def plot_all_lines(results, outcomes, grouped, save, title):
if grouped == True:
for kpi in list(outcomes.keys())[1:]: # drop first entry (TIME)
fig, axes = lines(results,
density=u'kde',
show_envelope=False,
outcomes_to_show=kpi,
group_by='policy')
if save == True:
plt.savefig('plots/scenario_policies/' +
time.strftime('%Y%m%d') + title +
'_lines_grouped_' + kpi + '.png',
dpi=1200)
else:
print(kpi + ' was not saved')
else:
for kpi in list(outcomes.keys())[1:]: # drop first entry (TIME)
fig, axes = lines(results,
density=u'kde',
show_envelope=False,
outcomes_to_show=kpi)
if save == True:
plt.savefig('plots/scenario_policies/' +
time.strftime('%Y%m%d') + title + '_lines_' + kpi +
'.png',
dpi=1200)
else:
print(kpi + ' was not saved')
def plot_individual_policies_lines(experiments, outcomes, policy,
outcomes_to_show):
'''Plot individual policy experiment sets'''
sliced_result = slice_results(experiments, outcomes, policy)
fig = lines(sliced_result, outcomes_to_show=outcomes_to_show, density=KDE)
print('Plot of policy: ', policy)
self.uncertainties = [LookupUncertainty('hearne2', [(-1, 3), (-2, 1), (0, 0.9), (0.1, 1), (0.99, 1.01), (0.99, 1.01)],
"accomplishments per hour lookup", self, 0, 1),
LookupUncertainty('hearne2', [(-0.75, 0.75), (-0.75, 0.75), (0, 1.5), (0.1, 1.6), (-0.3, 1.5), (0.25, 2.5)],
"fractional change in expectations from perceived adequacy lookup", self, -1, 1),
LookupUncertainty('hearne2', [(-2, 2), (-1, 2), (0, 1.5), (0.1, 1.6), (0.5, 2), (0.5, 2)],
"effect of perceived adequacy on energy drain lookup", self, 0, 10),
LookupUncertainty('hearne2', [(-2, 2), (-1, 2), (0, 1.5), (0.1, 1.6), (0.5, 1.5), (0.1, 2)],
"effect of perceived adequacy of hours worked lookup", self, 0, 2.5),
LookupUncertainty('hearne2', [(-1, 1), (-1, 1), (0, 0.9), (0.1, 1), (0.5, 1.5), (1, 1.5)],
"effect of energy levels on hours worked lookup", self, 0, 1.5),
LookupUncertainty('hearne2', [(-1, 1), (-1, 1), (0, 0.9), (0.1, 1), (0.5, 1.5), (1, 1.5)],
"effect of high energy on further recovery lookup", self, 0, 1.25),
LookupUncertainty('hearne2', [(-2, 2), (-1, 1), (0, 100), (20, 120), (0.5, 1.5), (0.5, 2)],
"effect of hours worked on energy recovery lookup", self, 0, 1.5),
LookupUncertainty('approximation', [(-0.5, 0.35), (3, 5), (1, 10), (0.2, 0.4), (0, 120)],
"effect of hours worked on energy drain lookup", self, 0, 3),
LookupUncertainty('hearne1', [(0, 1), (0, 0.15), (1, 1.5), (0.75, 1.25)],
"effect of low energy on further depletion lookup", self, 0, 1)]
self._delete_lookup_uncertainties()
if __name__ == "__main__":
ema_logging.log_to_stderr(ema_logging.INFO)
model = Burnout(r'./models/burnout', "burnout")
# run policy with old cases
results = perform_experiments(model, 100)
lines(results, 'Energy Level', density=BOXPLOT)
plt.show()
RealParameter("x22", 0.0001, 0.1),
RealParameter("x31", 0, 0.5),
RealParameter("x32", 0, 0.5),
RealParameter("x41", 0, 0.9),
RealParameter("x51", 0, 0.5),
RealParameter("x52", 0, 0.5),
RealParameter("x61", 0, 0.8),
RealParameter("x62", 0, 0.8),
RealParameter("x81", 1, 10),
RealParameter("x82", 1, 10),
RealParameter("x91", 0, 0.1),
RealParameter("x92", 0, 0.1),
RealParameter("x101", 0, 200),
RealParameter("x102", 0, 200),
]
model.outcomes = [TimeSeriesOutcome("TIME"), TimeSeriesOutcome("deceased_population_region_1")]
nr_experiments = 500
results = perform_experiments(model, nr_experiments, parallel=True)
lines(
results,
outcomes_to_show="deceased_population_region_1",
show_envelope=True,
density=KDE,
titles=None,
experiments_to_show=np.arange(0, nr_experiments, 10),
)
plt.show()
RealParameter('x61', 0, 0.8),
RealParameter('x62', 0, 0.8),
RealParameter('x81', 1, 10),
RealParameter('x82', 1, 10),
RealParameter('x91', 0, 0.1),
RealParameter('x92', 0, 0.1),
RealParameter('x101', 0, 200),
RealParameter('x102', 0, 200)
]
model.outcomes = [
TimeSeriesOutcome("TIME"),
TimeSeriesOutcome("deceased_population_region_1")
]
nr_experiments = 500
results = perform_experiments(model, 10)
# with MultiprocessingPoolEvaluator(model) as evaluator:
# results = perform_experiments(model, nr_experiments,
# evaluator=evaluator)
lines(results,
outcomes_to_show="deceased_population_region_1",
show_envelope=True,
density=KDE,
titles=None,
experiments_to_show=np.arange(0, nr_experiments, 10))
plt.show()
ema_logging.log_to_stderr(ema_logging.INFO)
model = VensimModel(
"PredPrey", wd='.', model_file='PredPrey.vpmx'
) # CANNOT MAKE A VPM FILE, SO THIS CONNECTOR CANNOT BE USED?
