def test_box():
EMAlogging.log_to_stderr(EMAlogging.INFO)
x = np.loadtxt(r'quasiflow x.txt')
y = np.loadtxt(r'quasiflow y.txt')
# prim = prim_box(x, y, pasting=True, threshold = 0, threshold_type = -1)
prim = perform_prim(x, y, pasting=True, threshold = 0, threshold_type =-1)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(x[:,0], x[:, 1], c=y)
print ' \tmass\tmean'
for i, entry in enumerate(prim[0:-1]):
print 'found box %s:\t%s\t%s' %(i, entry.box_mass, entry.y_mean)
print 'rest box :\t%s\t%s' %(prim[-1].box_mass, prim[-1].y_mean)
colors = graphs.COLOR_LIST
for i, box in enumerate(prim):
box = box.box
# print box
x = np.array([box[0,0], box[1,0], box[1,0], box[0,0], box[0,0]])
y = np.array([box[0,1], box[0,1], box[1,1], box[1,1], box[0,1]])
# print x
# print y
ax.plot(x,y, c=colors[i%len(colors)], lw=4)
plt.show()
def perform_experiments():
logger = EMAlogging.log_to_stderr(level=EMAlogging.INFO)
model = SalinizationModel(r"C:\eclipse\workspace\EMA-workbench\models\salinization", "verzilting")
model.step = 4
ensemble = SimpleModelEnsemble()
ensemble.set_model_structure(model)
policies=[{'name': 'no policy',
'file': r'\verzilting 2.vpm'},
{'name': 'policy group 8',
'file': r'\group 8 best policy.vpm'},
{'name': 'policy other group',
'file': r'\other group best policy.vpm'},
{'name': 'policies combined',
'file': r'\best policies combined.vpm'}
]
ensemble.add_policies(policies)
ensemble.parallel = True
nr_of_experiments = 1000
results = ensemble.perform_experiments(nr_of_experiments)
return results
# try:
# self.modelFile = policy['file']
# except:
# EMAlogging.debug("no policy specified")
super(EnergyTrans, self).model_init(policy, kwargs)
#pop name
policy = copy.copy(policy)
policy.pop('name')
for key, value in policy.items():
vensim.set_value(key, value)
if __name__ == "__main__":
logger = logging.log_to_stderr(logging.INFO)
model = EnergyTrans(r"..\VensimModels\TFSC", "ESDMAElecTrans")
ensemble = SimpleModelEnsemble()
ensemble.set_model_structure(model)
cases, uncertainties = ensemble._generate_cases(1)
valuelist = [15.467089994193 , 18.3948367845855 , 17.5216359599053 , 0.0323513175268276 , 0.0267216806566911 , 0.0252897989265933 , 0.0211748970259063 , 0.0192967619764282 , 0.0298868721235403 , 0.026846492561752 , 0.0282265728603356 , 0.0274643497911105 , 0.0206173186487346 , 0.930953610229856 , 1.05807449426449 , 58.6261672319115 , 1.0959476696141 , 48.4897275078371 , 79.8968117041453 , 2.03012275630195 , 2.33576352581696 , 2.60266175740213 , 1.24700542123355 , 3.06884098418713 , 1 , 0 , 0 , 0 , 0 , 1.45807445678444 , 3.53395235847141 , 1.75257486371618 , 2.9795030911447 , 4.00199168664975 , 1.97473349200058 , 4.74196793795403 , 4.72730891245437 , 0 , 0 , 14826.4074143275 , 1.24609526886412 , 1.18827514220571 , 1.09824115488565 , 1245886.83942348 , 6282282.69560999 , 6118827.67237203 , 9531496.10651471 , 8693813.50295679 , 32.948697875027 , 17.1705785135149 , 13.0971274404015 , 3.74255065304761 , 1.36231655867486 , 1.92101352688469 , 3.8941723138427 , 0.898745338298322 , 0.782806406356795 , 0.817631734201507 , 0.705822656618514 , 43.3820783577107]
newcases = []
case = {}
i=0
for uncertainty in uncertainties:
print uncertainty.name
'''
Created on 29 sep. 2011
@author: jhkwakkel
'''
import numpy as np
import matplotlib.pyplot as plt
from expWorkbench import load_results
from analysis import prim
from expWorkbench import EMAlogging
from analysis.graphs import envelopes
EMAlogging.log_to_stderr(EMAlogging.INFO)
results = load_results(r'C:\workspace\EMA-workbench\models\TFSC_all_policies.cPickle')
envelopes(results,
column='policy',
categories=['adaptive policy',
'ap with op'])
#exp, res = results
#
##get out only the results related to the last policy
#exp, res = results
#
#logical = exp['policy'] == 'adaptive policy'
#exp = exp[logical]
#
#temp_res = {}
#for key, value in res.items():
ParameterUncertainty((10, 100), "normal contact rate region 1"),
ParameterUncertainty((10, 200), "normal contact rate region 2"),
]
def model_init(self, policy, kwargs):
"""initializes the model"""
try:
self.modelFile = policy["file"]
except KeyError:
