def compare_travel_data_sets(self):
# get copied travel data csv
copied_travel_data_location = os.path.join(self.destination, "opus_matsim", "tmp")
if not os.path.exists(copied_travel_data_location):
raise StandardError("Travel data not found: %s" % copied_travel_data_location)
logger.log_status("Get copied travel data: %s" % copied_travel_data_location)
# convert travel data csv into travel data set matrix
in_storage = csv_storage(storage_location=copied_travel_data_location)
table_name = "travel_data"
travel_data_attribute = "single_vehicle_to_work_travel_cost"
travel_data_set = TravelDataDataset(in_storage=in_storage, in_table_name=table_name)
travel_data_attribute_mat = travel_data_set.get_attribute_as_matrix(travel_data_attribute, fill=999)
# get exsisting travel data set and convert it also into travel data set matrix
year = self.run_config["base_year"] + 2
attribute_cache = AttributeCache(cache_directory=self.run_config["cache_directory"])
cache_storage = attribute_cache.get_flt_storage_for_year(year)
existing_travel_data_set = TravelDataDataset(in_storage=cache_storage, in_table_name=table_name)
existing_travel_data_attribute_mat = existing_travel_data_set.get_attribute_as_matrix(
travel_data_attribute, fill=999
)
from numpy import savetxt # for debugging
savetxt(os.path.join(self.destination, "origin_travel_data.txt"), travel_data_attribute_mat, fmt="%f")
savetxt(os.path.join(self.destination, "existing_travel_data"), existing_travel_data_attribute_mat, fmt="%f")
# compare both data set matices
compare = travel_data_attribute_mat == existing_travel_data_attribute_mat
# return result
return compare.all()
def compare_travel_data_sets(self):
# get copied travel data csv
copied_travel_data_location = os.path.join( self.destination, 'opus_matsim', 'tmp')
if not os.path.exists(copied_travel_data_location):
raise StandardError('Travel data not found: %s' % copied_travel_data_location)
logger.log_status('Get copied travel data: %s' % copied_travel_data_location)
# convert travel data csv into travel data set matrix
in_storage = csv_storage(storage_location = copied_travel_data_location)
table_name = "travel_data"
travel_data_attribute = 'single_vehicle_to_work_travel_cost'
travel_data_set = TravelDataDataset( in_storage=in_storage, in_table_name=table_name )
travel_data_attribute_mat = travel_data_set.get_attribute_as_matrix(travel_data_attribute, fill=999)
# get exsisting travel data set and convert it also into travel data set matrix
year = self.run_config['base_year']+2
attribute_cache = AttributeCache(cache_directory=self.run_config['cache_directory'])
cache_storage = attribute_cache.get_flt_storage_for_year(year)
existing_travel_data_set = TravelDataDataset( in_storage=cache_storage, in_table_name=table_name )
existing_travel_data_attribute_mat = existing_travel_data_set.get_attribute_as_matrix(travel_data_attribute, fill=999)
from numpy import savetxt # for debugging
savetxt( os.path.join(self.destination, 'origin_travel_data.txt'), travel_data_attribute_mat , fmt="%f")
savetxt( os.path.join(self.destination, 'existing_travel_data') , existing_travel_data_attribute_mat, fmt="%f")
# compare both data set matices
compare = travel_data_attribute_mat == existing_travel_data_attribute_mat
# return result
return compare.all()
def test_run(self):
print "Entering test run"
logger.log_status("Loading travel data: %s" % self.travel_data_source)
# get travel data as an attribute marix
in_storage = csv_storage(storage_location=self.travel_data_source_dir)
table_name = "travel_data"
travel_data_attribute = "single_vehicle_to_work_travel_cost"
travel_data_set = TravelDataDataset(in_storage=in_storage, in_table_name=table_name)
travel_data_attribute_mat = travel_data_set.get_attribute_as_matrix(travel_data_attribute, fill=0)
# determine location to store and read attribute matrix
location1 = os.path.join(self.tempDir, "attrib_matrix1.txt")
location2 = os.path.join(self.tempDir, "attrib_matrix2.txt")
# store attribute matrix
savetxt(location1, travel_data_attribute_mat, fmt="%i")
savetxt(location2, travel_data_attribute_mat, fmt="%i")
# read attribute matrix
matrix1 = genfromtxt(location1)
matrix2 = genfromtxt(location2)
# compare both matices
result = matrix1 == matrix2
self.assertTrue(result.all())
print "Leaving test run"
def test_run(self):
print "Entering test run"
logger.log_status('Loading travel data: %s' % self.travel_data_source)
# get travel data as an attribute marix
in_storage = csv_storage(storage_location=self.travel_data_source_dir)
table_name = "travel_data"
travel_data_attribute = 'single_vehicle_to_work_travel_cost'
travel_data_set = TravelDataDataset(in_storage=in_storage,
in_table_name=table_name)
travel_data_attribute_mat = travel_data_set.get_attribute_as_matrix(
travel_data_attribute, fill=0)
# determine location to store and read attribute matrix
location1 = os.path.join(self.tempDir, 'attrib_matrix1.txt')
location2 = os.path.join(self.tempDir, 'attrib_matrix2.txt')
# store attribute matrix
savetxt(location1, travel_data_attribute_mat, fmt="%i")
savetxt(location2, travel_data_attribute_mat, fmt="%i")
# read attribute matrix
matrix1 = genfromtxt(location1)
matrix2 = genfromtxt(location2)
# compare both matices
result = (matrix1 == matrix2)
self.assertTrue(result.all())
print "Leaving test run"
def test_run(self):
print "Entering test run"
path = paths.get_opus_home_path('opus_matsim', 'tmp')
# check if travel data exsits
travel_data = os.path.join( path, "travel_data.csv" )
if not os.path.exists(travel_data):
print "Travel Data not found!!!"
