tell you, how sensitive your parameters are based 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. + +In our case, we provide the hymod model as an [example](https://github.com/thouska/spotpy/blob/master/spotpy/examples/spot_setup_hymod_python.py) +You can start the simulation with + + +So the first step is as always to initialize the spotpy setup class example: + import spotpy + from spotpy.examples.spot_setup_hymod_python import spot_setup + spot_setup=spot_setup() + +As a next step we apply the above formular to determine the number of repetitions needed for the FAST analysis. In our case we have k=5 parameter +in the hymod model, so we need N=1345 repetitions to get reliable results: + + rep = 1345 + +And that's already it. Now we can start the sensitivity analysis: + sampler = spotpy.algorithms.fast(spot_setup, dbname='FAST_hymod', dbformat='csv') + sampler.sample(rep) + +This will take some time. Meanwhile SPOTPY will report you about the progress and the approximate duration. +Further SPOTPY will create a database for you with the name 'Fast_hymod.csv', depending on the users selection above. +This file will contain the return objectivefunction values, the sampled parameter sets, the simulation results and some information +about a chain (not relevant for FAST). + +These results can be loaded, e.g. directly from the sampler: + + results = sampler.get_data() + +OR, by loading the results from the database: + + results = spotpy.analyser.load_csv_results('FAST_hymod') + +Finally SPOTPY gives you some tools at hand, to analyse these results. E.g. if you want to determine, which are the three most important parameter of your +model, you can use the following: + + spotpy.analyser.plot_fast_sensitivity(results, number_of_sensitiv_pars=3) + + + 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). +Herein we use a already create spotpy setup class from the tutorial. The code for this class +is available + + +![FAST sensitivity results](../img/FAST_sensitivity.png) + +*Figure 1: Example output of a sensitivity analysis using the FAST (Fourier Amplitude Sensitivity Test). +Total sensitivity index is plotted for every model parameter. Sensitive parameters are plotted in blue, insensitive parameter in orange.* + +The corresponding code is available for download [here](https://github.com/thouska/spotpy/blob/master/spotpy/examples/tutorial_fast_hymod.py). \ No newline at end of file diff --git a/docs/index.md b/docs/index.md index 2afedac2..be990ea0 100644 --- a/docs/index.md +++ b/docs/index.md @@ -157,8 +157,10 @@ The results can be analysed with some pre-build statistics and plotting features 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 + fscabc.py # Fitness Scaled Chaotic Artificial Bee Colony + dream.py # Differential Evolution Adaptive Metropolis + dds.py # Dynamically Dimensioned Search + parallel/ mpi.py #Basic Parralel Computing features diff --git a/mkdocs.yml b/mkdocs.yml index a68e12e2..5348b679 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -20,7 +20,10 @@ nav: - Rosenbrock: Rosenbrock.md - Griewank: Griewank.md - Ackley: Ackley.md - - Calibrating a hydrological model with DREAM: Calibrating a hydrological model with DREAM.md + - How to link a model to SPOTPY: How_to_link_a_model_to_SPOTPY.md + - Sensitivity analysis with FAST: Sensitivity_analysis_with_FAST.md + - Calibrating with SCE-UA: Calibration_with_SCE-UA.md + - Bayesian uncertainty analysis with DREAM: Bayesian_uncertainty_analysis_with_DREAM.md - Advanced_hints: Advanced_hints.md - Algorithm_guide: Algorithm_guide.md diff --git a/spotpy/algorithms/sceua.py