notebook.community

Edit and run



In [2]:

    
%load_ext autoreload
%autoreload 2



In [3]:

    
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import hashlib
import time
import shutil



In [4]:

    
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 [ ]:

    
from MNM_nb import *
import MNMAPI
from DODE import *



In [6]:

    
data_folder = os.path.join('..', '..', '..', 'data', 'input_files_2link_fix')



In [2]:

    
nb = MNM_network_builder()
nb.load_from_folder(data_folder)









    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-2-b10882e7e688> in <module>()
----> 1 nb = MNM_network_builder()
      2 nb.load_from_folder(data_folder)

NameError: name 'MNM_network_builder' is not defined



In [1]:

    
nb.link_lisk









    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-1d92c00049aa> in <module>()
----> 1 nb.link_lisk

NameError: name 'nb' is not defined



In [42]:

    
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'] = [3]
config['paths_list'] = [0, 1]



In [10]:

    
x_real = np.random.rand(10) * 10



In [11]:

    
data_dict = dict()
link_df = pd.DataFrame(index = range(10), columns = [3], data = x_real)
data_dict['link_flow'] = [link_df]



In [12]:

    
link_df



In [13]:

    
dode = DODE(nb, config)



In [14]:

    
dode.add_data(data_dict)



In [ ]:

    
dode.estimate_path_flow()



In [ ]:

SR41



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 [7]:

    
nb.config









    Out[7]:





[DTA]
network_name = Snap_graph
total_interval = 3000
unit_time = 5
assign_frq = 180
start_assign_interval = 0
max_interval = 6
flow_scalar = 10
num_of_link = 2065
num_of_node = 1441
num_of_O = 174
num_of_D = 174
OD_pair = 7110
routing_type = Fixed

[STAT]
rec_mode = LRn
rec_mode_para = 2
rec_folder = record
rec_volume = 0
volume_load_automatic_rec = 0
volume_record_automatic_rec = 0
rec_tt = 0
tt_load_automatic_rec = 0
tt_record_automatic_rec = 0

[FIXED]
path_file_name = path_table
num_path = 39561
choice_portion = Buffer
route_frq = 180



In [23]:

    
whole_observable_list = list(map(lambda x: x.ID, filter(lambda x: x.ffs < 1000, nb.link_list)))



In [24]:

    
obserable_list = list(filter(lambda x: np.random.rand() < 0.2, whole_observable_list))



In [25]:

    
paths_list = range(39561)



In [26]:

    
x_real = np.random.rand(nb.config.config_dict['DTA']['max_interval'], len(obserable_list) ) * 100



In [27]:

    
data_dict = dict()
link_df = pd.DataFrame(index = range(nb.config.config_dict['DTA']['max_interval']), 
                       columns = obserable_list, data = x_real)
data_dict['link_flow'] = [link_df]



In [28]:

    
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 [29]:

    
dode = DODE(nb, config)



In [30]:

    
dode.add_data(data_dict)



In [ ]:

    
dode.estimate_path_flow()









    



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Evaluating grad
Getting Loss
Epoch: 708 Loss: 1705.55909117
Running simulation
Getting DAR
Evaluating grad
Getting Loss
Epoch: 709 Loss: 1704.20281467
Running simulation
Getting DAR
Evaluating grad
Getting Loss
Epoch: 710 Loss: 1705.33680971
Running simulation
Getting DAR
Evaluating grad
Getting Loss
Epoch: 711 Loss: 1704.09622821
Running simulation
Getting DAR
Evaluating grad
Getting Loss
Epoch: 712 Loss: 1704.5491831
Running simulation
Getting DAR
Evaluating grad
Getting Loss
Epoch: 713 Loss: 1704.47577457
Running simulation
Getting DAR
Evaluating grad
Getting Loss
Epoch: 714 Loss: 1704.60283277
Running simulation
Getting DAR
Evaluating grad
Getting Loss
Epoch: 715 Loss: 1704.53068271
Running simulation
Getting DAR
Evaluating grad
Getting Loss
Epoch: 716 Loss: 1704.73218875
Running simulation



In [38]:

    
f_e = dode.init_path_flow()



In [ ]:

    
hash = hashlib.sha1()
hash.update(str(time.time()))
new_folder = str(hash.hexdigest())
nb.update_demand_path(f_e)
nb.dump_to_folder(new_folder)
a = MNMAPI.dta_api()
a.initialize(new_folder)
# shutil.rmtree(new_folder)
a.register_links(obserable_list)
a.register_paths(paths_list)
a.install_cc()
a.install_cc_tree()
a.run_whole()



In [1]:

    
nb.demand









    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-7a499a2d6a78> in <module>()
----> 1 nb.demand

NameError: name 'nb' is not defined



In [ ]:

Speed



In [43]:

    
dode = DODE(nb, config)



In [44]:

    
f_e = dode.init_path_flow()



In [45]:

    
dta = dode._run_simulation(f_e)



In [52]:

    
tt = dta.get_link_tt(np.arange(0, dode.num_loading_interval, dode.ass_freq))



In [53]:

    
tt.shape









    Out[53]:





(1, 10)



In [48]:

    
config









    Out[48]:





{'link_flow_weight': 1,
 'link_tt_weight': 1,
 'num_data': 1,
 'observed_links': [3],
 'paths_list': [0, 1],
 'use_link_flow': True,
 'use_link_tt': False}



In [49]:

    
tt









    Out[49]:





array([ 64.,  13.,  13.,  13.,  13.,  13.,  13.,  13.,  13.,  10.])



In [51]:

    
nb.get_link(3).get_fft()









    Out[51]:





64.0



In [3]:

    
from scipy.sparse import csr_matrix









    



---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-f3ac46944ccb> in <module>()
      1 from scipy.sparse import csr_matrix
----> 2 csr_matrix(10,5)

/home/lemma/anaconda2/lib/python2.7/site-packages/scipy/sparse/compressed.pyc in __init__(self, arg1, shape, dtype, copy)
     81         # Read matrix dimensions given, if any
     82         if shape is not None:
---> 83             self.shape = shape   # spmatrix will check for errors
     84         else:
     85             if self.shape is None:

/home/lemma/anaconda2/lib/python2.7/site-packages/scipy/sparse/base.pyc in set_shape(self, shape)
     78     def set_shape(self, shape):
     79         """See `reshape`."""
---> 80         shape = tuple(shape)
     81 
     82         if len(shape) != 2:

TypeError: 'int' object is not iterable



In [5]:

    
csr_matrix()









    Out[5]:





<10x5 sparse matrix of type '<type 'numpy.float64'>'
	with 0 stored elements in Compressed Sparse Row format>



In [ ]:

	3
0	8.463512
1	5.434778
2	6.338295
3	8.372635
4	3.430133
5	6.417450
6	4.953019
7	3.435557
8	4.198736
9	3.266298