def test_pairs_density():
results = load_results(r'..\data\eng_trans_100.cPickle', zipped=False)
# pairs_density(results)
# pairs_density(results, colormap='binary')
pairs_density(results, group_by='policy', grouping_specifiers=['no policy'])
plt.show()
def test_envelopes3d_group_by():
results = expWorkbench.load_results(r'1000 flu cases.cPickle')
envelopes3d_group_by(results,
outcome='infected fraction R1',
groupBy="normal interregional contact rate",
logSpace=True)
def test_prim_init_select(self):
self.results = load_results(r'../data/1000 flu cases no policy.bz2')
self.classify = flu_classify
experiments, outcomes = self.results
unc = experiments.dtype.descr
# test initialization, including t_coi calculation in case of searching
# for results equal to or higher than the threshold
outcomes['death toll'] = outcomes['deceased population region 1'][:, -1]
results = experiments, outcomes
threshold = 10000
prim_obj = prim.Prim(results, classify='death toll',
threshold_type=prim.ABOVE, threshold=threshold,
inc_unc=unc)
value = np.ones((experiments.shape[0],))
value = value[outcomes['death toll'] >= threshold].shape[0]
self.assertTrue(prim_obj.t_coi==value)
# test initialization, including t_coi calculation in case of searching
# for results equal to or lower than the threshold
threshold = 1000
prim_obj = prim.Prim(results, classify='death toll',
threshold_type=prim.BELOW,
threshold=threshold)
value = np.ones((experiments.shape[0],))
value = value[outcomes['death toll'] <= threshold].shape[0]
self.assertTrue(prim_obj.t_coi==value)
prim.Prim(self.results, self.classify, threshold=prim.ABOVE)
def test_kde_over_time():
results = load_results(r'./../data/eng_trans_100.cPickle', zipped=False)
# kde_over_time(results, log=False)
# kde_over_time(results, log=True)
kde_over_time(results, group_by='policy', grouping_specifiers=['no policy', 'adaptive policy'])
plt.show()
def test_prepare_outcomes(self):
fn = r'../data/1000 flu cases no policy.tar.gz'
results = load_results(fn)
# string type correct
ooi = 'nr deaths'
results[1][ooi] = results[1]['deceased population region 1'][:,-1]
y, categorical = fs._prepare_outcomes(results[1], ooi)
self.assertFalse(categorical)
self.assertTrue(len(y.shape)==1)
# string type not correct --> KeyError
with self.assertRaises(KeyError):
fs._prepare_outcomes(results[1], "non existing key")
# classify function correct
def classify(data):
result = data['deceased population region 1']
classes = np.zeros(result.shape[0])
classes[result[:, -1] > 1000000] = 1
return classes
y, categorical = fs._prepare_outcomes(results[1], classify)
self.assertTrue(categorical)
self.assertTrue(len(y.shape)==1)
# neither string nor classify function --> TypeError
with self.assertRaises(TypeError):
fs._prepare_outcomes(results[1], 1)
def test_get_univariate_feature_scores(self):
fn = r'../data/1000 flu cases no policy.tar.gz'
results = load_results(fn)
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
# f classify
scores = fs.get_univariate_feature_scores(results, classify)
self.assertEqual(len(scores), len(results[0].dtype.fields))
# chi2
scores = fs.get_univariate_feature_scores(results, classify,
score_func='chi2')
self.assertEqual(len(scores), len(results[0].dtype.fields))
# f regression
ooi = 'nr deaths'
results[1][ooi] = results[1]['deceased population region 1'][:,-1]
scores = fs.get_univariate_feature_scores(results, ooi)
self.assertEqual(len(scores), len(results[0].dtype.fields))
def test_get_rf_feature_scores(self):
fn = r'../data/1000 flu cases no policy.tar.gz'
results = load_results(fn)
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
scores, forest = fs.get_rf_feature_scores(results, classify,
random_state=10)
self.assertEqual(len(scores), len(results[0].dtype.fields))
self.assertTrue(isinstance(forest, RandomForestClassifier))
ooi = 'nr deaths'
results[1][ooi] = results[1]['deceased population region 1'][:,-1]
scores, forest = fs.get_rf_feature_scores(results, ooi,
random_state=10)
self.assertEqual(len(scores), len(results[0].dtype.fields))
self.assertTrue(isinstance(forest, RandomForestRegressor))
def test_pairs_lines():
results = load_results(r'..\data\eng_trans_100.cPickle', zipped=False)
pairs_lines(results)
# set_fig_to_bw(pairs_lines(results)[0])
pairs_lines(results, group_by='policy')