b/spotpy/algorithms/sceua.py index 86679eb1..fe5e59bf 100644 --- a/spotpy/algorithms/sceua.py +++ b/spotpy/algorithms/sceua.py @@ -15,7 +15,7 @@ class sceua(_algorithm): """ - This class holds the Shuffled Complex Evolution Algortithm (SCE-UA) algorithm, + This class holds the Shuffled Complex Evolution Algorithm (SCE-UA) algorithm, based on: Duan, Q., Sorooshian, S. and Gupta, V. K. (1994) Optimal use of the SCE-UA global optimization method for calibrating watershed models, J. Hydrol. diff --git a/spotpy/analyser.py b/spotpy/analyser.py index 1c7db8de..f7eb0aa6 100644 --- a/spotpy/analyser.py +++ b/spotpy/analyser.py @@ -187,7 +187,7 @@ def get_minlikeindex(results): textv=text+str(index[0][0])+text2+value print(textv) - return index, minimum + return index[0][0], minimum def get_percentiles(results,sim_number=''): diff --git a/spotpy/examples/spot_setup_hymod_python.py b/spotpy/examples/spot_setup_hymod_python.py index 876f1f41..38f6d6a1 100644 --- a/spotpy/examples/spot_setup_hymod_python.py +++ b/spotpy/examples/spot_setup_hymod_python.py @@ -68,7 +68,7 @@ def evaluation(self): def objectivefunction(self,simulation,evaluation, params=None): #SPOTPY expects to get one or multiple values back, - #that define the performence of the model run + #that define the performance of the model run if not self.obj_func: # This is used if not overwritten by user like = rmse(evaluation,simulation) diff --git a/spotpy/examples/tutorial_fast_hymod.py b/spotpy/examples/tutorial_fast_hymod.py index 4ea0f05e..8c350cf4 100644 --- a/spotpy/examples/tutorial_fast_hymod.py +++ b/spotpy/examples/tutorial_fast_hymod.py @@ -5,15 +5,10 @@ :author: Tobias Houska -This class holds example code how to use the dream algorithm +This class holds example code how to use the FAST algorithm ''' -try: - import spotpy -except ImportError: - import sys - sys.path.append(".") - import spotpy +import spotpy from spotpy.examples.spot_setup_hymod_python import spot_setup @@ -23,7 +18,7 @@ spot_setup = spot_setup() #Select number of maximum repetitions - rep = 1000 + rep = 1345 #Start a sensitivity analysis sampler = spotpy.algorithms.fast(spot_setup, dbname='FAST_hymod', dbformat='csv') diff --git a/spotpy/examples/tutorial_sceua_hymod.py b/spotpy/examples/tutorial_sceua_hymod.py index 3fd3626f..171ce0d9 100644 --- a/spotpy/examples/tutorial_sceua_hymod.py +++ b/spotpy/examples/tutorial_sceua_hymod.py @@ -24,11 +24,7 @@ spot_setup=spot_setup(spotpy.objectivefunctions.rmse) #Select number of maximum allowed repetitions - rep=1000 - 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) + rep=5000 sampler=spotpy.algorithms.sceua(spot_setup, dbname='SCEUA_hymod', dbformat='csv') #Start the sampler, one can specify ngs, kstop, peps and pcento id desired @@ -37,114 +33,32 @@ # 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.') + #Plot how the objective function was minimized during sampling 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) + fig.savefig('SCEUA_objectivefunctiontrace.png',dpi=300) - - 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):] + # Plot the best model run + #Find the run_id with the minimal objective function value + bestindex,bestobjf = spotpy.analyser.get_minlikeindex(results) - 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) + # Select best model run + best_model_run = results[bestindex] - - 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 + #Filter results for simulation results + fields=[word for word in best_model_run.dtype.names if word.startswith('sim')] + best_simulation = list(best_model_run[fields]) + + fig= plt.figure(figsize=(16,9)) + ax = plt.subplot(1,1,1) + ax.plot(best_simulation,color='black',linestyle='solid', label='Best objf.