# population that is ill at any given point in time
deaths = outcomes['deceased population region 1'][:, -1]
peak = np.max(outcomes['infected fraction R1'], axis=1)
deaths = [(deaths[i], i) for i in range(deaths.shape[0])]
deaths = sorted(deaths, reverse=True)
death_indices = [death[1] for death in deaths]
peak = [(peak[i], i) for i in range(peak.shape[0])]
peak = sorted(peak, reverse=True)
peak_indices = [element[1] for element in peak]
# combine the top 20 of both
indices = death_indices[0:20]
[indices.append(entry) for entry in peak_indices[0:20]]
# how much can we nest....
# we first do the set to remove duplicates, next we cast it to a list
# so we can sort it. The sorting might be superfluous.
indices = sorted(list(set(indices)))
indices = np.asarray(indices)
#lets modify the default time label
plotting.TIME_LABEL = "Time (months)"
# visualize the results on top of an envelope
# do not show titles for the axes
lines(results, experiments_to_show=indices, show_envelope=True, titles=None)
plt.savefig("./pictures/advanced_lines.png", dpi=75)
''' Created on 26 sep. 2011 @author: jhkwakkel ''' import matplotlib.pyplot as plt from expWorkbench import load_results from analysis.plotting import lines data = load_results(r'../../../src/analysis/1000 flu cases.cPickle') fig = lines(data, group_by='fatality ratio region 1') plt.show()
death_indices = [death[1] for death in deaths]
peak = [(peak [i], i) for i in range(peak.shape[0])]
peak = sorted(peak, reverse=True)
peak_indices = [element[1] for element in peak]
# combine the top 20 of both
indices = death_indices[0:20]
[indices.append(entry) for entry in peak_indices[0:20]]
# how much can we nest....
# we first do the set to remove duplicates, next we cast it to a list
# so we can sort it. The sorting might be superfluous.
indices = sorted(list(set(indices)))
indices = np.asarray(indices)
#lets modify the default time label
plotting.TIME_LABEL = "Time (months)"
# visualize the results on top of an envelope
# do not show titles for the axes
lines(results,
experiments_to_show=indices,
show_envelope=True,
titles=None)
plt.savefig("./pictures/advanced_lines.png", dpi=75)
susceptible_population_region_1 = susceptible_population_region_1_NEXT
susceptible_population_region_2 = susceptible_population_region_2_NEXT
immune_population_region_1 = immune_population_region_1_NEXT
immune_population_region_2 = immune_population_region_2_NEXT
deceased_population_region_1.append(deceased_population_region_1_NEXT)
deceased_population_region_2.append(deceased_population_region_2_NEXT)
#End of main code
return (runTime, deceased_population_region_1) #, Max_infected, Max_time)
if __name__ == "__main__":
import expWorkbench.ema_logging as logging
np.random.seed(150) #set the seed for replication purposes
logging.log_to_stderr(logging.INFO)
fluModel = MexicanFlu(None, "mexicanFluExample")
ensemble = ModelEnsemble()
ensemble.parallel = True
ensemble.set_model_structure(fluModel)
nr_experiments = 500
results = ensemble.perform_experiments(nr_experiments, reporting_interval=100)
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()
''' Created on 26 sep. 2011 @author: jhkwakkel ''' import matplotlib.pyplot as plt from expWorkbench import load_results from analysis.plotting import lines, KDE data = load_results(r'../../../src/analysis/1000 flu cases.cPickle', zipped=False) fig = lines(data, density=KDE, hist=True) plt.show()
new_outcomes['avg. price'] = outcomes['avg price']
new_outcomes['fraction non-fossil'] = new_outcomes['total non-fossil'] / new_outcomes['total generation']
# create the time dimension including 2006 as a starting year
time = np.arange(0, new_outcomes['avg. price'].shape[1])+2006
time = np.tile(time, (new_outcomes['avg. price'].shape[0],1))
new_outcomes["TIME"] = time
results = (experiments, new_outcomes)