sys.exit()
in_storage = csv_storage(storage_location = path)
table_name = "travel_data"
travel_data_set = TravelDataDataset( in_storage=in_storage, in_table_name=table_name )
origin_zones = travel_data_set.get_attribute_as_column(self.origin_zone_id)
l = numpy.atleast_1d(origin_zones).tolist()
origin_list = set(l) # removes duplicates and sorts the list in ascending order
# destination_list = len(origin_list) * self.cbd # creates a list that contains the zone id of the cbd an has the same length as "origin_list"
# set high travel costs for all origin to cbd pairs
for id in origin_list:
travel_data_set.set_values_of_one_attribute_with_od_pairs(self.travel_data_attribute, self.high_travel_cost, id, self.cbd)
# adjust cbd to cbd
travel_data_set.set_values_of_one_attribute_with_od_pairs(self.travel_data_attribute, 0.0, self.cbd, self.cbd)
# adjust prefered zone to cbd
travel_data_set.set_values_of_one_attribute_with_od_pairs(self.travel_data_attribute, self.low_travel_cost, self.preferential_zone, self.cbd)
w = travel_data_set.get_index_by_origin_and_destination_ids(110, 129)
x = travel_data_set.get_index_by_origin_and_destination_ids(129, 129)
y = travel_data_set.get_index_by_origin_and_destination_ids(20, 129)
z = travel_data_set.get_index_by_origin_and_destination_ids(20, 20)
print w
print x
print y
print z
origin_zones = travel_data_set.get_attribute_as_column(self.origin_zone_id)
destination_zones = travel_data_set.get_attribute_as_column(self.destination_zone_id)
my_travel_data_attr_mat = travel_data_set.get_attribute_as_matrix('travel_data.single_vehicle_to_work_travel_cost',
fill=999)
my_travel_data_attr_mat[origin_zones, destination_zones] = 1.03
cbd_ids = where(origin_zones == 129)
print "Leaving test run"
def test_run(self):
print "Entering test run"
path = os.path.join(os.environ['OPUS_HOME'], 'opus_matsim/tmp')
# check if travel data exsits
travel_data = os.path.join( path, "travel_data.csv" )
if not os.path.exists(travel_data):
print "Travel Data not found!!!"
sys.exit()
in_storage = csv_storage(storage_location = path)
table_name = "travel_data"
travel_data_set = TravelDataDataset( in_storage=in_storage, in_table_name=table_name )
origin_zones = travel_data_set.get_attribute_as_column(self.origin_zone_id)
l = numpy.atleast_1d(origin_zones).tolist()
origin_list = set(l) # removes duplicates and sorts the list in ascending order
# destination_list = len(origin_list) * self.cbd # creates a list that contains the zone id of the cbd an has the same length as "origin_list"
# set high travel costs for all origin to cbd pairs
for id in origin_list:
travel_data_set.set_values_of_one_attribute_with_od_pairs(self.travel_data_attribute, self.high_travel_cost, id, self.cbd)
# adjust cbd to cbd
travel_data_set.set_values_of_one_attribute_with_od_pairs(self.travel_data_attribute, 0.0, self.cbd, self.cbd)
# adjust prefered zone to cbd
travel_data_set.set_values_of_one_attribute_with_od_pairs(self.travel_data_attribute, self.low_travel_cost, self.preferential_zone, self.cbd)
w = travel_data_set.get_index_by_origin_and_destination_ids(110, 129)
x = travel_data_set.get_index_by_origin_and_destination_ids(129, 129)
y = travel_data_set.get_index_by_origin_and_destination_ids(20, 129)
z = travel_data_set.get_index_by_origin_and_destination_ids(20, 20)
print w
print x
print y
print z
origin_zones = travel_data_set.get_attribute_as_column(self.origin_zone_id)
destination_zones = travel_data_set.get_attribute_as_column(self.destination_zone_id)
my_travel_data_attr_mat = travel_data_set.get_attribute_as_matrix('travel_data.single_vehicle_to_work_travel_cost',
fill=999)
my_travel_data_attr_mat[origin_zones, destination_zones] = 1.03
cbd_ids = where(origin_zones == 129)
print "Leaving test run"
def test_run(self):
print "Entering test run"