='+str(bestobjf)) + ax.plot(spot_setup.evaluation(),'r.',markersize=3, label='Observation data') + plt.xlabel('Number of Observation Points') + plt.ylabel ('Discharge [l s-1]') + plt.legend(loc='upper right') + fig.savefig('SCEUA_best_modelrun.png',dpi=300) + From 1fc21100627ef189fbb476cf6c5fb5903455df4a Mon Sep 17 00:00:00 2001 From: thouska Date: Fri, 25 Sep 2020 14:12:51 +0200 Subject: [PATCH 26/28] Upload pictures for new documentation slides --- docs/Sensitivity_analysis_with_FAST.md | 1 + docs/img/FAST_sensitivity.png | Bin 0 -> 108421 bytes docs/img/SCEUA_best_modelrun.png | Bin 0 -> 404134 bytes docs/img/SCEUA_objectivefunctiontrace.png | Bin 0 -> 105561 bytes 4 files changed, 1 insertion(+) create mode 100644 docs/img/FAST_sensitivity.png create mode 100644 docs/img/SCEUA_best_modelrun.png create mode 100644 docs/img/SCEUA_objectivefunctiontrace.png diff --git a/docs/Sensitivity_analysis_with_FAST.md b/docs/Sensitivity_analysis_with_FAST.md index 72d2b874..941b2932 100644 --- a/docs/Sensitivity_analysis_with_FAST.md +++ b/docs/Sensitivity_analysis_with_FAST.md @@ -1,3 +1,4 @@ + # Sensitivity analysis of HYMOD with FAST SPOTPY gives you the opportunity to start a sensitivity analysis of your model. 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zNnN&J4EqEWmX*URyK~+5x&P}JvBx18lnvWhnh9vh5TU@Wjvp$=Md;qX-S=ko6F5UWXfO_G>PYh)T}30u$dea=D2Xx#@v&V$^2&jp65eYOsD#=zI{W#Ar1*Mx~jnPEJr`SC7kL zk9_`WZ|KgRf06IM?tQBQyLa|nNcmP`ckewq_*ZG%z4zZ4{C|Gze~23Y*^>W#O9ppX Ze(`$qYsH>BWE4_2u4=0m{Bq~_{|7jdRr&w` literal 0 HcmV?d00001 From b1bc7e0ce7f9c314aa47cdd822b28bc498ac0568 Mon Sep 17 00:00:00 2001 From: thouska Date: Fri, 25 Sep 2020 14:35:33 +0200 Subject: [PATCH 27/28] Change of resolution for figures in docs --- docs/Advanced_hints.md | 1 + ...ayesian_uncertainty_analysis_with_DREAM.md | 8 ++++---- docs/Calibration_with_SCE-UA.md | 5 +++-- docs/Sensitivity_analysis_with_FAST.md | 3 ++- docs/img/FAST_sensitivity.png | Bin 108421 -> 39509 bytes docs/img/SCEUA_best_modelrun.png | Bin 404134 -> 105764 bytes docs/img/SCEUA_objectivefunctiontrace.png | Bin 105561 -> 48080 bytes spotpy/analyser.py | 14 +++++++------- spotpy/examples/tutorial_dream_hymod.py | 17 ++++++++++------- spotpy/examples/tutorial_sceua_hymod.py | 8 ++++---- 10 files changed, 31 insertions(+), 25 deletions(-) diff --git a/docs/Advanced_hints.md b/docs/Advanced_hints.md index a86e1166..e8f90c0d 100644 --- a/docs/Advanced_hints.md +++ b/docs/Advanced_hints.md @@ -1,4 +1,5 @@ + # Advanced settings This chapter will show you, how to get the full power out of SPOTPY: diff --git a/docs/Bayesian_uncertainty_analysis_with_DREAM.md b/docs/Bayesian_uncertainty_analysis_with_DREAM.md index c0a4d104..cbeabb2b 100644 --- a/docs/Bayesian_uncertainty_analysis_with_DREAM.md +++ b/docs/Bayesian_uncertainty_analysis_with_DREAM.md @@ -92,7 +92,7 @@ If you want to see the remaining posterior model uncertainty: ax.legend() fig.savefig('python_hymod.png',dpi=300) -![Posterior model uncertainty](../img/python_hymod_simulation.png) +![Posterior simulation uncertainty](../img/DREAM_simulation_uncertainty_Hymod.png) *Figure 1: Posterior model uncertainty of HYMOD.* ## Plot convergence diagnostic @@ -100,7 +100,7 @@ If you want to check the convergence of the DREAM algorithm: spotpy.analyser.plot_gelman_rubin(results, r_hat, fig_name='python_hymod_convergence.png') -![Convergence diagnostic](../img/python_hymod_convergence.png) +![Convergence diagnostic](../img/DREAM_r_hat.png) *Figure 2: Gelman-Rubin onvergence diagnostic of DREAM results.