Beispiel #1
0
def load_flu_data():
    path = os.path.dirname(__file__)
    fn = './data/1000 flu cases no policy.tar.gz'
    fn = os.path.join(path, fn)

    experiments, outcomes = load_results(fn)
    return experiments, outcomes
def load_flu_data():
    path = os.path.dirname(__file__)
    fn = './data/1000 flu cases no policy.tar.gz'
    fn = os.path.join(path, fn)

    experiments, outcomes = load_results(fn)
    return experiments, outcomes
Beispiel #3
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def load_eng_trans_data():
    path = os.path.dirname(__file__)
    fn = './data/eng_trans.tar.gz'
    fn = os.path.join(path, fn)

    experiments, outcomes = load_results(fn)
    return experiments, outcomes
Beispiel #4
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def load_scarcity_data():
    path = os.path.dirname(__file__)
    fn = './data/1000 runs scarcity.tar.gz'
    fn = os.path.join(path, fn)

    experiments, outcomes = load_results(fn)
    return experiments, outcomes
Beispiel #5
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def load_eng_trans_data():
    path = os.path.dirname(__file__)
    fn = './data/eng_trans.tar.gz'
    fn = os.path.join(path, fn)

    experiments, outcomes = load_results(fn)
    return experiments, outcomes
Beispiel #6
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def load_scarcity_data():
    path = os.path.dirname(__file__)
    fn = './data/1000 runs scarcity.tar.gz'
    fn = os.path.join(path, fn)

    experiments, outcomes = load_results(fn)
    return experiments, outcomes
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
fn = r'./data/1000 flu cases.tar.gz'
results = load_results(fn)
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_obj = prim.setup_prim(results, classify, threshold=0.8, threshold_type=1)
'''
Created on Jul 8, 2014

@author: [email protected]
'''
import matplotlib.pyplot as plt

from ema_workbench.util import ema_logging, load_results

from ema_workbench.analysis.plotting import envelopes 
from ema_workbench.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()
Beispiel #9
0
import sys
sys.path
sys.path.append('/home/philipp/Dropbox/Workspace_Zika_Models_Philipp/src')
del sys

from ema_workbench.util import merge_results, load_results, save_results

filename = 'EMA108_vFinal_FullRUNLaptop'
results2 = load_results(r'simulation_results/' + filename + '.tar.gz')

filename = 'EMA324_vFinal_FullRUN_12core'
results1 = load_results(r'simulation_results/' + filename + '.tar.gz')

merged = merge_results(results1, results2, downsample=None)
print('merge_1_successful')

results2, results1 = None, None

filename = 'EMA108_vFinal_FullRUN_hometv'
results3 = load_results(r'simulation_results/' + filename + '.tar.gz')
merged = merge_results(merged, results3, downsample=None)
print('merge_1_successful')

experiments, outcomes = merged

import pandas as pd
for key, value in outcomes.iteritems():
    outcomes[key] = pd.DataFrame.from_dict(value)
experiments = pd.DataFrame.from_dict(experiments)
experiments.dropna(axis=0, inplace=True)
for key, value in outcomes.iteritems():
Created on 20 sep. 2011

.. codeauthor:: jhkwakkel <j.h.kwakkel (at) tudelft (dot) nl>
'''
import numpy as np
import matplotlib.pyplot as plt

from ema_workbench.analysis.pairs_plotting import (pairs_lines, pairs_scatter, 
                                                   pairs_density)
from ema_workbench.util import load_results, ema_logging

ema_logging.log_to_stderr(level=ema_logging.DEFAULT_LEVEL)

# load the data
fh = r'.\data\1000 flu cases no policy.tar.gz'
experiments, outcomes = load_results(fh)

# transform the results to the required format
# that is, we want to know the max peak and the casualties at the end of the 
# run
tr = {}

# get time and remove it from the dict
time = outcomes.pop('TIME')

for key, value in outcomes.items():
    if key == 'deceased population region 1':
        tr[key] = value[:,-1] #we want the end value
    else:
        # we want the maximum value of the peak
        max_peak = np.max(value, axis=1) 
Created on Sep 8, 2011

.. codeauthor:: jhkwakkel <j.h.kwakkel (at) tudelft (dot) nl>
                gonengyucel

'''
import matplotlib.pyplot as plt

from ema_workbench.analysis.clusterer import cluster

from ema_workbench.util import ema_logging, load_results

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,