# set_fig_to_bw(pairs_lines(results, group_by='policy')[0])
plt.show()
def test_pca(self):
results = load_results(r'../data/1000 flu cases no policy.bz2')
classify = flu_classify
prim_obj = prim.Prim(results, classify,
threshold=0.8)
prim_obj.perform_pca()
def test_in_box(self):
results = load_results(r'../data/1000 flu cases no policy.bz2')
prim_obj = prim.Prim(results, flu_classify, threshold=0.8)
box = prim_obj.make_box(results[0])
# I need an encompassing box
# the shape[0] of the return should be equal to experiment.shape[0]
# assuming that the box is an encompassing box
self.assertEqual(prim_obj.in_box(box).shape[0], results[0].shape[0])
def test_select(self):
results = load_results(r'../data/1000 flu cases no policy.bz2')
classify = flu_classify
prim_obj = prim.Prim(results, classify,
threshold=0.8)
box = prim_obj.find_box()
sb = 27
box.select(sb)
self.assertEqual(len(box.mean), sb+1)
def test_envelopes3d():
results = expWorkbench.load_results(r"1000 flu cases.cPickle")
exp, res = results
logical = exp["policy"] == "adaptive policy"
new_exp = exp[logical][0:100]
new_res = {}
for key, value in res.items():
new_res[key] = value[logical][0:100, :]
envelopes3d((new_exp, new_res), "infected fraction R1", logSpace=True)
def test_prim_init_exception(self):
results = load_results(r'../data/1000 flu cases no policy.bz2')
self.assertRaises(prim.PrimException,
prim.Prim,
results,
'deceased population region 1',
threshold=0.8)
def faulty_classify(outcomes):
return outcomes['deceased population region 1'][:, 0:10]
self.assertRaises(prim.PrimException, prim.Prim, results,
faulty_classify, threshold=0.8)
def test_pairs_scatter():
results = load_results(r'..\data\eng_trans_100.cPickle', zipped=False)
pairs_scatter(results)
# set_fig_to_bw(pairs_scatter(results)[0])
pairs_scatter(results, group_by='policy',
grouping_specifiers='basic policy', legend=False)
# set_fig_to_bw(pairs_scatter(results, group_by='policy')[0])
pairs_scatter(results, group_by='policy',
grouping_specifiers=['no policy', 'adaptive policy'])
# set_fig_to_bw(pairs_scatter(results, group_by='policy', legend=False)[0])
plt.show()
def test_multiple_densities():
results = load_results(r'..\data\eng_trans_100.cPickle', zipped=False)
# multiple_densities(results,
# outcome_to_show="total fraction new technologies",
# group_by="policy",
# points_in_time = [2000])
# multiple_densities(results,
# outcome_to_show="total fraction new technologies",
# group_by="policy",
# points_in_time = [2000, 2100])
# multiple_densities(results,
# outcome_to_show="total fraction new technologies",
# group_by="policy",
# points_in_time = [2000, 2020, 2100])
# multiple_densities(results,
# outcome_to_show="total fraction new technologies",
# group_by="policy",
# points_in_time = [2000, 2020, 2040, 2060])
# multiple_densities(results,
# outcome_to_show="total fraction new technologies",
# group_by="policy",
# points_in_time = [2020, 2040, 2060, 2080, 2100])
# multiple_densities(results,
# outcome_to_show="total fraction new technologies",
# group_by="policy",
# grouping_specifiers="no policy",
# points_in_time = [2000, 2020, 2040, 2060, 2080, 2100],
# plot_type=ENV_LIN,
# experiments_to_show=[1,2,10])
# multiple_densities(results,
# outcome_to_show="total fraction new technologies",
# group_by="policy",
# grouping_specifiers="no policy",
# points_in_time = [2000, 2020, 2040, 2060, 2080, 2100],
# plot_type=ENVELOPE,
# experiments_to_show=[1,2,10])
multiple_densities(results,
# group_by="policy",
# grouping_specifiers="no policy",
points_in_time = [2040, 2045, 2050, 2060,2070,2080],
plot_type=ENVELOPE,
density=KDE,
log=False
# experiments_to_show=[np.arange(0, 100, 20)]
)
plt.show()
def test_group_results():
results = load_results(r'./../data/eng_trans_100.cPickle', zipped=False)
experiments, outcomes = results
# test indices
grouping_specifiers = {'set1':np.arange(0,11),
'set2':np.arange(11,25),
'set3':np.arange(25,experiments.shape[0])}
groups = group_results(experiments, outcomes,
group_by='index',
grouping_specifiers=grouping_specifiers)
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print experiments.shape[0], total_data
# test continuous parameter type
array = experiments['average planning and construction period T1']
grouping_specifiers = make_continuous_grouping_specifiers(array, nr_of_groups=5)
