'''
Created on Jul 8, 2014

@author: <EMAIL>
'''
import matplotlib.pyplot as plt

from ema_workbench import ema_logging, load_results

from ema_workbench.analysis.plotting import lines
from ema_workbench.analysis.plotting_util import VIOLIN

ema_logging.log_to_stderr(ema_logging.INFO)

file_name = r'./data/1000 flu cases no policy.tar.gz'
experiments, outcomes = load_results(file_name)

# the plotting functions return the figure and a dict of axes
fig, axes = lines(experiments, outcomes, density=VIOLIN)

# we can access each of the axes and make changes
for key, value in axes.items():
    # the key is the name of the outcome for the normal plot
    # and the name plus '_density' for the endstate distribution
    if key.endswith('_density'):
        value.set_xscale('log')

plt.show()
'''
Created on Jul 8, 2014

@author: <EMAIL>
'''
import matplotlib.pyplot as plt

from ema_workbench import ema_logging, load_results

from ema_workbench.analysis.plotting import lines
from ema_workbench.analysis.plotting_util import VIOLIN

ema_logging.log_to_stderr(ema_logging.INFO)

file_name = r'./data/1000 flu cases no policy.tar.gz'
experiments, outcomes = load_results(file_name)

# the plotting functions return the figure and a dict of axes
fig, axes = lines(experiments, outcomes, density=VIOLIN)

# we can access each of the axes and make changes
for key, value in axes.items():
    # the key is the name of the outcome for the normal plot
    # and the name plus '_density' for the endstate distribution
    if key.endswith('_density'):
        value.set_xscale('log')

plt.show()
Created on Jul 8, 2014  @author: <EMAIL> # the plotting functions return the figure and a dict of axes # we can access each of the axes and make changes # the key is the name of the outcome for the normal plot # and the name plus '_density' for the endstate distribution