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')
Exemplo n.º 2
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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')
Exemplo n.º 5
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# 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()
Exemplo n.º 6
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# 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()