wiver.wiver_python module

exception wiver.wiver_python.InvalidWiverInputData

Bases: ValueError

Invalid input data for Wiver model

class wiver.wiver_python.WIVER(n_groups, n_zones, n_time_slices=5, n_time_series=1, n_modes=4, n_sectors=2, n_threads=None, logger=None)

Bases: _WIVER, _ArrayProperties

WIVER Business Trip Model

Parameters:

  • n_groups (int) –

  • n_zones (int) –

  • n_time_slices (int) –

  • n_time_series (int) –

  • n_modes (int) –

  • n_sectors (int) –

  • n_threads (int) –

  • logger (Logger) –

__init__(n_groups, n_zones, n_time_slices=5, n_time_series=1, n_modes=4, n_sectors=2, n_threads=None, logger=None)
Parameters:

  • n_groups (int) – the number of groups

  • n_zones (int) – the number of zones

  • n_time_slices (int) – the number of time slices

  • n_time_series (int) – the number of time series

  • n_modes (int) – the number of transport modes

  • n_sectors (int) – the number of economic sectors

  • n_threads (int) – the maximum number of threads to use for calculation

  • logger (Logger) – an optional logger to use from within wiver. If not provided, a logger is created

adjust_balancing_factor(iteration, threshold=0.1)

adjust the balancing factor for the destination choice model

Parameters:

  • threshold (float) – if the difference between the modelled and the target trips is < threshold, the model converges

  • iteration (int) –

assign_coordinates()

set the coordinates from self._coordinates to self.data

calc()

calc the daily trips and the trips by time slice

calc_starting_and_ending_trips()

calculate the starting trips per zone and group and return them as Dataframe

Return type:

DataFrame

calc_with_balancing(max_iterations=10, threshold=0.1)

calculate with balancing the destination choice model

Parameters:

  • max_iterations (int) – the maximum number of iterations

  • threshold (float) – if the difference between the modelled and the target trips is < threshold, the model converges

define_arrays()

Define the arrays

define_balancing()

Define the balancing factors and return them as dataset

Return type:

Dataset

define_matrices()

Define the matrices and return them as dataset

Return type:

Dataset

define_params()

Define the params and return them as dataset

Return type:

Dataset

define_results()

Define the results and return them as dataset

Return type:

Dataset

define_zonal_data()

Define the zonal data and return them as dataset

Return type:

Dataset

merge_datasets()

Merge the datasets

read_all_data(datasets)

Read Datasets from netcdf-file

Parameters:

datasets (Dict[str, str]) – dict with the filenames of the input and output data

read_data(dataset_name, fn)

read single dataset from file

Parameters:

  • dataset_name (str) – the dataset that should be read

  • fn (str) – filename to read from

classmethod read_from_netcdf(files, n_threads=None)

Read a Wiver Model from a set of netcdf-Filename defined in files

Parameters:

  • files (Dict[str, str]) – dict with the file names of the input data

  • n_threads (int) – the maximum number of threads to use for calculation

Return type:

WIVER

save_all_data(wiver_files)

Save Dataset to netcdf-files

Parameters:

wiver_files (Dict[str, str]) – dict with the input and output filenames

save_data(dataset_name, fn)

Save Dataset to netcdf-file

Parameters:

  • dataset_name (str) – the dataset that should be saved

  • fn (str) – filename to write to

save_detailed_results_to_visum(folder, visum_format='BK')

Save the detailed results to VISUM-Format including the matrices for each group

Parameters:

  • folder (str) – the folder where the Visum-Matrices will be written

  • visum_format (str) – the Visum-Matrix format (Default=Binary Compressed (BK))

save_results(wiver_files)

Save results except trips_gsij to folder

Parameters:

wiver_files (Dict[str, str]) – dict with the filenames of the input and output data

save_results_to_visum(folder, visum_format='BK')

Save the results to VISUM-Format

Parameters:

  • folder (str) – the folder where the Visum-Matrices will be written

  • visum_format (str) – the Visum-Matrix format (Default=Binary Compressed (BK))

set_arrays_from_balancing_ds(ds)

Set arrays from balancing dataset

Parameters:

ds (Dataset) – Dataset with the balancing factors

set_arrays_from_dataset()

Sets the arrays with values from the dataset

set_n_threads(n_threads=None)

Set the number of threads

Parameters:

n_threads (int) – the maximum number of threads to use for calculation

validate_input_data()

make checks on the input data

_coordinates = {'groups': 'group_names', 'modes': 'modes', 'sectors': 'sector_short', 'time_series': 'lbl_time_series', 'time_slices': 'lbl_time_slices', 'zone_no': 'zone_no'}
property balancing: Dataset

return the balancing-Dataset and define it, if not yet done

property base_trips_g: DataArray

the total trips from and to the home base per group

property base_trips_km_g: DataArray

the total vehicle km of trips from and to the home base per group

property linking_trips_g: DataArray

the total linking trips per group

property linking_trips_km_g: DataArray

the total vehicle km of linking trips per group

property matrices: Dataset

return the matrices-Dataset and define it, if not yet done

property params: Dataset

return the results-Dataset and define it, if not yet done

property results: Dataset

return the results-Dataset and define it, if not yet done

property trips_g: DataArray

the total trips per group

property vehicle_km_g: DataArray

the total vehicle kilometers per group

property zonal_data: Dataset

return the zonal_data-Dataset and define it, if not yet done