def test_pairs_lines(): results = test_utilities.load_eng_trans_data() pairs_lines(results) pairs_lines(results, group_by='policy') plt.draw() plt.close('all')
def test_pairs_lines(): results = load_results(r'..\data\eng_trans_100.cPickle', zipped=False) pairs_lines(results) # set_fig_to_bw(pairs_lines(results)[0]) pairs_lines(results, group_by='policy') # set_fig_to_bw(pairs_lines(results, group_by='policy')[0]) plt.show()
def test_pairs_lines(): results = util.load_eng_trans_data() pairs_lines(results) # set_fig_to_bw(pairs_lines(results)[0]) pairs_lines(results, group_by='policy') # set_fig_to_bw(pairs_lines(results, group_by='policy')[0]) 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 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()
import numpy as np import matplotlib.pyplot as plt from analysis.pairs_plotting import pairs_lines from expWorkbench.util import load_results #load the data data = load_results(r'../../../src/analysis/100 flu cases.cPickle', zipped=False) pairs_lines(data, group_by='policy') 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()