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The python framework pyphant allows for the creation and application of data flow models. The central idea of this approach is to encapsulate each data processing step in one unit which is called a worker. A worker receives input via sockets and provides the results of its data processing via plugs. These can be inserted into other workers' sockets. The resulting directed graph is called a recipe. Classes for these objects comprise the Pyphant core. To implement actual processing steps, Pyphant relies on third party plug-ins, also referred to as toolboxes, which extend the basic worker class, e.g. py-pyphant-imageprocessing. On top of the core, Pyphant offers a data exchange layer on basis of numpy arrays which facilitates the interoperability of the workers and fully supports physical quantities with errors and units. The third layer is a graphical user interface allowing for the interactive construction of recipes as well as the calculation and visualization of data from any worker in the recipe.

The python framework pyphant allows for the creation and application of data flow models. The central idea of this approach is to encapsulate each data processing step in one unit which is called a worker. A worker receives input via sockets and provides the results of its data processing via plugs. These can be inserted into other workers' sockets. The resulting directed graph is called a recipe. Classes for these objects comprise the Pyphant core. To implement actual processing steps, Pyphant relies on third party plug-ins, also referred to as toolboxes, which extend the basic worker class, e.g. py-pyphant-imageprocessing. On top of the core, Pyphant offers a data exchange layer on basis of numpy arrays which facilitates the interoperability of the workers and fully supports physical quantities with errors and units. The third layer is a graphical user interface allowing for the interactive construction of recipes as well as the calculation and visualization of data from any worker in the recipe.

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