def test_pairs_density():
results = load_results(r'..\data\eng_trans_100.cPickle', zipped=False)
# pairs_density(results)
# pairs_density(results, colormap='binary')
pairs_density(results, group_by='policy', grouping_specifiers=['no policy'])
plt.show()
def test_pairs_density():
results = util.load_eng_trans_data()
# pairs_density(results)
# pairs_density(results, colormap='binary')
pairs_density(results, group_by='policy', grouping_specifiers=['no policy'])
plt.show()
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():
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')
from expWorkbench.util import load_results
from expWorkbench import ema_logging
ema_logging.log_to_stderr(level=ema_logging.DEFAULT_LEVEL)
#load the data
experiments, outcomes = load_results(r'.\data\100 flu cases no policy.bz2')
#transform the results to the required format
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
tr['max peak'] = np.max(value, axis=1)
# 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
logicalIndex = value.T==np.max(value, axis=1)
tr['time of max'] = time[logicalIndex.T]
pairs_scatter((experiments, tr), filter_scalar=False)
pairs_lines((experiments, outcomes))
pairs_density((experiments, tr), filter_scalar=False)
plt.show()
from expWorkbench.util import load_results
from expWorkbench import ema_logging
ema_logging.log_to_stderr(level=ema_logging.DEFAULT_LEVEL)
#load the data
experiments, outcomes = load_results(r'.\data\100 flu cases no policy.bz2')
#transform the results to the required format
newResults = {}
#get time and remove it from the dict
time = outcomes.pop('TIME')
for key, value in outcomes.items():
if key == 'deceased population region 1':
newResults[key] = value[:,-1] #we want the end value
else:
# we want the maximum value of the peak
newResults['max peak'] = np.max(value, axis=1)
# 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
logicalIndex = value.T==np.max(value, axis=1)
newResults['time of max'] = time[logicalIndex.T]
pairs_scatter((experiments, newResults))
pairs_lines((experiments, newResults))
pairs_density((experiments, newResults))
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()
import numpy as np
import matplotlib.pyplot as plt
from analysis.pairs_plotting import pairs_scatter, pairs_density, pairs_lines
from expWorkbench.util import load_results
#load the data
experiments, results = load_results(
r'../../../src/analysis/1000 flu cases.cPickle', zipped=False)
#transform the results to the required format
newResults = {}
#get time and remove it from the dict
time = results.pop('TIME')
for key, value in results.items():
if key == 'deceased population region 1':
newResults[key] = value[:, -1] #we want the end value
else:
# we want the maximum value of the peak
newResults['max peak'] = np.max(value, axis=1)
# 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
logicalIndex = value.T == np.max(value, axis=1)
newResults['time of max'] = time[logicalIndex.T]
pairs_density((experiments, newResults))
plt.show()