# This test checks if the pre-computed MATSim travel data
# is loaded correctly into UrbanSim. Therefore the UrbanSim
# travel data matrix is converted into a numpy array and two
# predefined values are checked whether they match with MATSim data.
#
# Here an example:
# my_list = [[1,2,3],
# [4,5,6],
# [7,8,9]]
#
# my_list[0][1] should be = 2
# my_list[2][2] should be = 9
table_name = 'travel_data'
travel_data_attribute = 'single_vehicle_to_work_travel_cost'
# location of pre-calculated MATSim travel costs
in_storage = csv_storage(storage_location = self.input_directory)
# create travel data set (travel costs)
travel_data_set = TravelDataDataset( in_storage=in_storage, in_table_name=table_name )
travel_data_attribute_mat = travel_data_set.get_attribute_as_matrix(travel_data_attribute, fill=31)
# converting from numpy array into a 2d list
travel_list = numpy.atleast_2d(travel_data_attribute_mat).tolist()
# get two values for validation
value1 = int(travel_list[1][1]) # should be = 0
value2 = int(travel_list[2][1]) # should be = 120
logger.log_status('First validation value should be 0. Current value is %i' % value1)
logger.log_status('Second validation value should be 120. Current value is %i' % value2)
self.assertTrue( value1 == 0 )
self.assertTrue( value2 == 120 )
# self.dump_travel_list(travel_list) # for debugging
print "Leaving test run"
def test_run(self):
print "Entering test run"
# This test loads an exising travel data as a TravelDataSet (numpy array)
# and accesses single (pre-known) values to validate the conversion process
# (numpy array into standard python list).
#
# Here an example:
# my_list = [[1,2,3],
# [4,5,6],
# [7,8,9]]
#
# my_list[0][1] should be = 2
# my_list[2][2] should be = 9
table_name = 'travel_data'
travel_data_attribute = 'single_vehicle_to_work_travel_cost'
# location of pre-calculated MATSim travel costs
in_storage = csv_storage(storage_location = self.input_directory)
# create travel data set (travel costs)
travel_data_set = TravelDataDataset( in_storage=in_storage, in_table_name=table_name )
travel_data_attribute_mat = travel_data_set.get_attribute_as_matrix(travel_data_attribute, fill=31)
# converting from numpy array into a 2d list
travel_list = numpy.atleast_2d(travel_data_attribute_mat).tolist()
# get two values for validation
value1 = int(travel_list[1][1]) # should be = 0
value2 = int(travel_list[2][1]) # should be = 120
logger.log_status('First validation value should be 0. Current value is %i' % value1)
logger.log_status('Second validation value should be 120. Current value is %i' % value2)
self.assertTrue( value1 == 0 )
self.assertTrue( value2 == 120 )
# self.dump_travel_list(travel_list) # for debugging
print "Leaving test run"
def get_travel_cost_matrix(self):
''' Returns the pre-calculated MATSim travel costs as 2d list
'''
# get sensitivity test path
test_dir_path = test_dir.__path__[0]
input_directory = os.path.join( test_dir_path, 'data', 'travel_cost')
print "input_directory: %s" % input_directory
# check source file
if not os.path.exists( input_directory ):
print 'File not found! %s' % input_directory
sys.exit()
table_name = 'travel_data'
travel_data_attribute = 'single_vehicle_to_work_travel_cost'
# location of pre-calculated MATSim travel costs
in_storage = csv_storage(storage_location = input_directory)
# create travel data set (travel costs)
travel_data_set = TravelDataDataset( in_storage=in_storage, in_table_name=table_name )
travel_data_attribute_mat = travel_data_set.get_attribute_as_matrix(travel_data_attribute, fill=999)
# travel cost matris as 2d array
travel_list = numpy.atleast_2d(travel_data_attribute_mat).tolist()
return travel_list
def get_travel_cost_matrix(self):
''' Returns the pre-calculated MATSim travel costs as 2d list
'''
# get sensitivity test path
test_dir_path = test_dir.__path__[0]
input_directory = os.path.join( test_dir_path, 'data', 'travel_cost')
print "input_directory: %s" % input_directory
# check source file
if not os.path.exists( input_directory ):
print 'File not found! %s' % input_directory
sys.exit()
table_name = 'travel_data'
travel_data_attribute = 'single_vehicle_to_work_travel_cost'
# location of pre-calculated MATSim travel costs
in_storage = csv_storage(storage_location = input_directory)
# create travel data set (travel costs)
travel_data_set = TravelDataDataset( in_storage=in_storage, in_table_name=table_name )
travel_data_attribute_mat = travel_data_set.get_attribute_as_matrix(travel_data_attribute, fill=999)
# travel cost matris as 2d array
travel_list = numpy.atleast_2d(travel_data_attribute_mat).tolist()
return travel_list