def test_pairs_scatter(): experiments, outcomes = utilities.load_eng_trans_data() pairs_scatter(experiments, outcomes) pairs_scatter(experiments, outcomes, group_by='policy', grouping_specifiers='basic policy', legend=False) pairs_scatter(experiments, outcomes, group_by='policy', grouping_specifiers=['no policy', 'adaptive policy']) plt.draw() plt.close('all')
def test_pairs_scatter(): results = test_utilities.load_eng_trans_data() pairs_scatter(results) pairs_scatter(results, group_by='policy', grouping_specifiers='basic policy', legend=False) pairs_scatter(results, group_by='policy', grouping_specifiers=['no policy', 'adaptive policy']) plt.draw() plt.close('all')
policies=4, levers_sampling=MC) ##################################################################################################### # process the results of the experiments experiments, outcomes = results print(experiments.shape) print(list(outcomes.keys())) stop = time.time() print(f"Runtime in minutes: { ((stop-start)/60) }") from ema_workbench.analysis import pairs_plotting fig, axes = pairs_plotting.pairs_scatter(experiments, outcomes, group_by='policy', legend=False) hue = 'policy' fig.set_size_inches(8, 8) plt.show() ##PLOTTEN LUKT NIET # from ema_workbench.analysis import plotting, plotting_util # # # for outcome in outcomes.keys(): # plotting.lines(experiments, outcomes, outcomes_to_show=outcome, # density=plotting_util.Density.HIST) # plt.show() # # Printing as done on workbench website example
# load the data fh = './data/1000 flu cases no policy.tar.gz' experiments, outcomes = load_results(fh) # transform the results to the required format # that is, we want to know the max peak and the casualties at the end of the # run tr = {} # get time and remove it from the dict time = outcomes.pop('TIME') for key, value in outcomes.items(): if key == 'deceased population region 1': tr[key] = value[:, -1] #we want the end value else: # we want the maximum value of the peak max_peak = np.max(value, axis=1) tr['max peak'] = max_peak # we want the time at which the maximum occurred # the code here is a bit obscure, I don't know why the transpose # of value is needed. This however does produce the appropriate results logical = value.T == np.max(value, axis=1) tr['time of max'] = time[logical.T] pairs_scatter((experiments, tr), filter_scalar=False) pairs_lines((experiments, outcomes)) pairs_density((experiments, tr), filter_scalar=False) plt.show()
# load the data fh = r'.\data\1000 flu cases no policy.tar.gz' experiments, outcomes = load_results(fh) # transform the results to the required format # that is, we want to know the max peak and the casualties at the end of the # run tr = {} # get time and remove it from the dict time = outcomes.pop('TIME') for key, value in outcomes.items(): if key == 'deceased population region 1': tr[key] = value[:,-1] #we want the end value else: # we want the maximum value of the peak max_peak = np.max(value, axis=1) tr['max peak'] = max_peak # we want the time at which the maximum occurred # the code here is a bit obscure, I don't know why the transpose # of value is needed. This however does produce the appropriate results logical = value.T==np.max(value, axis=1) tr['time of max'] = time[logical.T] pairs_scatter((experiments, tr), filter_scalar=False) pairs_lines((experiments, outcomes)) pairs_density((experiments, tr), filter_scalar=False) plt.show()