def test_pairs_density():
results = test_utilities.load_eng_trans_data()
pairs_density(results)
pairs_density(results, colormap='binary')
pairs_density(results, group_by='policy', grouping_specifiers=['no policy'])
plt.draw()
plt.close('all')
def test_pairs_density():
experiments, outcomes = utilities.load_eng_trans_data()
pairs_density(experiments, outcomes)
pairs_density(experiments, outcomes, colormap='binary')
pairs_density(experiments, outcomes, group_by='policy',
grouping_specifiers=['no policy'])
plt.draw()
plt.close('all')
def test_pairs_density():
results = test_utilities.load_eng_trans_data()
pairs_density(results)
pairs_density(results, colormap='binary')
pairs_density(results,
group_by='policy',
grouping_specifiers=['no policy'])
plt.draw()
plt.close('all')
def test_pairs_density():
experiments, outcomes = utilities.load_eng_trans_data()
pairs_density(experiments, outcomes)
pairs_density(experiments, outcomes, colormap='binary')
pairs_density(experiments,
outcomes,
group_by='policy',
grouping_specifiers=['no policy'])
plt.draw()
plt.close('all')
# 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()