''' Created on 26 sep. 2011 @author: jhkwakkel ''' import matplotlib.pyplot as plt from expWorkbench.util import load_results from analysis.plotting import envelopes data = load_results(r'./data/2000 flu cases no policy.bz2') fig, axes_dict = envelopes(data, group_by='policy') plt.savefig("./pictures/basic_envelope.png", dpi=75)
figure = plt.figure() outcomes = result.keys() for i, field in enumerate(outcomes): number = str(len(outcomes))+'1'+str(i+1) ax = figure.add_subplot(number) ax.plot(result.get(field)) ax.text(1, 0.05, field, ha = 'right', transform = ax.transAxes) plt.show() def perform_experiments(): ema_logging.log_to_stderr(level=ema_logging.INFO) model = SalinizationModel(r"C:\workspace\EMA-workbench\models\salinization", "verzilting") model.step = 4 ensemble = ModelEnsemble() ensemble.set_model_structure(model) ensemble.parallel = True nr_of_experiments = 10000 results = ensemble.perform_experiments(nr_of_experiments) return results if __name__ == "__main__": results = perform_experiments() plotting.envelopes(results) plt.show()
''' Created on Jul 8, 2014 @author: [email protected] ''' import matplotlib.pyplot as plt from util import ema_logging from util.util import load_results from analysis.plotting import envelopes from analysis.plotting_util import KDE ema_logging.log_to_stderr(ema_logging.INFO) file_name = r'./data/1000 flu cases.tar.gz' results = load_results(file_name) # the plotting functions return the figure and a dict of axes fig, axes = envelopes(results, group_by='policy', density=KDE, fill=True) # we can access each of the axes and make changes for key, value in axes.iteritems(): # the key is the name of the outcome for the normal plot # and the name plus '_density' for the endstate distribution if key.endswith('_density'): value.set_xscale('log') plt.show()
''' Created on 26 sep. 2011 @author: jhkwakkel ''' import matplotlib.pyplot as plt from expWorkbench import load_results from analysis.plotting import envelopes data = load_results(r'../../../src/analysis/1000 flu cases.cPickle', zipped=False) fig = envelopes(data, group_by='policy') plt.show()
''' import matplotlib.pyplot as plt from expWorkbench import load_results from analysis.plotting import envelopes import analysis.plotting_util as plottingUtil # force matplotlib to use tight layout # see http://matplotlib.sourceforge.net/users/tight_layout_guide.html # for details plottingUtil.TIGHT= True #get the data results = load_results(r'.\data\TFSC_corrected.bz2') # make an envelope fig, axesdict = envelopes(results, outcomes_to_show=['total fraction new technologies'], group_by='policy', grouping_specifiers=['No Policy', 'Basic Policy', 'Optimized Adaptive Policy'], legend=False, density='kde', fill=True,titles=None) # set the size of the figure to look reasonable nice fig.set_size_inches(8,5) # save figure plt.savefig("./pictures/policy_comparison.png", dpi=75)
''' Created on 26 sep. 2011 @author: jhkwakkel ''' import matplotlib.pyplot as plt from expWorkbench import load_results from analysis.plotting import envelopes data = load_results(r'../../../src/analysis/1000 flu cases.cPickle', zipped=False) fig = envelopes(data, group_by='policy', grouping_specifiers=['static policy', 'adaptive policy']) plt.show()
model = EnergyTrans(r'..\..\models\EnergyTrans', "ESDMAElecTrans") model.step = 4 #reduce data to be stored ensemble = ModelEnsemble() ensemble.set_model_structure(model) policies = [{'name': 'no policy', 'file': r'\ESDMAElecTrans_NoPolicy.vpm'}, {'name': 'basic policy', 'file': r'\ESDMAElecTrans_basic_policy.vpm'}, {'name': 'tech2', 'file': r'\ESDMAElecTrans_tech2.vpm'}, {'name': 'econ', 'file': r'\ESDMAElecTrans_econ.vpm'}, {'name': 'adaptive policy', 'file': r'\ESDMAElecTrans_adaptive_policy.vpm'}, {'name': 'ap with op', 'file': r'\ESDMAElecTrans_ap_with_op.vpm'}, ] ensemble.add_policies(policies) #turn on parallel ensemble.parallel = True #run policy with old cases results = ensemble.perform_experiments(10) envelopes(results, column='policy') plt.show()