groups = group_results(experiments, outcomes,
group_by='average planning and construction period T1',
grouping_specifiers=grouping_specifiers)
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print experiments.shape[0], total_data
# test integer type
array = experiments['seed PR T1']
grouping_specifiers = make_continuous_grouping_specifiers(array, nr_of_groups=10)
groups = group_results(experiments, outcomes,
group_by='seed PR T1',
grouping_specifiers=grouping_specifiers)
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print experiments.shape[0], total_data
# test categorical type
grouping_specifiers = set(experiments["policy"])
groups = group_results(experiments, outcomes,
group_by='policy',
grouping_specifiers=grouping_specifiers)
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print experiments.shape[0], total_data
def test_show_boxes(self):
# results = load_results(r'../data/1000 flu cases no policy.bz2')
# classify = flu_classify
results = load_results(r'../data/scarcity 1000.bz2')
classify = scarcity_classify
prim_obj = prim.Prim(results, classify,
threshold=0.7)
prim_obj.find_box()
prim_obj.find_box()
prim_obj.write_boxes_to_stdout()
prim_obj.show_boxes()
plt.show()
def test_compare(self):
self.results = load_results(r'../data/scarcity 1000.bz2')
self.classify = scarcity_classify
prim_obj = prim.Prim(self.results, self.classify, threshold=0.8)
# all dimensions the same
a = np.array([(0,1),
(0,1)],
dtype=[('a', np.float),
('b', np.float)])
b = np.array([(0,1),
(0,1)],
dtype=[('a', np.float),
('b', np.float)])
self.assertTrue(np.all(prim_obj.compare(a,b)))
# all dimensions different
a = np.array([(0,1),
(0,1)],
dtype=[('a', np.float),
('b', np.float)])
b = np.array([(1,1),
(0,0)],
dtype=[('a', np.float),
('b', np.float)])
test = prim_obj.compare(a,b)==False
self.assertTrue(np.all(test))
# dimensions 1 different and dimension 2 the same
a = np.array([(0,1),
(0,1)],
dtype=[('a', np.float),
('b', np.float)])
b = np.array([(1,1),
(0,1)],
dtype=[('a', np.float),
('b', np.float)])
test = prim_obj.compare(a,b)
test = (test[0]==False) & (test[1]==True)
self.assertTrue(test)
def test_find_boxes(self):
results = load_results(r'../data/1000 flu cases no policy.bz2')
classify = flu_classify
prim_obj = prim.Prim(results, classify,
threshold=0.8)
box_1 = prim_obj.find_box()
prim_obj._update_yi_remaining()
after_find = box_1.yi.shape[0] + prim_obj.yi_remaining.shape[0]
self.assertEqual(after_find, prim_obj.y.shape[0])
box_2 = prim_obj.find_box()
prim_obj._update_yi_remaining()
after_find = box_1.yi.shape[0] +\
box_2.yi.shape[0] +\
prim_obj.yi_remaining.shape[0]
self.assertEqual(after_find, prim_obj.y.shape[0])
ema_logging.log_to_stderr(ema_logging.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] > 1500000] = 1
return classes
results = load_results(r".\data\1000 flu cases no policy.cPickle")
#perform prim on modified results tuple
res = pca_prim.perform_pca_prim(results,
classify,
mass_min=0.075,
threshold=0.8,
threshold_type=1)
rotation_matrix, row_names, column_names, rotated_experiments, boxes = res
#visualize results
prim.write_prim_to_stdout(boxes)
# we need to use the rotated experiments now
results = (rotated_experiments, results[1])
''' Created on 16 okt. 2012 @author: localadmin ''' from expWorkbench import load_results import numpy as np import matplotlib.pyplot as plt from analysis.plotting import lines from analysis import plotting results = load_results(r'.\data\2000 flu cases no policy.bz2') experiments, outcomes = results # get indices with of worst runs in terms of deaths and max fraction of # 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]
Created on Sep 8, 2011
.. codeauthor:: jhkwakkel <j.h.kwakkel (at) tudelft (dot) nl>
gonengyucel
'''
import matplotlib.pyplot as plt
from analysis.clusterer import cluster
from expWorkbench import load_results, ema_logging
ema_logging.log_to_stderr(ema_logging.INFO)
#load the data
data = load_results(r'..\examples\100 flu cases no policy.cPickle')
# specify the number of desired clusters
# note: the meaning of cValue is tied to the value for cMethod
cValue = 5
#perform cluster analysis
dist, clusteraloc, runlog, z = cluster(data=data,
outcome='deceased population region 1',
distance='gonenc',
interClusterDistance='complete',