* @@ -121,8 +121,8 @@ Or if you want to check the posterior parameter distribution: plt.show() fig.savefig('hymod_parameters.png',dpi=300) -![Parameter distribution](../img/python_hymod_parameters.png) +![DREAM_parameter_uncertainty_Hymod](../img/DREAM_parameter_uncertainty_Hymod.png) -*Figure 3: Posterior parameter distribution of HYMOD.* +*Figure 3: Posterior parameter distribution of HYMOD. Plotting the last 100 repetitions of the algorithm as a histogram.* The corresponding code is available for download [here](https://github.com/thouska/spotpy/blob/master/spotpy/examples/tutorial_dream_hymod.py). \ No newline at end of file diff --git a/docs/Calibration_with_SCE-UA.md b/docs/Calibration_with_SCE-UA.md index fac6380e..d522f4e7 100644 --- a/docs/Calibration_with_SCE-UA.md +++ b/docs/Calibration_with_SCE-UA.md @@ -57,14 +57,15 @@ Herein we show a example how the objective function was minimized during samplin *Figure 1: Root Mean Squared Error of Hymod model results during the optimization with SCE-UA algorithm.* -## Plot parameter uncertainty +## Plot best model run Or if you want to check the best model run, we first need to find the run_id with the minimal objective function value + bestindex,bestobjf = spotpy.analyser.get_minlikeindex(results) Than we select the best model run: best_model_run = results[bestindex] - + And filter results for simulation results only: fields=[word for word in best_model_run.dtype.names if word.startswith('sim')] diff --git a/docs/Sensitivity_analysis_with_FAST.md b/docs/Sensitivity_analysis_with_FAST.md index 941b2932..4015d351 100644 --- a/docs/Sensitivity_analysis_with_FAST.md +++ b/docs/Sensitivity_analysis_with_FAST.md @@ -1,4 +1,5 @@ + # Sensitivity analysis of HYMOD with FAST SPOTPY gives you the opportunity to start a sensitivity analysis of your model. In this case, we included a global sensitivity analysis called "FAST" based on @@ -8,7 +9,7 @@ The algorithm will tell you, how sensitive your parameters are based on whatever 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): -$$N = (1+4M^2(1+(k-2)d))k$$ + $$N = (1+4M^2(1+(k-2)d))k$$ 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. diff --git a/docs/img/FAST_sensitivity.png b/docs/img/FAST_sensitivity.png index 0aa706b624c7e200222c09665961c095570b672d..e0815f854e27198f1302eea7dd67256905701526 100644 GIT binary patch literal 39509 zcmeFa2UL~!nl(TQm$1RS0?`UdkXJKKX>Y;4UXXO3fq`eyVzk94HEgkvY)A_v5q&5F`YT?g6ZENcvKeuA|gQKL& z_MVLA?s>+E{VvYc`e(X(YloA1J1XnW_!YL=%HdA%h!s({<}lwyNk3de-|XM=>jCdhaOTWvEMUhJ&8u>IW3)}eZv#Oms7=l5254pIVQml)zK*)r^er$qF+3?n8@C-=cn^B7FCLT*H?zC_=U$|MGFXMOL(u^r`%N?pZES8*YC;2 z^0Ip)wVL7T9a7_dHMwmifoTnGGw`~zlCjSHZKiF-{)3zI7KDX{S=Xnslb)=26sMQW 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zNnN&J4EqEWmX*URyK~+5x&P}JvBx18lnvWhnh9vh5TU@Wjvp$=Md;qX-S=ko6F5UWXfO_G>PYh)T}30u$dea=D2Xx#@v&V$^2&jp65eYOsD#=zI{W#Ar1*Mx~jnPEJr`SC7kL zk9_`WZ|KgRf06IM?tQBQyLa|nNcmP`ckewq_*ZG%z4zZ4{C|Gze~23Y*^>W#O9ppX Ze(`$qYsH>BWE4_2u4=0m{Bq~_{|7jdRr&w` diff --git a/spotpy/analyser.py b/spotpy/analyser.py index f7eb0aa6..690d363e 100644 --- a/spotpy/analyser.py +++ b/spotpy/analyser.py @@ -283,13 +283,13 @@ def plot_parameter_trace(ax, results, parameter): #THis function plots the parameter setting for each run for i in range(int(max(results['chain']))): index=np.where(results['chain']==i) - ax.plot(results['par'+parameter['name']][index],'.') + ax.plot(results['par'+parameter['name']][index],'.', markersize=2) ax.set_ylabel(parameter['name']) ax.set_ylim(parameter['minbound'], parameter['maxbound']) def