# outcomes
model.outcomes = [
TimeSeriesOutcome('predators'),
TimeSeriesOutcome('prey')
]
# Plain Parametric Uncertainties
model.uncertainties = [
RealParameter('prey birth rate', 0.015, 0.35),
RealParameter('predation rate', 0.0005, 0.003),
RealParameter('predator efficiency', 0.001, 0.004),
RealParameter('predator loss rate', 0.04, 0.08),
]
nr_experiments = 10
with MultiprocessingEvaluator(model) as evaluator:
results = perform_experiments(model,
nr_experiments,
evaluator=evaluator)
import matplotlib.pyplot as plt
from ema_workbench.analysis.plotting import lines
figure = lines(results, density=True) #show lines, and end state density
plt.show() #show figure
from ema_workbench import load_results
from ema_workbench.analysis import clusterer, plotting, Density
experiments, outcomes = load_results('./data/1000 flu cases no policy.tar.gz')
data = outcomes['infected fraction R1']
# calcuate distances
distances = clusterer.calculate_cid(data)
# plot dedrog
clusterer.plot_dendrogram(distances)
# do agglomerative clustering on the distances
clusters = clusterer.apply_agglomerative_clustering(distances,
n_clusters=5)
# show the clusters in the output space
x = experiments.copy()
x['clusters'] = clusters.astype('object')
plotting.lines(x, outcomes, group_by='clusters',
density=Density.BOXPLOT)
# show the input space
sns.pairplot(x, hue='clusters',
vars=['infection ratio region 1', 'root contact rate region 1',
'normal contact rate region 1', 'recovery time region 1',
'permanent immune population fraction R1'],
plot_kws=dict(s=7))
plt.show()
ooi_name = "sheep"
outcome = outcomes[ooi_name]
outcome = outcome/default_flow
ooi = np.zeros(outcome.shape[0])
temp_outcomes = {ooi_name: ooi}
print ooi.shape
desired__nr_lines = 5
nr_cases = ooi.shape[0]
indices = np.arange(0, nr_cases, nr_cases/desired__nr_lines)
lines(results, outcomes_to_show = 'sheep', density=KDE,
show_envelope=True,
experiments_to_show=indices)
#n = key
#plt.savefig("./pictures/adopter_category_bounded_no.png".format(key), dpi=75)
plt.show()
print(outcome)
#print(results)
Outcome('grass', time=True) # TODO patches not working in reporting
]
if __name__ == "__main__":
import multiprocessing
ema_logging.LOG_FORMAT = multiprocessing.util.DEFAULT_LOGGING_FORMAT
#turn on logging
ema_logging.log_to_stderr(ema_logging.DEBUG)
ema_logging.info('in main')
#instantiate a model
fh = r"./models/predatorPreyNetlogo"
model = PredatorPrey(fh, "simpleModel")
#instantiate an ensemble
ensemble = ModelEnsemble()
#set the model on the ensemble
ensemble.model_structure = model
#run in parallel, if not set, FALSE is assumed
ensemble.parallel = True
ensemble.processes = 2
#perform experiments
results = ensemble.perform_experiments(10, reporting_interval=1)
plotting.lines(results, density=plotting_util.KDE)
plt.show()
import numpy as np
from ema_workbench import ema_logging, load_results
from ema_workbench.analysis.plotting import lines
from ema_workbench.analysis.plotting_util import KDE
ema_logging.log_to_stderr(ema_logging.INFO)
file_name = r'./data/1000 flu cases with policies.tar.gz'
experiments, outcomes = load_results(file_name)
# let's specify a few scenarios that we want to show for
# each of the policies
scenario_ids = np.arange(0, 1000, 100)
experiments_to_show = experiments['scenario'].isin(scenario_ids)
# the plotting functions return the figure and a dict of axes
fig, axes = lines(experiments, outcomes, group_by='policy',
density=KDE, show_envelope=True,
experiments_to_show=experiments_to_show)
# 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 protected] ''' 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()
from ema_workbench import load_results
from ema_workbench.analysis import clusterer, plotting, Density
experiments, outcomes = load_results('./data/1000 flu cases no policy.tar.gz')
data = outcomes['infected fraction R1']
# calcuate distances
distances = clusterer.calculate_cid(data)
# plot dedrog
clusterer.plot_dendrogram(distances)
# do agglomerative clustering on the distances
clusters = clusterer.apply_agglomerative_clustering(distances, n_clusters=5)
# show the clusters in the output space
x = experiments.copy()
x['clusters'] = clusters.astype('object')
plotting.lines(x, outcomes, group_by='clusters', density=Density.BOXPLOT)
# show the input space
sns.pairplot(x,
hue='clusters',
vars=[
'infection ratio region 1', 'root contact rate region 1',
'normal contact rate region 1', 'recovery time region 1',
'permanent immune population fraction R1'
],
plot_kws=dict(s=7))
plt.show()
