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')
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')
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')
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()