Welcome to Data fusion tools’ documentation!

DataFusionTools is a collection of tools that can be used to perform data fusion tasks. The tools are developed by the consortium of Deltares, TNO, HKV, Fugro and Geodan. The tools are developed in the context of the project DigiTwin Waterkering en Ondergrond. The tools are developed in Python and are available as a package on the following link.

Installation

DataFusionTools is a collection of several subpackages or distributions that can be either installed separately or as a whole project. Each subpackage may have a few heavy dependencies attached, therefore it is not recommended to install the whole DataFusionTools package if only a subpackage is needed in a project.

It is recommended to install DataFusionTools in a python virtual environment. The main purpose of Python virtual environments is to create an isolated environment for Python projects. This means that each project can have its own dependencies, regardless of what dependencies every other project has. This avoids issues with packages dependencies. The virtual environment should be installed and activated before the installation of DataFusionTools. To create a virtual environment with python/pip follow this link. To create a virtual environment with conda follow this link.

Installing DataFusionTools as a package

To install DataFusionTools, run the following code:

pip install git+https://bitbucket.org/DeltaresGEO/datafusiontools.git

Installing a subpackage

To install a specific subpackage (sensitivity in this example), run the following:

pip install git+https://bitbucket.org/DeltaresGEO/datafusiontools.git@master#subdirectory=DataFusionTools/sensitivity

The subpackage is then imported in python:

import datafusiontools.sensitivity

The installable sukpackages are: interpolation, machine_learning, sensitivity, spatial_utils, d_series_parser and visualisation.

Using the package as a developer

To install the package as a developer, you need first to check out the repository. Then, run the following command in the root of the repository:

pip install -e .[testing]

This will install the package in editable mode, so that any changes you make to the code will be reflected in the installed package. The [testing] flag will also install the dependencies needed for running the tests.

Developers

Consortium consists of:

• Deltares

• TNO

• Fugro

• Geodan

• HKV

The Tutorials

This part of the documentation includes the tutorials.

• Tutorial neural network
• How to get a 2d interpolated slice from a list of data
• Get ahn values from a list of points
• Bayesian Neural Networks
  • What is a BNN?
  • Model example
• reate D-Stability .sli file from the DataFusionTools
• Sensitivity analysis on a model
  • Available methods
    • Application
    • Sensitivity analysis
  • Morris method
  • Sobol method
  • Other uses
• Cluster 2d surface to create polygons for DSeries models
  • Step 1: Call from the user
  • Step 2: Normalizing the data
  • Step 3: Clustering with the Spatially Constrained Hierarchical Clustering method
    • Step 3.1 : Create Voronoi regions and polygonize them
    • Step 3.1 : Create the spatial connectivity matrix
    • Step 3.1 : Perform the clustering using the Agglomerative Clustering method
  • Step 4: Polygons are created by merging clusters
  • Step 5: Polygons are simplified
  • Step 6: IC values per polygon are extracted

Indices and tables

• Index

• Module Index

• Search Page

Package documentation

• DataFusionTools package
• Subpackages
  • DataFusionTools.interpolation package
    • Submodules
    • DataFusionTools.interpolation.interpolation module
    • Module contents
  • DataFusionTools.machine_learning package
    • Submodules
    • DataFusionTools.machine_learning.baseclass module
    • DataFusionTools.machine_learning.convolutional module
    • DataFusionTools.machine_learning.enumeration_classes module
    • DataFusionTools.machine_learning.mpl module
    • DataFusionTools.machine_learning.neural_networks module
    • DataFusionTools.machine_learning.random_forest module
    • DataFusionTools.machine_learning.support_vector_machine module
    • DataFusionTools.machine_learning.bayesian_neural_network module
    • Module contents
  • DataFusionTools.d_series_parser
    • DataFusionTools.clustering module
    • DataFusionTools.d_stability_parser module
  • DataFusionTools.sensitivity
  • DataFusionTools.visualization
• Submodules
  • DataFusionTools.data_input module
  • datafusiontools._core.utils module
  • Module contents

Bibliography

• References