logging.warning("key 'file' not found in policy")
super(FluModel, self).model_init(policy, kwargs)
if __name__ == "__main__":
logging.log_to_stderr(logging.INFO)
model = FluModel(r"..\..\models\flu", "fluCase")
ensemble = SimpleModelEnsemble()
ensemble.set_model_structure(model)
# add policies
policies = [
{"name": "no policy", "file": r"\FLUvensimV1basecase.vpm"},
{"name": "static policy", "file": r"\FLUvensimV1static.vpm"},
{"name": "adaptive policy", "file": r"\FLUvensimV1dynamic.vpm"},
]
ensemble.add_policies(policies)
ensemble.parallel = True # turn on parallel processing
import numpy as np
import matplotlib.pyplot as plt
import analysis.prim as prim
from expWorkbench import load_results
import expWorkbench.EMAlogging as EMAlogging
#perform_prim logs information to the logger
EMAlogging.log_to_stderr(level=EMAlogging.INFO)
def classify(data):
#get the output for deceased population
result = data['deceased population region 1']
#make an empty array of length equal to number of cases
classes = np.zeros(result.shape[0])
#if deceased population is higher then 1.000.000 people, classify as 1
classes[result[:, -1] > 1000000] = 1
return classes
#load data
results = load_results(r'../analysis/1000 flu cases.cPickle')
experiments, results = results
#extract results for 1 policy
logicalIndex = experiments['policy'] == 'no policy'
newExperiments = experiments[ logicalIndex ]
newResults = {}
for key, value in results.items():
def perform_loop_knockout():
unique_edges = [['In Goods', 'lost'],
['loss unprofitable extraction capacity', 'decommissioning extraction capacity'],
['production', 'In Goods'],
['production', 'lost'],
['production', 'Supply'],
['Real Annual Demand', 'substitution losses'],
['Real Annual Demand', 'price elasticity of demand losses'],
['Real Annual Demand', 'desired extraction capacity'],
['Real Annual Demand', 'economic demand growth'],
['average recycling cost', 'relative market price'],
['recycling fraction', 'lost'],
['commissioning recycling capacity', 'Recycling Capacity Under Construction'],
['maximum amount recyclable', 'recycling fraction'],
['profitability recycling', 'planned recycling capacity'],
['relative market price', 'price elasticity of demand losses'],
['constrained desired recycling capacity', 'gap between desired and constrained recycling capacity'],
['profitability extraction', 'planned extraction capacity'],
['commissioning extraction capacity', 'Extraction Capacity Under Construction'],
['desired recycling', 'gap between desired and constrained recycling capacity'],
['Installed Recycling Capacity', 'decommissioning recycling capacity'],
['Installed Recycling Capacity', 'loss unprofitable recycling capacity'],
['average extraction costs', 'profitability extraction'],
['average extraction costs', 'relative attractiveness recycling']]
unique_cons_edges = [['recycling', 'recycling'],
['recycling', 'supply demand ratio'],
['decommissioning recycling capacity', 'recycling fraction'],
['returns to scale', 'relative attractiveness recycling'],
['shortage price effect', 'relative price last year'],
['shortage price effect', 'profitability extraction'],
['loss unprofitable extraction capacity', 'loss unprofitable extraction capacity'],
['production', 'recycling fraction'],
['production', 'constrained desired recycling capacity'],
['production', 'new cumulatively recycled'],
['production', 'effective fraction recycled of supplied'],
['loss unprofitable recycling capacity', 'recycling fraction'],
['average recycling cost', 'loss unprofitable recycling capacity'],
['recycling fraction', 'new cumulatively recycled'],
['substitution losses', 'supply demand ratio'],
['Installed Extraction Capacity', 'Extraction Capacity Under Construction'],
['Installed Extraction Capacity', 'commissioning extraction capacity'],
['Installed Recycling Capacity', 'Recycling Capacity Under Construction'],
['Installed Recycling Capacity', 'commissioning recycling capacity'],
['average extraction costs', 'profitability extraction']]
# CONSTRUCTING THE ENSEMBLE AND SAVING THE RESULTS
EMAlogging.log_to_stderr(EMAlogging.INFO)
results = load_results(r'base.cPickle')
