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()
Esempio n. 2
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 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"
Esempio n. 5
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    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"
Esempio n. 6
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    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"
Esempio n. 7
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 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"
Esempio n. 8
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 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"
Esempio n. 9
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 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
Esempio n. 10
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 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