plot_posterior_parameter_histogram(ax, results, parameter): - #This functing is plotting the last 10% of the results - ax.hist(results['par'+parameter['name']][int(len(results)*0.9):], + #This functing is the last 100 runs + ax.hist(results['par'+parameter['name']][-100:], bins =np.linspace(parameter['minbound'],parameter['maxbound'],20)) ax.set_ylabel('Density') ax.set_xlim(parameter['minbound'], parameter['maxbound']) @@ -468,7 +468,7 @@ def plot_fast_sensitivity(results,like_index=1,number_of_sensitiv_pars=10,fig_na import matplotlib.pyplot as plt parnames=get_parameternames(results) - fig=plt.figure(figsize=(16,6)) + fig=plt.figure(figsize=(9,6)) ax = plt.subplot(1,1,1) Si = get_sensitivity_of_fast(results, like_index=like_index) @@ -530,7 +530,7 @@ def plot_fast_sensitivity(results,like_index=1,number_of_sensitiv_pars=10,fig_na ax.plot(np.arange(-1,len(parnames)+1,1),[threshold]*(len(parnames)+2),'r--') ax.set_xlim(-0.5,len(parnames)-0.5) plt.tight_layout() - fig.savefig(fig_name,dpi=300) + fig.savefig(fig_name,dpi=150) def plot_heatmap_griewank(results,algorithms, fig_name='heatmap_griewank.png'): @@ -989,7 +989,7 @@ def plot_gelman_rubin(results, r_hat_values,fig_name='gelman_rub.png'): Output: Plot as seen for e.g. in (Sadegh and Vrugt 2014)''' import matplotlib.pyplot as plt - fig= plt.figure(figsize=(12,16)) + fig= plt.figure(figsize=(9,6)) ax1 = plt.subplot(2,1,1) for i in range(int(max(results['chain']))+1): index=np.where(results['chain']==i) @@ -1008,7 +1008,7 @@ def plot_gelman_rubin(results, r_hat_values,fig_name='gelman_rub.png'): ax2.set_ylabel('R$^d$ - convergence diagnostic') ax2.set_xlabel('Number of chainruns') ax2.legend() - fig.savefig(fig_name,dpi=300) + fig.savefig(fig_name,dpi=150) def gelman_rubin(x): '''NOT USED YET''' diff --git a/spotpy/examples/tutorial_dream_hymod.py b/spotpy/examples/tutorial_dream_hymod.py index 36de14fc..57da1852 100644 --- a/spotpy/examples/tutorial_dream_hymod.py +++ b/spotpy/examples/tutorial_dream_hymod.py @@ -40,8 +40,8 @@ acceptance_test_option = 6 sampler=spotpy.algorithms.dream(spot_setup, dbname='DREAM_hymod', dbformat='csv') - r_hat = sampler.sample(rep, nChains, nCr, eps, convergence_limit, - runs_after_convergence,acceptance_test_option) + #r_hat = sampler.sample(rep, nChains, nCr, eps, convergence_limit, + # runs_after_convergence,acceptance_test_option) @@ -53,7 +53,7 @@ # Example plot to show remaining parameter uncertainty # - fig= plt.figure(figsize=(16,9)) + fig= plt.figure(figsize=(9,6)) ax = plt.subplot(1,1,1) q5,q25,q75,q95=[],[],[],[] for field in fields: @@ -63,16 +63,16 @@ ax.plot(q95,color='dimgrey',linestyle='solid') ax.fill_between(np.arange(0,len(q5),1),list(q5),list(q95),facecolor='dimgrey',zorder=0, linewidth=0,label='parameter uncertainty') - ax.plot(spot_setup.evaluation(),'r.',label='data') + ax.plot(spot_setup.evaluation(),color='red', markersize=2,label='data') ax.set_ylim(-50,450) ax.set_xlim(0,729) ax.legend() - fig.savefig('python_hymod.png',dpi=300) + fig.savefig('DREAM_simulation_uncertainty_Hymod.png',dpi=150) ######################################################### # Example plot to show the convergence ################# - spotpy.analyser.plot_gelman_rubin(results, r_hat) + spotpy.analyser.plot_gelman_rubin(results, r_hat, fig_name='DREAM_r_hat.png') ######################################################## @@ -82,6 +82,8 @@ parameters = spotpy.parameter.get_parameters_array(spot_setup) fig, ax = plt.subplots(nrows=5, ncols=2) + fig.set_figheight(9) + fig.set_figwidth(9) for par_id in