# GETTING OUT THOSE BEHAVIOURS AND EXPERIMENT SETTINGS
# Indices of a number of examples, these will be looked at.
runs = [526,781,911,988,10,780,740,943,573,991]
VOI = 'relative market price'
results_of_interest = experiment_settings(results,runs,VOI)
cases_of_interest = experiments_to_cases(results_of_interest[0])
behaviour_int = results_of_interest[1][VOI]
# CONSTRUCTING INTERVALS OF ATOMIC BEHAVIOUR PATTERNS
ints = intervals(behaviour_int,False)
# GETTING OUT ONLY THOSE OF MAXIMUM LENGTH PER BEHAVIOUR
max_intervals = intervals_interest(ints)
# THIS HAS TO DO WITH THE MODEL FORMULATION OF THE SWITCHES/VALUES
double_list = [6,9,11,17,19]
indCons = len(unique_edges)
# for elem in unique_cons_edges:
# unique_edges.append(elem)
current = os.getcwd()
for beh_no in range(0,10):
# beh_no = 0 # Varies between 0 and 9, index style.
interval = max_intervals[beh_no]
rmp = behaviour_int[beh_no]
# rmp = rmp[interval[0]:interval[1]]
x = range(0,len(rmp))
fig = plt.figure()
ax = fig.add_subplot(111)
vensim.be_quiet()
# for loop_index in range(7,8):
for loop_index in range(1,len(unique_edges)+1):
if loop_index-indCons > 0:
model_location = current + r'\Models\Consecutive\Metals EMA.vpm'
elif loop_index == 0:
model_location = current + r'\Models\Base\Metals EMA.vpm'
else:
model_location = current + r'\Models\Switches\Metals EMA.vpm'
serie = run_interval(model_location,loop_index,
interval,'relative market price',
unique_edges,indCons,double_list,
cases_of_interest[beh_no])
if serie.shape != rmp.shape:
EMAlogging.info('Loop %s created a floating point error' % (loop_index))
EMAlogging.info('Caused by trying to switch %s' % (unique_edges[loop_index-1]))
if serie.shape == rmp.shape:
ax.plot(x,serie,'b')
# data = np.zeros(rmp.shape[0])
# data[0:serie.shape[0]] = serie
# ax.plot(x,data,'b')
ax.plot(x,rmp,'r')
ax.axvspan(interval[0]-1,interval[1], facecolor='lightgrey', alpha=0.5)
f_name = 'switched unique edges only'+str(beh_no)
plt.savefig(f_name)
plt.xlim(0, 801)
color = ['grey', 'lightgrey']
for i, int in enumerate(intervals):
no = np.mod(i, len(color))
begin = int[0]
end = int[1]
plt.axvspan(begin, end, facecolor=color[no], alpha=0.5)
plt.show()
if __name__ == "__main__":
# CONSTRUCTING THE ENSEMBLE AND SAVING THE RESULTS
EMAlogging.log_to_stderr(EMAlogging.DEBUG)
#
# model = ScarcityModel(r'D:\tbm-g367\workspace\EMA workbench\src\sandbox\sils',"scarcity")
#
# ensemble = ModelEnsemble()
# ensemble.set_model_structure(model)
## ensemble.parallel = True
# results = ensemble.perform_experiments(1000)
# save_results(results, r'base.cPickle')
results = load_results(r'base.cPickle')
# PLOTS FOR ENSEMBLE
# fig = make_interactive_plot(results, outcomes=['relative market price'], type='lines')
# fig = lines(results, outcomes = ['relative market price'], density=True, hist=False)
# plt.show()
'''
Created on 20 sep. 2011
.. codeauthor:: jhkwakkel <j.h.kwakkel (at) tudelft (dot) nl>
'''
import numpy as np
import matplotlib.pyplot as plt
from analysis.graphs import multiplot_scatter, multiplot_density, multiplot_lines
from expWorkbench.util import load_results
import expWorkbench.EMAlogging as logging
logging.log_to_stderr(level=logging.DEFAULT_LEVEL)
#load the data
experiments, results = load_results(r'..\..\src\analysis\1000 flu cases.cPickle')
#transform the results to the required format
newResults = {}
#get time and remove it from the dict
time = results.pop('TIME')
for key, value in results.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
"""Method for retrieving output after a model run """
return self.result
def optimize(self, case, policy):
"""method called when using the model in an optimization context
this method should return a single value that represents the performance of the policy
params are the same as for run model
"""
raise NotImplementedError
def reset_model(self):
"""Method for reseting the model to its initial state before runModel was called"""
self.modelInterface.resetModel()
if __name__ == '__main__':
logger = logging.log_to_stderr(logging.DEBUG)
# emailHander = logging.TlsSMTPHandler(("smtp.gmail.com", 587),
# '*****@*****.**',
# ['*****@*****.**'],
# 'finished!',
# ('*****@*****.**', 'password'))
# emailHander.setLevel(logging.WARNING)
# logger.addHandler(emailHander)
model = ElectTransEMA(r'C:\workspace\ElectTransEMA\workingDirectory', "test")
ensemble = SimpleModelEnsemble()
ensemble.set_model_structure(model)
ensemble.parallel=True
results = ensemble.perform_experiments(10)