'''
Created on 26 sep. 2011
@author: jhkwakkel
'''
import matplotlib.pyplot as plt
from expWorkbench.util import load_results
from analysis.plotting import envelopes
data = load_results(r'./data/2000 flu cases no policy.bz2')
fig, axes_dict = envelopes(data, group_by='policy')
plt.savefig("./pictures/basic_envelope.png", dpi=75)
figure = plt.figure()
outcomes = result.keys()
for i, field in enumerate(outcomes):
number = str(len(outcomes))+'1'+str(i+1)
ax = figure.add_subplot(number)
ax.plot(result.get(field))
ax.text(1, 0.05,
field,
ha = 'right',
transform = ax.transAxes)
plt.show()
def perform_experiments():
ema_logging.log_to_stderr(level=ema_logging.INFO)
model = SalinizationModel(r"C:\workspace\EMA-workbench\models\salinization", "verzilting")
model.step = 4
ensemble = ModelEnsemble()
ensemble.set_model_structure(model)
ensemble.parallel = True
nr_of_experiments = 10000
results = ensemble.perform_experiments(nr_of_experiments)
return results
if __name__ == "__main__":
results = perform_experiments()
plotting.envelopes(results)
plt.show()
''' Created on Jul 8, 2014 @author: [email protected] ''' import matplotlib.pyplot as plt from util import ema_logging from util.util import load_results from analysis.plotting import envelopes from analysis.plotting_util import KDE ema_logging.log_to_stderr(ema_logging.INFO) file_name = r'./data/1000 flu cases.tar.gz' results = load_results(file_name) # the plotting functions return the figure and a dict of axes fig, axes = envelopes(results, group_by='policy', density=KDE, fill=True) # we can access each of the axes and make changes for key, value in axes.iteritems(): # 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 26 sep. 2011
@author: jhkwakkel
'''
import matplotlib.pyplot as plt
from expWorkbench import load_results
from analysis.plotting import envelopes
data = load_results(r'../../../src/analysis/1000 flu cases.cPickle',
zipped=False)
fig = envelopes(data, group_by='policy')
plt.show()
'''
import matplotlib.pyplot as plt
from expWorkbench import load_results
from analysis.plotting import envelopes
import analysis.plotting_util as plottingUtil
# force matplotlib to use tight layout
# see http://matplotlib.sourceforge.net/users/tight_layout_guide.html
# for details
plottingUtil.TIGHT= True
#get the data
results = load_results(r'.\data\TFSC_corrected.bz2')
# make an envelope
fig, axesdict = envelopes(results,
outcomes_to_show=['total fraction new technologies'],
group_by='policy',
grouping_specifiers=['No Policy',
'Basic Policy',
'Optimized Adaptive Policy'],
legend=False,
density='kde', fill=True,titles=None)
# set the size of the figure to look reasonable nice
fig.set_size_inches(8,5)
# save figure
plt.savefig("./pictures/policy_comparison.png", dpi=75)
'''
Created on 26 sep. 2011
@author: jhkwakkel
'''
import matplotlib.pyplot as plt
from expWorkbench import load_results
from analysis.plotting import envelopes
data = load_results(r'../../../src/analysis/1000 flu cases.cPickle',
zipped=False)
fig = envelopes(data,
group_by='policy',
grouping_specifiers=['static policy', 'adaptive policy'])
plt.show()
'''
Created on 26 sep. 2011
@author: jhkwakkel
'''
import matplotlib.pyplot as plt
from expWorkbench import load_results
from analysis.plotting import envelopes
data = load_results(r'../../../src/analysis/1000 flu cases.cPickle', zipped=False)
fig = envelopes(data,
group_by='policy',
grouping_specifiers=['static policy', 'adaptive policy'])
plt.show()
''' Created on 26 sep. 2011 @author: jhkwakkel ''' import matplotlib.pyplot as plt from expWorkbench import load_results from analysis.plotting import envelopes data = load_results(r'../../../src/analysis/1000 flu cases.cPickle', zipped=False) fig = envelopes(data, group_by='policy') plt.show()
model = EnergyTrans(r'..\..\models\EnergyTrans', "ESDMAElecTrans")
model.step = 4 #reduce data to be stored
ensemble = ModelEnsemble()
ensemble.set_model_structure(model)
policies = [{'name': 'no policy',
'file': r'\ESDMAElecTrans_NoPolicy.vpm'},
{'name': 'basic policy',
'file': r'\ESDMAElecTrans_basic_policy.vpm'},
{'name': 'tech2',
'file': r'\ESDMAElecTrans_tech2.vpm'},
{'name': 'econ',
'file': r'\ESDMAElecTrans_econ.vpm'},
{'name': 'adaptive policy',
'file': r'\ESDMAElecTrans_adaptive_policy.vpm'},
{'name': 'ap with op',
'file': r'\ESDMAElecTrans_ap_with_op.vpm'},
]
ensemble.add_policies(policies)
#turn on parallel
ensemble.parallel = True
#run policy with old cases
results = ensemble.perform_experiments(10)
envelopes(results, column='policy')
plt.show()
'''
import matplotlib.pyplot as plt
from expWorkbench import load_results
from analysis.plotting import envelopes
import analysis.plotting_util as plottingUtil
# force matplotlib to use tight layout
# see http://matplotlib.sourceforge.net/users/tight_layout_guide.html
# for details
plottingUtil.TIGHT= True
#get the data
results = load_results(r'.\data\TFSC_corrected.bz2')
# make an envelope
fig, axesdict = envelopes(results,
outcomes_to_show=['total fraction new technologies'],
group_by='policy',
grouping_specifiers=['No Policy',
'Basic Policy',
'Optimized Adaptive Policy'],
legend=False,
density='kde', fill=True,titles=None)
# set the size of the figure to look reasonable nice
fig.set_size_inches(8,5)
# save figure
plt.savefig("./pictures/policy_comparison.png", dpi=75)