# create a lines plot on top of an envelope
fig, axes_dict = lines(results,
density='kde',
outcomes_to_show=['total capacity',
'total generation',
'avg. price',
'fraction non-fossil'],
show_envelope=True,
experiments_to_show=np.random.randint(0, new_outcomes['avg. price'].shape[0], (5,)),
titles=None,
)
# use the returned axes dict to modify the ylim on one of the outcomes
axes_dict['fraction non-fossil'].set_ylim(ymin=0, ymax=1)
axes_dict['fraction non-fossil_density'].set_ylim(ymin=0, ymax=1)
# transform the figure to black and white
set_fig_to_bw(fig)
plt.savefig("./pictures/jotke_envelopes.png", dpi=75)
outcomes = [
Outcome('sheep', time=True),
Outcome('wolves', time=True),
Outcome('grass', time=True) # TODO patches not working in reporting
]
if __name__ == "__main__":
#turn on logging
ema_logging.log_to_stderr(ema_logging.INFO)
#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
#perform experiments
results = ensemble.perform_experiments(100, reporting_interval=1)
plotting.lines(results, density=plotting_util.KDE)
plt.show()
susceptible_population_region_2 = susceptible_population_region_2_NEXT
immune_population_region_1 = immune_population_region_1_NEXT
immune_population_region_2 = immune_population_region_2_NEXT
deceased_population_region_1.append(deceased_population_region_1_NEXT)
deceased_population_region_2.append(deceased_population_region_2_NEXT)
#End of main code
return (runTime, deceased_population_region_1) #, Max_infected, Max_time)
if __name__ == "__main__":
np.random.seed(150) #set the seed for replication purposes
ema_logging.log_to_stderr(ema_logging.INFO)
fluModel = MexicanFlu(None, "mexicanFluExample")
ensemble = ModelEnsemble()
ensemble.parallel = True
ensemble.model_structure = fluModel
nr_experiments = 500
results = ensemble.perform_experiments(nr_experiments, reporting_interval=100)
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()
#load the data
experiments, outcomes = load_results(r'Data/200 runs.bz2')
results = (experiments, outcomes)
'''==============================================================================
print out the lines, envelope and KDE
=============================================================================='''
desired__nr_lines = 200
nr_cases = experiments.shape[0]
indices = np.arange(0, nr_cases, nr_cases/desired__nr_lines)
#for key in ['repayment months']:
for key, value in outcomes.items():
fig, axes = lines(results, outcomes_to_show=key, density=KDE,
show_envelope=True, experiments_to_show=indices,
titles="")
# plt.show()
#
n = key
plt.savefig("./pictures/KDE_200_{}.png".format(n), dpi=75)
'''==============================================================================
to print for only selected outomes and group by model:
=============================================================================='''
#for key in ['total revenue Yearly']:
# fig, axes = envelopes(results, outcomes_to_show=key, density=KDE,
# group_by='model', titles="",fill=True)
# create the time dimension including 2006 as a starting year
time = np.arange(0, new_outcomes['avg. price'].shape[1]) + 2006
time = np.tile(time, (new_outcomes['avg. price'].shape[0], 1))
new_outcomes["TIME"] = time
results = (experiments, new_outcomes)
# create a lines plot on top of an envelope
fig, axes_dict = lines(
results,
density='kde',
outcomes_to_show=[
'total capacity', 'total generation', 'avg. price',
'fraction non-fossil'
],
show_envelope=True,
experiments_to_show=np.random.randint(0,
new_outcomes['avg. price'].shape[0],
(5, )),
titles=None,
)
# use the returned axes dict to modify the ylim on one of the outcomes
axes_dict['fraction non-fossil'].set_ylim(ymin=0, ymax=1)
axes_dict['fraction non-fossil_density'].set_ylim(ymin=0, ymax=1)
# transform the figure to black and white
set_fig_to_bw(fig)
