Welcome to the Matrixconverters documentation!¶
Matrixconverters has been developed to read and write PTV Matrices as xarray-Datasets
Readme File¶
tools to read and write matrices in the format or PTV VISUM
It implements:
Reading and writing PTV-Matrices
It can read and write the following formats:
Text-Formats: O-Format, V-Format, S-Format
Binary formats: BI-Format, BK-Format, BL-Format
# read a matrix into a xarray-Dataset
from matrixconverters import ReadPTVMatrix, SavePTVMatrix
ds = ReadPTVMatrix(filepath)
# save a xr.Dataset as PTV-Matrix
import xarray as xr
da = xr.DataArray(np.arange(9).reshape(3, 3))
zones = xr.DataArray([100, 200, 300])
names = xr.DataArray(['A-Town', 'B-Village', 'C-City'])
ds = xr.Dataset({'matrix': da,
'zone_no': zones,
'zone_name': names,})
from matrixconverters.save_ptv import SavePTV
s = SavePTV(ds)
s.savePTVMatrix(file_name=matrix_fn_out, file_type='BK')
Writing PSV-Matrices
Programmsystem Verkehr by Software-Kontor Helmert-Hilke)
File-Types CC and CN
from matrixconverters.save_ptv import SavePTV
s = SavePTV(ds)
s.savePSVMatrix(file_name=matrix_fn_out, ftype='CC')
Export xarray-Dataset as compressed NetCDF-File
from matrixconverters.xarray2netcdf import xarray2netcdf
xarray2netcdf(ds, file_path)
ds_saved = xr.open_dataset(file_path)
Installation¶
You can use pip to install the packages (and the requirements like numpy).
pip install matrixconverters
Another way to handle dependencies is to use conda.
There conda packages for python 3.8-3.12 for windows and linux are generated in the channel MaxBo in Anaconda Cloud.
conda create -n myenv python=3.12
conda activate myenv
conda config --add channels conda-forge
conda config --add channels MaxBo
conda install matrixconverters
This documentation contains¶
Code Documentation