now support links from one node to many nodes.Changelog 0.2.1Įnhancement #22 - how to build the model or the graph model programmatically #22įix bug #5 - I would like to be able to define any number of key levelsĪdded integration tests for each sample datasetįix bug #8 GraphMl generation fail when nodes already exists 0.1.8įix bug #2 PyYed generation failed if graph use connections 0.1.7įix bug #3 Missing links while using resources connections Thanks to the maintainers and contributors of these modules. TODO links and description from the module This project relies on the following libraries If you want to build from the sources, please clone the repository and run setup Main dependencies To use this library please use pip install dependencynet GraphMLConverter : turns the graph and the style into a GraplML file for Yed.StyleBuilder : generates a convenient cytoscape style from the schema.GraphViewer : encapsulate the steps to show the cytoscape graph.GraphModel : represents the Model as a networkx graph.Schema : describe the Model metadata (levels, resources, connections).Model : build datasource for each type of elements and organize the levels hierarchy.The graph production is done in 4 major steps it is not intended to cover all the needs but a provide a specific subset required to easily build, clean up and show directed graphs of connected elements.This is also a way to learn how to make a module library in Python. I factored out functions I found useful in the process of building the analyze tool. Again, I had a lot of code to maintain in order to achieve my goals. I also had a lot of pandas code to collect and build data structues required by the graph libraries. Hopefully there are ways to integrate networkx and cytoscape. I found ipycytoscape (a port of cytoscape) easy to use with charts. It has a lot of graph manipulation features but charts are not so easy, and charts was the main goal. In the Python ecosystem, networkx is the library to go with graphs. Graphs are powerful, but power comes with lots of concepts to master. I started to rework those scripts in Python notebooks as a side project. When this step is done it is easier to select what kind of nodes and links I want to show for a given view or documentation. I could clean up a little bit by representing all these elements as a graph. This pointed me to the fact that this system was a graph, a network of workflows, treatments, tasks, classes, tables, exported files, external packages, delivery bundles. I ended up with a lot of very messy scripts to extract the most visible dependencies and it helped me to draw some schemas in Powerpoint. I started by trying to list and connect elements manually, then I automated some parts. This project stems from the need of featuring out how a large very messy project is made. Show me the data flow zoomed out at the workflow level and hide out data flow within the workflow Stems of this projects Given lists of input and output tables of a task, what is the data flow ? Which modules may be inderectly impacted by a change in some module ? analyse of data flows between tasks or microservices Which modules are depending of a given module ? What can I do with this tool ? analyse dependencies in code bases towns may be connected to form a trip if one has a flight out and the other a flight in with the same label.files in and out of a workflow task might be connected to each other to form data flows.resources having the same label might be connected to each other to form new dependencies, for instance.
resource is any element related to a level (and only one), for instance.levels consists of composite keys forming a hierarchy, for instance.elements can be categorized as levels and resources.This modules make it easier to build graphs having the properties listed below This module wraps some data and graph manipulation tools to help analyzing and building representations of directed graphs.
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