plt.savefig("./pictures/jotke_envelopes.png", dpi=75)
#specification of the outcomes
outcomes = [Outcome("B4:B1076", time=True), #we can refer to a range in the normal way
Outcome("P_t", time=True)] # we can also use named range
#name of the sheet
sheet = "Sheet1"
#relative path to the Excel file
workbook = r'\excel example.xlsx'
if __name__ == "__main__":
logger = ema_logging.log_to_stderr(level=ema_logging.INFO)
model = ExcelModel(r"..\..\models\excelModel", "predatorPrey")
ensemble = ModelEnsemble()
ensemble.set_model_structure(model)
ensemble.parallel = True #turn on parallel computing
ensemble.processes = 2 #using only 2 cores
#generate 100 cases
results = ensemble.perform_experiments(10)
lines(results)
plt.show()
#save results
# save_results(results, r'..\..\excel runs.cPickle')
"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")
ensemble = ModelEnsemble()
ensemble.set_model_structure(model)
#run policy with old cases
results = ensemble.perform_experiments(100)
lines(results, 'Energy Level', density=BOXPLOT)
plt.show()
[(-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")
ensemble = ModelEnsemble()
ensemble.model_structure = model
# run policy with old cases
results = ensemble.perform_experiments(100)
lines(results, "Energy Level", density=BOXPLOT)
plt.show()
=============================================================================='''
#for key in ['repayment months']:
for key, value in outcomes.items():
ooi_name = 'percentage_of_early_majority_with_ISG_appliances'
ooi = outcomes[ooi_name]
ooi = np.reshape(ooi, (ooi.shape[0]*ooi.shape[1], ooi.shape[2]))
temp_outcomes = {ooi_name: ooi}
desired__nr_lines = 200
nr_cases = experiments.shape[0]
indices = np.arange(0, nr_cases, nr_cases/desired__nr_lines)
lines((ooi, temp_outcomes), density=KDE,
show_envelope=True, experiments_to_show=indices,
titles="")
plt.show()
# n = key
# plt.savefig("./pictures/KDE_200_{}.png".format(n), dpi=75)
'''==============================================================================
to print for only selected outomes and group by model:
=============================================================================='''
#for key in ['total revenue Yearly']:
# fig, axes = envelopes(results, outcomes_to_show=key, density=KDE,
# group_by='model', titles="",fill=True)
]
outcomes = [
Outcome("sheep", time=True),
Outcome("wolves", time=True),
Outcome("grass", time=True), # TODO patches not working in reporting
]
if __name__ == "__main__":
# turn on logging
ema_logging.log_to_stderr(ema_logging.INFO)
# instantiate a model
vensimModel = PredatorPrey(r"..\..\models\predatorPreyNetlogo", "simpleModel")
# instantiate an ensemble
ensemble = ModelEnsemble()
# set the model on the ensemble
ensemble.set_model_structure(vensimModel)
# run in parallel, if not set, FALSE is assumed
ensemble.parallel = True
# perform experiments
results = ensemble.perform_experiments(100)
plotting.lines(results, density=plotting.KDE)
plt.show()
''' Created on 26 sep. 2011 @author: jhkwakkel ''' import matplotlib.pyplot as plt from expWorkbench import load_results from analysis.plotting import lines data = load_results(r'../../../src/analysis/1000 flu cases.cPickle') fig = lines(data, group_by='fatality ratio region 1') plt.show()
"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")
ensemble = ModelEnsemble()
ensemble.model_structure = model
#run policy with old cases
results = ensemble.perform_experiments(100)
lines(results, 'Energy Level', density=BOXPLOT)
plt.show()
'''
Created on 26 sep. 2011
@author: jhkwakkel
'''
import matplotlib.pyplot as plt
from expWorkbench import load_results
from analysis.plotting import lines, KDE
data = load_results(r'../../../src/analysis/1000 flu cases.cPickle',
zipped=False)
fig = lines(data, density=KDE, hist=True)
plt.show()