cMethod = 'maxclust',
cValue = cValue,
plotDendrogram=False,
plotClusters=False,
groupPlot=False,
#self.uncertainties.pop()
self.uncertainties.append(LookupUncertainty([(0, 4), (1, 5), (1, 5), (0, 2), (0, 2)], "TF2", 'approximation', self, 0, 10))
#self.uncertainties.pop()
self.uncertainties.append(ParameterUncertainty((0.02, 0.08), "rate1"))
self.uncertainties.append(ParameterUncertainty((0.02, 0.08), "rate2"))
self.uncertainties.append(LookupUncertainty([[(0.0, 0.05), (0.25, 0.15), (0.5, 0.4), (0.75, 1), (1, 1.25)],
[(0.0, 0.1), (0.25, 0.25), (0.5, 0.75), (1, 1.25)],
[(0.0, 0.0), (0.1, 0.2), (0.3, 0.6), (0.6, 0.9), (1, 1.25)]], "TF3", 'categories', self, 0, 2))
#self.uncertainties.pop()
self.delete_lookup_uncertainties()
if __name__ == "__main__":
logger = logging.log_to_stderr(logging.INFO)
model = lookup_model(r'..\lookups', "sampleModel")
#model.step = 4 #reduce data to be stored
ensemble = ModelEnsemble()
ensemble.set_model_structure(model)
#turn on parallel
ensemble.parallel = False
#run policy with old cases
results = ensemble.perform_experiments(10)
save_results(results, 'lookup_3methods.cpickle')
results = load_results('lookup_3methods.cpickle')
outcomes =['TF', 'TF2', 'TF3', 'flow1']
lines(results, outcomes, density=True, hist=True)
plt.show()
'''
Created on 7 Sep 2011
@author: chamarat
'''
import matplotlib.pyplot as plt
from expWorkbench import load_results
from analysis.graphs import envelopes
results = load_results(r'.\data\TFSC_policies.cPickle')
fig = envelopes(results, column='policy', fill=True, legend=False)
fig = plt.gcf()
fig.set_size_inches(15, 5)
plt.savefig("policycomparison.png", dpi=75)
''' Created on 16 okt. 2012 @author: localadmin ''' from expWorkbench import load_results import numpy as np import matplotlib.pyplot as plt from analysis.plotting import lines from analysis import plotting results = load_results(r'.\data\2000 flu cases no policy.bz2') experiments, outcomes = results # get indices with of worst runs in terms of deaths and max fraction of # 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]
'''
Created on Aug 21, 2012
@author: jhkwakkel
'''
from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from expWorkbench import load_results
from analysis.plotting import lines, envelopes
from analysis.b_and_w_plotting import set_fig_to_bw
# load results
results = load_results(r'.\data\JotKE 50000.bz2')
experiments, outcomes = results
# pre process the data
new_outcomes = {}
new_outcomes[
'total capacity'] = outcomes['capa central'] + outcomes['capa decentral']
new_outcomes[
'total generation'] = outcomes['gen central'] + outcomes['gen decentral']
new_outcomes['total fossil'] = outcomes['central coal'] + outcomes[
'central gas'] + outcomes['decentral gas']
new_outcomes['total non-fossil'] = new_outcomes[
'total generation'] - new_outcomes['total fossil']
new_outcomes['avg. price'] = outcomes['avg price']
new_outcomes['fraction non-fossil'] = new_outcomes[
'total non-fossil'] / new_outcomes['total generation']
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()
# CONSTRUCTING THE CLUSTERS
# dRow, clusters, z = clusterer.cluster(results,
# outcome='relative market price',
# distance='gonenc',
# cMethod='maxclust',
# cValue=20,
# plotDendrogram=True,
# plotClusters=True,
# groupPlot=True)
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)
'''
Created on Sep 8, 2011
@author: gonengyucel, jhkwakkel
'''
import matplotlib.pyplot as plt
from analysis.clusterer import cluster
from expWorkbench import load_results
from expWorkbench import ema_logging
ema_logging.log_to_stderr(ema_logging.INFO)
#load the data
data = load_results(r'..\gallery\data\100 flu cases no policy.cPickle')
# specify the number of desired clusters
# note: the meaning of cValue is tied to the value for cMethod
cValue = 5
#perform cluster analysis
dist, clusteraloc, runlog, z = cluster(data=data,
outcome='deceased population region 1',
distance='gonenc',
interClusterDistance='complete',
cMethod = 'maxclust',
cValue = cValue,
plotDendrogram=False,
plotClusters=False,
groupPlot=False,
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
ema_logging.log_to_stderr(ema_logging.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:
ema_logging.info('Loop %s created a floating point error' % (loop_index))
