In [1]:

    

%load_ext autoreload
%autoreload 2


    

In [2]:

    

import os
import sys
import numpy as np
import pandas as pd
import time


    

In [3]:

    

MNM_nb_folder = os.path.join('..', '..', '..', 'side_project', 'network_builder')
sys.path.append(MNM_nb_folder)
python_lib_folder = os.path.join('..', '..', 'pylib')
sys.path.append(python_lib_folder)


    

In [4]:

    

from MNM_nb import *
import MNMAPI
from DODE import *


    

In [7]:

    

a = MNMAPI.dta_api()


    

In [9]:

    

# a.initialize(os.path.join('..', '..', '..', 'data', 'input_files_PGH_emission'))


    

In [10]:

    

a.initialize(os.path.join('..', '..', '..', 'data', 'input_files_2link_fix'))


    

    
Out[10]:





0L

In [11]:

    

a.register_links(np.arange(1,5))


    

    
Out[11]:





0L

In [12]:

    

a.install_cc()


    

    
Out[12]:





0L

In [13]:

    

a.run_whole()


    

    
Out[13]:





0L

In [14]:

    

a.get_link_inflow(np.arange(0, 30), np.arange(0, 30) + 1)


    

    
Out[14]:





array([[ 50. ,   0. ,   0. ,   0. ,   0. ,   0. ,   0. ,   0. ,   0. ,
          0. ,   4. ,   0. ,   0. ,   0. ,   0. ,   0. ,   0. ,   0. ,
          0. ,   0. ,   4. ,   0. ,   0. ,   0. ,   0. ,   0. ,   0. ,
          0. ,   0. ,   0. ],
       [ 30. ,   0. ,   0. ,   0. ,   0. ,   0. ,   0. ,   0. ,   0. ,
          0. ,   4. ,   0. ,   0. ,   0. ,   0. ,   0. ,   0. ,   0. ,
          0. ,   0. ,   4. ,   0. ,   0. ,   0. ,   0. ,   0. ,   0. ,
          0. ,   0. ,   0. ],
       [  0. ,   3. ,   3. ,   3. ,   3. ,   3. ,   3. ,   3. ,   3. ,
          3. ,   3. ,   3. ,   3. ,   3. ,   3. ,   3. ,   3. ,   3. ,
          3. ,   3. ,   3. ,   3. ,   3. ,   3. ,   3. ,   3. ,   3. ,
          3. ,   3. ,   3. ],
       [  0. ,   0. ,   0. ,   0. ,   0. ,   0. ,   0. ,   0. ,   0. ,
          0. ,   0. ,   0. ,   0. ,   3. ,   3. ,   2.6,   2.7,   2.7,
          2.7,   2.6,   2.7,   2.7,   2.7,   2.7,   2.7,   2.7,   2.7,
          2.7,   2.7,   2.6]])

In [ ]:

    




    

In [5]:

    

data_folder = os.path.join('..', '..', '..', 'data', 'input_files_SR41_fix')


    

In [6]:

    

nb = MNM_network_builder()
nb.load_from_folder(data_folder)


    

    




MNM_config
MNM_pathtable

In [22]:

    

whole_observable_list = list(map(lambda x: x.ID, filter(lambda x: x.ffs < 1000, nb.link_list)))
obserable_list = list(filter(lambda x: np.random.rand() < 0.8, whole_observable_list))
paths_list = range(39561)

config = dict()
config['use_link_flow'] = True
config['use_link_tt'] = False
config['link_flow_weight'] = 1
config['link_tt_weight'] = 1
config['num_data'] = 1
config['observed_links'] = obserable_list
config['paths_list'] = paths_list


    

In [23]:

    

dode = DODE(nb, config)


    

In [24]:

    

f_e = dode.init_path_flow()


    

In [25]:

    

dta = dode._run_simulation(f_e)


    

In [26]:

    

dta


    

    
Out[26]:





<MNMAPI.dta_api at 0x7fb4dd591260>

In [27]:

    

start = time.time()
dar1 = dode.get_dar(dta, f_e)
end = time.time()
print(end - start)


    

    




169.585990906

In [28]:

    

start = time.time()
dar2 = dode.get_dar2(dta, f_e)
end = time.time()
print(end - start)


    

    




124.004991055

In [29]:

    

dar1


    

    
Out[29]:





<8196x237366 sparse matrix of type '<type 'numpy.float64'>'
	with 1003868 stored elements in Compressed Sparse Row format>

In [30]:

    

dar2


    

    
Out[30]:





<8196x237366 sparse matrix of type '<type 'numpy.float64'>'
	with 1003868 stored elements in Compressed Sparse Row format>

In [31]:

    

np.abs(dar1 - dar2).sum()


    

    
Out[31]:





0.0

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