range(len(parameters)): plot_parameter_trace(ax[par_id][0], results, parameters[par_id]) plot_posterior_parameter_histogram(ax[par_id][1], results, parameters[par_id]) @@ -90,5 +92,6 @@ ax[-1][1].set_xlabel('Parameter range') plt.show() - fig.savefig('hymod_parameters.png',dpi=300) + fig.tight_layout() + fig.savefig('DREAM_parameter_uncertainty_Hymod.png',dpi=300) ####################################################### \ No newline at end of file diff --git a/spotpy/examples/tutorial_sceua_hymod.py b/spotpy/examples/tutorial_sceua_hymod.py index 171ce0d9..abd2b380 100644 --- a/spotpy/examples/tutorial_sceua_hymod.py +++ b/spotpy/examples/tutorial_sceua_hymod.py @@ -35,12 +35,12 @@ #Plot how the objective function was minimized during sampling - fig= plt.figure(1,figsize=(9,5)) + fig= plt.figure(1,figsize=(9,6)) plt.plot(results['like1']) plt.show() plt.ylabel('RMSE') plt.xlabel('Iteration') - fig.savefig('SCEUA_objectivefunctiontrace.png',dpi=300) + fig.savefig('SCEUA_objectivefunctiontrace.png',dpi=150) # Plot the best model run #Find the run_id with the minimal objective function value @@ -53,12 +53,12 @@ fields=[word for word in best_model_run.dtype.names if word.startswith('sim')] best_simulation = list(best_model_run[fields]) - fig= plt.figure(figsize=(16,9)) + fig= plt.figure(figsize=(9,6)) ax = plt.subplot(1,1,1) ax.plot(best_simulation,color='black',linestyle='solid', label='Best objf.='+str(bestobjf)) ax.plot(spot_setup.evaluation(),'r.',markersize=3, label='Observation data') plt.xlabel('Number of Observation Points') plt.ylabel ('Discharge [l s-1]') plt.legend(loc='upper right') - fig.savefig('SCEUA_best_modelrun.png',dpi=300) + fig.savefig('SCEUA_best_modelrun.png',dpi=150) From ad10c09e16115c73755b2b99465c00931ff36a75 Mon Sep 17 00:00:00 2001 From: thouska Date: Fri, 25 Sep 2020 14:48:21 +0200 Subject: [PATCH 28/28] Add figures to docs --- docs/Sensitivity_analysis_with_FAST.md | 2 +- docs/img/DREAM_parameter_uncertainty_Hymod.png | Bin 0 -> 649645 bytes docs/img/DREAM_r_hat.png | Bin 0 -> 135143 bytes .../img/DREAM_simulation_uncertainty_Hymod.png | Bin 0 -> 61843 bytes spotpy/examples/tutorial_dream_hymod.py | 12 +++++++----- 5 files changed, 8 insertions(+), 6 deletions(-) create mode 100644 docs/img/DREAM_parameter_uncertainty_Hymod.png create mode 100644 docs/img/DREAM_r_hat.png create mode 100644 docs/img/DREAM_simulation_uncertainty_Hymod.png diff --git a/docs/Sensitivity_analysis_with_FAST.md b/docs/Sensitivity_analysis_with_FAST.md index 4015d351..a0ed499b 100644 --- a/docs/Sensitivity_analysis_with_FAST.md +++ b/docs/Sensitivity_analysis_with_FAST.md @@ -9,7 +9,7 @@ The algorithm will tell you, how sensitive your parameters are based on whatever iterations you need to get an reliable information about your parameter. 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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): - $$N = (1+4M^2(1+(k-2)d))k$$ +$$N = (1+4M^2(1+(k-2)d))k$$ 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. diff --git a/docs/img/DREAM_parameter_uncertainty_Hymod.png b/docs/img/DREAM_parameter_uncertainty_Hymod.png new file mode 100644 index 0000000000000000000000000000000000000000..e29b48e21a795f4dee080179a3aeee477520c8d9 GIT binary patch literal 649645 zcmeFZcTiN_(msm$RS70giLVMGAVJ9)MZf_8$r(f>N68sXpdg?~P;$;N0z;09f`F2P zWDv;=h%n@Edo$yE4(B_+e{R*UZr!SnDh0>Q-fOSbtNZDvpI$HS%1MzNqdZ1LL_~tR zeM^yu=-5l-&tHe(lSw+sCHTf~FQIO)WNl>cq;G3TB&%<4V_|J?VS4{>M?+gXQ)??u zHcmE?Oib8_ojCJr|2|o)cbI~%;|qY{a-x0Yd{NEwWIw(vRF8Z-aOBs+s)15$ zIlIXG%1ld~fQU;KNjsM;Jo?|i;u`Z-OaJ`?1C+xMhlf?%yN>LB 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