ema_logging.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)
@author: chamarat
'''
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
from expWorkbench import load_results
from analysis.prim import perform_prim, write_prim_to_stdout
from analysis.prim import show_boxes_individually
def classify(data):
result = data["total fraction new technologies"]
classes = np.zeros(result.shape[0])
classes[result[:, -1] > 0.8] = 1
return classes
if __name__ == "__main__":
results = load_results(r"CESUN_optimized_1000_new.cPickle")
experiments, results = results
logicalIndex = experiments["policy"] == "Optimized Adaptive Policy"
newExperiments = experiments[logicalIndex]
newResults = {}
for key, value in results.items():
newResults[key] = value[logicalIndex]
results = (newExperiments, newResults)
boxes = perform_prim(results, "total fraction new technologies", threshold=0.6, threshold_type=-1)
write_prim_to_stdout(boxes)
show_boxes_individually(boxes, results)
plt.show()
''' Created on 26 sep. 2011 @author: jhkwakkel ''' import matplotlib.pyplot as plt from expWorkbench import load_results from analysis.graphs import lines data = load_results(r'../../../src/analysis/1000 flu cases.cPickle') fig = lines(data, column='fatality ratio region 1', density=False) plt.show()
from analysis.prim import perform_prim, write_prim_to_stdout
from analysis.prim import show_boxes_individually
def classify(data):
result = data['total fraction new technologies']
classes = np.zeros(result.shape[0])
classes[result[:, -1] > 0.8] = 1
return classes
if __name__ == '__main__':
results = load_results(r'CESUN_optimized_1000_new.cPickle')
experiments, results = results
logicalIndex = experiments['policy'] == 'Optimized Adaptive Policy'
newExperiments = experiments[logicalIndex]
newResults = {}
for key, value in results.items():
newResults[key] = value[logicalIndex]
results = (newExperiments, newResults)
boxes = perform_prim(results,
'total fraction new technologies',
threshold=0.6,
threshold_type=-1)
write_prim_to_stdout(boxes)
show_boxes_individually(boxes, results)
def test_lines3d():
results = expWorkbench.load_results(r'eng_trans_100.cPickle')
lines3d(results, outcomes=['installed capacity T1',
'installed capacity T2'])
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'../../../src/analysis/1000 flu cases.cPickle',
zipped=False)
experiments, results = results
#extract results for 1 policy
logicalIndex = experiments['policy'] == 'no policy'
newExperiments = experiments[logicalIndex]
newResults = {}
for key, value in results.items():
newResults[key] = value[logicalIndex]
results = (newExperiments, newResults)
#perform prim on modified results tuple
prims, uncertainties, x = prim.perform_prim(results,
classify,
threshold=0.8,
ema_logging.log_to_stderr(level=ema_logging.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'./data/1000 flu cases.bz2')
experiments, results = results
#extract results for 1 policy
logical = experiments['policy'] == 'no policy'
new_experiments = experiments[ logical ]
new_results = {}
for key, value in results.items():
new_results[key] = value[logical]
results = (new_experiments, new_results)
#perform prim on modified results tuple
prim = prim.Prim(results, classify, threshold=0.8, threshold_type=1)
box_1 = prim.find_box()
''' 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()
'''
Created on Oct 2, 2012
@author: sibeleker
'''
from expWorkbench import load_results
import matplotlib
import matplotlib.pyplot as plt
from analysis.graphs import lines, envelopes
results = load_results('burnout_1000_approx.cpickle')
'''
Created on Sep 8, 2011
@author: gonengyucel, jhkwakkel
'''
import matplotlib.pyplot as plt
from analysis.clusterer import cluster
from expWorkbench import load_results
from expWorkbench import ema_logging
ema_logging.log_to_stderr(ema_logging.INFO)
#load the data
data = load_results(r'..\gallery\data\100 flu cases no policy.bz2')
# specify the number of desired clusters
# note: the meaning of cValue is tied to the value for cMethod
cValue = 5
#perform cluster analysis
dRow, clusters, z = cluster(data=data,
outcome='deceased population region 1',
distance='gonenc',
interClusterDistance='complete',
cMethod='maxclust',
cValue=cValue,
plotDendrogram=False,
plotClusters=False,
groupPlot=False,